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

Author(s)

da Silva Guerra, Joao Victor ^{[1, 2]} ; Ribeiro-Filho, Helder Veras ^[1] ; Jara, Gabriel Ernesto ^[1] ; Bortot, Leandro Oliveira ^[1] ; de Carvalho Pereira, Jose Geraldo ^[1] ; Lopes-de-Oliveira, Paulo Sergio ^{[1, 2]}

Affiliation

^[1] Brazilian Ctr Res Energy & Mat CNPEM, Brazilian Biosci Natl Lab LNBio, R Giuseppe Maximo Scolfaro 10000, BR-13083100 Campinas, SP - Brazil

^[2] Univ Estadual Campinas, Grad Program Pharmaceut Sci, Fac Pharmaceut Sci, Campinas, SP - Brazil

• Document type: Journal article
• Source: BMC Bioinformatics; v. 22, n. 1 DEC 20 2021.
• Web of Science Citations: 0

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. (AU)

• FAPESP's process: 18/00629-0 - Detection and characterization of protein cavities using parallel computing and molecular descriptors
• Grantee: Paulo Sergio Lopes de Oliveira
• Support Opportunities: Regular Research Grants