Broaching Digital Twin to Predict Forces, Local Overloads, and Surface Topography Irregularities.

Broaching is a key manufacturing process that directly influences the surface integrity of critical components, impacting their functional performance in sectors such as aeronautics, automotive, and energy. Such components are subjected to severe conditions, including high thermomechanical loads, fatigue, and corrosion. For this reason, the development of predictive models is essential for determining the optimal tool design and machining conditions to ensure proper in-service performance.

Case Report: Severe Eosinophilic Asthma Associated With ANCA-Negative EGPA in a Young Adult Successfully Treated With Benralizumab.

Traditionally, Eosinophilic Granulomatosis with Polyangiitis (EGPA) has been treated with systemic corticosteroids and immunosuppressants. In recent years, therapeutic efforts have been directed towards targeting eosinophils which represent a major player in the pathogenesis of EGPA. In 2017 the Food and Drug Administration (FDA) approved mepolizumab, a humanized monoclonal antibody targeting interleukin 5 (IL-5) which reduces the production and survival of eosinophils, already used to treat severe eosinophilic asthma, for the management of EGPA.

aquaPELE: A Monte Carlo-Based Algorithm to Sample the Effects of Buried Water Molecules in Proteins.

Water is frequently found inside proteins, carrying out important roles in catalytic reactions or molecular recognition tasks. Therefore, computational models that aim to study protein-ligand interactions usually have to include water effects through explicit or implicit approaches to obtain reliable results. While full explicit models might be too computationally daunting for some applications, implicit models are normally faster but omit some of the most important contributions of water.

Monte Carlo simulations using PELE to identify a protein-protein inhibitor binding site and pose.

binding site location and pose prediction for a molecule targeted at a large protein surface is a challenging task. We report a blind test with two peptidomimetic molecules that bind the flu virus hemagglutinin (HA) surface antigen, JNJ7918 and JNJ4796 (recently disclosed in van Dongen , , 2019, ). Tests with a series of conventional approaches such as rigid (receptor) docking against available X-ray crystal structures or against an ensemble of structures generated by quick methodologies (NMA, homology modeling) gave mixed results, due to the shallowness and flexibility of the binding site and the sheer size of the target.

FragPELE: Dynamic Ligand Growing within a Binding Site. A Novel Tool for Hit-To-Lead Drug Design.

The early stages of drug discovery rely on hit-to-lead programs, where initial hits undergo partial optimization to improve binding affinities for their biological target. This is an expensive and time-consuming process, requiring multiple iterations of trial and error designs, an ideal scenario for applying computer simulation. However, most state-of-the-art modeling techniques fail to provide a fast and reliable answer to the Induced-Fit protein-ligand problem.

PELE-MSM: A Monte Carlo Based Protocol for the Estimation of Absolute Binding Free Energies.

In this study, we present a fully automatic platform based on our Monte Carlo algorithm, the Protein Energy Landscape Exploration method (PELE), for the estimation of absolute protein-ligand binding free energies, one of the most significant challenges in computer aided drug design. Based on a ligand pathway approach, an initial short enhanced sampling simulation is performed to identify reasonable starting positions for more extended sampling. This stepwise approach allows for a significant faster convergence of the free energy estimation using the Markov State Model (MSM) technique.

Extensive benchmark of rDock as a peptide-protein docking tool.

Peptide-protein interactions are ubiquitous in living cells and essential to a wide range of biological processes, as well as pathologies such as cancer or cardiovascular disease. Yet, obtaining reliable binding mode predictions in peptide-protein docking remains a great challenge for most computational docking programs. The main goal of this study was to assess the performance of the small molecule docking program rDock in comparison to other widely used small molecule docking programs, using 100 peptide-protein systems with peptides ranging from 2 to 12 residues.