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Hao Y, Liu X, Fu H, Shao X, Cai W. PGAT-ABPp: harnessing protein language models and graph attention networks for antibacterial peptide identification with remarkable accuracy. Bioinformatics 2024; 40:btae497. [PMID: 39120878 PMCID: PMC11338452 DOI: 10.1093/bioinformatics/btae497] [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: 03/29/2024] [Revised: 07/24/2024] [Accepted: 08/08/2024] [Indexed: 08/10/2024] Open
Abstract
MOTIVATION The emergence of drug-resistant pathogens represents a formidable challenge to global health. Using computational methods to identify the antibacterial peptides (ABPs), an alternative antimicrobial agent, has demonstrated advantages in further drug design studies. Most of the current approaches, however, rely on handcrafted features and underutilize structural information, which may affect prediction performance. RESULTS To present an ultra-accurate model for ABP identification, we propose a novel deep learning approach, PGAT-ABPp. PGAT-ABPp leverages structures predicted by AlphaFold2 and a pretrained protein language model, ProtT5-XL-U50 (ProtT5), to construct graphs. Then the graph attention network (GAT) is adopted to learn global discriminative features from the graphs. PGAT-ABPp outperforms the other fourteen state-of-the-art models in terms of accuracy, F1-score and Matthews Correlation Coefficient on the independent test dataset. The results show that ProtT5 has significant advantages in the identification of ABPs and the introduction of spatial information further improves the prediction performance of the model. The interpretability analysis of key residues in known active ABPs further underscores the superiority of PGAT-ABPp. AVAILABILITY AND IMPLEMENTATION The datasets and source codes for the PGAT-ABPp model are available at https://github.com/moonseter/PGAT-ABPp/.
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Affiliation(s)
- Yuelei Hao
- Research Center for Analytical Sciences, Tianjin Key Laboratory of Biosensing and Molecular Recognition, State Key Laboratory of Medicinal Chemical Biology, College of Chemistry, Nankai University, Tianjin 300071, China
- Haihe Laboratory of Sustainable Chemical Transformations, Tianjin 300192, China
| | - Xuyang Liu
- Research Center for Analytical Sciences, Tianjin Key Laboratory of Biosensing and Molecular Recognition, State Key Laboratory of Medicinal Chemical Biology, College of Chemistry, Nankai University, Tianjin 300071, China
- Haihe Laboratory of Sustainable Chemical Transformations, Tianjin 300192, China
| | - Haohao Fu
- Research Center for Analytical Sciences, Tianjin Key Laboratory of Biosensing and Molecular Recognition, State Key Laboratory of Medicinal Chemical Biology, College of Chemistry, Nankai University, Tianjin 300071, China
- Haihe Laboratory of Sustainable Chemical Transformations, Tianjin 300192, China
| | - Xueguang Shao
- Research Center for Analytical Sciences, Tianjin Key Laboratory of Biosensing and Molecular Recognition, State Key Laboratory of Medicinal Chemical Biology, College of Chemistry, Nankai University, Tianjin 300071, China
- Haihe Laboratory of Sustainable Chemical Transformations, Tianjin 300192, China
| | - Wensheng Cai
- Research Center for Analytical Sciences, Tianjin Key Laboratory of Biosensing and Molecular Recognition, State Key Laboratory of Medicinal Chemical Biology, College of Chemistry, Nankai University, Tianjin 300071, China
- Haihe Laboratory of Sustainable Chemical Transformations, Tianjin 300192, China
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Kang Y, Zhang H, Wang X, Yang Y, Jia Q. MMDB: Multimodal dual-branch model for multi-functional bioactive peptide prediction. Anal Biochem 2024; 690:115491. [PMID: 38460901 DOI: 10.1016/j.ab.2024.115491] [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: 11/12/2023] [Revised: 01/21/2024] [Accepted: 02/19/2024] [Indexed: 03/11/2024]
Abstract
Bioactive peptides can hinder oxidative processes and microbial spoilage in foodstuffs and play important roles in treating diverse diseases and disorders. While most of the methods focus on single-functional bioactive peptides and have obtained promising prediction performance, it is still a significant challenge to accurately detect complex and diverse functions simultaneously with the quick increase of multi-functional bioactive peptides. In contrast to previous research on multi-functional bioactive peptide prediction based solely on sequence, we propose a novel multimodal dual-branch (MMDB) lightweight deep learning model that designs two different branches to effectively capture the complementary information of peptide sequence and structural properties. Specifically, a multi-scale dilated convolution with Bi-LSTM branch is presented to effectively model the different scales sequence properties of peptides while a multi-layer convolution branch is proposed to capture structural information. To the best of our knowledge, this is the first effective extraction of peptide sequence features using multi-scale dilated convolution without parameter increase. Multimodal features from both branches are integrated via a fully connected layer for multi-label classification. Compared to state-of-the-art methods, our MMDB model exhibits competitive results across metrics, with a 9.1% Coverage increase and 5.3% and 3.5% improvements in Precision and Accuracy, respectively.
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Affiliation(s)
- Yan Kang
- National Pilot School of Software, Yunnan University, Kunming, 650091, Yunnan, China; Yunnan Key Laboratory of Software Engineering, China
| | - Huadong Zhang
- National Pilot School of Software, Yunnan University, Kunming, 650091, Yunnan, China
| | - Xinchao Wang
- National Pilot School of Software, Yunnan University, Kunming, 650091, Yunnan, China
| | - Yun Yang
- National Pilot School of Software, Yunnan University, Kunming, 650091, Yunnan, China; Yunnan Key Laboratory of Software Engineering, China.
| | - Qi Jia
- School of Information Science, Yunnan University, Kunming, 650091, Yunnan, China
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3
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Singh V, Singh SK, Sharma R. A novel framework based on explainable AI and genetic algorithms for designing neurological medicines. Sci Rep 2024; 14:12807. [PMID: 38834718 DOI: 10.1038/s41598-024-63561-3] [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: 02/29/2024] [Accepted: 05/30/2024] [Indexed: 06/06/2024] Open
Abstract
The advent of the fourth industrial revolution, characterized by artificial intelligence (AI) as its central component, has resulted in the mechanization of numerous previously labor-intensive activities. The use of in silico tools has become prevalent in the design of biopharmaceuticals. Upon conducting a comprehensive analysis of the genomes of many organisms, it has been discovered that their tissues can generate specific peptides that confer protection against certain diseases. This study aims to identify a selected group of neuropeptides (NPs) possessing favorable characteristics that render them ideal for production as neurological biopharmaceuticals. Until now, the construction of NP classifiers has been the primary focus, neglecting to optimize these characteristics. Therefore, in this study, the task of creating ideal NPs has been formulated as a multi-objective optimization problem. The proposed framework, NPpred, comprises two distinct components: NSGA-NeuroPred and BERT-NeuroPred. The former employs the NSGA-II algorithm to explore and change a population of NPs, while the latter is an interpretable deep learning-based model. The utilization of explainable AI and motifs has led to the proposal of two novel operators, namely p-crossover and p-mutation. An online application has been deployed at https://neuropred.anvil.app for designing an ideal collection of synthesizable NPs from protein sequences.
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Affiliation(s)
- Vishakha Singh
- Department of Computer Science and Engineering, Indian Institute of Technology (BHU), Varanasi, 221005, Uttar Pradesh, India.
| | - Sanjay Kumar Singh
- Department of Computer Science and Engineering, Indian Institute of Technology (BHU), Varanasi, 221005, Uttar Pradesh, India.
| | - Ritesh Sharma
- Department of ICT, Manipal Institute of Technology, Manipal, 576104, Uttar Pradesh, India
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4
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Chen Z, Wang R, Guo J, Wang X. The role and future prospects of artificial intelligence algorithms in peptide drug development. Biomed Pharmacother 2024; 175:116709. [PMID: 38713945 DOI: 10.1016/j.biopha.2024.116709] [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: 03/10/2024] [Revised: 05/01/2024] [Accepted: 05/02/2024] [Indexed: 05/09/2024] Open
Abstract
Peptide medications have been more well-known in recent years due to their many benefits, including low side effects, high biological activity, specificity, effectiveness, and so on. Over 100 peptide medications have been introduced to the market to treat a variety of illnesses. Most of these peptide medications are developed on the basis of endogenous peptides or natural peptides, which frequently required expensive, time-consuming, and extensive tests to confirm. As artificial intelligence advances quickly, it is now possible to build machine learning or deep learning models that screen a large number of candidate sequences for therapeutic peptides. Therapeutic peptides, such as those with antibacterial or anticancer properties, have been developed by the application of artificial intelligence algorithms.The process of finding and developing peptide drugs is outlined in this review, along with a few related cases that were helped by AI and conventional methods. These resources will open up new avenues for peptide drug development and discovery, helping to meet the pressing needs of clinical patients for disease treatment. Although peptide drugs are a new class of biopharmaceuticals that distinguish them from chemical and small molecule drugs, their clinical purpose and value cannot be ignored. However, the traditional peptide drug research and development has a long development cycle and high investment, and the creation of peptide medications will be substantially hastened by the AI-assisted (AI+) mode, offering a new boost for combating diseases.
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Affiliation(s)
- Zhiheng Chen
- School of Biological Science and Medical Engineering, Beihang University, Beijing 100083, China.
| | - Ruoxi Wang
- School of Biological Science and Medical Engineering, Beihang University, Beijing 100083, China.
| | - Junqi Guo
- School of Biological Science and Medical Engineering, Beihang University, Beijing 100083, China.
| | - Xiaogang Wang
- Guangdong Provincial Key Laboratory of Bone and Joint Degenerative Diseases, The Third Affiliated Hospital of Southern Medical University, Guangzhou, Guangdong 510630, China.
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Cheng Z, Aitha M, Thomas CA, Sturgill A, Fairweather M, Hu A, Bethel CR, Rivera DD, Dranchak P, Thomas PW, Li H, Feng Q, Tao K, Song M, Sun N, Wang S, Silwal SB, Page RC, Fast W, Bonomo RA, Weese M, Martinez W, Inglese J, Crowder MW. Machine Learning Models Identify Inhibitors of New Delhi Metallo-β-lactamase. J Chem Inf Model 2024; 64:3977-3991. [PMID: 38727192 PMCID: PMC11129921 DOI: 10.1021/acs.jcim.3c02015] [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] [Indexed: 05/13/2024]
Abstract
The worldwide spread of the metallo-β-lactamases (MBL), especially New Delhi metallo-β-lactamase-1 (NDM-1), is threatening the efficacy of β-lactams, which are the most potent and prescribed class of antibiotics in the clinic. Currently, FDA-approved MBL inhibitors are lacking in the clinic even though many strategies have been used in inhibitor development, including quantitative high-throughput screening (qHTS), fragment-based drug discovery (FBDD), and molecular docking. Herein, a machine learning-based prediction tool is described, which was generated using results from HTS of a large chemical library and previously published inhibition data. The prediction tool was then used for virtual screening of the NIH Genesis library, which was subsequently screened using qHTS. A novel MBL inhibitor was identified and shown to lower minimum inhibitory concentrations (MICs) of Meropenem for a panel of E. coli and K. pneumoniae clinical isolates expressing NDM-1. The mechanism of inhibition of this novel scaffold was probed utilizing equilibrium dialyses with metal analyses, native state electrospray ionization mass spectrometry, UV-vis spectrophotometry, and molecular docking. The uncovered inhibitor, compound 72922413, was shown to be 9-hydroxy-3-[(5-hydroxy-1-oxa-9-azaspiro[5.5]undec-9-yl)carbonyl]-4H-pyrido[1,2-a]pyrimidin-4-one.
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Affiliation(s)
- Zishuo Cheng
- Department of Chemistry and Biochemistry, Miami University, Oxford, OH 45056, USA
| | - Mahesh Aitha
- Division of Preclinical Innovation, National Center for Advancing Translational Sciences, National Institutes of Health, Rockville, MD 20850, USA
| | - Caitlyn A. Thomas
- Department of Chemistry and Biochemistry, Miami University, Oxford, OH 45056, USA
| | - Aidan Sturgill
- Department of Chemistry and Biochemistry, Miami University, Oxford, OH 45056, USA
| | - Mitch Fairweather
- Department of Chemistry and Biochemistry, Miami University, Oxford, OH 45056, USA
| | - Amy Hu
- Department of Chemistry and Biochemistry, Miami University, Oxford, OH 45056, USA
| | - Christopher R. Bethel
- Research Service, Louis Stokes Cleveland Department of Veterans Affairs Medical Center, Cleveland, OH 44106, USA
| | - Dann D. Rivera
- Division of Chemical Biology and Medicinal Chemistry, College of Pharmacy, University of Texas, Austin, TX 78712, USA
| | - Patricia Dranchak
- Division of Preclinical Innovation, National Center for Advancing Translational Sciences, National Institutes of Health, Rockville, MD 20850, USA
| | - Pei W. Thomas
- Division of Chemical Biology and Medicinal Chemistry, College of Pharmacy, University of Texas, Austin, TX 78712, USA
| | - Han Li
- Department of Chemistry and Biochemistry, Miami University, Oxford, OH 45056, USA
| | - Qi Feng
- Department of Chemistry and Biochemistry, Miami University, Oxford, OH 45056, USA
| | - Kaicheng Tao
- Department of Chemistry and Biochemistry, Miami University, Oxford, OH 45056, USA
| | - Minshuai Song
- Department of Chemistry and Biochemistry, Miami University, Oxford, OH 45056, USA
| | - Na Sun
- Department of Chemistry and Biochemistry, Miami University, Oxford, OH 45056, USA
| | - Shuo Wang
- Department of Chemistry and Biochemistry, Miami University, Oxford, OH 45056, USA
| | | | - Richard C. Page
- Department of Chemistry and Biochemistry, Miami University, Oxford, OH 45056, USA
| | - Walt Fast
- Division of Chemical Biology and Medicinal Chemistry, College of Pharmacy, University of Texas, Austin, TX 78712, USA
| | - Robert A. Bonomo
- Research Service, Louis Stokes Cleveland Department of Veterans Affairs Medical Center, Cleveland, OH 44106, USA
- Departments of Medicine, Biochemistry, Molecular Biology and Microbiology, Pharmacology, and Proteomics and Bioinformatics, Case Western Reserve University School of Medicine, Cleveland, OH 44106, USA
- Clinician Scientist Investigator, Louis Stokes Cleveland Department of Veterans Affairs Medical Center, Cleveland, OH 44106, USA
- CWRU-Cleveland VAMC Center for Antimicrobial Resistance and Epidemiology (Case VA CARES) Cleveland, OH 44106, USA
| | - Maria Weese
- Department of Chemistry and Biochemistry, Miami University, Oxford, OH 45056, USA
| | - Waldyn Martinez
- Department of Chemistry and Biochemistry, Miami University, Oxford, OH 45056, USA
| | - James Inglese
- Division of Preclinical Innovation, National Center for Advancing Translational Sciences, National Institutes of Health, Rockville, MD 20850, USA
- Metabolic Medicine Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20817, USA
| | - Michael W. Crowder
- Department of Chemistry and Biochemistry, Miami University, Oxford, OH 45056, USA
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Lin L, Li C, Zhang T, Xia C, Bai Q, Jin L, Shen Y. An in silico scheme for optimizing the enzymatic acquisition of natural biologically active peptides based on machine learning and virtual digestion. Anal Chim Acta 2024; 1298:342419. [PMID: 38462343 DOI: 10.1016/j.aca.2024.342419] [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: 08/20/2023] [Revised: 12/23/2023] [Accepted: 02/26/2024] [Indexed: 03/12/2024]
Abstract
BACKGROUND As a potential natural active substance, natural biologically active peptides (NBAPs) are recently attracting increasing attention. The traditional proteolysis methods of obtaining effective NBAPs are considerably vexing, especially since multiple proteases can be used, which blocks the exploration of available NBAPs. Although the development of virtual digesting brings some degree of convenience, the activity of the obtained peptides remains unclear, which would still not allow efficient access to the NBAPs. It is necessary to develop an efficient and accurate strategy for acquiring NBAPs. RESULTS A new in silico scheme named SSA-LSTM-VD, which combines a sparrow search algorithm-long short-term memory (SSA-LSTM) deep learning and virtually digested, was presented to optimize the proteolysis acquisition of NBAPs. Therein, SSA-LSTM reached the highest Efficiency value reached 98.00 % compared to traditional machine learning algorithms, and basic LSTM algorithm. SSA-LSTM was trained to predict the activity of peptides in the proteins virtually digested results, obtain the percentage of target active peptide, and select the appropriate protease for the actual experiment. As an application, SSA-LSTM was employed to predict the percentage of neuroprotective peptides in the virtual digested result of walnut protein, and trypsin was ultimately found to possess the highest value (85.29 %). The walnut protein was digested by trypsin (WPTrH) and the peptide sequence obtained was analyzed closely matches the theoretical neuroprotective peptide. More importantly, the neuroprotective effects of WPTrH had been demonstrated in nerve damage mouse models. SIGNIFICANCE The proposed SSA-LSTM-VD in this paper makes the acquisition of NBAPs efficient and accurate. The approach combines deep learning and virtually digested skillfully. Utilizing the SSA-LSTM-VD based strategy holds promise for discovering and developing peptides with neuroprotective properties or other desired biological activities.
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Affiliation(s)
- Like Lin
- Key Laboratory of Synthetic and Natural Functional Molecule of Ministry of Education, College of Chemistry and Materials Science, National Demonstration Center for Experimental Chemistry Education, Northwest University, Xi'an, Shaanxi, 710127, People's Republic of China
| | - Cong Li
- Key Laboratory of Synthetic and Natural Functional Molecule of Ministry of Education, College of Chemistry and Materials Science, National Demonstration Center for Experimental Chemistry Education, Northwest University, Xi'an, Shaanxi, 710127, People's Republic of China.
| | - Tianlong Zhang
- Key Laboratory of Synthetic and Natural Functional Molecule of Ministry of Education, College of Chemistry and Materials Science, National Demonstration Center for Experimental Chemistry Education, Northwest University, Xi'an, Shaanxi, 710127, People's Republic of China
| | - Chaoshuang Xia
- Center for Biomedical Mass Spectrometry, Boston University Chobanian and Avedisian School of Medicine, Boston, MA, 02118, United States
| | - Qiuhong Bai
- Key Laboratory of Synthetic and Natural Functional Molecule of Ministry of Education, College of Chemistry and Materials Science, National Demonstration Center for Experimental Chemistry Education, Northwest University, Xi'an, Shaanxi, 710127, People's Republic of China
| | - Lihua Jin
- Key Laboratory of Synthetic and Natural Functional Molecule of Ministry of Education, College of Chemistry and Materials Science, National Demonstration Center for Experimental Chemistry Education, Northwest University, Xi'an, Shaanxi, 710127, People's Republic of China
| | - Yehua Shen
- Key Laboratory of Synthetic and Natural Functional Molecule of Ministry of Education, College of Chemistry and Materials Science, National Demonstration Center for Experimental Chemistry Education, Northwest University, Xi'an, Shaanxi, 710127, People's Republic of China.
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7
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Martínez‐Mauricio KL, García‐Jacas CR, Cordoves‐Delgado G. Examining evolutionary scale modeling-derived different-dimensional embeddings in the antimicrobial peptide classification through a KNIME workflow. Protein Sci 2024; 33:e4928. [PMID: 38501511 PMCID: PMC10949403 DOI: 10.1002/pro.4928] [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: 09/07/2023] [Revised: 01/28/2024] [Accepted: 01/30/2024] [Indexed: 03/20/2024]
Abstract
Molecular features play an important role in different bio-chem-informatics tasks, such as the Quantitative Structure-Activity Relationships (QSAR) modeling. Several pre-trained models have been recently created to be used in downstream tasks, either by fine-tuning a specific model or by extracting features to feed traditional classifiers. In this regard, a new family of Evolutionary Scale Modeling models (termed as ESM-2 models) was recently introduced, demonstrating outstanding results in protein structure prediction benchmarks. Herein, we studied the usefulness of the different-dimensional embeddings derived from the ESM-2 models to classify antimicrobial peptides (AMPs). To this end, we built a KNIME workflow to use the same modeling methodology across experiments in order to guarantee fair analyses. As a result, the 640- and 1280-dimensional embeddings derived from the 30- and 33-layer ESM-2 models, respectively, are the most valuable since statistically better performances were achieved by the QSAR models built from them. We also fused features of the different ESM-2 models, and it was concluded that the fusion contributes to getting better QSAR models than using features of a single ESM-2 model. Frequency studies revealed that only a portion of the ESM-2 embeddings is valuable for modeling tasks since between 43% and 66% of the features were never used. Comparisons regarding state-of-the-art deep learning (DL) models confirm that when performing methodologically principled studies in the prediction of AMPs, non-DL based QSAR models yield comparable-to-superior performances to DL-based QSAR models. The developed KNIME workflow is available-freely at https://github.com/cicese-biocom/classification-QSAR-bioKom. This workflow can be valuable to avoid unfair comparisons regarding new computational methods, as well as to propose new non-DL based QSAR models.
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Affiliation(s)
- Karla L. Martínez‐Mauricio
- Departamento de Ciencias de la ComputaciónCentro de Investigación Científica y de Educación Superior de Ensenada (CICESE)EnsenadaMexico
| | - César R. García‐Jacas
- Cátedras CONAHCYT – Departamento de Ciencias de la ComputaciónCentro de Investigación Científica y de Educación Superior de Ensenada (CICESE)EnsenadaMexico
| | - Greneter Cordoves‐Delgado
- Departamento de Ciencias de la ComputaciónCentro de Investigación Científica y de Educación Superior de Ensenada (CICESE)EnsenadaMexico
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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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9
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Iwaniak A, Minkiewicz P, Darewicz M. Bioinformatics and bioactive peptides from foods: Do they work together? ADVANCES IN FOOD AND NUTRITION RESEARCH 2024; 108:35-111. [PMID: 38461003 DOI: 10.1016/bs.afnr.2023.09.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/11/2024]
Abstract
We live in the Big Data Era which affects many aspects of science, including research on bioactive peptides derived from foods, which during the last few decades have been a focus of interest for scientists. These two issues, i.e., the development of computer technologies and progress in the discovery of novel peptides with health-beneficial properties, are closely interrelated. This Chapter presents the example applications of bioinformatics for studying biopeptides, focusing on main aspects of peptide analysis as the starting point, including: (i) the role of peptide databases; (ii) aspects of bioactivity prediction; (iii) simulation of peptide release from proteins. Bioinformatics can also be used for predicting other features of peptides, including ADMET, QSAR, structure, and taste. To answer the question asked "bioinformatics and bioactive peptides from foods: do they work together?", currently it is almost impossible to find examples of peptide research with no bioinformatics involved. However, theoretical predictions are not equivalent to experimental work and always require critical scrutiny. The aspects of compatibility of in silico and in vitro results are also summarized herein.
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Affiliation(s)
- Anna Iwaniak
- Chair of Food Biochemistry, Faculty of Food Science, University of Warmia and Mazury in Olsztyn, Olsztyn-Kortowo, Poland.
| | - Piotr Minkiewicz
- Chair of Food Biochemistry, Faculty of Food Science, University of Warmia and Mazury in Olsztyn, Olsztyn-Kortowo, Poland
| | - Małgorzata Darewicz
- Chair of Food Biochemistry, Faculty of Food Science, University of Warmia and Mazury in Olsztyn, Olsztyn-Kortowo, Poland
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10
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Bajiya N, Choudhury S, Dhall A, Raghava GPS. AntiBP3: A Method for Predicting Antibacterial Peptides against Gram-Positive/Negative/Variable Bacteria. Antibiotics (Basel) 2024; 13:168. [PMID: 38391554 PMCID: PMC10885866 DOI: 10.3390/antibiotics13020168] [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: 12/26/2023] [Revised: 02/03/2024] [Accepted: 02/06/2024] [Indexed: 02/24/2024] Open
Abstract
Most of the existing methods developed for predicting antibacterial peptides (ABPs) are mostly designed to target either gram-positive or gram-negative bacteria. In this study, we describe a method that allows us to predict ABPs against gram-positive, gram-negative, and gram-variable bacteria. Firstly, we developed an alignment-based approach using BLAST to identify ABPs and achieved poor sensitivity. Secondly, we employed a motif-based approach to predict ABPs and obtained high precision with low sensitivity. To address the issue of poor sensitivity, we developed alignment-free methods for predicting ABPs using machine/deep learning techniques. In the case of alignment-free methods, we utilized a wide range of peptide features that include different types of composition, binary profiles of terminal residues, and fastText word embedding. In this study, a five-fold cross-validation technique has been used to build machine/deep learning models on training datasets. These models were evaluated on an independent dataset with no common peptide between training and independent datasets. Our machine learning-based model developed using the amino acid binary profile of terminal residues achieved maximum AUC 0.93, 0.98, and 0.94 for gram-positive, gram-negative, and gram-variable bacteria, respectively, on an independent dataset. Our method performs better than existing methods when compared with existing approaches on an independent dataset. A user-friendly web server, standalone package and pip package have been developed to facilitate peptide-based therapeutics.
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Affiliation(s)
- Nisha Bajiya
- Department of Computational Biology, Indraprastha Institute of Information Technology, Okhla Phase 3, New Delhi 110020, India
| | - Shubham Choudhury
- Department of Computational Biology, Indraprastha Institute of Information Technology, Okhla Phase 3, New Delhi 110020, India
| | - Anjali Dhall
- Department of Computational Biology, Indraprastha Institute of Information Technology, Okhla Phase 3, New Delhi 110020, India
| | - Gajendra P S Raghava
- Department of Computational Biology, Indraprastha Institute of Information Technology, Okhla Phase 3, New Delhi 110020, India
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Xing W, Zhang J, Li C, Huo Y, Dong G. iAMP-Attenpred: a novel antimicrobial peptide predictor based on BERT feature extraction method and CNN-BiLSTM-Attention combination model. Brief Bioinform 2023; 25:bbad443. [PMID: 38055840 PMCID: PMC10699745 DOI: 10.1093/bib/bbad443] [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: 09/08/2023] [Revised: 10/31/2023] [Accepted: 11/11/2023] [Indexed: 12/08/2023] Open
Abstract
As a kind of small molecule protein that can fight against various microorganisms in nature, antimicrobial peptides (AMPs) play an indispensable role in maintaining the health of organisms and fortifying defenses against diseases. Nevertheless, experimental approaches for AMP identification still demand substantial allocation of human resources and material inputs. Alternatively, computing approaches can assist researchers effectively and promptly predict AMPs. In this study, we present a novel AMP predictor called iAMP-Attenpred. As far as we know, this is the first work that not only employs the popular BERT model in the field of natural language processing (NLP) for AMPs feature encoding, but also utilizes the idea of combining multiple models to discover AMPs. Firstly, we treat each amino acid from preprocessed AMPs and non-AMP sequences as a word, and then input it into BERT pre-training model for feature extraction. Moreover, the features obtained from BERT method are fed to a composite model composed of one-dimensional CNN, BiLSTM and attention mechanism for better discriminating features. Finally, a flatten layer and various fully connected layers are utilized for the final classification of AMPs. Experimental results reveal that, compared with the existing predictors, our iAMP-Attenpred predictor achieves better performance indicators, such as accuracy, precision and so on. This further demonstrates that using the BERT approach to capture effective feature information of peptide sequences and combining multiple deep learning models are effective and meaningful for predicting AMPs.
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Affiliation(s)
- Wenxuan Xing
- School of Computer Science and Engineering, Northeastern University, No.195 Chuangxin Road, Hunnan District, Shenyang 110170, China
| | - Jie Zhang
- College of Computer and Information Engineering, Inner Mongolia Agricultural University, No.29 Erdos East Street, Saihan District, Hohhot 010011, China
| | - Chen Li
- School of Computer Science and Engineering, Northeastern University, No.195 Chuangxin Road, Hunnan District, Shenyang 110170, China
| | - Yujia Huo
- College of Computer and Information Engineering, Inner Mongolia Agricultural University, No.29 Erdos East Street, Saihan District, Hohhot 010011, China
| | - Gaifang Dong
- College of Computer and Information Engineering, Inner Mongolia Agricultural University, No.29 Erdos East Street, Saihan District, Hohhot 010011, China
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12
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Singh V, Singh SK. A separable temporal convolutional networks based deep learning technique for discovering antiviral medicines. Sci Rep 2023; 13:13722. [PMID: 37608092 PMCID: PMC10444765 DOI: 10.1038/s41598-023-40922-y] [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: 04/09/2023] [Accepted: 08/18/2023] [Indexed: 08/24/2023] Open
Abstract
An alarming number of fatalities caused by the COVID-19 pandemic has forced the scientific community to accelerate the process of therapeutic drug discovery. In this regard, the collaboration between biomedical scientists and experts in artificial intelligence (AI) has led to a number of in silico tools being developed for the initial screening of therapeutic molecules. All living organisms produce antiviral peptides (AVPs) as a part of their first line of defense against invading viruses. The Deep-AVPiden model proposed in this paper and its corresponding web app, deployed at https://deep-avpiden.anvil.app , is an effort toward discovering novel AVPs in proteomes of living organisms. Apart from Deep-AVPiden, a computationally efficient model called Deep-AVPiden (DS) has also been developed using the same underlying network but with point-wise separable convolutions. The Deep-AVPiden and Deep-AVPiden (DS) models show an accuracy of 90% and 88%, respectively, and both have a precision of 90%. Also, the proposed models were statistically compared using the Student's t-test. On comparing the proposed models with the state-of-the-art classifiers, it was found that they are much better than them. To test the proposed model, we identified some AVPs in the natural defense proteins of plants, mammals, and fishes and found them to have appreciable sequence similarity with some experimentally validated antimicrobial peptides. These AVPs can be chemically synthesized and tested for their antiviral activity.
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Affiliation(s)
- Vishakha Singh
- Department of Computer Science and Engineering, Indian Institute of Technology (BHU) Varanasi, Varanasi, Uttar Pradesh, 221005, India.
| | - Sanjay Kumar Singh
- Department of Computer Science and Engineering, Indian Institute of Technology (BHU) Varanasi, Varanasi, Uttar Pradesh, 221005, India.
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13
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Xu J, Li F, Li C, Guo X, Landersdorfer C, Shen HH, Peleg AY, Li J, Imoto S, Yao J, Akutsu T, Song J. iAMPCN: a deep-learning approach for identifying antimicrobial peptides and their functional activities. Brief Bioinform 2023; 24:bbad240. [PMID: 37369638 PMCID: PMC10359087 DOI: 10.1093/bib/bbad240] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Revised: 05/30/2023] [Accepted: 06/08/2023] [Indexed: 06/29/2023] Open
Abstract
Antimicrobial peptides (AMPs) are short peptides that play crucial roles in diverse biological processes and have various functional activities against target organisms. Due to the abuse of chemical antibiotics and microbial pathogens' increasing resistance to antibiotics, AMPs have the potential to be alternatives to antibiotics. As such, the identification of AMPs has become a widely discussed topic. A variety of computational approaches have been developed to identify AMPs based on machine learning algorithms. However, most of them are not capable of predicting the functional activities of AMPs, and those predictors that can specify activities only focus on a few of them. In this study, we first surveyed 10 predictors that can identify AMPs and their functional activities in terms of the features they employed and the algorithms they utilized. Then, we constructed comprehensive AMP datasets and proposed a new deep learning-based framework, iAMPCN (identification of AMPs based on CNNs), to identify AMPs and their related 22 functional activities. Our experiments demonstrate that iAMPCN significantly improved the prediction performance of AMPs and their corresponding functional activities based on four types of sequence features. Benchmarking experiments on the independent test datasets showed that iAMPCN outperformed a number of state-of-the-art approaches for predicting AMPs and their functional activities. Furthermore, we analyzed the amino acid preferences of different AMP activities and evaluated the model on datasets of varying sequence redundancy thresholds. To facilitate the community-wide identification of AMPs and their corresponding functional types, we have made the source codes of iAMPCN publicly available at https://github.com/joy50706/iAMPCN/tree/master. We anticipate that iAMPCN can be explored as a valuable tool for identifying potential AMPs with specific functional activities for further experimental validation.
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Affiliation(s)
- Jing Xu
- Monash Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC 3800, Australia
- Monash Data Futures Institute, Monash University, Melbourne, VIC 3800, Australia
| | - Fuyi Li
- Monash Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC 3800, Australia
- College of Information Engineering, Northwest A&F University, Shaanxi 712100, China
- The Peter Doherty Institute for Infection and Immunity, The University of Melbourne, Melbourne, VIC 3800, Australia
| | - Chen Li
- Monash Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC 3800, Australia
- Monash Data Futures Institute, Monash University, Melbourne, VIC 3800, Australia
| | - Xudong Guo
- College of Information Engineering, Northwest A&F University, Shaanxi 712100, China
| | - Cornelia Landersdorfer
- Monash Institute of Pharmaceutical Sciences, Monash University, Melbourne, VIC 3800, Australia
| | - Hsin-Hui Shen
- Monash Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC 3800, Australia
- Department of Materials Science and Engineering, Faculty of Engineering, Monash University, Clayton, VIC, 3800, Australia
| | - Anton Y Peleg
- Monash Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC 3800, Australia
- Department of Infectious Diseases, Alfred Hospital, Alfred Health, Melbourne, Victoria, Australia
| | - Jian Li
- Monash Biomedicine Discovery Institute and Department of Microbiology, Monash University, Melbourne, VIC 3800, Australia
| | - Seiya Imoto
- Division of Health Medical Intelligence, Human Genome Center, Institute of Medical Science, The University of Tokyo, Minato-ku, Tokyo, Japan
- Collaborative Research Institute for Innovative Microbiology, The University of Tokyo, Bunkyo-ku, Tokyo, Japan
| | | | - Tatsuya Akutsu
- Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji 611-0011, Japan
| | - Jiangning Song
- Monash Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC 3800, Australia
- Monash Data Futures Institute, Monash University, Melbourne, VIC 3800, Australia
- Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji 611-0011, Japan
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14
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Lin TT, Yang LY, Lin CY, Wang CT, Lai CW, Ko CF, Shih YH, Chen SH. Intelligent De Novo Design of Novel Antimicrobial Peptides against Antibiotic-Resistant Bacteria Strains. Int J Mol Sci 2023; 24:ijms24076788. [PMID: 37047760 PMCID: PMC10095442 DOI: 10.3390/ijms24076788] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Revised: 03/24/2023] [Accepted: 04/02/2023] [Indexed: 04/09/2023] Open
Abstract
Because of the growing number of clinical antibiotic resistance cases in recent years, novel antimicrobial peptides (AMPs) may be ideal for next-generation antibiotics. This study trained a Wasserstein generative adversarial network with gradient penalty (WGAN-GP) based on known AMPs to generate novel AMP candidates. The quality of the GAN-designed peptides was evaluated in silico, and eight of them, named GAN-pep 1–8, were selected by an AMP Artificial Intelligence (AI) classifier and synthesized for further experiments. Disc diffusion testing and minimum inhibitory concentration (MIC) determinations were used to identify the antibacterial effects of the synthesized GAN-designed peptides. Seven of the eight synthesized GAN-designed peptides displayed antibacterial activity. Additionally, GAN-pep 3 and GAN-pep 8 presented a broad spectrum of antibacterial effects and were effective against antibiotic-resistant bacteria strains, such as methicillin-resistant Staphylococcus aureus and carbapenem-resistant Pseudomonas aeruginosa. GAN-pep 3, the most promising GAN-designed peptide candidate, had low MICs against all the tested bacteria. In brief, our approach shows an efficient way to discover AMPs effective against general and antibiotic-resistant bacteria strains. In addition, such a strategy also allows other novel functional peptides to be quickly designed, identified, and synthesized for validation on the wet bench.
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Affiliation(s)
- Tzu-Tang Lin
- Institute of Information Science, Academia Sinica, Taipei 11529, Taiwan
| | - Li-Yen Yang
- Institute of Information Science, Academia Sinica, Taipei 11529, Taiwan
| | - Chung-Yen Lin
- Institute of Information Science, Academia Sinica, Taipei 11529, Taiwan
| | - Ching-Tien Wang
- Institute of Information Science, Academia Sinica, Taipei 11529, Taiwan
| | - Chia-Wen Lai
- Department of Agricultural Chemistry, National Taiwan University, Taipei 10617, Taiwan
| | - Chi-Fong Ko
- Department of Agricultural Chemistry, National Taiwan University, Taipei 10617, Taiwan
| | - Yang-Hsin Shih
- Department of Agricultural Chemistry, National Taiwan University, Taipei 10617, Taiwan
| | - Shu-Hwa Chen
- TMU Research Center of Cancer Translational Medicine, Taipei Medical University, Taipei 110301, Taiwan
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15
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Qin D, Jiao L, Wang R, Zhao Y, Hao Y, Liang G. Prediction of antioxidant peptides using a quantitative structure-activity relationship predictor (AnOxPP) based on bidirectional long short-term memory neural network and interpretable amino acid descriptors. Comput Biol Med 2023; 154:106591. [PMID: 36701965 DOI: 10.1016/j.compbiomed.2023.106591] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Revised: 01/15/2023] [Accepted: 01/22/2023] [Indexed: 01/25/2023]
Abstract
Antioxidant peptides can protect against free radical-mediated diseases, especially food-derived antioxidant peptides are considered as potential competitors among synthetic antioxidants due to their safety, high activity and abundant sources. However, wet experimental methods can not meet the need for effectively screening and clearly elucidating the structure-activity relationship of antioxidant peptides. Therefore, it is particularly important to build a reliable prediction platform for antioxidant peptides. In this work, we developed a platform, AnOxPP, for prediction of antioxidant peptides using the bidirectional long short-term memory (BiLSTM) neural network. The sequence characteristics of peptides were converted into feature codes based on amino acid descriptors (AADs). Our results showed that the feature conversion ability of the combined-AADs optimized by the forward feature selection method was more accurate than that of the single-AADs. Especially, the model trained by the optimal descriptor SDPZ27 significantly outperformed the existing predictor on two independent test sets (Accuracy = 0.967 and 0.819, respectively). The SDPZ27-based AnOxPP learned four key structure-activity features of antioxidant peptides, with the following importance as steric properties > hydrophobic properties > electronic properties > hydrogen bond contributions. AnOxPP is a valuable tool for screening and design of peptide drugs, and the web-server is accessible at http://www.cqudfbp.net/AnOxPP/index.jsp.
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Affiliation(s)
- Dongya Qin
- Key Laboratory of Biorheological Science and Technology, Ministry of Education, Bioengineering College, Chongqing University, Chongqing, 400030, China
| | - Linna Jiao
- Key Laboratory of Biorheological Science and Technology, Ministry of Education, Bioengineering College, Chongqing University, Chongqing, 400030, China
| | - Ruihong Wang
- Key Laboratory of Biorheological Science and Technology, Ministry of Education, Bioengineering College, Chongqing University, Chongqing, 400030, China
| | - Yi Zhao
- Key Laboratory of Biorheological Science and Technology, Ministry of Education, Bioengineering College, Chongqing University, Chongqing, 400030, China
| | - Youjin Hao
- College of Life Sciences, Chongqing Normal University, Chongqing, 401331, China
| | - Guizhao Liang
- Key Laboratory of Biorheological Science and Technology, Ministry of Education, Bioengineering College, Chongqing University, Chongqing, 400030, China.
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16
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Chen S, Li Q, Zhao J, Bin Y, Zheng C. NeuroPred-CLQ: incorporating deep temporal convolutional networks and multi-head attention mechanism to predict neuropeptides. Brief Bioinform 2022; 23:6672901. [DOI: 10.1093/bib/bbac319] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Revised: 06/27/2022] [Accepted: 07/14/2022] [Indexed: 11/13/2022] Open
Abstract
Abstract
Neuropeptides (NPs) are a particular class of informative substances in the immune system and physiological regulation. They play a crucial role in regulating physiological functions in various biological growth and developmental stages. In addition, NPs are crucial for developing new drugs for the treatment of neurological diseases. With the development of molecular biology techniques, some data-driven tools have emerged to predict NPs. However, it is necessary to improve the predictive performance of these tools for NPs. In this study, we developed a deep learning model (NeuroPred-CLQ) based on the temporal convolutional network (TCN) and multi-head attention mechanism to identify NPs effectively and translate the internal relationships of peptide sequences into numerical features by the Word2vec algorithm. The experimental results show that NeuroPred-CLQ learns data information effectively, achieving 93.6% accuracy and 98.8% AUC on the independent test set. The model has better performance in identifying NPs than the state-of-the-art predictors. Visualization of features using t-distribution random neighbor embedding shows that the NeuroPred-CLQ can clearly distinguish the positive NPs from the negative ones. We believe the NeuroPred-CLQ can facilitate drug development and clinical trial studies to treat neurological disorders.
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Affiliation(s)
- Shouzhi Chen
- School of Mathematics and System Science, Xinjiang University , Urumqi, China
| | - Qing Li
- School of Mathematics and System Science, Xinjiang University , Urumqi, China
| | - Jianping Zhao
- School of Mathematics and System Science, Xinjiang University , Urumqi, China
| | - Yannan Bin
- School of Computer Science and Technology, Anhui University , Hefei, China
| | - Chunhou Zheng
- School of Mathematics and System Science, Xinjiang University , Urumqi, China
- School of Computer Science and Technology, Anhui University , Hefei, China
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17
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MLCPP 2.0: An updated cell-penetrating peptides and their uptake efficiency predictor. J Mol Biol 2022; 434:167604. [DOI: 10.1016/j.jmb.2022.167604] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Revised: 04/03/2022] [Accepted: 04/19/2022] [Indexed: 12/12/2022]
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18
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Wasp Venom Peptide (Polybia MP-1) Shows Antimicrobial Activity Against Multi Drug Resistant Bacteria Isolated from Mastitic Cow Milk. Int J Pept Res Ther 2022. [DOI: 10.1007/s10989-021-10355-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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