Background

We aim to propose interactions between the parathyroid hormone-2 receptor (PTH2R) and its ligand the tuberoinfundibular peptide of 39 residues (TIP39) by constructing a homology model of their complex. The two related peptides parathyroid hormone (PTH) and parathyroid hormone related protein (PTHrP) are compared with the complex to examine their interactions.

Findings

In the model, the hydrophobic N-terminus of TIP39 is buried in a hydrophobic part of the central cavity between helices 3 and 7. Comparison of the peptide sequences indicates that the main discriminator between the agonistic peptides TIP39 and PTH and the inactive PTHrP is a tryptophan-phenylalanine replacement. The model indicates that the smaller phenylalanine in PTHrP does not completely occupy the binding site of the larger tryptophan residue in the other peptides. As only TIP39 causes internalisation of the receptor and the primary difference being an aspartic acid in position 7 of TIP39 that interacts with histidine 396 in the receptor, versus isoleucine/histidine residues in the related hormones, this might be a trigger interaction for the events that cause internalisation.

Conclusions

A model is constructed for the complex and a trigger interaction for full agonistic activation between aspartic acid 7 of TIP39 and histidine 396 in the receptor is proposed.

The Collaborative Computational Project No. 4 (CCP4) is a UK-led international collective with a mission to develop, test, distribute and promote software for macromolecular crystallography. The CCP4 suite is a multiplatform collection of programs brought together by familiar execution routines, a set of common libraries and graphical interfaces. The CCP4 suite has experienced several considerable changes since its last reference article, involving new infrastructure, original programs and graphical interfaces. This article, which is intended as a general literature citation for the use of the CCP4 software suite in structure determination, will guide the reader through such transformations, offering a general overview of the new features and outlining future developments. As such, it aims to highlight the individual programs that comprise the suite and to provide the latest references to them for perusal by crystallographers around the world.

Some viral metagenomic serum samples contain a huge amount of Anellovirus, which is a genetically diverse family with a few conserved regions making it hard to efficiently characterize. Multiple sequence alignment of the Anelloviruses found in the sample must be constructed to get a clear picture of Anellovirus diversity and to identify stable regions. Using available multiple sequence alignment software directly on these fragments results in an MSA of a very poor quality due to their diversity, misaligned regions and low-quality regions present in the sequence.

An efficient MSA must be constructed in order to characterize these Anellovirus present in the samples. Pairwise alignment is used to align one fragment to the database sequences at a time. The fragments are then aligned to the database sequences using the start and end position from the pairwise alignment results. The algorithm will also exclude non-aligned portions of the fragments, as these are very hard to handle properly and are often products of misassembly or chimeric sequenced fragments. Other tools to aid further analysis were developed, such as finding a non-overlapping window that contains the most fragments, find consensus of the alignment and extract any regions from the MSA for further analysis.

An MSA was constructed with a high percent of correctly aligned bases compared to an MSA constructed using MSA softwares. The minimal number of genomes found in the sampled sequence was found as well as a distribution of the fragments along the database sequence. Moreover, highly conserved region and the window containing most fragments were extracted from the MSA and phylogenetic trees were constructed for these regions. 

This thesis provides an in-depth analyze of expressed sequence tags (EST) that represent pieces of eukaryotic mRNA by using unsupervised hidden Markov model (HMM). ESTs are short nucleotide sequences that are used primarily for rapid identificationof new genes with potential coding regions (CDS). ESTs are made by sequencing on double-stranded cDNA and the synthesizedESTs are stored in digital form, usually in FASTA format. Since sequencing is often randomized and that parts of mRNA contain non-coding regions, some ESTs will not represent CDS.It is desired to remove these unwanted ESTs if the purpose is to identifygenes associated with CDS. Application of stochastic HMM allow identification of region contents in a EST. Softwares like ESTScanuse HMM in which a training of the HMM is done by supervised learning with annotated data. However, because there are not always annotated data at hand this thesis focus on the ability to train an HMM with unsupervised learning on data containing ESTs, both with and without CDS. But the data used for training is not annotated, i.e. the regions that an EST consists of are unknown. In this thesis a new HMM is introduced where the parameters of the HMM are in focus so that they are reasonablyconsistent with biologically important regionsof an mRNA such as the Kozak sequence, poly(A)-signals and poly(A)-tails to guide the training and decoding correctly with ESTs to proper statesin the HMM. Transition probabilities in the HMMhas been adapted so that it represents the mean length and distribution of the different regions in mRNA. Testing of the HMM's specificity and sensitivityhave been performed via BLAST by blasting each EST and compare the BLAST results with the HMM prediction results.A regression analysis test shows that the length of ESTs used when training the HMM is significantly important, the longer the better. The final resultsshows that it is possible to train an HMM with unsupervised machine learning but to be comparable to supervised machine learning as ESTScan, further expansion of the HMM is necessary such as frame-shift correction of ESTs byimproving the HMM's ability to choose correctly positioned start codons or nucleotides. Usually the false positive results are because of incorrectly positioned start codons leadingto too short CDS lengths. Since no frame-shift correction is implemented, short predicted CDS lengths are not acceptable and is hence not counted as coding regionsduring prediction. However, when there is a lack of supervised models then unsupervised HMM is a potential replacement with stable performance and able to be adapted forany eukaryotic organism.

Most loss-of-function diseases are caused by aberrant folding of important proteins. These proteins often misfold due to mutations. The disease marble brain syndrome (MBS), known also as carbonic anhydrase II deficiency syndrome (CADS), can manifest in carriers of point mutations in the human carbonic anhydrase II (HCA II) gene. One mutation associated with MBS entails the His107Tyr substitution. Here, we demonstrate that this mutation is a remarkably destabilizing folding mutation. The loss-of-function is clearly a folding defect, since the mutant shows 64% of CO₂ hydration activity compared to that of the wild-type at low temperature where the mutant is folded. On the contrary, its stability towards thermal and guanidine hydrochloride (GuHCl) denaturation is highly compromised. Using activity assays, CD, fluorescence, NMR, cross-linking, aggregation measurements and molecular modeling, we have mapped the properties of this remarkable mutant. Loss of enzymatic activity had a midpoint temperature of denaturation (T_m) of 16 °C for the mutant compared to 55 °C for the wild-type protein. GuHCl-denaturation (at 4 °C) showed that the native state of the mutant was destabilized by 9.2 kcal/mol. The mutant unfolds through at least two equilibrium intermediates; one novel intermediate that we have termed the molten globule light state and, after further denaturation, the classical molten globule state is populated. Under physiological conditions (neutral pH; 37 °C), the His107Tyr mutant will populate the molten globule light state, likely due to novel interactions between Tyr107 and the surroundings of the critical residue Ser29 that destabilize the native conformation. This intermediate binds the hydrophobic dye 8-anilino-1-naphthalene sulfonic acid (ANS) but not as strong as the molten globule state, and near-UV CD reveals the presence of significant tertiary structure. Notably, this intermediate is not as prone to aggregation as the classical molten globule. As a proof of concept for an intervention strategy with small molecules, we showed that binding of the CA inhibitor acetazolamide increases the stability of the native state of the mutant by 2.9 kcal/mol in accordance with its strong affinity. Acetazolamide shifts the T_m to 34 °C that protects from misfolding and will enable a substantial fraction of the enzyme pool to survive physiological conditions.

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.

The crucial role of Myc as an oncoprotein and as a key regulator of cell growth makes it essential to understand the molecular basis of Myc function. The N-terminal region of c-Myc coordinates a wealth of protein interactions involved in transformation, differentiation and apoptosis. We have characterized in detail the intrinsically disordered properties of Myc-1-88, where hierarchical phosphorylation of S62 and T58 regulates activation and destruction of the Myc protein. By nuclear magnetic resonance (NMR) chemical shift analysis, relaxation measurements and NOE analysis, we show that although Myc occupies a very heterogeneous conformational space, we find transiently structured regions in residues 22-33 and in the Myc homology box I (MBI; residues 45-65); both these regions are conserved in other members of the Myc family. Binding of Bin1 to Myc-1-88 as assayed by NMR and surface plasmon resonance (SPR) revealed primary binding to the S62 region in a dynamically disordered and multivalent complex, accompanied by population shifts leading to altered intramolecular conformational dynamics. These findings expand the increasingly recognized concept of intrinsically disordered regions mediating transient interactions to Myc, a key transcriptional regulator of major medical importance, and have important implications for further understanding its multifaceted role in gene regulation.

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.

Cellulose nanomaterial (CNM)-based foams and aerogels with thermal conductivities substantially below the value for air attract significant interest as super-insulating materials in energy-efficient green buildings. However, the moisture dependence of the thermal conductivity of hygroscopic CNM-based materials is poorly understood, and the importance of phonon scattering in nanofibrillar foams remains unexplored. Here, we show that the thermal conductivity perpendicular to the aligned nanofibrils in super-insulating icetemplated nanocellulose foams is lower for thinner fibrils and depends strongly on relative humidity (RH), with the lowest thermal conductivity (14 mW m(-1) K-1) attained at 35% RH. Molecular simulations show that the thermal boundary conductance is reduced by the moisture-uptake-controlled increase of the fibril-fibril separation distance and increased by the replacement of air with water in the foam walls. Controlling the heat transport of hygroscopic super-insulating nanofibrillar foams by moisture uptake and release is of potential interest in packaging and building applications.

Unconjugated pterins are ubiquitous molecules that participate in countless enzymatic processes and are potentially involved in the photosensitization of singlet oxygen, amino acids, and nucleotides. Following electronic excitation with UV-A light, some of these pterins degrade, producing hydrogen peroxide as the main side product. This process, which is known to take place in vivo, contributes to oxidative stress and melanocyte destruction in vitiligo. In this work, we present for the first time mechanistic insight into the formation of transient triplet species that simultaneously trigger Type I and Type II photosensitizing processes and the initiation of degradation processes. Our calculations reveal that photodegradation of 6-biopterin, which accumulates in the skin of vitiligo patients, leads to 6-formylpterin through a retro-aldol reaction, and subsequently to 6-carboxypterin through a water-mediated aldehyde oxidation. Additionally, we show that the changes in the photosensitizing potential of these systems with pH come from the modulation of their excited-state redox potentials.

Excited-state aromaticity (ESA) and antiaromaticity (ESAA) are by now well-established concepts for explaining photophysical properties and photochemical reactivities of cyclic, conjugated molecules. However, their application is less straightforward than the corresponding process by which the thermal chemistry of such systems is rationalized in terms of ground-state aromaticity (GSA) and antiaromaticity (GSAA). Recognizing that the harmonic oscillator model of aromaticity (HOMA) provides an easy way to measure aromaticity on geometric grounds, it is therefore notable that this model is yet to be parameterized for excited states. Against this background, we here present a new parameterization of HOMA - termed HOMER - for the T-1 state of both carbocyclic and heterocyclic compounds based on high-level quantum-chemical calculations. Considering CC, CN, NN and CO bonds and testing the parametrization using calculated magnetic data as reference, we find that the description of ESA and ESAA by HOMER is superior to that afforded by the original HOMA scheme, and that it reaches the same overall quality as HOMA does for GSA and GSAA. Furthermore, we demonstrate that the derived HOMER parameters can be used for predictive modeling of ESA and ESAA at very different levels of theory. Altogether, the results highlight the potential of HOMER to facilitate future studies of ESA and ESAA.

Photochemical reactions enabling efficient transformation of aromatic systems into energetic but stable non-aromatic isomers have a long history in organic chemistry. One recently discovered reaction in this realm is that where derivatives of 1,2-azaborine, a compound isoelectronic with benzene in which two adjacent C atoms are replaced by B and N atoms, form the non-hexagon Dewar isomer. Here, we report quantum-chemical calculations that explain both why 1,2-azaborine is intrinsically more reactive toward Dewar formation than benzene, and how suitable substitutions at the B and N atoms are able to increase the corresponding quantum yield. We find that Dewar formation from 1,2-azaborine is favored by a pronounced driving force that benzene lacks, and that a large improvement in quantum yield arises when the reaction of substituted 1,2-azaborines proceeds without involvement of an intermediary ground-state species. Overall, we report new insights into making photochemical use of the Dewar isomers of aromatic compounds. Quantum-chemical calculations combined with molecular-dynamics simulations reveal mechanisms for improving the quantum yields by which aromatic compounds form their non-aromatic Dewar isomers, with potential implications in solar-energy storage.

A major challenge in the development of molecular photoswitches capable of storing and releasing solar energy is to simultaneously realize many of the performance criteria required of the switches for such applications. Here, we take on this challenge by introducing an all-around performance descriptor that combines three key criteria (related to energy density, storage time and light-absorption characteristics), and by using density functional theory methods to calculate its values for 52 newly designed dihydroazulene/vinylheptafulvene (DHA/VHF) switches. Thereby, we are able to identify several switches with excellent overall properties that contain a structural motif absent in all DHA/VHF compounds previously considered for solar-energy storage. For some of these switches, we also provide retrosynthetic analyses and demonstrate that they form the energy-storing VHF isomer through a facile DHA!VHF photoisomerization reaction. All in all, we conclude that these switches show great promise for further development towards applications in solar-energy storage.

The common approach to investigate the impact of aromaticity on excited-state proton transfer by probing the (anti)aromatic character of reactants and products alone is scrutinized by modelling such reactions involving 2-pyridone. Thereby, it is found that energy barriers can be strongly influenced by transient changes in aromaticity unaccounted for by this approach, particularly when the photoexcited state interacts with a second excited state. Overall, the modelling identifies a pronounced effect overlooked by most studies on this topic.

The construction of molecular photogears that can achieve through-space transmission of the unidirectional double-bond rotary motion of light-driven molecular motors onto a remote single-bond axis is a formidable challenge in the field of artificial molecular machines. Here, we present a proof-of-principle design of such photogears that is based on the possibility of using stereogenic substituents to control both the relative stabilities of two helical forms of the photogear and the double-bond photoisomerization reaction that connects them. The potential of the design was verified by quantum-chemical modeling through which photogearing was found to be a favorable process compared to free-standing single-bond rotation ("slippage"). Overall, our study unveils a surprisingly simple approach to realizing unidirectional photogearing. A stereochemical approach to transmitting the directional double-bond rotary motion of light-driven molecular motors through space onto a remote single-bond axis is put forth and successfully tested by means of quantum-chemical modeling. A key result in the assessment of the approach is that the desired photogearing process is favorable compared to the undesired, free-standing single-bond rotation process ("slippage") with which it competes.**image

We report the synthesis and self-assembly of chiral, conformationally flexible C-3-symmetrical trisamides. A strong Cotton effect is observed for the supramolecular polymers in linear alkanes but not in cyclic alkanes. MD simulations suggest 2:1 conformations of the amides within the aggregates in both types of solvents, but a chiral bias in only linear alkanes.

Background There exist few, if any, practical guidelines for predictive and falsifiable multi-omic data integration that systematically integrate existing knowledge. Disease modules are popular concepts for interpreting genome-wide studies in medicine but have so far not been systematically evaluated and may lead to corroborating multi-omic modules. Result We assessed eight module identification methods in 57 previously published expression and methylation studies of 19 diseases using GWAS enrichment analysis. Next, we applied the same strategy for multi-omic integration of 20 datasets of multiple sclerosis (MS), and further validated the resulting module using both GWAS and risk-factor-associated genes from several independent cohorts. Our benchmark of modules showed that in immune-associated diseases modules inferred from clique-based methods were the most enriched for GWAS genes. The multi-omic case study using MS data revealed the robust identification of a module of 220 genes. Strikingly, most genes of the module were differentially methylated upon the action of one or several environmental risk factors in MS (n = 217, P = 10(- 47)) and were also independently validated for association with five different risk factors of MS, which further stressed the high genetic and epigenetic relevance of the module for MS. Conclusions We believe our analysis provides a workflow for selecting modules and our benchmark study may help further improvement of disease module methods. Moreover, we also stress that our methodology is generally applicable for combining and assessing the performance of multi-omic approaches for complex diseases.

Epigenetics may play a central, yet unexplored, role in the profound changes that the maternal immune system undergoes during pregnancy and could be involved in the pregnancy-induced modulation of several autoimmune diseases. We investigated changes in the methylome in isolated circulating CD4(+) T-cells in non-pregnant and pregnant women, during the 1(st) and 2(nd) trimester, using the Illumina Infinium Human Methylation 450K array, and explored how these changes were related to autoimmune diseases that are known to be affected during pregnancy. Pregnancy was associated with several hundreds of methylation differences, particularly during the 2(nd) trimester. A network-based modular approach identified several genes, e.g., CD28, FYN, VAV1 and pathways related to T-cell signalling and activation, highlighting T-cell regulation as a central component of the observed methylation alterations. The identified pregnancy module was significantly enriched for disease-associated methylation changes related to multiple sclerosis, rheumatoid arthritis and systemic lupus erythematosus. A negative correlation between pregnancy-associated methylation changes and disease-associated changes was found for multiple sclerosis and rheumatoid arthritis, diseases that are known to improve during pregnancy whereas a positive correlation was found for systemic lupus erythematosus, a disease that instead worsens during pregnancy. Thus, the directionality of the observed changes is in line with the previously observed effect of pregnancy on disease activity. Our systems medicine approach supports the importance of the methylome in immune regulation of T-cells during pregnancy. Our findings highlight the relevance of using pregnancy as a model for understanding and identifying disease-related mechanisms involved in the modulation of autoimmune diseases.

Biological systems encompass various molecular entities such as genes, proteins, and other biological molecules, including interactions among those components. Understanding a given phenotype, the functioning of a cell or tissue, aetiology of disease, or cellular organization, requires accurate measurements of the abundance profiles of these molecular entities in the form of biomedical data. The analysis of the interplay between these different entities at various levels represented in the form of biological network provides a mechanistic understanding of the observed phenotype. In order to study this interplay, there is a requirement of a conceptual and intuitive framework which can model multiple omics such as genome, transcriptome, or a proteome. This can be addressed by application of network-based strategies.

Translational bioinformatics deals with the development of analytic and interpretive methods to optimize the transformation of different omics and clinical data to understanding of complex diseases and improving human health. Complex diseases such as multiple sclerosis (MS), rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), and non-small cell lung cancer (NSCLC) etc., are hypothesized to be a result of a disturbance in the omic networks rendering the healthy cells to be in a state of malfunction. Even though there are numerous methods to layout the relation of the interactions among omics in complex diseases, the output network modules were not clearly interpreted.

In this PhD thesis, we showed how different omic data such as transcriptome and methylome can be mapped to the network of interactions to extract highly interconnected gene sets relevant to the disease, so called disease modules. First, we selected common module identification methods and assembled them into a unified framework of the methods implemented in an Rpackage MODifieR (Paper I). Secondly, we showed that the concept of the network modules can be applied on the whole genome sequencing data for developing a tested model for predicting myelosuppressive toxicity (Paper II).

Furthermore, we demonstrated that network modules extracted using the methylome data helped identifying several genes that were associated with pregnancy-induced pathways and were enriched for disease-associated methylation changes that were also shared by three auto-immune and inflammatory diseases, namely MS, RA, and SLE (Paper III). Remarkably, those methylation changes correlated with the expected outcome from clinical experience in those diseases. Last, we benchmarked the omic network modules on 19 different complex diseases using both transcriptomic and methylomic data. This led to the identification of a multi-omic MS module that was highly enriched disease-associated genes identified by genome-wide association studies, but also genes associated with the most common environmental risk factors of MS (Paper IV).

The application of the network modules concept on different omics is the centrepiece of the research presented in this PhD thesis. The thesis represents the application of omic network modules in complex diseases and how these modules should be integrated and interpreted. In particular, it aimed to show the importance of networks owing to the incomplete knowledge of the genes dysregulated in complex diseases and the contribution of this thesis that provides tools and benchmarks for the methods as well as insights into how a network module can be extracted and interpreted from the omic data in complex diseases.

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.

Massive amounts of metagenomics data are currently being produced, and in all such projects a sizeable fraction of the resulting data shows no or little homology to known sequences. It is likely that this fraction contains novel viruses, but identification is challenging since they frequently lack homology to known viruses. To overcome this problem, we developed a strategy to detect ORFan protein families in shotgun metagenomics data, using similarity-based clustering and a set of filters to extract bona fide protein families. We applied this method to 17 virus-enriched libraries originating from human nasopharyngeal aspirates, serum, feces, and cerebrospinal fluid samples. This resulted in 32 predicted putative novel gene families. Some families showed detectable homology to sequences in metagenomics datasets and protein databases after reannotation. Notably, one predicted family matches an ORF from the highly variable Torque Teno virus (TTV). Furthermore, follow-up from a predicted ORFan resulted in the complete reconstruction of a novel circular genome. Its organisation suggests that it most likely corresponds to a novel bacteriophage in the microviridae family, hence it was named bacteriophage HFM.

Visualizing data through Czekanowski’s diagram has as its aim the illus-tration of the relationships between objects. Often, obvious clusters of observationsare directly visible. However, it is not straightforward to precisely delineate theseclusters. This paper presents the development of the package RMaCzek, which nowincludes features for cluster identification in Czekanowski diagrams.

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.

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.

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.

Using the ImmunoChip custom genotyping array, we analyzed 14,498 subjects with multiple sclerosis and 24,091 healthy controls for 161,311 autosomal variants and identified 135 potentially associated regions (P andlt; 1.0 x 10(-4)). In a replication phase, we combined these data with previous genome-wide association study (GWAS) data from an independent 14,802 subjects with multiple sclerosis and 26,703 healthy controls. In these 80,094 individuals of European ancestry, we identified 48 new susceptibility variants (P andlt; 5.0 x 10(-8)), 3 of which we found after conditioning on previously identified variants. Thus, there are now 110 established multiple sclerosis risk variants at 103 discrete loci outside of the major histocompatibility complex. With high-resolution Bayesian fine mapping, we identified five regions where one variant accounted for more than 50% of the posterior probability of association. This study enhances the catalog of multiple sclerosis risk variants and illustrates the value of fine mapping in the resolution of GWAS signals.

Pediatric T-cell acute lymphoblastic leukemia (T-ALL) is an aggres-sive malignancy resulting from overproduction of immature T-cells in the thymus and is typified by widespread alterations in DNA methyl-ation. As survival rates for relapsed T-ALL remain dismal (10 to 25%), development of targeted therapies to prevent relapse is key to improv-ing prognosis. Whereas mutations in the DNA demethylating enzyme TET2 are frequent in adult T-cell malignancies, TET2 mutations in T-ALL are rare. Here, we analyzed RNA-sequencing data of 321 primary T-ALLs, 20 T-ALL cell lines, and 25 normal human tissues, revealing that TET2 is transcriptionally repressed or silenced in 71% and 17% of T-ALL, respec-tively. Furthermore, we show that TET2 silencing is often associated with hypermethylation of the TET2 promoter in primary T-ALL. Impor-tantly, treatment with the DNA demethylating agent, 5-azacytidine (5-aza), was significantly more toxic to TET2-silenced T-ALL cells and resulted in stable re-expression of the TET2 gene. Additionally, 5-aza led to up-regulation of methylated genes and human endogenous ret-roviruses (HERVs), which was further enhanced by the addition of phys-iological levels of vitamin C, a potent enhancer of TET activity. Together, our results clearly identify 5-aza as a potential targeted therapy for TET2-silenced T-ALL.

With the Neolithic transition, human lifestyle shifted from hunting and gathering to farming. This change altered subsistence patterns, cultural expression, and population structures as shown by the archaeological/zooarchaeological record, as well as by stable isotope and ancient DNA data. Here, we used metagenomic data to analyse if the transitions also impacted the microbiome composition in 25 Mesolithic and Neolithic hunter-gatherers and 13 Neolithic farmers from several Scandinavian Stone Age cultural contexts. Salmonella enterica, a bacterium that may have been the cause of death for the infected individuals, was found in two Neolithic samples from Battle Axe culture contexts. Several species of the bacterial genus Yersinia were found in Neolithic individuals from Funnel Beaker culture contexts as well as from later Neolithic context. Transmission of e.g. Y. enterocolitica may have been facilitated by the denser populations in agricultural contexts.

Protein-protein interactions occur in nature and have different functions. The interacting surface between two interacting proteins contains the respective protein's interface residues.

In this thesis, a series of Python scripts are presented which can perform interface-interface comparisons with the method InterComp, to obtain a distance matrix of different protein interfaces. The distance matrix can be studied with the use of clustering algorithms such as DBSCAN.

The result from clustering using DBSCAN shows that for the 77,017 protein interfaces studied, a majority of the protein interfaces are part of a single cluster while most of the remaining interfaces are noise for the tested parameters Eps and MinPts.

The conclusion of this thesis is the effect on the number of clusters for the tested parameters Eps and MinPts when performing DBSCAN.

Comprehensive analyses of structural features of non-canonical base pairs within a nucleic acid double helix are limited by the availability of a small number of three dimensional structures. Therefore, a procedure for model building of double helices containing any given nucleotide sequence and base pairing information, either canonical or non-canonical, is seriously needed. Here we describe a program RNAHelix, which is an updated version of our widely used software, NUCGEN. The program can regenerate duplexes using the dinucleotide step and base pair orientation parameters for a given double helical DNA or RNA sequence with defined Watson-Crick or non-Watson-Crick base pairs. The original structure and the corresponding regenerated structure of double helices were found to be very close, as indicated by the small RMSD values between positions of the corresponding atoms. Structures of several usual and unusual double helices have been regenerated and compared with their original structures in terms of base pair RMSD, torsion angles and electrostatic potentials and very high agreements have been noted. RNAHelix can also be used to generate a structure with a sequence completely different from an experimentally determined one or to introduce single to multiple mutation, but with the same set of parameters and hence can also be an important tool in homology modeling and study of mutation induced structural changes.

Under irradiation in the visible range, the glyoxal-methanol complex in a cryogenic argon matrix undergoes a double proton transfer (DPT) reaction through which the glyoxal molecule isomerizes into hydroxyketene. In this work, we employ electronic structure simulations in order to shed more light on the underlying mechanism. Rewardingly, we find that the lowest singlet excited state (S-1) of the complex acts as a gateway to two previously unknown isomerization pathways, of which one takes place entirely in the singlet manifold and the other also involves the lowest triplet state (T-1). Both of these pathways are fully compatible with the available experimental data, implying that either or both are operative under experimental conditions. In either pathway, the methanol molecule acts as a proton shuttle between the proton-donating and proton-accepting sites of glyoxal, resulting in a dramatic lowering of the potential energy barrier to isomerization with respect to the case of isolated glyoxal. The occurrence of DPT in the singlet manifold is demonstrated directly with the use of nonadiabatic molecular dynamics simulations at the spin-flip time-dependent density functional theory level.

The experimentally confirmed double proton transfer (DPT) reaction in glyoxal-methanol complex follows a complicated mechanism triggered by the excitation of the complex to the lowest singlet excited electronic state. The mechanism involves two complementary pathways, the first of which proceeds exclusively in the singlet manifold. The competing one, which is studied here with the use of quantum chemical topological methods, involves the lowest triplet state. Both Atoms-in-Molecules approach and Electron Localizability Indicator based approach demonstrate that protons dressed with some amount of electron density migrate to and from the methanol, which leads to the rearrangement of four covalent bonds. The transition structure nearly coincides with the dissociation of the covalent C-H bond in glyoxal. DPT proceeds through a structure formed by the protonated [MeOH2](+0.)(66), with H atoms equidistant to O atom, and deprotonated [C2O2H](-0.)(66). The key stages of the evolution of the crucial covalent bonds along the intrinsic reaction path have been demonstrated. (C) 2020 Elsevier B.V. All rights reserved.

The ultrafast dynamics of unsubstituted spironaphthopyran (SNP) were investigated using femtosecond transient UV and visible absorption spectroscopy in three different solvents and by semi-classical nuclear dynamics simulations. The primary ring-opening of the pyran unit was found to occur in 300 fs yielding a non-planar intermediate in the first singlet excited state (S-1). Subsequent planarisation and relaxation to the product ground state proceed through barrier crossing on the S-1 potential energy surface (PES) and take place within 1.1 ps after excitation. Simulations show that more than 90% of the trajectories involving C-O bond elongation lead to the planar, open-ring product, while relaxation back to the S-0 of the closed-ring form is accompanied by C-N elongation. All ensuing spectral dynamics are ascribed to vibrational relaxation and thermalisation of the product with a time constant of 13 ps. The latter shows dependency on characteristics of the solvent with solvent relaxation kinetics playing a role.

Gemcitabine/carboplatin chemotherapy commonly induces myelosuppression, including neutropenia, leukopenia, and thrombocytopenia. Predicting patients at risk of these adverse drug reactions (ADRs) and adjusting treatments accordingly is a long-term goal of personalized medicine. This study used whole-genome sequencing (WGS) of blood samples from 96 gemcitabine/carboplatin-treated non-small cell lung cancer (NSCLC) patients and gene network modules for predicting myelosuppression. Association of genetic variants in PLINK found 4594, 5019, and 5066 autosomal SNVs/INDELs with p ≤ 1 × 10−3 for neutropenia, leukopenia, and thrombocytopenia, respectively. Based on the SNVs/INDELs we identified the toxicity module, consisting of 215 unique overlapping genes inferred from MCODE-generated gene network modules of 350, 345, and 313 genes, respectively. These module genes showed enrichment for differentially expressed genes in rat bone marrow, human bone marrow, and human cell lines exposed to carboplatin and gemcitabine (p < 0.05). Then using 80% of the patients as training data, random LASSO reduced the number of SNVs/INDELs in the toxicity module into a feasible prediction model consisting of 62 SNVs/INDELs that accurately predict both the training and the test (remaining 20%) data with high (CTCAE 3–4) and low (CTCAE 0–1) maximal myelosuppressive toxicity completely, with the receiver-operating characteristic (ROC) area under the curve (AUC) of 100%. The present study shows how WGS, gene network modules, and random LASSO can be used to develop a feasible and tested model for predicting myelosuppressive toxicity. Although the proposed model predicts myelosuppression in this study, further evaluation in other studies is required to determine its reproducibility, usability, and clinical effect.

Personalized medicine requires the integration and processing of vast amounts of data. Here, we propose a solution to this challenge that is based on constructing Digital Twins. These are high-resolution models of individual patients that are computationally treated with thousands of drugs to find the drug that is optimal for the patient.

BackgroundMicroRNAs (miRNAs) are small RNAs that regulate gene expression at a post-transcriptional level and are emerging as potentially important biomarkers for various disease states, including pancreatic cancer. In silico-based functional analysis of miRNAs usually consists of miRNA target prediction and functional enrichment analysis of miRNA targets. Since miRNA target prediction methods generate a large number of false positive target genes, further validation to narrow down interesting candidate miRNA targets is needed. One commonly used method correlates miRNA and mRNA expression to assess the regulatory effect of a particular miRNA.The aim of this study was to build a bioinformatics pipeline in R for miRNA functional analysis including correlation analyses between miRNA expression levels and its targets on mRNA and protein expression levels available from the cancer genome atlas (TCGA) and the cancer proteome atlas (TCPA). TCGA-derived expression data of specific mature miRNA isoforms from pancreatic cancer tissue was used.ResultsFifteen circulating miRNAs with significantly altered expression levels detected in pancreatic cancer patients were queried separately in the pipeline. The pipeline generated predicted miRNA target genes, enriched gene ontology (GO) terms and Kyoto encyclopedia of genes and genomes (KEGG) pathways. Predicted miRNA targets were evaluated by correlation analyses between each miRNA and its predicted targets. MiRNA functional analysis in combination with Kaplan-Meier survival analysis suggest that hsa-miR-885-5p could act as a tumor suppressor and should be validated as a potential prognostic biomarker in pancreatic cancer.ConclusionsOur miRNA functional analysis (miRFA) pipeline can serve as a valuable tool in biomarker discovery involving mature miRNAs associated with pancreatic cancer and could be developed to cover additional cancer types. Results for all mature miRNAs in TCGA pancreatic adenocarcinoma dataset can be studied and downloaded through a shiny web application at https://emmbor.shinyapps.io/mirfa/.

Background: In order to perform taxonomically unbiased analyses of protein relationships, there is a need ofcomplete proteomes rather than databases with bias towards well characterized protein families. However, nocomprehensive resource of completed proteomes is currently available. Instead, the proteomes need to be down-loaded manually from di®erent servers, all using different filename conventions and fasta header formats.

Results: We have developed a semi-automatic algorithm that retrieves complete proteomes from multiple FTP-servers and maps the species-speci¯c sequence entries to the NCBI taxonomy. The compiled data is provided ina sequence database named genomeLKPG.

Conclusions: The usefulness of genomeLKPG is proven in several published taxonomical studies.

Bioinformatics involves storing, analyzing and making predictions on massive amounts of protein and nucleotide sequence data. The thesis consists of six papers and is focused on proteins. It describes the utilization of bioinformatics techniques to characterize protein families and to detect patterns in gene expression and in polypeptide occurrences. Two protein families were bioinformatically characterized - the membrane associated proteins in eicosanoid and glutathione metabolism (MAPEG) and the Tripartite motif (TRIM) protein families.

In the study of the MAPEG super-family, application of different bioinformatic methods made it possible to characterize many new members leading to a doubling of the family size. Furthermore, the MAPEG members were subdivided into families. Remarkably, in six families with previously predominantly mammalian members, fish representatives were also now detected, which dated the origin of these families back to the Cambrium ”species explosion”, thus earlier than previously anticipated. Sequence comparisons made it possible to define diagnostic sequence patterns that can be used in genome annotations. Upon publication of several MAPEG structures, these patterns were confirmed to be part of the active sites.

In the TRIM study, the bioinformatic analyses made it possible to subdivide the proteins into three subtypes and to characterize a large number of members. In addition, the analyses showed crucial structural dependencies between the RING and the B-box domains of the TRIM member

Ro52. The linker region between the two domains, denoted RBL, is known

to be disease associated. Now, an amphipathic helix was found to be a

characteristic feature of the RBL region, which also was used to divide the family into three subtypes.

The ontology annotation treebrowser (OAT) tool was developed to detect functional similarities or common concepts in long lists of proteins or genes, typically generated from proteomics or microarray experiments. OAT was the first annotation browser to include both Gene Ontology (GO) and Medical Subject Headings (MeSH) into the same framework. The complementarity of these two ontologies was demonstrated. OAT was used in the TRIM study to detect differences in functional annotations between the subtypes.

In the oligopeptide study, we investigated pentapeptide patterns that were over- or under-represented in the current de facto standard database of protein knowledge and a set of completed genomes, compared to what could be expected from amino acid compositions. We found three predominant categories of patterns: (i) patterns originating from frequently occurring families, e.g. respiratory chain-associated proteins and translation machinery proteins; (ii) proteins with structurally and/or functionally favored patterns; (iii) multicopy species-specific retrotransposons, only found in the genome set. Such patterns may influence amino acid residue based prediction algorithms. These findings in the oligopeptide study were utilized for development of a new method that detects translated introns in unverified protein predictions, which are available in great numbers due to the many completed and ongoing genome projects.

A new comprehensive database of protein sequences from completed genomes was developed, denoted genomeLKPG. This database was of central importance in the MAPEG, TRIM and oligopeptide studies. The new sequence database has also been proven useful in several other studies.

Background: Recent sequencing projects and the growth of sequence data banks enable oligopeptide patterns to be characterized on a genome or kingdom level. Several studies have focused on kingdom or habitat classifications based on the abundance of short peptide patterns. There have also been efforts at local structural prediction based on short sequence motifs. Oligopeptide patterns undoubtedly carry valuable information content. Therefore, it is important to characterize these informational peptide patterns to shed light on possible new applications and the pitfalls implicit in neglecting bias in peptide patterns.

Results: We have studied four classes of pentapeptide patterns (designated POP, NEP, ORP and URP) in the kingdoms archaea, bacteria and eukaryotes. POP are highly abundant patterns statistically not expected to exist; NEP are patterns that do not exist but are statistically expected to; ORP are patterns unique to a kingdom; and URP are patterns excluded from a kingdom. We used two data sources: the de facto standard of protein knowledge Swiss-Prot, and a set of 386 completely sequenced genomes. For each class of peptides we looked at the 100 most extreme and found both known and unknown sequence features. Most of the known sequence motifs can be explained on the basis of the protein families from which they originate.

Conclusion: We find an inherent bias of certain oligopeptide patterns in naturally occurring proteins that cannot be explained solely on the basis of residue distribution in single proteins, kingdoms or databases. We see three predominant categories of patterns: (i) patterns widespread in a kingdom such as those originating from respiratory chain-associated proteins and translation machinery; (ii) proteins with structurally and/or functionally favored patterns, which have not yet been ascribed this role; (iii) multicopy species-specific retrotransposons, only found in the genome set. These categories will affect the accuracy of sequence pattern algorithms that rely mainly on amino acid residue usage. Methods presented in this paper may be used to discover targets for antibiotics, as we identify numerous examples of kingdom-specific antigens among our peptide classes. The methods may also be useful for detecting coding regions of genes.

Gene expression and proteomics analysis allow the investigation of thousands of biomolecules in parallel. This results in a long list of interesting genes or proteins and a list of annotation terms in the order of thousands. It is not a trivial task to understand such a gene list and it would require extensive efforts to bring together the overwhelming amounts of associated information from the literature and databases. Thus, it is evident that we need ways of condensing and filtering this information. An excellent way to represent knowledge is to use ontologies, where it is possible to group genes or terms with overlapping context, rather than studying one-dimensional lists of keywords. Therefore, we have built the ontology annotation treebrowser (OAT) to represent, condense, filter and summarise the knowledge associated with a list of genes or proteins.

The OAT system consists of two disjointed parts; a MySQL^® database named OATdb, and a treebrowser engine that is implemented as a web interface. The OAT system is implemented using Perl scripts on an Apache web server and the gene, ontology and annotation data is stored in a relational MySQL^® database. In OAT, we have harmonized the two ontologies of medical subject headings (MeSH) and gene ontology (GO), to enable us to use knowledge both from the literature and the annotation projects in the same tool. OAT includes multiple gene identifier sets, which are merged internally in the OAT database. We have also generated novel MeSH annotations by mapping accession numbers to MEDLINE entries.

The ontology browser OAT was created to facilitate the analysis of gene lists. It can be browsed dynamically, so that a scientist can interact with the data and govern the outcome. Test statistics show which branches are enriched. We also show that the two ontologies complement each other, with surprisingly low overlap, by mapping annotations to the Unified Medical Language System^®.

We have developed a novel interactive annotation browser that is the first to incorporate both MeSH and GO for improved interpretation of gene lists. With OAT, we illustrate the benefits of combining MeSH and GO for understanding gene lists. OAT is available as a public web service at: http://www.ifm.liu.se/bioinfo/oat

The membrane associated proteins in eicosanoid and glutathione metabolism (MAPEG) superfamily includes structurally related membrane proteins with diverse functions of widespread origin. A total of 136 proteins belonging to the MAPEG superfamily were found in database and genome screenings. The members were found in prokaryotes and eukaryotes, but not in any archaeal organism. Multiple sequence alignments and calculations of evolutionary trees revealed a clear subdivision of the eukaryotic MAPEG members, corresponding to the six families of microsomal glutathione transferases (MGST) 1, 2 and 3, leukotriene C₄ synthase (LTC₄), 5-lipoxygenase activating protein (FLAP), and prostaglandin E synthase. Prokaryotes contain at least two distinct potential ancestral subfamilies, of which one is unique, whereas the other most closely resembles enzymes that belong to the MGST2/FLAP/LTC₄ synthase families. The insect members are most similar to MGST1/prostaglandin E synthase. With the new data available, we observe that fish enzymes are present in all six families, showing an early origin for MAPEG family differentiation. Thus, the evolutionary origins and relationships of the MAPEG superfamily can be defined, including distinct sequence patterns characteristic for each of the subfamilies. We have further investigated and functionally characterized representative gene products from Escherichia coli, Synechocystis sp., Arabidopsis thaliana and Drosophila melanogaster, and the fish liver enzyme, purified from pike (Esox lucius). Protein overexpression and enzyme activity analysis demonstrated that all proteins catalyzed the conjugation of 1-chloro-2,4-dinitrobenzene with reduced glutathione. The E. coli protein displayed glutathione transferase activity of 0.11 µmol·min⁻¹·mg⁻¹ in the membrane fraction from bacteria overexpressing the protein. Partial purification of the Synechocystis sp. protein yielded an enzyme of the expected molecular mass and an N-terminal amino acid sequence that was at least 50% pure, with a specific activity towards 1-chloro-2,4-dinitrobenzene of 11 µmol·min⁻¹·mg⁻¹. Yeast microsomes expressing the Arabidopsis enzyme showed an activity of 0.02 µmol·min⁻¹·mg⁻¹, whereas the Drosophila enzyme expressed in E. coli was highly active at 3.6 µmol·min⁻¹·mg⁻¹. The purified pike enzyme is the most active MGST described so far with a specific activity of 285 µmol·min⁻¹·mg⁻¹. Drosophila and pike enzymes also displayed glutathione peroxidase activity towards cumene hydroperoxide (0.4 and 2.2 µmol·min⁻¹·mg⁻¹, respectively). Glutathione transferase activity can thus be regarded as a common denominator for a majority of MAPEG members throughout the kingdoms of life whereas glutathione peroxidase activity occurs in representatives from the MGST1, 2 and 3 and PGES subfamilies.