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

Title:
pyKVFinder: an efficient and integrable Python package for biomolecular cavity detection and characterization in data science.
Authors:
Guerra JVDS; Brazilian Center for Research in Energy and Materials (CNPEM), Brazilian Biosciences National Laboratory (LNBio), R. Giuseppe Máximo Scolfaro, 10000 - Bosque das Palmeiras, Campinas, SP, 13083-100, Brazil. joao.guerra@lnbio.cnpem.br.; Graduate Program in Pharmaceutical Sciences, Faculty of Pharmaceutical Sciences, University of Campinas, Campinas, SP, Brazil. joao.guerra@lnbio.cnpem.br., Ribeiro-Filho HV; Brazilian Center for Research in Energy and Materials (CNPEM), Brazilian Biosciences National Laboratory (LNBio), R. Giuseppe Máximo Scolfaro, 10000 - Bosque das Palmeiras, Campinas, SP, 13083-100, Brazil., Jara GE; Brazilian Center for Research in Energy and Materials (CNPEM), Brazilian Biosciences National Laboratory (LNBio), R. Giuseppe Máximo Scolfaro, 10000 - Bosque das Palmeiras, Campinas, SP, 13083-100, Brazil., Bortot LO; Brazilian Center for Research in Energy and Materials (CNPEM), Brazilian Biosciences National Laboratory (LNBio), R. Giuseppe Máximo Scolfaro, 10000 - Bosque das Palmeiras, Campinas, SP, 13083-100, Brazil., Pereira JGC; Brazilian Center for Research in Energy and Materials (CNPEM), Brazilian Biosciences National Laboratory (LNBio), R. Giuseppe Máximo Scolfaro, 10000 - Bosque das Palmeiras, Campinas, SP, 13083-100, Brazil., Lopes-de-Oliveira PS; Brazilian Center for Research in Energy and Materials (CNPEM), Brazilian Biosciences National Laboratory (LNBio), R. Giuseppe Máximo Scolfaro, 10000 - Bosque das Palmeiras, Campinas, SP, 13083-100, Brazil. paulo.oliveira@lnbio.cnpem.br.; Graduate Program in Pharmaceutical Sciences, Faculty of Pharmaceutical Sciences, University of Campinas, Campinas, SP, Brazil. paulo.oliveira@lnbio.cnpem.br.
Source:
BMC bioinformatics [BMC Bioinformatics] 2021 Dec 20; Vol. 22 (1), pp. 607. Date of Electronic Publication: 2021 Dec 20.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: BioMed Central Country of Publication: England NLM ID: 100965194 Publication Model: Electronic Cited Medium: Internet ISSN: 1471-2105 (Electronic) Linking ISSN: 14712105 NLM ISO Abbreviation: BMC Bioinformatics Subsets: MEDLINE
Imprint Name(s):
Original Publication: [London] : BioMed Central, 2000-
MeSH Terms:
COVID-19* , Data Science*, Data Analysis ; Ecosystem ; Humans ; SARS-CoV-2
References:
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Grant Information:
2018/00629-0 Fundação de Amparo à Pesquisa do Estado de São Paulo; 350244/2020-0 Conselho Nacional de Desenvolvimento Científico e Tecnológico
Contributed Indexing:
Keywords: Automated pipelines; Cavity characterization; Cavity detection; Data science; Data structure; Molecular dynamics; NumPy; Python
Entry Date(s):
Date Created: 20211221 Date Completed: 20211222 Latest Revision: 20211224
Update Code:
20250114
PubMed Central ID:
PMC8685811
DOI:
10.1186/s12859-021-04519-4
PMID:
34930115
Database:
MEDLINE

Weitere Informationen

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.
(© 2021. The Author(s).)