VLDP web server: a powerful geometric tool for analysing protein structures in their environment.

International audienceProtein structures are an ensemble of atoms determined experimentally mostly by X-ray crystallography or Nuclear Magnetic Resonance. Studying 3D protein structures is a key point for better understanding protein function at a molecular level. We propose a set of accurate tools, for analysing protein structures, based on the reliable method of Voronoi-Laguerre tessellations. The Voronoi Laguerre Delaunay Protein web server (VLDPws) computes the Laguerre tessellation on a whole given system first embedded in solvent. Through this fine description, VLDPws gives the following data: (i) Amino acid volumes evaluated with high precision, as confirmed by good correlations with experimental data. (ii) A novel definition of inter-residue contacts within the given protein. (iii) A measure of the residue exposure to solvent that significantly improves the standard notion of accessibility in some cases. At present, no equivalent web server is available. VLDPws provides output in two complementary forms: direct visualization of the Laguerre tessellation, mostly its polygonal molecular surfaces; files of volumes; and areas, contacts and similar data for each residue and each atom. These files are available for download for further analysis. VLDPws can be accessed at http://www.dsimb.inserm.fr/dsimb_tools/vldp

The G\=oMartini approach: Revisiting the concept of contact maps and the modelling of protein complexes

We present a review of a series of contact maps for the determination of native interactions in proteins and nucleic acids based on a distance-threshold. Such contact maps are mostly based on physical and chemical construction, and yet they are sensitive to some parameters (e.g. distances or atomic radii) and can neglect some key interactions. Furthermore, we also comment on a new class of contact maps that only requires geometric arguments. The contact map is a necessary ingredient to build a robust G\=oMartini model for proteins and their complexes in the Martini 3 force field. We present the extension of a popular structure-based G\=o-like approach for the study of protein-sugar complexes, and also limitations of this approach are discussed. The G\=oMartini approach was first introduced by Poma et al. J. Chem. Theory Comput. 2017, 13(3), 1366-1374 in Martini 2 force field and recently, it has gained the status of gold-standard for protein simulation undergoing conformational changes in Martini 3 force field. We discuss several studies that have provided support to this approach in the context of the biophysical community.Comment: 19 pages, 3 figure

Knowledge-based energy functions for computational studies of proteins

This chapter discusses theoretical framework and methods for developing knowledge-based potential functions essential for protein structure prediction, protein-protein interaction, and protein sequence design. We discuss in some details about the Miyazawa-Jernigan contact statistical potential, distance-dependent statistical potentials, as well as geometric statistical potentials. We also describe a geometric model for developing both linear and non-linear potential functions by optimization. Applications of knowledge-based potential functions in protein-decoy discrimination, in protein-protein interactions, and in protein design are then described. Several issues of knowledge-based potential functions are finally discussed.Comment: 57 pages, 6 figures. To be published in a book by Springe

From Isotropic to Anisotropic Side Chain Representations: Comparison of Three Models for Residue Contact Estimation

The criterion to determine residue contact is a fundamental problem in deriving knowledge-based mean-force potential energy calculations for protein structures. A frequently used criterion is to require the side chain center-to-center distance or the -to- atom distance to be within a pre-determined cutoff distance. However, the spatially anisotropic nature of the side chain determines that it is challenging to identify the contact pairs. This study compares three side chain contact models: the Atom Distance criteria (ADC) model, the Isotropic Sphere Side chain (ISS) model and the Anisotropic Ellipsoid Side chain (AES) model using 424 high resolution protein structures in the Protein Data Bank. The results indicate that the ADC model is the most accurate and ISS is the worst. The AES model eliminates about 95% of the incorrectly counted contact-pairs in the ISS model. Algorithm analysis shows that AES model is the most computational intensive while ADC model has moderate computational cost. We derived a dataset of the mis-estimated contact pairs by AES model. The most misjudged pairs are Arg-Glu, Arg-Asp and Arg-Tyr. Such a dataset can be useful for developing the improved AES model by incorporating the pair-specific information for the cutoff distance

Optimized Null Model for Protein Structure Networks

Much attention has recently been given to the statistical significance of topological features observed in biological networks. Here, we consider residue interaction graphs (RIGs) as network representations of protein structures with residues as nodes and inter-residue interactions as edges. Degree-preserving randomized models have been widely used for this purpose in biomolecular networks. However, such a single summary statistic of a network may not be detailed enough to capture the complex topological characteristics of protein structures and their network counterparts. Here, we investigate a variety of topological properties of RIGs to find a well fitting network null model for them. The RIGs are derived from a structurally diverse protein data set at various distance cut-offs and for different groups of interacting atoms. We compare the network structure of RIGs to several random graph models. We show that 3-dimensional geometric random graphs, that model spatial relationships between objects, provide the best fit to RIGs. We investigate the relationship between the strength of the fit and various protein structural features. We show that the fit depends on protein size, structural class, and thermostability, but not on quaternary structure. We apply our model to the identification of significantly over-represented structural building blocks, i.e., network motifs, in protein structure networks. As expected, choosing geometric graphs as a null model results in the most specific identification of motifs. Our geometric random graph model may facilitate further graph-based studies of protein conformation space and have important implications for protein structure comparison and prediction. The choice of a well-fitting null model is crucial for finding structural motifs that play an important role in protein folding, stability and function. To our knowledge, this is the first study that addresses the challenge of finding an optimized null model for RIGs, by comparing various RIG definitions against a series of network models

Integrated mining of feature spaces for bioinformatics domain discovery

One of the major challenges in the field of bioinformatics is the elucidation of protein folding for the functional annotation of proteins. The factors that govern protein folding include the chemical, physical, and environmental conditions of the protein\u27s surroundings, which can be measured and exploited for computational discovery purposes. These conditions enable the protein to transform from a sequence of amino acids to a globular three-dimensional structure. Information concerning the folded state of a protein has significant potential to explain biochemical pathways and their involvement in disorders and diseases. This information impacts the ways in which genetic diseases are characterized and cured and in which designer drugs are created. With the exponential growth of protein databases and the limitations of experimental protein structure determination, sophisticated computational methods have been developed and applied to search for, detect, and compare protein homology. Most computational tools developed for protein structure prediction are primarily based on sequence similarity searches. These approaches have improved the prediction accuracy of high sequence similarity proteins but have failed to perform well with proteins of low sequence similarity. Data mining offers unique algorithmic computational approaches that have been used widely in the development of automatic protein structure classification and prediction. In this dissertation, we present a novel approach for the integration of physico-chemical properties and effective feature extraction techniques for the classification of proteins. Our approaches overcome one of the major obstacles of data mining in protein databases, the encapsulation of different hydrophobicity residue properties into a much reduced feature space that possess high degrees of specificity and sensitivity in protein structure classification. We have developed three unique computational algorithms for coherent feature extraction on selected scale properties of the protein sequence. When plagued by the problem of the unequal cardinality of proteins, our proposed integration scheme effectively handles the varied sizes of proteins and scales well with increasing dimensionality of these sequences. We also detail a two-fold methodology for protein functional annotation. First, we exhibit our success in creating an algorithm that provides a means to integrate multiple physico-chemical properties in the form of a multi-layered abstract feature space, with each layer corresponding to a physico-chemical property. Second, we discuss a wavelet-based segmentation approach that efficiently detects regions of property conservation across all layers of the created feature space. Finally, we present a unique graph-theory based algorithmic framework for the identification of conserved hydrophobic residue interaction patterns using identified scales of hydrophobicity. We report that these discriminatory features are specific to a family of proteins, which consist of conserved hydrophobic residues that are then used for structural classification. We also present our rigorously tested validation schemes, which report significant degrees of accuracy to show that homologous proteins exhibit the conservation of physico-chemical properties along the protein backbone. We conclude our discussion by summarizing our results and contributions and by listing our goals for future research

Mass & secondary structure propensity of amino acids explain their mutability and evolutionary replacements

Why is an amino acid replacement in a protein accepted during evolution? The answer given by bioinformatics relies on the frequency of change of each amino acid by another one and the propensity of each to remain unchanged. We propose that these replacement rules are recoverable from the secondary structural trends of amino acids. A distance measure between high-resolution Ramachandran distributions reveals that structurally similar residues coincide with those found in substitution matrices such as BLOSUM: Asn Asp, Phe Tyr, Lys Arg, Gln Glu, Ile Val, Met → Leu; with Ala, Cys, His, Gly, Ser, Pro, and Thr, as structurally idiosyncratic residues. We also found a high average correlation (\overline{R} R = 0.85) between thirty amino acid mutability scales and the mutational inertia (I X ), which measures the energetic cost weighted by the number of observations at the most probable amino acid conformation. These results indicate that amino acid substitutions follow two optimally-efficient principles: (a) amino acids interchangeability privileges their secondary structural similarity, and (b) the amino acid mutability depends directly on its biosynthetic energy cost, and inversely with its frequency. These two principles are the underlying rules governing the observed amino acid substitutions. © 2017 The Author(s)

Structural and Functional Analysis of Multi-Interface Domains

10.1371/journal.pone.0050821PLoS ONE71

Molecular characterization and evolutionary plasticity of protein-protein interfaces

Abstract The sequencing of the human genome provides the parts list for understanding cellular processes. However, as 70% of eukaryotic genes work through multi-protein systems, it is only through detailed study of the interactions of these components, that a more complete, systems-level understanding can be gained. This thesis is centred on the establishment of PICCOLO - a comprehensive database of structurally characterized protein interactions. In generating the resource, issues of interface definition, quaternary structure, data redundancy, structural environment and interaction type are addressed. The resource enables a variety of analyses to be performed concerning interface properties including residue propensity, hydropathy, polarity, interface size, sequence entropy and residue contact preference. PICCOLO has been applied to probing the patterns of substitutions that are accepted in protein interfaces across evolution, and whether these patterns are distinguishable from those seen in other structural environments. The derivation of a high-quality set of multiple structural alignments in the form of the database TOCCATA, a prerequisite for such analysis, is described, as well as procedures to derive environment-specific substitution tables. The Blundell group has contributed a series of methods to predict the likely effect of non-synonymous Single Nucleotide Polymorphisms (nsSNPs) on protein stability, function and interactions in order to triage the large volumes of data created from high-throughput genetic screening studies, enabling prioritization of those nsSNPs most likely to be phenotypically detrimental. PICCOLO's contribution to these predictions is described. Historically there has been little focus on protein-protein interactions as drug targets for small-molecule therapeutics. However, alanine-scanning mutagenesis studies have revealed that only a subset of residues contribute the greater part of free energy to binding - so-called "hot-spots". Molecular characterization of hot-spots performed using PICCOLO, probes the molecular basis underlying this important phenomenon leading to the possibility of predictive methods to identify hot-spots 'in silico'