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Amjad A, Ahmed S, Kabir M, Arif M, Alam T. A novel deep learning identifier for promoters and their strength using heterogeneous features. Methods 2024; 230:119-128. [PMID: 39168294 DOI: 10.1016/j.ymeth.2024.08.005] [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: 06/25/2024] [Revised: 07/24/2024] [Accepted: 08/17/2024] [Indexed: 08/23/2024] Open
Abstract
Promoters, which are short (50-1500 base-pair) in DNA regions, have emerged to play a critical role in the regulation of gene transcription. Numerous dangerous diseases, likewise cancer, cardiovascular, and inflammatory bowel diseases, are caused by genetic variations in promoters. Consequently, the correct identification and characterization of promoters are significant for the discovery of drugs. However, experimental approaches to recognizing promoters and their strengths are challenging in terms of cost, time, and resources. Therefore, computational techniques are highly desirable for the correct characterization of promoters from unannotated genomic data. Here, we designed a powerful bi-layer deep-learning based predictor named "PROCABLES", which discriminates DNA samples as promoters in the first-phase and strong or weak promoters in the second-phase respectively. The proposed method utilizes five distinct features, such as word2vec, k-spaced nucleotide pairs, trinucleotide propensity-based features, trinucleotide composition, and electron-ion interaction pseudopotentials, to extract the hidden patterns from the DNA sequence. Afterwards, a stacked framework is formed by integrating a convolutional neural network (CNN) with bidirectional long-short-term memory (LSTM) using multi-view attributes to train the proposed model. The PROCABLES model achieved an accuracy of 0.971 and 0.920 and the MCC 0.940 and 0.840 for the first and second-layer using the ten-fold cross-validation test, respectively. The predicted results anticipate that the proposed PROCABLES protocol outperformed the advanced computational predictors targeting promoters and their types. In summary, this research will provide useful hints for the recognition of large-scale promoters in particular and other DNA problems in general.
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Affiliation(s)
- Aqsa Amjad
- School of Systems and Technology, University of Management and Technology, Lahore 54770, Pakistan
| | - Saeed Ahmed
- School of Systems and Technology, University of Management and Technology, Lahore 54770, Pakistan
| | - Muhammad Kabir
- School of Systems and Technology, University of Management and Technology, Lahore 54770, Pakistan.
| | - Muhammad Arif
- College of Science and Engineering, Hamad Bin Khalifa University, Doha 34110, Qatar.
| | - Tanvir Alam
- College of Science and Engineering, Hamad Bin Khalifa University, Doha 34110, Qatar.
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2
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Bernett J, Blumenthal DB, Grimm DG, Haselbeck F, Joeres R, Kalinina OV, List M. Guiding questions to avoid data leakage in biological machine learning applications. Nat Methods 2024; 21:1444-1453. [PMID: 39122953 DOI: 10.1038/s41592-024-02362-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Accepted: 06/26/2024] [Indexed: 08/12/2024]
Abstract
Machine learning methods for extracting patterns from high-dimensional data are very important in the biological sciences. However, in certain cases, real-world applications cannot confirm the reported prediction performance. One of the main reasons for this is data leakage, which can be seen as the illicit sharing of information between the training data and the test data, resulting in performance estimates that are far better than the performance observed in the intended application scenario. Data leakage can be difficult to detect in biological datasets due to their complex dependencies. With this in mind, we present seven questions that should be asked to prevent data leakage when constructing machine learning models in biological domains. We illustrate the usefulness of our questions by applying them to nontrivial examples. Our goal is to raise awareness of potential data leakage problems and to promote robust and reproducible machine learning-based research in biology.
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Affiliation(s)
- Judith Bernett
- TUM School of Life Sciences, Technical University of Munich, Freising, Germany
| | - David B Blumenthal
- Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.
| | - Dominik G Grimm
- TUM Campus Straubing for Biotechnology and Sustainability, Technical University of Munich, Straubing, Germany.
- Bioinformatics, Weihenstephan-Triesdorf University of Applied Sciences, Straubing, Germany.
- TUM School of Computation, Information and Technology, Technical University of Munich, Garching, Germany.
| | - Florian Haselbeck
- TUM Campus Straubing for Biotechnology and Sustainability, Technical University of Munich, Straubing, Germany
- Bioinformatics, Weihenstephan-Triesdorf University of Applied Sciences, Straubing, Germany
- Smart Farming, Weihenstephan-Triesdorf University of Applied Sciences, Freising, Germany
| | - Roman Joeres
- Department of Chemistry and Molecular Biology, University of Gothenburg, Gothenburg, Sweden
- Wallenberg Centre for Molecular and Translational Medicine, University of Gothenburg, Gothenburg, Sweden
- Helmholtz Institute for Pharmaceutical Research Saarland (HIPS), Helmholtz Centre for Infection Research (HZI), Saarbrücken, Germany
- Center for Bioinformatics, Saarland University, Saarbrücken, Germany
| | - Olga V Kalinina
- Helmholtz Institute for Pharmaceutical Research Saarland (HIPS), Helmholtz Centre for Infection Research (HZI), Saarbrücken, Germany.
- Center for Bioinformatics, Saarland University, Saarbrücken, Germany.
- Medical Faculty, Saarland University, Homburg, Germany.
| | - Markus List
- TUM School of Life Sciences, Technical University of Munich, Freising, Germany.
- Munich Data Science Institute (MDSI), Technical University of Munich, Garching, Germany.
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3
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Susanty M, Mursalim MKN, Hertadi R, Purwarianti A, LE Rajab T. Leveraging protein language model embeddings and logistic regression for efficient and accurate in-silico acidophilic proteins classification. Comput Biol Chem 2024; 112:108163. [PMID: 39098138 DOI: 10.1016/j.compbiolchem.2024.108163] [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: 04/11/2024] [Revised: 07/02/2024] [Accepted: 07/24/2024] [Indexed: 08/06/2024]
Abstract
The increasing demand for eco-friendly technologies in biotechnology necessitates effective and sustainable catalysts. Acidophilic proteins, functioning optimally in highly acidic environments, hold immense promise for various applications, including food production, biofuels, and bioremediation. However, limited knowledge about these proteins hinders their exploration. This study addresses this gap by employing in silico methods utilizing computational tools and machine learning. We propose a novel approach to predict acidophilic proteins using protein language models (PLMs), accelerating discovery without extensive lab work. Our investigation highlights the potential of PLMs in understanding and harnessing acidophilic proteins for scientific and industrial advancements. We introduce the ACE model, which combines a simple Logistic Regression model with embeddings derived from protein sequences processed by the ProtT5 PLM. This model achieves high performance on an independent test set, with accuracy (0.91), F1-score (0.93), and Matthew's correlation coefficient (0.76). To our knowledge, this is the first application of pre-trained PLM embeddings for acidophilic protein classification. The ACE model serves as a powerful tool for exploring protein acidophilicity, paving the way for future advancements in protein design and engineering.
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Affiliation(s)
- Meredita Susanty
- Institut Teknologi Bandung School of Electrical Engineering and Informatics, Jl. Ganesa 10, Bandung, Jawa Barat, Indonesia; Universitas Pertamina, School of Computer Science, Jl Teuku Nyak Arief Jakarta Selatan DKI, Jakarta, Indonesia
| | - Muhammad Khaerul Naim Mursalim
- Institut Teknologi Bandung School of Electrical Engineering and Informatics, Jl. Ganesa 10, Bandung, Jawa Barat, Indonesia; Universitas UniversalKompleks Maha Vihara Duta Maitreya Bukit Beruntung, Sei Panas Batam, Kepulauan, Riau 29456, Indonesia
| | - Rukman Hertadi
- Institut Teknologi Bandung Faculty of Math and Natural Sciences, Jl. Ganesa 10, Bandung, Jawa Barat, Indonesia
| | - Ayu Purwarianti
- Institut Teknologi Bandung School of Electrical Engineering and Informatics, Jl. Ganesa 10, Bandung, Jawa Barat, Indonesia; Center for Artificial Intelligence (U-CoE AI-VLB), Institut Teknologi Bandung, Bandung, Indonesia
| | - Tati LE Rajab
- Institut Teknologi Bandung School of Electrical Engineering and Informatics, Jl. Ganesa 10, Bandung, Jawa Barat, Indonesia.
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4
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Rodella C, Lazaridi S, Lemmin T. TemBERTure: advancing protein thermostability prediction with deep learning and attention mechanisms. BIOINFORMATICS ADVANCES 2024; 4:vbae103. [PMID: 39040220 PMCID: PMC11262459 DOI: 10.1093/bioadv/vbae103] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/30/2024] [Revised: 06/14/2024] [Accepted: 07/12/2024] [Indexed: 07/24/2024]
Abstract
Motivation Understanding protein thermostability is essential for numerous biotechnological applications, but traditional experimental methods are time-consuming, expensive, and error-prone. Recently, deep learning (DL) techniques from natural language processing (NLP) was extended to the field of biology, since the primary sequence of proteins can be viewed as a string of amino acids that follow a physicochemical grammar. Results In this study, we developed TemBERTure, a DL framework that predicts thermostability class and melting temperature from protein sequences. Our findings emphasize the importance of data diversity for training robust models, especially by including sequences from a wider range of organisms. Additionally, we suggest using attention scores from Deep Learning models to gain deeper insights into protein thermostability. Analyzing these scores in conjunction with the 3D protein structure can enhance understanding of the complex interactions among amino acid properties, their positioning, and the surrounding microenvironment. By addressing the limitations of current prediction methods and introducing new exploration avenues, this research paves the way for more accurate and informative protein thermostability predictions, ultimately accelerating advancements in protein engineering. Availability and implementation TemBERTure model and the data are available at: https://github.com/ibmm-unibe-ch/TemBERTure.
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Affiliation(s)
- Chiara Rodella
- Institute of Biochemistry and Molecular Medicine (IBMM), University of Bern, Bern CH-3012, Switzerland
- Graduate School for Cellular and Biomedical Sciences (GCB), University of Bern, Bern CH-3012, Switzerland
| | - Symela Lazaridi
- Institute of Biochemistry and Molecular Medicine (IBMM), University of Bern, Bern CH-3012, Switzerland
- Graduate School for Cellular and Biomedical Sciences (GCB), University of Bern, Bern CH-3012, Switzerland
| | - Thomas Lemmin
- Institute of Biochemistry and Molecular Medicine (IBMM), University of Bern, Bern CH-3012, Switzerland
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5
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Yu H, Luo X. ThermoFinder: A sequence-based thermophilic proteins prediction framework. Int J Biol Macromol 2024; 270:132469. [PMID: 38761901 DOI: 10.1016/j.ijbiomac.2024.132469] [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: 03/17/2024] [Revised: 05/14/2024] [Accepted: 05/15/2024] [Indexed: 05/20/2024]
Abstract
Thermophilic proteins are important for academic research and industrial processes, and various computational methods have been developed to identify and screen them. However, their performance has been limited due to the lack of high-quality labeled data and efficient models for representing protein. Here, we proposed a novel sequence-based thermophilic proteins prediction framework, called ThermoFinder. The results demonstrated that ThermoFinder outperforms previous state-of-the-art tools on two benchmark datasets, and feature ablation experiments confirmed the effectiveness of our approach. Additionally, ThermoFinder exhibited exceptional performance and consistency across two newly constructed datasets, one of these was specifically constructed for the regression-based prediction of temperature optimum values directly derived from protein sequences. The feature importance analysis, using shapley additive explanations, further validated the advantages of ThermoFinder. We believe that ThermoFinder will be a valuable and comprehensive framework for predicting thermophilic proteins, and we have made our model open source and available on Github at https://github.com/Luo-SynBioLab/ThermoFinder.
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Affiliation(s)
- Han Yu
- Shenzhen Key Laboratory for the Intelligent Microbial Manufacturing of Medicines, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; University of Chinese Academy of Sciences, Beijing 100049, China; CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; Center for Synthetic Biochemistry, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Xiaozhou Luo
- Shenzhen Key Laboratory for the Intelligent Microbial Manufacturing of Medicines, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; University of Chinese Academy of Sciences, Beijing 100049, China; CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; Center for Synthetic Biochemistry, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.
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6
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Abbasi AF, Asim MN, Ahmed S, Dengel A. Long extrachromosomal circular DNA identification by fusing sequence-derived features of physicochemical properties and nucleotide distribution patterns. Sci Rep 2024; 14:9466. [PMID: 38658614 PMCID: PMC11043385 DOI: 10.1038/s41598-024-57457-5] [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: 09/19/2023] [Accepted: 03/18/2024] [Indexed: 04/26/2024] Open
Abstract
Long extrachromosomal circular DNA (leccDNA) regulates several biological processes such as genomic instability, gene amplification, and oncogenesis. The identification of leccDNA holds significant importance to investigate its potential associations with cancer, autoimmune, cardiovascular, and neurological diseases. In addition, understanding these associations can provide valuable insights about disease mechanisms and potential therapeutic approaches. Conventionally, wet lab-based methods are utilized to identify leccDNA, which are hindered by the need for prior knowledge, and resource-intensive processes, potentially limiting their broader applicability. To empower the process of leccDNA identification across multiple species, the paper in hand presents the very first computational predictor. The proposed iLEC-DNA predictor makes use of SVM classifier along with sequence-derived nucleotide distribution patterns and physicochemical properties-based features. In addition, the study introduces a set of 12 benchmark leccDNA datasets related to three species, namely Homo sapiens (HM), Arabidopsis Thaliana (AT), and Saccharomyces cerevisiae (SC/YS). It performs large-scale experimentation across 12 benchmark datasets under different experimental settings using the proposed predictor, more than 140 baseline predictors, and 858 encoder ensembles. The proposed predictor outperforms baseline predictors and encoder ensembles across diverse leccDNA datasets by producing average performance values of 81.09%, 62.2% and 81.08% in terms of ACC, MCC and AUC-ROC across all the datasets. The source code of the proposed and baseline predictors is available at https://github.com/FAhtisham/Extrachrosmosomal-DNA-Prediction . To facilitate the scientific community, a web application for leccDNA identification is available at https://sds_genetic_analysis.opendfki.de/iLEC_DNA/.
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Affiliation(s)
- Ahtisham Fazeel Abbasi
- Department of Computer Science, Rhineland-Palatinate Technical University of Kaiserslautern-Landau, 67663, Kaiserslautern, Germany.
- German Research Center for Artificial Intelligence GmbH, 67663, Kaiserslautern, Germany.
| | - Muhammad Nabeel Asim
- German Research Center for Artificial Intelligence GmbH, 67663, Kaiserslautern, Germany.
| | - Sheraz Ahmed
- German Research Center for Artificial Intelligence GmbH, 67663, Kaiserslautern, Germany
| | - Andreas Dengel
- Department of Computer Science, Rhineland-Palatinate Technical University of Kaiserslautern-Landau, 67663, Kaiserslautern, Germany
- German Research Center for Artificial Intelligence GmbH, 67663, Kaiserslautern, Germany
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7
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Khalid M, Ali F, Alghamdi W, Alzahrani A, Alsini R, Alzahrani A. An ensemble computational model for prediction of clathrin protein by coupling machine learning with discrete cosine transform. J Biomol Struct Dyn 2024:1-9. [PMID: 38498362 DOI: 10.1080/07391102.2024.2329777] [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: 10/09/2023] [Accepted: 02/19/2024] [Indexed: 03/20/2024]
Abstract
Clathrin protein (CP) plays a pivotal role in numerous cellular processes, including endocytosis, signal transduction, and neuronal function. Dysregulation of CP has been associated with a spectrum of diseases. Given its involvement in various cellular functions, CP has garnered significant attention for its potential applications in drug design and medicine, ranging from targeted drug delivery to addressing viral infections, neurological disorders, and cancer. The accurate identification of CP is crucial for unraveling its function and devising novel therapeutic strategies. Computational methods offer a rapid, cost-effective, and less labor-intensive alternative to traditional identification methods, making them especially appealing for high-throughput screening. This paper introduces CL-Pred, a novel computational method for CP identification. CL-Pred leverages three feature descriptors: Dipeptide Deviation from Expected Mean (DDE), Bigram Position Specific Scoring Matrix (BiPSSM), and Position Specific Scoring Matrix-Tetra Slice-Discrete Cosine Transform (PSSM-TS-DCT). The model is trained using three classifiers: Support Vector Machine (SVM), Extremely Randomized Tree (ERT), and Light eXtreme Gradient Boosting (LiXGB). Notably, the LiXGB-based model achieves outstanding performance, demonstrating accuracies of 94.63% and 93.65% on the training and testing datasets, respectively. The proposed CL-Pred method is poised to significantly advance our comprehension of clathrin-mediated endocytosis, cellular physiology, and disease pathogenesis. Furthermore, it holds promise for identifying potential drug targets across a spectrum of diseases.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Majdi Khalid
- Department of Computer Science and Artificial Intelligence, College of Computing, Umm Al-Qura University, Makkah, Saudi Arabia
| | - Farman Ali
- Sarhad University of Science and Information Technology Peshawar, Mardan Campus, Mardan, Pakistan
| | - Wajdi Alghamdi
- Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Abdulrahman Alzahrani
- Department of Information System and Technology, College of Computer Science and Engineering, University of Jeddah, Jeddah, Saudi Arabia
| | - Raed Alsini
- Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Ahmed Alzahrani
- College of Computer Science and Engineering, University of Jeddah, Jeddah, Saudi Arabia
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8
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Hassan MT, Tayara H, Chong KT. An integrative machine learning model for the identification of tumor T-cell antigens. Biosystems 2024; 237:105177. [PMID: 38458346 DOI: 10.1016/j.biosystems.2024.105177] [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: 10/03/2023] [Revised: 01/28/2024] [Accepted: 03/05/2024] [Indexed: 03/10/2024]
Abstract
The escalating global incidence of cancer poses significant health challenges, underscoring the need for innovative and more efficacious treatments. Cancer immunotherapy, a promising approach leveraging the body's immune system against cancer, emerges as a compelling solution. Consequently, the identification and characterization of tumor T-cell antigens (TTCAs) have become pivotal for exploration. In this manuscript, we introduce TTCA-IF, an integrative machine learning-based framework designed for TTCAs identification. TTCA-IF employs ten feature encoding types in conjunction with five conventional machine learning classifiers. To establish a robust foundation, these classifiers are trained, resulting in the creation of 150 baseline models. The outputs from these baseline models are then fed back into the five classifiers, generating their respective meta-models. Through an ensemble approach, the five meta-models are seamlessly integrated to yield the final predictive model, the TTCA-IF model. Our proposed model, TTCA-IF, surpasses both baseline models and existing predictors in performance. In a comparative analysis involving nine novel peptide sequences, TTCA-IF demonstrated exceptional accuracy by correctly identifying 8 out of 9 peptides as TTCAs. As a tool for screening and pinpointing potential TTCAs, we anticipate TTCA-IF to be invaluable in advancing cancer immunotherapy.
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Affiliation(s)
- Mir Tanveerul Hassan
- Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju, 54896, South Korea
| | - Hilal Tayara
- School of International Engineering and Science, Jeonbuk National University, Jeonju, 54896, South Korea.
| | - Kil To Chong
- Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju, 54896, South Korea; Advances Electronics and Information Research Centre, Jeonbuk National University, Jeonju, 54896, South Korea.
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9
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Charoenkwan P, Chumnanpuen P, Schaduangrat N, Shoombuatong W. Accelerating the identification of the allergenic potential of plant proteins using a stacked ensemble-learning framework. J Biomol Struct Dyn 2024:1-13. [PMID: 38385478 DOI: 10.1080/07391102.2024.2318482] [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: 12/07/2023] [Accepted: 02/08/2024] [Indexed: 02/23/2024]
Abstract
Plant-allergenic proteins (PAPs) have the potential to induce allergic reactions in certain individuals. While these proteins are generally innocuous for the majority of people, they can elicit an immune response in those with particular sensitivities. Thus, screening and prioritizing the allergenic potential of plant proteins is indispensable for the development of diagnostic tools, therapeutic interventions or medications to treat allergic reactions. However, investigating the allergenic potential of plant proteins based on experimental methods is costly and labour-intensive. Therefore, we develop StackPAP, a three-layer stacking ensemble framework for accurate large-scale identification of PAPs. In StackPAP, at the first layer, we conducted a comprehensive analysis of an extensive set of feature descriptors. Subsequently, we selected and fused five potential sequence-based feature descriptors, including amphiphilic pseudo-amino acid composition, dipeptide deviation from expected mean, amino acid composition, pseudo amino acid composition and dipeptide composition. Additionally, we applied an efficient genetic algorithm (GA-SAR) to determine informative feature sets. In the second layer, 12 powerful machine learning (ML) methods, in combination with all the informative feature sets, were employed to construct a pool of base classifiers. Finally, 13 potential base classifiers were selected using the GA-SAR method and combined to develop the final meta-classifier. Our experimental results revealed the promising prediction performance of StackPAP, with an accuracy, Matthew's correlation coefficient and AUC of 0.984, 0.969 and 0.993, respectively, as judged by the independent test dataset. In conclusion, both cross-validation and independent test results indicated the superior performance of StackPAP compared with several ML-based classifiers. To accelerate the identification of the allergenicity of plant proteins, we developed a user-friendly web server for StackPAP (https://pmlabqsar.pythonanywhere.com/StackPAP). We anticipate that StackPAP will be an efficient and useful tool for rapidly screening PAPs from a vast number of plant proteins.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Phasit Charoenkwan
- Modern Management and Information Technology, College of Arts, Media and Technology, Chiang Mai University, Thailand
| | - Pramote Chumnanpuen
- Department of Zoology, Faculty of Science, Kasetsart University, Bangkok, Thailand
- Omics Center for Agriculture, Bioresources, Food, and Health, Kasetsart University (OmiKU), Bangkok, Thailand
| | - Nalini Schaduangrat
- Center for Research Innovation and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, Thailand
| | - Watshara Shoombuatong
- Center for Research Innovation and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, Thailand
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10
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Xiang X, Gao J, Ding Y. DeepPPThermo: A Deep Learning Framework for Predicting Protein Thermostability Combining Protein-Level and Amino Acid-Level Features. J Comput Biol 2024; 31:147-160. [PMID: 38100126 DOI: 10.1089/cmb.2023.0097] [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] [Indexed: 02/15/2024] Open
Abstract
Using wet experimental methods to discover new thermophilic proteins or improve protein thermostability is time-consuming and expensive. Machine learning methods have shown powerful performance in the study of protein thermostability in recent years. However, how to make full use of multiview sequence information to predict thermostability effectively is still a challenge. In this study, we proposed a deep learning-based classifier named DeepPPThermo that fuses features of classical sequence features and deep learning representation features for classifying thermophilic and mesophilic proteins. In this model, deep neural network (DNN) and bi-long short-term memory (Bi-LSTM) are used to mine hidden features. Furthermore, local attention and global attention mechanisms give different importance to multiview features. The fused features are fed to a fully connected network classifier to distinguish thermophilic and mesophilic proteins. Our model is comprehensively compared with advanced machine learning algorithms and deep learning algorithms, proving that our model performs better. We further compare the effects of removing different features on the classification results, demonstrating the importance of each feature and the robustness of the model. Our DeepPPThermo model can be further used to explore protein diversity, identify new thermophilic proteins, and guide directed mutations of mesophilic proteins.
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Affiliation(s)
- Xiaoyang Xiang
- School of Science, Jiangnan University, Wuxi, P. R. China
| | - Jiaxuan Gao
- School of Science, Jiangnan University, Wuxi, P. R. China
| | - Yanrui Ding
- School of Science, Jiangnan University, Wuxi, P. R. China
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11
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Singh S, Le NQK, Wang C. VF-Pred: Predicting virulence factor using sequence alignment percentage and ensemble learning models. Comput Biol Med 2024; 168:107662. [PMID: 37979206 DOI: 10.1016/j.compbiomed.2023.107662] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Revised: 10/02/2023] [Accepted: 10/31/2023] [Indexed: 11/20/2023]
Abstract
This study introduces VF-Pred, a novel framework developed for the purpose of detecting virulence factors (VFs) through the analysis of genomic data. VFs are crucial for pathogens to successfully infect host tissue and evade the immune system, leading to the onset of infectious diseases. Identifying VFs accurately is of utmost importance in the quest for developing potent drugs and vaccines to counter these diseases. To accomplish this, VF-Pred combines various feature engineering techniques to generate inputs for distinct machine learning classification models. The collective predictions of these models are then consolidated by a final downstream model using an innovative ensembling approach. One notable aspect of VF-Pred is the inclusion of a novel Seq-Alignment feature, which significantly enhances the accuracy of the employed machine learning algorithms. The framework was meticulously trained on 982 features obtained from extensive feature engineering, utilizing a comprehensive ensemble of 25 models. The new downstream ensembling technique adopted by VF-Pred surpasses existing stacking strategies and other ensembling methods, delivering superior performance in VF detection. There have been similar studies done earlier, VF-Pred stands out in comparison showing higher accuracy (83.5 %), higher sensitivity (87 %) towards identification of VFs. Accessible through a user-friendly web page, VF-Pred can be accessed by providing the identifier and protein sequence, enabling the prediction of high or low likelihoods of VFs. Overall, VF-Pred showcases a highly promising methodology for the identification of VFs, potentially paving the way for the development of more effective strategies in the battle against infectious diseases.
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Affiliation(s)
- Shreya Singh
- NUS-ISS, National University of Singapore, 119615, Singapore
| | - Nguyen Quoc Khanh Le
- Professional Master Program in Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University, Taipei, 110, Taiwan; AIBioMed Research Group, Taipei Medical University, Taipei, 110, Taiwan; Translational Imaging Research Center, Taipei Medical University Hospital, Taipei, 110, Taiwan.
| | - Cheng Wang
- NUS-ISS, National University of Singapore, 119615, Singapore
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12
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Gaffar S, Hassan MT, Tayara H, Chong KT. IF-AIP: A machine learning method for the identification of anti-inflammatory peptides using multi-feature fusion strategy. Comput Biol Med 2024; 168:107724. [PMID: 37989075 DOI: 10.1016/j.compbiomed.2023.107724] [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: 08/30/2023] [Revised: 10/16/2023] [Accepted: 11/15/2023] [Indexed: 11/23/2023]
Abstract
BACKGROUND The most commonly used therapy currently for inflammatory and autoimmune diseases is nonspecific anti-inflammatory drugs, which have various hazardous side effects. Recently, some anti-inflammatory peptides (AIPs) have been found to be a substitute therapy for inflammatory diseases like rheumatoid arthritis and Alzheimer's. Therefore, the identification of these AIPs is an emerging topic that is equally important. METHODS In this work, we have proposed an identification model for AIPs using a voting classifier. We used eight different feature descriptors and five conventional machine-learning classifiers. The eight feature encodings were concatenated to get a hybrid feature set. The five baseline models trained on the hybrid feature set were integrated via a voting classifier. Finally, a feature selection algorithm was used to select the optimal feature set for the construction of our final model, named IF-AIP. RESULTS We tested the proposed model on two independent datasets. On independent data 1, the IF-AIP model shows an improvement of 3%-5.6% in terms of accuracies and 6.7%-10.8% in terms of MCC compared to the existing methods. On the independent dataset 2, our model IF-AIP shows an overall improvement of 2.9%-5.7% in terms of accuracy and 8.3%-8.6% in terms of MCC score compared to the existing methods. A comparative performance analysis was conducted between the proposed model and existing methods using a set of 24 novel peptide sequences. Notably, the IF-AIP method exhibited exceptional accuracy, correctly identifying all 24 peptides as AIPs. The source code, pre-trained models, and all datasets are made available at https://github.com/Mir-Saima/IF-AIP.
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Affiliation(s)
- Saima Gaffar
- Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju, 54896, South Korea
| | - Mir Tanveerul Hassan
- Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju, 54896, South Korea
| | - Hilal Tayara
- School of International Engineering and Science, Jeonbuk National University, Jeonju, 54896, South Korea.
| | - Kil To Chong
- Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju, 54896, South Korea; Advances Electronics and Information Research Centre, Jeonbuk National University, Jeonju, 54896, South Korea.
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13
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Schaduangrat N, Homdee N, Shoombuatong W. StackER: a novel SMILES-based stacked approach for the accelerated and efficient discovery of ERα and ERβ antagonists. Sci Rep 2023; 13:22994. [PMID: 38151513 PMCID: PMC10752908 DOI: 10.1038/s41598-023-50393-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Accepted: 12/19/2023] [Indexed: 12/29/2023] Open
Abstract
The role of estrogen receptors (ERs) in breast cancer is of great importance in both clinical practice and scientific exploration. However, around 15-30% of those affected do not see benefits from the usual treatments owing to the innate resistance mechanisms, while 30-40% will gain resistance through treatments. In order to address this problem and facilitate community-wide efforts, machine learning (ML)-based approaches are considered one of the most cost-effective and large-scale identification methods. Herein, we propose a new SMILES-based stacked approach, termed StackER, for the accelerated and efficient identification of ERα and ERβ inhibitors. In StackER, we first established an up-to-date dataset consisting of 1,996 and 1,207 compounds for ERα and ERβ, respectively. Using the up-to-date dataset, StackER explored a wide range of different SMILES-based feature descriptors and ML algorithms in order to generate probabilistic features (PFs). Finally, the selected PFs derived from the two-step feature selection strategy were used for the development of an efficient stacked model. Both cross-validation and independent tests showed that StackER surpassed several conventional ML classifiers and the existing method in precisely predicting ERα and ERβ inhibitors. Remarkably, StackER achieved MCC values of 0.829-0.847 and 0.712-0.786 in terms of the cross-validation and independent tests, respectively, which were 5.92-8.29 and 1.59-3.45% higher than the existing method. In addition, StackER was applied to determine useful features for being ERα and ERβ inhibitors and identify FDA-approved drugs as potential ERα inhibitors in efforts to facilitate drug repurposing. This innovative stacked method is anticipated to facilitate community-wide efforts in efficiently narrowing down ER inhibitor screening.
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Affiliation(s)
- Nalini Schaduangrat
- Center for Research Innovation and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, 10700, Thailand
| | - Nutta Homdee
- Center for Research Innovation and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, 10700, Thailand
| | - Watshara Shoombuatong
- Center for Research Innovation and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, 10700, Thailand.
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14
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Haselbeck F, John M, Zhang Y, Pirnay J, Fuenzalida-Werner J, Costa R, Grimm D. Superior protein thermophilicity prediction with protein language model embeddings. NAR Genom Bioinform 2023; 5:lqad087. [PMID: 37829176 PMCID: PMC10566323 DOI: 10.1093/nargab/lqad087] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Revised: 07/14/2023] [Accepted: 09/18/2023] [Indexed: 10/14/2023] Open
Abstract
Protein thermostability is important in many areas of biotechnology, including enzyme engineering and protein-hybrid optoelectronics. Ever-growing protein databases and information on stability at different temperatures allow the training of machine learning models to predict whether proteins are thermophilic. In silico predictions could reduce costs and accelerate the development process by guiding researchers to more promising candidates. Existing models for predicting protein thermophilicity rely mainly on features derived from physicochemical properties. Recently, modern protein language models that directly use sequence information have demonstrated superior performance in several tasks. In this study, we evaluate the usefulness of protein language model embeddings for thermophilicity prediction with ProLaTherm, a Protein Language model-based Thermophilicity predictor. ProLaTherm significantly outperforms all feature-, sequence- and literature-based comparison partners on multiple evaluation metrics. In terms of the Matthew's correlation coefficient, ProLaTherm outperforms the second-best competitor by 18.1% in a nested cross-validation setup. Using proteins from species not overlapping with species from the training data, ProLaTherm outperforms all competitors by at least 9.7%. On these data, it misclassified only one nonthermophilic protein as thermophilic. Furthermore, it correctly identified 97.4% of all thermophilic proteins in our test set with an optimal growth temperature above 70°C.
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Affiliation(s)
- Florian Haselbeck
- Technical University of Munich, Campus Straubing for Biotechnology and Sustainability, Bioinformatics, 94315 Straubing, Germany
- Weihenstephan-Triesdorf University of Applied Sciences, Bioinformatics, 94315 Straubing, Germany
| | - Maura John
- Technical University of Munich, Campus Straubing for Biotechnology and Sustainability, Bioinformatics, 94315 Straubing, Germany
- Weihenstephan-Triesdorf University of Applied Sciences, Bioinformatics, 94315 Straubing, Germany
| | - Yuqi Zhang
- Technical University of Munich, Campus Straubing for Biotechnology and Sustainability, Bioinformatics, 94315 Straubing, Germany
| | - Jonathan Pirnay
- Technical University of Munich, Campus Straubing for Biotechnology and Sustainability, Bioinformatics, 94315 Straubing, Germany
- Weihenstephan-Triesdorf University of Applied Sciences, Bioinformatics, 94315 Straubing, Germany
| | - Juan Pablo Fuenzalida-Werner
- Technical University of Munich, Campus Straubing for Biotechnology and Sustainability, Chair of Biogenic Functional Materials, 94315 Straubing, Germany
| | - Rubén D Costa
- Technical University of Munich, Campus Straubing for Biotechnology and Sustainability, Chair of Biogenic Functional Materials, 94315 Straubing, Germany
| | - Dominik G Grimm
- Technical University of Munich, Campus Straubing for Biotechnology and Sustainability, Bioinformatics, 94315 Straubing, Germany
- Weihenstephan-Triesdorf University of Applied Sciences, Bioinformatics, 94315 Straubing, Germany
- Technical University of Munich, TUM School of Computation, Information and Technology (CIT), 85748 Garching, Germany
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15
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Charoenkwan P, Kongsompong S, Schaduangrat N, Chumnanpuen P, Shoombuatong W. TIPred: a novel stacked ensemble approach for the accelerated discovery of tyrosinase inhibitory peptides. BMC Bioinformatics 2023; 24:356. [PMID: 37735626 PMCID: PMC10512532 DOI: 10.1186/s12859-023-05463-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Accepted: 09/01/2023] [Indexed: 09/23/2023] Open
Abstract
BACKGROUND Tyrosinase is an enzyme involved in melanin production in the skin. Several hyperpigmentation disorders involve the overproduction of melanin and instability of tyrosinase activity resulting in darker, discolored patches on the skin. Therefore, discovering tyrosinase inhibitory peptides (TIPs) is of great significance for basic research and clinical treatments. However, the identification of TIPs using experimental methods is generally cost-ineffective and time-consuming. RESULTS Herein, a stacked ensemble learning approach, called TIPred, is proposed for the accurate and quick identification of TIPs by using sequence information. TIPred explored a comprehensive set of various baseline models derived from well-known machine learning (ML) algorithms and heterogeneous feature encoding schemes from multiple perspectives, such as chemical structure properties, physicochemical properties, and composition information. Subsequently, 130 baseline models were trained and optimized to create new probabilistic features. Finally, the feature selection approach was utilized to determine the optimal feature vector for developing TIPred. Both tenfold cross-validation and independent test methods were employed to assess the predictive capability of TIPred by using the stacking strategy. Experimental results showed that TIPred significantly outperformed the state-of-the-art method in terms of the independent test, with an accuracy of 0.923, MCC of 0.757 and an AUC of 0.977. CONCLUSIONS The proposed TIPred approach could be a valuable tool for rapidly discovering novel TIPs and effectively identifying potential TIP candidates for follow-up experimental validation. Moreover, an online webserver of TIPred is publicly available at http://pmlabstack.pythonanywhere.com/TIPred .
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Affiliation(s)
- Phasit Charoenkwan
- Modern Management and Information Technology, College of Arts, Media and Technology, Chiang Mai University, Chiang Mai, 50200, Thailand
| | - Sasikarn Kongsompong
- Interdisciplinary Graduate Program in Bioscience, Faculty of Science, Kasetsart University, Bangkok, 10900, Thailand
| | - Nalini Schaduangrat
- Center for Research Innovation and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, 10700, Thailand
| | - Pramote Chumnanpuen
- Department of Zoology, Faculty of Science, Kasetsart University, Bangkok, 10900, Thailand.
- Omics Center for Agriculture, Bioresources, Food, and Health, Kasetsart University (OmiKU), Bangkok, 10900, Thailand.
| | - Watshara Shoombuatong
- Center for Research Innovation and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, 10700, Thailand.
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16
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Charoenkwan P, Waramit S, Chumnanpuen P, Schaduangrat N, Shoombuatong W. TROLLOPE: A novel sequence-based stacked approach for the accelerated discovery of linear T-cell epitopes of hepatitis C virus. PLoS One 2023; 18:e0290538. [PMID: 37624802 PMCID: PMC10456195 DOI: 10.1371/journal.pone.0290538] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Accepted: 08/10/2023] [Indexed: 08/27/2023] Open
Abstract
Hepatitis C virus (HCV) infection is a concerning health issue that causes chronic liver diseases. Despite many successful therapeutic outcomes, no effective HCV vaccines are currently available. Focusing on T cell activity, the primary effector for HCV clearance, T cell epitopes of HCV (TCE-HCV) are considered promising elements to accelerate HCV vaccine efficacy. Thus, accurate and rapid identification of TCE-HCVs is recommended to obtain more efficient therapy for chronic HCV infection. In this study, a novel sequence-based stacked approach, termed TROLLOPE, is proposed to accurately identify TCE-HCVs from sequence information. Specifically, we employed 12 different sequence-based feature descriptors from heterogeneous perspectives, such as physicochemical properties, composition-transition-distribution information and composition information. These descriptors were used in cooperation with 12 popular machine learning (ML) algorithms to create 144 base-classifiers. To maximize the utility of these base-classifiers, we used a feature selection strategy to determine a collection of potential base-classifiers and integrated them to develop the meta-classifier. Comprehensive experiments based on both cross-validation and independent tests demonstrated the superior predictive performance of TROLLOPE compared with conventional ML classifiers, with cross-validation and independent test accuracies of 0.745 and 0.747, respectively. Finally, a user-friendly online web server of TROLLOPE (http://pmlabqsar.pythonanywhere.com/TROLLOPE) has been developed to serve research efforts in the large-scale identification of potential TCE-HCVs for follow-up experimental verification.
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Affiliation(s)
- Phasit Charoenkwan
- Modern Management and Information Technology, College of Arts, Media and Technology, Chiang Mai University, Chiang Mai, Thailand
| | - Sajee Waramit
- Department of Zoology, Faculty of Science, Kasetsart University, Bangkok, Thailand
| | - Pramote Chumnanpuen
- Department of Zoology, Faculty of Science, Kasetsart University, Bangkok, Thailand
- Omics Center for Agriculture, Bioresources, Food, and Health, Kasetsart University (OmiKU), Bangkok, Thailand
| | - Nalini Schaduangrat
- Center for Research Innovation and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, Thailand
| | - Watshara Shoombuatong
- Center for Research Innovation and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, Thailand
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17
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Charoenkwan P, Schaduangrat N, Shoombuatong W. StackTTCA: a stacking ensemble learning-based framework for accurate and high-throughput identification of tumor T cell antigens. BMC Bioinformatics 2023; 24:301. [PMID: 37507654 PMCID: PMC10386778 DOI: 10.1186/s12859-023-05421-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Accepted: 07/19/2023] [Indexed: 07/30/2023] Open
Abstract
BACKGROUND The identification of tumor T cell antigens (TTCAs) is crucial for providing insights into their functional mechanisms and utilizing their potential in anticancer vaccines development. In this context, TTCAs are highly promising. Meanwhile, experimental technologies for discovering and characterizing new TTCAs are expensive and time-consuming. Although many machine learning (ML)-based models have been proposed for identifying new TTCAs, there is still a need to develop a robust model that can achieve higher rates of accuracy and precision. RESULTS In this study, we propose a new stacking ensemble learning-based framework, termed StackTTCA, for accurate and large-scale identification of TTCAs. Firstly, we constructed 156 different baseline models by using 12 different feature encoding schemes and 13 popular ML algorithms. Secondly, these baseline models were trained and employed to create a new probabilistic feature vector. Finally, the optimal probabilistic feature vector was determined based the feature selection strategy and then used for the construction of our stacked model. Comparative benchmarking experiments indicated that StackTTCA clearly outperformed several ML classifiers and the existing methods in terms of the independent test, with an accuracy of 0.932 and Matthew's correlation coefficient of 0.866. CONCLUSIONS In summary, the proposed stacking ensemble learning-based framework of StackTTCA could help to precisely and rapidly identify true TTCAs for follow-up experimental verification. In addition, we developed an online web server ( http://2pmlab.camt.cmu.ac.th/StackTTCA ) to maximize user convenience for high-throughput screening of novel TTCAs.
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Affiliation(s)
- Phasit Charoenkwan
- Modern Management and Information Technology, College of Arts, Media and Technology, Chiang Mai University, Chiang Mai, 50200, Thailand
| | - Nalini Schaduangrat
- Center for Research Innovation and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, 10700, Thailand
| | - Watshara Shoombuatong
- Center for Research Innovation and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, 10700, Thailand.
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18
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Schaduangrat N, Anuwongcharoen N, Charoenkwan P, Shoombuatong W. DeepAR: a novel deep learning-based hybrid framework for the interpretable prediction of androgen receptor antagonists. J Cheminform 2023; 15:50. [PMID: 37149650 PMCID: PMC10163717 DOI: 10.1186/s13321-023-00721-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2022] [Accepted: 04/08/2023] [Indexed: 05/08/2023] Open
Abstract
Drug resistance represents a major obstacle to therapeutic innovations and is a prevalent feature in prostate cancer (PCa). Androgen receptors (ARs) are the hallmark therapeutic target for prostate cancer modulation and AR antagonists have achieved great success. However, rapid emergence of resistance contributing to PCa progression is the ultimate burden of their long-term usage. Hence, the discovery and development of AR antagonists with capability to combat the resistance, remains an avenue for further exploration. Therefore, this study proposes a novel deep learning (DL)-based hybrid framework, named DeepAR, to accurately and rapidly identify AR antagonists by using only the SMILES notation. Specifically, DeepAR is capable of extracting and learning the key information embedded in AR antagonists. Firstly, we established a benchmark dataset by collecting active and inactive compounds against AR from the ChEMBL database. Based on this dataset, we developed and optimized a collection of baseline models by using a comprehensive set of well-known molecular descriptors and machine learning algorithms. Then, these baseline models were utilized for creating probabilistic features. Finally, these probabilistic features were combined and used for the construction of a meta-model based on a one-dimensional convolutional neural network. Experimental results indicated that DeepAR is a more accurate and stable approach for identifying AR antagonists in terms of the independent test dataset, by achieving an accuracy of 0.911 and MCC of 0.823. In addition, our proposed framework is able to provide feature importance information by leveraging a popular computational approach, named SHapley Additive exPlanations (SHAP). In the meanwhile, the characterization and analysis of potential AR antagonist candidates were achieved through the SHAP waterfall plot and molecular docking. The analysis inferred that N-heterocyclic moieties, halogenated substituents, and a cyano functional group were significant determinants of potential AR antagonists. Lastly, we implemented an online web server by using DeepAR (at http://pmlabstack.pythonanywhere.com/DeepAR ). We anticipate that DeepAR could be a useful computational tool for community-wide facilitation of AR candidates from a large number of uncharacterized compounds.
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Affiliation(s)
- Nalini Schaduangrat
- Center for Research Innovation and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, 10700, Thailand
| | - Nuttapat Anuwongcharoen
- Department of Community Medical Technology, Faculty of Medical Technology, Mahidol University, Bangkok, 10700, Thailand
| | - Phasit Charoenkwan
- Modern Management and Information Technology, College of Arts, Media and Technology, Chiang Mai University, Chiang Mai, 50200, Thailand.
| | - Watshara Shoombuatong
- Center for Research Innovation and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, 10700, Thailand.
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19
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Charoenkwan P, Chumnanpuen P, Schaduangrat N, Oh C, Manavalan B, Shoombuatong W. PSRQSP: An effective approach for the interpretable prediction of quorum sensing peptide using propensity score representation learning. Comput Biol Med 2023; 158:106784. [PMID: 36989748 DOI: 10.1016/j.compbiomed.2023.106784] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2022] [Revised: 02/07/2023] [Accepted: 03/10/2023] [Indexed: 03/14/2023]
Abstract
Quorum sensing peptides (QSPs) are microbial signaling molecules involved in several cellular processes, such as cellular communication, virulence expression, bioluminescence, and swarming, in various bacterial species. Understanding QSPs is essential for identifying novel drug targets for controlling bacterial populations and pathogenicity. In this study, we present a novel computational approach (PSRQSP) for improving the prediction and analysis of QSPs. In PSRQSP, we develop a novel propensity score representation learning (PSR) scheme. Specifically, we utilized the PSR approach to extract and learn a comprehensive set of estimated propensities of 20 amino acids, 400 dipeptides, and 400 g-gap dipeptides from a pool of scoring card method-based models. Finally, to maximize the utility of the propensity scores, we explored a set of optimal propensity scores and combined them to construct a final meta-predictor. Our experimental results showed that combining multiview propensity scores was more beneficial for identifying QSPs than the conventional feature descriptors. Moreover, extensive benchmarking experiments based on the independent test were sufficient to demonstrate the predictive capability and effectiveness of PSRQSP by outperforming the conventional ML-based and existing methods, with an accuracy of 94.44% and AUC of 0.967. PSR-derived propensity scores were employed to determine the crucial physicochemical properties for a better understanding of the functional mechanisms of QSPs. Finally, we constructed an easy-to-use web server for the PSRQSP (http://pmlabstack.pythonanywhere.com/PSRQSP). PSRQSP is anticipated to be an efficient computational tool for accelerating the data-driven discovery of potential QSPs for drug discovery and development.
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Affiliation(s)
- Phasit Charoenkwan
- Modern Management and Information Technology, College of Arts, Media and Technology, Chiang Mai University, Chiang Mai, 50200, Thailand
| | - Pramote Chumnanpuen
- Department of Zoology, Faculty of Science, Kasetsart University, Bangkok, 10900, Thailand; Omics Center for Agriculture, Bioresources, Food, and Health, Kasetsart University (OmiKU), Bangkok, 10900, Thailand
| | - Nalini Schaduangrat
- Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, 10700, Thailand
| | - Changmin Oh
- Computational Biology and Bioinformatics Laboratory, Department of Integrative Biotechnology, College of Biotechnology and Bioengineering, Sungkyunkwan University, Suwon, 16419, Gyeonggi-do, Republic of Korea
| | - Balachandran Manavalan
- Computational Biology and Bioinformatics Laboratory, Department of Integrative Biotechnology, College of Biotechnology and Bioengineering, Sungkyunkwan University, Suwon, 16419, Gyeonggi-do, Republic of Korea.
| | - Watshara Shoombuatong
- Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, 10700, Thailand.
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20
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Charoenkwan P, Schaduangrat N, Pham NT, Manavalan B, Shoombuatong W. Pretoria: An effective computational approach for accurate and high-throughput identification of CD8+ t-cell epitopes of eukaryotic pathogens. Int J Biol Macromol 2023; 238:124228. [PMID: 36996953 DOI: 10.1016/j.ijbiomac.2023.124228] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Revised: 03/11/2023] [Accepted: 03/25/2023] [Indexed: 03/31/2023]
Abstract
T-cells recognize antigenic epitopes present on major histocompatibility complex (MHC) molecules, triggering an adaptive immune response in the host. T-cell epitope (TCE) identification is challenging because of the extensive number of undetermined proteins found in eukaryotic pathogens, as well as MHC polymorphisms. In addition, conventional experimental approaches for TCE identification are time-consuming and expensive. Thus, computational approaches that can accurately and rapidly identify CD8+ T-cell epitopes (TCEs) of eukaryotic pathogens based solely on sequence information may facilitate the discovery of novel CD8+ TCEs in a cost-effective manner. Here, Pretoria (Predictor of CD8+ TCEs of eukaryotic pathogens) is proposed as the first stack-based approach for accurate and large-scale identification of CD8+ TCEs of eukaryotic pathogens. In particular, Pretoria enabled the extraction and exploration of crucial information embedded in CD8+ TCEs by employing a comprehensive set of 12 well-known feature descriptors extracted from multiple groups, including physicochemical properties, composition-transition-distribution, pseudo-amino acid composition, and amino acid composition. These feature descriptors were then utilized to construct a pool of 144 different machine learning (ML)-based classifiers based on 12 popular ML algorithms. Finally, the feature selection method was used to effectively determine the important ML classifiers for the construction of our stacked model. The experimental results indicated that Pretoria is an accurate and effective computational approach for CD8+ TCE prediction; it was superior to several conventional ML classifiers and the existing method in terms of the independent test, with an accuracy of 0.866, MCC of 0.732, and AUC of 0.921. Additionally, to maximize user convenience for high-throughput identification of CD8+ TCEs of eukaryotic pathogens, a user-friendly web server of Pretoria (http://pmlabstack.pythonanywhere.com/Pretoria) was developed and made freely available.
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Affiliation(s)
- Phasit Charoenkwan
- Modern Management and Information Technology, College of Arts, Media and Technology, Chiang Mai University, Chiang Mai 50200, Thailand
| | - Nalini Schaduangrat
- Center for Research Innovation and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand
| | - Nhat Truong Pham
- Computational Biology and Bioinformatics Laboratory, Department of Integrative Biotechnology, College of Biotechnology and Bioengineering, Sungkyunkwan University, Suwon 16419, Gyeonggi-do, Republic of Korea
| | - Balachandran Manavalan
- Computational Biology and Bioinformatics Laboratory, Department of Integrative Biotechnology, College of Biotechnology and Bioengineering, Sungkyunkwan University, Suwon 16419, Gyeonggi-do, Republic of Korea.
| | - Watshara Shoombuatong
- Center for Research Innovation and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand.
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21
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Zhao J, Yan W, Yang Y. DeepTP: A Deep Learning Model for Thermophilic Protein Prediction. Int J Mol Sci 2023; 24:ijms24032217. [PMID: 36768540 PMCID: PMC9917291 DOI: 10.3390/ijms24032217] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2022] [Revised: 01/19/2023] [Accepted: 01/19/2023] [Indexed: 01/26/2023] Open
Abstract
Thermophilic proteins have important value in the fields of biopharmaceuticals and enzyme engineering. Most existing thermophilic protein prediction models are based on traditional machine learning algorithms and do not fully utilize protein sequence information. To solve this problem, a deep learning model based on self-attention and multiple-channel feature fusion was proposed to predict thermophilic proteins, called DeepTP. First, a large new dataset consisting of 20,842 proteins was constructed. Second, a convolutional neural network and bidirectional long short-term memory network were used to extract the hidden features in protein sequences. Different weights were then assigned to features through self-attention, and finally, biological features were integrated to build a prediction model. In a performance comparison with existing methods, DeepTP had better performance and scalability in an independent balanced test set and validation set, with AUC values of 0.944 and 0.801, respectively. In the unbalanced test set, DeepTP had an average precision (AP) of 0.536. The tool is freely available.
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Affiliation(s)
- Jianjun Zhao
- School of Computer Science and Technology, Soochow University, Suzhou 215006, China
- Collaborative Innovation Center of Novel Software Technology and Industrialization, Nanjing 210000, China
| | - Wenying Yan
- Department of Bioinformatics, School of Biology and Basic Medical Sciences, Suzhou Medical College of Soochow University, Soochow University, Suzhou 215123, China
- Center for Systems Biology, Soochow University, Suzhou 215123, China
- Jiangsu Province Engineering Research Center of Precision Diagnostics and Therapeutics Development, Suzhou 215123, China
- Correspondence: (W.Y.); (Y.Y.)
| | - Yang Yang
- School of Computer Science and Technology, Soochow University, Suzhou 215006, China
- Collaborative Innovation Center of Novel Software Technology and Industrialization, Nanjing 210000, China
- Correspondence: (W.Y.); (Y.Y.)
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22
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PSRTTCA: A new approach for improving the prediction and characterization of tumor T cell antigens using propensity score representation learning. Comput Biol Med 2023; 152:106368. [PMID: 36481763 DOI: 10.1016/j.compbiomed.2022.106368] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Revised: 10/19/2022] [Accepted: 11/25/2022] [Indexed: 11/27/2022]
Abstract
Despite the arsenal of existing cancer therapies, the ongoing recurrence and new cases of cancer pose a serious health concern that necessitates the development of new and effective treatments. Cancer immunotherapy, which uses the body's immune system to combat cancer, is a promising treatment option. As a result, in silico methods for identifying and characterizing tumor T cell antigens (TTCAs) would be useful for better understanding their functional mechanisms. Although few computational methods for TTCA identification have been developed, their lack of model interpretability is a major drawback. Thus, developing computational methods for the effective identification and characterization of TTCAs is a critical endeavor. PSRTTCA, a new machine learning (ML)-based approach for improving the identification and characterization of TTCAs based on their primary sequences, is proposed in this study. Specifically, we introduce a new propensity score representation learning algorithm that allows one to generate various sets of propensity scores of amino acids, dipeptides, and g-gap dipeptides to be TTCAs. To enhance the predictive performance, optimal sets of variant propensity scores were determined and fed into the final meta-predictor (PSRTTCA). Benchmarking results revealed that PSRTTCA was a more precise and promising tool for the identification and characterization of TTCAs than conventional ML classifiers and existing methods. Furthermore, PSR-derived propensities of amino acids in becoming TTCAs are used to reveal the relationship between TTCAs and their informative physicochemical properties in order to provide insights into TTCA characteristics. Finally, a user-friendly online computational platform of PSRTTCA is publicly available at http://pmlabstack.pythonanywhere.com/PSRTTCA. The PSRTTCA predictor is anticipated to facilitate community-wide efforts in accelerating the discovery of novel TTCAs for cancer immunotherapy and other clinical applications.
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Converting the genomic knowledge base to build protein specific machine learning prediction models; a classification study on thermophilic serine protease. Biologia (Bratisl) 2022. [DOI: 10.1007/s11756-022-01214-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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