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...
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| Format: | Article |
| Language: | English |
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BMC
2021-12-01
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| Series: | BMC Bioinformatics |
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| Online Access: | https://doi.org/10.1186/s12859-021-04519-4 |
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| author | João Victor da Silva Guerra Helder Veras Ribeiro-Filho Gabriel Ernesto Jara Leandro Oliveira Bortot José Geraldo de Carvalho Pereira Paulo Sérgio Lopes-de-Oliveira |
| author_facet | João Victor da Silva Guerra Helder Veras Ribeiro-Filho Gabriel Ernesto Jara Leandro Oliveira Bortot José Geraldo de Carvalho Pereira Paulo Sérgio Lopes-de-Oliveira |
| author_sort | João Victor da Silva Guerra |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-22T01:43:02Z |
| format | Article |
| id | doaj.art-8ed22d93639c46f7a27d48425ed79d7f |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-12-22T01:43:02Z |
| publishDate | 2021-12-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-8ed22d93639c46f7a27d48425ed79d7f2022-12-21T18:43:09ZengBMCBMC Bioinformatics1471-21052021-12-0122111310.1186/s12859-021-04519-4pyKVFinder: an efficient and integrable Python package for biomolecular cavity detection and characterization in data scienceJoão Victor da Silva Guerra0Helder Veras Ribeiro-Filho1Gabriel Ernesto Jara2Leandro Oliveira Bortot3José Geraldo de Carvalho Pereira4Paulo Sérgio Lopes-de-Oliveira5Brazilian Center for Research in Energy and Materials (CNPEM), Brazilian Biosciences National Laboratory (LNBio)Brazilian Center for Research in Energy and Materials (CNPEM), Brazilian Biosciences National Laboratory (LNBio)Brazilian Center for Research in Energy and Materials (CNPEM), Brazilian Biosciences National Laboratory (LNBio)Brazilian Center for Research in Energy and Materials (CNPEM), Brazilian Biosciences National Laboratory (LNBio)Brazilian Center for Research in Energy and Materials (CNPEM), Brazilian Biosciences National Laboratory (LNBio)Brazilian Center for Research in Energy and Materials (CNPEM), Brazilian Biosciences National Laboratory (LNBio)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.https://doi.org/10.1186/s12859-021-04519-4Cavity detectionCavity characterizationNumPyPythonData structureData science |
| spellingShingle | João Victor da Silva Guerra Helder Veras Ribeiro-Filho Gabriel Ernesto Jara Leandro Oliveira Bortot José Geraldo de Carvalho Pereira Paulo Sérgio Lopes-de-Oliveira pyKVFinder: an efficient and integrable Python package for biomolecular cavity detection and characterization in data science BMC Bioinformatics Cavity detection Cavity characterization NumPy Python Data structure Data science |
| title | pyKVFinder: an efficient and integrable Python package for biomolecular cavity detection and characterization in data science |
| title_full | pyKVFinder: an efficient and integrable Python package for biomolecular cavity detection and characterization in data science |
| title_fullStr | pyKVFinder: an efficient and integrable Python package for biomolecular cavity detection and characterization in data science |
| title_full_unstemmed | pyKVFinder: an efficient and integrable Python package for biomolecular cavity detection and characterization in data science |
| title_short | pyKVFinder: an efficient and integrable Python package for biomolecular cavity detection and characterization in data science |
| title_sort | pykvfinder an efficient and integrable python package for biomolecular cavity detection and characterization in data science |
| topic | Cavity detection Cavity characterization NumPy Python Data structure Data science |
| url | https://doi.org/10.1186/s12859-021-04519-4 |
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