ProPythia: A Python package for protein classification based on machine and deep learning
The field of protein data mining has been growing rapidly in the last years. To characterize proteins and determine their function from their amino acid sequences are challenging and long-standing problems, where Bioinformatics and Machine Learning have an emergent role. A myriad of machine and deep...
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| Vydáno v: | Neurocomputing (Amsterdam) Ročník 484; s. 172 - 182 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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Elsevier B.V
01.05.2022
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| ISSN: | 0925-2312, 1872-8286 |
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| Abstract | The field of protein data mining has been growing rapidly in the last years. To characterize proteins and determine their function from their amino acid sequences are challenging and long-standing problems, where Bioinformatics and Machine Learning have an emergent role. A myriad of machine and deep learning algorithms have been applied in these tasks with exciting results. However, tools and platforms to calculate protein features and perform both Machine Learning (ML) and Deep Learning (DL) pipelines, taking as inputs protein sequences, are still lacking and have their limitations in terms of performance, user-friendliness and restricted domains of application.
Here, to address these limitations, we propose ProPythia, a generic and modular Python package that allows to easily deploy ML and DL approaches for a plethora of problems in protein sequence analysis and classification. It facilitates the implementation, comparison and validation of the major tasks in ML or DL pipelines including modules to read and alter sequences, calculate protein features, preprocess datasets, execute feature selection and dimensionality reduction, perform clustering and manifold analysis, as well as to train and optimize ML/DL models and use them to make predictions.
ProPythia has an adaptable modular architecture being a versatile and easy-to-use tool, which will be useful to transform protein data in valuable knowledge even for people not familiarized with ML code. This platform was tested in several applications comparing with results from literature. Here, we illustrate its applicability in two cases studies: the prediction of antimicrobial peptides and the prediction of enzymes Enzyme commission (EC) numbers. Furthermore, we assess the performance of the different descriptors on four different protein classification challenges. Its source code and documentation, including an user guide and case studies are freely available at https://github.com/BioSystemsUM/propythia. |
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| AbstractList | The field of protein data mining has been growing rapidly in the last years. To characterize proteins and determine their function from their amino acid sequences are challenging and long-standing problems, where Bioinformatics and Machine Learning have an emergent role. A myriad of machine and deep learning algorithms have been applied in these tasks with exciting results. However, tools and platforms to calculate protein features and perform both Machine Learning (ML) and Deep Learning (DL) pipelines, taking as inputs protein sequences, are still lacking and have their limitations in terms of performance, user-friendliness and restricted domains of application.
Here, to address these limitations, we propose ProPythia, a generic and modular Python package that allows to easily deploy ML and DL approaches for a plethora of problems in protein sequence analysis and classification. It facilitates the implementation, comparison and validation of the major tasks in ML or DL pipelines including modules to read and alter sequences, calculate protein features, preprocess datasets, execute feature selection and dimensionality reduction, perform clustering and manifold analysis, as well as to train and optimize ML/DL models and use them to make predictions.
ProPythia has an adaptable modular architecture being a versatile and easy-to-use tool, which will be useful to transform protein data in valuable knowledge even for people not familiarized with ML code. This platform was tested in several applications comparing with results from literature. Here, we illustrate its applicability in two cases studies: the prediction of antimicrobial peptides and the prediction of enzymes Enzyme commission (EC) numbers. Furthermore, we assess the performance of the different descriptors on four different protein classification challenges. Its source code and documentation, including an user guide and case studies are freely available at https://github.com/BioSystemsUM/propythia. |
| Author | Lousa, Diana Rocha, Miguel Sequeira, Ana Marta |
| Author_xml | – sequence: 1 givenname: Ana Marta surname: Sequeira fullname: Sequeira, Ana Marta email: id9417@alunos.uminho.pt organization: CEB-Centre Biological Engineering, University of Minho, 4710-057 Braga, Portugal – sequence: 2 givenname: Diana surname: Lousa fullname: Lousa, Diana email: dlousa@itqb.unl.pt organization: Protein Modelling Laboratory, Instituto de Tecnologia Química e Biológica António Xavier (ITQB NOVA), Universidade Nova de Lisboa, 2780-157 Oeiras, Portugal – sequence: 3 givenname: Miguel surname: Rocha fullname: Rocha, Miguel email: mrocha@di.uminho.pt organization: CEB-Centre Biological Engineering, University of Minho, 4710-057 Braga, Portugal |
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| Keywords | Deep learning Python Package Enzyme Protein/peptide classification Machine learning Antimicrobial peptide |
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| SubjectTerms | Antimicrobial peptide Deep learning Enzyme Machine learning Protein/peptide classification Python Package |
| Title | ProPythia: A Python package for protein classification based on machine and deep learning |
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