Jacques Chomilier

DECIPHERING PROTEIN SEQUENCE INFORMATION THROUGH HYDROPHOBIC CLUSTER ANALYSIS (HCA): CURRENT STATUS AND PERSPECTIVES

Abstract. Ten years after the idea of hydrophobic cluster analysis (HCA) was conceived and first pub lished, theoretical and practical experience has shown this unconventional method of protein sequence anal ysis to be particularly efficient and sensitive, especially with families of sequences sharing low levels of se quence identity. This extreme sensitivity has made it possible to predict the functions of genes whose se quence similarities are hardly if at all detectable by current one-dimensional (1D) methods alone, and of fers a new way to explore the enormous amount of data generated by genome sequencing. HCA also pro vides original tools to understand fundamental fea tures of protein stability and folding. Since the last review of HCA published in 1990 [1], significant im provements have been made and several new facets have been addressed. Here we wish to update and summarize this information.

Nonatomic Solvent-Driven Voronoi Tessellation of Proteins:An Open Tool to Analyze Protein Folds

A three‐dimensional Voronoi tessellation of folded proteins is used to analyze geometrical and topological properties of a set of proteins. To each amino acid is associated a central point surrounded by a Voronoi cell. Voronoi cells describe the packing of the amino acids. Special attention is given to reproduction of the protein surface. Once the Voronoi cells are built, a lot of tools from geometrical analysis can be applied to investigate the protein structure; volume of cells, number of faces per cell, and number of sides per face are the usual signatures of the protein structure. A distinct difference between faces related to primary, secondary, and tertiary structures has been observed. Faces threaded by the main‐chain have on average more than six edges, whereas those related to helical packing of the amino acid chain have less than five edges. The faces on the protein surface have on average five edges within 1% error. The average number of faces on the protein surface for a given type of amino acid brings a new point of view in the characterization of the exposition to the solvent and the classification of amino acid as hydrophilic or hydrophobic. It may be a convenient tool for model validation. Proteins 2002;49:446–456.

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 and the specific services at .

Correction for multiple scattering in Compton profile experiments: Application for synchrotron source photons

Abstract New improvements in the Monte Carlo multiple scattering correction for Compton profiles are described. They consider the linear and well-defined polarization of a synchrotron beam and the actual geometry of the Compton spectrometer at LURE-DCI: the monochromatization by two Bragg reflections as well as the transmission of the scattered photons through the crystal analyzer enhance the main component of the electric field vector. These two properties tend to lower the multiple scattering cross-section. Measurements on beryllium performed with samples of various thicknesses prove the quality of the present simulation. A graphite Compton profile, obtained in different conditions of incident energy and sample geometry, is also corrected for multiple scattering contribution. It is finally shown that the correction is quite small, for low energy and polarized photons.

Automat and BLAST: comparison of two protein sequence similarity search programs

Since the early 1980s, protein/DNA sequence similarity search has become of major importance to biologists, and the need for fast and efficient tools grows with the size of databanks. Two programs use the strategy of finite state deterministic automatons to accomplish these searches. One of these two is BLAST, which is now widely used, and the other Automat, which has just been published. The differences and similarities in their basic principles, their use and their performances are analysed in this paper in order to allow optimal use of these important softwares.

Contribution to the Prediction of the Fold Code: Application to Immunoglobulin and Flavodoxin Cases

Background Folding nucleus of globular proteins formation starts by the mutual interaction of a group of hydrophobic amino acids whose close contacts allow subsequent formation and stability of the 3D structure. These early steps can be predicted by simulation of the folding process through a Monte Carlo (MC) coarse grain model in a discrete space. We previously defined MIRs (Most Interacting Residues), as the set of residues presenting a large number of non-covalent neighbour interactions during such simulation. MIRs are good candidates to define the minimal number of residues giving rise to a given fold instead of another one, although their proportion is rather high, typically [15-20]% of the sequences. Having in mind experiments with two sequences of very high levels of sequence identity (up to 90%) but different folds, we combined the MIR method, which takes sequence as single input, with the “fuzzy oil drop” (FOD) model that requires a 3D structure, in order to estimate the residues coding for the fold. FOD assumes that a globular protein follows an idealised 3D Gaussian distribution of hydrophobicity density, with the maximum in the centre and minima at the surface of the “drop”. If the actual local density of hydrophobicity around a given amino acid is as high as the ideal one, then this amino acid is assigned to the core of the globular protein, and it is assumed to follow the FOD model. Therefore one obtains a distribution of the amino acids of a protein according to their agreement or rejection with the FOD model. Results We compared and combined MIR and FOD methods to define the minimal nucleus, or keystone, of two populated folds: immunoglobulin-like (Ig) and flavodoxins (Flav). The combination of these two approaches defines some positions both predicted as a MIR and assigned as accordant with the FOD model. It is shown here that for these two folds, the intersection of the predicted sets of residues significantly differs from random selection. It reduces the number of selected residues by each individual method and allows a reasonable agreement with experimentally determined key residues coding for the particular fold. In addition, the intersection of the two methods significantly increases the specificity of the prediction, providing a robust set of residues that constitute the folding nucleus.

Protein intrachain contact prediction with most interacting residues (MIR)

Abstract The transition state ensemble during the folding process of globular proteins occurs when a sufficient number of intrachain contacts are formed, mainly, but not exclusively, due to hydrophobic interactions. These contacts are related to the folding nucleus, and they contribute to the stability of the native structure, although they may disappear after the energetic barrier of transition states has been passed. A number of structure and sequence analyses, as well as protein engineering studies, have shown that the signature of the folding nucleus is surprisingly present in the native three-dimensional structure, in the form of closed loops, and also in the early folding events. These findings support the idea that the residues of the folding nucleus become buried in the very first folding events, therefore helping the formation of closed loops that act as anchor structures, speed up the process, and overcome the Levinthal paradox. We present here a review of an algorithm intended to simulate in a discrete space the early steps of the folding process. It is based on a Monte Carlo simulation where perturbations, or moves, are randomly applied to residues within a sequence. In contrast with many technically similar approaches, this model does not intend to fold the protein but to calculate the number of non-covalent neighbors of each residue, during the early steps of the folding process. Amino acids along the sequence are categorized as most interacting residues (MIRs) or least interacting residues. The MIR method can be applied under a variety of circumstances. In the cases tested thus far, MIR has successfully identified the exact residue whose mutation causes a switch in conformation. This follows with the idea that MIR identifies residues that are important in the folding process. Most MIR positions correspond to hydrophobic residues; correspondingly, MIRs have zero or very low accessible surface area. Alongside the review of the MIR method, we present a new postprocessing method called smoothed MIR (SMIR), which refines the original MIR method by exploiting the knowledge of residue hydrophobicity. We review known results and present new ones, focusing on the ability of MIR to predict structural changes, secondary structure, and the improved precision with the SMIR method.

Managing and Documenting Legacy Scientific Workflows

Scientific legacy workflows are often developed over many years, poorly documented and implemented with scripting languages. In the context of our cross-disciplinary projects we face the problem of maintaining such scientific workflows. This paper presents the Workflow Instrumentation for Structure Extraction (WISE) method used to process several ad-hoc legacy workflows written in Python and automatically produce their workflow structural skeleton. Unlike many existing methods, WISE does not assume input workflows to be preprocessed in a known workflow formalism. It is also able to identify and analyze calls to external tools. We present the method and report its results on several scientific workflows.

Hydrophobic core in domains of immunoglobulin-like fold.

This work analyzes proteins which contain an immunoglobulin fold, focusing on their hydrophobic core structure. The “fuzzy oil drop” model was used to measure the regularity of hydrophobicity distribution in globular domains belonging to proteins which exhibit the above-mentioned fold. Light-chain IgG domains are found to frequently contain regular hydrophobic cores, unlike the corresponding heavy-chain domains. Enzymes and DNA binding proteins present in the data-set are found to exhibit poor accordance with the hydrophobic core model.

Prediction of Stability upon Point Mutation in the Context of the Folding Nucleus

Proteins come in all shapes and sizes. Although it is possible to predict with reasonable success their structure from their sequence, the process of folding a chain of amino acids into its tertiary structure remains partially understood. This article addresses several characteristics pertaining to protein folding. The development of the Most Interacting Residues (MIR) algorithm, which dynamically simulates the early folding events, permits a reasonable ab initio prediction of the deeply buried critical residues involved in the formation of the protein core. The analysis of MIR positions with respect to protein 3D topology, in particular, to fragments called Tightened End Fragments (TEF) that might be good candidate for autonomous folding units, suggests that they are also essential for defining core stability. To validate this hypothesis, this study measures the sensitivity of MIR residues to point mutations. It is performed on a set of 385 proteins from a database that contains stability data calculated with five different algorithms. Tools have been developed to help the analysis and a consensus of the five methods is proposed. It results that positions predicted both as a MIR and a minimum of stability for the consensus are good candidates for the folding nucleus, and consequently their mutations may be hazardous.