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.
Siteomix is 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.
Kira M. Velieva, E. Skorb, S. Shityakov· Journal of Computer-Aided Mo...· 0 citations
Protein–protein interaction (PPI) information is distributed across resources that differ in organism coverage, identifier systems, evidence models, confidence scores and access mechanisms, so assembling and comparing evidence for a protein requires source-specific queries, identifier conversion and extensive post-processing. We present KlinkPPI, a web server that retrieves, compares and exports PPI evidence from STRING, BioGRID, IntAct, CORUM, HuRI and Predictomes from a single query. KlinkPPI accepts UniProtKB accessions, NCBI Gene and Ensembl identifiers and gene names, and performs taxonomy-aware mapping to a common identifier space. Users can query individual proteins across all resources available for an organism, or retrieve organism-wide interaction sets. Results are presented per source so that database-specific evidence, annotations and confidence values are retained, while an integrated view exposes coverage and agreement between resources. KlinkPPI deliberately does not merge heterogeneous confidence scores, nor collapse functional associations, complex co-membership, binary interactions and structural predictions into a single consensus network. Results can be exported in PSI-MI TAB 2.8-compatible or Apache Parquet format with user-selected evidence fields. KlinkPPI is freely available at https://rappsilberlab.org/KlinkPPI/ and the source code at https://github.com/Rappsilber-Laboratory/KlinkPPI. Graphical abstract
A. Lutfi, Sukrit Dang, R. Warneke et al.· bioRxiv· 0 citations
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
Understanding protein mechanisms in health and disease requires characterizing the functional roles of individual amino acid residues. To explore the role of residues and their mutations, we have developed Atlantis, a database that integrates structural and functional information at the human proteome residue level. A graph database enables complex queries and the retrieval of integrated information for multiple functional analysis of protein systems. A Model Context Protocol (MCP) connector allows the interrogation of the resource through Large Language Models (LLMs) or agentic frameworks for biomedical research. Atlantis annotates over 11M residues across 20k human proteins, identifying hundreds thousands intra- and inter-protein contacts in PDB as well as AlphaFoldDB structures. We also provide the possibility to analyze and integrate predicted 3D complexes inputted by the user, and we showcased these features on hundreds of AlphaFold-multimer complexes of GPCRs and LRRK2 interaction networks. The tool is freely accessible at https://atlantis.bioinfolab.sns.it/. GRAPHICAL ABSTRACT
Natalia De Oliveira Rosa, Piergiorgio Ferronato, M. Varisco et al.· bioRxiv· 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
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
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