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Nature chemical biology 16(9), 1013 - 1018 (2020). doi:10.1038/s41589-020-0569-y, $\small{D}$-amino acids endow peptides with diverse, desirable properties, but the post-translational and site-specific epimerization of $\small{L}$-amino acids into their d-counterparts is rare and chemically challenging. Bottromycins are ribosomally synthesized and post-translationally modified peptides that have overcome this challenge and feature a $\small{D}$-aspartate ($\small{D}$-Asp), which was proposed to arise spontaneously during biosynthesis. We have identified the highly unusual $α/β$-hydrolase (ABH) fold enzyme BotH as a peptide epimerase responsible for the post-translational epimerization of $\small{L}$-Asp to $\small{D}$-Asp during bottromycin biosynthesis. The biochemical characterization of BotH combined with the structures of BotH and the BotH–substrate complex allowed us to propose a mechanism for this reaction. Bioinformatic analyses of BotH homologs show that similar ABH enzymes are found in diverse biosynthetic gene clusters. This places BotH as the founding member of a group of atypical ABH enzymes that may be able to epimerize non-Asp stereocenters across different families of secondary metabolites., Published by Nature Publishing Group, Basingstoke

Fitness conferred by the same allele may differ between genotypes, and these differences shape variation and evolution. Changes in amino acid propensities at protein sites over the course of evolution have been inferred from sequence alignments statistically, but the existing methods are data-intensive and aggregate multiple sites. Here, we develop an approach to detect individual amino acids that confer different fitness in different groups of species from combined sequence and phylogenetic data. Using the fact that the probability of a substitution to an amino acid depends on its fitness, our method looks for amino acids such that substitutions to them occur more frequently in one group of lineages than in another. We validate our method using simulated evolution of a protein site under different scenarios and show that it has high specificity for a wide range of assumptions regarding the underlying changes in selection, while its sensitivity differs between scenarios. We apply our method to the env gene of two HIV-1 subtypes, A and B, and to the HA gene of two influenza A subtypes, H1 and H3, and show that the inferred fitness changes are consistent with the fitness differences observed in deep mutational scanning experiments. We find that changes in relative fitness of different amino acid variants within a site do not always trigger episodes of positive selection and therefore may not result in an overall increase in the frequency of substitutions, but can still be detected from changes in relative frequencies of different substitutions.Author summaryWhich amino acids are acceptable at a certain protein site can change with time. In viruses, for example, this can be due to changes in mechanisms of drug resistance and immune escape in the course of evolution. Here, we develop a method for detecting such changes from how evolutionary events are distributed over an evolutionary tree. Informally, we infer that a certain amino acid is favored in a certain group of lineages if substitutions giving rise to it repeatedly occur in the evolution of this group, and disfavored if such substitutions are rare. In surface proteins of HIV-1 and influenza A, we find that changes in preferences detected with d-test match those observed in deep mutational scanning experiments. Our purely bioinformatic approach allows inference of changes in selection between lineages from sequences alone, shedding light on the functional differences between strains or species even in the absence of any structural or functional data.

Severe side effects often restrict clinical application of the widely used chemotherapeutic drug doxorubicin. In order to decrease required substance concentrations, new concepts for successful combination therapy are needed. Since doxorubicin causes DNA damage, combination with compounds that modulate DNA repair could be a promising strategy. Very recently, a role of nuclear actin for DNA damage repair has been proposed, making actin a potential target for cancer therapy in combination with DNA-damaging therapeutics. This is of special interest, since actin-binding compounds have not yet found their way into clinics. We find that low-dose combination treatment of doxorubicin with the actin polymerizer chondramide B (ChB) synergistically inhibits tumor growth in vivo. On the cellular level we demonstrate that actin binders inhibit distinctive double strand break (DSB) repair pathways. Actin manipulation impairs the recruitment of replication factor A (RPA) to the site of damage, a process crucial for homologous recombination. In addition, actin binders reduce autophosphorylation of DNA-dependent protein kinase (DNA-PK) during nonhomologous end joining. Our findings substantiate a direct involvement of actin in nuclear DSB repair pathways, and propose actin as a therapeutic target for combination therapy with DNA-damaging agents such as doxorubicin.

To combat the rise of antimicrobial resistance, the discovery of new antibiotics is paramount. Albicidin and cystobactamid are related natural product antibiotics with potent activity against Gram-positive and, crucially, Gram-negative pathogens. AlbA has been reported to neutralize albicidin by binding it with nanomolar affinity. To understand this potential resistance mechanism, we determined structures of AlbA and its complex with albicidin. The structures revealed AlbA to be comprised of two domains, each unexpectedly resembling the multiantibiotic neutralizing protein TipA. Binding of the long albicidin molecule was shared pseudosymmetrically between the two domains. The structure also revealed an unexpected chemical modification of albicidin, which we demonstrate to be promoted by AlbA, and to reduce albicidin potency; we propose a mechanism for this reaction. Overall, our findings suggest that AlbA arose through internal duplication in an ancient TipA-like gene, leading to a new binding scaffold adapted to the sequestration of long-chain antibiotics.

New investigation results point to the potential participation of extracellular vesicles (EVs) in the pathogenesis of coronavirus infection, its progression, and mechanisms of the therapy effectiveness. This dictates the necessity to transfer scientific testing technologies to medical practice. Here, we demonstrated the method of phenotyping and quantitative analysis of plasma EVs based on differential centrifugation, immunostaining, and high-sensitivity multicolor flow cytometry. We used EV markers that were potentially associated with SARS-CoV-2 dissemination via vesicles and cell-origination markers, characterizing objects from different cell types that could influence clinical manifestation of COVID-19. Plasma levels of CD235a+ and CD14+ EVs in patients with moderate infection were significantly increased while CD8+ and CD19+ EVs were decreased comparing with HD. Patients with severe infection had lower levels of CD4+, CD19+, and CD146+ EVs than HD. These findings demonstrate that EV concentrations in COVID-19 are severity related. Moreover, the three-point dynamic assessment demonstrated significant loss of CD63+ and CD147+ plasma EVs. The used method can be a convenient tool for vital infection pathogenesis investigation and for COVID-19 diagnostics.

Motivation Due to their high genomic variability, RNA viruses and retroviruses present a unique opportunity for detailed study of molecular evolution. Lentiviruses, with HIV being a notable example, are one of the best studied viral groups: hundreds of thousands of sequences are available together with experimentally resolved three-dimensional structures for most viral proteins. In this work, we use these data to study specific patterns of evolution of the viral proteins, and their relationship to protein interactions and immunogenicity. Results We propose a method for identification of two types of surface residues clusters with abnormal conservation: extremely conserved and extremely variable clusters. We identify them on the surface of proteins from HIV and other animal immunodeficiency viruses. Both types of clusters are overrepresented on the interaction interfaces of viral proteins with other proteins, nucleic acids or low molecular-weight ligands, both in the viral particle and between the virus and its host. In the immunodeficiency viruses, the interaction interfaces are not more conserved than the corresponding proteins on an average, and we show that extremely conserved clusters coincide with protein–protein interaction hotspots, predicted as the residues with the largest energetic contribution to the interaction. Extremely variable clusters have been identified here for the first time. In the HIV-1 envelope protein gp120, they overlap with known antigenic sites. These antigenic sites also contain many residues from extremely conserved clusters, hence representing a unique interacting interface enriched both in extremely conserved and in extremely variable clusters of residues. This observation may have important implication for antiretroviral vaccine development. Availability and Implementation A Python package is available at https://bioinf.mpi-inf.mpg.de/publications/viral-ppi-pred/ Contact voitenko@mpi-inf.mpg.de or kalinina@mpi-inf.mpg.de Supplementary information Supplementary data are available at Bioinformatics online.

Conformational changes of molecules are crucial elements in many biochemical processes, and also in molecular recognition. Here, we present a novel exact mathematical equation for the binding free energy of a receptor-ligand pair. It shows that the energetic contribution due to conformational changes upon molecular recognition is defined by the so-called Kullback-Leibler (KL) divergence between the probability distributions of the conformational ensemble of the biomolecule in the bound and free states. We show that conformational changes always contribute positively to the change in free energy and therefore disfavor the association process. Using the example of ligands binding to a flexible cavity of T4 lysozyme, we illustrate that, due to enthalpy-entropy compensation, the conformational entropy is a misleading quantity for assessing the conformational contribution to the binding free energy, in contrast to the KL divergence, which is the correct quantity to use in this context.

Periodization of the evolution of epidemic process of hepatitis C is given based on the results of phylodynamic, phylogeographic, historic and demographic studies: invasion of the virus into European and North American population in 1700 - 1850; primary activation of the epidemic process in the years of the World War 1; expansive growth of prevalence in 40 - 60s of the 20th century due to mass parenteral interventions; new rise due to heroine drug abuse in 60 - 80s of the 20th century; manifold reduction of incidence of acute hepatitis C in industrial countries for the last 10 - 15 years as a result of general medical measures of prevention of hemocontact infections. A problem of possibility of hepatitis C management and necessity of evaluation of effectiveness of existing prophylaxis measures involving quantitative analytical methods of epidemiology is discussed. Data from phylogenetic studies on stages of hepatitis C virus evolution (HCV) are provided: division of its root genetic lineage with homologous hepaciviruses of animals 985 - 2013 years ago; division ofHCV into genotypes 500 - 2000 years ago; division ofgenotypes into subtypes 70 - 300 years ago. Contribution of mutations and genetic recombinations into HCV evolution is discussed. Genotyping is stated as an inefficient approach for determination of pathogenicity determinants, immune evasion, non-responsiveness to therapy, as well as search for predictors of infection outcome. A necessity of genomic approach for these aims is justified, as well as for risk monitoring, ensuing from continuing evolution and biodiversity of HCV and other hepaciviruses.

Hepatitis C virus is an actual public health problem worldwide since its discovering in 1989. It is explained not only by the wide spreading andfrequent adverse outcomes of disease, the lack of effective preventive vaccine, but also by the high genetic variability of the virus. The current review summarizes the results of phylodynamic and phylogeographic studies of different HCV populations that allowed to characterize epidemic processes, to analyze the divergence of HCV into genotypes and subtypes, and to determine the geographic origin of the current HCV epidemic variants.

A new method termed Relative Principal Components analysis (RPCA) is introduced that extracts optimal relevant principal components to describe the change between two data samples representing two macroscopic states. The method is applicable in all areas of data-driven science. Mining of the components is based on a unified physical framework which introduces the objective function, namely the Kullback-Leibler divergence, appropriate for quantifying the change of the macroscopic state as it is effected by the microscopic features. Moreover, we provide a proof of existence of a low-dimensional space for latent informative features of the change. To demonstrate the applicability of RPCA, we analyze the thermodynamically relevant conformational changes of the protein HIV-1 protease upon binding to different drug molecules. In this case, the RPCA method provides a sound thermodynamic foundation for the analysis of the binding process. The relevant collective (global) conformational changes can be reconstructed from the informative latent variables to exhibit both the enhanced and the restricted conformational fluctuations upon ligand association. Moreover, RPCA characterizes the locally relevant conformational changes which can be presented on the structure of the protein.

Proteogenomics is based on the use of customized genome or RNA sequencing databases for interrogation of shotgun proteomics data in search for proteome-level evidence of genome variations or RNA editing. In this work, the products of adenosine-to-inosine RNA editing in human and murine brain proteomes are identified using publicly available brain proteome LC-MS/MS datasets and an RNA editome database compiled from several sources. After filtering of false-positive results, 20 and 37 sites of editing in proteins belonging to 14 and 32 genes are identified for murine and human brain proteomes, respectively. Eight sites of editing identified with high spectral counts overlapped between human and mouse brain samples. Some of these sites have been previously reported using orthogonal methods, such as α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) glutamate receptors, CYFIP2, coatomer alpha. Also, differential editing between neurons and microglia is demonstrated in this work for some of the proteins from primary murine brain cell cultures. Because many edited sites are still not characterized functionally at the protein level, the results provide a necessary background for their further analysis in normal and diseased cells and tissues using targeted proteomic approaches.

Hepatitis C virus (HCV) causes a major health burden and can be effectively treated by direct-acting antivirals (DAAs). The non-structural protein 5A (NS5A), which plays a role in the viral genome replication, is one of the DAAs&rsquo, targets. Resistance-associated viruses (RAVs) harbouring NS5A resistance-associated mutations (RAMs) have been described at baseline and after therapy failure. A mutation from glutamine to arginine at position 30 (Q30R) is a characteristic RAM for the HCV sub/genotype (GT) 1a, but arginine corresponds to the wild type in the GT-1b, still, GT-1b strains are susceptible to NS5A-inhibitors. In this study, we show that GT-1b strains with R30Q often display other specific NS5A substitutions, particularly in positions 24 and 34. We demonstrate that in GT-1b secondary substitutions usually happen after initial R30Q development in the phylogeny, and that the chemical properties of the corresponding amino acids serve to restore the positive charge in this region, acting as compensatory mutations. These findings may have implications for RAVs treatment.

Macromolecular oligomeric assemblies are involved in many biochemical processes of living organisms. The benefits of such assemblies in crowded cellular environments include increased reaction rates, efficient feedback regulation, cooperativity and protective functions. However, an atom‐level structural determination of large assemblies is challenging due to the size of the complex and the difference in binding affinities of the involved proteins. In this study, we propose a novel combinatorial greedy algorithm for assembling large oligomeric complexes from information on the approximate position of interaction interfaces of pairs of monomers in the complex. Prior information on complex symmetry is not required but rather the symmetry is inferred during assembly. We implement an efficient geometric score, the transformation match score, that bypasses the model ranking problems of state‐of‐the‐art scoring functions by scoring the similarity between the inferred dimers of the same monomer simultaneously with different binding partners in a (sub)complex with a set of pregenerated docking poses. We compiled a diverse benchmark set of 308 homo and heteromeric complexes containing 6 to 60 monomers. To explore the applicability of the method, we considered 48 sets of parameters and selected those three sets of parameters, for which the algorithm can correctly reconstruct the maximum number, namely 252 complexes (81.8%) in, at least one of the respective three runs. The crossvalidation coverage, that is, the mean fraction of correctly reconstructed benchmark complexes during crossvalidation, was 78.1%, which demonstrates the ability of the presented method to correctly reconstruct topology of a large variety of biological complexes. Proteins 2015; 83:1887–1899. © 2015 The Authors. Proteins: Structure, Function, and Bioinformatics Published by Wiley Periodicals, Inc.

Modern data on hepatitis C virus genome and polyprotein organization are summarized in this review. The functional roles of structural and nonstructural virus proteins, including their interaction with cellular regulatory proteins and structural cell elements, are described. Special features of the life cycle of the hepatitis C virus (that supplement our knowledge of viral hepatitis C pathogenesis) are presented.

Primary liver cancer (PLC) is a major public health concern worldwide, ranking third among the causes of death from cancer. Molecular pathogenesis of PLC is known to be especially sensitive to ethno-environmental variations that modulate mutation spectra in tumours. Despite a high prevalence of chronic liver diseases, the molecular epidemiology of PLC is still poorly known in Russia. To characterize the major genetic features of liver tumours in Russian populations, we conducted a pilot study on 34 PLC cases (28 hepatocellular, two cholangiocellular, and four mixed cases) among patients attending the Radiology and Roentgenology Hospital in Saint Petersburg. Point mutations were searched in 9 genes that are commonly altered in PLC, viz. TP53, CTNNB1, AXIN1, H/K/N-RAS, BRAF, PTEN, and NFE2L2. The genes TP53 and AXIN1 were mutated in 16% and 10% of the cases, respectively, whereas mutations of β-catenin were present in only 7% of samples, an unusual situation for Europe but common in East Asia. No R249S mutation indicative of exposure to aflatoxin B1 was detected in TP53. A single case harboured an NFE2L2 mutation. The loss of chromosome 18q was associated with early onset of tumours (mean 50 vs 62yrs, p=0.0252) and with the patient's place of birth in Caucasus or Siberia. A lack of any risk factor was noted in 47% of the patients, whereas only 23% of the patients were infected either by hepatitis virus B or C. An extension of the present cohort as well as further molecular studies are now warranted in order to understand the processes governing liver carcinogenesis affecting Russian populations.

The next generation sequencing technologies produce unprecedented amounts of data on the genetic sequence of individual organisms. These sequences carry a substantial amount of variation that may or may be not related to a phenotype. Phenotypically important part of this variation often comes in form of protein-sequence altering (non-synonymous) single nucleotide variants (nsSNVs). Here we present StructMAn, a Web-based tool for annotation of human and non-human nsSNVs in the structural context. StructMAn analyzes the spatial location of the amino acid residue corresponding to nsSNVs in the three-dimensional (3D) protein structure relative to other proteins, nucleic acids and low molecular-weight ligands. We make use of all experimentally available 3D structures of query proteins, and also, unlike other tools in the field, of structures of proteins with detectable sequence identity to them. This allows us to provide a structural context for around 20% of all nsSNVs in a typical human sequencing sample, for up to 60% of nsSNVs in genes related to human diseases and for around 35% of nsSNVs in a typical bacterial sample. Each nsSNV can be visualized and inspected by the user in the corresponding 3D structure of a protein or protein complex. The StructMAn server is available at http://structman.mpi-inf.mpg.de.

The change in free energy is the dominant factor in all chemical processes; it usually encompasses enthalpy–entropy compensation (EEC). Here, we use the free energy perturbation formalism to show that EEC is influenced by the molecular conformational changes (CCs) of the entire system comprising the solute and by the already known solvent reorganization. The internal changes of enthalpy and the entropy due to CCs upon modifying the interactions (perturbation) cancel each other exactly. The CCs influence the dissipation of the modified interactions and their contributions to the free energy. Using molecular simulations, we show that, for solvation of six different HIV-1 protease inhibitors, CCs in the solute cause EEC as large as 10–30 kcal/mol. Moreover, the EEC due to CCs in HIV-1 protease is shown to vary significantly upon modifying its bound ligand. These findings have important implications for understanding of EEC phenomena and for interpretation of thermodynamic measurements.

A simple quantitative relationship between the molecular conformational changes and the corresponding changes in the free energy is presented. The change in free energy is the sum of that part of the enthalpic change that is due to the externally applied work (perturbation) and of that part of the entropic change, termed dissipative entropy, that is related to the conformational changes. The dissipative entropy is equivalent to the relative entropy, a concept from information theory, between the distributions of the conformations in the initial and the final states. The remaining change in entropy (nondissipative) cancels exactly with the remaining enthalpic change. The calculation of the dissipative entropy is demonstrated to pose the main difficulty in free energy computation. The straightforward decomposition of the dissipative entropy into contributions from different parts of the system promises to improve the understanding of the role of conformational changes in biochemical reactions.

Mitogen‐activated protein kinases (MAPK) are broadly used regulators of cellular signaling. However, how these enzymes can be involved in such a broad spectrum of physiological functions is not understood. Systematic discovery of MAPK networks both experimentally and in silico has been hindered because MAPKs bind to other proteins with low affinity and mostly in less‐characterized disordered regions. We used a structurally consistent model on kinase‐docking motif interactions to facilitate the discovery of short functional sites in the structurally flexible and functionally under‐explored part of the human proteome and applied experimental tools specifically tailored to detect low‐affinity protein–protein interactions for their validation in vitro and in cell‐based assays. The combined computational and experimental approach enabled the identification of many novel MAPK‐docking motifs that were elusive for other large‐scale protein–protein interaction screens. The analysis produced an extensive list of independently evolved linear binding motifs from a functionally diverse set of proteins. These all target, with characteristic binding specificity, an ancient protein interaction surface on evolutionarily related but physiologically clearly distinct three MAPKs (JNK, ERK, and p38). This inventory of human protein kinase binding sites was compared with that of other organisms to examine how kinase‐mediated partnerships evolved over time. The analysis suggests that most human MAPK‐binding motifs are surprisingly new evolutionarily inventions and newly found links highlight (previously hidden) roles of MAPKs. We propose that short MAPK‐binding stretches are created in disordered protein segments through a variety of ways and they represent a major resource for ancient signaling enzymes to acquire new regulatory roles.

Progress in structure determination methods means that the set of experimentally determined 3D structures of proteins in complex with small molecules is growing exponentially. ProtChemSI exploits and extends this useful set of structures by both collecting and annotating the existing data as well as providing models of potential complexes inferred by protein or chemical structure similarity. The database currently includes 7704 proteins from 1803 organisms, 11,324 chemical compounds and 202, 289 complexes including 178,974 predicted. It is publicly available at http://pcidb.russelllab.org.

The currently available body of decoded amino acid sequences of various proteins exceeds manifold the experimental capabilities of their functional annotation. Therefore, in silico annotation using bioinformatics methods becomes increasingly important. Such annotation is actually a prediction; however, this can be an important starting point for further laboratory research. This work describes a new method for predicting functionally important protein sites, SDPsite, on the basis of identification of specificity determinants. The algorithm proposed utilizes a protein family aglinment and a phylogenetic tree to predict the conserved positions and specificity determinants, map them onto the protein structure, and search for clusters of the predicted positions. Comparison of the resulting predictions with experimental data and published predictions of functional sites by other methods demonstrates that the results of SDPsite agree well with experimental data and exceed the results obtained with the majority of previous methods. SDPsite is publicly available at http://bioinf.fbb.msu.ru/SDPsite .

Summary: In medical research, it is crucial to understand the functional consequences of genetic alterations, for example, non-synonymous single nucleotide variants (nsSNVs). NsSNVs are known to be causative for several human diseases. However, the genetic basis of complex disorders such as diabetes or cancer comprises multiple factors. Methods to analyze putative synergetic effects of multiple such factors, however, are limited. Here, we concentrate on nsSNVs and present BALL-SNPgp, a tool for structural and functional characterization of nsSNVs, which is aimed to improve pathogenicity assessment in computational diagnostics. Based on annotated SNV data, BALL-SNPgp creates a three-dimensional visualization of the encoded protein, collects available information from different resources concerning disease relevance and other functional annotations, performs cluster analysis, predicts putative binding pockets and provides data on known interaction sites. Availability and implementation: BALL-SNPgp is based on the comprehensive C ++ framework Biochemical Algorithms Library (BALL) and its visualization front-end BALLView. Our tool is available at www.ccb.uni-saarland.de/BALL-SNPgp. Contact: ballsnp@milaman.cs.uni-saarland.de

Termination of protein synthesis is promoted in ribosomes by proper stop codon discrimination by class 1 polypeptide release factors (RFs). A large set of prokaryotic RFs differing in stop codon specificity, RF1 for UAG and UAA, and RF2 for UGA and UAA, was analyzed by means of a recently developed computational method allowing identification of the specificity-determining positions (SDPs) in families composed of proteins with similar but not identical function. Fifteen SDPs were identified within the RF1/2 superdomain II/IV known to be implicated in stop codon decoding. Three of these SDPs had particularly high scores. Five residues invariant for RF1 and RF2 [invariant amino acid residues (IRs)] were spatially clustered with the highest-scoring SDPs that in turn were located in two zones within the SDP/IR area. Zone 1 (domain II) included PxT and SPF motifs identified earlier by others as 'discriminator tripeptides'. We suggest that IRs in this zone take part in the recognition of U, the first base of all stop codons. Zone 2 (domain IV) possessed two SDPs with the highest scores not identified earlier. Presumably, they also take part in stop codon binding and discrimination. Elucidation of potential functional role(s) of the newly identified SDP/IR zones requires further experiments.

The full-length ORFs for the hepatitis C virus recombinant RF1_2k/1b (N687) and the non-recombinant 1b strain N589 were sequenced. A single recombination point was found and the sizes of the genes (C, E1, E2, p7, NS2, NS3, NS4 and NS5) were according to the parental subtypes. The PKR-eIF2α phosphorylation site homology domain sequence of the E2 protein was identical to those of genotype 2 strains, while the IFN-α-sensitivity-determining region of the NS5A protein was identical to those of interferon-resistant 1b strains. For the parental strains, two hairpin structures, HS1 and HS2, were predicted for the plus-strand up- and downstream of the crossover site, which were not present in the recombinant strain. HS2 shared similarity with the motif1 hairpin of turnip crinkle virus RNA that binds to the RNA-dependent RNA polymerase and facilitates 3′-terminal extension during recombination. This study suggests that RF1_2k/1b has emerged by homologous recombination during minus-strand synthesis via template switching because of constraints imposed by the HS1 hairpin of the 3′-parental genome.

SDPpred (Specificity Determining Position prediction) is a tool for prediction of residues in protein sequences that determine the proteins' functional specificity. It is designed for analysis of protein families whose members have biochemically similar but not identical interaction partners (e.g. different substrates for a family of transporters). SDPpred predicts residues that could be responsible for the proteins' choice of their correct interaction partners. The input of SDPpred is a multiple alignment of a protein family divided into a number of specificity groups, within which the interaction partner is believed to be the same. SDPpred does not require information about the secondary or three-dimensional structure of proteins. It produces a set of the alignment positions (specificity determining positions) that determine differences in functional specificity. SDPpred is available at http://math.genebee.msu.ru/~psn/.

The increasing volume of genomic data opens new possibilities for analysis of protein function. We introduce a method for automated selection of residues that determine the functional specificity of proteins with a common general function (the specificity-determining positions [SDP] prediction method). Such residues are assumed to be conserved within groups of orthologs (that may be assumed to have the same specificity) and to vary between paralogs. Thus, considering a multiple sequence alignment of a protein family divided into orthologous groups, one can select positions where the distribution of amino acids correlates with this division. Unlike previously published techniques, the introduced method directly takes into account nonuniformity of amino acid substitution frequencies. In addition, it does not require setting arbitrary thresholds. Instead, a formal procedure for threshold selection using the Bernoulli estimator is implemented. We tested the SDP prediction method on the LacI family of bacterial transcription factors and a sample of bacterial water and glycerol transporters belonging to the major intrinsic protein (MIP) family. In both cases, the comparison with available experimental and structural data strongly supported our predictions.

The induction of apoptotic cell death is a prominent cytopathic effect of dengue (DEN) viruses. One of the key questions to be addressed is which viral components induce apoptosis in DEN virus-infected cells. This study investigated whether the small membrane (M) protein was involved in the induction of apoptosis by DEN virus. This was addressed by using a series of enhanced green fluorescent protein-fused DEN proteins. Evidence is provided that intracellular production of the M ectodomains (residues M-1 to M-40) of all four DEN serotypes triggered apoptosis in host cells such as mouse neuroblastoma Neuro 2a and human hepatoma HepG2 cells. The M ectodomains of the wild-type strains of Japanese encephalitis, West Nile and yellow fever viruses also had proapoptotic properties. The export of the M ectodomain from the Golgi apparatus to the plasma membrane appeared to be essential for the initiation of apoptosis. The study found that anti-apoptosis protein Bcl-2 protected HepG2 cells against the death-promoting activity of the DEN M ectodomain. This suggests that the M ectodomain exerts its cytotoxic effects by activating a mitochondrial apoptotic pathway. The cytotoxicity of the DEN M ectodomain reflected the intrinsic proapoptotic properties of the nine carboxy-terminal amino acids (residues M-32 to M-40) designatedApoptoM. Residue M-36 was unique in that it modulated the death-promoting activity of the M ectodomain. Defining theApoptoM-activated signalling pathways leading to apoptosis will provide the basis for studying how the M protein might play a key role in the fate of the flavivirus-infected cells.

Next-generation sequencing enables simultaneous analysis of hundreds of human genomes associated with a particular phenotype, for example, a disease. These genomes naturally contain a lot of sequence variation that ranges from single-nucleotide variants (SNVs) to large-scale structural rearrangements. In order to establish a functional connection between genotype and disease-associated phenotypes, one needs to distinguish disease drivers from neutral passenger variants. Functional annotation based on experimental assays is feasible only for a limited number of candidate mutations. Thus alternative computational tools are needed. A possible approach to annotating mutations functionally is to consider their spatial location relative to functionally relevant sites in three-dimensional (3D) structures of the harboring proteins. This is impeded by the lack of available protein 3D structures. Complementing experimentally resolved structures with reliable computational models is an attractive alternative. We developed a structure-based approach to characterizing comprehensive sets of non-synonymous single-nucleotide variants (nsSNVs): associated with cancer, non-cancer diseases and putatively functionally neutral. We searched experimentally resolved protein 3D structures for potential homology-modeling templates for proteins harboring corresponding mutations. We found such templates for all proteins with disease-associated nsSNVs, and 51 and 66% of proteins carrying common polymorphisms and annotated benign variants. Many mutations caused by nsSNVs can be found in protein–protein, protein–nucleic acid or protein–ligand complexes. Correction for the number of available templates per protein reveals that protein–protein interaction interfaces are not enriched in either cancer nsSNVs, or nsSNVs associated with non-cancer diseases. Whereas cancer-associated mutations are enriched in DNA-binding proteins, they are rarely located directly in DNA-interacting interfaces. In contrast, mutations associated with non-cancer diseases are in general rare in DNA-binding proteins, but enriched in DNA-interacting interfaces in these proteins. All disease-associated nsSNVs are overrepresented in ligand-binding pockets, and nsSNVs associated with non-cancer diseases are additionally enriched in protein core, where they probably affect overall protein stability.

New exact equations are derived for the terms contributing to the binding free energy (ΔG0) of a ligand-receptor pair using our recently introduced formalism which we here call perturbation-divergence formalism (PDF). Specifically, ΔG0 equals the sum of the average of the perturbation (pertaining to new interactions) and additional dissipative terms. The average of the perturbation includes the sum of the average receptor-ligand interactions and the average of the change of solvation energies upon association. The Kullback-Leibler (KL) divergence quantifies the energetically dissipative terms, which are due to the configurational changes and, using the chain rule of KL divergence, can be decomposed into (i) dissipation due to limiting the external liberation (translation and rotation) of the ligand relative to the receptor and (ii) dissipation due to conformational (internal) changes inside the receptor and the ligand. We also identify all exactly canceling energetic terms which do not contribute to ΔG0. Furthermore, the PDF provides a new approach towards dimensionality reduction in the representation of the association process and towards relating the dynamic (high dimensional) with the thermodynamic (one-dimensional) changes.

Three-dimensional structures of proteins are the key to understanding their molecular function. Most reliably protein structures are determined by experiment. Recent advances in experimental techniques have lead to a large increase in numbers of both protein sequences and 3D structures. Yet, the number of experimentally resolved proteins 3D structures is three orders of magnitude lower than that of sequences. This calls for computer support of protein structure prediction. Today several databases complement the comparatively small set of experimentally resolved protein structures with much larger sets of protein models generated by computer. Key Concepts: Protein structure prediction relies heavily on the experimental data on protein structures; the volume of such data is the prime determinant for the quality of protein structure predictions. The three major types of methods for protein structure prediction are homology, or template-based modelling; fold recognition, or threading; de novo, or ab initio prediction. Homology modelling is the most reliable class of methods, but require experimental knowledge of a structure of a homologous – and thus structurally similar – protein, called the template. Sensitive sequence similarity search tools are used for detection of potential templates. The protein structure is modelled step-wise: (1) aligning the target protein to the template, (2) placing the aligned target residues onto their respective template residues, (3) placing the side chains of nonconserved residues, healing backbone breaks and modelling loops that form gaps in the alignment, and (4) refining the model. The two most popular computational tools for homology modelling are MODELLER and SWISS-MODEL; the two protein model databases based on them are ModBase and the SWISS-MODEL Repository, respectively. The Protein Modelling Portal unites data from these and other databases, and provides an independent system for model evaluation called CAMEO. Keywords: protein structure; protein structure prediction; protein structure databases; structural genomics; protein structure modelling

The best understanding of complex biological systems ultimately comes from details of the underlying atomic structures within it. In the absence of known structures of all protein complexes and interactions in a system, structural bioinformatics or modeling fill an important niche in providing predicted mechanistic information which can guide experiments, aid the interpretation of high-throughput datasets and help provide key details to model biological systems. This introductory review discusses the current state of this field and suggests how current datasets in systems studies can profit from a better integration of predicted or known structural information.

The genotypes of 149 HCV strains from St. Petersburg were determined by limited sequencing and phylogenetic analysis within the NS5B region. One hundred two strains derived from patients that attended infectious disease clinics, of whom 48 admitted injecting drug use, and 47 derived from dialysis patients. Subtype 3a was predominant in the patients from infectious disease clinics, both in patients that admitted injecting drug use (56%) and in those with unknown source of infection (46%). However, 89% of the strains from dialysis patients belonged to subtype 1b. Eleven of twelve characterised strains from recent cases of hepatitis C at these units were at phylogenetic analysis shown to be related to strains already circulating there, demonstrating that within the dialysis units nosocomial transmission is the most important route of HCV infection. The predominance of subtype 1b strains in dialysis patients indicates that these strains have been circulating for a long time in dialysis units. The predominance of subtype 3a also among patients who did not admit drug use and that their strains were intermixed with the strains from injecting drug users in the phylogenetic analysis shows that the increase in injecting drug use is the major factor that explains the recent spread of HCV in the St. Petersburg population. This supports the concept that injecting drug use remains the major route for HCV infection in developed countries and that the control of drug abuse is the most important measure to prevent its spread.

Pairs of amino acid positions that evolve in a correlated manner are proposed to play important roles in protein structure or function. Methods to detect them might fare better with families for which sequences of thousands of closely related homologs are available than families with only a few distant relatives. We applied co-evolution analysis to thousands of sequences of HIV Gag, finding that the most significantly co-evolving positions are proximal in the quaternary structures of the viral capsid. A reduction in infectivity caused by mutating one member of a significant pair could be rescued by a compensatory mutation of the other.

International audience; The biological role of the protein encoded by the alternative open reading frame (core+1/ARF) of the Hepatitis C virus (HCV) genome remains elusive, as does the significance of the production of corresponding antibodies in HCV infection. We investigated the prevalence of anti-core and anti-core+1/ARFP antibodies in HCV-positive blood donors from Cambodia, using peptide and recombinant protein-based ELISAs. We detected unusual serological profiles in 3 out of 58 HCV positive plasma of genotype 1a. These patients were negative for anti-core antibodies by commercial and peptide-based assays using C-terminal fragments of core but reacted by Western Blot with full-length core protein. All three patients had high levels of anti-core+1/ARFP antibodies. Cloning of the cDNA that corresponds to the core-coding region from these sera resulted in the expression of both core and core+1/ARFP in mammalian cells. The core protein exhibited high amino-acid homology with a consensus HCV1a sequence. However, 10 identical synonymous mutations were found, and 7 were located in the aa(99-124) region of core. All mutations concerned the third base of a codon, and 5/10 represented a T>C mutation. Prediction analyses of the RNA secondary structure revealed conformational changes within the stem-loop region that contains the core+1/ARFP internal AUG initiator at position 85/87. Using the luciferase tagging approach, we showed that core+1/ARFP expression is more efficient from such a sequence than from the prototype HCV1a RNA. We provide additional evidence of the existence of core+1/ARFP in vivo and new data concerning expression of HCV core protein. We show that HCV patients who do not produce normal anti-core antibodies have unusually high levels of antit-core+1/ARFP and harbour several identical synonymous mutations in the core and core+1/ARFP coding region that result in major changes in predicted RNA structure. Such HCV variants may favour core+1/ARFP production during HCV infection.

International audience; BACKGROUND: According to previous data, the antibodies produced during natural hepatitis C virus (HCV) infection frequently recognize amino acids 10-43 in the core protein and 1689-1740 or 1921-1940 in the non-structural 4B (NS4B) protein. The reactivity of these peptides with the corresponding antibodies has mainly been evaluated using serum samples from Western countries where HCV genotype 1 (HCV-1) is predominant, and no information is available concerning samples from sub-Saharan countries where high HCV variability has been reported. OBJECTIVE OF THIS STUDY: To evaluate the performance of HCV core and NS4B peptide-based immunoassays in the serodiagnosis of HCV infection in Cameroon subjects. STUDY DESIGN: Three core and four NS4B-based synthetic peptides derived from HCV genotypes 1b and 2a were designed and tested against a panel of 151 serum samples from Cameroon (40 positive for HCV-1, 32 for HCV-2, 39 HCV-4, and 40 HCV-negative). RESULTS: The three core peptides all demonstrated strong immunoreactivity, regardless of the HCV genotype from which they were derived, with greater than 90% and 92% sensitivity and specificity. In contrast, the NS4B-derived peptides exhibited lower sensitivities (24.3-65.8% depending on the HCV genotype) but higher specificities (100% for all four peptides tested). CONCLUSIONS: Our findings indicate that an HCV core peptide could be used for the diagnosis of chronic HCV infection. Among the NS4B peptides tested, a chimeric NS4B peptide encompassing both N- and C-terminal portions of the NS4B protein gave a much better performance than the two component N- and C-terminal peptides used individually.

Background Recent progress in sequencing and 3 D structure determination techniques stimulated development of approaches aimed at more precise annotation of proteins, that is, prediction of exact specificity to a ligand or, more broadly, to a binding partner of any kind. Results We present a method, SDPclust, for identification of protein functional subfamilies coupled with prediction of specificity-determining positions (SDPs). SDPclust predicts specificity in a phylogeny-independent stochastic manner, which allows for the correct identification of the specificity for proteins that are separated on a phylogenetic tree, but still bind the same ligand. SDPclust is implemented as a Web-server http://bioinf.fbb.msu.ru/SDPfoxWeb/ and a stand-alone Java application available from the website. Conclusions SDPclust performs a simultaneous identification of specificity determinants and specificity groups in a statistically robust and phylogeny-independent manner.

The host-virus interactions leading to cell infection with hepatitis C virus (HCV) are not fully understood. The tetraspanin CD-81 and human scavenger receptor SR-BI/Cla1 are major receptors mediating virus cell entry. However, HCV in patients' sera is associated with lipoproteins and infectious potential of the virus depends on lipoproteins associated to virus particles. We show here that lipoprotein lipase (LPL), targeting triglyceride-rich lipoproteins (TRL) to the liver, mediates binding and internalization of HCV to different types of cells, acting as a bridge between virus-associated lipoproteins and cell surface heparan sulfate proteoglycans (HSPG). The dimeric structure and catalytic activity of LPL are required for LPL-mediated HCV uptake to cells. Unexpectedly, exogenous LPL significantly inhibits HCVcc infection in vitro. This effect is prevented by anti-LPL antibodies and by tetrahydrolipstatin (THL) a specific inhibitor of LPL enzymatic activity. In addition, we show that antibodies directed to apolipoprotein B (ApoB)-containing lipoproteins efficiently inhibits HCVcc infection. Our findings suggest that LPL mediates HCV cell entry by a mechanism similar to hepatic clearance of TRL from the circulation, promoting a non-productive virus uptake. These data provide new insight into mechanisms of HCV cell entry and suggest that LPL could modulate HCV infectivity in vivo.

Isocitrate and isopropylmalalte dehydrogenases are homologous enzymes important for the cell metabolism. They oxidize their substrates using NAD or NADP as cofactors. Thus, they have two specificities, towards the substrate and the cofactor, appearing in three combinations. Although many three-dimensional (3D) structures are resolved, identification of amino acids determining these specificities remains a challenge. We present computational identification and analysis of specificity-determining positions (SDPs). Besides many experimentally proven SDPs, we predict new SDPs, for example, four substrate-specific positions (103Leu, 105Thr, 337Ala, and 341Thr in IDH from E. coli) that contact the cofactor and may play a role in the recognition process. Proteins 2006. © 2006 Wiley-Liss, Inc.

Background Heavy metal resistance (HMR) in Eubacteria is regulated by a variety of systems including transcription factors from the MerR family (COG0789). The HMR systems are characterized by the complex signal structure (strong palindrome within a 19 or 20 bp promoter spacer), and usually consist of transporter and regulator genes. Some HMR regulons also include detoxification systems. The number of sequenced bacterial genomes is constantly increasing and even though HMR resistance regulons of the COG0789 type usually consist of few genes per genome, the computational analysis may contribute to the understanding of the cellular systems of metal detoxification. Results We studied the mercury (MerR), copper (CueR and HmrR), cadmium (CadR), lead (PbrR), and zinc (ZntR) resistance systems and demonstrated that combining protein sequence analysis and analysis of DNA regulatory signals it was possible to distinguish metal-dependent members of COG0789, assign specificity towards particular metals to uncharacterized loci, and find new genes involved in the metal resistance, in particular, multicopper oxidase and copper chaperones, candidate cytochromes from the copper regulon, new cadmium transporters and, possibly, glutathione-S-transferases. Conclusion Our data indicate that the specificity of the COG0789 systems can be determined combining phylogenetic analysis and identification of DNA regulatory sites. Taking into account signal structure, we can adequately identify genes that are activated using the DNA bending-unbending mechanism. In the case of regulon members that do not reside in single loci, analysis of potential regulatory sites could be crucial for the correct annotation and prediction of the specificity.

Hepatitis C virus genotyping was assessed for 257 chronic hepatitis C patients with viral loads above 1,000 IU/ml. Twelve patients were coinfected with more than one genotype. Their median viral loads did not differ significantly from those observed for monoinfected patients, which in turn did not vary significantly among different genotypes.

Transmembrane transport is an essential component of the cell life. Many genes encoding known or putative transport proteins are found in bacterial genomes. In most cases their substrate specificity is not experimentally determined and only approximately predicted by comparative genomic analysis. Even less is known about the 3D structure of transporters. Nevertheless, the published experimental data demonstrate that channel-forming residues determine the substrate specificity of secondary transporters and analysis of these residues would provide better understanding of the transport mechanism. We developed a simple computational method for identification of channel-forming residues in transporter sequences. It is based on the analysis of amino acids frequencies in bacterial secondary transporters. We applied this method to a variety of transmembrane proteins with resolved 3D structure. The predictions are in sufficiently good agreement with the real protein structure.

Hepatitis C virus (HCV) evolution is thought to proceed by mutations within the six genotypes. Here, we report on a viable spontaneous HCV recombinant and we show that recombination may play a role in the evolution of this virus. Previously, 149 HCV strains from St. Petersburg had been subtyped by limited sequencing within the NS5B region. In the present study, the core regions of 41 of these strains were sequenced to investigate the concordance of HCV genotyping for these two genomic regions. Two phylogenetically related HCV strains were found to belong to different subtypes, 2k and 1b, according to sequence analysis of the 5′ untranslated region (5′UTR)-core and the NS5B regions, respectively. By sequencing of the E2-p7-NS2 region, the crossover point was mapped within the NS2 region, probably between positions 3175 and 3176 (according to the numbering system for strain pj6CF). Sequencing of the 5′UTR-core regions of four other HCV strains, phylogenetically related to the above-mentioned two strains (based on analysis within the NS5B region), revealed that these four strains were also recombinants. Since a nonrecombinant 2k strain was found in St. Petersburg, the recombination may have taken place there around a decade ago. Since the frequency of this recombinant is now high enough to allow the detection of the recombinant in a fraction of the city's population, it seems to be actively spreading there. The reported recombinant is tentatively designated RF1_2k/1b, in agreement with the nomenclature used for HIV recombinants. Recombination between HCV genotypes must now be considered in the classification, laboratory diagnosis, and treatment of HCV infection.

Experimental thermodynamic data of the ligand-receptor association showed that the entropy changes upon binding are positive and large enough to be important driving forces of the binding process for a considerable number of ligand-receptor complexes [1]. The expected source behind such an entropy increase is the release of the water molecules from the binding pocket and from around the ligand to get more freedom in the bulk phase. However, this important source of entropy is usually ignored due to the lake of a method to compute it. Therefore, we developed a method to compute the entropic gain due to water release upon ligand binding. Water molecules close to a charged residue are restricted from free rotation due to the interaction between their dipole moments and the electric field. This restriction result in a decrease in their entropy. The loss of entropy can be gained again when the water molecules displaced from area that under the electric field upon the binding process. To compute the loss of entropy due to the electric field, we developed a method based on the knowledge of the electric field which affects the rotation of the water molecules using the continuum electrostatic methods. This provides a possibility for the calculation of the entropic loss from the partition function of the water molecules under an electric field. For monovalent ions, the computed loss in entropy upon solvation correlates well with experimentally measured one. The loss of entropy of water close to hydrophobic molecules (Hydrophobic entropy) cannot be explained by the effect of the electric field from the molecule because the magnitude of the electric field is not strong enough to restrict the mobility of the water molecules. However, the loss in entropy results from the asymmetric interactions of the water molecules with the hydrophobic molecules and the neighbour water molecules. To compute the hydrophobic entropy we developed an empirical method which is derived from a linear relation between the hydration entropy of small hydrophobic molecules and the hydrophobicity of the water molecules around the surface of the small molecule. A training data set of 142 small molecules shows a significant linear relation between the hydrophobic entropy and the computed hydrophobicity.