Biology subjects

Devignes, M.-D.

Publications and source records attributed to Devignes, M.-D..

4 recordsLinked to original sources

bioRxiv2024

Subtle changes at the RBD/hACE2 interface during SARS-CoV2 variant evolution: a molecular dynamics study

The SARS-CoV-2 Omicron variants present a different behavior compared to the previous variants, all particularly in respect to the Delta variant, as it seems to promote a lower morbidity although being much more contagious. In this perspective, we performed new molecular dynamics (MD) simulations of the various spike RBD/hACE2 complexes corresponding to the WT, Delta and Omicron variants (BA.1 up to BA.4/5) over 1.5 {micro}s timescale. Then, carrying out a comprehensive analysis of residue interactions within and between the two partners, allowed us to draw the profile of each variant by using complementary methods (PairInt, hydrophobic potential, contact PCA). Main results of PairInt calculations highlighted the most involved residues in electrostatic interactions that represent a strong contribution in the binding with highly stable contacts between spike RBD and hACE2 (importance of mutated residues at positions 417, 493 and 498). In addition to the swappable arginine residues (493/498), the apolar contacts made a substantial and complementary contribution in Omicron with the detection of two hydrophobic patches, one of which was correlated with energetic contribution calculations. This study brings new highlights on the global dynamics of spike RBD/hACE2 complexes resulting from the analysis of contact networks and cross-correlation matrices able to detect subtle changes at point mutations. The results of our study are also consistent with alternative approaches such as binding free energy calculations but are more informative and sensitive to transient or low-energy interactions. Nevertheless, the energetic contributions of residues at positions 501 and 505 were in good agreement with hydrophobic interactions measurements. The contact PCA networks could identify the intramolecular incidence of the S375F mutation occurring in all Omicron variants and likely conferring them an advantage in binding stability. Collectively, these data revealed the major differences observed between WT/Delta and Omicron variants at the RBD/hACE2 interface, which may explain the greater persistence of Omicron. Author SummaryThe evolution of SARS-CoV-2 was extremely rapid, leading to the global predominance of Omicron variants, despite the many mutations identified in the spike protein. Some of these were introduced to evade the immune system, but many others were located in the Receptor Binding Domain (RBD) without affecting its efficient binding to hACE2 and preserving the high infectivity of this variant. To unravel the mechanism by which this protein-protein connection remains strong or stable, it is necessary to study the different types of interactions at the atomic level and over time using molecular dynamics (MD) simulations. Indeed, in contrast to crystal or cryo-EM structures providing only a fixed image of the binding process, MD simulations have allowed to unambiguously identify the sustainability of some interactions mediated by key residues of spike RBD. This study could also highlight the interchangeable role of certain residues in compensating for a mutation, which in turn allows the virus to maintain durable binding to the host cell receptor. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=83 SRC="FIGDIR/small/628120v1_ufig1.gif" ALT="Figure 1"> View larger version (35K): org.highwire.dtl.DTLVardef@b2a6c4org.highwire.dtl.DTLVardef@e29044org.highwire.dtl.DTLVardef@6d9835org.highwire.dtl.DTLVardef@123c6f9_HPS_FORMAT_FIGEXP M_FIG Graphical abstract C_FIG

bioinformatics↗

bioRxiv2023

HLA-EpiCheck: A B-cell epitope prediction tool for HLA proteins using molecular dynamics simulation data.

1The Human Leukocyte Antigen (HLA) system is the main cause of organ transplant loss through the recognition of HLA proteins by Donor-Specific Antibodies (DSA). Therefore, the identification of potentially immunogenic epitopes is a key task to refine organ allocation and then to improve the survival of transplanted organs. Here, we present HLA-EpiCheck, a machine learning predictor for B-cell epitopes on HLA proteins that leverages an unprecedented dataset of high-quality molecular dynamics simulations of 207 HLA proteins. Candidate epitopes are represented as surface patches centered on solvent-accessible residues and described by a set of 18 descriptors. The descriptors include both static and dynamic properties, such as hydrophobicity, electrostatic charges, relative solvent-accessible surface area and side-chain flexibility. The HLA-EpiCheck was trained using an Extra Trees ensemble learning method and was compared to DiscoTope-3.0, a state-of-the-art B-cell epitope predictor. HLA-EpiCheck largely outperformed DiscoTope-3.0 in the task of predicting HLA epitopes. HLA-EpiCheck was also used to assess the epitope status of a subset of non-confirmed eplets. The predictions were compared to experimental data and a notable consistency was found. These results suggest that HLA-EpiCheck could be used to better define HLA matching between donor and recipient to reduce de novo DSA formation and graft rejection.

bioinformatics↗

bioRxiv2021

PPIDomainMiner : Inferring domain-domain interactions from multiple sources of protein-protein interactions

Many biological processes are mediated by protein-protein interactions (PPIs). Because protein domains are the building blocks of proteins, PPIs likely rely on domain-domain interactions (DDIs). Several attempts exist to infer DDIs from PPI networks but the produced datasets are heterogeneous and sometimes not accessible, while the PPI interactome data keeps growing. We describe a new computational approach called "PPIDM" (Protein-Protein Interactions Domain Miner) for inferring DDIs using multiple sources of PPIs. The approach is an extension of our previously described "CODAC" (Computational Discovery of Direct Associations using Common neighbors) method for inferring new edges in a tripartite graph. The PPIDM method has been applied to seven widely used PPI resources, using as "Gold-Standard" a set of DDIs extracted from 3D structural databases. Overall, PPIDM has produced a dataset of 84, 552 non-redundant DDIs. Statistical significance (p-value) is calculated for each source of PPI and used to classify the PPIDM DDIs in Gold (9,175 DDIs), Silver (24, 934 DDIs) and Bronze (50, 443 DDIs) categories. Dataset comparison reveals that PPIDM has inferred from the 2017 releases of PPI sources about 46% of the DDIs present in the 2020 release of the 3did database, not counting the DDIs present in the Gold-Standard. The PPIDM dataset contains 10, 229 DDIs that are consistent with more than 13, 300 PPIs extracted from the IMEx database, and nearly 23,300 DDIs (27.5%) that are consistent with more than 214,000 human PPIs extracted from the STRING database. Examples of newly inferred DDIs covering more than 10 PPIs in the IMEx database are provided. Further exploitation of the PPIDM DDI reservoir includes the inventory of possible partners of a protein of interest and characterization of protein interactions at the domain level in combination with other methods. The result is publicly available at http://ppidm.loria.fr/. Author summaryWe revisit at a large scale the question of inferring DDIs from PPIs. Compared to previous studies, we take a unified approach accross multiple sources of PPIs. This approach is a method for inferring new edges in a tripartite graph setting and can be compared to link prediction approaches in knowledge graphs. Aggregation of several sources is performed using an optimized weighted average of the individual scores calculated in each source. A huge dataset of over 84K DDIs is produced which far exceeds the previous datasets. We show that a significant portion of the PPIDM dataset covers a large number of PPIs from curated (IMEx) or non curated (STRING) databases. Such a reservoir of DDIs deserves further exploration and can be combined with high-throughput methods such as cross-linking mass spectrometry to identify plausible protein partners of proteins of interest.

bioinformatics↗

bioRxiv2019

PGxCorpus: a Manually Annotated Corpus for Pharmacogenomics

Pharmacogenomics (PGx) studies how individual gene variations impact drug response phenotypes, which makes knowledge related to PGx a key component towards precision medicine. A significant part of the state-of-the-art knowledge in PGx is accumulated in scientific publications, where it is hardly usable to humans or software. Natural language processing techniques have been developed and are indeed employed for guiding experts curating this amount of knowledge. But, existing works are limited by the absence of high quality annotated corpora focusing on the domain. This absence restricts in particular the use of supervised machine learning approaches. This article introduces PGxCorpus, a manually annotated corpus, designed for the automatic extraction of PGx relationships from text. It comprises 945 sentences from 911 PubMed abstracts, annotated with PGx entities of interest (mainly genes variations, gene, drugs and phenotypes), and relationships between those. We present in this article the method used to annotate consistently texts, and a baseline experiment that illustrates how this resource may be leveraged to synthesize and summarize PGx knowledge.

bioinformatics↗