Determining functionally important amino acid residues of the E1 protein of Venezuelan equine encephalitis virus

A new method for predicting interacting residues in protein complexes, InterProSurf, was applied to the E1 envelope protein of Venezuelan equine encephalitis (VEEV). Monomeric and trimeric models of VEEV-E1 were constructed with our MPACK program, using the crystal structure of the E1 protein of Semliki forest virus as a template. An alignment of the E1 sequences from representative alphavirus sequences was used to determine physical chemical property motifs (likely functional areas) with our PCPMer program. Information on residue variability, propensity to be in protein interfaces, and surface exposure on the model was combined to predict surface clusters likely to interact with other viral or cellular proteins. Mutagenesis of these clusters indicated that the predictions accurately detected areas crucial for virus infection. In addition to the fusion peptide area in domain 2, at least two other surface areas play an important role in virus infection. We propose that these may be sites of interaction between the E1–E1 and E1–E2 subdomains of the envelope proteins that are required to assemble the functional unit. The InterProSurf method is, thus, an important new tool for predicting viral protein interactions. These results can aid in the design of new vaccines against alphaviruses and other viruses.     

ProPred: prediction of HLA-DR binding sites.

ProPred is a graphical web tool for predicting MHC class II binding regions in antigenic protein sequences. The server implement matrix based prediction algorithm, employing amino-acid/position coefficient table deduced from literature. The predicted binders can be visualized either as peaks in graphical interface or as colored residues in HTML interface. This server might be a useful tool in locating the promiscuous binding regions that can bind to several HLA-DR alleles. AVAILABILITY: The server is available at http://www.imtech.res.in/raghava/propred/ CONTACT: raghava@imtech.res.in SUPPLEMENTARY INFORMATION: http://www.imtech.res.in/raghava/propred/page3.html     

ConSurf: identification of functional regions in proteins by surface-mapping of phylogenetic information

SUMMARY: We recently developed algorithmic tools for the identification of functionally important regions in proteins of known three dimensional structure by estimating the degree of conservation of the amino-acid sites among their close sequence homologues. Projecting the conservation grades onto the molecular surface of these proteins reveals patches of highly conserved (or occasionally highly variable) residues that are often of important biological function. We present a new web server, ConSurf, which automates these algorithmic tools. ConSurf may be used for high-throughput characterization of functional regions in proteins. AVAILABILITY: The ConSurf web server is available at:http://consurf.tau.ac.il. SUPPLEMENTARY INFORMATION: A set of examples is available at http://consurf.tau.ac.il under 'GALLERY'.     

FoldUnfold: web server for the prediction of disordered regions in protein chain

Identification of disordered regions in polypeptide chains is very important because such regions are essential for protein function. A new parameter, namely mean packing density of residues has been introduced to detect disordered regions in a protein sequence. We have demonstrated that regions with weak expected packing density would be responsible for the appearance of disordered regions. Our method (FoldUnfold) has been tested on datasets of globular proteins (559 proteins) and long disordered protein segments (129 proteins) and showed improved performance over some other widely used methods, such as DISOPRED, PONDR VL3H, IUPred and GlobPlot. AVAILABILITY: The FoldUnfold server is available for users at http://skuld.protres.ru/~mlobanov/ogu/ogu.cgi. There is a link to our server through the web site of DisProt (http://www.disprot.org/predictors.php).     

PRICE (PRotein Interface Conservation and Energetics): a server for the analysis of protein–protein interfaces

Residues in a protein–protein interface that are important for forming and stabilizing the interaction can usually be identified by looking at patterns of evolutionary conservation in groups of homologous proteins and also by the computational identification of binding hotspots. The PRICE (PRotein Interface Conservation and Energetics) server takes the coordinates of a protein–protein complex, dissects the interface into core and rim regions, and calculates (1) the degree of conservation (measured as the sequence entropy), as well as (2) the change in free energy of binding (∆∆G, due to alanine scanning mutagenesis) of interface residues. Results are displayed as color-coded plots and also made available for download. This enables the computational identification of binding hot spots, based on which further experiments can be designed. The method will aid in protein functional prediction by correct assignment of hot regions involved in binding. Consideration of sequence entropies for residues with large ∆∆G values may provide an indication of the biological relevance of the interface. Finally, the results obtained on a test set of alanine mutants has been compared to those obtained using other servers/methods. The PRICE server is a web application available at .     

ModLoop: automated modeling of loops in protein structures

SUMMARY: ModLoop is a web server for automated modeling of loops in protein structures. The input is the atomic coordinates of the protein structure in the Protein Data Bank format, and the specification of the starting and ending residues of one or more segments to be modeled, containing no more than 20 residues in total. The output is the coordinates of the non-hydrogen atoms in the modeled segments. A user provides the input to the server via a simple web interface, and receives the output by e-mail. The server relies on the loop modeling routine in MODELLER that predicts the loop conformations by satisfaction of spatial restraints, without relying on a database of known protein structures. For a rapid response, ModLoop runs on a cluster of Linux PC computers. AVAILABILITY: The server is freely accessible to academic users at http://salilab.org/modloop     

PROFbval: predict flexible and rigid residues in proteins

SUMMARY: The mobility of a residue on the protein surface is closely linked to its function. The identification of extremely rigid or flexible surface residues can therefore contribute information crucial for solving the complex problem of identifying functionally important residues in proteins. Mobility is commonly measured by B-value data from high-resolution three-dimensional X-ray structures. Few methods predict B-values from sequence. Here, we present PROFbval, the first web server to predict normalized B-values from amino acid sequence. The server handles amino acid sequences (or alignments) as input and outputs normalized B-value and two-state (flexible/rigid) predictions. The server also assigns a reliability index for each prediction. For example, PROFbval correctly identifies residues in active sites on the surface of enzymes as particularly rigid. AVAILABILITY: http://www.rostlab.org/services/profbval CONTACT: profbval@rostlab.org SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.     

Crystal structure of the chi:psi sub-assembly of the Escherichia coli DNA polymerase clamp-loader complex.

The chi (chi) and psi (psi) subunits of Escherichia coli DNA polymerase III form a heterodimer that is associated with the ATP-dependent clamp-loader machinery. In E. coli, the chi:psi heterodimer serves as a bridge between the clamp-loader complex and the single-stranded DNA-binding protein. We determined the crystal structure of the chi:psi heterodimer at 2.1 A resolution. Although neither chi (147 residues) nor psi (137 residues) bind to nucleotides, the fold of each protein is similar to the folds of mononucleotide-(chi) or dinucleotide-(psi) binding proteins, without marked similarity to the structures of the clamp-loader subunits. Genes encoding chi and psi proteins are found to be readily identifiable in several bacterial genomes and sequence alignments showed that residues at the chi:psi interface are highly conserved in both proteins, suggesting that the heterodimeric interaction is of functional significance. The conservation of surface-exposed residues is restricted to the interfacial region and to just two other regions in the chi:psi complex. One of the conserved regions was found to be located on chi, distal to the psi interaction region, and we identified this as the binding site for a C-terminal segment of the single-stranded DNA-binding protein. The other region of sequence conservation is localized to an N-terminal segment of psi (26 residues) that is disordered in the crystal structure. We speculate that psi is linked to the clamp-loader complex by this flexible, but conserved, N-terminal segment, and that the chi:psi unit is linked to the single-stranded DNA-binding protein via the distal surface of chi. The base of the clamp-loader complex has an open C-shaped structure, and the shape of the chi:psi complex is suggestive of a loose docking within the crevice formed by the open faces of the delta and delta' subunits of the clamp-loader.     

Complex between Peptostreptococcus magnus protein L and a human antibody reveals structural convergence in the interaction modes of Fab binding proteins

BACKGROUND: Peptostreptococcus magnus protein L (PpL) is a multidomain, bacterial surface protein whose presence correlates with virulence. It consists of up to five homologous immunoglobulin binding domains that interact with the variable (VL) regions of kappa light chains found on two thirds of mammalian antibodies. RESULTS: We refined the crystal structure of the complex between a human antibody Fab fragment (2A2) and a single PpL domain (61 residues) to 2.7 A. The asymmetric unit contains two Fab molecules sandwiching a single PpL domain, which contacts similar VL framework regions of two light chains via independent interfaces. The residues contacted on VL are remote from the hypervariable loops. One PpL-Vkappa interface agrees with previous biochemical data, while the second is novel. Site-directed mutagenesis and analytical-centrifugation studies suggest that the two PpL binding sites have markedly different affinities for VL. The PpL residues in both interactions are well conserved among different Peptostreptococcus magnus strains. The Fab contact positions identified in the complex explain the high specificity of PpL for antibodies with kappa rather than lambda chains. CONCLUSIONS: The PpL-Fab complex shows the first interaction of a bacterial virulence factor with a Fab light chain outside the conventional combining site. Structural comparison with two other bacterial proteins interacting with the Fab heavy chain shows that PpL, structurally homologous to streptococcal SpG domains, shares with the latter a similar binding mode. These two bacterial surface proteins interact with their respective immunoglobulin regions through a similar beta zipper interaction.     

Expression cartography of human tissues using self organizing maps

Background

Although homology-based methods are among the most widely used methods for predicting the structure and function of proteins, the question as to whether interface sequence conservation can be effectively exploited in predicting protein-protein interfaces has been a subject of debate.

Results

We studied more than 300,000 pair-wise alignments of protein sequences from structurally characterized protein complexes, including both obligate and transient complexes. We identified sequence similarity criteria required for accurate homology-based inference of interface residues in a query protein sequence. Based on these analyses, we developed HomPPI, a class of sequence homology-based methods for predicting protein-protein interface residues. We present two variants of HomPPI: (i) NPS-HomPPI (Non partner-specific HomPPI), which can be used to predict interface residues of a query protein in the absence of knowledge of the interaction partner; and (ii) PS-HomPPI (Partner-specific HomPPI), which can be used to predict the interface residues of a query protein with a specific target protein. Our experiments on a benchmark dataset of obligate homodimeric complexes show that NPS-HomPPI can reliably predict protein-protein interface residues in a given protein, with an average correlation coefficient (CC) of 0.76, sensitivity of 0.83, and specificity of 0.78, when sequence homologs of the query protein can be reliably identified. NPS-HomPPI also reliably predicts the interface residues of intrinsically disordered proteins. Our experiments suggest that NPS-HomPPI is competitive with several state-of-the-art interface prediction servers including those that exploit the structure of the query proteins. The partner-specific classifier, PS-HomPPI can, on a large dataset of transient complexes, predict the interface residues of a query protein with a specific target, with a CC of 0.65, sensitivity of 0.69, and specificity of 0.70, when homologs of both the query and the target can be reliably identified. The HomPPI web server is available at http://homppi.cs.iastate.edu/.

Conclusions

Sequence homology-based methods offer a class of computationally efficient and reliable approaches for predicting the protein-protein interface residues that participate in either obligate or transient interactions. For query proteins involved in transient interactions, the reliability of interface residue prediction can be improved by exploiting knowledge of putative interaction partners.     

Non‐interacting surface solvation and dynamics in protein–protein interactions

Protein–protein interactions control a plethora of cellular processes, including cell proliferation, differentiation, apoptosis, and signal transduction. Understanding how and why proteins interact will inevitably lead to novel structure‐based drug design methods, as well as design of de novo binders with preferred interaction properties. At a structural and molecular level, interface and rim regions are not enough to fully account for the energetics of protein–protein binding, even for simple lock‐and‐key rigid binders. As we have recently shown, properties of the global surface might also play a role in protein–protein interactions. Here, we report on molecular dynamics simulations performed to understand solvent effects on protein–protein surfaces. We compare properties of the interface, rim, and non‐interacting surface regions for five different complexes and their free components. Interface and rim residues become, as expected, less mobile upon complexation. However, non‐interacting surface appears more flexible in the complex. Fluctuations of polar residues are always lower compared with charged ones, independent of the protein state. Further, stable water molecules are often observed around polar residues, in contrast to charged ones. Our analysis reveals that (a) upon complexation, the non‐interacting surface can have a direct entropic compensation for the lower interface and rim entropy and (b) the mobility of the first hydration layer, which is linked to the stability of the protein–protein complex, is influenced by the local chemical properties of the surface. These findings corroborate previous hypotheses on the role of the hydration layer in shielding protein–protein complexes from unintended protein–protein interactions. Proteins 2015; 83:445–458. © 2014 Wiley Periodicals, Inc.     

BetaTPred: prediction of beta-TURNS in a protein using statistical algorithms

MOTIVATION: beta-turns play an important role from a structural and functional point of view. beta-turns are the most common type of non-repetitive structures in proteins and comprise on average, 25% of the residues. In the past numerous methods have been developed to predict beta-turns in a protein. Most of these prediction methods are based on statistical approaches. In order to utilize the full potential of these methods, there is a need to develop a web server. RESULTS: This paper describes a web server called BetaTPred, developed for predicting beta-TURNS in a protein from its amino acid sequence. BetaTPred allows the user to predict turns in a protein using existing statistical algorithms. It also allows to predict different types of beta-TURNS e.g. type I, I', II, II', VI, VIII and non-specific. This server assists the users in predicting the consensus beta-TURNS in a protein. AVAILABILITY: The server is accessible from http://imtech.res.in/raghava/betatpred/     

Residue conservation in viral capsid assembly

To evaluate the evolutionary constraints placed on viral proteins by the structure and assembly of the capsid, we calculate Shannon entropies in the aligned sequences of 45 polypeptide chains in 32 icosahedral viruses, and relate these entropies to the residue location in the three-dimensional structure of the capsids. Three categories of residues have entropies lower than the chain average implying that they are better conserved than average: residues that are buried within a subunit (the protein core), residues that contain atoms buried at an interface between subunits (the interface core), and residues that contribute to several such interfaces. The interface core is also conserved in homomeric proteins and in transient protein-protein complexes, which have only one interface whereas capsids have many. In capsids, the subunit interfaces implicate most of the polypeptide chain: on average, 66% of the capsid residues are at an interface, 34% at more than one, and 47% at the interface core. Nevertheless, we observe that the degree of residue conservation can vary widely between interfaces within a capsid and between regions within an interface. The interfaces and regions of interfaces that show a low sequence variability are likely to play major roles in the self-assembly of the capsid, with implications on its mechanism that we discuss taking adeno-associated virus as an example.     

PRINTR: Prediction of RNA binding sites in proteins using SVM and profiles

Protein–RNA interactions play a key role in a number of biological processes such as protein synthesis, mRNA processing, assembly and function of ribosomes and eukaryotic spliceosomes. A reliable identification of RNA-binding sites in RNA-binding proteins is important for functional annotation and site-directed mutagenesis. We developed a novel method for the prediction of protein residues that interact with RNA using support vector machine (SVM) and position-specific scoring matrices (PSSMs). Two cases have been considered in the prediction of protein residues at RNA-binding surfaces. One is given the sequence information of a protein chain that is known to interact with RNA; the other is given the structural information. Thus, five different inputs have been tested. Coupled with PSI-BLAST profiles and predicted secondary structure, the present approach yields a Matthews correlation coefficient (MCC) of 0.432 by a 7-fold cross-validation, which is the best among all previous reported RNA-binding sites prediction methods. When given the structural information, we have obtained the MCC value of 0.457, with PSSMs, observed secondary structure and solvent accessibility information assigned by DSSP as input. A web server implementing the prediction method is available at the following URL: .     

MuSiC: a tool for multiple sequence alignment with constraints

SUMMARY: MuSiC is a web server to perform the constrained alignment of a set of sequences, such that the user-specified residues/nucleotides are aligned with each other. The input of the MuSiC system consists of a set of protein/DNA/RNA sequences and a set of user-specified constraints, each with a fragment of residue/nucleotide that (approximately) appears in all input sequences. The output of MuSiC is a constrained multiple sequence alignment in which the fragments of the input sequences whose residues/nucleotides exhibit a given degree of similarity to a constraint are aligned together. The current MuSiC system is implemented in Java language and can be accessed via a simple web interface. AVAILABILITY: http://genome.life.nctu.edu.tw/MUSIC     

TESE: generating specific protein structure test set ensembles

TESE is a web server for the generation of test sets of protein sequences and structures fulfilling a number of different criteria. At least three different use cases can be envisaged: (i) benchmarking of novel methods; (ii) test sets tailored for special needs and (iii) extending available datasets. The CATH structure classification is used to control structural/sequence redundancy and a variety of structural quality parameters can be used to interactively select protein subsets with specific characteristics, e.g. all X-ray structures of alpha-helical repeat proteins with more than 120 residues and resolution <2.0 A. The output includes FASTA-formatted sequences, PDB files and a clickable HTML index file containing images of the selected proteins. Multiple subsets for cross-validation are also supported. AVAILABILITY: The TESE server is available for non-commercial use at URL: http://protein.bio.unipd.it/tese/.     

SPOT-Disorder2: Improved Protein Intrinsic Disorder Prediction by Ensembled Deep Learning

Intrinsically disordered or unstructured proteins (or regions in proteins) have been found to be important in a wide range of biological functions and implicated in many diseases. Due to the high cost and low efficiency of experimental determination of intrinsic disorder and the exponential increase of unannotated protein sequences, developing complementary computational prediction methods has been an active area of research for several decades. Here, we employed an ensemble of deep Squeeze-and-Excitation residual inception and long short-term memory (LSTM) networks for predicting protein intrinsic disorder with input from evolutionary information and predicted one-dimensional structural properties. The method, called SPOT-Disorder2, offers substantial and consistent improvement not only over our previous technique based on LSTM networks alone, but also over other state-of-the-art techniques in three independent tests with different ratios of disordered to ordered amino acid residues, and for sequences with either rich or limited evolutionary information. More importantly, semi-disordered regions predicted in SPOT-Disorder2 are more accurate in identifying molecular recognition features (MoRFs) than methods directly designed for MoRFs prediction. SPOT-Disorder2 is available as a web server and as a standalone program at https://sparks-lab.org/server/spot-disorder2/.