Biology subjects

Marechal, J.-D.

Publications and source records attributed to Marechal, J.-D..

3 recordsLinked to original sources

bioRxiv2025

HemeFinder: a Computational Predictor for Heme-Binding Sites in Proteins

HemeFinder has been developed to predict heme-binding sites in natural and heme-dependent de novo enzymes. This tool relies on the structural and physicochemical characteristics of heme-binding sites, including shape, residue composition, and geometric descriptors. HemeFinder benchmarks more than 94% accuracy in identifying the correct heme location, considering the complete set of solutions, and 72% accuracy for the proper location with the correct iron-coordinating residues among the three best-ranked solutions. HemeFinder performs within seconds for monomeric systems and takes minutes for larger multimeric ones, demonstrating that its speed does not compromise its performance. An illustrative case of its potential is provided. HemeFinder is applied to the heme carrier protein 1 (HCP1), a transmembrane protein involved in heme recruitment in evolved organisms, for which no ligand-bound structures have been revealed. HemeFinder provides a relevant prediction of the binding of porphyrin and, when combined with protein-ligand docking, offers the first evidence of low-energy Heme-HCP1 complexes and unveils possible heme pathways. HemeFinder is an interesting, fast, and accurate tool for identifying heme-binding sites in proteins. Source code, documentation, and data are available at https://github.com/laura-tiessler/hemefinder and ESI.

bioinformatics↗

bioRxiv2024

BioBrigit, A Hybrid Deep Learning and Knowledge-based Approach to Model Metal Pathways in Proteins: Application to a Di-Copper Tyrosinase

The interaction of metallic species with proteins has been fundamental in evolution and key in many physiological processes. How metals bind to proteins also holds promise in many fields, like the design of new biocatalysts or the fight against pathogens. Nonetheless, uncovering the mechanism under which proteins recruit metal ions is far from understood and is one of the challenges in bioinorganic chemistry and structural biology. Computational methods are potentially among the most promising tools for this endeavor. Only a handful of efficient structural predictors of metal binding sites exist to date. Most focus on identifying the most stable binding sites in the protein scaffolds. Although these methods are very interesting, they do not consider the exploration of transient, sub-optimal binding sites that could be relevant in metal binding pathways in proteins. At the far end of modeling capabilities nowadays, we introduce BioBrigit, a hybrid Deep Learning - knowledge-based approach that suggests metal binding pathways in proteins. To demonstrate the methods viability, we apply it to the di-copper tyrosinase from Streptomyces castaneoglobisporus, a system for which crystallographic experiments allowed the identification of a series of transient sites of the copper in its path from a chaperone to the final catalytic site. Combined with homology modeling and large-scale molecular dynamics, BioBrigit allows for computational characterization of all experimental sites and for better understanding of the copper recruitment mechanism. BioBrigit appears as an asset in a field full of unknowns like metal binding to proteins and opens the way to further algorithms in this area. Source code, documentation, and data are available at https://github.com/insilichem/BioBrigit

bioinformatics↗

bioRxiv2022

TALAIA: A 3D visual dictionary for protein structures

SummaryGraphical analysis of the molecular structure of proteins can be very complex. Full atom representations retain most of the geometric information but are generally crowded and key structural patterns can be difficult to catch. Non-full atom representations could be more instructive on physicochemical aspects but be insufficiently detailed regarding shapes (e.g., entity beans-like models in coarse grain approaches) or individualistic properties of amino acids (e.g., representation of superficial electrostatic properties). TALAIA aims at providing another layer of structural representations. It is a visual dictionary where each amino acid is represented by a unique object with differentiated shapes and colors. It makes it easier to spot important molecular information including patches of amino acids or key interactions between side chains. Most of the conventions used in TALAIA are common in chemistry and biochemistry so that experimentalists and modelers can rapidly grasp the meaning of any TALAIA depictions. MotivationThe aim of the work is to offer a visual grammar that combines simplistic representations of amino acids while retaining their general geometry and physicochemical properties. ResultsWe propose a tool that renders protein structures and encodes both structure and physicochemical aspects as a simple visual grammar. The approach is fast, highly informative, and simple, allowing the identification of possible interactions, hydrophobic patches, and other characteristic structural features at a simple glance. Availabilityhttps://github.com/insilichem/talaia Contactjeandidier.marechal@uab.cat

scientific communication and education↗