Aug 2026· Journal of Computer-Aided Molecular Design· Vol 40· 0 citations· 33 references
Medicine
TL;DR
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.
Abstract
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.
A program with the DockAnalyzer graphical interface has been developed to automate the analysis of molecular docking results in CIF format. The need for such a tool is caused by the exponential growth of data during virtual screening, when manual processing of a large number of files becomes impossible, and existing solutions require complex configuration or transfer of confidential structures to external servers. The aim of the work is to create a lightweight desktop application for local analysis and visualization of intermolecular interactions in protein-ligand complexes.
The program is implemented in Python using the GEMMI, NumPy, and Tkinter libraries. The architecture includes modules for parsing CIF files, geometric classification of contacts (hydrogen bonds, hydrophobic interactions, flares) and an interactive table with color coding of bond types. Additionally, integration with the P2Rank tool for predicting ligand-binding pockets is implemented, which allows comparing calculated contacts with predicted binding regions.
Testing on the reference complex of HIV-1 protease with an inhibitor confirmed the correctness of the operation: 470 contacts were automatically identified, the distribution by type of interactions and the remnants of the binding site coincided with those annotated in the PDB. A comparative analysis with analogues (PLIP, Arpeggio, BINANA 2) showed that the advantages of DockAnalyzer are visual visualization of data directly in the contact table, the absence of dependencies on external servers, and confidentiality of processing due to local startup. The program exports the results to TXT and CSV formats for further statistical processing. The modular architecture makes expanded functionality possible, including support for new file formats and machine learning methods for predicting binding pockets.
Igor Ananchenko, R. Sakhabeev, Ivan Melnikov et al.· Bulletin of the Saint Peters...· 0 citations
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.
BACKGROUND
Protein sequence and structure similarity-based search is an important task, which underpins protein annotation, evolutionary analysis, large-scale functional inference, and the exploration of the protein "dark space". The rapid growth of sequence and predicted structure databases has spurred diverse search methods, yet their evaluation remains limited to fold-level similarity and inconsistent benchmarking protocols.
RESULTS
We present a comprehensive benchmark for protein sequence and structure search. Using this framework, we evaluate 14 representative methods spanning sequence alignment, structure alignment, and representation-based approaches across multiple biologically relevant scenarios. Our results show pronounced and context-dependent differences among methods. Structure alignment methods excel at detecting fold-level and geometric similarity, while representation-based searching approaches show advantages in capturing functional similarity under low sequence identity and robustness to predicted structures. Notably, all evaluated methods show limited effectiveness on intrinsically disordered proteins.
CONCLUSIONS
This benchmark establishes a standardized framework for evaluating protein similarity search methods, providing a practical resource for method selection and a foundation for the development of next-generation approaches capable of addressing diverse homology search challenges.
Yuan Liu, Yingquan Zhou, Yan Huang et al.· Genome Biology· 1 citation
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
Accurate identification of near-native ligand binding poses is a central challenge in structure-based drug design. From a physical point of view, the successful construction of a protein-ligand complex structure is dependent on whether protein and ligand can form enough atomically pairwise interactions that result in a global energy minimum. In this work, we report a machine learning scoring strategy for protein-ligand screening which explicitly considers the Native Contact Ratio (NCR), a topology inspired metric that quantifies the preservation of protein-ligand interfacial contacts as well as interaction energy. This physics-awared supervision strategy provides a simple but efficient gradient field that faithfully reflects the complicated protein energy landscape than conventional 3D coordinate-based objectives. Building on this principle, we present DeepNCR, an energy-informed Transformer framework that encodes approximate Coulombic and dispersive interaction potentials across the protein-ligand binding interface. Furthermore, we introduce a feature pruning step that compresses the interaction tensor from 1470 to 868 dimensions, further improving signal-to-noise ratio and directing model attention toward the interaction motifs critical for binding specificity. The model optimizes topological objectives and at inference drives pose refinement through a differentiable hybrid gradient field integrating predicted NCR and AutoDock Vina energetics. Extensive evaluation on the CASF-2016 benchmark and the 3D-DISCO cross-docking data set demonstrates consistently high performance: a Top-1 docking success rate of 94.7%, a 1% Enrichment Factor of 21.21 in virtual screening, and a Top-1 cross-docking success rate of 34.8%. Mechanistic analysis reveals that NCR-guided optimization enables decoy escaping from local energy minima and drives the recovery of disrupted native interactions, confirming that NCR captures the physical determinants of binding rather than mere geometric proximity.
Zhen-Qiang Zhang, Zhihao Wang, Yang Liu et al.· Journal of Chemical Informat...· 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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