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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.

S. D. de Vries, A. V. van Dijk, A. M. J. J. Bonvin · 0 citations
Open access Jul 2026

Integrating AlphaFold2 with physics-based ensemble docking for high-efficiency nanobody discovery

A computational framework that progressed from next-generation sequencing (NGS)derived candidate nanobody sequences to predicted structures using AlphaFold2, and prioritized nanobodies based on predicted binding energy scores supported the practical utility of the framework for enriching functional nanobodies from experimentally pre-enriched NGS-derived pools.

Yinghao Guo, Renfang Guan, Lunde Jin et al. · 0 citations
Open access Jul 2026

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

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. · 0 citations
Open access Aug 2026

HDOCK-Multimer: integrating docking and combinatorial assembly for structure prediction of large protein complexes

Deep learning methods, such as AlphaFold and RosettaFold, achieve high accuracy in protein structure prediction. However, predicting the structure of large protein complexes remains challenging due to their large size and intricate multi-chain interactions. Docking-based methods can handle large proteins, but are limited by the huge combinatorial binding space of multichains. Assembly-based approaches offer an alternative, but their accuracy critically relies on the precision of predicted subcomponents. Addressing the challenges, we propose HDOCK-Multimer (HDM), a structure prediction framework of large protein complexes by integrating ab initio docking and combinatorial assembly. HDM can efficiently reduce reliance on subcom-ponent accuracy through docking process, while leveraging the pairwise interactions of subcom-ponents through assembly strategy. HDM is extensively validated on three benchmarks of 35 large heteromeric complexes, 172 large protein complexes, and 7 CASP15 targets, and compared with state-of-the-art methods including MoLPC, CombFold, AlphaFold-Multimer (AFM), and AlphaFold3 (AF3). It is shown that HDOCK-Multimer substantially outperforms the other methods. In addition, HDM also shows ability to predict the stoichiometry and model the complex without stoichiometry input. It is anticipated that HDM will serve as a powerful tool for studying large protein complexes or molecular machines. The HDM package is freely available at https://github.com/huang-laboratory/HDOCK-Multimer/.

Xuan Yao, Yifan Ya, Hao Li et al. · 0 citations
Open access Aug 2026

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.

P. Panda · 0 citations
Open access Aug 2026

FlexAutoDock: A Flexible Platform for Automated Molecular Docking and Virtual Screening of Natural and Synthetic Compounds

FlexAutoDock is an automated cloud-based molecular docking platform that provides a unified environment for protein-ligand docking and large-scale virtual screening, providing researchers with an accessible computational resource for accelerating early-stage drug discovery.

Md. Feroj Ahmed, M. Faysal, Khalid Muntasir Sawad et al. · 0 citations

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