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  • 1.
    Anandapadmanaban, Madhanagopal
    et al.
    Linköping University, Department of Physics, Chemistry and Biology. Linköping University, Faculty of Science & Engineering.
    Pilstål, Robert
    Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics. Linköping University, Faculty of Science & Engineering.
    Andrésen, Cecilia
    Linköping University, Department of Physics, Chemistry and Biology, Chemistry. Linköping University, Faculty of Science & Engineering.
    Trewhella, Jill
    Linköping University, Department of Physics, Chemistry and Biology, Chemistry. Linköping University, Faculty of Science & Engineering. University of Sydney, Australia.
    Moche, Martin
    Karolinska Institute, Sweden.
    Wallner, Björn
    Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics. Linköping University, Faculty of Science & Engineering.
    Sunnerhagen, Maria
    Linköping University, Department of Physics, Chemistry and Biology, Chemistry. Linköping University, Faculty of Science & Engineering.
    Mutation-Induced Population Shift in the MexR Conformational Ensemble Disengages DNA Binding: A Novel Mechanism for MarR Family Derepression2016In: Structure, ISSN 0969-2126, E-ISSN 1878-4186, Vol. 24, no 8, p. 1311-1321Article in journal (Refereed)
    Abstract [en]

    MexR is a repressor of the MexAB-OprM multidrug efflux pump operon of Pseudomonas aeruginosa, where DNA-binding impairing mutations lead to multidrug resistance (MDR). Surprisingly, the crystal structure of an MDR-conferring MexR mutant R21W (2.19 angstrom) presented here is closely similar to wildtype MexR. However, our extended analysis, by molecular dynamics and small-angle X-ray scattering, reveals that the mutation stabilizes a ground state that is deficient of DNA binding and is shared by both mutant and wild-type MexR, whereas the DNA-binding state is only transiently reached by the more flexible wild-type MexR. This population shift in the conformational ensemble is effected by mutation-induced allosteric coupling of contact networks that are independent in the wild-type protein. We propose that the MexR-R21W mutant mimics derepression by small-molecule binding to MarR proteins, and that the described allosteric model based on population shifts may also apply to other MarR family members.

  • 2.
    Andrésen, Cecilia
    et al.
    Linköping University, Department of Physics, Chemistry and Biology, Chemistry. Linköping University, Faculty of Science & Engineering.
    Niklasson, Markus
    Linköping University, Department of Physics, Chemistry and Biology, Chemistry. Linköping University, Faculty of Science & Engineering.
    Cassman Eklöf, Sofie
    Linköping University, Department of Physics, Chemistry and Biology, Chemistry. Linköping University, Faculty of Science & Engineering.
    Wallner, Björn
    Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics. Linköping University, Faculty of Science & Engineering.
    Lundström, Patrik
    Linköping University, Department of Physics, Chemistry and Biology, Chemistry. Linköping University, Faculty of Science & Engineering.
    Biophysical characterization of the calmodulin-like domain of Plasmodium falciparum calcium dependent protein kinase 32017In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 12, no 7, article id e0181721Article in journal (Refereed)
    Abstract [en]

    Calcium dependent protein kinases are unique to plants and certain parasites and comprise an N-terminal segment and a kinase domain that is regulated by a C-terminal calcium binding domain. Since the proteins are not found in man they are potential drug targets. We have characterized the calcium binding lobes of the regulatory domain of calcium dependent protein kinase 3 from the malaria parasite Plasmodium falciparum. Despite being structurally similar, the two lobes differ in several other regards. While the monomeric N-terminal lobe changes its structure in response to calcium binding and shows global dynamics on the sub-millisecond time-scale both in its apo and calcium bound states, the C-terminal lobe could not be prepared calcium-free and forms dimers in solution. If our results can be generalized to the full-length protein, they suggest that the C-terminal lobe is calcium bound even at basal levels and that activation is caused by the structural reorganization associated with binding of a single calcium ion to the N-terminal lobe.

  • 3.
    Bano-Polo, Manuel
    et al.
    University of Valencia, Spain .
    Martinez-Gill, Luis
    University of Valencia, Spain .
    Wallner, Björn
    Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics. Linköping University, The Institute of Technology.
    Nieva, Jose L.
    University of Pais Vasco UPV EHU, Spain .
    Elofsson, Arne
    Stockholm University, Sweden .
    Mingarro, Ismael
    University of Valencia, Spain .
    Charge Pair Interactions in Transmembrane Helices and Turn Propensity of the Connecting Sequence Promote Helical Hairpin Insertion2013In: Journal of Molecular Biology, ISSN 0022-2836, E-ISSN 1089-8638, Vol. 425, no 4, p. 830-840Article in journal (Refereed)
    Abstract [en]

    alpha-Helical hairpins, consisting of a pair of closely spaced transmembrane (TM) helices that are connected by a short interfacial turn, are the simplest structural motifs found in multi-spanning membrane proteins. In naturally occurring hairpins, the presence of polar residues is common and predicted to complicate membrane insertion. We postulate that the pre-packing process offsets any energetic cost of allocating polar and charged residues within the hydrophobic environment of biological membranes. Consistent with this idea, we provide here experimental evidence demonstrating that helical hairpin insertion into biological membranes can be driven by electrostatic interactions between closely separated, poorly hydrophobic sequences. Additionally, we observe that the integral hairpin can be stabilized by a short loop heavily populated by turn-promoting residues. We conclude that the combined effect of TM-TM electrostatic interactions and tight turns plays an important role in generating the functional architecture of membrane proteins and propose that helical hairpin motifs can be acquired within the context of the Sec61 translocon at the early stages of membrane protein biosynthesis. Taken together, these data further underline the potential complexities involved in accurately predicting TM domains from primary structures.

  • 4.
    Basu, Sankar Chandra
    et al.
    Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics. Linköping University, Faculty of Science & Engineering. University of Calcutta, India.
    Söderquist, Fredrik
    Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics. Linköping University, Faculty of Science & Engineering.
    Wallner, Björn
    Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics. Linköping University, Faculty of Science & Engineering.
    Proteus: a random forest classifier to predict disorder-to-order transitioning binding regions in intrinsically disordered proteins2017In: Journal of Computer-Aided Molecular Design, ISSN 0920-654X, E-ISSN 1573-4951, Vol. 31, no 5, p. 453-466Article in journal (Refereed)
    Abstract [en]

    The focus of the computational structural biology community has taken a dramatic shift over the past one-and-a-half decades from the classical protein structure prediction problem to the possible understanding of intrinsically disordered proteins (IDP) or proteins containing regions of disorder (IDPR). The current interest lies in the unraveling of a disorder-to-order transitioning code embedded in the amino acid sequences of IDPs/ IDPRs. Disordered proteins are characterized by an enormous amount of structural plasticity which makes them promiscuous in binding to different partners, multi-functional in cellular activity and atypical in folding energy landscapes resembling partially folded molten globules. Also, their involvement in several deadly human diseases (e.g. cancer, cardiovascular and neurodegenerative diseases) makes them attractive drug targets, and important for a biochemical understanding of the disease(s). The study of the structural ensemble of IDPs is rather difficult, in particular for transient interactions. When bound to a structured partner, an IDPR adapts an ordered conformation in the complex. The residues that undergo this disorder-to-order transition are called protean residues, generally found in short contiguous stretches and the first step in understanding the modus operandi of an IDP/IDPR would be to predict these residues. There are a few available methods which predict these protean segments from their amino acid sequences; however, their performance reported in the literature leaves clear room for improvement. With this background, the current study presents Proteus, a random forest classifier that predicts the likelihood of a residue undergoing a disorder-toorder transition upon binding to a potential partner protein. The prediction is based on features that can be calculated using the amino acid sequence alone. Proteus compares favorably with existing methods predicting twice as many true positives as the second best method (55 vs. 27%) with a much higher precision on an independent data set. The current study also sheds some light on a possible disorderto-order transitioning consensus, untangled, yet embedded in the amino acid sequence of IDPs. Some guidelines have also been suggested for proceeding with a real-life structural modeling involving an IDPR using Proteus.

  • 5.
    Basu, Sankar Chandra
    et al.
    Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics. Linköping University, Faculty of Science & Engineering.
    Wallner, Björn
    Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics. Linköping University, Faculty of Science & Engineering.
    DockQ: A Quality Measure for Protein-Protein Docking Models2016In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 11, no 8, p. e0161879-Article in journal (Refereed)
    Abstract [en]

    The state-of-the-art to assess the structural quality of docking models is currently based on three related yet independent quality measures: F-nat, LRMS, and iRMS as proposed and standardized by CAPRI. These quality measures quantify different aspects of the quality of a particular docking model and need to be viewed together to reveal the true quality, e.g. a model with relatively poor LRMS (amp;gt; 10 angstrom) might still qualify as acceptable with a descent F-nat (amp;gt; 0.50) and iRMS (amp;lt; 3.0 angstrom). This is also the reason why the so called CAPRI criteria for assessing the quality of docking models is defined by applying various ad-hoc cutoffs on these measures to classify a docking model into the four classes: Incorrect, Acceptable, Medium, or High quality. This classification has been useful in CAPRI, but since models are grouped in only four bins it is also rather limiting, making it difficult to rank models, correlate with scoring functions or use it as target function in machine learning algorithms. Here, we present DockQ, a continuous protein-protein docking model quality measure derived by combining F-nat, LRMS, and iRMS to a single score in the range [0, 1] that can be used to assess the quality of protein docking models. By using DockQ on CAPRI models it is possible to almost completely reproduce the original CAPRI classification into Incorrect, Acceptable, Medium and High quality. An average PPV of 94% at 90% Recall demonstrating that there is no need to apply predefined ad-hoc cutoffs to classify docking models. Since DockQ recapitulates the CAPRI classification almost perfectly, it can be viewed as a higher resolution version of the CAPRI classification, making it possible to estimate model quality in a more quantitative way using Z-scores or sum of top ranked models, which has been so valuable for the CASP community. The possibility to directly correlate a quality measure to a scoring function has been crucial for the development of scoring functions for protein structure prediction, and DockQ should be useful in a similar development in the protein docking field.

  • 6.
    Basu, Sankar Chandra
    et al.
    Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics. Linköping University, Faculty of Science & Engineering.
    Wallner, Björn
    Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics. Linköping University, Faculty of Science & Engineering.
    Finding correct protein-protein docking models using ProQDock2016In: Bioinformatics, ISSN 1367-4803, E-ISSN 1367-4811, Vol. 32, no 12, p. 262-270Article in journal (Refereed)
    Abstract [en]

    Motivation: Protein-protein interactions are a key in virtually all biological processes. For a detailed understanding of the biological processes, the structure of the protein complex is essential. Given the current experimental techniques for structure determination, the vast majority of all protein complexes will never be solved by experimental techniques. In lack of experimental data, computational docking methods can be used to predict the structure of the protein complex. A common strategy is to generate many alternative docking solutions (atomic models) and then use a scoring function to select the best. The success of the computational docking technique is, to a large degree, dependent on the ability of the scoring function to accurately rank and score the many alternative docking models. Results: Here, we present ProQDock, a scoring function that predicts the absolute quality of docking model measured by a novel protein docking quality score (DockQ). ProQDock uses support vector machines trained to predict the quality of protein docking models using features that can be calculated from the docking model itself. By combining different types of features describing both the protein-protein interface and the overall physical chemistry, it was possible to improve the correlation with DockQ from 0.25 for the best individual feature (electrostatic complementarity) to 0.49 for the final version of ProQDock. ProQDock performed better than the state-of-the-art methods ZRANK and ZRANK2 in terms of correlations, ranking and finding correct models on an independent test set. Finally, we also demonstrate that it is possible to combine ProQDock with ZRANK and ZRANK2 to improve performance even further.

  • 7.
    Bunkoczi, Gabor
    et al.
    University of Cambridge, England.
    Wallner, Björn
    Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics. Linköping University, The Institute of Technology.
    Read, Randy J.
    University of Cambridge, England.
    Local Error Estimates Dramatically Improve the Utility of Homology Models for Solving Crystal Structures by Molecular Replacement2015In: Structure, ISSN 0969-2126, E-ISSN 1878-4186, Vol. 23, no 2, p. 397-406Article in journal (Refereed)
    Abstract [en]

    Predicted structures submitted for CASP10 have been evaluated as molecular replacement models against the corresponding sets of structure factor amplitudes. It has been found that the log- likelihood gain score computed for each prediction correlates well with common structure quality indicators but is more sensitive when the accuracy of the models is high. In addition, it was observed that using coordinate error estimates submitted by predictors to weight the model can improve its utility in molecular replacement dramatically, and several groups have been identified who reliably provide accurate error estimates that could be used to extend the application of molecular replacement for low-homology cases.

  • 8.
    Chene, Jianlin
    et al.
    Univ Missouri, MO USA.
    Choe, Myong-Ho
    Univ Sci, North Korea.
    Elofsson, Arne
    Stockholm Univ, Sweden.
    Han, Kun-Sop
    Univ Sci, North Korea.
    Hoe, Jie
    Univ Missouri, MO USA.
    Maghrabi, Ali H. A.
    Univ Reading, England.
    McGuffin, Liam J.
    Univ Reading, England.
    Menendez-Hurtado, David
    Stockholm Univ, Sweden.
    Olechnovic, Klinnent
    Vilnius Univ, Lithuania.
    Schwede, Torsten
    Univ Basel, Switzerland.
    Studer, Gabriel
    Univ Basel, Switzerland; Univ Basel, Switzerland.
    Uziela, Karolis
    Stockholm Univ, Sweden.
    Venclovas, Ceslovas
    Vilnius Univ, Lithuania.
    Wallner, Björn
    Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics. Linköping University, Faculty of Science & Engineering.
    Estimation of model accuracy in CASP132019In: Proteins: Structure, Function, and Bioinformatics, ISSN 0887-3585, E-ISSN 1097-0134Article in journal (Refereed)
    Abstract [en]

    Methods to reliably estimate the accuracy of 3D models of proteins are both a fundamental part of most protein folding pipelines and important for reliable identification of the best models when multiple pipelines are used. Here, we describe the progress made from CASP12 to CASP13 in the field of estimation of model accuracy (EMA) as seen from the progress of the most successful methods in CASP13. We show small but clear progress, that is, several methods perform better than the best methods from CASP12 when tested on CASP13 EMA targets. Some progress is driven by applying deep learning and residue-residue contacts to model accuracy prediction. We show that the best EMA methods select better models than the best servers in CASP13, but that there exists a great potential to improve this further. Also, according to the evaluation criteria based on local similarities, such as lDDT and CAD, it is now clear that single model accuracy methods perform relatively better than consensus-based methods.

  • 9.
    Elofsson, Arne
    et al.
    Stockholm Univ, Sweden.
    Joo, Keehyoung
    Korea Inst Adv Study, South Korea.
    Keasar, Chen
    Ben Gurion Univ Negev, Israel.
    Lee, Jooyoung
    Korea Inst Adv Study, South Korea.
    Maghrabi, Ali H. A.
    Univ Reading, England.
    Manavalan, Balachandran
    Korea Inst Adv Study, South Korea.
    McGuffin, Liam J.
    Univ Reading, England.
    Hurtado, David Menendez
    Stockholm Univ, Sweden.
    Mirabello, Claudio
    Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics. Linköping University, Faculty of Science & Engineering.
    Pilstål, Robert
    Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics. Linköping University, Faculty of Science & Engineering.
    Sidi, Tomer
    Ben Gurion Univ Negev, Israel.
    Uziela, Karolis
    Stockholm Univ, Sweden.
    Wallner, Björn
    Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics. Linköping University, Faculty of Science & Engineering.
    Methods for estimation of model accuracy in CASP122018In: Proteins: Structure, Function, and Bioinformatics, ISSN 0887-3585, E-ISSN 1097-0134, Vol. 86, p. 361-373Article in journal (Refereed)
    Abstract [en]

    Methods to reliably estimate the quality of 3D models of proteins are essential drivers for the wide adoption and serious acceptance of protein structure predictions by life scientists. In this article, the most successful groups in CASP12 describe their latest methods for estimates of model accuracy (EMA). We show that pure single model accuracy estimation methods have shown clear progress since CASP11; the 3 top methods (MESHI, ProQ3, SVMQA) all perform better than the top method of CASP11 (ProQ2). Although the pure single model accuracy estimation methods outperform quasi-single (ModFOLD6 variations) and consensus methods (Pcons, ModFOLDclust2, Pcomb-domain, and Wallner) in model selection, they are still not as good as those methods in absolute model quality estimation and predictions of local quality. Finally, we show that when using contact-based model quality measures (CAD, lDDT) the single model quality methods perform relatively better.

  • 10.
    Helander, Sara
    et al.
    Linköping University, Department of Physics, Chemistry and Biology, Chemistry. Linköping University, The Institute of Technology.
    Montecchio, Meri
    Linköping University, Department of Physics, Chemistry and Biology, Chemistry. Linköping University, The Institute of Technology.
    Pilstål, Robert
    Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics. Linköping University, The Institute of Technology.
    Su, Yulong
    Department of Molecular and Medical Genetics, Oregon Health and Science University, Portland, Oregon, USA.
    Kuruvilla, Jacob
    Linköping University, Department of Clinical and Experimental Medicine, Division of Cell Biology. Linköping University, Faculty of Health Sciences. Department of Medical Sciences, Molecular Medicine and Science for Life Laboratory, Uppsala University, Sweden.
    Johansson, Malin
    Division of Dermatology and Venereology, Department of Clinical Sciences, Lund University, Sweden.
    Mohammed, Javed
    Linköping University, Department of Physics, Chemistry and Biology, Chemistry. Linköping University, The Institute of Technology.
    Cristobal, Susana
    Linköping University, Department of Clinical and Experimental Medicine, Division of Cell Biology. Linköping University, Faculty of Health Sciences.
    Lundström, Patrik
    Linköping University, Department of Physics, Chemistry and Biology, Chemistry. Linköping University, The Institute of Technology.
    Sears, Rosalie
    Department of Molecular and Medical Genetics, Oregon Health and Science University, Portland, Oregon, USA.
    Wallner, Björn
    Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics. Linköping University, Department of Social and Welfare Studies, Learning, Aesthetics, Natural science. Linköping University, Faculty of Educational Sciences. Linköping University, Faculty of Science & Engineering.
    Sunnerhagen, Maria
    Linköping University, Department of Physics, Chemistry and Biology, Chemistry. Linköping University, The Institute of Technology.
    Pre-Anchoring of Pin1 to Unphosphorylated c-Myc in a Fuzzy Complex Regulates c-Myc Activity2015In: Structure, ISSN 0969-2126, E-ISSN 1878-4186, Vol. 23, no 12, p. 2267-2279Article in journal (Refereed)
    Abstract [en]

    Hierarchic phosphorylation and concomitant Pin1-mediated proline isomerization of the oncoprotein c-Myc controls its cellular stability and activity. However, the molecular basis for Pin1 recognition and catalysis of c-Myc and other multisite, disordered substrates in cell regulation and disease is unclear. By nuclear magnetic resonance, surface plasmon resonance, and molecular modeling, we show that Pin1 subdomains jointly pre-anchor unphosphorylated c-Myc1–88 in the Pin1 interdomain cleft in a disordered, or “fuzzy”, complex at the herein named Myc Box 0 (MB0) conserved region N-terminal to the highly conserved Myc Box I (MBI). Ser62 phosphorylation in MBI intensifies previously transient MBI-Pin1 interactions in c-Myc1–88 binding, and increasingly engages Pin1PPIase and its catalytic region with maintained MB0 interactions. In cellular assays, MB0 mutated c-Myc shows decreased Pin1 interaction, increased protein half-life, but lowered rates of Myc-driven transcription and cell proliferation. We propose that dynamic Pin1 recognition of MB0 contributes to the regulation of c-Myc activity in cells

  • 11.
    Henrion, Ulrike
    et al.
    Linköping University, Faculty of Health Sciences. Linköping University, Department of Clinical and Experimental Medicine, Division of Cell Biology.
    Renhorn, Jakob
    Linköping University, Department of Clinical and Experimental Medicine, Cell Biology. Linköping University, Faculty of Health Sciences.
    Börjesson, Sara
    Linköping University, Department of Clinical and Experimental Medicine, Cell Biology. Linköping University, Faculty of Health Sciences.
    Nelson, Erin M
    Linköping University, Department of Clinical and Experimental Medicine, Cell Biology. Linköping University, Faculty of Health Sciences.
    Schwaiger, Christine S
    Royal Institute of Technology, Sweden .
    Bjelkmar, Par
    Royal Institute of Technology, Sweden Stockholm University, Sweden .
    Wallner, Björn
    Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics. Linköping University, The Institute of Technology.
    Lindahl, Erik
    Royal Institute of Technology, Sweden Stockholm University, Sweden .
    Elinder, Fredrik
    Linköping University, Department of Clinical and Experimental Medicine, Cell Biology. Linköping University, Faculty of Health Sciences.
    Tracking a complete voltage-sensor cycle with metal-ion bridges2012In: Proceedings of the National Academy of Sciences of the United States of America, ISSN 0027-8424, E-ISSN 1091-6490, Vol. 109, no 22, p. 8552-8557Article in journal (Refereed)
    Abstract [en]

    Voltage-gated ion channels open and close in response to changes in membrane potential, thereby enabling electrical signaling in excitable cells. The voltage sensitivity is conferred through four voltage-sensor domains (VSDs) where positively charged residues in the fourth transmembrane segment (S4) sense the potential. While an open state is known from the Kv1.2/2.1 X-ray structure, the conformational changes underlying voltage sensing have not been resolved. We present 20 additional interactions in one open and four different closed conformations based on metal-ion bridges between all four segments of the VSD in the voltage-gated Shaker K channel. A subset of the experimental constraints was used to generate Rosetta models of the conformations that were subjected to molecular simulation and tested against the remaining constraints. This achieves a detailed model of intermediate conformations during VSD gating. The results provide molecular insight into the transition, suggesting that S4 slides at least 12 angstrom along its axis to open the channel with a 3(10) helix region present that moves in sequence in S4 in order to occupy the same position in space opposite F290 from open through the three first closed states.

  • 12.
    Johansson-Åkhe, Isak
    et al.
    Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics. Linköping University, Faculty of Science & Engineering.
    Mirabello, Claudio
    Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics. Linköping University, Faculty of Science & Engineering.
    Wallner, Björn
    Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics. Linköping University, Faculty of Science & Engineering.
    Predicting protein-peptide interaction sites using distant protein complexes as structural templates2019In: Scientific Reports, ISSN 2045-2322, E-ISSN 2045-2322, Vol. 9, article id 4267Article in journal (Refereed)
    Abstract [en]

    Protein-peptide interactions play an important role in major cellular processes, and are associated with several human diseases. To understand and potentially regulate these cellular function and diseases it is important to know the molecular details of the interactions. However, because of peptide flexibility and the transient nature of protein-peptide interactions, peptides are difficult to study experimentally. Thus, computational methods for predicting structural information about protein-peptide interactions are needed. Here we present InterPep, a pipeline for predicting protein-peptide interaction sites. It is a novel pipeline that, given a protein structure and a peptide sequence, utilizes structural template matches, sequence information, random forest machine learning, and hierarchical clustering to predict what region of the protein structure the peptide is most likely to bind. When tested on its ability to predict binding sites, InterPep successfully pinpointed 255 of 502 (50.7%) binding sites in experimentally determined structures at rank 1 and 348 of 502 (69.3%) among the top five predictions using only structures with no significant sequence similarity as templates. InterPep is a powerful tool for identifying peptide-binding sites; with a precision of 80% at a recall of 20% it should be an excellent starting point for docking protocols or experiments investigating peptide interactions. The source code for InterPred is available at http://wallnerlab.org/InterPep/.

  • 13.
    Keasar, Chen
    et al.
    Ben Gurion Univ Negev, Israel.
    McGuffin, Liam J.
    Univ Reading, England.
    Wallner, Björn
    Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics. Linköping University, Faculty of Science & Engineering.
    Chopra, Gaurav
    Purdue Univ, IN USA.
    Adhikari, Badri
    Univ Missouri, MO USA.
    Bhattacharya, Debswapna
    Auburn Univ, AL 36849 USA.
    Blake, Lauren
    Lawrence Berkeley Natl Lab, CA 94720 USA.
    Bortot, Leandro Oliveira
    Univ Sao Paulo, Brazil.
    Cao, Renzhi
    Univ Missouri, MO USA.
    Dhanasekaran, B. K.
    Indian Inst Sci, India.
    Dimas, Itzhel
    Lawrence Berkeley Natl Lab, CA 94720 USA.
    Faccioli, Rodrigo Antonio
    Univ Sao Paulo, Brazil.
    Faraggi, Eshel
    Res and Informat Syst LLC, CA USA; IU Sch Med, IN USA; Nationwide Childrens Hosp, OH USA.
    Ganzynkowicz, Robert
    Univ Gdansk, Poland.
    Ghosh, Sambit
    Indian Inst Sci, India.
    Ghosh, Soma
    Indian Inst Sci, India.
    Gieldon, Artur
    Univ Gdansk, Poland.
    Golon, Lukasz
    Univ Gdansk, Poland.
    He, Yi
    Univ Calif, CA USA.
    Heo, Lim
    Seoul Natl Univ, South Korea.
    Hou, Jie
    Univ Missouri, MO USA.
    Khan, Main
    Univ Massachusetts, MA USA.
    Khatib, Firas
    Univ Massachusetts, MA USA.
    Khoury, George A.
    Princeton Univ, NJ 08544 USA.
    Kieslich, Chris
    Texas AandM Univ, TX USA.
    Kim, David E.
    Univ Washington, WA 98195 USA; Univ Washington, WA 98195 USA.
    Krupa, Pawel
    Univ Gdansk, Poland.
    Lee, Gyu Rie
    Seoul Natl Univ, South Korea.
    Li, Hongbo
    Univ Missouri, MO USA; NorthEast Normal Univ, Peoples R China; Univ Missouri, MO USA.
    Li, Jilong
    Univ Missouri, MO USA.
    Lipska, Agnieszka
    Univ Gdansk, Poland.
    Liwo, Adam
    Univ Gdansk, Poland.
    Maghrabi, Ali Hassan A.
    Univ Reading, England.
    Mirdita, Milot
    Max Planck Inst Biophys Chem, Germany.
    Mirzaei, Shokoufeh
    Calif State Polytech Univ Pomona, CA 91768 USA.
    Mozolewska, Magdalena A.
    Univ Gdansk, Poland.
    Onel, Melis
    Texas AandM Univ, TX USA.
    Ovchinnikov, Sergey
    Univ Washington, WA 98195 USA.
    Shah, Anand
    Univ Massachusetts, MA USA.
    Shah, Utkarsh
    Texas AandM Univ, TX USA.
    Sidi, Tomer
    Ben Gurion Univ Negev, Israel.
    Sieradzan, Adam K.
    Univ Gdansk, Poland.
    Slusarz, Magdalena
    Univ Gdansk, Poland.
    Slusarz, Rafal
    Univ Gdansk, Poland.
    Smadbeck, James
    Princeton Univ, NJ 08544 USA.
    Tamamis, Phanourios
    Texas AandM Univ, TX USA.
    Trieber, Nicholas
    Univ Massachusetts, MA USA.
    Wirecki, Tomasz
    Univ Gdansk, Poland.
    Yin, Yanping
    Cornell Univ, NY USA.
    Zhang, Yang
    Univ Michigan, MI 48109 USA.
    Bacardit, Jaume
    Newcastle Univ, England.
    Baranowski, Maciej
    Univ Gdansk, Poland; Med Univ Gdansk, Poland.
    Chapman, Nicholas
    Univ Washington, WA 98195 USA.
    Cooper, Seth
    Northeastern Univ, MA 02115 USA.
    Defelicibus, Alexandre
    Univ Sao Paulo, Brazil.
    Flatten, Jeff
    Univ Washington, WA 98195 USA.
    Koepnick, Brian
    Univ Washington, WA 98195 USA.
    Popovic, Zoran
    Univ Washington, WA 98195 USA.
    Zaborowski, Bartlomiej
    Univ Gdansk, Poland.
    Baker, David
    Univ Washington, WA 98195 USA.
    Cheng, Jianlin
    Univ Missouri, MO USA.
    Czaplewski, Cezary
    Univ Gdansk, Poland.
    Botazzo Delbem, Alexandre Claudio
    Univ Sao Paulo, Brazil.
    Floudas, Christodoulos
    Texas AandM Univ, TX USA.
    Kloczkowski, Andrzej
    Univ Gdansk, Poland.
    Oldziej, Stanislaw
    Univ Gdansk, Poland; Med Univ Gdansk, Poland.
    Levitt, Michael
    Stanford Univ, CA 94305 USA.
    Scheraga, Harold
    Cornell Univ, NY USA.
    Seok, Chaok
    Seoul Natl Univ, South Korea.
    Soeding, Johannes
    Max Planck Inst Biophys Chem, Germany.
    Vishveshwara, Saraswathi
    Indian Inst Sci, India.
    Xu, Dong
    Univ Missouri, MO USA.
    Crivelli, Silvia N.
    Lawrence Berkeley Natl Lab, CA 94720 USA; Univ Calif Davis, CA 95616 USA.
    An analysis and evaluation of the WeFold collaborative for protein structure prediction and its pipelines in CASP11 and CASP122018In: Scientific Reports, ISSN 2045-2322, E-ISSN 2045-2322, Vol. 8, article id 9939Article in journal (Refereed)
    Abstract [en]

    Every two years groups worldwide participate in the Critical Assessment of Protein Structure Prediction (CASP) experiment to blindly test the strengths and weaknesses of their computational methods. CASP has significantly advanced the field but many hurdles still remain, which may require new ideas and collaborations. In 2012 a web-based effort called WeFold, was initiated to promote collaboration within the CASP community and attract researchers from other fields to contribute new ideas to CASP. Members of the WeFold coopetition (cooperation and competition) participated in CASP as individual teams, but also shared components of their methods to create hybrid pipelines and actively contributed to this effort. We assert that the scale and diversity of integrative prediction pipelines could not have been achieved by any individual lab or even by any collaboration among a few partners. The models contributed by the participating groups and generated by the pipelines are publicly available at the WeFold website providing a wealth of data that remains to be tapped. Here, we analyze the results of the 2014 and 2016 pipelines showing improvements according to the CASP assessment as well as areas that require further adjustments and research.

  • 14.
    Liin, Sara
    et al.
    Linköping University, Department of Clinical and Experimental Medicine, Divison of Neurobiology. Linköping University, Faculty of Medicine and Health Sciences.
    Lund, Per-Eric
    Linköping University, Department of Clinical and Experimental Medicine, Division of Cell Biology. Linköping University, Faculty of Medicine and Health Sciences.
    Larsson, Johan
    Linköping University, Department of Clinical and Experimental Medicine, Divison of Neurobiology. Linköping University, Faculty of Medicine and Health Sciences.
    Brask, Johan
    Linköping University, Department of Clinical and Experimental Medicine, Divison of Neurobiology. Linköping University, Faculty of Medicine and Health Sciences.
    Wallner, Björn
    Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics. Linköping University, Faculty of Science & Engineering.
    Elinder, Fredrik
    Linköping University, Department of Clinical and Experimental Medicine, Divison of Neurobiology. Linköping University, Faculty of Medicine and Health Sciences.
    Biaryl sulfonamide motifs up- or down-regulate ion channel activity by activating voltage sensors2018In: The Journal of General Physiology, ISSN 0022-1295, E-ISSN 1540-7748, Vol. 150, no 8, p. 1215-1230Article in journal (Refereed)
    Abstract [en]

    Voltage-gated ion channels are key molecules for the generation of cellular electrical excitability. Many pharmaceutical drugs target these channels by blocking their ion-conducting pore, but in many cases, channel-opening compounds would be more beneficial. Here, to search for new channel-opening compounds, we screen 18,000 compounds with high-throughput patch-clamp technology and find several potassium-channel openers that share a distinct biaryl-sulfonamide motif. Our data suggest that the negatively charged variants of these compounds bind to the top of the voltage-sensor domain, between transmembrane segments 3 and 4, to open the channel. Although we show here that biaryl-sulfonamide compounds open a potassium channel, they have also been reported to block sodium and calcium channels. However, because they inactivate voltage-gated sodium channels by promoting activation of one voltage sensor, we suggest that, despite different effects on the channel gates, the biaryl-sulfonamide motif is a general ion-channel activator motif. Because these compounds block action potential-generating sodium and calcium channels and open an action potential-dampening potassium channel, they should have a high propensity to reduce excitability. This opens up the possibility to build new excitability-reducing pharmaceutical drugs from the biaryl-sulfonamide scaffold.

  • 15.
    Mirabello, Claudio
    et al.
    Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics. Linköping University, Faculty of Science & Engineering.
    Wallner, Björn
    Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics. Linköping University, Faculty of Science & Engineering.
    InterPred: A pipeline to identify and model protein-protein interactions2017In: Proteins: Structure, Function, and Bioinformatics, ISSN 0887-3585, E-ISSN 1097-0134, Vol. 85, no 6, p. 1159-1170Article in journal (Refereed)
    Abstract [en]

    Protein-protein interactions (PPI) are crucial for protein function. There exist many techniques to identify PPIs experimentally, but to determine the interactions in molecular detail is still difficult and very time-consuming. The fact that the number of PPIs is vastly larger than the number of individual proteins makes it practically impossible to characterize all interactions experimentally. Computational approaches that can bridge this gap and predict PPIs and model the interactions in molecular detail are greatly needed. Here we present InterPred, a fully automated pipeline that predicts and model PPIs from sequence using structural modeling combined with massive structural comparisons and molecular docking. A key component of the method is the use of a novel random forest classifier that integrate several structural features to distinguish correct from incorrect protein-protein interaction models. We show that InterPred represents a major improvement in protein-protein interaction detection with a performance comparable or better than experimental high-throughput techniques. We also show that our full-atom protein-protein complex modeling pipeline performs better than state of the art protein docking methods on a standard benchmark set. In addition, InterPred was also one of the top predictors in the latest CAPRI37 experiment. InterPred source code can be downloaded from http://wallnerlab.org/InterPred (C) 2017 Wiley Periodicals, Inc.

  • 16.
    Mirabello, Claudio
    et al.
    Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics. Linköping University, Faculty of Science & Engineering.
    Wallner, Björn
    Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics. Linköping University, Faculty of Science & Engineering.
    rawMSA: End-to-end Deep Learning using raw Multiple Sequence Alignments2019In: PLoS ONE, E-ISSN 1932-6203, Vol. 14, no 8, article id e0220182Article in journal (Refereed)
    Abstract [en]

    In the last decades, huge efforts have been made in the bioinformatics community to develop machine learning-based methods for the prediction of structural features of proteins in the hope of answering fundamental questions about the way proteins function and their involvement in several illnesses. The recent advent of Deep Learning has renewed the interest in neural networks, with dozens of methods being developed taking advantage of these new architectures. However, most methods are still heavily based pre-processing of the input data, as well as extraction and integration of multiple hand-picked, and manually designed features. Multiple Sequence Alignments (MSA) are the most common source of information in de novo prediction methods. Deep Networks that automatically refine the MSA and extract useful features from it would be immensely powerful. In this work, we propose a new paradigm for the prediction of protein structural features called rawMSA. The core idea behind rawMSA is borrowed from the field of natural language processing to map amino acid sequences into an adaptively learned continuous space. This allows the whole MSA to be input into a Deep Network, thus rendering pre-calculated features such as sequence profiles and other features calculated from MSA obsolete. We showcased the rawMSA methodology on three different prediction problems: secondary structure, relative solvent accessibility and inter-residue contact maps. We have rigorously trained and bench-marked rawMSA on a large set of proteins and have determined that it outperforms classical methods based on position-specific scoring matrices (PSSM) when predicting secondary structure and solvent accessibility, while performing on par with methods using more pre-calculated features in the inter-residue contact map prediction category in CASP12 and CASP13. Clearly demonstrating that rawMSA represents a promising development that can pave the way for improved methods using rawMSA instead of sequence profiles to represent evolutionary information in the coming years.

  • 17.
    Mirabello, Claudio
    et al.
    Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics. Linköping University, Faculty of Science & Engineering.
    Wallner, Björn
    Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics. Linköping University, Faculty of Science & Engineering.
    Topology independent structural matching discovers novel templates for protein interfaces2018In: Bioinformatics, ISSN 1367-4803, E-ISSN 1367-4811, Vol. 34, no 17, p. 787-794Article in journal (Refereed)
    Abstract [en]

    Motivation: Protein-protein interactions (PPI) are essential for the function of the cellular machinery. The rapid growth of protein-protein complexes with known 3D structures offers a unique opportunity to study PPI to gain crucial insights into protein function and the causes of many diseases. In particular, it would be extremely useful to compare interaction surfaces of monomers, as this would enable the pinpointing of potential interaction surfaces based solely on the monomer structure, without the need to predict the complete complex structure. While there are many structural alignment algorithms for individual proteins, very few have been developed for protein interfaces, and none that can align only the interface residues to other interfaces or surfaces of interacting monomer subunits in a topology independent (non-sequential) manner. Results: We present InterComp, a method for topology and sequence-order independent structural comparisons. The method is general and can be applied to various structural comparison applications. By representing residues as independent points in space rather than as a sequence of residues, InterComp can be applied to a wide range of problems including interface-surface comparisons and interface-interface comparisons. We demonstrate a use-case by applying InterComp to find similar protein interfaces on the surface of proteins. We show that InterComp pinpoints the correct interface for almost half of the targets (283 of 586) when considering the top 10 hits, and for 24% of the top 1, even when no templates can be found with regular sequence-order dependent structural alignment methods.

  • 18.
    Ping Heidi Iu, Yan
    et al.
    Queen Elizabeth Hospital, Peoples R China.
    Helander, Sara
    Linköping University, Department of Medical and Health Sciences, Division of Drug Research. Linköping University, Faculty of Medicine and Health Sciences.
    Zimdahl Kahlin, Anna
    Linköping University, Department of Medical and Health Sciences, Division of Drug Research. Linköping University, Faculty of Medicine and Health Sciences.
    Wah Cheng, Chun
    Queen Elizabeth Hospital, Peoples R China.
    Chung Shek, Chi
    Queen Elizabeth Hospital, Peoples R China.
    Ho Leung, Moon
    Queen Elizabeth Hospital, Peoples R China.
    Wallner, Björn
    Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics. Linköping University, Faculty of Science & Engineering.
    Mårtensson, Lars-Göran
    Linköping University, Department of Physics, Chemistry and Biology, Chemistry. Linköping University, Faculty of Science & Engineering.
    Lindqvist Appell, Malin
    Linköping University, Department of Medical and Health Sciences, Division of Drug Research. Linköping University, Faculty of Medicine and Health Sciences.
    One amino acid makes a difference-Characterization of a new TPMT allele and the influence of SAM on TPMT stability2017In: Scientific Reports, ISSN 2045-2322, E-ISSN 2045-2322, Vol. 7, article id 46428Article in journal (Refereed)
    Abstract [en]

    Thiopurine induced toxicity is associated with defects in the thiopurine methyltransferase (TPMT) gene. TPMT is a polymorphic enzyme, with most of the single nucleotide polymorphisms (SNPs) causing an amino acid change, altering the enzymatic activity of the TPMT protein. In this study, we characterize a novel patient allele c.719A amp;gt; C, named TPMT*41, together with the more common variant *3C c.719A amp;gt; G, resulting in an amino acid shift at tyrosine 240 to serine, p.Y240S and cysteine, p.Y240C respectively. We show that the patient heterozygote for c.719A amp;gt; C has intermediate enzymatic activity in red blood cells. Furthermore, in vitro studies, using recombinant protein, show that TPMT p.Y240S is less stable than both TPMTwt and TPMT p.Y240C. The addition of SAM increases the stability and, in agreement with Isothermal Titration Calorimetry (ITC) data, higher molar excess of SAM is needed in order to stabilize TPMT p.Y240C and TPMT p.Y240S compared to TPMTwt. Molecular dynamics simulations show that the loss of interactions is most severe for Y240S, which agrees with the thermal stability of the mutations. In conclusion, our study shows that SAM increases the stability of TPMT and that changing only one amino acid can have a dramatic effect on TPMT stability and activity.

  • 19.
    Ray, Arjun
    et al.
    Department of Theoretical Physics & Swedish eScience Research Center, Royal Institute of Technology, Stockholm, Sweden.
    Lindahl, Erik
    Department of Theoretical Physics & Swedish eScience Research Center, Royal Institute of Technology, Stockholm, Sweden and Center for Biomembrane Research, Department of Biochemistry & Biophysics, Stockholm University, Stockholm, Sweden.
    Wallner, Björn
    Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics. Linköping University, The Institute of Technology.
    Improved model quality assessment using ProQ22012In: BMC Bioinformatics, ISSN 1471-2105, E-ISSN 1471-2105, Vol. 13Article in journal (Refereed)
    Abstract [en]

    Background

    Employing methods to assess the quality of modeled protein structures is now standard practice in bioinformatics. In a broad sense, the techniques can be divided into methods relying on consensus prediction on the one hand, and single-model methods on the other. Consensus methods frequently perform very well when there is a clear consensus, but this is not always the case. In particular, they frequently fail in selecting the best possible model in the hard cases (lacking consensus) or in the easy cases where models are very similar. In contrast, single-model methods do not suffer from these drawbacks and could potentially be applied on any protein of interest to assess quality or as a scoring function for sampling-based refinement.

    Results

    Here, we present a new single-model method, ProQ2, based on ideas from its predecessor, ProQ. ProQ2 is a model quality assessment algorithm that uses support vector machines to predict local as well as global quality of protein models. Improved performance is obtained by combining previously used features with updated structural and predicted features. The most important contribution can be attributed to the use of profile weighting of the residue specific features and the use features averaged over the whole model even though the prediction is still local.

    Conclusions

    ProQ2 is significantly better than its predecessors at detecting high quality models, improving the sum of Z-scores for the selected first-ranked models by 20% and 32% compared to the second-best single-model method in CASP8 and CASP9, respectively. The absolute quality assessment of the models at both local and global level is also improved. The Pearson’s correlation between the correct and local predicted score is improved from 0.59 to 0.70 on CASP8 and from 0.62 to 0.68 on CASP9; for global score to the correct GDT_TS from 0.75 to 0.80 and from 0.77 to 0.80 again compared to the second-best single methods in CASP8 and CASP9, respectively. ProQ2 is available at http://proq2.wallnerlab.org.

  • 20.
    Schwaiger, Christine S
    et al.
    Science Life Lab, Sweden Royal Institute Technology, Sweden .
    Börjesson, Sara
    Linköping University, Department of Clinical and Experimental Medicine, Cell Biology. Linköping University, Faculty of Health Sciences.
    Hess, Berk
    Science Life Lab, Sweden Royal Institute Technology, Sweden .
    Wallner, Björn
    Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics. Linköping University, The Institute of Technology.
    Elinder, Fredrik
    Linköping University, Department of Clinical and Experimental Medicine, Cell Biology. Linköping University, Faculty of Health Sciences.
    Lindahl, Erik
    Science Life Lab, Sweden Royal Institute Technology, Sweden Stockholm University, Sweden .
    The Free Energy Barrier for Arginine Gating Charge Translation Is Altered by Mutations in the Voltage Sensor Domain2012In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 7, no 10, p. E45880-Article in journal (Refereed)
    Abstract [en]

    The gating of voltage-gated ion channels is controlled by the arginine-rich S4 helix of the voltage-sensor domain moving in response to an external potential. Recent studies have suggested that S4 moves in three to four steps to open the conducting pore, thus visiting several intermediate conformations during gating. However, the exact conformational changes are not known in detail. For instance, it has been suggested that there is a local rotation in the helix corresponding to short segments of a 3(10)-helix moving along S4 during opening and closing. Here, we have explored the energetics of the transition between the fully open state (based on the X-ray structure) and the first intermediate state towards channel closing (C-1), modeled from experimental constraints. We show that conformations within 3 angstrom of the X-ray structure are obtained in simulations starting from the C-1 model, and directly observe the previously suggested sliding 3(10)-helix region in S4. Through systematic free energy calculations, we show that the C-1 state is a stable intermediate conformation and determine free energy profiles for moving between the states without constraints. Mutations indicate several residues in a narrow hydrophobic band in the voltage sensor contribute to the barrier between the open and C-1 states, with F233 in the S2 helix having the largest influence. Substitution for smaller amino acids reduces the transition cost, while introduction of a larger ring increases it, largely confirming experimental activation shift results. There is a systematic correlation between the local aromatic ring rotation, the arginine barrier crossing, and the corresponding relative free energy. In particular, it appears to be more advantageous for the F233 side chain to rotate towards the extracellular side when arginines cross the hydrophobic region.

  • 21.
    Tu, William B.
    et al.
    University of Toronto, Canada; Princess Margaret Cancer Centre, Canada.
    Helander, Sara
    Linköping University, Department of Physics, Chemistry and Biology, Chemistry. Linköping University, Faculty of Science & Engineering.
    Pilstål, Robert
    Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics. Linköping University, Faculty of Science & Engineering.
    Ashley Hickman, K.
    University of Toronto, Canada; Princess Margaret Cancer Centre, Canada.
    Lourenco, Corey
    University of Toronto, Canada; Princess Margaret Cancer Centre, Canada.
    Jurisica, Igor
    University of Toronto, Canada; Princess Margaret Cancer Centre, Canada.
    Raught, Brian
    University of Toronto, Canada; Princess Margaret Cancer Centre, Canada.
    Wallner, Björn
    Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics. Linköping University, Faculty of Science & Engineering.
    Sunnerhagen, Maria
    Linköping University, Department of Physics, Chemistry and Biology, Chemistry. Linköping University, Faculty of Science & Engineering.
    Penn, Linda Z.
    University of Toronto, Canada; Princess Margaret Cancer Centre, Canada.
    Myc and its interactors take shape2015In: Biochimica et Biophysica Acta. Gene Regulatory Mechanisms, ISSN 1874-9399, E-ISSN 1876-4320, Vol. 1849, no 5, p. 469-483Article, review/survey (Refereed)
    Abstract [en]

    The Myc oncoprotein is a key contributor to the development of many human cancers. As such, understanding its molecular activities and biological functions has been a field of active research since its discovery more than three decades ago. Genome-wide studies have revealed Myc to be a global regulator of gene expression. The identification of its DNA-binding partner protein, Max, launched an area of extensive research into both the protein-protein interactions and protein structure of Myc. In this review, we highlight key insights with respect to Myc interactors and protein structure that contribute to the understanding of Mycs roles in transcriptional regulation and cancer. Structural analyses of Myc show many critical regions with transient structures that mediate protein interactions and biological functions. Interactors, such as Max, TRRAP, and PTEF-b, provide mechanistic insight into Mycs transcriptional activities, while others, such as ubiquitin ligases, regulate the Myc protein itself. It is appreciated that Myc possesses a large interactome, yet the functional relevance of many interactors remains unknown. Here, we discuss future research trends that embrace advances in genome-wide and proteome-wide approaches to systematically elucidate mechanisms of Myc action. This article is part of a Special Issue entitled: Myc proteins in cell biology and pathology. (C) 2014 Elsevier B.V. All rights reserved.

  • 22.
    Uziela, Karolis
    et al.
    Stockholm Univ, Sweden.
    Menendez Hurtado, David
    Stockholm Univ, Sweden.
    Shu, Nanjiang
    Stockholm Univ, Sweden; Sci Life Lab, Sweden.
    Wallner, Björn
    Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics. Linköping University, Faculty of Science & Engineering.
    Elofsson, Ame
    Stockholm Univ, Sweden.
    Improved protein model quality assessments by changing the target function2018In: Proteins: Structure, Function, and Bioinformatics, ISSN 0887-3585, E-ISSN 1097-0134, Vol. 86, no 6, p. 654-663Article in journal (Refereed)
    Abstract [en]

    Protein modeling quality is an important part of protein structure prediction. We have for more than a decade developed a set of methods for this problem. We have used various types of description of the protein and different machine learning methodologies. However, common to all these methods has been the target function used for training. The target function in ProQ describes the local quality of a residue in a protein model. In all versions of ProQ the target function has been the S-score. However, other quality estimation functions also exist, which can be divided into superposition- and contact-based methods. The superposition-based methods, such as S-score, are based on a rigid body superposition of a protein model and the native structure, while the contact-based methods compare the local environment of each residue. Here, we examine the effects of retraining our latest predictor, ProQ3D, using identical inputs but different target functions. We find that the contact-based methods are easier to predict and that predictors trained on these measures provide some advantages when it comes to identifying the best model. One possible reason for this is that contact based methods are better at estimating the quality of multi-domain targets. However, training on the S-score gives the best correlation with the GDT_TS score, which is commonly used in CASP to score the global model quality. To take the advantage of both of these features we provide an updated version of ProQ3D that predicts local and global model quality estimates based on different quality estimates.

  • 23.
    Uziela, Karolis
    et al.
    Stockholm University, Sweden.
    Menendez Hurtado, David
    Stockholm University, Sweden.
    Shu, Nanjiang
    Stockholm University, Sweden; Science Life Lab, Sweden.
    Wallner, Björn
    Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics. Linköping University, Faculty of Science & Engineering.
    Elofsson, Arne
    Stockholm University, Sweden.
    ProQ3D: improved model quality assessments using deep learning2017In: Bioinformatics, ISSN 1367-4803, E-ISSN 1367-4811, Vol. 33, no 10, p. 1578-1580Article in journal (Refereed)
    Abstract [en]

    A Summary: Protein quality assessment is a long-standing problem in bioinformatics. For more than a decade we have developed state-of-art predictors by carefully selecting and optimising inputs to a machine learning method. The correlation has increased from 0.60 in ProQ to 0.81 in ProQ2 and 0.85 in ProQ3 mainly by adding a large set of carefully tuned descriptions of a protein. Here, we show that a substantial improvement can be obtained using exactly the same inputs as in ProQ2 or ProQ3 but replacing the support vector machine by a deep neural network. This improves the Pearson correlation to 0.90 (0.85 using ProQ2 input features). Supplementary information: Supplementary data are available at Bioinformatics online.

  • 24.
    Uziela, Karolis
    et al.
    Stockholm University, Sweden.
    Shu, Nanjiang
    Stockholm University, Sweden; Science Life Lab, Sweden.
    Wallner, Björn
    Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics. Linköping University, Faculty of Science & Engineering.
    Elofsson, Arne
    Stockholm University, Sweden.
    ProQ3: Improved model quality assessments using Rosetta energy terms2016In: Scientific Reports, ISSN 2045-2322, E-ISSN 2045-2322, Vol. 6, article id 33509Article in journal (Refereed)
    Abstract [en]

    Quality assessment of protein models using no other information than the structure of the model itself has been shown to be useful for structure prediction. Here, we introduce two novel methods, ProQRosFA and ProQRosCen, inspired by the state-of-art method ProQ2, but using a completely different description of a protein model. ProQ2 uses contacts and other features calculated from a model, while the new predictors are based on Rosetta energies: ProQRosFA uses the full-atom energy function that takes into account all atoms, while ProQRosCen uses the coarse-grained centroid energy function. The two new predictors also include residue conservation and terms corresponding to the agreement of a model with predicted secondary structure and surface area, as in ProQ2. We show that the performance of these predictors is on par with ProQ2 and significantly better than all other model quality assessment programs. Furthermore, we show that combining the input features from all three predictors, the resulting predictor ProQ3 performs better than any of the individual methods. ProQ3, ProQRosFA and ProQRosCen are freely available both as a webserver and stand-alone programs at http://proq3.bioinfo.se/.

  • 25.
    Uziela, Karolis
    et al.
    Stockholm University, Sweden.
    Wallner, Björn
    Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics. Linköping University, Faculty of Science & Engineering. Swedish e-science Research Centre (SeRC).
    ProQ2: estimation of model accuracy implemented in Rosetta2016In: Bioinformatics, ISSN 1367-4803, E-ISSN 1367-4811, Vol. 32, no 9, p. 1411-1413Article in journal (Refereed)
    Abstract [en]

    Motivation: Model quality assessment programs are used to predict the quality of modeled protein structures. They can be divided into two groups depending on the information they are using: ensemble methods using consensus of many alternative models and methods only using a single model to do its prediction. The consensus methods excel in achieving high correlations between prediction and true quality measures. However, they frequently fail to pick out the best possible model, nor can they be used to generate and score new structures. Single-model methods on the other hand do not have these inherent shortcomings and can be used both to sample new structures and to improve existing consensus methods. Results: Here, we present an implementation of the ProQ2 program to estimate both local and global model accuracy as part of the Rosetta modeling suite. The current implementation does not only make it possible to run large batch runs locally, but it also opens up a whole new arena for conformational sampling using machine learned scoring functions and to incorporate model accuracy estimation in to various existing modeling schemes. ProQ2 participated in CASP11 and results from CASP11 are used to benchmark the current implementation. Based on results from CASP11 and CAMEO-QE, a continuous benchmark of quality estimation methods, it is clear that ProQ2 is the single-model method that performs best in both local and global model accuracy.

  • 26.
    Virkki, Minttu T.
    et al.
    Stockholm University, Sweden .
    Peters, Christoph
    Stockholm University, Sweden; Swedish e-Science Research Center (SeRC), Stockholm, Sweden.
    Nilsson, Daniel
    Stockholm University, Sweden .
    Sörensen, Therese
    Stockholm University, Sweden .
    Cristobal, Susana
    Linköping University, Department of Clinical and Experimental Medicine, Division of Cell Biology. Linköping University, Faculty of Health Sciences. University of the Basque Country, Leioa, Spain.
    Wallner, Björn
    Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics. Linköping University, The Institute of Technology.
    Elofsson, Arne
    Stockholm University, Sweden; Swedish E Science Research Centre SeRC, Stockholm, Sweden .
    The Positive Inside Rule Is Stronger When Followed by a Transmembrane Helix2014In: Journal of Molecular Biology, ISSN 0022-2836, E-ISSN 1089-8638, Vol. 426, no 16, p. 2982-2991Article in journal (Refereed)
    Abstract [en]

    The translocon recognizes transmembrane helices with sufficient level of hydrophobicity and inserts them into the membrane. However, sometimes less hydrophobic helices are also recognized. Positive inside rule, orientational preferences of and specific interactions with neighboring helices have been shown to aid in the recognition of these helices, at least in artificial systems. To better understand how the translocon inserts marginally hydrophobic helices, we studied three naturally occurring marginally hydrophobic helices, which were previously shown to require the subsequent helix for efficient translocon recognition. We find no evidence for specific interactions when we scan all residues in the subsequent helices. Instead, we identify arginines located at the N-terminal part of the subsequent helices that are crucial for the recognition of the marginally hydrophobic transmembrane helices, indicating that the positive inside rule is important. However, in two of the constructs, these arginines do not aid in the recognition without the rest of the subsequent helix; that is, the positive inside rule alone is not sufficient. Instead, the improved recognition of marginally hydrophobic helices can here be explained as follows: the positive inside rule provides an orientational preference of the subsequent helix, which in turn allows the marginally hydrophobic helix to be inserted; that is, the effect of the positive inside rule is stronger if positively charged residues are followed by a transmembrane helix. Such a mechanism obviously cannot aid C-terminal helices, and consequently, we find that the terminal helices in multi-spanning membrane proteins are more hydrophobic than internal helices.

  • 27.
    Wallenhammar, Amélie
    et al.
    Linköping University, Department of Physics, Chemistry and Biology, Chemistry. Linköping University, Faculty of Science & Engineering.
    Anandapadmanaban, Madhanagopal
    Linköping University, Department of Physics, Chemistry and Biology, Chemistry. Linköping University, Faculty of Science & Engineering. MRC, England.
    Lemak, Alexander
    University of Toronto, Canada; University of Toronto, Canada.
    Mirabello, Claudio
    Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics. Linköping University, Faculty of Science & Engineering.
    Lundström, Patrik
    Linköping University, Department of Physics, Chemistry and Biology, Chemistry. Linköping University, Faculty of Science & Engineering.
    Wallner, Björn
    Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics. Linköping University, Faculty of Science & Engineering.
    Sunnerhagen, Maria
    Linköping University, Department of Physics, Chemistry and Biology, Chemistry. Linköping University, Faculty of Science & Engineering.
    Solution NMR structure of the TRIM21 B-box2 and identification of residues involved in its interaction with the RING domain2017In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 12, no 7, article id e0181551Article in journal (Refereed)
    Abstract [en]

    Tripartite motif-containing (TRIM) proteins are defined by the sequential arrangement of RING, B-box and coiled-coil domains (RBCC), where the B-box domain is a unique feature of the TRIM protein family. TRIM21 is an E3 ubiquitin-protein ligase implicated in innate immune signaling by acting as an autoantigen and by modifying interferon regulatory factors. Here we report the three-dimensional solution structure of the TRIM21 B-box2 domain by nuclear magnetic resonance (NMR) spectroscopy. The structure of the B-box2 domain, comprising TRIM21 residues 86-130, consists of a short alpha-helical segment with an N-terminal short beta-strand and two anti-parallel beta-strands jointly found the core, and adopts a RING-like fold. This beta beta alpha beta core largely defines the overall fold of the TRIM21 B-box2 and the coordination of one Zn2+ ion stabilizes the tertiary structure of the protein. Using NMR titration experiments, we have identified an exposed interaction surface, a novel interaction patch where the B-box2 is likely to bind the N-terminal RING domain. Our structure together with comparisons with other TRIM B-box domains jointly reveal how its different surfaces are employed for various modular interactions, and provides extended understanding of how this domain relates to flanking domains in TRIM proteins.

  • 28.
    Wallner, Björn
    Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics. Linköping University, Faculty of Science & Engineering.
    Estimating local protein model quality: prospects for molecular replacement2020In: ACTA CRYSTALLOGRAPHICA SECTION D-STRUCTURAL BIOLOGY, ISSN 2059-7983, Vol. 76, p. 285-290Article in journal (Refereed)
    Abstract [en]

    Model quality assessment programs estimate the quality of protein models and can be used to estimate local error in protein models. ProQ3D is the most recent and most accurate version of our software. Here, it is demonstrated that it is possible to use local error estimates to substantially increase the quality of the models for molecular replacement (MR). Adjusting the B factors using ProQ3D improved the log-likelihood gain (LLG) score by over 50% on average, resulting in significantly more successful models in MR compared with not using error estimates. On a data set of 431 homology models to address difficult MR targets, models with error estimates from ProQ3D received an LLG of amp;gt;50 for almost half of the models 209/431 (48.5%), compared with 175/431 (40.6%) for the previous version, ProQ2, and only 74/431 (17.2%) for models with no error estimates, clearly demonstrating the added value of using error estimates to enable MR for more targets. ProQ3D is available from http://proq3.bioinfo.se/ both as a server and as a standalone download.

  • 29.
    Wallner, Björn
    Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics. Linköping University, The Institute of Technology. Swedish e-Science Research Center, Stockholm, Sweden.
    ProQM-resample: improved model quality assessment for membrane proteins by limited conformational sampling2014In: Bioinformatics, ISSN 1367-4803, E-ISSN 1367-4811, Vol. 30, no 15, p. 2221-2223Article in journal (Refereed)
    Abstract [en]

    Model Quality Assessment Programs (MQAPs) are used to predict the quality of modeled protein structures. These usually use two approaches: methods using consensus of many alternative models and methods requiring only a single model to do its prediction. The consensus methods are useful to improve overall accuracy; however, they frequently fail to pick out the best possible model and cannot be used to generate and score new structures. Single-model methods, on the other hand, do not have these inherent shortcomings and can be used to both sample new structures and improve existing consensus methods. Here, we present ProQM-resample, a membrane protein-specific single-model MQAP, that couples side-chain resampling with MQAP rescoring by ProQM to improve model selection. The side-chain resampling is able to improve side-chain packing for 96% of all models, and improve model selection by 24% as measured by the sum of the Z-score for the first-ranked model (from 25.0 to 31.1), even better than the state-of-the-art consensus method Pcons. The improved model selection can be attributed to the improved side-chain quality, which enables the MQAP to rescue good backbone models with poor side-chain packing.

  • 30.
    Wei, Yong
    et al.
    Linköping University, Department of Physics, Chemistry and Biology. Linköping University, Faculty of Science & Engineering. Univ Hlth Network, Canada; Struct Genom Consortium, Canada.
    Resetca, Diana
    Univ Hlth Network, Canada; Univ Toronto, Canada.
    Li, Zhe
    Yokohama City Univ, Japan.
    Johansson-Åkhe, Isak
    Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics. Linköping University, Faculty of Science & Engineering.
    Ahlner, Alexandra
    Linköping University, Department of Physics, Chemistry and Biology, Chemistry. Linköping University, Faculty of Science & Engineering.
    Helander, Sara
    Linköping University, Department of Clinical and Experimental Medicine, Division of Clinical Chemistry. Linköping University, Faculty of Medicine and Health Sciences.
    Wallenhammar, Amélie
    Linköping University, Department of Physics, Chemistry and Biology, Chemistry. Linköping University, Faculty of Science & Engineering.
    Morad, Vivian
    Linköping University, Department of Physics, Chemistry and Biology, Chemistry. Linköping University, Faculty of Science & Engineering.
    Raught, Brian
    Univ Hlth Network, Canada; Univ Toronto, Canada.
    Wallner, Björn
    Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics. Linköping University, Faculty of Science & Engineering.
    Kokubo, Tetsuro
    Yokohama City Univ, Japan.
    Tong, Yufeng
    Struct Genom Consortium, Canada; Univ Windsor, Canada.
    Penn, Linda Z.
    Univ Hlth Network, Canada; Univ Toronto, Canada.
    Sunnerhagen, Maria
    Linköping University, Department of Physics, Chemistry and Biology, Chemistry. Linköping University, Faculty of Science & Engineering.
    Multiple direct interactions of TBP with the MYC oncoprotein2019In: Nature Structural & Molecular Biology, ISSN 1545-9993, E-ISSN 1545-9985, Vol. 26, no 11, p. 1035-+Article in journal (Refereed)
    Abstract [en]

    Transcription factor c-MYC is a potent oncoprotein; however, the mechanism of transcriptional regulation via MYC-protein interactions remains poorly understood. The TATA-binding protein (TBP) is an essential component of the transcription initiation complex TFIID and is required for gene expression. We identify two discrete regions mediating MYC-TBP interactions using structural, biochemical and cellular approaches. A 2.4 -angstrom resolution crystal structure reveals that human MYC amino acids 98-111 interact with TBP in the presence of the amino-terminal domain 1 of TBP-associated factor 1 (TAF1(TAND1)). Using biochemical approaches, we have shown that MYC amino acids 115-124 also interact with TBP independently of TAF1(TAND1). Modeling reveals that this region of MYC resembles a TBP anchor motif found in factors that regulate TBP promoter loading. Site-specific MYC mutants that abrogate MYC-TBP interaction compromise MYC activity. We propose that MYC-TBP interactions propagate transcription by modulating the energetic landscape of transcription initiation complex assembly.

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