COACH-D 2.0: A Server for Template-based Modeling of Protein-ligand Interactions.

Does Surface Conservation Yield? Application to Data-Driven Docking

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.

ProtLID2.0: A Residue-Specific Pharmacophore Framework for Protein–Protein Docking

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.

Siteomix: a PyMOL plugin for the detection, alignment, and similarity analysis of protein‒ligand binding sites

The analysis of structural similarity among protein active sites is fundamental to understanding functional relationships between proteins and plays a critical role in structure-based drug design. Detailed characterization of protein‒ligand binding site similarity requires not only fast and accurate computational workflows but also effective platforms for visualizing aligned binding sites and interpreting similarity relationships at the molecular level. Here, we present Siteomix, an integrated plugin for the PyMOL molecular graphics system that automates the detection of binding pockets via the LIGSITE algorithm, visualizes them as discrete point clouds colored by cavity depth, and performs a two-step alignment combining the rigid iterative closest point (ICP) algorithm with differential evolution optimization. The plugin quantifies binding site similarity through three complementary metrics: normalized volumetric overlap (equivalent to the Tanimoto coefficient), the mean nearest neighbor distance after ICP alignment, and root-mean-square deviation after refined alignment, enabling researchers to distinguish global shape complementarity from local geometric congruence. By embedding the entire comparison pipeline within an interactive three-dimensional PyMOL environment, Siteomix facilitates visual assessment of aligned binding sites and supports structure-based drug discovery applications. The plugin is implemented in Python, works on all major operating systems (Windows, macOS, Linux), and is freely available with its source code at https://github.com/KiraVelieva-CH/Siteomix-Plugin.

In Silico Laboratory: New and Updated Tools for Protein-Centered Drug Discovery.

PandaMap: A Python Package for Comprehensive Visualization of Protein–Ligand Interaction Networks

Protein–ligand interaction diagrams are a routine part of structural and medicinal chemistry, but the tools that produce them tend to force a choice: comprehensive detection with tabular output, publication-quality figures behind a licence, or a scripting environment that assumes expertise. PandaMap (Protein AND ligAnd interaction MAPper) is an open-source Python package that produces a 2D interaction diagram, an interactive 3D viewer, a text report, a machine-readable CSV, and a four-panel graphical summary from a single command. It reads PDB, mmCIF and PDBQT files, detects 15 interaction classes using crystallographically validated distance thresholds, and depends only on NumPy, Matplotlib, BioPython and Requests; RDKit improves the 2D ligand layout when present but is not required. Hydrogen bonds are filtered on the true D–H· · · A angle when the structure contains explicit hydrogens, matching PLIP’s 100◦ criterion on the same evidence, and on distance alone otherwise, with the provenance of each measurement recorded. We benchmarked the package on three complexes chosen for different chemistry: enolase with a phosphonate transition-state analogue (PDB 1ELS), the EGFR kinase with erlotinib (1M17), and aldose reductase with IDD594 (1US0). PandaMap recovers the contacts these structures are known for, including the EGFR hinge hydrogen bond to MET769 and the IDD594 bromine· · · THR113 halogen bond, both at distances identical to PLIP’s. All detection thresholds, scoring weights and the exact commands used are given in the Supplementary Information, and the release carries a regression suite covering each interaction class. PandaMap 4.3.0 is available on PyPI under the MIT licence.

Mavchen-1: A Conformational Ensemble Platform for Protein–Ligand Pose Prediction That Substantially Outperforms Static Structure Prediction in a Category-Stratified Benchmark

A category-stratified, statistically powered benchmark comparing pose prediction from receptor conformational ensembles against AlphaFold2, used as a matched static-structure baseline, across 29 protein–ligand systems spanning cryptic-pocket, induced-fit, water-mediated, and autoimmune-indication target classes is presented.