β-bulges are irregularities inside the β-sheets. They represent more than 3 percent of the protein residues, i.e., they are as frequent as 3.10helices. In terms of evolution, β-bulges are not more conserved than any other local protein conformations within homologous protein structures. In a first of its kind study, we have investigated the dynamical behaviour of β-bulges using the largest known set of protein molecular dynamics simulations. We observed that more than 50 percent of the existing β-bulges in protein crystal structures remained stable during dynamics while more than1/6thwere not stable at all and disappeared entirely. Surprisingly, 1.1 percent of β-bulges that appeared remained stable. β-bulges have been categorized in different subtypes. The most common β-bulges’ types are the smallest insertion in β-strands (namely AC and AG); they are found as stable as the whole β-bulges dataset. Low occurring types (namely PC and AS), that have the largest insertions, are significantly more stable than expected. Thus, this pioneer study allowed to precisely quantify the stability of the β-bulges, demonstrating their structural robustness, with few unexpected cases raising structural questions.

BACKGROUND: Bioinformatics methods are helpful to identify new molecules for diagnostic or therapeutic applications. For example, the use of peptides capable of mimicking binding sites has several benefits in replacing a protein which is difficult to produce, or toxic. Using peptides is less expensive. Peptides are easier to manipulate, and can be used as drugs. Continuous epitopes predicted by bioinformatics tools are commonly used and these sequential epitopes are used as is in further experiments. Numerous discontinuous epitope predictors have been developed but only two bioinformatics tools have been proposed so far to predict peptide sequences: Superficial and PEPOP 2.0. PEPOP 2.0 can generate series of peptide sequences that can replace continuous or discontinuous epitopes in their interaction with their cognate antibody. RESULTS: We have developed an improved version of PEPOP (PEPOP 2.0) dedicated to answer to experimentalists' need for a tool able to handle proteins and to turn them into peptides. The PEPOP 2.0 web site has been reorganized by peptide prediction category and is therefore better formulated to experimental designs. Since the first version of PEPOP, 32 new methods of peptide design were developed. In total, PEPOP 2.0 proposes 35 methods in which 34 deal specifically with discontinuous epitopes, the most represented epitope type in nature. CONCLUSION: Through the presentation of its user-friendly, well-structured new web site conceived in close proximity to experimentalists, we report original methods that show how PEPOP 2.0 can assist biologists in dealing with discontinuous epitopes.

BACKGROUND: Computational methods provide approaches to identify epitopes in protein Ags to help characterizing potential biomarkers identified by high-throughput genomic or proteomic experiments. PEPOP version 1.0 was developed as an antigenic or immunogenic peptide prediction tool. We have now improved this tool by implementing 32 new methods (PEPOP version 2.0) to guide the choice of peptides that mimic discontinuous epitopes and thus potentially able to replace the cognate protein Ag in its interaction with an Ab. In the present work, we describe these new methods and the benchmarking of their performances. RESULTS: Benchmarking was carried out by comparing the peptides predicted by the different methods and the corresponding epitopes determined by X-ray crystallography in a dataset of 75 Ag-Ab complexes. The Sensitivity (Se) and Positive Predictive Value (PPV) parameters were used to assess the performance of these methods. The results were compared to that of peptides obtained either by chance or by using the SUPERFICIAL tool, the only available comparable method. CONCLUSION: The PEPOP methods were more efficient than, or as much as chance, and 33 of the 34 PEPOP methods performed better than SUPERFICIAL. Overall, "optimized" methods (tools that use the traveling salesman problem approach to design peptides) can predict peptides that best match true epitopes in most cases.

The majority of the proteins encoded in the genomes of eukaryotes contain more than one domain. Reasons for high prevalence of multi-domain proteins in various organisms have been attributed to higher stability and functional and folding advantages over single-domain proteins. Despite these advantages, many proteins are composed of only one domain while their homologous domains are part of multi-domain proteins. In the study presented here, differences in the properties of protein domains in single-domain and multi-domain systems and their influence on functions are discussed. We studied 20 pairs of identical protein domains, which were crystallized in two forms (a) tethered to other proteins domains and (b) tethered to fewer protein domains than (a) or not tethered to any protein domain. Results suggest that tethering of domains in multi-domain proteins influences the structural, dynamic and energetic properties of the constituent protein domains. 50% of the protein domain pairs show significant structural deviations while 90% of the protein domain pairs show differences in dynamics and 12% of the residues show differences in the energetics. To gain further insights on the influence of tethering on the function of the domains, 4 pairs of homologous protein domains, where one of them is a full-length single-domain protein and the other protein domain is a part of a multi-domain protein, were studied. Analyses showed that identical and structurally equivalent functional residues show differential dynamics in homologous protein domains; though comparable dynamics between in-silico generated chimera protein and multi-domain proteins were observed. From these observations, the differences observed in the functions of homologous proteins could be attributed to the presence of tethered domain. Overall, we conclude that tethered domains in multi-domain proteins not only provide stability or folding advantages but also influence pathways resulting in differences in function or regulatory properties.

MOTIVATION: Template-based modeling, the most successful approach for predicting protein 3D structure, often requires detecting distant evolutionary relationships between the target sequence and proteins of known structure. Developed for this purpose, fold recognition methods use elaborate strategies to exploit evolutionary information, mainly by encoding amino acid sequence into profiles. Since protein structure is more conserved than sequence, the inclusion of structural information can improve the detection of remote homology. RESULTS: Here, we present ORION, a new fold recognition method based on the pairwise comparison of hybrid profiles that contain evolutionary information from both protein sequence and structure. Our method uses the 16-state structural alphabet Protein Blocks, which provides an accurate 1D description of protein structure local conformations. ORION systematically outperforms PSI-BLAST and HHsearch on several benchmarks, including target sequences from the modeling competitions CASP8, 9 and 10, and detects ∼10% more templates at fold and superfamily SCOP levels. AVAILABILITY: Software freely available for download at http://www.dsimb.inserm.fr/orion/. CONTACT: [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

UNLABELLED: We present an improved version of our Protein Peeling web server dedicated to the analysis of protein structure architecture through the identification of protein units produced by an iterative splitting algorithm. New features include identification of structural domains, detection of unstructured terminal elements and evaluation of the stability of protein unit structures. AVAILABILITY: The website is free and open to all users with no login requirements at http://www.dsimb.inserm.fr/dsimb-tools/peeling3.

MOTIVATION: The object of this study is to propose a new method to identify small compact units that compose protein three-dimensional structures. These fragments, called 'protein units (PU)', are a new level of description to well understand and analyze the organization of protein structures. The method only works from the contact probability matrix, i.e. the inter Calpha-distances translated into probabilities. It uses the principle of conventional hierarchical clustering, leading to a series of nested partitions of the 3D structure. Every step aims at dividing optimally a unit into 2 or 3 subunits according to a criterion called 'partition index' assessing the structural independence of the subunits newly defined. Moreover, an entropy-derived squared correlation R is used for assessing globally the protein structure dissection. The method is compared to other splitting algorithms and shows relevant performance. AVAILABILITY: An Internet server with dedicated tools is available at http://www.ebgm.jussieu.fr/~gelly/

BACKGROUND: Microarray technologies produced large amount of data. The hierarchical clustering is commonly used to identify clusters of co-expressed genes. However, microarray datasets often contain missing values (MVs) representing a major drawback for the use of the clustering methods. Usually the MVs are not treated, or replaced by zero or estimated by the k-Nearest Neighbor (kNN) approach. The topic of the paper is to study the stability of gene clusters, defined by various hierarchical clustering algorithms, of microarrays experiments including or not MVs. RESULTS: In this study, we show that the MVs have important effects on the stability of the gene clusters. Moreover, the magnitude of the gene misallocations is depending on the aggregation algorithm. The most appropriate aggregation methods (e.g. complete-linkage and Ward) are highly sensitive to MVs, and surprisingly, for a very tiny proportion of MVs (e.g. 1%). In most of the case, the MVs must be replaced by expected values. The MVs replacement by the kNN approach clearly improves the identification of co-expressed gene clusters. Nevertheless, we observe that kNN approach is less suitable for the extreme values of gene expression. CONCLUSION: The presence of MVs (even at a low rate) is a major factor of gene cluster instability. In addition, the impact depends on the hierarchical clustering algorithm used. Some methods should be used carefully. Nevertheless, the kNN approach constitutes one efficient method for restoring the missing expression gene values, with a low error level. Our study highlights the need of statistical treatments in microarray data to avoid misinterpretation.

BACKGROUND: Because loops connect regular secondary structures, analysis of the former depends directly on the definition of the latter. The numerous assignment methods, however, can offer different definitions. In a previous study, we defined a structural alphabet composed of 16 average protein fragments, which we called Protein Blocks (PBs). They allow an accurate description of every region of 3D protein backbones and have been used in local structure prediction. In the present study, we use this structural alphabet to analyze and predict the loops connecting two repetitive structures. RESULTS: We first analyzed the secondary structure assignments. Use of five different assignment methods (DSSP, DEFINE, PCURVE, STRIDE and PSEA) showed the absence of consensus: 20% of the residues were assigned to different states. The discrepancies were particularly important at the extremities of the repetitive structures. We used PBs to describe and predict the short loops because they can help analyze and in part explain these discrepancies. An analysis of the PB distribution in these regions showed some specificities in the sequence-structure relationship. Of the amino acid over- or under-representations observed in the short loop databank, 20% did not appear in the entire databank. Finally, predicting 3D structure in terms of PBs with a Bayesian approach yielded an accuracy rate of 36.0% for all loops and 41.2% for the short loops. Specific learning in the short loops increased the latter by 1%. CONCLUSION: This work highlights the difficulties of assigning repetitive structures and the advantages of using more precise descriptions, that is, PBs. We observed some new amino acid distributions in the short loops and used this information to enhance local prediction. Instead of describing entire loops, our approach predicts each position in the loops locally. It can thus be used to propose many different structures for the loops and to probe and sample their flexibility. It can be a useful tool in ab initio loop prediction.

MOTIVATION: Our aim is to develop a process that automatically defines a repertory of contiguous 3D protein structure fragments and can be used in homology modeling. We present here improvements to the method we introduced previously: the 'hybrid protein model' (de Brevern and Hazout, THEOR: Chem. Acc., 106, 36-47, (2001)) The hybrid protein learns a non-redundant databank encoded in a structural alphabet composed of 16 Protein Blocks (PBs; de Brevern et al., Proteins, 41, 271-287, (2000)). Every local fold is learned by looking for the most similar pattern present in the hybrid protein and modifying it slightly. Finally each position corresponds to a cluster of similar 3D local folds. RESULTS: In this paper, we describe improvements to our method for building an optimal hybrid protein: (i) 'baby training,' which is defined as the introduction of large structure fragments and the progressive reduction in the size of training fragments; and (ii) the deletion of the redundant parts of the hybrid protein. This repertory of contiguous 3D protein structure fragments should be a useful tool for molecular modeling.

The transformation of protein 1D-sequence to protein 3D-structure is one of the main difficulties of structural biology. A structural alphabet has been previously defined from dihedral angles describing the protein backbone as structural information by using an unsupervised classifier. The 16 protein blocks (PBs), basis element of the structural alphabet, allows a correct 3D structure approximation. Local prediction had been estimated by a Bayesian approach and shown that sequence information induces strongly the local fold, but stays coarse (prediction rate of 40.7% with one PB, 75,8% with the four most probable PBs). The Hybrid Protein Model presented in this study learns both the sequence and structure of the proteins. The analysis made along the hybrid protein has permitted to appreciate more precisely the spatial location of some types of amino acid residues in the secondary structures and their flanking regions. This study leads to a fuzzy model of dependence between sequence and structure.