Aug 2026· Journal of Chemical Information and Modeling· Vol 66 16, pp.
10277-10286
· 0 citations· 39 references
Medicine
TL;DR
ProtLID2.0 is presented, a residue-specific pharmacophore framework that generates coordinate-based interaction preferences from molecular dynamics simulations of single-residue probes at receptor interfaces and uses these preferences to score docked complexes and identify cognate ligands.
Abstract
Predicting biologically relevant protein-protein interactions remains a major challenge in structural biology. Here, we present ProtLID2.0, a residue-specific pharmacophore framework that generates coordinate-based interaction preferences from molecular dynamics simulations of single-residue probes at receptor interfaces and uses these preferences to score docked complexes and identify cognate ligands. Benchmarking across 30 receptor systems and multiple docking methods demonstrated that ProtLID2.0 performs comparably to or better than HADDOCK for identifying near-native poses and ranking cognate ligands from decoy sets, while combining both scoring functions consistently improved performance. These results establish ProtLID2.0 as a robust and complementary scoring framework for protein-protein docking and ligand identification.
COACH-D 2.0 is introduced, a substantially enhanced template-based method for predicting protein-ligand binding sites and features three key advances: integration of multimeric templates from Q-BioLiP into the authors' in-house library, a new multimeric structure processing module enabling binding site prediction for protein complexes, and an efficient template screening strategy that significantly boosts both prediction speed and accuracy.
Xiao-Yu An, Hong Wei, Wenkai Wang et al.· Genomics, Proteomics & Bioin...· 0 citations
The fragment molecular orbital (FMO) quantum mechanics method offers a comprehensive and computationally inexpensive means of identifying the strength and the chemical nature of the molecular interactions taking place at the protein-protein interface of large biomolecular systems.
Dustin C. Woods, Stefania Monteleone, Stéphanie Labouille· Methods in molecular biology· 0 citations
The interface prediction program WHISCY is presented, which combines surface conservation and structural information to predict protein–protein interfaces and demonstrates the potential of using interface predictions to drive protein–protein docking.
S. D. de Vries, A. V. van Dijk, A. M. J. J. Bonvin· 0 citations
HydroDock is extended to challenging targets with open binding pockets, populated by highly mobile water molecules that are the most troublesome for drug design, and improved docking accuracy with increases of 28% (10%) in median structural (and ranking) performance.
Bayar Bayarsaikhan, C. Hetényi, B. Z. Zsidó· ACS Physical Chemistry Au· 0 citations
Summary Prot2Surf is a software tool designed for the characterization and prediction of protein association to surfaces. In this application note, Prot2Surf was tested using catalytic domains of the lytic polysaccharide monooxygenases (LPMOs), interacting with native surfaces. The results show that the software can efficiently analyze key binding features, including protein–surface distances, distances between catalytically reactive atoms, and the orientation angle between surface chains and the protein. These features are essential for distinguishing productive binding poses in these protein–surface systems and for understanding interaction patterns that provide guidance on protein engineering. Prot2Surf performs these analyses within seconds to a few minutes, providing a fast and accessible framework to post-process and characterize protein-surface encounter complexes. Availability and implementation Prot2Surf, which is written in Fortran90, is documented and freely available as open source on GitHub: https://github.com/TUNNELING-GROUP/Prot2Surf. In order to run Prot2Surf, users should also install the SDA software package which is freely available at https://www.h-its.org/downloads/sda7/.
Abraham Muñiz-Chicharro, Gamze Tanriver, Artur Góra· bioRxiv· 0 citations
AlphaFold 3 (AF3) and Boltz-2 are state-of-the-art AI-based tools for biomolecular structure prediction, but whether their predictions provide useful guidance for lead optimization, SAR interpretation, and virtual screening remains insufficiently characterized. We benchmarked their performance using newly determined soluble epoxide hydrolase co-crystal structures and matched activity data together with a curated post-training-cutoff dataset spanning kinases, allosteric modulators, covalent systems, PROTACs, molecular glues, fragments, membrane proteins, RNA binders, and activity-cliff pairs. Both models recovered canonical orthosteric enzyme and kinase complexes, including key DFG/αC conformational states, whereas allosteric, membrane-protein, and induced-proximity complexes remained challenging. Pharmacophore RMSD was often lower than overall ligand RMSD, indicating preservation of key recognition features despite imperfect whole-ligand alignment. AF3 minPAE correlated with pose accuracy, and very low minPAE values (<0.85 Å) were strongly enriched for accurate poses. Model confidence scores were not associated with experimental activity, whereas Boltz-2 predicted affinity captured relative activity trends and distinguished the activity-cliff pair, although its performance varied across ligand series.
Ke Chen, Zuo-Huang Qi, Omar Lozano Ramos et al.· bioRxiv· 0 citations
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