Gautier Moroy

The MSMP (MicroSeminoProtein, Prostate-associated) protein is overexpressed in several cancers, including prostate, ovarian, and breast cancers. Its overexpression is particularly prevalent in tumors resistant to hormonal therapy, as well as to antiangiogenic treatments (e.g., anti-VEGF therapies). In hypoxic tumor microenvironments, characteristic of solid tumors, MSMP expression increases and facilitates tumor growth. MSMP binds to the transmembrane receptor CCR2 (C-C chemokine receptor type 2), a GPCR present on monocytes and lymphocytes. This interaction stabilizes an active conformation of CCR2, enabling downstream MAP kinase signaling pathways to reactivate androgen synthesis and promote tumor progression. In this study, we employed molecular modeling, molecular dynamics simulations, and machine learning techniques to elucidate the structural basis of the MSMP-CCR2 interaction. High-resolution models of the MSMP-CCR2-G protein complex were generated using AlphaFold2 and refined with MD simulations. Comparative analyses with the CCL2-CCR2-G protein complex and the unbound CCR2-G protein complex revealed key conformational rearrangements that modulate and stabilize the active state of the receptor. Binding free energy calculations revealed similar energetic profiles for MSMP and CCL2, despite distinct binding modes. Residue-level energy decomposition identified critical residues at the MSMP-CCR2 interface, offering insights into the receptor's activation mechanism. Additionally, machine learning models classified molecular dynamics trajectories, highlighting key structural features associated with stable MSMP-CCR2-G protein states. This integrative approach identified residues essential for maintaining a stable conformation of the MSMP-CCR2-G protein complex, providing a basis for targeting MSMP-CCR2 interactions in therapeutic development.

Amyloids are protein aggregates having a cross-β structure, and they reveal some unusual properties, like interactions with specific dyes and resistance to actions of detergents and proteases, as well as the capability to force some proteins to change their conformation from a soluble form to aggregates. The occurrence of amyloids is not restricted to humans and animals, as they also exist in microbial cells. However, contrary to animals, where amyloids are usually pathological molecules, bacterial amyloids are often functional, participating in various physiological processes. In this review, we focus on a specific property of bacterial amyloids, namely their ability to interact with nucleic acids and resultant regulatory mechanisms. Moreover, some of these interactions might play indirect roles in the pathomechanisms of human neurodegenerative and inflammatory diseases; these aspects are also summarized and discussed in this review.

Mycobacterium tuberculosis, the infectious agent behind tuberculosis (TB), underscores the significance of targeting enzymes such as arabinosyltransferases in drug development efforts. Benzothiozinone derivatives, which have been assessed for their effectiveness against TB, present a promising avenue for treatment. Utilizing a high virtual screening quantitative structure–activity relationship (QSAR-VS), a set of forty Benzothiozinone (C1–C40) compounds were investigated to build a robust model with satisfactory performance metrics (R² = 0.82, R²_adj = 0.78, N_test = 10, R²_test = 0.70). This model enabled the creation of databases containing new derivatives for screening drug-like properties and predicting MIC activity in TB treatment. The best-scoring compounds were screened by molecular docking with Mycobacterium tuberculosis kinases A and B (PDB code: 6B2P) and validated by molecular dynamics simulations to elucidate the most stable drug–protein interactions. Additionally, the MM-PBSA analysis shows that the strongest binding occurs in complexes X3, X4, and X6 with ΔG_bind values of −8.2, −15.3, and −12.0 kcal/mol, respectively. Our in silico study aims to prospect these new anti-tubercular drugs and their potential development through perspective in vitro and in vivo assays.

Molecular dynamics (MD) simulations are powerful tools for studying the movement and interactions of molecules, but they can be computationally expensive, especially for large biomolecules like proteins. This is problematic because accurately simulating the motions of these molecules is key to understanding their function. Enhanced sampling methods, such as Simulated Tempering (ST), temperature Replica Exchange Molecular Dynamics (REMD) and Replica Exchange with Solute Tempering (REST and REST2), have been developed to overcome this challenge by improving the efficiency of MD simulations. This article presents a new enhanced sampling method called Simulated Solute Tempering 2 (SST2) that builds upon the strengths of ST and REST2. SST2 selectively scales the interactions inside a biomolecule and with its surrounding environment, effectively accelerating the exploration of its different structural states and their stability at various temperatures. SST2 was tested on three different systems (chignolin CLN025, Trp-Cage, and a protein-peptide complex, p97/PNGase) and found to achieve comparable or superior sampling efficiency to ST, SST1 and REST2 while requiring fewer temperature rungs. Notably, SST2 is particularly well-suited for investigating large biomolecular systems, making it a valuable tool for studying a wide range of biomolecular processes, from protein folding to ligand binding.

Human infection with the coronavirus disease 2019 (COVID-19) is mediated by the binding of the spike protein of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) to the human angiotensin-converting enzyme 2 (ACE2). The frequent mutations in the receptor-binding domain (RBD) of the spike protein induced the emergence of variants with increased contagion and can hinder vaccine efficiency. Hence, it is crucial to better understand the binding mechanisms of variant RBDs to human ACE2 and develop efficient methods to characterize this interaction. In this work, we present an approach that uses machine learning to analyze the molecular dynamics simulations of RBD variant trajectories bound to ACE2. Along with the binding free energy calculation, this method was used to characterize the major differences in ACE2-binding capacity of three SARS-CoV-2 RBD variants—namely the original Wuhan strain, Omicron BA.1, and the more recent Omicron BA.5 sublineages. Our analyses assessed the differences in binding free energy and shed light on how it affects the infectious rates of different variants. Furthermore, this approach successfully characterized key binding interactions and could be deployed as an efficient tool to predict different binding inhibitors to pave the way for new preventive and therapeutic strategies.

SARS-CoV-2 strains have made an appearance across the globe, causing over 757 million cases and over 6.85 million deaths at the time of writing. The emergence of these variants shows the amplitude of genetic variation to which the wild-type strains have been subjected. The rise of the different SARS-CoV-2 variants resulting from such genetic modification has significantly affected COVD-19's major impact on proliferation, virulence, and clinics. With the emergence of the variants of concern, the spike protein has been identified as a possible therapeutic target due to its critical role in binding to human cells and pathogenesis. These mutations could be linked to functional heterogeneity and use a different infection strategy. For example, the Omicron variant's multiple mutations should be carefully examined, as they represent one of the most widely spread strains and hint to us that there may be more genetic changes in the virus. As a result, we applied a common protocol where we reconstructed SARS-CoV-2 variants of concern and performed molecular dynamics simulations to study the stability of the ACE2-RBD complex in each variant. We also carried out free energy calculations to compare the binding and biophysical properties of the different SARS-CoV-2 variants when they interact with ACE2. Therefore, we were able to obtain consistent results and uncover new crucial residues that were essential for preserving a balance between maintaining a high affinity for ACE2 and the capacity to evade RBD-targeted antibodies. Our detailed structural analysis showed that SARS-CoV-2 variants of concern show a higher affinity for ACE2 compared to the Wuhan strain. Additionally, residues K417N and E484K/A might play a crucial role in antibody evasion, whereas Q498R and N501Y are specifically mutated to strengthen RBD affinity to ACE2 and, thereby, increase the viral effect of the COVID-19 virus.

Motivation: Peptides are molecules involved in many essential biological activities by interacting with proteins in the body. They can also be used as therapeutic molecules, particularly to disrupt protein-protein interactions involved in disease. However, it is difficult to identify potential therapeutic peptides if the structure of the protein-protein complex to be inhibited has not been solved. Results: The PepIT program was developed to propose peptides that can interact with a given protein. PepIT is based on a non-sequential alignment algorithm to identify peptide binding sites that share geometrical and physicochemical properties with a surface region of the target protein. PepIT compares the entire surface of the target protein with the peptide binding sites of the Propedia dataset, which contains more than 19,000 high-resolution protein-peptide structures. Once a peptide binding site similar to a portion of the protein surface is found, the peptide bound to the binding site is repositioned on the corresponding portion of the protein surface. Availability and implementation: The PepIT source code is freely available at https://github.com/DSIMB/pepit Supplementary information: Supplementary data are available online.

Plant-derived polyphenols flavonoids are increasingly being recognized for their medicinal potential. These bioactive compounds derived from plants are gaining more interest in ameliorating adverse health risks because of their low toxicity and few side effects. Among them, therapeutic approaches demonstrated the efficacy of catechins, a major group of flavonoids, in reverting several aspects of Down syndrome, the most common genomic disorder that causes intellectual disability. Down syndrome is characterized by increased incidence of developing Alzheimer’s disease, obesity, and subsequent metabolic disorders. In this focused review, we examine the main effects of catechins on comorbidities linked with Down syndrome. We also provide evidence of catechin effects on DYRK1A, a dosage-sensitive gene encoding a protein kinase involved in brain defects and metabolic disease associated with Down syndrome.

Insulin-degrading enzyme (IDE) is a ubiquitously expressed metallopeptidase that degrades insulin and a large panel of amyloidogenic peptides. IDE is thought to be a potential therapeutic target for type-2 diabetes and neurodegenerative diseases, such as Alzheimer’s disease. IDE catalytic chamber, known as a crypt, is formed, so that peptides can be enclosed and degraded. However, the molecular mechanism of the IDE function and peptide recognition, as well as its conformation changes, remains elusive. Our study elucidates IDE structural changes and explains how IDE conformational dynamics is important to modulate the catalytic cycle of IDE. In this aim, a free-substrate IDE crystallographic structure (PDB ID: 2JG4) was used to model a complete structure of IDE. IDE stability and flexibility were studied through molecular dynamics (MD) simulations to witness IDE conformational dynamics switching from a closed to an open state. The description of IDE structural changes was achieved by analysis of the cavity and its expansion over time. Moreover, the quasi-harmonic analysis of the hinge connecting IDE domains and the angles formed over the simulations gave more insights into IDE shifts. Overall, our results could guide toward the use of different approaches to study IDE with different substrates and inhibitors, while taking into account the conformational states resolved in our study.