A critical assessment of hidden markov model sub-optimal sampling strategies applied to the generation of peptide 3D models.

Hidden Markov Model derived structural alphabets are a probabilistic framework in which the complete conformational space of a peptidic chain is described in terms of probability distributions that can be sampled to identify conformations of largest probabilities. Here, we assess how three strategies to sample sub-optimal conformations-Viterbi k-best, forward backtrack and a taboo sampling approach-can lead to the efficient generation of peptide conformations. We show that the diversity of sampling is essential to compensate biases introduced in the estimates of the probabilities, and we find that only the forward backtrack and a taboo sampling strategies can efficiently generate native or near-native models. Finally, we also find such approaches are as efficient as former protocols, while being one order of magnitude faster, opening the door to the large scale de novo modeling of peptides and mini-proteins. © 2016 Wiley Periodicals, Inc.

wwLigCSRre: a 3D ligand-based server for hit identification and optimization.

The wwLigCSRre web server performs ligand-based screening using a 3D molecular similarity engine. Its aim is to provide an online versatile facility to assist the exploration of the chemical similarity of families of compounds, or to propose some scaffold hopping from a query compound. The service allows the user to screen several chemically diversified focused banks, such as Kinase-, CNS-, GPCR-, Ion-channel-, Antibacterial-, Anticancer- and Analgesic-focused libraries. The server also provides the possibility to screen the DrugBank and DSSTOX/Carcinogenic compounds databases. User banks can also been downloaded. The 3D similarity search combines both geometrical (3D) and physicochemical information. Starting from one 3D ligand molecule as query, the screening of such databases can lead to unraveled compound scaffold as hits or help to optimize previously identified hit molecules in a SAR (Structure activity relationship) project. wwLigCSRre can be accessed at http://bioserv.rpbs.univ-paris-diderot.fr/wwLigCSRre.html.

Frog: a FRee Online druG 3D conformation generator.

In silico screening methods based on the 3D structures of the ligands or of the proteins have become an essential tool to facilitate the drug discovery process. To achieve such process, the 3D structures of the small chemical compounds have to be generated. In addition, for ligand-based screening computations or hierarchical structure-based screening projects involving a rigid-body docking step, it is necessary to generate multi-conformer 3D models for each input ligand to increase the efficiency of the search. However, most academic or commercial compound collections are delivered in 1D SMILES (simplified molecular input line entry system) format or in 2D SDF (structure data file), highlighting the need for free 1D/2D to 3D structure generators. Frog is an on-line service aimed at generating 3D conformations for drug-like compounds starting from their 1D or 2D descriptions. Given the atomic constitution of the molecules and connectivity information, Frog can identify the different unambiguous isomers corresponding to each compound, and generate single or multiple low-to-medium energy 3D conformations, using an assembly process that does not presently consider ring flexibility. Tests show that Frog is able to generate bioactive conformations close to those observed in crystallographic complexes. Frog can be accessed at http://bioserv.rpbs.jussieu.fr/Frog.html.

Exploring an alignment free approach for protein classification and structural class prediction.

Alignment free methods based on Chaos Game Representation (CGR), also known as sequence signature approaches, have proven of great interest for DNA sequence analysis. Indeed, they have been successfully applied for sequence comparison, phylogeny, detection of horizontal transfers or extraction of representative motifs in regulation sequences. Transposing such methods to proteins poses several fundamental questions related to representation space dimensionality. Several studies have tackled these points, but none has, so far, brought the application of CGRs to proteins to their fully expected potential. Yet, several studies have shown that techniques based on n-peptide frequencies can be relevant for proteins. Here, we investigate the effectiveness of a strategy based on the CGR approach using a fixed reverse encoding of amino acids into nucleic sequences. We first explore its relevance to protein classification into functional families. We then attempt to apply it to the prediction of protein structural classes. Our results suggest that the reverse encoding approach could be relevant in both cases. We show that it is able to classify functional families of proteins by extracting signatures close to the ProSite patterns. Applied to structural classification, the approach reaches scores of correct classification close to 84%, i.e. close to the scores of related methods in the field. Various optimizations of the approach are still possible, which open the door for future applications.

A coarse-grained protein force field for folding and structure prediction.

We have revisited the protein coarse-grained optimized potential for efficient structure prediction (OPEP). The training and validation sets consist of 13 and 16 protein targets. Because optimization depends on details of how the ensemble of decoys is sampled, trial conformations are generated by molecular dynamics, threading, greedy, and Monte Carlo simulations, or taken from publicly available databases. The OPEP parameters are varied by a genetic algorithm using a scoring function which requires that the native structure has the lowest energy, and the native-like structures have energy higher than the native structure but lower than the remote conformations. Overall, we find that OPEP correctly identifies 24 native or native-like states for 29 targets and has very similar capability to the all-atom discrete optimized protein energy model (DOPE), found recently to outperform five currently used energy models.

RPBS: a web resource for structural bioinformatics.

RPBS (Ressource Parisienne en Bioinformatique Structurale) is a resource dedicated primarily to structural bioinformatics. It is the result of a joint effort by several teams to set up an interface that offers original and powerful methods in the field. As an illustration, we focus here on three such methods uniquely available at RPBS: AUTOMAT for sequence databank scanning, YAKUSA for structure databank scanning and WLOOP for homology loop modelling. The RPBS server can be accessed at http://bioserv.rpbs.jussieu.fr/ and the specific services at http://bioserv.rpbs.jussieu.fr/SpecificServices.html.

Hidden Markov model-derived structural alphabet for proteins: the learning of protein local shapes captures sequence specificity.

Understanding and predicting protein structures depend on the complexity and the accuracy of the models used to represent them. We have recently set up a Hidden Markov Model to optimally compress protein three-dimensional conformations into a one-dimensional series of letters of a structural alphabet. Such a model learns simultaneously the shape of representative structural letters describing the local conformation and the logic of their connections, i.e. the transition matrix between the letters. Here, we move one step further and report some evidence that such a model of protein local architecture also captures some accurate amino acid features. All the letters have specific and distinct amino acid distributions. Moreover, we show that words of amino acids can have significant propensities for some letters. Perspectives point towards the prediction of the series of letters describing the structure of a protein from its amino acid sequence.

SCit: web tools for protein side chain conformation analysis.

SCit is a web server providing services for protein side chain conformation analysis and side chain positioning. Specific services use the dependence of the side chain conformations on the local backbone conformation, which is described using a structural alphabet that describes the conformation of fragments of four-residue length in a limited library of structural prototypes. Based on this concept, SCit uses sets of rotameric conformations dependent on the local backbone conformation of each protein for side chain positioning and the identification of side chains with unlikely conformations. The SCit web server is accessible at http://bioserv.rpbs.jussieu.fr/SCit.

A hidden markov model derived structural alphabet for proteins.

Understanding and predicting protein structures depends on the complexity and the accuracy of the models used to represent them. We have set up a hidden Markov model that discretizes protein backbone conformation as series of overlapping fragments (states) of four residues length. This approach learns simultaneously the geometry of the states and their connections. We obtain, using a statistical criterion, an optimal systematic decomposition of the conformational variability of the protein peptidic chain in 27 states with strong connection logic. This result is stable over different protein sets. Our model fits well the previous knowledge related to protein architecture organisation and seems able to grab some subtle details of protein organisation, such as helix sub-level organisation schemes. Taking into account the dependence between the states results in a description of local protein structure of low complexity. On an average, the model makes use of only 8.3 states among 27 to describe each position of a protein structure. Although we use short fragments, the learning process on entire protein conformations captures the logic of the assembly on a larger scale. Using such a model, the structure of proteins can be reconstructed with an average accuracy close to 1.1A root-mean-square deviation and for a complexity of only 3. Finally, we also observe that sequence specificity increases with the number of states of the structural alphabet. Such models can constitute a very relevant approach to the analysis of protein architecture in particular for protein structure prediction.

Prediction of the positioning of the seven transmembrane alpha-helices of bacteriorhodopsin. A molecular simulation study.

We have applied a search strategy for determining the optimal packing of protein secondary structure elements to the rotational positioning of the seven transmembrane helices of bacteriorhodopsin. The search is based on the assumption that the relative orientations of the helices within the bundle are conditioned principally by inter-helix side-chain interactions and that the extra-helical parts of the protein have only a minor influence on the bundle conformation. Our approach performs conformational energy optimization using a predetermined set of side-chain rotamers and appropriate methods for sampling the conformational space of peptide fragments with fixed backbone geometries. The final solution obtained for bacteriorhodopsin places each of the seven helices to a precision of a few degrees in rotation around the helical axis and to a few tenths of an ångström in translation along the helical axis with respect to the best experimental structure obtained by electron diffraction, except for helix D, where our results support the suggestion that this helix should be displaced along its axis toward its N terminus. The perspectives of such an approach for the determination of the structures of other transmembrane helical bundles are discussed.

A new approach to the rapid determination of protein side chain conformations.

Two efficient algorithms have been developed which allow amino acid side chain conformations to be optimized rapidly for a given peptide backbone conformation. Both these approaches are based on the assumption that each side chain can be represented by a small number of rotameric states. These states have been obtained by a dynamic cluster analysis of a large data base of known crystallographic structures. Successful applications of these algorithms to the prediction of known protein conformations are presented.

XmMol: an X11 and motif program for macromolecular visualization and modeling.

XmMol is a desktop tool designed to provide both interactive molecular graphics on X11 displays and easy interface with external applications. A kernel provides an interactive wire-frame display of macromolecules. It supports depth cueing, 3D clipping, and stereo. Various representations, coloring, and labeling modes are proposed. Docking and interactive backbone deformation tools are also supported. Communication protocols allow the user to develop new external features or to use XmMol as a visualization tool for external numerical programs.

CS-PSeq-Gen: simulating the evolution of protein sequence under constraints.

UNLABELLED: CS-PSeq-Gen is a program derived from PSeq-Gen, designed to perform simulations of the evolution of protein sequences under the constraints of a reconstructed phylogeny. It also provides a basis for the investigation of the correlated evolution of sites. AVAILABILITY: http://condor.urbb.jussieu.fr/CS-PSeq-Gen.html

Critical assessment of side-chain conformational space sampling procedures designed for quantifying the effect of side-chain environment.

We introduce a family of procedures designed to sample side-chain conformational space at particular locations in protein structures. These procedures (CRSP) use intensive cycles of random assignment of side-chain conformations followed by minimization to determine all the conformations that a group of side-chains can adopt simultaneously. First, we consider a procedure evolving in the dihedral space (dCRSP). Our results suggest that it can accurately map low-energy conformations adopted by clusters of side-chains of a protein. dCRSP is relatively insensitive to various important parameters, and it is sufficiently accurate to capture efficiently the constraint induced by the environment on the conformations a particular side-chain can adopt. Our results show that dCRSP, compared with molecular dynamics (MD), can overcome the problem of the limited set of conformations reached in a reasonable amount of simulations. Next, we introduce procedures (vCRSP) in which valence angles are relaxed, and we assess how efficiently they quantify the conformational entropy of side-chains in the protein native state. For simple peptides, entropies obtained with vCRSP are fully compatible with those obtained with a Monte Carlo procedure. For side-chains in a protein environment, however, vCRSP appears of limited use. Finally, we consider a two-step procedure that combines dCRSP and vCRSP. Our tests suggest that it is able to overcome the limitations of vCRSP. We also note that dCRSP provides a reasonable initial approximation. This family of procedures offers promise in quantifying the contribution of conformational entropy to the energetics of protein structures.

Packing and recognition of protein structural elements: a new approach applied to the 4-helix bundle of myohemerythrin.

We present a novel search strategy for determining the optimal packing of protein secondary structure elements. The approach is based on conformational energy optimization using a predetermined set of side chain rotamers and appropriate methods for sampling the conformational space of peptide fragments having fixed backbone geometries. An application to the 4-helix bundle of myohemerythrin is presented. It is shown that the conformations of the amino acid side chains are largely determined at the level of helix pairs and that superposition of these results can be used to construct the full bundle. The final solution obtained, taking into account restrictions due to the lateral amphiphilicity of the helices, differs from the native structure by only a 20 degrees rotation of a single helix.

Prediction of protein side chain conformations: a study on the influence of backbone accuracy on conformation stability in the rotamer space.

We have studied the effect of backbone inaccuracy on the efficiency of protein side chain conformation prediction using rotamer libraries. The backbones were generated by randomly perturbing the crystallographic conformation of 12 proteins and exhibit C alpha r.m.s.d.s of up to 2 A. Our results show that, even for a perturbation of the backbone fully compatible with the temperature factors of the proteins, the predicted side chain conformations of approximately 10% of the buried side chains remain variable. This fraction increases further for larger backbone deviations. However, for backbone deviations of up to 2 A r.m.s.d., the predicted side chain r.m.s.d. varies only in a ratio of < 1.4. Moreover, a possible strategy for obtaining side chain conformations close to the experimental ones consists of extracting the consensus conformations of the side chains from a series of backbone conformations. Such a procedure allows the computation of the side chain conformations with no loss of accuracy for backbones exhibiting r.m.s.d.s of up to 1 A from the crystallographic coordinates. For larger backbone deviations (up to 2 A r.m.s.d.) the r.m.s.d. of the buried side chains increases from 1.33 up to 1.60 A. We also discuss the influence of the size of the rotamer library on the quality of the prediction.

Predicting the disulfide bonding state of cysteines using protein descriptors.

Knowledge of the disulfide bonding state of the cysteines of proteins is of major interest in designing numerous molecular biology experiments, or in predicting their three-dimensional structure. Previous methods using the information gained from aligned sets of sequences have reached up to 82% of success in predicting the oxidation state of cysteines. In the present study, we assess the relative efficiency of different descriptors in predicting the cysteine disulfide bonding states. Our results suggest that the information on the residues flanking the cysteines is less informative about the disulfide bonding state than about the amino acid content of the whole protein. Using a combination of logistic functions learned with subsets of proteins homogeneous in terms of their amino acid content, we propose a simple prediction approach, starting from a single sequence, that reaches success rates close to 84%. This score can be improved by avoiding predictions regarding cysteines for which the decision is not well marked. For example, we obtain a score close to 87% correct prediction when we exclude predicting 10% of the cysteines.

Restriction map construction using a 'complete sentences compatibility' algorithm.

We have developed a new algorithm 'Complete sentences compatibility' (CSC) which uses single and double digestion fragments to rapidly determine restriction maps of circular DNA. From possible combinations of fragments of each simple digestion, which we call 'sentences of decomposition', we construct a restriction map which combines the sentences while taking into account compatibility rules. The algorithm can also deal with experimental errors of fragment weight and can suggest solutions that account for non-readable bands (fragments of zero length or multiple bands) on the gel. Because experiments using pairs of restrictive enzymes often result in multiple solutions, a complementary algorithm tries to reduce the number of proposed solutions by establishing consensus maps. The restriction map construction algorithm was tested on real cases, some containing more than fifteen fragments. Execution times range from 1-10 s on an IBM PC compatible microcomputer.

PredAcc: prediction of solvent accessibility.

UNLABELLED: PredAcc is a tool for predicting the solvent accessibility of protein residues from the sequence at different relative accessibility levels (0-55%). The prediction rate varies between 70. 7% (for 25% relative accessibility) and 85.7% (for 0% relative accessibility). Amino acids are predicted in four categories: almost certainly hidden and almost certainly exposed with a given a posteriori prediction error, probably hidden and probably exposed otherwise. AVAILABILITY: http://condor.urbb.jussieu.fr/PredAccCfg.html CONTACT: tuffery@urbb.jussieu.fr

FORME: an interactive package for protein backbone deformation.

The FORME package presented herein is designed for modeling purposes: It allows interactive deformation of the protein backbone. General formalism on transformations is introduced and the operators of stretching inside an "acceptance area" and stretching with end-block invariance (i.e., governed by a translational moving) are described. A discussion is presented on the choice of strategy to achieve an interactive deformation tool. Perspectives about complex transformations are presented.