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Breimann S, Kamp F, Steiner H, Frishman D. AAontology: An Ontology of Amino Acid Scales for Interpretable Machine Learning. J Mol Biol 2024; 436:168717. [PMID: 39053689 DOI: 10.1016/j.jmb.2024.168717] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2024] [Revised: 07/15/2024] [Accepted: 07/19/2024] [Indexed: 07/27/2024]
Abstract
Amino acid scales are crucial for protein prediction tasks, many of them being curated in the AAindex database. Despite various clustering attempts to organize them and to better understand their relationships, these approaches lack the fine-grained classification necessary for satisfactory interpretability in many protein prediction problems. To address this issue, we developed AAontology-a two-level classification for 586 amino acid scales (mainly from AAindex) together with an in-depth analysis of their relations-using bag-of-word-based classification, clustering, and manual refinement over multiple iterations. AAontology organizes physicochemical scales into 8 categories and 67 subcategories, enhancing the interpretability of scale-based machine learning methods in protein bioinformatics. Thereby it enables researchers to gain a deeper biological insight. We anticipate that AAontology will be a building block to link amino acid properties with protein function and dysfunctions as well as aid informed decision-making in mutation analysis or protein drug design.
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Affiliation(s)
- Stephan Breimann
- Department of Bioinformatics, School of Life Sciences, Technical University of Munich, Freising, Germany; Ludwig-Maximilians-University Munich, Biomedical Center, Division of Metabolic Biochemistry, Munich, Germany; German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
| | - Frits Kamp
- Ludwig-Maximilians-University Munich, Biomedical Center, Division of Metabolic Biochemistry, Munich, Germany
| | - Harald Steiner
- Ludwig-Maximilians-University Munich, Biomedical Center, Division of Metabolic Biochemistry, Munich, Germany; German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
| | - Dmitrij Frishman
- Department of Bioinformatics, School of Life Sciences, Technical University of Munich, Freising, Germany.
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Liu Y, Zhang Y, Chen Z, Peng J. POLAT: Protein function prediction based on soft mask graph network and residue-Label ATtention. Comput Biol Chem 2024; 110:108064. [PMID: 38677014 DOI: 10.1016/j.compbiolchem.2024.108064] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2023] [Revised: 01/19/2024] [Accepted: 03/26/2024] [Indexed: 04/29/2024]
Abstract
MOTIVATION Elucidating protein function is a central problem in biochemistry, genetics, and molecular biology. Developing computational methods for protein function prediction is critical due to the significant gap between sequence and functional data. Recent advances in protein structure prediction, which strongly correlates with function, make it feasible to use structure to predict function. However, current structure-based methods overlook the fact that individual residues may contribute differently to the protein's function and do not take into account the correlation between protein residues and their functions. The challenge of effectively utilizing the relationship between protein residues and function-level information to predict protein function remains unsolved. RESULT We proposed a protein function prediction method based on Soft Mask Graph Networks and Residue-Label Attention (POLAT), which could combine sequence features, predicted structure features, and function-level information to get an accurate prediction. We use soft mask graph networks to adaptively extract the residues relevant to functions. A residue-label attention mechanism is adopted to obtain the protein-level encoded features of a protein, which are then concatenated with a protein-level embedding and fed into a dense classifier to determine the probabilities of each function. POLAT achieves 0.670, 0.515, 0.578 Fmax and 0.677, 0.409, 0.507 AUPR on the PDB cdhit test set for the MFO, BPO, and CCO domains, respectively, outperforming the existing structure-based SOTA method GAT-GO (Fmax 0.633, 0.492, 0.547; AUPR 0.660, 0.381, 0.479). POLAT is also competitive in extensive experiments among sequence-based and multimodal methods and achieves the SOTA performance in three out of six metrics.
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Affiliation(s)
- Yang Liu
- Intelligent Bioinformatics Laboratory, School of Computer and Artificial Intelligence, Wuhan University of Technology, Wuhan, 430070, China.
| | - Yi Zhang
- Intelligent Bioinformatics Laboratory, School of Computer and Artificial Intelligence, Wuhan University of Technology, Wuhan, 430070, China.
| | - ZiHao Chen
- Intelligent Bioinformatics Laboratory, School of Computer and Artificial Intelligence, Wuhan University of Technology, Wuhan, 430070, China.
| | - Jing Peng
- Intelligent Bioinformatics Laboratory, School of Computer and Artificial Intelligence, Wuhan University of Technology, Wuhan, 430070, China.
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Computational prediction of disordered binding regions. Comput Struct Biotechnol J 2023; 21:1487-1497. [PMID: 36851914 PMCID: PMC9957716 DOI: 10.1016/j.csbj.2023.02.018] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Revised: 02/08/2023] [Accepted: 02/08/2023] [Indexed: 02/12/2023] Open
Abstract
One of the key features of intrinsically disordered regions (IDRs) is their ability to interact with a broad range of partner molecules. Multiple types of interacting IDRs were identified including molecular recognition fragments (MoRFs), short linear sequence motifs (SLiMs), and protein-, nucleic acids- and lipid-binding regions. Prediction of binding IDRs in protein sequences is gaining momentum in recent years. We survey 38 predictors of binding IDRs that target interactions with a diverse set of partners, such as peptides, proteins, RNA, DNA and lipids. We offer a historical perspective and highlight key events that fueled efforts to develop these methods. These tools rely on a diverse range of predictive architectures that include scoring functions, regular expressions, traditional and deep machine learning and meta-models. Recent efforts focus on the development of deep neural network-based architectures and extending coverage to RNA, DNA and lipid-binding IDRs. We analyze availability of these methods and show that providing implementations and webservers results in much higher rates of citations/use. We also make several recommendations to take advantage of modern deep network architectures, develop tools that bundle predictions of multiple and different types of binding IDRs, and work on algorithms that model structures of the resulting complexes.
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Kabir A, Shehu A. GOProFormer: A Multi-Modal Transformer Method for Gene Ontology Protein Function Prediction. Biomolecules 2022; 12:1709. [PMID: 36421723 PMCID: PMC9687818 DOI: 10.3390/biom12111709] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Revised: 11/14/2022] [Accepted: 11/15/2022] [Indexed: 09/19/2023] Open
Abstract
Protein Language Models (PLMs) are shown to be capable of learning sequence representations useful for various prediction tasks, from subcellular localization, evolutionary relationships, family membership, and more. They have yet to be demonstrated useful for protein function prediction. In particular, the problem of automatic annotation of proteins under the Gene Ontology (GO) framework remains open. This paper makes two key contributions. It debuts a novel method that leverages the transformer architecture in two ways. A sequence transformer encodes protein sequences in a task-agnostic feature space. A graph transformer learns a representation of GO terms while respecting their hierarchical relationships. The learned sequence and GO terms representations are combined and utilized for multi-label classification, with the labels corresponding to GO terms. The method is shown superior over recent representative GO prediction methods. The second major contribution in this paper is a deep investigation of different ways of constructing training and testing datasets. The paper shows that existing approaches under- or over-estimate the generalization power of a model. A novel approach is proposed to address these issues, resulting in a new benchmark dataset to rigorously evaluate and compare methods and advance the state-of-the-art.
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Affiliation(s)
- Anowarul Kabir
- Department of Computer Science, George Mason University, Fairfax, VA 22030, USA
| | - Amarda Shehu
- Department of Computer Science, George Mason University, Fairfax, VA 22030, USA
- Center for Advancing Human-Machine Partnerships, George Mason University, Fairfax, VA 22030, USA
- Department of Bioengineering, George Mason University, Fairfax, VA 22030, USA
- School of Systems Biology, George Mason University, Fairfax, VA 22030, USA
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Abdullah-Zawawi MR, Govender N, Harun S, Muhammad NAN, Zainal Z, Mohamed-Hussein ZA. Multi-Omics Approaches and Resources for Systems-Level Gene Function Prediction in the Plant Kingdom. PLANTS (BASEL, SWITZERLAND) 2022; 11:2614. [PMID: 36235479 PMCID: PMC9573505 DOI: 10.3390/plants11192614] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Revised: 09/05/2022] [Accepted: 09/13/2022] [Indexed: 06/16/2023]
Abstract
In higher plants, the complexity of a system and the components within and among species are rapidly dissected by omics technologies. Multi-omics datasets are integrated to infer and enable a comprehensive understanding of the life processes of organisms of interest. Further, growing open-source datasets coupled with the emergence of high-performance computing and development of computational tools for biological sciences have assisted in silico functional prediction of unknown genes, proteins and metabolites, otherwise known as uncharacterized. The systems biology approach includes data collection and filtration, system modelling, experimentation and the establishment of new hypotheses for experimental validation. Informatics technologies add meaningful sense to the output generated by complex bioinformatics algorithms, which are now freely available in a user-friendly graphical user interface. These resources accentuate gene function prediction at a relatively minimal cost and effort. Herein, we present a comprehensive view of relevant approaches available for system-level gene function prediction in the plant kingdom. Together, the most recent applications and sought-after principles for gene mining are discussed to benefit the plant research community. A realistic tabulation of plant genomic resources is included for a less laborious and accurate candidate gene discovery in basic plant research and improvement strategies.
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Affiliation(s)
- Muhammad-Redha Abdullah-Zawawi
- UKM Medical Molecular Biology Institute (UMBI), Universiti Kebangsaan Malaysia, Kuala Lumpur 56000, Malaysia
- Institute of System Biology (INBIOSIS), Universiti Kebangsaan Malaysia (UKM), Bangi 43600, Malaysia
| | - Nisha Govender
- Institute of System Biology (INBIOSIS), Universiti Kebangsaan Malaysia (UKM), Bangi 43600, Malaysia
| | - Sarahani Harun
- Institute of System Biology (INBIOSIS), Universiti Kebangsaan Malaysia (UKM), Bangi 43600, Malaysia
| | - Nor Azlan Nor Muhammad
- Institute of System Biology (INBIOSIS), Universiti Kebangsaan Malaysia (UKM), Bangi 43600, Malaysia
| | - Zamri Zainal
- Institute of System Biology (INBIOSIS), Universiti Kebangsaan Malaysia (UKM), Bangi 43600, Malaysia
- Faculty of Science and Technology, Universiti Kebangsaan Malaysia (UKM), Bangi 43600, Malaysia
| | - Zeti-Azura Mohamed-Hussein
- Institute of System Biology (INBIOSIS), Universiti Kebangsaan Malaysia (UKM), Bangi 43600, Malaysia
- Faculty of Science and Technology, Universiti Kebangsaan Malaysia (UKM), Bangi 43600, Malaysia
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Ma W, Zhang S, Li Z, Jiang M, Wang S, Lu W, Bi X, Jiang H, Zhang H, Wei Z. Enhancing Protein Function Prediction Performance by Utilizing AlphaFold-Predicted Protein Structures. J Chem Inf Model 2022; 62:4008-4017. [PMID: 36006049 DOI: 10.1021/acs.jcim.2c00885] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
The structure of a protein is of great importance in determining its functionality, and this characteristic can be leveraged to train data-driven prediction models. However, the limited number of available protein structures severely limits the performance of these models. AlphaFold2 and its open-source data set of predicted protein structures have provided a promising solution to this problem, and these predicted structures are expected to benefit the model performance by increasing the number of training samples. In this work, we constructed a new data set that acted as a benchmark and implemented a state-of-the-art structure-based approach for determining whether the performance of the function prediction model can be improved by putting additional AlphaFold-predicted structures into the training set and further compared the performance differences between two models separately trained with real structures only and AlphaFold-predicted structures only. Experimental results indicated that structure-based protein function prediction models could benefit from virtual training data consisting of AlphaFold-predicted structures. First, model performances were improved in all three categories of Gene Ontology terms (GO terms) after adding predicted structures as training samples. Second, the model trained only on AlphaFold-predicted virtual samples achieved comparable performances to the model based on experimentally solved real structures, suggesting that predicted structures were almost equally effective in predicting protein functionality.
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Affiliation(s)
- Wenjian Ma
- College of Computer Science and Technology, Ocean University of China, Qingdao 266100, China
| | - Shugang Zhang
- College of Computer Science and Technology, Ocean University of China, Qingdao 266100, China.,High Performance Computing Center, Pilot National Laboratory for Marine Science and Technology (Qingdao), Qingdao 266237, China
| | - Zhen Li
- College of Computer Science and Technology, Qingdao University, Qingdao 266071, China
| | - Mingjian Jiang
- School of Information and Control Engineering, Qingdao University of Technology, Qingdao 266033, China
| | - Shuang Wang
- College of Computer Science and Technology, China University of Petroleum (East China), Qingdao 266580, China
| | - Weigang Lu
- College of Computer Science and Technology, Ocean University of China, Qingdao 266100, China
| | - Xiangpeng Bi
- College of Computer Science and Technology, Ocean University of China, Qingdao 266100, China
| | - Huasen Jiang
- College of Computer Science and Technology, Ocean University of China, Qingdao 266100, China
| | - Henggui Zhang
- College of Computer Science and Technology, Ocean University of China, Qingdao 266100, China.,High Performance Computing Center, Pilot National Laboratory for Marine Science and Technology (Qingdao), Qingdao 266237, China.,Biological Physics Group, School of Physics and Astronomy, University of Manchester, Manchester M13 9PL, U.K
| | - Zhiqiang Wei
- College of Computer Science and Technology, Ocean University of China, Qingdao 266100, China.,High Performance Computing Center, Pilot National Laboratory for Marine Science and Technology (Qingdao), Qingdao 266237, China
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