Propensities Of Residues Research Articles

Sequence and structural features of binding site residues in protein-protein complexes: comparison with protein-nucleic acid complexes

BackgroundProtein-protein interactions are important for several cellular processes. Understanding the mechanism of protein-protein recognition and predicting the binding sites in protein-protein complexes are long standing goals in molecular and computational biology.MethodsWe have developed an energy based approach for identifying the binding site residues in protein–protein complexes. The binding site residues have been analyzed with sequence and structure based parameters such as binding propensity, neighboring residues in the vicinity of binding sites, conservation score and conformational switching.ResultsWe observed that the binding propensities of amino acid residues are specific for protein-protein complexes. Further, typical dipeptides and tripeptides showed high preference for binding, which is unique to protein-protein complexes. Most of the binding site residues are highly conserved among homologous sequences. Our analysis showed that 7% of residues changed their conformations upon protein-protein complex formation and it is 9.2% and 6.6% in the binding and non-binding sites, respectively. Specifically, the residues Glu, Lys, Leu and Ser changed their conformation from coil to helix/strand and from helix to coil/strand. Leu, Ser, Thr and Val prefer to change their conformation from strand to coil/helix.ConclusionsThe results obtained in this study will be helpful for understanding and predicting the binding sites in protein-protein complexes.

In silico prediction of the granzyme B degradome

BackgroundGranzyme B is a serine protease which cleaves at unique tetrapeptide sequences. It is involved in several signaling cross-talks with caspases and functions as a pivotal mediator in a broad range of cellular processes such as apoptosis and inflammation. The granzyme B degradome constitutes proteins from a myriad of functional classes with many more expected to be discovered. However, the experimental discovery and validation of bona fide granzyme B substrates require time consuming and laborious efforts. As such, computational methods for the prediction of substrates would be immensely helpful.ResultsWe have compiled a dataset of 580 experimentally verified granzyme B cleavage sites and found distinctive patterns of residue conservation and position-specific residue propensities which could be useful for in silico prediction using machine learning algorithms. We trained a series of support vector machines (SVM) classifiers employing Bayes Feature Extraction to predict cleavage sites using sequence windows of diverse lengths and compositions. The SVM classifiers achieved accuracy and AROC scores between 71.00% to 86.50% and 0.78 to 0.94 respectively on independent test sets. We have applied our prediction method on the Chikungunya viral proteome and identified several regulatory domains of viral proteins to be potential sites of granzyme B cleavage, suggesting direct antiviral activity of granzyme B during host-viral innate immune responses.ConclusionsWe have compiled a comprehensive dataset of granzyme B cleavage sites and developed an accurate SVM-based prediction method utilizing Bayes Feature Extraction to identify novel substrates of granzyme B in silico. The prediction server is available online, together with reference datasets and supplementary materials.

SVM-based prediction of linear B-cell epitopes using Bayes Feature Extraction

BackgoundThe identification of B-cell epitopes on antigens has been a subject of intense research as the knowledge of these markers has great implications for the development of peptide-based diagnostics, therapeutics and vaccines. As experimental approaches are often laborious and time consuming, in silico methods for prediction of these immunogenic regions are critical. Such efforts, however, have been significantly hindered by high variability in the length and composition of the epitope sequences, making naïve modeling methods difficult to apply.ResultsWe analyzed two benchmark datasets and found that linear B-cell epitopes possess distinctive residue conservation and position-specific residue propensities which could be exploited for epitope discrimination in silico. We developed a support vector machines (SVM) prediction model employing Bayes Feature Extraction to predict linear B-cell epitopes of diverse lengths (12- to 20-mers). The best SVM classifier achieved an accuracy of 74.50% and AROC of 0.84 on an independent test set and was shown to outperform existing linear B-cell epitope prediction algorithms. In addition, we applied our model to a dataset of antigenic proteins with experimentally-verified epitopes and found it to be generally effective for discriminating the epitopes from non-epitopes.ConclusionWe developed a SVM prediction model utilizing Bayes Feature Extraction and showed that it was effective in discriminating epitopes from non-epitopes in benchmark datasets and annotated antigenic proteins. A web server for predicting linear B-cell epitopes was developed and is available, together with supplementary materials, at http://www.immunopred.org/bayesb/index.html.

The Propensity of α‐Aminoisobutyric Acid (=2‐Methylalanine; Aib) to Induce Helical Secondary Structure in an α‐Heptapeptide: A Computational Study

AbstractWe present a molecular‐dynamics simulation study of an α‐heptapeptide containing an α‐aminoisobutyric acid (=2‐methylalanine; Aib) residue, Val1‐Ala2‐Leu3‐Aib4‐Ile5‐Met6‐Phe7, and a quantum‐mechanical (QM) study of simplified models to investigate the propensity of the Aib residue to induce 310/α‐helical conformation. For comparison, we have also performed simulations of three analogues of the peptide with the Aib residue being replaced by L‐Ala, D‐Ala, and Gly, respectively, which provide information on the subtitution effect at C(α) (two Me groups for Aib, one for L‐Ala and D‐Ala, and zero for Gly). Our simulations suggest that, in MeOH, the heptapeptide hardly folds into canonical helical conformations, but appears to populate multiple conformations, i.e., C7 and 310‐helical ones, which is in agreement with results from the QM calculations and NMR experiments. The populations of these conformations depend on the polarity of the solvent. Our study confirms that a short peptide, though with the presence of an Aib residue in the middle of the chain, does not have to fold to an α‐helical secondary structure. To generate a helical conformation for a linear peptide, several Aib residues should be present in the peptide, either sequentially or alternatively, to enhance the propensity of Aib‐containing peptides towards the helical conformation. A correction of a few of the published NMR data is reported.

Protein β-Sheet Nucleation Is Driven by Local Modular Formation

Despite its central role in the protein folding process, the specific mechanism(s) behind beta-sheet formation has yet to be determined. For example, whether the nucleation of beta-sheets, often containing strands separated in sequence by many residues, is local or not remains hotly debated. Here, we investigate the initial nucleation step of beta-sheet formation by performing an analysis of the smallest beta-sheets in a non-redundant dataset on the grounds that the smallest sheets, having undergone little growth after nucleation, will be enriched for nucleating characteristics. We find that the residue propensities are similar for small and large beta-sheets as are their interstrand pairing preferences, suggesting that nucleation is not primarily driven by specific residues or interacting pairs. Instead, an examination of the structural environments of the two-stranded sheets shows that virtually all of them are contained in single, compact structural modules, or when multiple modules are present, one or both of the chain termini are involved. We, therefore, find that beta-nucleation is a local phenomenon resulting either from sequential or topological proximity. We propose that beta-nucleation is a result of two opposite factors; that is, the relative rigidity of an associated folding module that holds two stretches of coil close together in topology coupled with sufficient chain flexibility that enables the stretches of coil to bring their backbones in close proximity. Our findings lend support to the hydrophobic zipper model of protein folding (Dill, K. A., Fiebig, K. M., and Chan, H. S. (1993) Proc. Natl. Acad. Sci. U.S.A. 90, 1942-1946). Implications for protein folding are discussed.

HORI: a web server to compute Higher Order Residue Interactions in protein structures

BackgroundFolding of a protein into its three dimensional structure is influenced by both local and global interactions within a protein. Higher order residue interactions, like pairwise, triplet and quadruplet ones, play a vital role in attaining the stable conformation of the protein structure. It is generally agreed that higher order interactions make significant contribution to the potential energy landscape of folded proteins and therefore it is important to identify them to estimate their contributions to overall stability of a protein structure.ResultsWe developed HORI [Higher order residue interactions in proteins], a web server for the calculation of global and local higher order interactions in protein structures. The basic algorithm of HORI is designed based on the classical concept of four-body nearest-neighbour propensities of amino-acid residues. It has been proved that higher order residue interactions up to the level of quadruple interactions plays a major role in the three-dimensional structure of proteins and is an important feature that can be used in protein structure analysis.ConclusionHORI server will be a useful resource for the structural bioinformatics community to perform analysis on protein structures based on higher order residue interactions. HORI server is a highly interactive web server designed in three modules that enables the user to analyse higher order residue interactions in protein structures. HORI server is available from the URL: http://caps.ncbs.res.in/hori

Exploiting structural and topological information to improve prediction of RNA-protein binding sites

BackgroundRNA-protein interactions are important for a wide range of biological processes. Current computational methods to predict interacting residues in RNA-protein interfaces predominately rely on sequence data. It is, however, known that interface residue propensity is closely correlated with structural properties. In this paper we systematically study information obtained from sequences and structures and compare their contributions in this prediction problem. Particularly, different geometrical and network topological properties of protein structures are evaluated to improve interface residue prediction accuracy.ResultsWe have quantified the impact of structural information on the prediction accuracy in comparison to the purely sequence based approach using two machine learning techniques: Naïve Bayes classifiers and Support Vector Machines. The highest AUC of 0.83 was achieved by a Support Vector Machine, exploiting PSI-BLAST profile, accessible surface area, betweenness-centrality and retention coefficient as input features. Taking into account that our results are based on a larger non-redundant data set, the prediction accuracy is considerably higher than reported in previous, comparable studies. A protein-RNA interface predictor (PRIP) and the data set have been made available at .ConclusionGraph-theoretic properties of residue contact maps derived from protein structures such as betweenness-centrality can supplement sequence or structure features to improve the prediction accuracy for binding residues in RNA-protein interactions. While Support Vector Machines perform better on this task, Naïve Bayes classifiers also have been found to achieve good prediction accuracies but require much less training time and are an attractive choice for large scale predictions.

Protein Structure Classification Based on Conserved Hydrophobic Residues

Protein folding is frequently guided by local residue interactions that form clusters in the protein core. The interactions between residue clusters serve as potential nucleation sites in the folding process. Evidence postulates that the residue interactions are governed by the hydrophobic propensities that the residues possess. An array of hydrophobicity scales has been developed to determine the hydrophobic propensities of residues under different environmental conditions. In this work, we propose a graph-theory-based data mining framework to extract and isolate protein structural features that sustain invariance in evolutionary-related proteins, through the integrated analysis of five well-known hydrophobicity scales over the 3D structure of proteins. We hypothesize that proteins of the same homology contain conserved hydrophobic residues and exhibit analogous residue interaction patterns in the folded state. The results obtained demonstrate that discriminatory residue interaction patterns shared among proteins of the same family can be employed for both the structural and the functional annotation of proteins. We obtained on the average 90 percent accuracy in protein classification with a significantly small feature vector compared to previous results in the area. This work presents an elaborate study, as well as validation evidence, to illustrate the efficacy of the method and the correctness of results reported.

Role of Conserved Salt Bridges in Homeodomain Stability and DNA Binding

The sequence information available for homeodomains reveals that salt bridges connecting pairs 19/30, 31/42, and 17/52 are frequent, whereas aliphatic residues at these sites are rare and mainly restricted to proteins from homeotherms. We have analyzed the influence of salt and hydrophobic bridges at these sites on the stability and DNA binding properties of human Hesx-1 homeodomain. Regarding the protein stability, our analysis shows that hydrophobic side chains are clearly preferred at positions 19/30 and 31/42. This stabilizing influence results from the more favorable packing of the aliphatic side chains with the protein core, as illustrated by the three-dimensional solution structure of a thermostable variant, herein reported. In contrast only polar side chains seem to be tolerated at positions 17/52. Interestingly, despite the significant influence of pairs 19/30 and 31/42 on the stability of the homeodomain, their effect on DNA binding ranges from modest to negligible. The observed lack of correlation between binding strength and conformational stability in the analyzed variants suggests that salt/hydrophobic bridges at these specific positions might have been employed by evolution to independently modulate both properties.

SEPPA: a computational server for spatial epitope prediction of protein antigens

In recent years, a lot of efforts have been made in conformational epitope prediction as antigen proteins usually bind antibodies with an assembly of sequentially discontinuous and structurally compact surface residues. Currently, only a few methods for spatial epitope prediction are available with focus on single residue propensity scales or continual segments clustering. In the method of SEPPA, a concept of ‘unit patch of residue triangle’ was introduced to better describe the local spatial context in protein surface. Besides that, SEPPA incorporated clustering coefficient to describe the spatial compactness of surface residues. Validated by independent testing datasets, SEPPA gave an average AUC value over 0.742 and produced a successful pick-up rate of 96.64%. Comparing with peers, SEPPA shows significant improvement over other popular methods like CEP, DiscoTope and BEpro. In addition, the threshold scores for certain accuracy, sensitivity and specificity are provided online to give the confidence level of the spatial epitope identification. The web server can be accessed at http://lifecenter.sgst.cn/seppa/index.php. Batch query is supported.

Combining interface core and whole interface descriptors in postscan processing of protein‐protein docking models

Computational protein-protein docking scans commonly produce correct and nearly correct interaction models, but sometimes these models are ranked low. We present here postscan processing procedures that dramatically enhance the distinction between nearly correct and false predictions. The procedures employ propensity descriptors calculated for the interface core, an interface-core clusters count, solvation energy, and a geometric-electrostatic-hydrophobic complementarity score. The various descriptors rank high different selections of false models (shuffling effect), and therefore, are used as Boolean yes/no classifiers in soft intersection filters, which eliminate large proportions of false models. Furthermore, the standardized descriptors are used in new scoring functions that highlight nearly correct models (NCMs). All the tests are performed on unbound docking models produced with MolFit without use of external data. We find that the discrimination between nearly correct and false models by the various descriptors is class dependent; hence, our postscan processing is class specific. The filters reduce the number of putative models from 10,726, 12,517, and 11,054 to 758, 157, and 1218 for enzyme-inhibitor, antibody-antigen, and nonclassified systems. When combined with the new scoring functions, they improve the average rank of the highest ranking NCMs from 673 to 122. Application to 23 CAPRI targets demonstrates the effectiveness of the postscan procedures in cases where external information is used in the production of the putative models. Our new per-molecule residue propensity descriptors show that interacting interfaces are enriched with high propensity residues except for antigenic sites, which resemble more the noninteracting regions of protein surfaces.

Integration of evolutionary and desolvation energy analysis identifies functional sites in a plant immunity protein

Plant immune responses often depend on leucine-rich repeat receptors that recognize microbe-associated molecular patterns or pathogen-specific virulence proteins, either directly or indirectly. When the recognition is direct, a molecular arms race takes place where plant receptors continually and rapidly evolve in response to virulence factor evolution. A useful model system to study ligand-receptor coevolution dynamics at the protein level is represented by the interaction between pathogen-derived polygalacturonases (PGs) and plant polygalacturonase-inhibiting proteins (PGIPs). We have applied codon substitution models to PGIP sequences of different eudicotyledonous families to identify putative positively selected sites and then compared these sites with the propensity of protein surface residues to interact with protein partners, based on desolvation energy calculations. The 2 approaches remarkably correlated in pinpointing several residues in the concave face of the leucine-rich repeat domain. These residues were mutated into alanine and their effect on the recognition of several PGs was tested, leading to the identification of unique hotspots for the PGIP-PG interaction. The combined approach used in this work can be of general utility in cases where structural information about a pattern-recognition receptor or resistance-gene product is available.

The role of entropy and polarity in intermolecular contacts in protein crystals.

The integrity and X-ray diffraction quality of protein crystals depend on the three-dimensional order of relatively weak but reproducible intermolecular contacts. Despite their importance, relatively little attention has been paid to the chemical and physical nature of these contacts, which are often regarded as stochastic and thus not different from randomly selected protein surface patches. Here, logistic regression was used to analyze crystal contacts in a database of 821 unambiguously monomeric proteins with structures determined to 2.5 A resolution or better. It is shown that the propensity of a surface residue for incorporation into a crystal contact is not a linear function of its solvent-accessible surface area and that amino acids with low exposed surfaces, which are typically small and hydrophobic, have been underestimated with respect to their contact-forming potential by earlier area-based calculations. For any given solvent-exposed surface, small and hydrophobic residues are more likely to be involved in crystal contacts than large and charged amino acids. Side-chain entropy is the single physicochemical property that is most negatively correlated with the involvement of amino acids in crystal contacts. It is also shown that crystal contacts with larger buried surfaces containing eight or more amino acids have cores that are depleted of polar amino acids.

BIPA: a database for protein–nucleic acid interaction in 3D structures

BIPA is a database for protein-nucleic acid interactions in 3D structures. The database provides various physicochemical features of protein-nucleic acid interface such as size, shape, residue propensity, secondary structure composition and intermolecular interactions. The database also contains multiple structural alignments of nucleic acid-binding protein families with annotations of local environments in order to allow definition of features that influence acceptability of mutations at a particular position in a protein family. A web interface has been designed to present the results of these analyses and facilitate navigation of protein-nucleic acid interfaces. http://www-cryst.bioc.cam.ac.uk/bipa Supplementary data are available at Bioinformatics online.

Discriminating acidic and alkaline enzymes using a random forest model with secondary structure amino acid composition

Understating the adaptation mechanism of enzymes to pH extremes and discriminating them is a challenging task and would help to design stable enzymes. In this work, we have systematically analyzed the secondary structure amino acid compositions of 105 acidic and 111 alkaline enzymes, respectively. We found that the propensity of the individual residues to participate in different secondary structures might be a general stability mechanism for their adaptation to pH extremes. Based on it, we present a secondary structure amino acid composition method for extracting useful features from sequence, and a novel ensemble classifier named random forest was used. The overall prediction accuracy evaluated by the 10-fold cross-validation reached 90.7%. Comparing our method with other feature extraction methods, the improvement of the overall prediction accuracy ranged from 5.5% to 21.2%. The random forests algorithm also outperformed other machine learning techniques with an improvement ranging from 3.2% to 19.9%.

What Determines the Structure and Stability of KFFE Monomers, Dimers, and Protofibrils?

The self-assembly of the KFFE peptide was studied using replica exchange molecular dynamics simulations with a fully atomic description of the peptide and explicit solvent. The relative roles of the aromatic residues and oppositely charged end groups in stabilizing the earliest oligomers and the end-products of aggregation were investigated. β and non- β-peptide conformations compete in the monomeric state as a result of a balancing between the high β-sheet propensity of the phenylalanine residues and charge-charge interactions that favor non- β-conformations. Dimers are present in β- and non- β-sheet conformations and are stabilized primarily by direct and water-mediated charge-charge interactions between oppositely charged side chains and between oppositely charged termini, with forces between aromatic residues playing a minor role. Dimerization to a β-sheet, fibril-competent state, is seen to be a cooperative process, with the association process inducing β-structure in otherwise non- β-monomers. We propose a model for the KFFE fibril, with mixed interface and antiparallel sheet and strand arrangements, which is consistent with experimental electron microscopy measurements. Both aromatic and charge-charge interactions contribute to the fibril stability, although the dominant contribution arises from electrostatic interactions.

Energy based approach for understanding the recognition mechanism in protein–protein complexes

Protein-protein interactions play an essential role in the regulation of various cellular processes. Understanding the recognition mechanism of protein-protein complexes is a challenging task in molecular and computational biology. In this work, we have developed an energy based approach for identifying the binding sites and important residues for binding in protein-protein complexes. The new approach is different from the traditional distance based contacts in which the repulsive interactions are treated as binding sites as well as the contacts within a specific cutoff have been treated in the same way. We found that the residues and residue-pairs with charged and aromatic side chains are important for binding. These residues influence to form cation-, electrostatic and aromatic interactions. Our observation has been verified with the experimental binding specificity of protein-protein complexes and found good agreement with experiments. Based on these results we have proposed a novel mechanism for the recognition of protein-protein complexes: the charged and aromatic residues in receptor and ligand initiate recognition by making suitable interactions between them; the neighboring hydrophobic residues assist the stability of complex along with other hydrogen bonding partners by the polar residues. Further, the propensity of residues in the binding sites of receptors and ligands, atomic contributions and the influence on secondary structure will be discussed.

Spatial Autocorrelation of Amino Acid Replacement Rates in the Vasopressin Receptor Family

Evolutionary rates of sites can be independent of one another or correlated in some fashion. Significant spatial autocorrelation was observed for site amino acid replacement rates in vasopressin receptor family proteins (VPRs). Spatial autocorrelation of rates is the propensity of residues to lie near other residues of similar rate in the folded protein structure. Optimal correlation occurred at a distance suggesting that residues in contact had correlated rates. As another way to study the same phenomenon, VPR was partitioned into >40 x 10 A(3) contiguous spatial clusters for amino acid replacement rate estimation. Partitioning was done without preconception of functional regions of the protein and with a random partition control. Cluster rates exhibited an overdispersed distribution suggesting that rates were not randomly distributed in the spatial partitions. In tests, cluster partitioning improved maximum likelihood and Bayesian likelihood models for VPR evolution. Spatial clusters with outlier rates, or lineage-specific clusters differing in rate, proved to contain VPR features likely to be under selection. Thus the spatial autocorrelation observed is probably not just a statistical finding, but likely has an evolutionary basis in protein function.

Role of side chains in collagen triple helix stabilization and partner recognition

Collagen is a widespread protein family involved in a variety of biological processes. The complexity of collagen and its fibrous nature prevent detailed investigations on the full-length protein. Reductionist approaches conducted by dissecting the protein complexity through the use of model peptides have proved to be quite effective. There are, however, several issues regarding structure-stability relationships, aggregation in higher-order assemblies, and partner recognition that are still extensively investigated. In this review, we discuss the role that side chains play in triple helix stabilization and in partner recognition. On the basis of recent literature data, we show that collagen triple helix stability is the result of the interplay of different factors. As a general trend, interactions established by amino/imino acid side chains within the triple helix scaffold effectively modulate the intrinsic residue propensity for this common structural motif. The use of peptide models has also highlighted the role that side chains play in collagen self-association and in its interactions with receptors. Valuable examples in these fields are illustrated. Finally, future actions required to obtain more detailed information on the structure and the function of this complex protein are also delineated.

The intrinsic conformational propensities of the 20 naturally occurring amino acids and reflection of these propensities in proteins

Here, we compare the distributions of main chain (Phi,Psi) angles (i.e., Ramachandran maps) of the 20 naturally occurring amino acids in three contexts: (i) molecular dynamics (MD) simulations of Gly-Gly-X-Gly-Gly pentapeptides in water at 298 K with exhaustive sampling, where X = the amino acid in question; (ii) 188 independent protein simulations in water at 298 K from our Dynameomics Project; and (iii) static crystal and NMR structures from the Protein Data Bank. The GGXGG peptide series is often used as a model of the unstructured denatured state of proteins. The sampling in the peptide MD simulations is neither random nor uniform. Instead, individual amino acids show preferences for particular conformations, but the peptide is dynamic, and interconversion between conformers is facile. For a given amino acid, the (Phi,Psi) distributions in the protein simulations and the Protein Data Bank are very similar and often distinct from those in the peptide simulations. Comparison between the peptide and protein simulations shows that packing constraints, solvation, and the tendency for particular amino acids to be used for specific structural motifs can overwhelm the "intrinsic propensities" of amino acids for particular (Phi,Psi) conformations. We also compare our helical propensities with experimental consensus values using the host-guest method, which appear to be determined largely by context and not necessarily the intrinsic conformational propensities of the guest residues. These simulations represent an improved coil library free from contextual effects to better model intrinsic conformational propensities and provide a detailed view of conformations making up the "random coil" state.