PyMOL plugin for Protein Circuit Topology

Pharmacon: A Molecular Dynamics Simulation Analysis Toolkit

We present the Pharmacon toolkit, a pure Python command-line software package for analyzing molecular dynamics (MD) simulations of biomacromolecules. Pharmacon supports trajectories generated by widely used MD simulation engines, such as Amber, Gromacs, CHARMM, NAMD, and OpenMM. Pharmacon is written entirely in Python and uses popular third-party libraries, including MDAnalysis for trajectory handling and NumPy for data processing. The toolkit provides a streamlined, automated workflow that simplifies the routine analysis and postprocessing of MD simulation results of biomacromolecular complexes, making them more accessible and reproducible by aggregating many tedious and time-consuming tasks into a single command-line workflow. As a proof of concept, we applied Pharmacon to analyze, in comparison with other toolkits, results from MD simulations, including intermolecular interactions and geometric measures, across different test protein complexes, including three membrane proteins and one soluble protein complex.

Molecular Architecture and Energy Visualization: Advancing Insights into Protein Structure, Interactions, and Activity.

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.

DynDom1D_Python: An Open-Source Python tool for the analysis of domain movements in proteins

Here we introduce DynDom1D_Python, an open-source Python implementation based on the original DynDom Fortran program for the analysis of domain movements in proteins. DynDom works on a single protein chain and can be used when two structures of the same protein are available representing a conformational change. If appropriate, it describes the conformational change in terms of the relative rotation of quasi-rigid regions called “dynamic domains” by way of hinge axes (more precisely, interdomain screw axes) and hinge-bending residues (more precisely, interdomain bending residues). This new implementation improves on the original standalone version in a number of ways.

DirectContacts2: a wiring diagram of human physical protein interactions

Cellular function is driven by the activity of proteins in stable complexes. Protein complex assembly depends on the direct physical association of component proteins. Advances in macromolecular structure prediction with tools like AlphaFold and RoseTTAFold have greatly improved our ability to model these interactions in silico, but an all-by-all analysis of the human proteome’s ~200 M possible pairs remains computationally intractable. A comprehensive cellular map of direct protein interactions will therefore be an invaluable resource to direct screening efforts. Here, we present DirectContacts2, a machine learning model that distinguishes direct from indirect protein interactions using features derived from over 25,000 mass spectrometry experiments. Applied to ~25 million human protein pairs, our model outperforms previous resources in identifying direct physical interactions and enriches for accurate structural models including ~2500 AlphaFold3 models. Our framework enables structural modeling of disease-relevant complexes (e.g. orofacial digital syndrome (OFDS) complex) offering insights into the molecular consequences of pathogenic mutations (OFD1) and broadly, establishes a highly accurate protein wiring diagram of the cell. Knowledge of the physical interactions of proteins provides mechanistic understanding of their function. Here, the authors develop a machine learning classifier through the integration of 25,000 proteomics experiments to construct a wiring diagram of human cells.

Rep3D : an algorithm to identify structurally similar motifs

Repeats in protein structures act as essential structural building blocks, commonly forming multiple complex structures and functional units. Identifying sequence repeats in the primary structure alone is not sufficient to find the function of the proteins. Therefore, a new method, repeats in the three-dimensional structure of proteins ( Rep3D ), has been developed using a dynamic programming approach. This method enables rapid and accurate identification of structural repeats by calculating the distance between Cα atoms in the peptide backbone. A standalone computing version of the tool has been developed and implemented in Python. The Rep3D source code, along with documentation, examples of use and instructions, is available in the Rep3D repository (https://github.com/srimaha0801/Rep3d-Stand_Alone_v1/).