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Liu X, Zhang L, Lai B, Li J, Zang J, Ma L. Harnessing Protein Hydrolysates and Peptides for Hyperuricemia Management: Insights into Sources, Mechanisms, Techniques, and Future Directions. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2024; 72:18758-18773. [PMID: 39161084 DOI: 10.1021/acs.jafc.4c03605] [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: 08/21/2024]
Abstract
Hyperuricemia (HUA) is a metabolic disorder characterized by an imbalance in uric acid production and excretion, frequently leading to gout and various chronic conditions. Novel bioactive compounds offer effective alternatives for managing HUA, reducing side effects of traditional medications. Recent studies have highlighted the therapeutic potential of protein hydrolysates and peptides in managing HUA. This review focuses on preparing and applying protein hydrolysates to treat HUA and explores peptides for xanthine oxidase inhibition. Particularly, we discuss their origins, enzymatic approaches, and mechanisms of action in detail. The review provides an updated understanding of HUA pathogenesis, current pharmacological interventions, and methodologies for the preparation, purification, identification, and assessment of these compounds. Furthermore, to explore the application of protein hydrolysates and peptides in the food industry, we also address challenges and propose solutions related to the safety, bitterness, oral delivery, and the integration of artificial intelligence in peptide discovery. Bridging traditional pharmacological approaches and innovative dietary interventions, this study paves the way for future research and development in HUA management, contributing to the utilization of proteins from different food sources. In conclusion, protein hydrolysates and peptides show significant promise as safe agents and dietary interventions for preventing and treating HUA.
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Affiliation(s)
- Xiaoyu Liu
- College of Food Science and Nutritional Engineering, China Agricultural University, Beijing 100083, China
| | - Lei Zhang
- College of Food Science and Nutritional Engineering, China Agricultural University, Beijing 100083, China
| | - Boyin Lai
- College of Food Science and Nutritional Engineering, China Agricultural University, Beijing 100083, China
| | - Jingming Li
- College of Food Science and Nutritional Engineering, China Agricultural University, Beijing 100083, China
| | - Jiachen Zang
- College of Food Science and Nutritional Engineering, China Agricultural University, Beijing 100083, China
| | - Liyan Ma
- College of Food Science and Nutritional Engineering, China Agricultural University, Beijing 100083, China
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2
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Cheong HH, Zuo W, Chen J, Un CW, Si YW, Wong KH, Kwok HF, Siu SWI. Identification of Anticancer Peptides from the Genome of Candida albicans: in Silico Screening, in Vitro and in Vivo Validations. J Chem Inf Model 2024; 64:6174-6189. [PMID: 39008832 DOI: 10.1021/acs.jcim.4c00501] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/17/2024]
Abstract
Anticancer peptides (ACPs) are promising future therapeutics, but their experimental discovery remains time-consuming and costly. To accelerate the discovery process, we propose a computational screening workflow to identify, filter, and prioritize peptide sequences based on predicted class probability, antitumor activity, and toxicity. The workflow was applied to identify novel ACPs with potent activity against colorectal cancer from the genome sequences of Candida albicans. As a result, four candidates were identified and validated in the HCT116 colon cancer cell line. Among them, PCa1 and PCa2 emerged as the most potent, displaying IC50 values of 3.75 and 56.06 μM, respectively, and demonstrating a 4-fold selectivity for cancer cells over normal cells. In the colon xenograft nude mice model, the administration of both peptides resulted in substantial inhibition of tumor growth without causing significant adverse effects. In conclusion, this work not only contributes a proven computational workflow for ACP discovery but also introduces two peptides, PCa1 and PCa2, as promising candidates poised for further development as targeted therapies for colon cancer. The method as a web service is available at https://app.cbbio.online/acpep/home and the source code at https://github.com/cartercheong/AcPEP_classification.git.
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Affiliation(s)
- Hong-Hin Cheong
- Department of Computer and Information Science, Faculty of Science and Technology, University of Macau, Avenida de Universidade, Taipa, Macau SAR 999078, China
| | - Weimin Zuo
- Department of Biomedical Sciences, Faculty of Health Sciences, University of Macau, Avenida de Universidade, Taipa, Macau SAR 999078, China
- Cancer Centre, Faculty of Health Sciences, University of Macau, Avenida de Universidade, Taipa, Macau SAR 999078, China
| | - Jiarui Chen
- Department of Computer and Information Science, Faculty of Science and Technology, University of Macau, Avenida de Universidade, Taipa, Macau SAR 999078, China
| | - Chon-Wai Un
- Department of Computer and Information Science, Faculty of Science and Technology, University of Macau, Avenida de Universidade, Taipa, Macau SAR 999078, China
| | - Yain-Whar Si
- Department of Computer and Information Science, Faculty of Science and Technology, University of Macau, Avenida de Universidade, Taipa, Macau SAR 999078, China
| | - Koon Ho Wong
- Department of Biomedical Sciences, Faculty of Health Sciences, University of Macau, Avenida de Universidade, Taipa, Macau SAR 999078, China
- MoE Frontiers Science Center for Precision Oncology, University of Macau, Avenida de Universidade, Taipa, Macau SAR 999078, China
- Cancer Centre, Faculty of Health Sciences, University of Macau, Avenida de Universidade, Taipa, Macau SAR 999078, China
| | - Hang Fai Kwok
- Department of Biomedical Sciences, Faculty of Health Sciences, University of Macau, Avenida de Universidade, Taipa, Macau SAR 999078, China
- MoE Frontiers Science Center for Precision Oncology, University of Macau, Avenida de Universidade, Taipa, Macau SAR 999078, China
- Cancer Centre, Faculty of Health Sciences, University of Macau, Avenida de Universidade, Taipa, Macau SAR 999078, China
| | - Shirley W I Siu
- Centre for Artificial Intelligence Driven Drug Discovery, Faculty of Applied Sciences, Macao Polytechnic University, R. de Luís Gonzaga Gomes, Macau SAR 999078, China
- Institute of Science and Environment, University of Saint Joseph, Estrada Marginal da Ilha Verde 14-17, Macau SAR 999078, China
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Sinha K, Parwez S, Mv S, Yadav A, Siddiqi MI, Banerjee D. Machine learning and biological evaluation-based identification of a potential MMP-9 inhibitor, effective against ovarian cancer cells SKOV3. J Biomol Struct Dyn 2024; 42:6823-6841. [PMID: 37504963 DOI: 10.1080/07391102.2023.2240416] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2023] [Accepted: 07/08/2023] [Indexed: 07/29/2023]
Abstract
MMP-9, also known as gelatinase B, is a zinc-metalloproteinase family protein that plays a key role in the degradation of the extracellular matrix (ECM). The normal function of MMP-9 includes the breakdown of ECM, a process that aids in normal physiological processes such as embryonic development, angiogenesis, etc. Interruptions in these processes due to the over-expression or downregulation of MMP-9 are reported to cause some pathological conditions like neurodegenerative diseases and cancer. In the present study, an integrated approach for ML-based virtual screening of the Maybridge library was carried out and their biological activity was tested in an attempt to identify novel small molecule scaffolds that can inhibit the activity of MMP-9. The top hits were identified and selected for target-based activity against MMP-9 protein using the kit (Biovision K844). Further, MTT assay was performed in various cancer cell lines such as breast (MCF-7, MDA-MB-231), colorectal (HCT119, DL-D-1), cervical (HeLa), lung (A549) and ovarian cancer (SKOV3). Interestingly, one compound viz., RJF02215 exhibited anti-cancer activity selectively in SKOV3. Wound healing assay and colony formation assay performed on SKOV3 cell line in the presence of RJF02215 confirmed that the compound had a significant inhibitory effect on this cell line. Thus, we have identified a novel molecule that can inhibit MMP-9 activity in vitro and inhibits the proliferation of SKOV3 cells. Novel molecules based on the structure of RJF02215 may become a good value addition for the treatment of ovarian cancer by exhibiting selective MMP-9 activity.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Khushboo Sinha
- Cancer Biology Division, CSIR-Central Drug Research Institute, Lucknow, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India
| | - Shahid Parwez
- Biochemistry and Structural Biology Division, CSIR-Central Drug Research Institute, Lucknow, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India
| | - Shahana Mv
- Cancer Biology Division, CSIR-Central Drug Research Institute, Lucknow, India
| | - Ananya Yadav
- Cancer Biology Division, CSIR-Central Drug Research Institute, Lucknow, India
| | - Mohammad Imran Siddiqi
- Biochemistry and Structural Biology Division, CSIR-Central Drug Research Institute, Lucknow, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India
| | - Dibyendu Banerjee
- Cancer Biology Division, CSIR-Central Drug Research Institute, Lucknow, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India
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Zhao F, Qiu J, Xiang D, Jiao P, Cao Y, Xu Q, Qiao D, Xu H, Cao Y. deepAMPNet: a novel antimicrobial peptide predictor employing AlphaFold2 predicted structures and a bi-directional long short-term memory protein language model. PeerJ 2024; 12:e17729. [PMID: 39040937 PMCID: PMC11262304 DOI: 10.7717/peerj.17729] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2024] [Accepted: 06/20/2024] [Indexed: 07/24/2024] Open
Abstract
Background Global public health is seriously threatened by the escalating issue of antimicrobial resistance (AMR). Antimicrobial peptides (AMPs), pivotal components of the innate immune system, have emerged as a potent solution to AMR due to their therapeutic potential. Employing computational methodologies for the prompt recognition of these antimicrobial peptides indeed unlocks fresh perspectives, thereby potentially revolutionizing antimicrobial drug development. Methods In this study, we have developed a model named as deepAMPNet. This model, which leverages graph neural networks, excels at the swift identification of AMPs. It employs structures of antimicrobial peptides predicted by AlphaFold2, encodes residue-level features through a bi-directional long short-term memory (Bi-LSTM) protein language model, and constructs adjacency matrices anchored on amino acids' contact maps. Results In a comparative study with other state-of-the-art AMP predictors on two external independent test datasets, deepAMPNet outperformed in accuracy. Furthermore, in terms of commonly accepted evaluation matrices such as AUC, Mcc, sensitivity, and specificity, deepAMPNet achieved the highest or highly comparable performances against other predictors. Conclusion deepAMPNet interweaves both structural and sequence information of AMPs, stands as a high-performance identification model that propels the evolution and design in antimicrobial peptide pharmaceuticals. The data and code utilized in this study can be accessed at https://github.com/Iseeu233/deepAMPNet.
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Affiliation(s)
- Fei Zhao
- Microbiology and Metabolic Engineering Laboratory of Sichuan Province, College of Life Science, Sichuan University, Chengdu, Sichuan, China
| | - Junhui Qiu
- Microbiology and Metabolic Engineering Laboratory of Sichuan Province, College of Life Science, Sichuan University, Chengdu, Sichuan, China
| | - Dongyou Xiang
- Microbiology and Metabolic Engineering Laboratory of Sichuan Province, College of Life Science, Sichuan University, Chengdu, Sichuan, China
| | - Pengrui Jiao
- Microbiology and Metabolic Engineering Laboratory of Sichuan Province, College of Life Science, Sichuan University, Chengdu, Sichuan, China
| | - Yu Cao
- Microbiology and Metabolic Engineering Laboratory of Sichuan Province, College of Life Science, Sichuan University, Chengdu, Sichuan, China
| | - Qingrui Xu
- Microbiology and Metabolic Engineering Laboratory of Sichuan Province, College of Life Science, Sichuan University, Chengdu, Sichuan, China
| | - Dairong Qiao
- Microbiology and Metabolic Engineering Laboratory of Sichuan Province, College of Life Science, Sichuan University, Chengdu, Sichuan, China
| | - Hui Xu
- Microbiology and Metabolic Engineering Laboratory of Sichuan Province, College of Life Science, Sichuan University, Chengdu, Sichuan, China
| | - Yi Cao
- Microbiology and Metabolic Engineering Laboratory of Sichuan Province, College of Life Science, Sichuan University, Chengdu, Sichuan, China
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Salam A, Ullah F, Amin F, Ahmad Khan I, Garcia Villena E, Kuc Castilla A, de la Torre I. Efficient prediction of anticancer peptides through deep learning. PeerJ Comput Sci 2024; 10:e2171. [PMID: 39145253 PMCID: PMC11323142 DOI: 10.7717/peerj-cs.2171] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2024] [Accepted: 06/11/2024] [Indexed: 08/16/2024]
Abstract
Background Cancer remains one of the leading causes of mortality globally, with conventional chemotherapy often resulting in severe side effects and limited effectiveness. Recent advancements in bioinformatics and machine learning, particularly deep learning, offer promising new avenues for cancer treatment through the prediction and identification of anticancer peptides. Objective This study aimed to develop and evaluate a deep learning model utilizing a two-dimensional convolutional neural network (2D CNN) to enhance the prediction accuracy of anticancer peptides, addressing the complexities and limitations of current prediction methods. Methods A diverse dataset of peptide sequences with annotated anticancer activity labels was compiled from various public databases and experimental studies. The sequences were preprocessed and encoded using one-hot encoding and additional physicochemical properties. The 2D CNN model was trained and optimized using this dataset, with performance evaluated through metrics such as accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC-ROC). Results The proposed 2D CNN model achieved superior performance compared to existing methods, with an accuracy of 0.87, precision of 0.85, recall of 0.89, F1-score of 0.87, and an AUC-ROC value of 0.91. These results indicate the model's effectiveness in accurately predicting anticancer peptides and capturing intricate spatial patterns within peptide sequences. Conclusion The findings demonstrate the potential of deep learning, specifically 2D CNNs, in advancing the prediction of anticancer peptides. The proposed model significantly improves prediction accuracy, offering a valuable tool for identifying effective peptide candidates for cancer treatment. Future Work Further research should focus on expanding the dataset, exploring alternative deep learning architectures, and validating the model's predictions through experimental studies. Efforts should also aim at optimizing computational efficiency and translating these predictions into clinical applications.
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Affiliation(s)
- Abdu Salam
- Department of Computer Science, Abdul Wali Khan University, Mardan, Pakistan
| | - Faizan Ullah
- Department of Computer Science, Bacha Khan University, Charsadda, Pakistan
| | - Farhan Amin
- School of Computer Science and Engineering, Yeungnam University, Gyeongsan, Republic of Korea
| | - Izaz Ahmad Khan
- Department of Computer Science, Bacha Khan University, Charsadda, Pakistan
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Zhong G, Liu H, Deng L. Ensemble Machine Learning and Predicted Properties Promote Antimicrobial Peptide Identification. Interdiscip Sci 2024:10.1007/s12539-024-00640-z. [PMID: 38972032 DOI: 10.1007/s12539-024-00640-z] [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: 01/22/2024] [Revised: 06/04/2024] [Accepted: 06/07/2024] [Indexed: 07/08/2024]
Abstract
The emergence of antibiotic-resistant microbes raises a pressing demand for novel alternative treatments. One promising alternative is the antimicrobial peptides (AMPs), a class of innate immunity mediators within the therapeutic peptide realm. AMPs offer salient advantages such as high specificity, cost-effective synthesis, and reduced toxicity. Although some computational methodologies have been proposed to identify potential AMPs with the rapid development of artificial intelligence techniques, there is still ample room to improve their performance. This study proposes a predictive framework which ensembles deep learning and statistical learning methods to screen peptides with antimicrobial activity. We integrate multiple LightGBM classifiers and convolution neural networks which leverages various predicted sequential, structural and physicochemical properties from their residue sequences extracted by diverse machine learning paradigms. Comparative experiments exhibit that our method outperforms other state-of-the-art approaches on an independent test dataset, in terms of representative capability measures. Besides, we analyse the discrimination quality under different varieties of attribute information and it reveals that combination of multiple features could improve prediction. In addition, a case study is carried out to illustrate the exemplary favorable identification effect. We establish a web application at http://amp.denglab.org to provide convenient usage of our proposal and make the predictive framework, source code, and datasets publicly accessible at https://github.com/researchprotein/amp .
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Affiliation(s)
- Guolun Zhong
- School of Computer Science and Engineering, Central South University, Changsha, 410083, China
| | - Hui Liu
- College of Computer and Information Engineering, Nanjing Tech University, Nanjing, 211816, China.
| | - Lei Deng
- School of Computer Science and Engineering, Central South University, Changsha, 410083, China.
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7
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Nguyen QH, Nguyen-Vo TH, Do TTT, Nguyen BP. An efficient hybrid deep learning architecture for predicting short antimicrobial peptides. Proteomics 2024; 24:e2300382. [PMID: 38837544 DOI: 10.1002/pmic.202300382] [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: 10/02/2023] [Revised: 05/02/2024] [Accepted: 05/07/2024] [Indexed: 06/07/2024]
Abstract
Short-length antimicrobial peptides (AMPs) have been demonstrated to have intensified antimicrobial activities against a wide spectrum of microbes. Therefore, exploration of novel and promising short AMPs is highly essential in developing various types of antimicrobial drugs or treatments. In addition to experimental approaches, computational methods have been developed to improve screening efficiency. Although existing computational methods have achieved satisfactory performance, there is still much room for model improvement. In this study, we proposed iAMP-DL, an efficient hybrid deep learning architecture, for predicting short AMPs. The model was constructed using two well-known deep learning architectures: the long short-term memory architecture and convolutional neural networks. To fairly assess the performance of the model, we compared our model with existing state-of-the-art methods using the same independent test set. Our comparative analysis shows that iAMP-DL outperformed other methods. Furthermore, to assess the robustness and stability of our model, the experiments were repeated 10 times to observe the variation in prediction efficiency. The results demonstrate that iAMP-DL is an effective, robust, and stable framework for detecting promising short AMPs. Another comparative study of different negative data sampling methods also confirms the effectiveness of our method and demonstrates that it can also be used to develop a robust model for predicting AMPs in general. The proposed framework was also deployed as an online web server with a user-friendly interface to support the research community in identifying short AMPs.
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Affiliation(s)
- Quang H Nguyen
- School of Information and Communication Technology, Hanoi University of Science and Technology, Hanoi, Vietnam
| | - Thanh-Hoang Nguyen-Vo
- School of Mathematics and Statistics, Victoria University of Wellington, Wellington, New Zealand
- School of Innovation, Design and Technology, Wellington Institute of Technology, Lower Hutt, New Zealand
| | - Trang T T Do
- Faculty of Information Technology, Ho Chi Minh City Open University, Ho Chi Minh City, Vietnam
| | - Binh P Nguyen
- School of Mathematics and Statistics, Victoria University of Wellington, Wellington, New Zealand
- Faculty of Information Technology, Ho Chi Minh City Open University, Ho Chi Minh City, Vietnam
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Orsi M, Reymond JL. Can large language models predict antimicrobial peptide activity and toxicity? RSC Med Chem 2024; 15:2030-2036. [PMID: 38911166 PMCID: PMC11187562 DOI: 10.1039/d4md00159a] [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: 03/08/2024] [Accepted: 04/19/2024] [Indexed: 06/25/2024] Open
Abstract
Antimicrobial peptides (AMPs) are naturally occurring or designed peptides up to a few tens of amino acids which may help address the antimicrobial resistance crisis. However, their clinical development is limited by toxicity to human cells, a parameter which is very difficult to control. Given the similarity between peptide sequences and words, large language models (LLMs) might be able to predict AMP activity and toxicity. To test this hypothesis, we fine-tuned LLMs using data from the Database of Antimicrobial Activity and Structure of Peptides (DBAASP). GPT-3 performed well but not reproducibly for activity prediction and hemolysis, taken as a proxy for toxicity. The later GPT-3.5 performed more poorly and was surpassed by recurrent neural networks (RNN) trained on sequence-activity data or support vector machines (SVM) trained on MAP4C molecular fingerprint-activity data. These simpler models are therefore recommended, although the rapid evolution of LLMs warrants future re-evaluation of their prediction abilities.
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Affiliation(s)
- Markus Orsi
- Department of Chemistry, Biochemistry and Pharmaceutical Sciences, University of Bern Freiestrasse 3 3012 Bern Switzerland
| | - Jean-Louis Reymond
- Department of Chemistry, Biochemistry and Pharmaceutical Sciences, University of Bern Freiestrasse 3 3012 Bern Switzerland
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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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10
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Cordoves-Delgado G, García-Jacas CR. Predicting Antimicrobial Peptides Using ESMFold-Predicted Structures and ESM-2-Based Amino Acid Features with Graph Deep Learning. J Chem Inf Model 2024; 64:4310-4321. [PMID: 38739853 DOI: 10.1021/acs.jcim.3c02061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
Currently, antimicrobial resistance constitutes a serious threat to human health. Drugs based on antimicrobial peptides (AMPs) constitute one of the alternatives to address it. Shallow and deep learning (DL)-based models have mainly been built from amino acid sequences to predict AMPs. Recent advances in tertiary (3D) structure prediction have opened new opportunities in this field. In this sense, models based on graphs derived from predicted peptide structures have recently been proposed. However, these models are not in correspondence with state-of-the-art approaches to codify evolutionary information, and, in addition, they are memory- and time-consuming because depend on multiple sequence alignment. Herein, we presented a framework to create alignment-free models based on graph representations generated from ESMFold-predicted peptide structures, whose nodes are characterized with amino acid-level evolutionary information derived from the Evolutionary Scale Modeling (ESM-2) models. A graph attention network (GAT) was implemented to assess the usefulness of the framework in the AMP classification. To this end, a set comprised of 67,058 peptides was used. It was demonstrated that the proposed methodology allowed to build GAT models with generalization abilities consistently better than 20 state-of-the-art non-DL-based and DL-based models. The best GAT models were developed using evolutionary information derived from the 36- and 33-layer ESM-2 models. Similarity studies showed that the best-built GAT models codified different chemical spaces, and thus they were fused to significantly improve the classification. In general, the results suggest that esm-AxP-GDL is a promissory tool to develop good, structure-dependent, and alignment-free models that can be successfully applied in the screening of large data sets. This framework should not only be useful to classify AMPs but also for modeling other peptide and protein activities.
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Affiliation(s)
- Greneter Cordoves-Delgado
- Departamento de Ciencias de la Computación, Centro de Investigación Científica y de Educación Superior de Ensenada (CICESE), 22860 Ensenada, Baja California, México
| | - César R García-Jacas
- Cátedras CONAHCYT - Departamento de Ciencias de la Computación, Centro de Investigación Científica y de Educación Superior de Ensenada (CICESE), 22860 Ensenada, Baja California, México
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11
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Chen N, Yu J, Zhe L, Wang F, Li X, Wong KC. TP-LMMSG: a peptide prediction graph neural network incorporating flexible amino acid property representation. Brief Bioinform 2024; 25:bbae308. [PMID: 38920345 PMCID: PMC11200197 DOI: 10.1093/bib/bbae308] [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: 04/08/2024] [Revised: 05/28/2024] [Accepted: 06/10/2024] [Indexed: 06/27/2024] Open
Abstract
Bioactive peptide therapeutics has been a long-standing research topic. Notably, the antimicrobial peptides (AMPs) have been extensively studied for its therapeutic potential. Meanwhile, the demand for annotating other therapeutic peptides, such as antiviral peptides (AVPs) and anticancer peptides (ACPs), also witnessed an increase in recent years. However, we conceive that the structure of peptide chains and the intrinsic information between the amino acids is not fully investigated among the existing protocols. Therefore, we develop a new graph deep learning model, namely TP-LMMSG, which offers lightweight and easy-to-deploy advantages while improving the annotation performance in a generalizable manner. The results indicate that our model can accurately predict the properties of different peptides. The model surpasses the other state-of-the-art models on AMP, AVP and ACP prediction across multiple experimental validated datasets. Moreover, TP-LMMSG also addresses the challenges of time-consuming pre-processing in graph neural network frameworks. With its flexibility in integrating heterogeneous peptide features, our model can provide substantial impacts on the screening and discovery of therapeutic peptides. The source code is available at https://github.com/NanjunChen37/TP_LMMSG.
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Affiliation(s)
- Nanjun Chen
- Department of Computer Science, City University of Hong Kong, 83 Tat Chee Ave, Kowloon Tong, Kowloon, Hong Kong SAR
| | - Jixiang Yu
- Department of Computer Science, City University of Hong Kong, 83 Tat Chee Ave, Kowloon Tong, Kowloon, Hong Kong SAR
| | - Liu Zhe
- Department of Computer Science, City University of Hong Kong, 83 Tat Chee Ave, Kowloon Tong, Kowloon, Hong Kong SAR
| | - Fuzhou Wang
- Department of Computer Science, City University of Hong Kong, 83 Tat Chee Ave, Kowloon Tong, Kowloon, Hong Kong SAR
| | - Xiangtao Li
- School of Artificial Intelligence, Jilin University, Chang Chun, Ji Lin, China
| | - Ka-Chun Wong
- Department of Computer Science, City University of Hong Kong, 83 Tat Chee Ave, Kowloon Tong, Kowloon, Hong Kong SAR
- Shenzhen Research Institute, City University of Hong Kong, Shenzhen, Guang Dong, China
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12
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Zervou MA, Doutsi E, Pantazis Y, Tsakalides P. De Novo Antimicrobial Peptide Design with Feedback Generative Adversarial Networks. Int J Mol Sci 2024; 25:5506. [PMID: 38791544 PMCID: PMC11122239 DOI: 10.3390/ijms25105506] [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: 04/15/2024] [Revised: 05/10/2024] [Accepted: 05/15/2024] [Indexed: 05/26/2024] Open
Abstract
Antimicrobial peptides (AMPs) are promising candidates for new antibiotics due to their broad-spectrum activity against pathogens and reduced susceptibility to resistance development. Deep-learning techniques, such as deep generative models, offer a promising avenue to expedite the discovery and optimization of AMPs. A remarkable example is the Feedback Generative Adversarial Network (FBGAN), a deep generative model that incorporates a classifier during its training phase. Our study aims to explore the impact of enhanced classifiers on the generative capabilities of FBGAN. To this end, we introduce two alternative classifiers for the FBGAN framework, both surpassing the accuracy of the original classifier. The first classifier utilizes the k-mers technique, while the second applies transfer learning from the large protein language model Evolutionary Scale Modeling 2 (ESM2). Integrating these classifiers into FBGAN not only yields notable performance enhancements compared to the original FBGAN but also enables the proposed generative models to achieve comparable or even superior performance to established methods such as AMPGAN and HydrAMP. This achievement underscores the effectiveness of leveraging advanced classifiers within the FBGAN framework, enhancing its computational robustness for AMP de novo design and making it comparable to existing literature.
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Affiliation(s)
- Michaela Areti Zervou
- Department of Computer Science, University of Crete, 700 13 Heraklion, Greece
- Institute of Computer Science, Foundation for Research and Technology-Hellas, 700 13 Heraklion, Greece;
| | - Effrosyni Doutsi
- Institute of Computer Science, Foundation for Research and Technology-Hellas, 700 13 Heraklion, Greece;
| | - Yannis Pantazis
- Institute of Applied and Computational Mathematics, Foundation for Research and Technology-Hellas, 700 13 Heraklion, Greece;
| | - Panagiotis Tsakalides
- Department of Computer Science, University of Crete, 700 13 Heraklion, Greece
- Institute of Computer Science, Foundation for Research and Technology-Hellas, 700 13 Heraklion, Greece;
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13
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Chen C, Shi J, Wang D, Kong P, Wang Z, Liu Y. Antimicrobial peptides as promising antibiotic adjuvants to combat drug-resistant pathogens. Crit Rev Microbiol 2024; 50:267-284. [PMID: 36890767 DOI: 10.1080/1040841x.2023.2186215] [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: 03/01/2022] [Revised: 07/19/2022] [Accepted: 10/26/2022] [Indexed: 03/10/2023]
Abstract
The widespread antimicrobial resistance (AMR) calls for the development of new antimicrobial strategies. Antibiotic adjuvant rescues antibiotic activity and increases the life span of the antibiotics, representing a more productive, timely, and cost-effective strategy in fighting drug-resistant pathogens. Antimicrobial peptides (AMPs) from synthetic and natural sources are considered new-generation antibacterial agents. Besides their direct antimicrobial activity, growing evidence shows that some AMPs effectively enhance the activity of conventional antibiotics. The combinations of AMPs and antibiotics display an improved therapeutic effect on antibiotic-resistant bacterial infections and minimize the emergence of resistance. In this review, we discuss the value of AMPs in the age of resistance, including modes of action, limiting evolutionary resistance, and their designing strategies. We summarise the recent advances in combining AMPs and antibiotics against antibiotic-resistant pathogens, as well as their synergistic mechanisms. Lastly, we highlight the challenges and opportunities associated with the use of AMPs as potential antibiotic adjuvants. This will shed new light on the deployment of synergistic combinations to address the AMR crisis.
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Affiliation(s)
- Chen Chen
- College of Veterinary Medicine, Yangzhou University, Yangzhou, China
| | - Jingru Shi
- College of Veterinary Medicine, Yangzhou University, Yangzhou, China
| | - Dejuan Wang
- College of Veterinary Medicine, Yangzhou University, Yangzhou, China
| | - Pan Kong
- College of Veterinary Medicine, Yangzhou University, Yangzhou, China
| | - Zhiqiang Wang
- College of Veterinary Medicine, Yangzhou University, Yangzhou, China
- Jiangsu Co-innovation Center for Prevention and Control of Important Animal Infectious Diseases and Zoonoses, Yangzhou University, Yangzhou, China
- Joint International Research Laboratory of Agriculture and Agri-Product Safety, Ministry of Education of China, Yangzhou University, Yangzhou, China
| | - Yuan Liu
- College of Veterinary Medicine, Yangzhou University, Yangzhou, China
- Institute of Comparative Medicine, Yangzhou University, Yangzhou, China
- Jiangsu Co-innovation Center for Prevention and Control of Important Animal Infectious Diseases and Zoonoses, Yangzhou University, Yangzhou, China
- Joint International Research Laboratory of Agriculture and Agri-Product Safety, Ministry of Education of China, Yangzhou University, Yangzhou, China
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14
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Lobanov MY, Slizen MV, Dovidchenko NV, Panfilov AV, Surin AA, Likhachev IV, Galzitskaya OV. Comparison of deep learning models with simple method to assess the problem of antimicrobial peptides prediction. Mol Inform 2024; 43:e202200181. [PMID: 36961202 DOI: 10.1002/minf.202200181] [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: 07/27/2022] [Revised: 03/20/2023] [Accepted: 03/23/2023] [Indexed: 03/25/2023]
Abstract
Antibiotic-resistant strains are an emerging threat to public health. The usage of antimicrobial peptides (AMPs) is one of the promising approaches to solve this problem. For the development of new AMPs, it is necessary to have reliable prediction methods. Recently, deep learning approaches have been used to predict AMP. In this paper, we want to compare simple and complex methods for these purposes. We used the BERT transformer to create sequence embeddings and the multilayer perceptron (MLP) and light attention (LA) approaches for classification. One of them reached about 80 % accuracy and specificity in benchmark testing, which is on par with the best available methods. For comparison, we proposed a simple method using only the amino acid composition of proteins or peptides. This method has shown good results, at the level of the best methods. We have prepared a special server for predicting the ability of AMPs by amino acid composition: http://bioproteom.protres.ru/antimicrob/.
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Affiliation(s)
- M Y Lobanov
- Laboratory of Bioinformatics and Proteomics, Institute of Protein Research, Russian Academy of Sciences, 142290, Pushchino, Moscow Region, Russia
| | - M V Slizen
- Laboratory of Bioinformatics and Proteomics, Institute of Protein Research, Russian Academy of Sciences, 142290, Pushchino, Moscow Region, Russia
| | - N V Dovidchenko
- Laboratory of Bioinformatics and Proteomics, Institute of Protein Research, Russian Academy of Sciences, 142290, Pushchino, Moscow Region, Russia
| | - A V Panfilov
- Laboratory of Bioinformatics and Proteomics, Institute of Protein Research, Russian Academy of Sciences, 142290, Pushchino, Moscow Region, Russia
| | - A A Surin
- Faculty of Applied math, MIREA - Russian Technological University, Moscow, 119454, Russia
| | - I V Likhachev
- Laboratory of Bioinformatics and Proteomics, Institute of Protein Research, Russian Academy of Sciences, 142290, Pushchino, Moscow Region, Russia
- Institute of Mathematical Problems of Biology branch of Keldysh Institute of Applied Mathematics, Russian Academy of Sciences, 142290, Pushchino, Russia
| | - O V Galzitskaya
- Laboratory of Bioinformatics and Proteomics, Institute of Protein Research, Russian Academy of Sciences, 142290, Pushchino, Moscow Region, Russia
- Laboratory of Structure and Function of Muscle Proteins, Institute of Theoretical and Experimental Biophysics, Russian Academy of Sciences, 142290, Pushchino, Moscow Region, Russia
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15
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Feng J, Sun M, Liu C, Zhang W, Xu C, Wang J, Wang G, Wan S. SAMP: Identifying Antimicrobial Peptides by an Ensemble Learning Model Based on Proportionalized Split Amino Acid Composition. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.25.590553. [PMID: 38712184 PMCID: PMC11071531 DOI: 10.1101/2024.04.25.590553] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Abstract
It is projected that 10 million deaths could be attributed to drug-resistant bacteria infections in 2050. To address this concern, identifying new-generation antibiotics is an effective way. Antimicrobial peptides (AMPs), a class of innate immune effectors, have received significant attention for their capacity to eliminate drug-resistant pathogens, including viruses, bacteria, and fungi. Recent years have witnessed widespread applications of computational methods especially machine learning (ML) and deep learning (DL) for discovering AMPs. However, existing methods only use features including compositional, physiochemical, and structural properties of peptides, which cannot fully capture sequence information from AMPs. Here, we present SAMP, an ensemble random projection (RP) based computational model that leverages a new type of features called Proportionalized Split Amino Acid Composition (PSAAC) in addition to conventional sequence-based features for AMP prediction. With this new feature set, SAMP captures the residue patterns like sorting signals at around both the N-terminus and the C-terminus, while also retaining the sequence order information from the middle peptide fragments. Benchmarking tests on different balanced and imbalanced datasets demonstrate that SAMP consistently outperforms existing state-of-the-art methods, such as iAMPpred and AMPScanner V2, in terms of accuracy, MCC, G-measure and F1-score. In addition, by leveraging an ensemble RP architecture, SAMP is scalable to processing large-scale AMP identification with further performance improvement, compared to those models without RP. To facilitate the use of SAMP, we have developed a Python package freely available at https://github.com/wan-mlab/SAMP .
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16
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Rusic D, Kumric M, Seselja Perisin A, Leskur D, Bukic J, Modun D, Vilovic M, Vrdoljak J, Martinovic D, Grahovac M, Bozic J. Tackling the Antimicrobial Resistance "Pandemic" with Machine Learning Tools: A Summary of Available Evidence. Microorganisms 2024; 12:842. [PMID: 38792673 PMCID: PMC11123121 DOI: 10.3390/microorganisms12050842] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2024] [Revised: 04/16/2024] [Accepted: 04/19/2024] [Indexed: 05/26/2024] Open
Abstract
Antimicrobial resistance is recognised as one of the top threats healthcare is bound to face in the future. There have been various attempts to preserve the efficacy of existing antimicrobials, develop new and efficient antimicrobials, manage infections with multi-drug resistant strains, and improve patient outcomes, resulting in a growing mass of routinely available data, including electronic health records and microbiological information that can be employed to develop individualised antimicrobial stewardship. Machine learning methods have been developed to predict antimicrobial resistance from whole-genome sequencing data, forecast medication susceptibility, recognise epidemic patterns for surveillance purposes, or propose new antibacterial treatments and accelerate scientific discovery. Unfortunately, there is an evident gap between the number of machine learning applications in science and the effective implementation of these systems. This narrative review highlights some of the outstanding opportunities that machine learning offers when applied in research related to antimicrobial resistance. In the future, machine learning tools may prove to be superbugs' kryptonite. This review aims to provide an overview of available publications to aid researchers that are looking to expand their work with new approaches and to acquaint them with the current application of machine learning techniques in this field.
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Affiliation(s)
- Doris Rusic
- Department of Pharmacy, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia; (D.R.); (A.S.P.); (D.L.); (J.B.); (D.M.)
| | - Marko Kumric
- Department of Pathophysiology, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia; (M.K.); (M.V.); (J.V.); (D.M.)
- Laboratory for Cardiometabolic Research, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia
| | - Ana Seselja Perisin
- Department of Pharmacy, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia; (D.R.); (A.S.P.); (D.L.); (J.B.); (D.M.)
| | - Dario Leskur
- Department of Pharmacy, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia; (D.R.); (A.S.P.); (D.L.); (J.B.); (D.M.)
| | - Josipa Bukic
- Department of Pharmacy, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia; (D.R.); (A.S.P.); (D.L.); (J.B.); (D.M.)
| | - Darko Modun
- Department of Pharmacy, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia; (D.R.); (A.S.P.); (D.L.); (J.B.); (D.M.)
| | - Marino Vilovic
- Department of Pathophysiology, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia; (M.K.); (M.V.); (J.V.); (D.M.)
- Laboratory for Cardiometabolic Research, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia
| | - Josip Vrdoljak
- Department of Pathophysiology, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia; (M.K.); (M.V.); (J.V.); (D.M.)
- Laboratory for Cardiometabolic Research, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia
| | - Dinko Martinovic
- Department of Pathophysiology, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia; (M.K.); (M.V.); (J.V.); (D.M.)
- Department of Maxillofacial Surgery, University Hospital of Split, Spinciceva 1, 21000 Split, Croatia
| | - Marko Grahovac
- Department of Pharmacology, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia;
| | - Josko Bozic
- Department of Pathophysiology, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia; (M.K.); (M.V.); (J.V.); (D.M.)
- Laboratory for Cardiometabolic Research, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia
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17
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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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18
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Lee B, Shin D. Contrastive learning for enhancing feature extraction in anticancer peptides. Brief Bioinform 2024; 25:bbae220. [PMID: 38725157 PMCID: PMC11082072 DOI: 10.1093/bib/bbae220] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Revised: 03/28/2024] [Accepted: 04/21/2024] [Indexed: 05/13/2024] Open
Abstract
Cancer, recognized as a primary cause of death worldwide, has profound health implications and incurs a substantial social burden. Numerous efforts have been made to develop cancer treatments, among which anticancer peptides (ACPs) are garnering recognition for their potential applications. While ACP screening is time-consuming and costly, in silico prediction tools provide a way to overcome these challenges. Herein, we present a deep learning model designed to screen ACPs using peptide sequences only. A contrastive learning technique was applied to enhance model performance, yielding better results than a model trained solely on binary classification loss. Furthermore, two independent encoders were employed as a replacement for data augmentation, a technique commonly used in contrastive learning. Our model achieved superior performance on five of six benchmark datasets against previous state-of-the-art models. As prediction tools advance, the potential in peptide-based cancer therapeutics increases, promising a brighter future for oncology research and patient care.
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Affiliation(s)
- Byungjo Lee
- Research Institute, National Cancer Center, 323, Ilsan-ro, Ilsandong-gu, Goyang, 10408, Republic of Korea
| | - Dongkwan Shin
- Research Institute, National Cancer Center, 323, Ilsan-ro, Ilsandong-gu, Goyang, 10408, Republic of Korea
- Department of Cancer Biomedical Science, National Cancer Center Graduate School of Cancer Science and Policy, 323, Ilsan-ro, Ilsandong-gu, Goyang, 10408, Republic of Korea
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19
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Wu H, Chen R, Li X, Zhang Y, Zhang J, Yang Y, Wan J, Zhou Y, Chen H, Li J, Li R, Zou G. ESKtides: a comprehensive database and mining method for ESKAPE phage-derived antimicrobial peptides. Database (Oxford) 2024; 2024:baae022. [PMID: 38531599 DOI: 10.1093/database/baae022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Revised: 12/06/2023] [Accepted: 03/06/2024] [Indexed: 03/28/2024]
Abstract
'Superbugs' have received increasing attention from researchers, such as ESKAPE bacteria (Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa and Enterobacter spp.), which directly led to about 1 270 000 death cases in 2019. Recently, phage peptidoglycan hydrolases (PGHs)-derived antimicrobial peptides were proposed as new antibacterial agents against multidrug-resistant bacteria. However, there is still a lack of methods for mining antimicrobial peptides based on phages or phage PGHs. Here, by using a collection of 6809 genomes of ESKAPE isolates and corresponding phages in public databases, based on a unified annotation process of all the genomes, PGHs were systematically identified, from which peptides were mined. As a result, a total of 12 067 248 peptides with high antibacterial activities were respectively determined. A user-friendly tool was developed to predict the phage PGHs-derived antimicrobial peptides from customized genomes, which also allows the calculation of peptide phylogeny, physicochemical properties, and secondary structure. Finally, a user-friendly and intuitive database, ESKtides (http://www.phageonehealth.cn:9000/ESKtides), was designed for data browsing, searching and downloading, which provides a rich peptide library based on ESKAPE prophages and phages. Database URL: 10.1093/database/baae022.
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Affiliation(s)
- Hongfang Wu
- National Key Laboratory of Agricultural Microbiology, College of Biomedicine and Health, Huazhong Agricultural University, Shenzhen Institute of Nutrition and Health, Huazhong Agricultural University, Shizishan Street No. 1, Wuhan 430070, China
- Hubei Hongshan Laboratory, College of Food Science and Technology, Huazhong Agricultural University, Shizishan Street No. 1, Wuhan 430070, China
| | - Rongxian Chen
- National Key Laboratory of Agricultural Microbiology, College of Biomedicine and Health, Huazhong Agricultural University, Shenzhen Institute of Nutrition and Health, Huazhong Agricultural University, Shizishan Street No. 1, Wuhan 430070, China
- Hubei Hongshan Laboratory, College of Food Science and Technology, Huazhong Agricultural University, Shizishan Street No. 1, Wuhan 430070, China
| | - Xuejian Li
- National Key Laboratory of Agricultural Microbiology, College of Biomedicine and Health, Huazhong Agricultural University, Shenzhen Institute of Nutrition and Health, Huazhong Agricultural University, Shizishan Street No. 1, Wuhan 430070, China
- Hubei Hongshan Laboratory, College of Food Science and Technology, Huazhong Agricultural University, Shizishan Street No. 1, Wuhan 430070, China
- College of Informatics, Huazhong Agricultural University, Shizishan Street No. 1, Wuhan 430070, China
| | - Yue Zhang
- National Key Laboratory of Agricultural Microbiology, College of Biomedicine and Health, Huazhong Agricultural University, Shenzhen Institute of Nutrition and Health, Huazhong Agricultural University, Shizishan Street No. 1, Wuhan 430070, China
- Hubei Hongshan Laboratory, College of Food Science and Technology, Huazhong Agricultural University, Shizishan Street No. 1, Wuhan 430070, China
| | - Jianwei Zhang
- National Key Laboratory of Crop Genetic Improvement, Shizishan Street No. 1, Wuhan 430070, China
| | - Yanbo Yang
- College of Informatics, Huazhong Agricultural University, Shizishan Street No. 1, Wuhan 430070, China
| | - Jun Wan
- Hubei Hongshan Laboratory, College of Food Science and Technology, Huazhong Agricultural University, Shizishan Street No. 1, Wuhan 430070, China
| | - Yang Zhou
- College of Fisheries, Huazhong Agricultural University, Shizishan Street No. 1, Wuhan 430070, China
| | - Huanchun Chen
- National Key Laboratory of Agricultural Microbiology, College of Biomedicine and Health, Huazhong Agricultural University, Shenzhen Institute of Nutrition and Health, Huazhong Agricultural University, Shizishan Street No. 1, Wuhan 430070, China
- College of Veterinary Medicine, Huazhong Agricultural University, Shizishan Street No. 1, Wuhan 430070, China
| | - Jinquan Li
- National Key Laboratory of Agricultural Microbiology, College of Biomedicine and Health, Huazhong Agricultural University, Shenzhen Institute of Nutrition and Health, Huazhong Agricultural University, Shizishan Street No. 1, Wuhan 430070, China
- Hubei Hongshan Laboratory, College of Food Science and Technology, Huazhong Agricultural University, Shizishan Street No. 1, Wuhan 430070, China
- College of Veterinary Medicine, Huazhong Agricultural University, Shizishan Street No. 1, Wuhan 430070, China
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Buxin Road No. 97, Shenzhen 518000, China
- Shenzhen Institute of Quality & Safety Inspection and Research, Buxin Road No. 97, Shenzhen 518000, China
| | - Runze Li
- National Key Laboratory of Agricultural Microbiology, College of Biomedicine and Health, Huazhong Agricultural University, Shenzhen Institute of Nutrition and Health, Huazhong Agricultural University, Shizishan Street No. 1, Wuhan 430070, China
- Hubei Hongshan Laboratory, College of Food Science and Technology, Huazhong Agricultural University, Shizishan Street No. 1, Wuhan 430070, China
| | - Geng Zou
- National Key Laboratory of Agricultural Microbiology, College of Biomedicine and Health, Huazhong Agricultural University, Shenzhen Institute of Nutrition and Health, Huazhong Agricultural University, Shizishan Street No. 1, Wuhan 430070, China
- Hubei Hongshan Laboratory, College of Food Science and Technology, Huazhong Agricultural University, Shizishan Street No. 1, Wuhan 430070, China
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20
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Pandey P, Srivastava A. sAMP-VGG16: Force-field assisted image-based deep neural network prediction model for short antimicrobial peptides. Proteins 2024. [PMID: 38520179 DOI: 10.1002/prot.26681] [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: 12/08/2023] [Revised: 02/15/2024] [Accepted: 02/28/2024] [Indexed: 03/25/2024]
Abstract
During the last three decades, antimicrobial peptides (AMPs) have emerged as a promising therapeutic alternative to antibiotics. The approaches for designing AMPs span from experimental trial-and-error methods to synthetic hybrid peptide libraries. To overcome the exceedingly expensive and time-consuming process of designing effective AMPs, many computational and machine-learning tools for AMP prediction have been recently developed. In general, to encode the peptide sequences, featurization relies on approaches based on (a) amino acid (AA) composition, (b) physicochemical properties, (c) sequence similarity, and (d) structural properties. In this work, we present an image-based deep neural network model to predict AMPs, where we are using feature encoding based on Drude polarizable force-field atom types, which can capture the peptide properties more efficiently compared to conventional feature vectors. The proposed prediction model identifies short AMPs (≤30 AA) with promising accuracy and efficiency and can be used as a next-generation screening method for predicting new AMPs. The source code is publicly available at the Figshare server sAMP-VGG16.
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Affiliation(s)
- Poonam Pandey
- Molecular Biophysics Unit, Indian Institute of Science, Bangalore, Karnataka, India
| | - Anand Srivastava
- Molecular Biophysics Unit, Indian Institute of Science, Bangalore, Karnataka, India
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21
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Kanwal S, Arif R, Ahmed S, Kabir M. A novel stacking-based predictor for accurate prediction of antimicrobial peptides. J Biomol Struct Dyn 2024:1-12. [PMID: 38500243 DOI: 10.1080/07391102.2024.2329298] [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: 09/21/2023] [Accepted: 03/06/2024] [Indexed: 03/20/2024]
Abstract
Antimicrobial peptides (AMPs) are gaining acceptance and support as a chief antibiotic substitute since they boost human immunity. They retain a wide range of actions and have a low risk of developing resistance, which are critical properties to the pharmaceutical industry for drug discovery. Antibiotic sensitivity, however, is an issue that affects people all around the world and has the potential to one day lead to an epidemic. As cutting-edge therapeutic agents, AMPs are also expected to cure microbial infections. In order to produce tolerable drugs, it is crucial to understand the significance of the basic architecture of AMPs. Traditional laboratory methods are expensive and time-consuming for AMPs testing and detection. Currently, bioinformatics techniques are being successfully applied to the detection of AMPs. In this study, we have developed a novel STacking-based ensemble learning framework for AntiMicrobial Peptide (STAMP) prediction. First, we constructed 84 different baseline models by using 12 different feature encoding schemes and 7 popular machine learning algorithms. Second, these baseline models were trained and employed to create a new probabilistic feature vector. Finally, based on the feature selection strategy, we determined the optimal probabilistic feature vector, which was further utilized for the construction of our stacked model. Resultantly, the STAMP predictor achieved excellent performance during cross-validation with an accuracy and Matthew's correlation coefficient of 0.930 and 0.860, respectively. The corresponding metrics during the independent test were 0.710 and 0.464, respectively. Overall, STAMP achieved a more accurate and stable performance than the baseline models and significantly outperformed the existing predictors, demonstrating the effectiveness of our proposed hybrid framework. Furthermore, STAMP is expected to assist community-wide efforts in identifying AMPs and will contribute to the development of novel therapeutic methods and drug-design for immunity.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Sameera Kanwal
- School of Systems and Technology, University of Management and Technology, Lahore, Pakistan
| | - Roha Arif
- School of Systems and Technology, University of Management and Technology, Lahore, Pakistan
| | - Saeed Ahmed
- School of Systems and Technology, University of Management and Technology, Lahore, Pakistan
| | - Muhammad Kabir
- School of Systems and Technology, University of Management and Technology, Lahore, Pakistan
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22
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Tsai CT, Lin CW, Ye GL, Wu SC, Yao P, Lin CT, Wan L, Tsai HHG. Accelerating Antimicrobial Peptide Discovery for WHO Priority Pathogens through Predictive and Interpretable Machine Learning Models. ACS OMEGA 2024; 9:9357-9374. [PMID: 38434814 PMCID: PMC10905719 DOI: 10.1021/acsomega.3c08676] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Revised: 12/19/2023] [Accepted: 01/19/2024] [Indexed: 03/05/2024]
Abstract
The escalating menace of multidrug-resistant (MDR) pathogens necessitates a paradigm shift from conventional antibiotics to innovative alternatives. Antimicrobial peptides (AMPs) emerge as a compelling contender in this arena. Employing in silico methodologies, we can usher in a new era of AMP discovery, streamlining the identification process from vast candidate sequences, thereby optimizing laboratory screening expenditures. Here, we unveil cutting-edge machine learning (ML) models that are both predictive and interpretable, tailored for the identification of potent AMPs targeting World Health Organization's (WHO) high-priority pathogens. Furthermore, we have developed ML models that consider the hemolysis of human erythrocytes, emphasizing their therapeutic potential. Anchored in the nuanced physical-chemical attributes gleaned from the three-dimensional (3D) helical conformations of AMPs, our optimized models have demonstrated commendable performance-boasting an accuracy exceeding 75% when evaluated against both low-sequence-identified peptides and recently unveiled AMPs. As a testament to their efficacy, we deployed these models to prioritize peptide sequences stemming from PEM-2 and subsequently probed the bioactivity of our algorithm-predicted peptides vis-à-vis WHO's priority pathogens. Intriguingly, several of these new AMPs outperformed the native PEM-2 in their antimicrobial prowess, thereby underscoring the robustness of our modeling approach. To elucidate ML model outcomes, we probe via Shapley Additive exPlanations (SHAP) values, uncovering intricate mechanisms guiding diverse actions against bacteria. Our state-of-the-art predictive models expedite the design of new AMPs, offering a robust countermeasure to antibiotic resistance. Our prediction tool is available to the public at https://ai-meta.chem.ncu.edu.tw/amp-meta.
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Affiliation(s)
- Cheng-Ting Tsai
- Department
of Chemistry, National Central University, No. 300, Zhongda Road, Zhongli District, Taoyuan 32001, Taiwan
| | - Chia-Wei Lin
- Department
of Chemistry, National Central University, No. 300, Zhongda Road, Zhongli District, Taoyuan 32001, Taiwan
| | - Gen-Lin Ye
- Department
of Chemistry, National Central University, No. 300, Zhongda Road, Zhongli District, Taoyuan 32001, Taiwan
| | - Shao-Chi Wu
- Department
of Chemistry, National Central University, No. 300, Zhongda Road, Zhongli District, Taoyuan 32001, Taiwan
| | - Philip Yao
- Aurora
High School, 109 W Pioneer Trail, Aurora, Ohio 44202, United States
| | - Ching-Ting Lin
- School
of Chinese Medicine, China Medical University, No. 91 Hsueh-Shih Road, Taichung 40402, Taiwan
| | - Lei Wan
- School
of Chinese Medicine, China Medical University, No. 91 Hsueh-Shih Road, Taichung 40402, Taiwan
| | - Hui-Hsu Gavin Tsai
- Department
of Chemistry, National Central University, No. 300, Zhongda Road, Zhongli District, Taoyuan 32001, Taiwan
- Research
Center of New Generation Light Driven Photovoltaic Modules, National Central University, Taoyuan 32001, Taiwan
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23
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Dong Q, Wang S, Miao Y, Luo H, Weng Z, Yu L. Novel antimicrobial peptides against Cutibacterium acnes designed by deep learning. Sci Rep 2024; 14:4529. [PMID: 38402320 PMCID: PMC10894229 DOI: 10.1038/s41598-024-55205-3] [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: 11/01/2023] [Accepted: 02/21/2024] [Indexed: 02/26/2024] Open
Abstract
The increasing prevalence of antibiotic resistance in Cutibacterium acnes (C. acnes) requires the search for alternative therapeutic strategies. Antimicrobial peptides (AMPs) offer a promising avenue for the development of new treatments targeting C. acnes. In this study, to design peptides with the specific inhibitory activity against C. acnes, we employed a deep learning pipeline with generators and classifiers, using transfer learning and pretrained protein embeddings, trained on publicly available data. To enhance the training data specific to C. acnes inhibition, we constructed a phylogenetic tree. A panel of 42 novel generated linear peptides was then synthesized and experimentally evaluated for their antimicrobial selectivity and activity. Five of them demonstrated their high potency and selectivity against C. acnes with MIC of 2-4 µg/mL. Our findings highlight the potential of these designed peptides as promising candidates for anti-acne therapeutics and demonstrate the power of computational approaches for the rational design of targeted antimicrobial peptides.
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Affiliation(s)
- Qichang Dong
- Shanghai MetaNovas Biotech Co., Ltd, Shanghai, 200120, China
| | - Shaohua Wang
- Shanghai MetaNovas Biotech Co., Ltd, Shanghai, 200120, China
| | - Ying Miao
- College of Biological Science and Engineering, Fuzhou University, Fuzhou, 350108, China
| | - Heng Luo
- Shanghai MetaNovas Biotech Co., Ltd, Shanghai, 200120, China
| | - Zuquan Weng
- College of Biological Science and Engineering, Fuzhou University, Fuzhou, 350108, China
| | - Lun Yu
- Metanovas Biotech Inc., Foster City, 94404, USA.
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24
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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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25
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Zhuang J, Gao W, Su R. EnAMP: A novel deep learning ensemble antibacterial peptide recognition algorithm based on multi-features. J Bioinform Comput Biol 2024; 22:2450001. [PMID: 38406833 DOI: 10.1142/s021972002450001x] [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: 02/27/2024]
Abstract
Antimicrobial peptides (AMPs), as the preferred alternatives to antibiotics, have wide application with good prospects. Identifying AMPs through wet lab experiments remains expensive, time-consuming and challenging. Many machine learning methods have been proposed to predict AMPs and achieved good results. In this work, we combine two kinds of word embedding features with the statistical features of peptide sequences to develop an ensemble classifier, named EnAMP, in which, two deep neural networks are trained based on Word2vec and Glove word embedding features of peptide sequences, respectively, meanwhile, we utilize statistical features of peptide sequences to train random forest and support vector machine classifiers. The average of four classifiers is the final prediction result. Compared with other state-of-the-art algorithms on six datasets, EnAMP outperforms most existing models with similar computational costs, even when compared with high computational cost algorithms based on Bidirectional Encoder Representation from Transformers (BERT), the performance of our model is comparable. EnAMP source code and the data are available at https://github.com/ruisue/EnAMP.
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Affiliation(s)
- Jujuan Zhuang
- School of Science, Dalian Maritime University, Dalian, Liaoning, P. R. China
| | - Wanquan Gao
- School of Science, Dalian Maritime University, Dalian, Liaoning, P. R. China
| | - Rui Su
- School of Science, Dalian Maritime University, Dalian, Liaoning, P. R. China
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26
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Liu GY, Yu D, Fan MM, Zhang X, Jin ZY, Tang C, Liu XF. Antimicrobial resistance crisis: could artificial intelligence be the solution? Mil Med Res 2024; 11:7. [PMID: 38254241 PMCID: PMC10804841 DOI: 10.1186/s40779-024-00510-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Accepted: 01/08/2024] [Indexed: 01/24/2024] Open
Abstract
Antimicrobial resistance is a global public health threat, and the World Health Organization (WHO) has announced a priority list of the most threatening pathogens against which novel antibiotics need to be developed. The discovery and introduction of novel antibiotics are time-consuming and expensive. According to WHO's report of antibacterial agents in clinical development, only 18 novel antibiotics have been approved since 2014. Therefore, novel antibiotics are critically needed. Artificial intelligence (AI) has been rapidly applied to drug development since its recent technical breakthrough and has dramatically improved the efficiency of the discovery of novel antibiotics. Here, we first summarized recently marketed novel antibiotics, and antibiotic candidates in clinical development. In addition, we systematically reviewed the involvement of AI in antibacterial drug development and utilization, including small molecules, antimicrobial peptides, phage therapy, essential oils, as well as resistance mechanism prediction, and antibiotic stewardship.
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Affiliation(s)
- Guang-Yu Liu
- Department of Immunology and Pathogen Biology, School of Basic Medical Sciences, Hangzhou Normal University, Key Laboratory of Aging and Cancer Biology of Zhejiang Province, Key Laboratory of Inflammation and Immunoregulation of Hangzhou, Hangzhou Normal University, Hangzhou, 311121, China
| | - Dan Yu
- National Key Discipline of Pediatrics Key Laboratory of Major Diseases in Children Ministry of Education, Laboratory of Dermatology, Beijing Pediatric Research Institute, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, 100045, China
| | - Mei-Mei Fan
- Department of Immunology and Pathogen Biology, School of Basic Medical Sciences, Hangzhou Normal University, Key Laboratory of Aging and Cancer Biology of Zhejiang Province, Key Laboratory of Inflammation and Immunoregulation of Hangzhou, Hangzhou Normal University, Hangzhou, 311121, China
| | - Xu Zhang
- Robert and Arlene Kogod Center on Aging, Mayo Clinic, Rochester, MN, 55905, USA
- Department of Biochemistry and Molecular Biology, Mayo Clinic, Rochester, MN, 55905, USA
| | - Ze-Yu Jin
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Christoph Tang
- Sir William Dunn School of Pathology, University of Oxford, Oxford, OX1 3RE, UK.
| | - Xiao-Fen Liu
- Institute of Antibiotics, Huashan Hospital, Fudan University, Key Laboratory of Clinical Pharmacology of Antibiotics, National Health Commission of the People's Republic of China, National Clinical Research Centre for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, 200040, China.
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27
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Wang R, Wang T, Zhuo L, Wei J, Fu X, Zou Q, Yao X. Diff-AMP: tailored designed antimicrobial peptide framework with all-in-one generation, identification, prediction and optimization. Brief Bioinform 2024; 25:bbae078. [PMID: 38446739 PMCID: PMC10939340 DOI: 10.1093/bib/bbae078] [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: 11/23/2023] [Revised: 01/25/2024] [Accepted: 02/08/2024] [Indexed: 03/08/2024] Open
Abstract
Antimicrobial peptides (AMPs), short peptides with diverse functions, effectively target and combat various organisms. The widespread misuse of chemical antibiotics has led to increasing microbial resistance. Due to their low drug resistance and toxicity, AMPs are considered promising substitutes for traditional antibiotics. While existing deep learning technology enhances AMP generation, it also presents certain challenges. Firstly, AMP generation overlooks the complex interdependencies among amino acids. Secondly, current models fail to integrate crucial tasks like screening, attribute prediction and iterative optimization. Consequently, we develop a integrated deep learning framework, Diff-AMP, that automates AMP generation, identification, attribute prediction and iterative optimization. We innovatively integrate kinetic diffusion and attention mechanisms into the reinforcement learning framework for efficient AMP generation. Additionally, our prediction module incorporates pre-training and transfer learning strategies for precise AMP identification and screening. We employ a convolutional neural network for multi-attribute prediction and a reinforcement learning-based iterative optimization strategy to produce diverse AMPs. This framework automates molecule generation, screening, attribute prediction and optimization, thereby advancing AMP research. We have also deployed Diff-AMP on a web server, with code, data and server details available in the Data Availability section.
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Affiliation(s)
- Rui Wang
- School of Data Science and Artificial Intelligence, Wenzhou University of Technology, 325000 Wenzhou, China
| | - Tao Wang
- School of Data Science and Artificial Intelligence, Wenzhou University of Technology, 325000 Wenzhou, China
| | - Linlin Zhuo
- School of Data Science and Artificial Intelligence, Wenzhou University of Technology, 325000 Wenzhou, China
| | - Jinhang Wei
- School of Data Science and Artificial Intelligence, Wenzhou University of Technology, 325000 Wenzhou, China
| | - Xiangzheng Fu
- College of Computer Science and Electronic Engineering, Hunan University, 410012 Changsha, China
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, 611730 Chengdu, China
| | - Xiaojun Yao
- Faculty of Applied Sciences, Macao Polytechnic University, 999078 Macao, China
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28
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Yue L, Song L, Zhu S, Fu X, Li X, He C, Li J. Machine learning assisted rational design of antimicrobial peptides based on human endogenous proteins and their applications for cosmetic preservative system optimization. Sci Rep 2024; 14:947. [PMID: 38200054 PMCID: PMC10781772 DOI: 10.1038/s41598-023-50832-8] [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: 07/25/2023] [Accepted: 12/26/2023] [Indexed: 01/12/2024] Open
Abstract
Preservatives are essential components in cosmetic products, but their safety issues have attracted widespread attention. There is an urgent need for safe and effective alternatives. Antimicrobial peptides (AMPs) are part of the innate immune system and have potent antimicrobial properties. Using machine learning-assisted rational design, we obtained a novel antibacterial peptide, IK-16-1, with significant antibacterial activity and maintaining safety based on β-defensins. IK-16-1 has broad-spectrum antimicrobial properties against Escherichia coli, Staphylococcus aureus, Pseudomonas aeruginosa, and Candida albicans, and has no haemolytic activity. The use of IK-16-1 holds promise in the cosmetics industry, since it can serve as a preservative synergist to reduce the amount of other preservatives in cosmetics. This study verified the feasibility of combining computational design with artificial intelligence prediction to design AMPs, achieving rapid screening and reducing development costs.
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Affiliation(s)
- Lizhi Yue
- Key Laboratory of Cosmetic of China National Light Industry, School of Light Industry Science and Engineering, Beijing Technology and Business University, Beijing, China
- School of Chemistry and Chemical Engineering, Qilu Normal University, Shandong, China
| | - Liya Song
- Key Laboratory of Cosmetic of China National Light Industry, School of Light Industry Science and Engineering, Beijing Technology and Business University, Beijing, China
| | - Siyu Zhu
- AGECODE R&D Center, Yangtze Delta Region Institute of Tsinghua University, Zhejiang, China
- Harvest Biotech (Zhejiang) Co., Ltd., Zhejiang, China
| | - Xiaolei Fu
- AGECODE R&D Center, Yangtze Delta Region Institute of Tsinghua University, Zhejiang, China
- Harvest Biotech (Zhejiang) Co., Ltd., Zhejiang, China
| | - Xuhui Li
- AGECODE R&D Center, Yangtze Delta Region Institute of Tsinghua University, Zhejiang, China
- Zhejiang Provincial Key Laboratory of Applied Enzymology, Yangtze Delta Region Institute of Tsinghua University, Zhejiang, China
| | - Congfen He
- Key Laboratory of Cosmetic of China National Light Industry, School of Light Industry Science and Engineering, Beijing Technology and Business University, Beijing, China.
| | - Junxiang Li
- AGECODE R&D Center, Yangtze Delta Region Institute of Tsinghua University, Zhejiang, China.
- Harvest Biotech (Zhejiang) Co., Ltd., Zhejiang, China.
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29
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Aguilera-Puga MDC, Cancelarich NL, Marani MM, de la Fuente-Nunez C, Plisson F. Accelerating the Discovery and Design of Antimicrobial Peptides with Artificial Intelligence. Methods Mol Biol 2024; 2714:329-352. [PMID: 37676607 DOI: 10.1007/978-1-0716-3441-7_18] [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] [Indexed: 09/08/2023]
Abstract
Peptides modulate many processes of human physiology targeting ion channels, protein receptors, or enzymes. They represent valuable starting points for the development of new biologics against communicable and non-communicable disorders. However, turning native peptide ligands into druggable materials requires high selectivity and efficacy, predictable metabolism, and good safety profiles. Machine learning models have gradually emerged as cost-effective and time-saving solutions to predict and generate new proteins with optimal properties. In this chapter, we will discuss the evolution and applications of predictive modeling and generative modeling to discover and design safe and effective antimicrobial peptides. We will also present their current limitations and suggest future research directions, applicable to peptide drug design campaigns.
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Affiliation(s)
- Mariana D C Aguilera-Puga
- Centro de Investigación y de Estudios Avanzados del IPN (CINVESTAV-IPN), Unidad de Genómica Avanzada, Laboratorio Nacional de Genómica para la Biodiversidad (Langebio), Irapuato, Guanajuato, Mexico
- CINVESTAV-IPN, Unidad Irapuato, Departamento de Biotecnología y Bioquímica, Irapuato, Guanajuato, Mexico
| | - Natalia L Cancelarich
- Instituto Patagónico para el Estudio de los Ecosistemas Continentales (IPEEC), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Puerto Madryn, Argentina
| | - Mariela M Marani
- Instituto Patagónico para el Estudio de los Ecosistemas Continentales (IPEEC), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Puerto Madryn, Argentina
| | - Cesar de la Fuente-Nunez
- Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
- Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA.
- Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, PA, USA.
| | - Fabien Plisson
- Centro de Investigación y de Estudios Avanzados del IPN (CINVESTAV-IPN), Unidad de Genómica Avanzada, Laboratorio Nacional de Genómica para la Biodiversidad (Langebio), Irapuato, Guanajuato, Mexico.
- CINVESTAV-IPN, Unidad Irapuato, Departamento de Biotecnología y Bioquímica, Irapuato, Guanajuato, Mexico.
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30
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La Paglia L, Vazzana M, Mauro M, Urso A, Arizza V, Vizzini A. Bioactive Molecules from the Innate Immunity of Ascidians and Innovative Methods of Drug Discovery: A Computational Approach Based on Artificial Intelligence. Mar Drugs 2023; 22:6. [PMID: 38276644 PMCID: PMC10817596 DOI: 10.3390/md22010006] [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: 11/20/2023] [Revised: 12/12/2023] [Accepted: 12/17/2023] [Indexed: 01/27/2024] Open
Abstract
The study of bioactive molecules of marine origin has created an important bridge between biological knowledge and its applications in biotechnology and biomedicine. Current studies in different research fields, such as biomedicine, aim to discover marine molecules characterized by biological activities that can be used to produce potential drugs for human use. In recent decades, increasing attention has been paid to a particular group of marine invertebrates, the Ascidians, as they are a source of bioactive products. We describe omics data and computational methods relevant to identifying the mechanisms and processes of innate immunity underlying the biosynthesis of bioactive molecules, focusing on innovative computational approaches based on Artificial Intelligence. Since there is increasing attention on finding new solutions for a sustainable supply of bioactive compounds, we propose that a possible improvement in the biodiscovery pipeline might also come from the study and utilization of marine invertebrates' innate immunity.
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Affiliation(s)
- Laura La Paglia
- Istituto di Calcolo e Reti ad Alte Prestazioni–Consiglio Nazionale delle Ricerche, Via Ugo La Malfa 153, 90146 Palermo, Italy; (L.L.P.); (A.U.)
| | - Mirella Vazzana
- Dipartimento di Scienze e Tecnologie Biologiche, Chimiche e Farmaceutiche–Università di Palermo, Via Archirafi 18, 90100 Palermo, Italy; (M.V.); (M.M.); (V.A.)
| | - Manuela Mauro
- Dipartimento di Scienze e Tecnologie Biologiche, Chimiche e Farmaceutiche–Università di Palermo, Via Archirafi 18, 90100 Palermo, Italy; (M.V.); (M.M.); (V.A.)
| | - Alfonso Urso
- Istituto di Calcolo e Reti ad Alte Prestazioni–Consiglio Nazionale delle Ricerche, Via Ugo La Malfa 153, 90146 Palermo, Italy; (L.L.P.); (A.U.)
| | - Vincenzo Arizza
- Dipartimento di Scienze e Tecnologie Biologiche, Chimiche e Farmaceutiche–Università di Palermo, Via Archirafi 18, 90100 Palermo, Italy; (M.V.); (M.M.); (V.A.)
| | - Aiti Vizzini
- Dipartimento di Scienze e Tecnologie Biologiche, Chimiche e Farmaceutiche–Università di Palermo, Via Archirafi 18, 90100 Palermo, Italy; (M.V.); (M.M.); (V.A.)
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31
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Xu K, Zhao X, Tan Y, Wu J, Cai Y, Zhou J, Wang X. A systematical review on antimicrobial peptides and their food applications. BIOMATERIALS ADVANCES 2023; 155:213684. [PMID: 37976831 DOI: 10.1016/j.bioadv.2023.213684] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Revised: 10/29/2023] [Accepted: 11/02/2023] [Indexed: 11/19/2023]
Abstract
Food safety issues are a major concern in food processing and packaging industries. Food spoilage is caused by microbial contamination, where antimicrobial peptides (APs) provide solutions by eliminating microorganisms. APs such as nisin have been successfully and commonly used in food processing and preservation. Here, we discuss all aspects of the functionalization of APs in food applications. We briefly review the natural sources of APs and their native functions. Recombinant expression of APs in microorganisms and their yields are described. The molecular mechanisms of AP antibacterial action are explained, and this knowledge can further benefit the design of functional APs. We highlight current utilities and challenges for the application of APs in the food industry, and address rational methods for AP design that may overcome current limitations.
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Affiliation(s)
- Kangjie Xu
- Engineering Research Center of Ministry of Education on Food Synthetic Biotechnology and School of Biotechnology, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu 214122, China; Science Center for Future Foods, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu 214122, China
| | - XinYi Zhao
- Engineering Research Center of Ministry of Education on Food Synthetic Biotechnology and School of Biotechnology, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu 214122, China; Science Center for Future Foods, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu 214122, China
| | - Yameng Tan
- Engineering Research Center of Ministry of Education on Food Synthetic Biotechnology and School of Biotechnology, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu 214122, China; Science Center for Future Foods, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu 214122, China
| | - Junheng Wu
- Engineering Research Center of Ministry of Education on Food Synthetic Biotechnology and School of Biotechnology, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu 214122, China; Science Center for Future Foods, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu 214122, China
| | - Yiqing Cai
- Engineering Research Center of Ministry of Education on Food Synthetic Biotechnology and School of Biotechnology, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu 214122, China; Science Center for Future Foods, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu 214122, China
| | - Jingwen Zhou
- Engineering Research Center of Ministry of Education on Food Synthetic Biotechnology and School of Biotechnology, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu 214122, China; Science Center for Future Foods, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu 214122, China; Jiangsu Province Engineering Research Center of Food Synthetic Biotechnology, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu 214122, China..
| | - Xinglong Wang
- Engineering Research Center of Ministry of Education on Food Synthetic Biotechnology and School of Biotechnology, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu 214122, China; Science Center for Future Foods, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu 214122, China.
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32
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Guan C, Luo J, Li S, Tan ZL, Wang Y, Chen H, Yamamoto N, Zhang C, Lu Y, Chen J, Xing XH. Exploration of DPP-IV Inhibitory Peptide Design Rules Assisted by the Deep Learning Pipeline That Identifies the Restriction Enzyme Cutting Site. ACS OMEGA 2023; 8:39662-39672. [PMID: 37901493 PMCID: PMC10601436 DOI: 10.1021/acsomega.3c05571] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/30/2023] [Accepted: 09/27/2023] [Indexed: 10/31/2023]
Abstract
The mining of antidiabetic dipeptidyl peptidase IV (DPP-IV) inhibitory peptides (DPP-IV-IPs) is currently a costly and laborious process. Due to the absence of rational peptide design rules, it relies on cumbersome screening of unknown enzyme hydrolysates. Here, we present an enhanced deep learning model called bidirectional encoder representation (BERT)-DPPIV, specifically designed to classify DPP-IV-IPs and explore their design rules to discover potent candidates. The end-to-end model utilizes a fine-tuned BERT architecture to extract structural/functional information from input peptides and accurately identify DPP-IV-Ips from input peptides. Experimental results in the benchmark data set showed BERT-DPPIV yielded state-of-the-art accuracy and MCC of 0.894 and 0.790, surpassing the 0.797 and 0.594 obtained by the sequence-feature model. Furthermore, we leveraged the attention mechanism to uncover that our model could recognize the restriction enzyme cutting site and specific residues that contribute to the inhibition of DPP-IV. Moreover, guided by BERT-DPPIV, proposed design rules for DPP-IV inhibitory tripeptides and pentapeptides were validated, and they can be used to screen potent DPP-IV-IPs.
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Affiliation(s)
- Changge Guan
- Key
Laboratory for Industrial Biocatalysis, Ministry of Education of China,
Department of Chemical Engineering, Tsinghua
University, Beijing 100084, China
| | - Jiawei Luo
- Department
of Computer Science and Technology, Harbin
Institute of Technology, Shenzhen 518055, China
| | - Shucheng Li
- Key
Laboratory for Industrial Biocatalysis, Ministry of Education of China,
Department of Chemical Engineering, Tsinghua
University, Beijing 100084, China
| | - Zheng Lin Tan
- School
of Life Science and Technology, Tokyo Institute
of Technology, 4259 Nagatsutacho, Midori Ward, Yokohama,
Kanagawa Prefecture 226-0026, Japan
| | - Yi Wang
- Key
Laboratory for Industrial Biocatalysis, Ministry of Education of China,
Department of Chemical Engineering, Tsinghua
University, Beijing 100084, China
| | - Haihong Chen
- Institute
of Biopharmaceutical and Health Engineering, Tsinghua Shenzhen International Graduate School, Shenzhen 518055, China
- Institute
of Biomedical Health Technology and Engineering, Shenzhen Bay Laboratory, Shenzhen 518118, China
| | - Naoyuki Yamamoto
- School
of Life Science and Technology, Tokyo Institute
of Technology, 4259 Nagatsutacho, Midori Ward, Yokohama,
Kanagawa Prefecture 226-0026, Japan
| | - Chong Zhang
- Key
Laboratory for Industrial Biocatalysis, Ministry of Education of China,
Department of Chemical Engineering, Tsinghua
University, Beijing 100084, China
- Center
for Synthetic and Systems Biology, Tsinghua
University, Beijing 100084, China
| | - Yuan Lu
- Key
Laboratory for Industrial Biocatalysis, Ministry of Education of China,
Department of Chemical Engineering, Tsinghua
University, Beijing 100084, China
| | - Junjie Chen
- Department
of Computer Science and Technology, Harbin
Institute of Technology, Shenzhen 518055, China
| | - Xin-Hui Xing
- Key
Laboratory for Industrial Biocatalysis, Ministry of Education of China,
Department of Chemical Engineering, Tsinghua
University, Beijing 100084, China
- Institute
of Biopharmaceutical and Health Engineering, Tsinghua Shenzhen International Graduate School, Shenzhen 518055, China
- Institute
of Biomedical Health Technology and Engineering, Shenzhen Bay Laboratory, Shenzhen 518118, China
- Center
for Synthetic and Systems Biology, Tsinghua
University, Beijing 100084, China
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33
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Zhang W, Xu Y, Wang A, Chen G, Zhao J. Fuse feeds as one: cross-modal framework for general identification of AMPs. Brief Bioinform 2023; 24:bbad336. [PMID: 37779248 DOI: 10.1093/bib/bbad336] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Revised: 09/05/2023] [Accepted: 09/06/2023] [Indexed: 10/03/2023] Open
Abstract
Antimicrobial peptides (AMPs) are promising candidates for the development of new antibiotics due to their broad-spectrum activity against a range of pathogens. However, identifying AMPs through a huge bunch of candidates is challenging due to their complex structures and diverse sequences. In this study, we propose SenseXAMP, a cross-modal framework that leverages semantic embeddings of and protein descriptors (PDs) of input sequences to improve the identification performance of AMPs. SenseXAMP includes a multi-input alignment module and cross-representation fusion module to explore the hidden information between the two input features and better leverage the fusion feature. To better address the AMPs identification task, we accumulate the latest annotated AMPs data to form more generous benchmark datasets. Additionally, we expand the existing AMPs identification task settings by adding an AMPs regression task to meet more specific requirements like antimicrobial activity prediction. The experimental results indicated that SenseXAMP outperformed existing state-of-the-art models on multiple AMP-related datasets including commonly used AMPs classification datasets and our proposed benchmark datasets. Furthermore, we conducted a series of experiments to demonstrate the complementary nature of traditional PDs and protein pre-training models in AMPs tasks. Our experiments reveal that SenseXAMP can effectively combine the advantages of PDs to improve the performance of protein pre-training models in AMPs tasks.
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Affiliation(s)
- Wentao Zhang
- College of Computer Science and Technology, Zhejiang University, 38 Zheda Road, 310027, Hangzhou,Zhejiang, P.R.China
| | - Yanchao Xu
- College of Computer Science and Technology, Zhejiang University, 38 Zheda Road, 310027, Hangzhou,Zhejiang, P.R.China
| | - Aowen Wang
- College of Computer Science and Technology, Zhejiang University, 38 Zheda Road, 310027, Hangzhou,Zhejiang, P.R.China
| | - Gang Chen
- College of Computer Science and Technology, Zhejiang University, 38 Zheda Road, 310027, Hangzhou,Zhejiang, P.R.China
| | - Junbo Zhao
- College of Computer Science and Technology, Zhejiang University, 38 Zheda Road, 310027, Hangzhou,Zhejiang, P.R.China
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34
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Tao H, Shan S, Fu H, Zhu C, Liu B. An Augmented Sample Selection Framework for Prediction of Anticancer Peptides. Molecules 2023; 28:6680. [PMID: 37764455 PMCID: PMC10535447 DOI: 10.3390/molecules28186680] [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: 08/09/2023] [Revised: 09/14/2023] [Accepted: 09/15/2023] [Indexed: 09/29/2023] Open
Abstract
Anticancer peptides (ACPs) have promising prospects for cancer treatment. Traditional ACP identification experiments have the limitations of low efficiency and high cost. In recent years, data-driven deep learning techniques have shown significant potential for ACP prediction. However, data-driven prediction models rely heavily on extensive training data. Furthermore, the current publicly accessible ACP dataset is limited in size, leading to inadequate model generalization. While data augmentation effectively expands dataset size, existing techniques for augmenting ACP data often generate noisy samples, adversely affecting prediction performance. Therefore, this paper proposes a novel augmented sample selection framework for the prediction of anticancer peptides (ACPs-ASSF). First, the prediction model is trained using raw data. Then, the augmented samples generated using the data augmentation technique are fed into the trained model to compute pseudo-labels and estimate the uncertainty of the model prediction. Finally, samples with low uncertainty, high confidence, and pseudo-labels consistent with the original labels are selected and incorporated into the training set to retrain the model. The evaluation results for the ACP240 and ACP740 datasets show that ACPs-ASSF achieved accuracy improvements of up to 5.41% and 5.68%, respectively, compared to the traditional data augmentation method.
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Affiliation(s)
- Huawei Tao
- Key Laboratory of Food Information Processing and Control, Ministry of Education, Henan University of Technology, Zhengzhou 450001, China; (H.T.); (S.S.); (H.F.); (C.Z.)
- Henan Engineering Laboratory of Grain IOT Technology, Henan University of Technology, Zhengzhou 450001, China
| | - Shuai Shan
- Key Laboratory of Food Information Processing and Control, Ministry of Education, Henan University of Technology, Zhengzhou 450001, China; (H.T.); (S.S.); (H.F.); (C.Z.)
- Henan Engineering Laboratory of Grain IOT Technology, Henan University of Technology, Zhengzhou 450001, China
| | - Hongliang Fu
- Key Laboratory of Food Information Processing and Control, Ministry of Education, Henan University of Technology, Zhengzhou 450001, China; (H.T.); (S.S.); (H.F.); (C.Z.)
- Henan Engineering Laboratory of Grain IOT Technology, Henan University of Technology, Zhengzhou 450001, China
| | - Chunhua Zhu
- Key Laboratory of Food Information Processing and Control, Ministry of Education, Henan University of Technology, Zhengzhou 450001, China; (H.T.); (S.S.); (H.F.); (C.Z.)
- Henan Engineering Laboratory of Grain IOT Technology, Henan University of Technology, Zhengzhou 450001, China
| | - Boye Liu
- College of Food Science and Engineering, Henan University of Technology, Zhengzhou 450001, China
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35
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Wang Y, Xie Y, Luo Y, Jia P, Wei J, Zhang J, Yan W, Huang J. iASMP: An interpretable in-silico predictive tool focusing on species-specific antimicrobial peptides. J Pept Sci 2023; 29:e3490. [PMID: 36994602 DOI: 10.1002/psc.3490] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Revised: 03/02/2023] [Accepted: 03/25/2023] [Indexed: 03/31/2023]
Abstract
Antimicrobial peptides (AMPs), a crucial part of the innate immune system, have been exploited as promising candidates for antibacterial agents. Many researchers have been devoting their efforts to develop novel AMPs in recent decades. In this term, many computational approaches have been developed to identify potential AMPs accurately. However, finding peptides specific to a particular bacterial species is challenging. Streptococcus mutans is a pathogen with an apparent cariogenic effect, and it is of great significance to study AMP that inhibit S. mutans for the prevention and treatment of caries. In this study, we proposed a sequence-based machine learning model, namely iASMP, to exactly identify potential anti-S. mutans peptides (ASMPs). After collecting ASMPs, the performances of models were compared by utilizing multiple feature descriptors and different classification algorithms. Among the baseline predictors, the model integrating the extra trees (ET) algorithm and the hybrid features exhibited optimal results. The feature selection method was utilized to remove redundant feature information to improve the model performance further. Finally, the proposed model achieved the maximum accuracy (ACC) of 0.962 on the training dataset and performed on the testing dataset with an ACC of 0.750. The results demonstrated that iASMP had an excellent predictive performance and was suitable for identifying potential ASMP. Furthermore, we also visualized the selected features and rationally explained the impact of individual features on the model output.
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Affiliation(s)
- Yuqiang Wang
- Key Laboratory of Dental Maxillofacial Reconstruction and Biological Intelligence Manufacturing of Gansu Province, School of Stomatology, Lanzhou University, Lanzhou, Gansu, China
| | - Yihao Xie
- The Institute of Pharmacology, Key Laboratory of Preclinical Study for New Drugs of Gansu Province, School of Basic Medical Sciences, Lanzhou University, Lanzhou, Gansu, China
| | - Yang Luo
- Key Laboratory of Dental Maxillofacial Reconstruction and Biological Intelligence Manufacturing of Gansu Province, School of Stomatology, Lanzhou University, Lanzhou, Gansu, China
| | - Pengfei Jia
- The Institute of Pharmacology, Key Laboratory of Preclinical Study for New Drugs of Gansu Province, School of Basic Medical Sciences, Lanzhou University, Lanzhou, Gansu, China
| | - Jiaqi Wei
- Key Laboratory of Dental Maxillofacial Reconstruction and Biological Intelligence Manufacturing of Gansu Province, School of Stomatology, Lanzhou University, Lanzhou, Gansu, China
| | - Jie Zhang
- Key Laboratory of Dental Maxillofacial Reconstruction and Biological Intelligence Manufacturing of Gansu Province, School of Stomatology, Lanzhou University, Lanzhou, Gansu, China
| | - Wenjin Yan
- The Institute of Pharmacology, Key Laboratory of Preclinical Study for New Drugs of Gansu Province, School of Basic Medical Sciences, Lanzhou University, Lanzhou, Gansu, China
| | - Jinqi Huang
- The Affiliated Hospital of Guangdong Medical University, Zhanjiang, Guangdong, China
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36
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Maasch JRMA, Torres MDT, Melo MCR, de la Fuente-Nunez C. Molecular de-extinction of ancient antimicrobial peptides enabled by machine learning. Cell Host Microbe 2023; 31:1260-1274.e6. [PMID: 37516110 DOI: 10.1016/j.chom.2023.07.001] [Citation(s) in RCA: 37] [Impact Index Per Article: 37.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Revised: 05/12/2023] [Accepted: 07/06/2023] [Indexed: 07/31/2023]
Abstract
Molecular de-extinction could offer avenues for drug discovery by reintroducing bioactive molecules that are no longer encoded by extant organisms. To prospect for antimicrobial peptides encrypted within extinct and extant human proteins, we introduce the panCleave random forest model for proteome-wide cleavage site prediction. Our model outperformed multiple protease-specific cleavage site classifiers for three modern human caspases, despite its pan-protease design. Antimicrobial activity was observed in vitro for modern and archaic protein fragments identified with panCleave. Lead peptides showed resistance to proteolysis and exhibited variable membrane permeabilization. Additionally, representative modern and archaic protein fragments showed anti-infective efficacy against A. baumannii in both a skin abscess infection model and a preclinical murine thigh infection model. These results suggest that machine-learning-based encrypted peptide prospection can identify stable, nontoxic peptide antibiotics. Moreover, we establish molecular de-extinction through paleoproteome mining as a framework for antibacterial drug discovery.
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Affiliation(s)
- Jacqueline R M A Maasch
- Department of Computer and Information Science, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA; Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Bioengineering, Department of Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA; Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Marcelo D T Torres
- Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Bioengineering, Department of Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA; Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Marcelo C R Melo
- Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Bioengineering, Department of Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA; Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Cesar de la Fuente-Nunez
- Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Bioengineering, Department of Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA; Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, PA 19104, USA.
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37
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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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38
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Fernandes FC, Cardoso MH, Gil-Ley A, Luchi LV, da Silva MGL, Macedo MLR, de la Fuente-Nunez C, Franco OL. Geometric deep learning as a potential tool for antimicrobial peptide prediction. FRONTIERS IN BIOINFORMATICS 2023; 3:1216362. [PMID: 37521317 PMCID: PMC10374423 DOI: 10.3389/fbinf.2023.1216362] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Accepted: 06/13/2023] [Indexed: 08/01/2023] Open
Abstract
Antimicrobial peptides (AMPs) are components of natural immunity against invading pathogens. They are polymers that fold into a variety of three-dimensional structures, enabling their function, with an underlying sequence that is best represented in a non-flat space. The structural data of AMPs exhibits non-Euclidean characteristics, which means that certain properties, e.g., differential manifolds, common system of coordinates, vector space structure, or translation-equivariance, along with basic operations like convolution, in non-Euclidean space are not distinctly established. Geometric deep learning (GDL) refers to a category of machine learning methods that utilize deep neural models to process and analyze data in non-Euclidean settings, such as graphs and manifolds. This emerging field seeks to expand the use of structured models to these domains. This review provides a detailed summary of the latest developments in designing and predicting AMPs utilizing GDL techniques and also discusses both current research gaps and future directions in the field.
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Affiliation(s)
- Fabiano C. Fernandes
- Centro de Análises Proteômicas e Bioquímicas, Pós-Graduação em Ciências Genômicas e Biotecnologia, Universidade Católica de Brasília, Brasília, Brazil
- Departamento de Ciência da Computação, Instituto Federal de Brasília, Brasília, Brazil
| | - Marlon H. Cardoso
- Centro de Análises Proteômicas e Bioquímicas, Pós-Graduação em Ciências Genômicas e Biotecnologia, Universidade Católica de Brasília, Brasília, Brazil
- S-Inova Biotech, Programa de Pós-Graduação em Biotecnologia, Universidade Católica Dom Bosco, Campo Grande, Brazil
- Laboratório de Purificação de Proteínas e suas Funções Biológicas, Universidade Federal de Mato Grosso do Sul, Cidade Universitária, Campo Grande, Mato Grosso do Sul, Brazil
| | - Abel Gil-Ley
- S-Inova Biotech, Programa de Pós-Graduação em Biotecnologia, Universidade Católica Dom Bosco, Campo Grande, Brazil
| | - Lívia V. Luchi
- S-Inova Biotech, Programa de Pós-Graduação em Biotecnologia, Universidade Católica Dom Bosco, Campo Grande, Brazil
| | - Maria G. L. da Silva
- Centro de Análises Proteômicas e Bioquímicas, Pós-Graduação em Ciências Genômicas e Biotecnologia, Universidade Católica de Brasília, Brasília, Brazil
| | - Maria L. R. Macedo
- Laboratório de Purificação de Proteínas e suas Funções Biológicas, Universidade Federal de Mato Grosso do Sul, Cidade Universitária, Campo Grande, Mato Grosso do Sul, Brazil
| | - Cesar de la Fuente-Nunez
- Machine Biology Group, Departments of Psychiatry and Microbiology, Perelman School of Medicine, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, University of Pennsylvania, Philadelphia, PA, United States
- Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, United States
- Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, PA, United States
| | - Octavio L. Franco
- Centro de Análises Proteômicas e Bioquímicas, Pós-Graduação em Ciências Genômicas e Biotecnologia, Universidade Católica de Brasília, Brasília, Brazil
- S-Inova Biotech, Programa de Pós-Graduação em Biotecnologia, Universidade Católica Dom Bosco, Campo Grande, Brazil
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Cesaro A, Bagheri M, Torres MDT, Wan F, de la Fuente-Nunez C. Deep learning tools to accelerate antibiotic discovery. Expert Opin Drug Discov 2023; 18:1245-1257. [PMID: 37794737 PMCID: PMC10790350 DOI: 10.1080/17460441.2023.2250721] [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: 05/19/2023] [Accepted: 08/18/2023] [Indexed: 10/06/2023]
Abstract
INTRODUCTION As machine learning (ML) and artificial intelligence (AI) expand to many segments of our society, they are increasingly being used for drug discovery. Recent deep learning models offer an efficient way to explore high-dimensional data and design compounds with desired properties, including those with antibacterial activity. AREAS COVERED This review covers key frameworks in antibiotic discovery, highlighting physicochemical features and addressing dataset limitations. The deep learning approaches here described include discriminative models such as convolutional neural networks, recurrent neural networks, graph neural networks, and generative models like neural language models, variational autoencoders, generative adversarial networks, normalizing flow, and diffusion models. As the integration of these approaches in drug discovery continues to evolve, this review aims to provide insights into promising prospects and challenges that lie ahead in harnessing such technologies for the development of antibiotics. EXPERT OPINION Accurate antimicrobial prediction using deep learning faces challenges such as imbalanced data, limited datasets, experimental validation, target strains, and structure. The integration of deep generative models with bioinformatics, molecular dynamics, and data augmentation holds the potential to overcome these challenges, enhance model performance, and utlimately accelerate antimicrobial discovery.
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Affiliation(s)
- Angela Cesaro
- Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
- Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
- Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Mojtaba Bagheri
- Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
- Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
- Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Marcelo D. T. Torres
- Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
- Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
- Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Fangping Wan
- Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
- Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
- Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Cesar de la Fuente-Nunez
- Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
- Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
- Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
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40
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Barman P, Joshi S, Sharma S, Preet S, Sharma S, Saini A. Strategic Approaches to Improvise Peptide Drugs as Next Generation Therapeutics. Int J Pept Res Ther 2023; 29:61. [PMID: 37251528 PMCID: PMC10206374 DOI: 10.1007/s10989-023-10524-3] [Citation(s) in RCA: 23] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/20/2023] [Indexed: 05/31/2023]
Abstract
In recent years, the occurrence of a wide variety of drug-resistant diseases has led to an increase in interest in alternate therapies. Peptide-based drugs as an alternate therapy hold researchers' attention in various therapeutic fields such as neurology, dermatology, oncology, metabolic diseases, etc. Previously, they had been overlooked by pharmaceutical companies due to certain limitations such as proteolytic degradation, poor membrane permeability, low oral bioavailability, shorter half-life, and poor target specificity. Over the last two decades, these limitations have been countered by introducing various modification strategies such as backbone and side-chain modifications, amino acid substitution, etc. which improve their functionality. This has led to a substantial interest of researchers and pharmaceutical companies, moving the next generation of these therapeutics from fundamental research to the market. Various chemical and computational approaches are aiding the production of more stable and long-lasting peptides guiding the formulation of novel and advanced therapeutic agents. However, there is not a single article that talks about various peptide design approaches i.e., in-silico and in-vitro along with their applications and strategies to improve their efficacy. In this review, we try to bring different aspects of peptide-based therapeutics under one article with a clear focus to cover the missing links in the literature. This review draws emphasis on various in-silico approaches and modification-based peptide design strategies. It also highlights the recent progress made in peptide delivery methods important for their enhanced clinical efficacy. The article would provide a bird's-eye view to researchers aiming to develop peptides with therapeutic applications. Graphical Abstract
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Affiliation(s)
- Panchali Barman
- Institute of Forensic Science and Criminology (UIEAST), Panjab University, Sector 14, Chandigarh, 160014 India
| | - Shubhi Joshi
- Energy Research Centre, Panjab University, Sector 14, Chandigarh, 160014 India
| | - Sheetal Sharma
- Department of Biophysics, Panjab University, Sector 25, Chandigarh, U.T 160014 India
| | - Simran Preet
- Department of Biophysics, Panjab University, Sector 25, Chandigarh, U.T 160014 India
| | - Shweta Sharma
- Institute of Forensic Science and Criminology (UIEAST), Panjab University, Sector 14, Chandigarh, 160014 India
| | - Avneet Saini
- Department of Biophysics, Panjab University, Sector 25, Chandigarh, U.T 160014 India
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41
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Yang S, Yang Z, Ni X. AMPFinder: A computational model to identify antimicrobial peptides and their functions based on sequence-derived information. Anal Biochem 2023; 673:115196. [PMID: 37236434 DOI: 10.1016/j.ab.2023.115196] [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: 04/13/2023] [Revised: 05/22/2023] [Accepted: 05/23/2023] [Indexed: 05/28/2023]
Abstract
Antimicrobial peptides (AMPs) called host defense peptides have existed among all classes of life with 5-100 amino acids generally and can kill mycobacteria, envelop viruses, bacteria, fungi, cancerous cells and so on. Owing to the non-drug resistance of AMP, it has been a wonderful agent to find novel therapies. Therefore, it is urgent to identify AMPs and predict their function in a high-throughput way. In this paper, we propose a cascaded computational model to identify AMPs and their functional type based on sequence-derived and life language embedding, called AMPFinder. Compared with other state-of-the-art methods, AMPFinder obtains higher performance both on AMP identification and AMP function prediction. AMPFinder shows better performance with improvement of F1-score (1.45%-6.13%), MCC (2.92%-12.86%) and AUC (5.13%-8.56%) and AP (9.20%-21.07%) on an independent test dataset. And AMPFinder achieve lower bias of R2 on a public dataset by 10-fold cross-validation with an improvement of (18.82%-19.46%). The comparison with other state-of-the-art methods shows that AMP can accurately identify AMP and its function types. The datasets, source code and user-friendly application are available at https://github.com/abcair/AMPFinder.
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Affiliation(s)
- Sen Yang
- The Affiliated Changzhou No 2 People's Hospital of Nanjing Medical University, Changzhou, 213164, China; School of Computer Science and Artificial Intelligence Aliyun School of Big Data, School of Software, Changzhou University, Changzhou, 213164, China
| | - Zexi Yang
- School of Computer Science and Artificial Intelligence Aliyun School of Big Data, School of Software, Changzhou University, Changzhou, 213164, China
| | - Xinye Ni
- The Affiliated Changzhou No 2 People's Hospital of Nanjing Medical University, Changzhou, 213164, China.
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42
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Varotsou C, Premetis GE, Labrou NE. Characterization and Engineering Studies of a New Endolysin from the Propionibacterium acnes Bacteriophage PAC1 for the Development of a Broad-Spectrum Artilysin with Altered Specificity. Int J Mol Sci 2023; 24:ijms24108523. [PMID: 37239874 DOI: 10.3390/ijms24108523] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Revised: 04/27/2023] [Accepted: 05/04/2023] [Indexed: 05/28/2023] Open
Abstract
The emergence of multidrug-resistant (MDR) bacteria has risen rapidly, leading to a great threat to global public health. A promising solution to this problem is the exploitation of phage endolysins. In the present study, a putative N-acetylmuramoyl-L-alanine type-2 amidase (NALAA-2, EC 3.5.1.28) from Propionibacterium bacteriophage PAC1 was characterized. The enzyme (PaAmi1) was cloned into a T7 expression vector and expressed in E. coli BL21 cells. Kinetics analysis using turbidity reduction assays allowed the determination of the optimal conditions for lytic activity against a range of Gram-positive and negative human pathogens. The peptidoglycan degradation activity of PaAmi1 was confirmed using isolated peptidoglycan from P. acnes. The antibacterial activity of PaAmi1 was investigated using live P. acnes cells growing on agar plates. Two engineered variants of PaAmi1 were designed by fusion to its N-terminus two short antimicrobial peptides (AMPs). One AMP was selected by searching the genomes of Propionibacterium bacteriophages using bioinformatics tools, whereas the other AMP sequence was selected from the antimicrobial peptide databases. Both engineered variants exhibited improved lytic activity towards P. acnes and the enterococci species Enterococcus faecalis and Enterococcus faecium. The results of the present study suggest that PaAmi1 is a new antimicrobial agent and provide proof of concept that bacteriophage genomes are a rich source of AMP sequences that can be further exploited for designing novel or improved endolysins.
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Affiliation(s)
- Christina Varotsou
- Laboratory of Enzyme Technology, Department of Biotechnology, School of Applied Biology and Biotechnology, Agricultural University of Athens, 75 Iera Odos Street, 11855 Athens, Greece
| | - Georgios E Premetis
- Laboratory of Enzyme Technology, Department of Biotechnology, School of Applied Biology and Biotechnology, Agricultural University of Athens, 75 Iera Odos Street, 11855 Athens, Greece
| | - Nikolaos E Labrou
- Laboratory of Enzyme Technology, Department of Biotechnology, School of Applied Biology and Biotechnology, Agricultural University of Athens, 75 Iera Odos Street, 11855 Athens, Greece
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43
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Hsueh HT, Chou RT, Rai U, Liyanage W, Kim YC, Appell MB, Pejavar J, Leo KT, Davison C, Kolodziejski P, Mozzer A, Kwon H, Sista M, Anders NM, Hemingway A, Rompicharla SVK, Edwards M, Pitha I, Hanes J, Cummings MP, Ensign LM. Machine learning-driven multifunctional peptide engineering for sustained ocular drug delivery. Nat Commun 2023; 14:2509. [PMID: 37130851 PMCID: PMC10154330 DOI: 10.1038/s41467-023-38056-w] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Accepted: 04/12/2023] [Indexed: 05/04/2023] Open
Abstract
Sustained drug delivery strategies have many potential benefits for treating a range of diseases, particularly chronic diseases that require treatment for years. For many chronic ocular diseases, patient adherence to eye drop dosing regimens and the need for frequent intraocular injections are significant barriers to effective disease management. Here, we utilize peptide engineering to impart melanin binding properties to peptide-drug conjugates to act as a sustained-release depot in the eye. We develop a super learning-based methodology to engineer multifunctional peptides that efficiently enter cells, bind to melanin, and have low cytotoxicity. When the lead multifunctional peptide (HR97) is conjugated to brimonidine, an intraocular pressure lowering drug that is prescribed for three times per day topical dosing, intraocular pressure reduction is observed for up to 18 days after a single intracameral injection in rabbits. Further, the cumulative intraocular pressure lowering effect increases ~17-fold compared to free brimonidine injection. Engineered multifunctional peptide-drug conjugates are a promising approach for providing sustained therapeutic delivery in the eye and beyond.
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Affiliation(s)
- Henry T Hsueh
- Center for Nanomedicine at the Wilmer Eye Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Chemical & Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Renee Ti Chou
- Center for Bioinformatics and Computational Biology, University of Maryland, College Park, College Park, MD, USA
| | - Usha Rai
- Center for Nanomedicine at the Wilmer Eye Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Ophthalmology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Wathsala Liyanage
- Center for Nanomedicine at the Wilmer Eye Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Ophthalmology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Yoo Chun Kim
- Center for Nanomedicine at the Wilmer Eye Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Ophthalmology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Matthew B Appell
- Center for Nanomedicine at the Wilmer Eye Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Pharmacology and Molecular Sciences, Johns Hopkins University, Baltimore, MD, USA
| | - Jahnavi Pejavar
- Center for Nanomedicine at the Wilmer Eye Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Chemical & Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Kirby T Leo
- Center for Nanomedicine at the Wilmer Eye Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Charlotte Davison
- Center for Nanomedicine at the Wilmer Eye Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Chemical & Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Patricia Kolodziejski
- Center for Nanomedicine at the Wilmer Eye Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Chemical & Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Ann Mozzer
- Center for Nanomedicine at the Wilmer Eye Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Ophthalmology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - HyeYoung Kwon
- Center for Nanomedicine at the Wilmer Eye Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Chemical & Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Maanasa Sista
- Center for Nanomedicine at the Wilmer Eye Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
| | - Nicole M Anders
- The Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins University, Baltimore, MD, USA
| | - Avelina Hemingway
- The Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins University, Baltimore, MD, USA
| | - Sri Vishnu Kiran Rompicharla
- Center for Nanomedicine at the Wilmer Eye Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Ophthalmology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Malia Edwards
- Department of Ophthalmology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Ian Pitha
- Center for Nanomedicine at the Wilmer Eye Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Ophthalmology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Justin Hanes
- Center for Nanomedicine at the Wilmer Eye Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Chemical & Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
- Department of Ophthalmology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Pharmacology and Molecular Sciences, Johns Hopkins University, Baltimore, MD, USA
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
- The Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins University, Baltimore, MD, USA
| | - Michael P Cummings
- Center for Bioinformatics and Computational Biology, University of Maryland, College Park, College Park, MD, USA.
| | - Laura M Ensign
- Center for Nanomedicine at the Wilmer Eye Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
- Department of Chemical & Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA.
- Department of Ophthalmology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
- Department of Pharmacology and Molecular Sciences, Johns Hopkins University, Baltimore, MD, USA.
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.
- The Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins University, Baltimore, MD, USA.
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44
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Redshaw J, Ting DSJ, Brown A, Hirst JD, Gärtner T. Krein support vector machine classification of antimicrobial peptides. DIGITAL DISCOVERY 2023; 2:502-511. [PMID: 37065679 PMCID: PMC10087059 DOI: 10.1039/d3dd00004d] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Accepted: 02/22/2023] [Indexed: 03/02/2023]
Abstract
Antimicrobial peptides (AMPs) represent a potential solution to the growing problem of antimicrobial resistance, yet their identification through wet-lab experiments is a costly and time-consuming process. Accurate computational predictions would allow rapid in silico screening of candidate AMPs, thereby accelerating the discovery process. Kernel methods are a class of machine learning algorithms that utilise a kernel function to transform input data into a new representation. When appropriately normalised, the kernel function can be regarded as a notion of similarity between instances. However, many expressive notions of similarity are not valid kernel functions, meaning they cannot be used with standard kernel methods such as the support-vector machine (SVM). The Kreĭn-SVM represents generalisation of the standard SVM that admits a much larger class of similarity functions. In this study, we propose and develop Kreĭn-SVM models for AMP classification and prediction by employing the Levenshtein distance and local alignment score as sequence similarity functions. Utilising two datasets from the literature, each containing more than 3000 peptides, we train models to predict general antimicrobial activity. Our best models achieve an AUC of 0.967 and 0.863 on the test sets of each respective dataset, outperforming the in-house and literature baselines in both cases. We also curate a dataset of experimentally validated peptides, measured against Staphylococcus aureus and Pseudomonas aeruginosa, in order to evaluate the applicability of our methodology in predicting microbe-specific activity. In this case, our best models achieve an AUC of 0.982 and 0.891, respectively. Models to predict both general and microbe-specific activities are made available as web applications.
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Affiliation(s)
- Joseph Redshaw
- School of Chemistry, University of Nottingham, University Park Nottingham NG7 2RD UK
| | - Darren S J Ting
- Academic Ophthalmology, School of Medicine, University of Nottingham Nottingham NG7 2UH UK
- Academic Unit of Ophthalmology, Institute of Inflammation and Ageing, University of Birmingham Birmingham UK
- Birmingham and Midland Eye Centre Birmingham UK
| | - Alex Brown
- Artificial Intelligence and Machine Learning, GSK Medicines Research Centre Gunnels Wood Road Stevenage SG1 2NY UK
| | - Jonathan D Hirst
- School of Chemistry, University of Nottingham, University Park Nottingham NG7 2RD UK
| | - Thomas Gärtner
- Machine Learning Group, TU Wien Informatics Vienna Austria
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45
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Bournez C, Riool M, de Boer L, Cordfunke RA, de Best L, van Leeuwen R, Drijfhout JW, Zaat SAJ, van Westen GJP. CalcAMP: A New Machine Learning Model for the Accurate Prediction of Antimicrobial Activity of Peptides. Antibiotics (Basel) 2023; 12:antibiotics12040725. [PMID: 37107088 PMCID: PMC10135148 DOI: 10.3390/antibiotics12040725] [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: 02/26/2023] [Revised: 03/24/2023] [Accepted: 03/31/2023] [Indexed: 04/29/2023] Open
Abstract
To combat infection by microorganisms host organisms possess a primary arsenal via the innate immune system. Among them are defense peptides with the ability to target a wide range of pathogenic organisms, including bacteria, viruses, parasites, and fungi. Here, we present the development of a novel machine learning model capable of predicting the activity of antimicrobial peptides (AMPs), CalcAMP. AMPs, in particular short ones (<35 amino acids), can become an effective solution to face the multi-drug resistance issue arising worldwide. Whereas finding potent AMPs through classical wet-lab techniques is still a long and expensive process, a machine learning model can be useful to help researchers to rapidly identify whether peptides present potential or not. Our prediction model is based on a new data set constructed from the available public data on AMPs and experimental antimicrobial activities. CalcAMP can predict activity against both Gram-positive and Gram-negative bacteria. Different features either concerning general physicochemical properties or sequence composition have been assessed to retrieve higher prediction accuracy. CalcAMP can be used as an promising prediction asset to identify short AMPs among given peptide sequences.
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Affiliation(s)
- Colin Bournez
- Computational Drug Discovery, Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden University, P.O. Box 9502, 2300 RA Leiden, The Netherlands
| | - Martijn Riool
- Department of Medical Microbiology and Infection Prevention, Amsterdam Institute for Infection and Immunity, Amsterdam UMC, University of Amsterdam, 1105 AZ Amsterdam, The Netherlands
| | - Leonie de Boer
- Department of Medical Microbiology and Infection Prevention, Amsterdam Institute for Infection and Immunity, Amsterdam UMC, University of Amsterdam, 1105 AZ Amsterdam, The Netherlands
| | - Robert A Cordfunke
- Department Immunology, Leiden University Medical Center, 2300 RC Leiden, The Netherlands
| | - Leonie de Best
- Madam Therapeutics B.V., Pivot Park Life Sciences Community, Kloosterstraat 9, 5349 AB Oss, The Netherlands
| | - Remko van Leeuwen
- Madam Therapeutics B.V., Pivot Park Life Sciences Community, Kloosterstraat 9, 5349 AB Oss, The Netherlands
| | - Jan Wouter Drijfhout
- Department Immunology, Leiden University Medical Center, 2300 RC Leiden, The Netherlands
| | - Sebastian A J Zaat
- Department of Medical Microbiology and Infection Prevention, Amsterdam Institute for Infection and Immunity, Amsterdam UMC, University of Amsterdam, 1105 AZ Amsterdam, The Netherlands
| | - Gerard J P van Westen
- Computational Drug Discovery, Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden University, P.O. Box 9502, 2300 RA Leiden, The Netherlands
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46
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Jahangirian E, Zargan J, Rabbani H, Zamani J. Investigating the inhibitory and penetrating properties of three novel anticancer and antimicrobial scorpion peptides via molecular docking and molecular dynamic simulation. J Biomol Struct Dyn 2023; 41:15354-15385. [PMID: 36927377 DOI: 10.1080/07391102.2023.2188956] [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: 12/01/2022] [Accepted: 02/28/2023] [Indexed: 03/18/2023]
Abstract
The two types of bladder cancer, muscle invasive and non-muscle invasive (NMIBC), are among the most prevalent cancers worldwide. Despite this, even though muscle-invasive bladder cancer is more deadly, NMIBC requires more therapy due to a greater recurrence rate and more extended and expensive care. Immunotherapy, intravesical chemotherapy, cystoscopy, and transurethral resection (TUR) are among the treatments available. Crude scorpion venomand purified proteins and peptides, can suppress cancer metastasis in an in vitro or in vivo context, suppress cancer growth, halt the cell cycle, and cause cell apoptosis, according to an increasing number of experimental and preclinical studies. In this research, three novels discovered peptides (P2, P3 and P4. ProteomeXchange: PXD036231) from Buthotus saulcyi and, Odontobuthus doriae scorpions were used along with a peptide called pantinin (as a control). The phylogenetic tree showed that the peptides belong to Chaperonin HSP60, Chrysophsin2 and Pheromone-binding protein2, respectively. These peptides were docked with four known antigens, BAGE, BLCAP, PRAME and ROR1 related to bladder cancer and three bacterial antigens FliC, FliD and FimH to investigate their antimicrobial and anticancer properties. The results showed that peptides 2 and 3 have the best binding rate. The MD simulation results also confirmed the binding of peptides 2 and 3 to antigens. The penetration power of peptides 2 and 3 in the membrane of cancer cells and bacterial cells was also simulated, and the results of RMSD and PD confirmed it. QSAR suggests that peptides 2 and 3 can act as anti-cancer and anti-microbial peptides.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Ehsan Jahangirian
- Department of Biology, Faculty of Basic Science, Imam Hossein University, Tehran, Iran
| | - Jamil Zargan
- Department of Biology, Faculty of Basic Science, Imam Hossein University, Tehran, Iran
| | - Hodjattallah Rabbani
- Monoclonal Antibody Research Center, Avicenna Research Institute, ACECR, Tehran, Iran
| | - Javad Zamani
- Department of Animal Biotechnology, National Institute of Genetic Engineering and Biotechnology, Tehran, Iran
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47
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You Y, Liu H, Zhu Y, Zheng H. Rational design of stapled antimicrobial peptides. Amino Acids 2023; 55:421-442. [PMID: 36781451 DOI: 10.1007/s00726-023-03245-w] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2022] [Accepted: 01/30/2023] [Indexed: 02/15/2023]
Abstract
The global increase in antimicrobial drug resistance has dramatically reduced the effectiveness of traditional antibiotics. Structurally diverse antibiotics are urgently needed to combat multiple-resistant bacterial infections. As part of innate immunity, antimicrobial peptides have been recognized as the most promising candidates because they comprise diverse sequences and mechanisms of action and have a relatively low induction rate of resistance. However, because of their low chemical stability, susceptibility to proteases, and high hemolytic effect, their usage is subject to many restrictions. Chemical modifications such as D-amino acid substitution, cyclization, and unnatural amino acid modification have been used to improve the stability of antimicrobial peptides for decades. Among them, a side-chain covalent bridge modification, the so-called stapled peptide, has attracted much attention. The stapled side-chain bridge stabilizes the secondary structure, induces protease resistance, and increases cell penetration and biological activity. Recent progress in computer-aided drug design and artificial intelligence methods has also been used in the design of stapled antimicrobial peptides and has led to the successful discovery of many prospective peptides. This article reviews the possible structure-activity relationships of stapled antimicrobial peptides, the physicochemical properties that influence their activity (such as net charge, hydrophobicity, helicity, and dipole moment), and computer-aided methods of stapled peptide design. Antimicrobial peptides under clinical trial: Pexiganan (NCT01594762, 2012-05-07). Omiganan (NCT02576847, 2015-10-13).
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Affiliation(s)
- YuHao You
- School of Life Science and Technology, China Pharmaceutical University, Nanjing, 210009, People's Republic of China
| | - HongYu Liu
- School of Life Science and Technology, China Pharmaceutical University, Nanjing, 210009, People's Republic of China
| | - YouZhuo Zhu
- School of Life Science and Technology, China Pharmaceutical University, Nanjing, 210009, People's Republic of China
| | - Heng Zheng
- School of Life Science and Technology, China Pharmaceutical University, Nanjing, 210009, People's Republic of China.
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48
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Yuan Q, Chen K, Yu Y, Le NQK, Chua MCH. Prediction of anticancer peptides based on an ensemble model of deep learning and machine learning using ordinal positional encoding. Brief Bioinform 2023; 24:6987656. [PMID: 36642410 DOI: 10.1093/bib/bbac630] [Citation(s) in RCA: 32] [Impact Index Per Article: 32.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Revised: 12/01/2022] [Accepted: 12/28/2022] [Indexed: 01/17/2023] Open
Abstract
Anticancer peptides (ACPs) are the types of peptides that have been demonstrated to have anticancer activities. Using ACPs to prevent cancer could be a viable alternative to conventional cancer treatments because they are safer and display higher selectivity. Due to ACP identification being highly lab-limited, expensive and lengthy, a computational method is proposed to predict ACPs from sequence information in this study. The process includes the input of the peptide sequences, feature extraction in terms of ordinal encoding with positional information and handcrafted features, and finally feature selection. The whole model comprises of two modules, including deep learning and machine learning algorithms. The deep learning module contained two channels: bidirectional long short-term memory (BiLSTM) and convolutional neural network (CNN). Light Gradient Boosting Machine (LightGBM) was used in the machine learning module. Finally, this study voted the three models' classification results for the three paths resulting in the model ensemble layer. This study provides insights into ACP prediction utilizing a novel method and presented a promising performance. It used a benchmark dataset for further exploration and improvement compared with previous studies. Our final model has an accuracy of 0.7895, sensitivity of 0.8153 and specificity of 0.7676, and it was increased by at least 2% compared with the state-of-the-art studies in all metrics. Hence, this paper presents a novel method that can potentially predict ACPs more effectively and efficiently. The work and source codes are made available to the community of researchers and developers at https://github.com/khanhlee/acp-ope/.
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Affiliation(s)
- Qitong Yuan
- Institute of Systems Science, National University of Singapore, 25 Heng Mui Keng Terrace, 119615, Singapore, Singapore
| | - Keyi Chen
- Institute of Systems Science, National University of Singapore, 25 Heng Mui Keng Terrace, 119615, Singapore, Singapore
| | - Yimin Yu
- Institute of Systems Science, National University of Singapore, 25 Heng Mui Keng Terrace, 119615, Singapore, Singapore
| | - Nguyen Quoc Khanh Le
- Professional Master Program in Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University, 250 Wuxing St, 106, Taipei, Taiwan.,Research Center for Artificial Intelligence in Medicine, Taipei Medical University, 250 Wuxing St, 106, Taipei, Taiwan.,Translational Imaging Research Center, Taipei Medical University Hospital, 252 Wuxing St, 110, Taipei, Taiwan
| | - Matthew Chin Heng Chua
- Institute of Systems Science, National University of Singapore, 25 Heng Mui Keng Terrace, 119615, Singapore, Singapore
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49
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Talat A, Khan AU. Artificial intelligence as a smart approach to develop antimicrobial drug molecules: A paradigm to combat drug-resistant infections. Drug Discov Today 2023; 28:103491. [PMID: 36646245 DOI: 10.1016/j.drudis.2023.103491] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Revised: 01/01/2023] [Accepted: 01/05/2023] [Indexed: 01/15/2023]
Abstract
Antimicrobial resistance (AMR) is a silent pandemic with the third highest global mortality. The antibiotic development pipeline is scarce even though AMR has escalated uncontrollably. Artificial intelligence (AI) is a revolutionary approach, accelerating drug discovery because of its fast pace, cost efficiency, lower labor requirements, and fewer chances of failure. AI has been used to discover several beta-lactamase inhibitors and antibiotic alternatives from antimicrobial peptides (AMPs), nonribosomal peptides, bacteriocins, and marine natural products. The significant recent increase in the use of AI platforms by pharmaceutical companies could result in the discovery of efficient antibiotic alternatives with lower chances of resistance generation.
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Affiliation(s)
- Absar Talat
- Medical Microbiology and Molecular Biology Laboratory, Interdisciplinary Biotechnology Unit, Aligarh Muslim University, Aligarh, India
| | - Asad U Khan
- Medical Microbiology and Molecular Biology Laboratory, Interdisciplinary Biotechnology Unit, Aligarh Muslim University, Aligarh, India.
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50
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McDonald EF, Jones T, Plate L, Meiler J, Gulsevin A. Benchmarking AlphaFold2 on peptide structure prediction. Structure 2023; 31:111-119.e2. [PMID: 36525975 PMCID: PMC9883802 DOI: 10.1016/j.str.2022.11.012] [Citation(s) in RCA: 25] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Revised: 10/15/2022] [Accepted: 11/18/2022] [Indexed: 12/23/2022]
Abstract
Recent advancements in computational tools have allowed protein structure prediction with high accuracy. Computational prediction methods have been used for modeling many soluble and membrane proteins, but the performance of these methods in modeling peptide structures has not yet been systematically investigated. We benchmarked the accuracy of AlphaFold2 in predicting 588 peptide structures between 10 and 40 amino acids using experimentally determined NMR structures as reference. Our results showed AlphaFold2 predicts α-helical, β-hairpin, and disulfide-rich peptides with high accuracy. AlphaFold2 performed at least as well if not better than alternative methods developed specifically for peptide structure prediction. AlphaFold2 showed several shortcomings in predicting Φ/Ψ angles, disulfide bond patterns, and the lowest RMSD structures failed to correlate with lowest pLDDT ranked structures. In summary, computation can be a powerful tool to predict peptide structures, but additional steps may be necessary to analyze and validate the results.
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Affiliation(s)
- Eli Fritz McDonald
- Department of Chemistry, Vanderbilt University, Nashville, TN 37212, USA; Center for Structural Biology, Vanderbilt University, Nashville, TN 37212, USA
| | - Taylor Jones
- Department of Chemistry, Vanderbilt University, Nashville, TN 37212, USA; Center for Structural Biology, Vanderbilt University, Nashville, TN 37212, USA
| | - Lars Plate
- Department of Chemistry, Vanderbilt University, Nashville, TN 37212, USA; Department of Biological Sciences, Vanderbilt University, Nashville, TN 37212, USA
| | - Jens Meiler
- Department of Chemistry, Vanderbilt University, Nashville, TN 37212, USA; Center for Structural Biology, Vanderbilt University, Nashville, TN 37212, USA; Institute for Drug Discovery, Leipzig University Medical School, 04103 Leipzig, Germany.
| | - Alican Gulsevin
- Department of Chemistry, Vanderbilt University, Nashville, TN 37212, USA; Center for Structural Biology, Vanderbilt University, Nashville, TN 37212, USA.
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