Showing papers in "Proteins in 2015"
TL;DR: This paper presents an ab initio protein folding method to build three‐dimensional models using predicted contacts and secondary structures that improves the quality and accuracy of structural models and in particular generates better β‐sheets than other algorithms.
Abstract: Predicted protein residue–residue contacts can be used to build three-dimensional models and consequently to predict protein folds from scratch. A considerable amount of effort is currently being spent to improve contact prediction accuracy, whereas few methods are available to construct protein tertiary structures from predicted contacts. Here, we present an ab initio protein folding method to build three-dimensional models using predicted contacts and secondary structures. Our method first translates contacts and secondary structures into distance, dihedral angle, and hydrogen bond restraints according to a set of new conversion rules, and then provides these restraints as input for a distance geometry algorithm to build tertiary structure models. The initially reconstructed models are used to regenerate a set of physically realistic contact restraints and detect secondary structure patterns, which are then used to reconstruct final structural models. This unique two-stage modeling approach of integrating contacts and secondary structures improves the quality and accuracy of structural models and in particular generates better β-sheets than other algorithms. We validate our method on two standard benchmark datasets using true contacts and secondary structures. Our method improves TM-score of reconstructed protein models by 45% and 42% over the existing method on the two datasets, respectively. On the dataset for benchmarking reconstructions methods with predicted contacts and secondary structures, the average TM-score of best models reconstructed by our method is 0.59, 5.5% higher than the existing method. The CONFOLD web server is available at http://protein.rnet.missouri.edu/confold/. Proteins 2015; 83:1436–1449. © 2015 Wiley Periodicals, Inc.
166 citations
TL;DR: The goal of this review is to outline how tunnels influence substrate specificity and catalytic efficiency in enzymes with buried active sites and to provide a brief summary of the computational tools used to identify and evaluate these tunnels.
Abstract: In enzymes, the active site is the location where incoming substrates are chemically converted to products. In some enzymes, this site is deeply buried within the core of the protein and in order to access the active site, substrates must pass through the body of the protein via a tunnel. In many systems, these tunnels act as filters and have been found to influence both substrate specificity and catalytic mechanism. Identifying and understanding how these tunnels exert such control has been of growing interest over the past several years due to implications in fields such as protein engineering and drug design. This growing interest has spurred the development of several computational methods to identify and analyze tunnels and how ligands migrate through these tunnels. The goal of this review is to outline how tunnels influence substrate specificity and catalytic efficiency in enzymes with tunnels and to provide a brief summary of the computational tools used to identify and evaluate these tunnels.
115 citations
TL;DR: Investigations showed that predictions for most of buried mutant residues of SNase could be improved by using higher dielectric constant values and an option to generate different hydrogen positions also improves pKa predictions for buried carboxyl residues.
Abstract: We developed a Poisson-Boltzmann based approach to calculate the pKa values of protein ionizable residues (Glu, Asp, His, Lys and Arg), nucleotides of RNA and single stranded DNA. Two novel features were utilized: the dielectric properties of the macromolecules and water phase were modeled via the smooth Gaussian-based dielectric function in DelPhi and the corresponding electrostatic energies were calculated without defining the molecular surface. We tested the algorithm by calculating pKa values for more than 300 residues from 32 proteins from the PPD dataset and achieved an overall RMSD of 0.77. Particularly, the RMSD of 0.55 was achieved for surface residues, while the RMSD of 1.1 for buried residues. The approach was also found capable of capturing the large pKa shifts of various single point mutations in staphylococcal nuclease (SNase) from pKa-cooperative dataset, resulting in an overall RMSD of 1.6 for this set of pKa's. Investigations showed that predictions for most of buried mutant residues of SNase could be improved by using higher dielectric constant values. Furthermore, an option to generate different hydrogen positions also improves pKa predictions for buried carboxyl residues. Finally, the pKa calculations on two RNAs demonstrated the capability of this approach for other types of biomolecules.
107 citations
TL;DR: A combinatorial backbone and sequence optimization algorithm called AbDesign is described, which leverages the large number of sequences and experimentally determined molecular structures of antibodies to construct new antibody models, dock them against target surfaces and optimize their sequence and backbone conformation for high stability and binding affinity.
Abstract: Computational design of protein function has made substantial progress, generating new enzymes, binders, inhibitors, and nanomaterials not previously seen in nature. However, the ability to design new protein backbones for function--essential to exert control over all polypeptide degrees of freedom--remains a critical challenge. Most previous attempts to design new backbones computed the mainchain from scratch. Here, instead, we describe a combinatorial backbone and sequence optimization algorithm called AbDesign, which leverages the large number of sequences and experimentally determined molecular structures of antibodies to construct new antibody models, dock them against target surfaces and optimize their sequence and backbone conformation for high stability and binding affinity. We used the algorithm to produce antibody designs that target the same molecular surfaces as nine natural, high-affinity antibodies; in five cases interface sequence identity is above 30%, and in four of those the backbone conformation at the core of the antibody binding surface is within 1 A root-mean square deviation from the natural antibodies. Designs recapitulate polar interaction networks observed in natural complexes, and amino acid sidechain rigidity at the designed binding surface, which is likely important for affinity and specificity, is high compared to previous design studies. In designed anti-lysozyme antibodies, complementarity-determining regions (CDRs) at the periphery of the interface, such as L1 and H2, show greater backbone conformation diversity than the CDRs at the core of the interface, and increase the binding surface area compared to the natural antibody, potentially enhancing affinity and specificity.
92 citations
TL;DR: This review presents current computational methods to predict MP structure, starting with secondary structure prediction, prediction of trans‐membrane spans, and topology, and describes recent developments in the prediction of 3D structures of both α‐helical MPs as well as β‐barrels.
Abstract: The determination of membrane protein (MP) structures has always trailed that of soluble proteins due to difficulties in their overexpression, reconstitution into membrane mimetics, and subsequent structure determination. The percentage of MP structures in the protein databank (PDB) has been at a constant 1-2% for the last decade. In contrast, over half of all drugs target MPs, only highlighting how little we understand about drug-specific effects in the human body. To reduce this gap, researchers have attempted to predict structural features of MPs even before the first structure was experimentally elucidated. In this review, we present current computational methods to predict MP structure, starting with secondary structure prediction, prediction of trans-membrane spans, and topology. Even though these methods generate reliable predictions, challenges such as predicting kinks or precise beginnings and ends of secondary structure elements are still waiting to be addressed. We describe recent developments in the prediction of 3D structures of both α-helical MPs as well as β-barrels using comparative modeling techniques, de novo methods, and molecular dynamics (MD) simulations. The increase of MP structures has (1) facilitated comparative modeling due to availability of more and better templates, and (2) improved the statistics for knowledge-based scoring functions. Moreover, de novo methods have benefited from the use of correlated mutations as restraints. Finally, we outline current advances that will likely shape the field in the forthcoming decade.
92 citations
TL;DR: A compound is identified by in silico screening that inhibits CbDHFR with at least 25‐fold greater potency than hDHFR, and could form the basis of a novel antibacterial agent effective against a broad spectrum of pathogenic bacteria.
Abstract: Coxiella burnetii is a highly infectious bacterium and potential agent of bioterrorism. However, it has not been studied as extensively as other biological agents, and very few of its proteins have been structurally characterized. To address this situation, we undertook a study of critical metabolic enzymes in C. burnetii that have great potential as drug targets. We used high-throughput techniques to produce novel crystal structures of 48 of these proteins. We selected one protein, C. burnetii dihydrofolate reductase (CbDHFR), for additional work to demonstrate the value of these structures for structure-based drug design. This enzyme's structure reveals a feature in the substrate binding groove that is different between CbDHFR and human dihydrofolate reductase (hDHFR). We then identified a compound by in silico screening that exploits this binding groove difference, and demonstrated that this compound inhibits CbDHFR with at least 25-fold greater potency than hDHFR. Since this binding groove feature is shared by many other prokaryotes, the compound identified could form the basis of a novel antibacterial agent effective against a broad spectrum of pathogenic bacteria.
73 citations
TL;DR: The BioGPS approach is described and its applicability to rationalize off‐target effects (ERα and SERCA), to classify protein families and explain polypharmacology (ABL1 kinase and NQO2), and torationalize selectivity between subfamilies (MAP kinases p38α/ERK2 and PPARδ/PPARγ) is demonstrated.
Abstract: The structural comparison of protein binding sites is increasingly important in drug design; identifying structurally similar sites can be useful for techniques such as drug repurposing, and also in a polypharmacological approach to deliberately affect multiple targets in a disease pathway, or to explain unwanted off-target effects Once similar sites are identified, identifying local differences can aid in the design of selectivity Such an approach moves away from the classical “one target one drug” approach and toward a wider systems biology paradigm Here, we report a semiautomated approach, called BioGPS, that is based on the software FLAP which combines GRID Molecular Interactions Fields (MIFs) and pharmacophoric fingerprints BioGPS comprises the automatic preparation of protein structure data, identification of binding sites, and subsequent comparison by aligning the sites and directly comparing the MIFs Chemometric approaches are included to reduce the complexity of the resulting data on large datasets, enabling focus on the most relevant information Individual site similarities can be analyzed in terms of their Pharmacophoric Interaction Field (PIF) similarity, and importantly the differences in their PIFs can be extracted Here we describe the BioGPS approach, and demonstrate its applicability to rationalize off-target effects (ERα and SERCA), to classify protein families and explain polypharmacology (ABL1 kinase and NQO2), and to rationalize selectivity between subfamilies (MAP kinases p38α/ERK2 and PPARδ/PPARγ) The examples shown demonstrate a significant validation of the method and illustrate the effectiveness of the approach Proteins 2015; 83:517–532 © 2015 Wiley Periodicals, Inc
64 citations
TL;DR: A calibration curve for backbone entropy vs. O2NH is developed, which accounts for both correlations between amide group motions of different residues, and correlations between backbone and side chain motions, and provides a prescription for determination of the total protein conformational entropy changes from NMR relaxation measurements.
Abstract: Molecular dynamics simulations are used to analyze the relationship between NMR-derived squared generalized order parameters of amide NH groups and backbone entropy. Amide order parameters (O(2) NH ) are largely determined by the secondary structure and average values appear unrelated to the overall flexibility of the protein. However, analysis of the more flexible subset (O(2) NH < 0.8) shows that these report both on the local flexibility of the protein and on a different component of the conformational entropy than that reported by the side chain methyl axis order parameters, O(2) axis . A calibration curve for backbone entropy vs. O(2) NH is developed, which accounts for both correlations between amide group motions of different residues, and correlations between backbone and side chain motions. This calibration curve can be used with experimental values of O(2) NH changes obtained by NMR relaxation measurements to extract backbone entropy changes, for example, upon ligand binding. In conjunction with our previous calibration for side chain entropy derived from measured O(2) axis values this provides a prescription for determination of the total protein conformational entropy changes from NMR relaxation measurements.
63 citations
TL;DR: The manual classification strategy in ECOD is illustrated by example, focusing on two major issues in protein classification: domain partitioning and the relationship between homology and similarity scores.
Abstract: ECOD (Evolutionary Classification Of protein Domains) is a comprehensive and up-to-date protein structure classification database. The majority of new structures released from the PDB (Protein Data Bank) every week already have close homologs in the ECOD hierarchy and thus can be reliably partitioned into domains and classified by software without manual intervention. However, those proteins that lack confidently detectable homologs require careful analysis by experts. Although many bioinformatics resources rely on expert curation to some degree, specific examples of how this curation occurs and in what cases it is necessary are not always described. Here, we illustrate the manual classification strategy in ECOD by example, focusing on two major issues in protein classification: domain partitioning and the relationship between homology and similarity scores. Most examples show recently released and manually classified PDB structures. We discuss multi-domain proteins, discordance between sequence and structural similarities, difficulties with assessing homology with scores, and integral membrane proteins homologous to soluble proteins. By timely assimilation of newly available structures into its hierarchy, ECOD strives to provide a most accurate and updated view of the protein structure world as a result of combined computational and expert-driven analysis.
61 citations
TL;DR: A new docking refinement approach, iATTRACT, has been developed which combines simultaneous full interface flexibility and rigid body optimizations during docking energy minimization.
Abstract: Protein-protein interactions are abundant in the cell but to date structural data for a large number of complexes is lacking. Computational docking methods can complement experiments by providing structural models of complexes based on structures of the individual partners. A major caveat for docking success is accounting for protein flexibility. Especially, interface residues undergo significant conformational changes upon binding. This limits the performance of docking methods that keep partner structures rigid or allow limited flexibility. A new docking refinement approach, iATTRACT, has been developed which combines simultaneous full interface flexibility and rigid body optimizations during docking energy minimization. It employs an atomistic molecular mechanics force field for intermolecular interface interactions and a structure-based force field for intramolecular contributions. The approach was systematically evaluated on a large protein-protein docking benchmark, starting from an enriched decoy set of rigidly docked protein–protein complexes deviating by up to 15 A from the native structure at the interface. Large improvements in sampling and slight but significant improvements in scoring/discrimination of near native docking solutions were observed. Complexes with initial deviations at the interface of up to 5.5 A were refined to significantly better agreement with the native structure. Improvements in the fraction of native contacts were especially favorable, yielding increases of up to 70%. Proteins 2015; 83:248–258. © 2014 Wiley Periodicals, Inc.
57 citations
TL;DR: It is proposed that pMD has the potential to predict surface patches responsible for protein‐biomolecule interactions by comparing the distribution of hotspots in nonpocket‐like regions with known protein‐ and membrane‐interacting interfaces.
Abstract: We have used probe-based molecular dynamics (pMD) simulations to search for interaction hotspots on the surface of the therapeutically highly relevant oncogenic K-Ras G12D. Combining the probe-based query with an ensemble-based pocket identification scheme and an analysis of existing Ras-ligand complexes, we show that (i) pMD is a robust and cost-effective strategy for binding site identification, (ii) all four of the previously reported ligand binding sites are suitable for structure-based ligand design, and (iii) in some cases probe binding and expanded sampling of configurational space enable pocket expansion and increase the likelihood of site identification. Furthermore, by comparing the distribution of hotspots in non-pocket-like regions with known protein- and membrane-interacting interfaces, we propose that pMD has the potential to predict surface patches responsible for protein-biomolecule interactions. These observations have important implications for future drug design efforts and will facilitate the search for potential interfaces responsible for the proposed transient oligomerization or interaction of Ras with other biomolecules in the cellular milieu.
TL;DR: Analysis of the radial distribution of IL constituents with respect to the enzyme reveals decreased interactions with the anion are prevalent in the modified systems when compared to the wild type, which is largely accompanied by an increase in cation contact.
Abstract: Molecular simulations of the enzymes Candida rugosa lipase and Bos taurus α-chymotrypsin in aqueous ionic liquids 1-butyl-3-methylimidazolium chloride and 1-ethyl-3-methylimidazolium ethyl sulfate were used to study the change in enzyme-solvent interactions induced by modification of the enzyme surface charge. The enzymes were altered by randomly mutating lysine surface residues to glutamate, effectively decreasing the net surface charge by two for each mutation. These mutations resemble succinylation of the enzyme by chemical modification, which has been shown to enhance the stability of both enzymes in ILs. After establishing that the enzymes were stable on the simulated time scales, we focused the analysis on the organization of the ionic liquid substituents about the enzyme surface. Calculated solvent charge densities show that for both enzymes and in both solvents that changing positively charged residues to negative charge does indeed increase the charge density of the solvent near the enzyme surface. The radial distribution of IL constituents with respect to the enzyme reveals decreased interactions with the anion are prevalent in the modified systems when compared to the wild type, which is largely accompanied by an increase in cation contact. Additionally, the radial dependence of the charge density and ion distribution indicates that the effect of altering enzyme charge is confined to short range (≤1 nm) ordering of the IL. Ultimately, these results, which are consistent with that from prior experiments, provide molecular insight into the effect of enzyme surface charge on enzyme stability in ILs.
TL;DR: The origin of the catalytic efficiency of COMT is explored by using the empirical valence bond and the linear response approximation approaches and it is demonstrated that the catalyst effect is mainly due to electrostatic preorganization effects.
Abstract: The enzyme catechol O-methyltransferase (COMT) catalyzes the transfer of a methyl group from S-adenosylmethionine to dopamine and related catechols. The search for the origin of COMT catalysis has led to different proposals and hypothesis, including the entropic, the NAC, and the compression proposals as well as the more reasonable electrostatic idea. Thus, it is important to understand the catalytic power of this enzyme and to examine the validity of different proposals and in particular the repeated recent implication of the compression idea. The corresponding analysis should be done by well-defined physically-based considerations that involve computations rather than circular interpretations of experimental results. Thus, we explore here the origin of the catalytic efficiency of COMT by using the empirical valence bond and the linear response approximation approaches. The results demonstrate that the catalytic effect of COMT is mainly due to electrostatic preorganization effects. It is also shown that the compression, NAC and entropic proposals do not account for the catalytic effect.
TL;DR: A computational model of the N‐term of the human DAT, obtained through an ab initio structure prediction, in combination with extensive atomistic molecular dynamics simulations in the context of a lipid membrane, reveals that whereas theN‐term is a highly dynamic domain, it contains secondary structure elements that remain stable in the long MD trajectories of interactions with the bilayer.
Abstract: The dopamine transporter (DAT) is a transmembrane protein belonging to the family of neurotransmitter:sodium symporters (NSS). Members of the NSS are responsible for the clearance of neurotransmitters from the synaptic cleft, and for their translocation back into the presynaptic nerve terminal. The DAT contains long intracellular N- and C-terminal domains that are strongly implicated in the transporter function. The N-terminus (N-term), in particular, regulates the reverse transport (efflux) of the substrate through DAT. Currently, the molecular mechanisms of the efflux remain elusive in large part due to lack of structural information on the N-terminal segment. Here we report a computational model of the N-term of the human DAT (hDAT), obtained through an ab initio structure prediction, in combination with extensive atomistic molecular dynamics (MD) simulations in the context of a lipid membrane. Our analysis reveals that whereas the N-term is a highly dynamic domain, it contains secondary structure elements that remain stable in the long MD trajectories of interactions with the bilayer (totaling >2.2 μs). Combining MD simulations with continuum mean-field modeling we found that the N-term engages with lipid membranes through electrostatic interactions with the charged lipids PIP2 (phosphatidylinositol 4,5-Biphosphate) or PS (phosphatidylserine) that are present in these bilayers. We identify specific motifs along the N-term implicated in such interactions and show that differential modes of N-term/membrane association result in differential positioning of the structured segments on the membrane surface. These results will inform future structure-based studies that will elucidate the mechanistic role of the N-term in DAT function.
TL;DR: A fast sequence‐based predictor that predicts VH–VL domain orientation with Q2 values ranging from 0.54 to 0.73 is presented, which translates into an improvement in the average root‐mean‐square deviation with regard to the crystal structures.
Abstract: The antigen-binding site of antibodies forms at the interface of their two variable domains, VH and VL, making VH-VL domain orientation a factor that codetermines antibody specificity and affinity. Preserving VH-VL domain orientation in the process of antibody engineering is important in order to retain the original antibody properties, and predicting the correct VH-VL orientation has also been recognized as an important factor in antibody homology modeling. In this article, we present a fast sequence-based predictor that predicts VH-VL domain orientation with Q(2) values ranging from 0.54 to 0.73 on the evaluation set. We describe VH-VL orientation in terms of the six absolute ABangle parameters that have recently been proposed as a means to separate the different degrees of freedom of VH-VL domain orientation. In order to assess the impact of adjusting VH-VL orientation according to our predictions, we use the set of antibody structures of the recently published Antibody Modeling Assessment (AMA) II study. In comparison to the original AMAII homology models, we find an improvement in the accuracy of VH-VL orientation modeling, which also translates into an improvement in the average root-mean-square deviation with regard to the crystal structures.
TL;DR: This approach extends applicability of the Rosetta redesign protocol into regimes without a priori precision structural information to create a library focused on the ligand‐contacting residues of an Acinetobacter transcription factor.
Abstract: Structure-based rational mutagenesis for engineering protein functionality has been limited by the scarcity and difficulty of obtaining crystal structures of desired proteins. On the other hand, when high-throughput selection is possible, directed evolution-based approaches for gaining protein functionalities have been random and fortuitous with limited rationalization. We combine comparative modeling of dimer structures, ab initio loop reconstruction, and ligand docking to select positions for mutagenesis to create a library focused on the ligand-contacting residues. The rationally reduced library requirement enabled conservative control of the substitutions by oligonucleotide synthesis and bounding its size within practical transformation efficiencies (∼ 10(7) variants). This rational approach was successfully applied on an inducer-binding domain of an Acinetobacter transcription factor (TF), pobR, which shows high specificity for natural effector molecule, 4-hydroxy benzoate (4HB), but no native response to 3,4-dihydroxy benzoate (34DHB). Selection for mutants with high transcriptional induction by 34DHB was carried out at the single-cell level under flow cytometry (via green fluorescent protein expression under the control of pobR promoter). Critically, this selection protocol allows both selection for induction and rejection of constitutively active mutants. In addition to gain-of-function for 34DHB induction, the selected mutants also showed enhanced sensitivity and response for 4HB (native inducer) while no sensitivity was observed for a non-targeted but chemically similar molecule, 2-hydroxy benzoate (2HB). This is unique application of the Rosetta modeling protocols for library design to engineer a TF. Our approach extends applicability of the Rosetta redesign protocol into regimes without a priori precision structural information.
TL;DR: This work proposes that BMS‐626529 binds to the UNLIG conformation of gp120 within the structurally conserved outer domain, under the antiparallel β20–β21 sheet, and adjacent to the CD4 binding loop, which prevents the initial interaction of HIV‐1 with the host CD4+ T cell, and subsequent HIV‐ 1 binding and entry.
Abstract: HIV-1 gp120 undergoes multiple conformational changes both before and after binding to the host CD4 receptor. BMS-626529 is an attachment inhibitor (AI) in clinical development (administered as prodrug BMS-663068) that binds to HIV-1 gp120. To investigate the mechanism of action of this new class of antiretroviral compounds, we constructed homology models of unliganded HIV-1 gp120 (UNLIG), a pre-CD4 binding-intermediate conformation (pCD4), a CD4 bound-intermediate conformation (bCD4), and a CD4/co-receptor-bound gp120 (LIG) from a series of partial structures. We also describe a simple pathway illustrating the transition between these four states. Guided by the positions of BMS-626529 resistance substitutions and structure–activity relationship data for the AI series, putative binding sites for BMS-626529 were identified, supported by biochemical and biophysical data. BMS-626529 was docked into the UNLIG model and molecular dynamics simulations were used to demonstrate the thermodynamic stability of the different gp120 UNLIG/BMS-626529 models. We propose that BMS-626529 binds to the UNLIG conformation of gp120 within the structurally conserved outer domain, under the antiparallel β20–β21 sheet, and adjacent to the CD4 binding loop. Through this binding mode, BMS-626529 can inhibit both CD4-induced and CD4-independent formation of the “open state” four-stranded gp120 bridging sheet, and the subsequent formation and exposure of the chemokine co-receptor binding site. This unique mechanism of action prevents the initial interaction of HIV-1 with the host CD4+ T cell, and subsequent HIV-1 binding and entry. Our findings clarify the novel mechanism of BMS-626529, supporting its ongoing clinical development. Proteins 2015; 83:331–350. © 2014 Wiley Periodicals, Inc.
TL;DR: The crystal structure of a fully glycosylated HIV‐1 gp120 core in complex with CD4 receptor and Fab 17b at 4.5‐Å resolution reveals 9 of the 15 N‐linked glycans of core gp120 to be partially ordered.
Abstract: The crystal structure of a fully glycosylated HIV-1 gp120 core in complex with CD4 receptor and Fab 17b at 4.5 A resolution reveals 9 of the 15 N-linked glycans of core gp120 to be partially ordered. The glycan at position Asn262 had the most extensive and well-ordered electron density, and a GlcNAc2Man7 was modeled. The GlcNAc stem of this glycan is largely buried in a cleft in gp120, suggesting a role in gp120 folding and stability. Its arms interact with the stems of neighboring glycans from the oligomannose patch, which is a major target for broadly neutralizing antibodies.
TL;DR: Findings illustrate that side chain methyl CH···O hydrogen bonding contributes to the energetics of protein structure and folding.
Abstract: The propensity of backbone Cα atoms to engage in carbon-oxygen (CH · · · O) hydrogen bonding is well-appreciated in protein structure, but side chain CH · · · O hydrogen bonding remains largely uncharacterized. The extent to which side chain methyl groups in proteins participate in CH · · · O hydrogen bonding is examined through a survey of neutron crystal structures, quantum chemistry calculations, and molecular dynamics simulations. Using these approaches, methyl groups were observed to form stabilizing CH · · · O hydrogen bonds within protein structure that are maintained through protein dynamics and participate in correlated motion. Collectively, these findings illustrate that side chain methyl CH · · · O hydrogen bonding contributes to the energetics of protein structure and folding.
TL;DR: A sequence‐based literature mining algorithm was designed to systematically analyze this wealth of information on SNPs, designed mutations, structural interactions, or functional roles of individual residues to identify universal selectivity‐determining positions.
Abstract: Cytochrome P450 monooxygenases (CYPs) are a large, highly diverse protein family with a common fold. The sequences, structures, and functions of CYPs have been extensively studied resulting in more than 53,000 scientific articles. A sequence-based literature mining algorithm was designed to systematically analyze this wealth of information on SNPs, designed mutations, structural interactions, or functional roles of individual residues. Structurally corresponding positions in different CYPs were compared and universal selectivity-determining positions were identified. Based on the Cytochrome P450 Engineering Database (www.CYPED.BioCatNet.de) and a standard numbering scheme for all CYPs, 4000 residues in 168 CYPs mentioned in 2400 articles could be assigned to 440 structurally corresponding standard positions of the CYP fold, covering 96% of all standard positions. Seventeen individual standard positions were mentioned in the context of more than 32 different CYPs. The majority of these most frequently mentioned positions are located on the six substrate recognition sites and are involved in control of selectivity, such as the well-studied position 87 in CYP102A1 (P450(BM-3)) which was mentioned in the articles on 63 different CYPs. The recurrent citation of the 17 frequently mentioned positions for different CYPs suggests their universal functional relevance.
TL;DR: This study provides a comprehensive comparison of the dynamics of all the three RAS isoforms using extensive molecular dynamics simulations, and presents the first simulation study showing GDP destabilization in the wild‐type RAS protein.
Abstract: RAS subfamily proteins regulates cell growth promoting signaling processes by cycling between active (GTP-bound) and inactive (GDP-bound) states. Different RAS isoforms, though structurally similar, exhibit functional specificity and are associated with different types of cancers and developmental disorders. Understanding the dynamical differences between the isoforms is crucial for the design of inhibitors that can selectively target a particular malfunctioning isoform. In this study, we provide a comprehensive comparison of the dynamics of all the three RAS isoforms (HRAS, KRAS, and NRAS) using extensive molecular dynamics simulations in both the GDP- (total of 3.06 μs) and GTP-bound (total of 2.4 μs) states. We observed significant differences in the dynamics of the isoforms, which rather interestingly, varied depending on the type of the nucleotide bound and the simulation temperature. Both SwitchI (Residues 25-40) and SwitchII (Residues 59-75) differ significantly in their flexibility in the three isoforms. Furthermore, Principal Component Analysis showed that there are differences in the conformational space sampled by the GTP-bound RAS isoforms. We also identified a previously unreported pocket, which opens transiently during MD simulations, and can be targeted to regulate nucleotide exchange reaction or possibly interfere with membrane localization. Further, we present the first simulation study showing GDP destabilization in the wild-type RAS protein. The destabilization of GDP/GTP occurred only in 1/50 simulations, emphasizing the need of guanine nucleotide exchange factors (GEFs) to accelerate such an extremely unfavorable process. This observation along with the other results presented in this article further support our previously hypothesized mechanism of GEF-assisted nucleotide exchange.
TL;DR: The results support models of the germinal center reaction in which two or more mutations can occur without concomitant selection and show how divergent pathways have yielded functionally equivalent antibodies.
Abstract: Affinity maturation, the process in which somatic hypermutation and positive selection generate antibodies with increasing affinity for an antigen, is pivotal in acquired humoral immunity. We have studied the mechanism of affinity gain in a human B-cell lineage in which two main maturation pathways, diverging from a common ancestor, lead to three mature antibodies that neutralize a broad range of H1 influenza viruses. Previous work showed that increased affinity in the mature antibodies derives primarily from stabilization of the CDR H3 loop in the antigen-binding conformation. We have now used molecular dynamics simulations and existing crystal structures to identify potentially key maturation mutations, and we have characterized their effects on the CDR H3 loop and on antigen binding using further simulations and experimental affinity measurements, respectively. In the two maturation pathways, different contacts between light and heavy chains stabilize the CDR H3 loop. As few as two single-site mutations in each pathway can confer substantial loop stability, but none of them confers experimentally detectable stability on its own. Our results support models of the germinal center reaction in which two or more mutations can occur without concomitant selection and show how divergent pathways have yielded functionally equivalent antibodies.
TL;DR: In this paper, the authors reported the crystal structures of the human anti-apoptotic protein Bcl-XL in complex with BH3-only BID(BH3) and BIM(BIM) peptides at 2.0 A and 1.5 A resolution, respectively.
Abstract: Apoptosis or programmed cell death is a regulatory process in cells in response to stimuli perturbing physiological conditions. The Bcl-2 family of proteins plays an important role in regulating homeostasis during apoptosis. In the process, the molecular interactions among the three members of this family, the pro-apoptotic, anti-apoptotic and BH3-only proteins at the mitochondrial outer membrane define the fate of a cell. Here, we report the crystal structures of the human anti-apoptotic protein Bcl-XL in complex with BH3-only BID(BH3) and BIM(BH3) peptides determined at 2.0 A and 1.5 A resolution, respectively. The BH3 peptides bind to the canonical hydrophobic pocket in Bcl-XL and adopt an alpha helical conformation in the bound form. Despite a similar structural fold, a comparison with other BH3 complexes revealed structural differences due to their sequence variations. In the Bcl-XL-BID(BH3) complex we observed a large pocket, in comparison with other BH3 complexes, lined by residues from helices α1, α2, α3, and α5 located adjacent to the canonical hydrophobic pocket. These results suggest that there are differences in the mode of interactions by the BH3 peptides that may translate into functional differences in apoptotic regulation.
TL;DR: A fast and consistent method for constructing tripartite structures is developed which leverages existing data‐driven models and molecular modeling approaches for constructing Tripartite structure of multidrug resistance efflux pumps are provided.
Abstract: Many bacterial pathogens are becoming increasingly resistant to antibiotic treatments, and a detailed understanding of the molecular basis of antibiotic resistance is critical for the development of next-generation approaches for combating bacterial infections. Studies focusing on pathogens have revealed the profile of resistance in these organisms to be due primarily to the presence of multidrug resistance efflux pumps: tripartite protein complexes which span the periplasm bridging the inner and outer membranes of Gram-negative bacteria. An atomic-level resolution tripartite structure remains imperative to advancing our understanding of the molecular mechanisms of pump function using both theoretical and experimental approaches. We develop a fast and consistent method for constructing tripartite structures which leverages existing data-driven models and provide molecular modeling approaches for constructing tripartite structures of multidrug resistance efflux pumps. Our modeling studies reveal that conformational changes in the inner membrane component responsible for drug translocation have limited impact on the conformations of the other pump components, and that two distinct models derived from conflicting experimental data are both consistent with all currently available measurements. Additionally, we investigate putative drug translocation pathways via geometric simulations based on the available crystal structures of the inner membrane pump component, AcrB, bound to two drugs which occupy distinct binding sites: doxorubicin and linezolid. These simulations suggest that smaller drugs may enter the pump through a channel from the cytoplasmic leaflet of the inner membrane, while both smaller and larger drug molecules may enter through a vestibule accessible from the periplasm.
TL;DR: This work highlights a new approach for studying minor conformational changes due to structural plasticity within a single dimeric interface in solution, and uses sparse data to limit conformational search during optimization of a physically realistic energy function.
Abstract: Oligomeric proteins are important targets for structure determination in solution. While in most cases the fold of individual subunits can be determined experimentally, or predicted by homology-based methods, protein-protein interfaces are challenging to determine de novo using conventional NMR structure determination protocols. Here we focus on a member of the bet-V1 superfamily, Aha1 from Colwellia psychrerythraea. This family displays a broad range of crystallographic interfaces none of which can be reconciled with the NMR and SAXS data collected for Aha1. Unlike conventional methods relying on a dense network of experimental restraints, the sparse data are used to limit conformational search during optimization of a physically realistic energy function. This work highlights a new approach for studying minor conformational changes due to structural plasticity within a single dimeric interface in solution.
TL;DR: Overall, the proteome‐wide conformational dynamic analysis indicates that certain interface sites play a critical role in functionally related dynamics (i.e., those with low dfi values), therefore mutations at those sites are more likely to be associated with disease.
Abstract: Recent studies have shown that the protein interface sites between individual monomeric units in biological assemblies are enriched in disease-associated non-synonymous single nucleotide variants (nsSNVs). To elucidate the mechanistic underpinning of this observation, we investigated the conformational dynamic properties of protein interface sites through a site-specific structural dynamic flexibility metric (dfi) for 333 multimeric protein assemblies. dfi measures the dynamic resilience of a single residue to perturbations that occurred in the rest of the protein structure and identifies sites contributing the most to functionally critical dynamics. Analysis of dfi profiles of over a thousand positions harboring variation revealed that amino acid residues at interfaces have lower average dfi (31%) than those present at non-interfaces (50%), which means that protein interfaces have less dynamic flexibility. Interestingly, interface sites with disease-associated nsSNVs have significantly lower average dfi (23%) as compared to those of neutral nsSNVs (42%), which directly relates structural dynamics to functional importance. We found that less conserved interface positions show much lower dfi for disease nsSNVs as compared to neutral nsSNVs. In this case, dfi is better as compared to the accessible surface area metric, which is based on the static protein structure. Overall, our proteome-wide conformational dynamic analysis indicates that certain interface sites play a critical role in functionally related dynamics (i.e., those with low dfi values), therefore mutations at those sites are more likely to be associated with disease.
TL;DR: The crystal structure of the GH78 family α‐rhamnosidase from Klebsiella oxytoca (KoRha) has been determined at 2.7 Å resolution with rhamnose bound in the active site of the catalytic domain, suggesting that the additional structural domains found in the related enzymes are dispensible for function.
Abstract: The crystal structure of the GH78 family α-rhamnosidase from Klebsiella oxytoca (KoRha) has been determined at 2.7 A resolution with rhamnose bound in the active site of the catalytic domain. Curiously, the putative catalytic acid, Asp 222, is preceded by an unusual non-proline cis-peptide bond which helps to project the carboxyl group into the active centre. This KoRha homodimeric structure is significantly smaller than those of the other previously determined GH78 structures. Nevertheless, the enzyme displays α-rhamnosidase activity when assayed in vitro, suggesting that the additional structural domains found in the related enzymes are dispensible for function. Proteins 2015; 83:1742–1749. © 2015 The Authors. Proteins: Structure, Function, and Bioinformatics Published by Wiley Periodicals, Inc.
TL;DR: The iron‐sulfur protein 1 (Isu1) and the J‐type co‐chaperone Jac1 from yeast are part of a huge ATP‐dependent system, and both interact with Hsp70 chaperones, and two most probable models were subjected to the coarse‐grained molecular dynamics simulations with the UNRES force field.
Abstract: The iron-sulfur protein 1 (Isu1) and the J-type co-chaperone Jac1 from yeast are part of a huge ATP-dependent system, and both interact with Hsp70 chaperones. Interaction of Isu1 and Jac1 is a part of the iron-sulfur cluster biogenesis system in mitochondria. In this study, the structure and dynamics of the yeast Isu1-Jac1 complex has been modeled. First, the complete structure of Isu1 was obtained by homology modeling using the I-TASSER server and YASARA software and thereafter tested for stability in the all-atom force field AMBER. Then, the known experimental structure of Jac1 was adopted to obtain initial models of the Isu1-Jac1 complex by using the ZDOCK server for global and local docking and the AutoDock software for local docking. Three most probable models were subsequently subjected to the coarse-grained molecular dynamics simulations with the UNRES force field to obtain the final structures of the complex. In the most probable model, Isu1 binds to the left face of the Γ-shaped Jac1 molecule by the β-sheet section of Isu1. Residues L105 , L109 , and Y163 of Jac1 have been assessed by mutation studies to be essential for binding (Ciesielski et al., J Mol Biol 2012; 417:1-12). These residues were also found, by UNRES/molecular dynamics simulations, to be involved in strong interactions between Isu1 and Jac1 in the complex. Moreover, N(95), T(98), P(102), H(112), V(159), L(167), and A(170) of Jac1, not yet tested experimentally, were also found to be important in binding.
TL;DR: In this article, the BCL::MP-Fold (BioChemical Library membrane protein fold) algorithm assembles secondary structure elements (SSEs) in the membrane using a Monte Carlo Metropolis (MCM) approach.
Abstract: For many membrane proteins, the determination of their topology remains a challenge for methods like X-ray crystallography and nuclear magnetic resonance (NMR) spectroscopy. Electron paramagnetic resonance (EPR) spectroscopy has evolved as an alternative technique to study structure and dynamics of membrane proteins. The present study demonstrates the feasibility of membrane protein topology determination using limited EPR distance and accessibility measurements. The BCL::MP-Fold (BioChemical Library membrane protein fold) algorithm assembles secondary structure elements (SSEs) in the membrane using a Monte Carlo Metropolis (MCM) approach. Sampled models are evaluated using knowledge-based potential functions and agreement with the EPR data and a knowledge-based energy function. Twenty-nine membrane proteins of up to 696 residues are used to test the algorithm. The RMSD100 value of the most accurate model is better than 8 A for 27, better than 6 A for 22, and better than 4 A for 15 of the 29 proteins, demonstrating the algorithms' ability to sample the native topology. The average enrichment could be improved from 1.3 to 2.5, showing the improved discrimination power by using EPR data.
TL;DR: A new balanced network deconvolution (BND) algorithm is presented to identify optimized dependency matrix without limit on the eigenvalue range in the applied network systems.
Abstract: Residue contact map is essential for protein three-dimensional structure determination. But most of the current contact prediction methods based on residue co-evolution suffer from high false-positives as introduced by indirect and transitive contacts (i.e., residues A–B and B–C are in contact, but A–C are not). Built on the work by Feizi et al. (Nat Biotechnol 2013; 31:726–733), which demonstrated a general network model to distinguish direct dependencies by network deconvolution, this study presents a new balanced network deconvolution (BND) algorithm to identify optimized dependency matrix without limit on the eigenvalue range in the applied network systems. The algorithm was used to filter contact predictions of five widely used co-evolution methods. On the test of proteins from three benchmark datasets of the 9th critical assessment of protein structure prediction (CASP9), CASP10, and PSICOV (precise structural contact prediction using sparse inverse covariance estimation) database experiments, the BND can improve the medium- and long-range contact predictions at the L/5 cutoff by 55.59% and 47.68%, respectively, without additional central processing unit cost. The improvement is statistically significant, with a P-value < 5.93 × 10−3 in the Student's t-test. A further comparison with the ab initio structure predictions in CASPs showed that the usefulness of the current co-evolution-based contact prediction to the three-dimensional structure modeling relies on the number of homologous sequences existing in the sequence databases. BND can be used as a general contact refinement method, which is freely available at: http://www.csbio.sjtu.edu.cn/bioinf/BND/. Proteins 2015; 83:485–496. © 2014 Wiley Periodicals, Inc.