MoleQCage: Geometric High-Throughput Screening for Molecular Caging Prediction.

Although being able to determine whether a host molecule can enclose a guest molecule and form a caging complex could benefit numerous chemical and medical applications, the experimental discovery of molecular caging complexes has not yet been achieved at scale. Here, we propose MoleQCage, a simple tool for the high-throughput screening of host and guest candidates based on an efficient robotics-inspired geometric algorithm for molecular caging prediction, providing theoretical guarantees and robustness assessment. MoleQCage is distributed as Linux-based software with a graphical user interface and is available online at https://hub.

EnGens: a computational framework for generation and analysis of representative protein conformational ensembles.

Proteins are dynamic macromolecules that perform vital functions in cells. A protein structure determines its function, but this structure is not static, as proteins change their conformation to achieve various functions. Understanding the conformational landscapes of proteins is essential to understand their mechanism of action.

EnGens: a computational framework for generation and analysis of representative protein conformational ensembles.

Proteins are dynamic macromolecules that perform vital functions in cells. A protein structure determines its function, but this structure is not static, as proteins change their conformation to achieve various functions. Understanding the conformational landscapes of proteins is essential to understand their mechanism of action.

3pHLA-score improves structure-based peptide-HLA binding affinity prediction.

Binding of peptides to Human Leukocyte Antigen (HLA) receptors is a prerequisite for triggering immune response. Estimating peptide-HLA (pHLA) binding is crucial for peptide vaccine target identification and epitope discovery pipelines. Computational methods for binding affinity prediction can accelerate these pipelines.

Computational Modeling of Molecular Structures Guided by Hydrogen-Exchange Data.

Data produced by hydrogen-exchange monitoring experiments have been used in structural studies of molecules for several decades. Despite uncertainties about the structural determinants of hydrogen exchange itself, such data have successfully helped guide the structural modeling of challenging molecular systems, such as membrane proteins or large macromolecular complexes. As hydrogen-exchange monitoring provides information on the dynamics of molecules in solution, it can complement other experimental techniques in so-called integrative modeling approaches.

DINC-COVID: A webserver for ensemble docking with flexible SARS-CoV-2 proteins.

An unprecedented research effort has been undertaken in response to the ongoing COVID-19 pandemic. This has included the determination of hundreds of crystallographic structures of SARS-CoV-2 proteins, and numerous virtual screening projects searching large compound libraries for potential drug inhibitors. Unfortunately, these initiatives have had very limited success in producing effective inhibitors against SARS-CoV-2 proteins.

DINC-COVID: A webserver for ensemble docking with flexible SARS-CoV-2 proteins.

Motivation: Recent efforts to computationally identify inhibitors for SARS-CoV-2 proteins have largely ignored the issue of receptor flexibility. We have implemented a computational tool for ensemble docking with the SARS-CoV-2 proteins, including the main protease (Mpro), papain-like protease (PLpro) and RNA-dependent RNA polymerase (RdRp).

Results: Ensembles of other SARS-CoV-2 proteins are being prepared and made available through a user-friendly docking interface.

Computational analysis of complement inhibitor compstatin using molecular dynamics.

The complement system plays a major role in human immunity, but its abnormal activation can have severe pathological impacts. By mimicking a natural mechanism of complement regulation, the small peptide compstatin has proven to be a very promising complement inhibitor. Over the years, several compstatin analogs have been created, with improved inhibitory potency.

HLA-Arena: A Customizable Environment for the Structural Modeling and Analysis of Peptide-HLA Complexes for Cancer Immunotherapy.

Purpose: HLA protein receptors play a key role in cellular immunity. They bind intracellular peptides and display them for recognition by T-cell lymphocytes. Because T-cell activation is partially driven by structural features of these peptide-HLA complexes, their structural modeling and analysis are becoming central components of cancer immunotherapy projects.

A Robotics-Inspired Screening Algorithm for Molecular Caging Prediction.

We define a as a pair of molecules in which one molecule (the "host" or "cage") possesses a cavity that can encapsulate the other molecule (the "guest") and prevent it from escaping. Molecular caging complexes can be useful in applications such as molecular shape sorting, drug delivery, and molecular immobilization in materials science, to name just a few. However, the design and computational discovery of new caging complexes is a challenging task, as it is hard to predict whether one molecule can encapsulate another because their shapes can be quite complex.

Using parallelized incremental meta-docking can solve the conformational sampling issue when docking large ligands to proteins.

Background: Docking large ligands, and especially peptides, to protein receptors is still considered a challenge in computational structural biology. Besides the issue of accurately scoring the binding modes of a protein-ligand complex produced by a molecular docking tool, the conformational sampling of a large ligand is also often considered a challenge because of its underlying combinatorial complexity. In this study, we evaluate the impact of using parallelized and incremental paradigms on the accuracy and performance of conformational sampling when docking large ligands.

Native State of Complement Protein C3d Analysed via Hydrogen Exchange and Conformational Sampling.

Hydrogen/deuterium exchange detected by mass spectrometry (HDXMS) provides valuable information on protein structure and dynamics. Although HDX-MS data is often interpreted using crystal structures, it was suggested that conformational ensembles produced by molecular dynamics simulations yield more accurate interpretations. In this paper, we analyse the complement protein C3d by performing an HDX-MS experiment, and evaluate several interpretation methodologies using an existing prediction model to derive HDX-MS data from protein structure.

Structure-based Methods for Binding Mode and Binding Affinity Prediction for Peptide-MHC Complexes.

Understanding the mechanisms involved in the activation of an immune response is essential to many fields in human health, including vaccine development and personalized cancer immunotherapy. A central step in the activation of the adaptive immune response is the recognition, by T-cell lymphocytes, of peptides displayed by a special type of receptor known as Major Histocompatibility Complex (MHC). Considering the key role of MHC receptors in T-cell activation, the computational prediction of peptide binding to MHC has been an important goal for many immunological applications.

Revealing Unknown Protein Structures Using Computational Conformational Sampling Guided by Experimental Hydrogen-Exchange Data.

Both experimental and computational methods are available to gather information about a protein's conformational space and interpret changes in protein structure. However, experimentally observing and computationally modeling large proteins remain critical challenges for structural biology. Our work aims at addressing these challenges by combining computational and experimental techniques relying on each other to overcome their respective limitations.

General Prediction of Peptide-MHC Binding Modes Using Incremental Docking: A Proof of Concept.

The class I major histocompatibility complex (MHC) is capable of binding peptides derived from intracellular proteins and displaying them at the cell surface. The recognition of these peptide-MHC (pMHC) complexes by T-cells is the cornerstone of cellular immunity, enabling the elimination of infected or tumoral cells. T-cell-based immunotherapies against cancer, which leverage this mechanism, can greatly benefit from structural analyses of pMHC complexes.

DINC 2.0: A New Protein-Peptide Docking Webserver Using an Incremental Approach.

Molecular docking is a standard computational approach to predict binding modes of protein-ligand complexes by exploring alternative orientations and conformations of the ligand (i.e., by exploring ligand flexibility).

Coarse-Grained Conformational Sampling of Protein Structure Improves the Fit to Experimental Hydrogen-Exchange Data.

Monitoring hydrogen/deuterium exchange (HDX) undergone by a protein in solution produces experimental data that translates into valuable information about the protein's structure. Data produced by HDX experiments is often interpreted using a crystal structure of the protein, when available. However, it has been shown that the correspondence between experimental HDX data and crystal structures is often not satisfactory.

Defining Low-Dimensional Projections to Guide Protein Conformational Sampling.

Exploring the conformational space of proteins is critical to characterize their functions. Numerous methods have been proposed to sample a protein's conformational space, including techniques developed in the field of robotics and known as sampling-based motion-planning algorithms (or sampling-based planners). However, these algorithms suffer from the curse of dimensionality when applied to large proteins.

Understanding the challenges of protein flexibility in drug design.

Introduction: Protein-ligand interactions play key roles in various metabolic pathways, and the proteins involved in these interactions represent major targets for drug discovery. Molecular docking is widely used to predict the structure of protein-ligand complexes, and protein flexibility stands out as one of the most important and challenging issues for binding mode prediction. Various docking methods accounting for protein flexibility have been proposed, tackling problems of ever-increasing dimensionality.

Characterizing Energy Landscapes of Peptides Using a Combination of Stochastic Algorithms.

Obtaining accurate representations of energy landscapes of biomolecules such as proteins and peptides is central to the study of their physicochemical properties and biological functions. Peptides are particularly interesting, as they exploit structural flexibility to modulate their biological function. Despite their small size, peptide modeling remains challenging due to the complexity of the energy landscape of such highly-flexible dynamic systems.

MoMA-LigPath: a web server to simulate protein-ligand unbinding.

Protein-ligand interactions taking place far away from the active site, during ligand binding or release, may determine molecular specificity and activity. However, obtaining information about these interactions with experimental or computational methods remains difficult. The computational tool presented in this article, MoMA-LigPath, is based on a mechanistic representation of the molecular system, considering partial flexibility, and on the application of a robotics-inspired algorithm to explore the conformational space.

An individual-based evolving predator-prey ecosystem simulation using a fuzzy cognitive map as the behavior model.

We present an individual-based predator-prey model with, for the first time, each agent behavior being modeled by a fuzzy cognitive map (FCM), allowing the evolution of the agent behavior through the epochs of the simulation. The FCM enables the agent to evaluate its environment (e.g.