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John C, Sahoo J, Sajan IK, Madhavan M, Mathew OK. CNN-BLSTM based deep learning framework for eukaryotic kinome classification: An explainability based approach. Comput Biol Chem 2024; 112:108169. [PMID: 39137619 DOI: 10.1016/j.compbiolchem.2024.108169] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2024] [Revised: 05/08/2024] [Accepted: 08/03/2024] [Indexed: 08/15/2024]
Abstract
Classification of protein families from their sequences is an enduring task in Proteomics and related studies. Numerous deep-learning models have been moulded to tackle this challenge, but due to the black-box character, they still fall short in reliability. Here, we present a novel explainability pipeline that explains the pivotal decisions of the deep learning model on the classification of the Eukaryotic kinome. Based on a comparative and experimental analysis of the most cutting-edge deep learning algorithms, the best deep learning model CNN-BLSTM was chosen to classify the eight eukaryotic kinase sequences to their corresponding families. As a substitution for the conventional class activation map-based interpretation of CNN-based models in the domain, we have cascaded the GRAD CAM and Integrated Gradient (IG) explainability modus operandi for improved and responsible results. To ensure the trustworthiness of the classifier, we have masked the kinase domain traces, identified from the explainability pipeline and observed a class-specific drop in F1-score from 0.96 to 0.76. In compliance with the Explainable AI paradigm, our results are promising and contribute to enhancing the trustworthiness of deep learning models for biological sequence-associated studies.
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Affiliation(s)
- Chinju John
- Department of Computer Science and Engineering, Indian Institute of Information Technology Kottayam, Kottayam, 686635, Kerala, India.
| | - Jayakrushna Sahoo
- Department of Computer Science and Engineering, Indian Institute of Information Technology Kottayam, Kottayam, 686635, Kerala, India
| | - Irish K Sajan
- Department of Computer Science and Engineering, Indian Institute of Information Technology Kottayam, Kottayam, 686635, Kerala, India
| | - Manu Madhavan
- Department of Computer Science and Engineering, Indian Institute of Information Technology Kottayam, Kottayam, 686635, Kerala, India
| | - Oommen K Mathew
- Department of Computer Science and Engineering, Indian Institute of Information Technology Kottayam, Kottayam, 686635, Kerala, India
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Dickson A, Mofrad MRK. Fine-tuning protein embeddings for functional similarity evaluation. Bioinformatics 2024; 40:btae445. [PMID: 38985218 PMCID: PMC11299545 DOI: 10.1093/bioinformatics/btae445] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Revised: 06/25/2024] [Accepted: 07/09/2024] [Indexed: 07/11/2024] Open
Abstract
MOTIVATION Proteins with unknown function are frequently compared to better characterized relatives, either using sequence similarity, or recently through similarity in a learned embedding space. Through comparison, protein sequence embeddings allow for interpretable and accurate annotation of proteins, as well as for downstream tasks such as clustering for unsupervised discovery of protein families. However, it is unclear whether embeddings can be deliberately designed to improve their use in these downstream tasks. RESULTS We find that for functional annotation of proteins, as represented by Gene Ontology (GO) terms, direct fine-tuning of language models on a simple classification loss has an immediate positive impact on protein embedding quality. Fine-tuned embeddings show stronger performance as representations for K-nearest neighbor classifiers, reaching stronger performance for GO annotation than even directly comparable fine-tuned classifiers, while maintaining interpretability through protein similarity comparisons. They also maintain their quality in related tasks, such as rediscovering protein families with clustering. AVAILABILITY AND IMPLEMENTATION github.com/mofradlab/go_metric.
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Affiliation(s)
- Andrew Dickson
- Departments of Bioengineering and Mechanical Engineering, Molecular Cell Biomechanics Laboratory, University of California, Berkeley, CA 94720, United States
| | - Mohammad R K Mofrad
- Departments of Bioengineering and Mechanical Engineering, Molecular Cell Biomechanics Laboratory, University of California, Berkeley, CA 94720, United States
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Yang M, Chen S, Huang Z, Gao S, Yu T, Du T, Zhang H, Li X, Liu CM, Chen S, Li H. Deep learning-enabled discovery and characterization of HKT genes in Spartina alterniflora. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2023; 116:690-705. [PMID: 37494542 DOI: 10.1111/tpj.16397] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Revised: 07/03/2023] [Accepted: 07/11/2023] [Indexed: 07/28/2023]
Abstract
Spartina alterniflora is a halophyte that can survive in high-salinity environments, and it is phylogenetically close to important cereal crops, such as maize and rice. It is of scientific interest to understand why S. alterniflora can live under such extremely stressful conditions. The molecular mechanism underlying its high-saline tolerance is still largely unknown. Here we investigated the possibility that high-affinity K+ transporters (HKTs), which function in salt tolerance and maintenance of ion homeostasis in plants, are responsible for salt tolerance in S. alterniflora. To overcome the imprecision and unstable of the gene screening method caused by the conventional sequence alignment, we used a deep learning method, DeepGOPlus, to automatically extract sequence and protein characteristics from our newly assemble S. alterniflora genome to identify SaHKTs. Results showed that a total of 16 HKT genes were identified. The number of S. alterniflora HKTs (SaHKTs) is larger than that in all other investigated plant species except wheat. Phylogenetically related SaHKT members had similar gene structures, conserved protein domains and cis-elements. Expression profiling showed that most SaHKT genes are expressed in specific tissues and are differentially expressed under salt stress. Yeast complementation expression analysis showed that type I members SaHKT1;2, SaHKT1;3 and SaHKT1;8 and type II members SaHKT2;1, SaHKT2;3 and SaHKT2;4 had low-affinity K+ uptake ability and that type II members showed stronger K+ affinity than rice and Arabidopsis HKTs, as well as most SaHKTs showed preference for Na+ transport. We believe the deep learning-based methods are powerful approaches to uncovering new functional genes, and the SaHKT genes identified are important resources for breeding new varieties of salt-tolerant crops.
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Affiliation(s)
- Maogeng Yang
- State Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences (CAAS), Beijing, China
- Nanfan Research Institute, CAAS, Sanya, Hainan, China
- Key Laboratory of Plant Molecular & Developmental Biology, College of Life Sciences, Yantai University, Yantai, Shandong, China
| | - Shoukun Chen
- State Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences (CAAS), Beijing, China
- Nanfan Research Institute, CAAS, Sanya, Hainan, China
- Hainan Yazhou Bay Seed Laboratory, Sanya, Hainan, China
| | - Zhangping Huang
- State Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences (CAAS), Beijing, China
- Nanfan Research Institute, CAAS, Sanya, Hainan, China
| | - Shang Gao
- State Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences (CAAS), Beijing, China
- Nanfan Research Institute, CAAS, Sanya, Hainan, China
| | - Tingxi Yu
- State Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences (CAAS), Beijing, China
- Nanfan Research Institute, CAAS, Sanya, Hainan, China
| | - Tingting Du
- State Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences (CAAS), Beijing, China
- Nanfan Research Institute, CAAS, Sanya, Hainan, China
| | - Hao Zhang
- State Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences (CAAS), Beijing, China
- Nanfan Research Institute, CAAS, Sanya, Hainan, China
| | - Xiang Li
- State Key Laboratory of Plant Genomics and National Center for Plant Gene Research, Institute of Genetics and Developmental Biology, Innovation Academy for Seed Design, Chinese Academy of Sciences, Beijing, China
| | - Chun-Ming Liu
- State Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences (CAAS), Beijing, China
- Key Laboratory of Plant Molecular Physiology, Institute of Botany, Chinese Academy of Sciences, Beijing, China
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing, China
- School of Advanced Agricultural Sciences, Peking University, Beijing, China
| | - Shihua Chen
- Key Laboratory of Plant Molecular & Developmental Biology, College of Life Sciences, Yantai University, Yantai, Shandong, China
| | - Huihui Li
- State Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences (CAAS), Beijing, China
- Nanfan Research Institute, CAAS, Sanya, Hainan, China
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Ardern Z, Chakraborty S, Lenk F, Kaster AK. Elucidating the functional roles of prokaryotic proteins using big data and artificial intelligence. FEMS Microbiol Rev 2023; 47:fuad003. [PMID: 36725215 PMCID: PMC9960493 DOI: 10.1093/femsre/fuad003] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Revised: 01/11/2023] [Accepted: 01/31/2023] [Indexed: 02/03/2023] Open
Abstract
Annotating protein sequences according to their biological functions is one of the key steps in understanding microbial diversity, metabolic potentials, and evolutionary histories. However, even in the best-studied prokaryotic genomes, not all proteins can be characterized by classical in vivo, in vitro, and/or in silico methods-a challenge rapidly growing alongside the advent of next-generation sequencing technologies and their enormous extension of 'omics' data in public databases. These so-called hypothetical proteins (HPs) represent a huge knowledge gap and hidden potential for biotechnological applications. Opportunities for leveraging the available 'Big Data' have recently proliferated with the use of artificial intelligence (AI). Here, we review the aims and methods of protein annotation and explain the different principles behind machine and deep learning algorithms including recent research examples, in order to assist both biologists wishing to apply AI tools in developing comprehensive genome annotations and computer scientists who want to contribute to this leading edge of biological research.
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Affiliation(s)
- Zachary Ardern
- Institute for Biological Interfaces 5 (Institut für Biologische Grenzflächen IBG 5), Karlsruhe Institute of Technology (KIT), 76344 Eggenstein-Leopoldshafen, Germany
- Wellcome Trust Sanger Institute, Hinxton, Saffron Walden CB10 1RQ, United Kingdom
| | - Sagarika Chakraborty
- Institute for Biological Interfaces 5 (Institut für Biologische Grenzflächen IBG 5), Karlsruhe Institute of Technology (KIT), 76344 Eggenstein-Leopoldshafen, Germany
| | - Florian Lenk
- Institute for Biological Interfaces 5 (Institut für Biologische Grenzflächen IBG 5), Karlsruhe Institute of Technology (KIT), 76344 Eggenstein-Leopoldshafen, Germany
| | - Anne-Kristin Kaster
- Institute for Biological Interfaces 5 (Institut für Biologische Grenzflächen IBG 5), Karlsruhe Institute of Technology (KIT), 76344 Eggenstein-Leopoldshafen, Germany
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Rezende PM, Xavier JS, Ascher DB, Fernandes GR, Pires DEV. Evaluating hierarchical machine learning approaches to classify biological databases. Brief Bioinform 2022; 23:6611916. [PMID: 35724625 PMCID: PMC9310517 DOI: 10.1093/bib/bbac216] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Revised: 04/29/2022] [Accepted: 05/09/2022] [Indexed: 12/04/2022] Open
Abstract
The rate of biological data generation has increased dramatically in recent years, which has driven the importance of databases as a resource to guide innovation and the generation of biological insights. Given the complexity and scale of these databases, automatic data classification is often required. Biological data sets are often hierarchical in nature, with varying degrees of complexity, imposing different challenges to train, test and validate accurate and generalizable classification models. While some approaches to classify hierarchical data have been proposed, no guidelines regarding their utility, applicability and limitations have been explored or implemented. These include ‘Local’ approaches considering the hierarchy, building models per level or node, and ‘Global’ hierarchical classification, using a flat classification approach. To fill this gap, here we have systematically contrasted the performance of ‘Local per Level’ and ‘Local per Node’ approaches with a ‘Global’ approach applied to two different hierarchical datasets: BioLip and CATH. The results show how different components of hierarchical data sets, such as variation coefficient and prediction by depth, can guide the choice of appropriate classification schemes. Finally, we provide guidelines to support this process when embarking on a hierarchical classification task, which will help optimize computational resources and predictive performance.
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Affiliation(s)
- Pâmela M Rezende
- Universidade Federal de Minas Gerais.,Instituto René Rachou, Fundação Oswaldo Cruz.,Stilingue Inteligência Artificial
| | - Joicymara S Xavier
- Universidade Federal de Minas Gerais.,Instituto René Rachou, Fundação Oswaldo Cruz.,Institute of Agricultural Sciences, Universidade Federal dos Vales do Jequitinhonha e Mucuri
| | - David B Ascher
- School of Chemistry and Molecular Biosciences, University of Queensland.,Systems and Computational Biology, Bio 21 Institute, University of Melbourne.,Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute
| | | | - Douglas E V Pires
- Systems and Computational Biology, Bio 21 Institute, University of Melbourne.,Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute.,School of Computing and Information Systems, University of Melbourne
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