Search · four archives
Search · four archives
11 papers · ranked by Valyu relevance
Authors not listed
The Protein Data Bank (PDB) is one of the richest open‑source repositories in biology, housing over 277,000 macromolecular structural models alongside much of the experimental data that underpins these models. By systematically collecting, validating, and indexing these models, the PDB has accelerated structural…
Pavel Kohout, Michal Vasina, Marika Majerova, Veronika Novakova + 5 more
Enzymes play a crucial role in sustainable industrial applications, with their optimization posing a formidable challenge due to the intricate interplay among residues. Computational methodologies predominantly rely on evolutionary insights, leveraging homologous sequences to pinpoint conserved and functionally…
Authors not listed
Proteochemometric models (PCM) are used in computational drug discovery to leverage both protein and ligand representations for bioactivity prediction. While machine learning (ML) and deep learning (DL) have come to dominate PCMs, often serving as scoring functions, rigorous evaluation standards have not always been…
Joseph Redshaw, Darren Ting, Alex Brown, Jonathan Hirst + 1 more
Antimicrobial peptides (AMPs) represent a potential solution to the growing problem of antimicrobial resistance, yet their identification through wet-lab experiments is a costly and timeconsuming process. Accurate computational predictions would allow rapid in silico screening of candidate AMPs, thereby accelerating…
Authors not listed
Generative deep-learning models have demonstrated significant potential in designing drug-like molecules. However, medicinal chemistry typically requires generating analogues that combine structural similarity with scaffold hopping—the replacement of molecular scaffolds while retaining biological relevance. To address…
Babu Bassa
In this communication the author describes a software tool named "ChameleonSort". The software program, developed by the present author is useful in the sorting of biological sequence variants like those accumulating mutations while diverging from the common ancestors. Examples include viral protein variants, protein…
Authors not listed
Recent advances in structural biology have led to the publica- tion of a wealth of high resolution x-ray crystallography and cryo-EM macromolecule structures, including many complexes with small molecules of interest for drug design. While it is com- mon to incorporate information from the atomic coordinates of these…
Authors not listed
Pose prediction of ligands to proteins remains a central challenge of structure-based drug design. Although data leakage and generalizability concerns remain, data-driven methods for pose prediction (i.e. based on deep learning and diffusion) now routinely outperform traditional techniques such as molecular docking. In…
Victor Daniel Aldas-Bulos, Fabien Plisson
Machine learning models provide an informed and efficient strategy to create novel peptide and protein sequences with the desired profiles. Nevertheless, they are primarily trained on sequences where the tridimensional structures of peptides and proteins are often overlooked. We need a fast and reliable approach to…
Authors not listed
Cyclic peptides become attractive therapeutic candidates due to their diverse biological activities. However, existing deep learning-based sequence design models, such as ProteinMPNN, are primarily optimized using cross-entropy loss and often overlook the unique topological constraints of cyclic peptides. This limits…
Theresa Ramelot, Roberto Tejero, Gaetano Montelione
Biomolecules exhibit dynamic behavior that single-state models of their structures cannot fully capture. We review some recent advances for investigating multiple conformations of biomolecules, including experimental methods, molecular dynamics simulations, and machine learning. We also address the challenges…