MOTIVATION: Evolutionary rates in protein-coding genes vary widely, reflecting functional and/or structural constraints. Essential or highly expressed proteins tend to evolve more slowly, and within a protein, different amino acid sites experience distinct selective pressures. Accurately modeling this variation is critical for identifying functional and/or structurally important amino acid sites. Standard methods assume independent substitution rates across sites, and the most conserved ones are widely distributed in protein tertiary structure. This is biologically unrealistic, as functional sites tend to cluster in 3D space. RESULTS: Here, we developed CONSTRUCT, an improved strategy for detecting functional and structurally important regions in protein tertiary structure. Given a set of orthologous sequences, CONSTRUCT first estimates site-specific substitution rates using the Rate4site model. These rates are then weighted by the rates of neighboring amino acid sites within an optimally defined window size, determined by the strongest spatial correlation. To refine clustering detection, CONSTRUCT can analyze either Cα atoms or the center of mass of amino acid sites, accounting for side chain orientation. Extensive simulations and validation on 14 functionally characterized proteins of diverse sizes, interspecies conservation levels, and taxonomic origins demonstrated the robustness of CONSTRUCT. The results highlight CONSTRUCT as a powerful tool for guiding site-directed mutagenesis experiments aimed at elucidating protein function. AVAILABILITY AND IMPLEMENTATION: The CONSTRUCT program and documentation are freely available at https://github.com/Rcoppee/CONSTRUCT.

In the era of constantly increasing amounts of the available protein data, a relevant and interpretable visualization becomes crucial, especially for tasks requiring human expertise. Poincaré disk projection has previously demonstrated its important efficiency for visualization of biological data such as single-cell RNAseq data. Here, we develop a new method PoincaréMSA for visual representation of complex relationships between protein sequences based on Poincaré maps embedding. We demonstrate its efficiency and potential for visualization of protein family topology as well as evolutionary and functional annotation of uncharacterized sequences. PoincaréMSA is implemented in open source Python code with available interactive Google Colab notebooks as described at https://www.dsimb.inserm.fr/POINCARE_MSA.

MOTIVATION: Alignment of protein structures is a major problem in structural biology. The first approach commonly used is to consider proteins as rigid bodies. However, alignment of protein structures can be very complex due to conformational variability, or complex evolutionary relationships between proteins such as insertions, circular permutations or repetitions. In such cases, introducing flexibility becomes useful for two reasons: (i) it can help compare two protein chains which adopted two different conformational states, such as due to proteins/ligands interaction or post-translational modifications, and (ii) it aids in the identification of conserved regions in proteins that may have distant evolutionary relationships. RESULTS: We propose ICARUS, a new approach for flexible structural alignment based on identification of Protein Units, evolutionarily preserved structural descriptors of intermediate size, between secondary structures and domains. ICARUS significantly outperforms reference methods on a dataset of very difficult structural alignments. AVAILABILITY AND IMPLEMENTATION: Code is freely available online at https://github.com/DSIMB/ICARUS.

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.

UNLABELLED: : The experimental determination of membrane protein orientation within the lipid bilayer is extremely challenging, such that computational methods are most often the only solution. Moreover, obtaining all-atom 3D structures of membrane proteins is also technically difficult, and many of the available data are either experimental low-resolution structures or theoretical models, whose structural quality needs to be evaluated. Here, to address these two crucial problems, we propose OREMPRO, a web server capable of both (i) positioning α-helical and β-sheet transmembrane domains in the lipid bilayer and (ii) assessing their structural quality. Most importantly, OREMPRO uses the sole alpha carbon coordinates, which makes it the only web server compatible with both high and low structural resolutions. Finally, OREMPRO is also interesting in its ability to process coarse-grained protein models, by using coordinates of backbone beads in place of alpha carbons. AVAILABILITY AND IMPLEMENTATION: http://www.dsimb.inserm.fr/OREMPRO/ CONTACT: : [email protected] or [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

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: Structure-dependent substitution matrices increase the accuracy of sequence alignments when the 3D structure of one sequence is known, and are successful e.g. in fold recognition. We propose a new automated method, EvDTree, based on a decision tree algorithm, for automatic derivation of amino acid substitution probabilities from a set of sequence-structure alignments. The main advantage over other approaches is an unbiased automatic selection of the most informative structural descriptors and associated values or thresholds. This feature allows automatic derivation of structure-dependent substitution scores for any specific set of structures, without the need to empirically determine best descriptors and parameters. RESULTS: Decision trees for residue substitutions were constructed for each residue type from sequence-structure alignments extracted from the HOMSTRAD database. For each tree cluster, environment-dependent substitution profiles were derived. The resulting structure-dependent substitution scores were assessed using a criterion based on the mean ranking of observed substitution among all possible substitutions and in sequence-structure alignments. The automatically built EvDTree substitution scores provide significantly better results than conventional matrices and similar or slightly better results than other structure-dependent matrices. EvDTree has been applied to small disulfide-rich proteins as a test case to automatically derive specific substitutions scores providing better results than non-specific substitution scores. Analyses of the decision tree classifications provide useful information on the relative importance of different structural descriptors. CONCLUSIONS: We propose a fully automatic method for the classification of structural environments and inference of structure-dependent substitution profiles. We show that this approach is more accurate than existing methods for various applications. The easy adaptation of EvDTree to any specific data set opens the way for class-specific structure-dependent substitution scores which can be used in threading-based remote homology searches.