pyKVFinder: an efficient and integrable Python package for biomolecular cavity detection and characterization in data science

Abstract

Background Biomolecular interactions that modulate biological processes occur mainly in cavities throughout the surface of biomolecular structures. In the data science era, structural biology has benefited from the increasing availability of biostructural data due to advances in structural determination and computational methods. In this scenario, data-intensive cavity analysis demands efficient scripting routines built on easily manipulated data structures. To fulfill this need, we developed pyKVFinder, a Python package to detect and characterize cavities in biomolecular structures for data science and automated pipelines. Results pyKVFinder efficiently detects cavities in biomolecular structures and computes their volume, area, depth and hydropathy, storing these cavity properties in NumPy arrays. Benefited from Python ecosystem interoperability and data structures, pyKVFinder can be integrated with third-party scientific packages and libraries for mathematical calculations, machine learning and 3D visualization in automated workflows. As proof of pyKVFinder’s capabilities, we successfully identified and compared ADRP substrate-binding site of SARS-CoV-2 and a set of homologous proteins with pyKVFinder, showing its integrability with data science packages such as matplotlib, NGL Viewer, SciPy and Jupyter notebook. Conclusions We introduce an efficient, highly versatile and easily integrable software for detecting and characterizing biomolecular cavities in data science applications and automated protocols. pyKVFinder facilitates biostructural data analysis with scripting routines in the Python ecosystem and can be building blocks for data science and drug design applications.

Structural and functional features of a broad-spectrum prophage-encoded enzybiotic from Enterococcus faecium

“…The algorithm pyKVFider 51 was used to map the predicted peptidoglycan-binding cavity and the amino acids that are in its vicinity (Fig. 7 E).…”

“…COACH is a meta-server approach that combines multiple function annotation results (ligand-binding sites) from the COFACTOR, TM-SITE and S-SITE programs 35 , 36 , 71 . The python package pyKVFider 51 was used to map the peptidoglycan-binding cavity and the amino acids that are in its vicinity. The structural analysis was performed using PyMOL 48 , UCSF Chimera 46 , PDBsum web server 47 , and LigPlot + v.2.2 55 .…”

Variability of Phenylalanine side chain conformations facilitates promiscuity of Fatty acid binding in Cockroach milk proteins

“…In the Lili-Mip1 structure, we found that Phe-98 and Phe-100 have moved upwards, causing a decrease in the size of the ligand binding pocket (Figure 4A-D). To better understand how the ligand binding pocket size changes between structures, we quantitated the cavity volumes of different Lili-Mip using parKVfinder [46]. This is seen better in contrast with Lili-Mip2, where the Phe-98 and Phe-100 face downwards, increasing the cavity volume (Figure 4E-G).…”

Cavity Characterization in Supramolecular Cages

“…However, KVFinder software, originally published in 2014, is deprecated, and the KVFinder project is currently composed of two independent software: Parallel KVFinder (parKVFinder) 21 and Python-C Parallel KVFinder (pyKVFinder). 15 parKVFinder is available with an easy-to-use PyMOL plugin with an intuitive graphical user interface that allows users to explore customizable parameters for cavity detection and characterization. On the other hand, pyKVFinder is available as a Python package with efficient scripting routines built on easy-to-handle data structures, facilitating complex structural data analysis.…”

“…A comprehensive taxonomy of geometry-based cavity detection approaches includes grid-, probe-, tessellation-, and surface-based techniques and their combination . The technique employed to extract cavities from the molecular structure is mainly what differentiates them. Grid-based algorithms (e.g., POVME 3.0) represent a set of atoms as discrete points, usually using an axis-aligned 3D grid such as a scalar field, i.e., density map, where each discrete point is an integer or a boolean.…”