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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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2
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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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3
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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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4
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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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5
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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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6
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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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7
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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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8
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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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9
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Wan F, de la Fuente-Nunez C. Mining for antimicrobial peptides in sequence space. Nat Biomed Eng 2023; 7:707-708. [PMID: 37095317 DOI: 10.1038/s41551-023-01027-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/26/2023]
Affiliation(s)
- 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, 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
| | - 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.
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10
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Carballo GM, Vázquez KG, García-González LA, Rio GD, Brizuela CA. Embedded-AMP: A Multi-Thread Computational Method for the Systematic Identification of Antimicrobial Peptides Embedded in Proteome Sequences. Antibiotics (Basel) 2023; 12:antibiotics12010139. [PMID: 36671338 PMCID: PMC9854971 DOI: 10.3390/antibiotics12010139] [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: 10/26/2022] [Revised: 01/03/2023] [Accepted: 01/05/2023] [Indexed: 01/12/2023] Open
Abstract
Antimicrobial peptides (AMPs) have gained the attention of the research community for being an alternative to conventional antimicrobials to fight antibiotic resistance and for displaying other pharmacologically relevant activities, such as cell penetration, autophagy induction, immunomodulation, among others. The identification of AMPs had been accomplished by combining computational and experimental approaches and have been mostly restricted to self-contained peptides despite accumulated evidence indicating AMPs may be found embedded within proteins, the functions of which are not necessarily associated with antimicrobials. To address this limitation, we propose a machine-learning (ML)-based pipeline to identify AMPs that are embedded in proteomes. Our method performs an in-silico digestion of every protein in the proteome to generate unique k-mers of different lengths, computes a set of molecular descriptors for each k-mer, and performs an antimicrobial activity prediction. To show the efficiency of the method we used the shrimp proteome, and the pipeline analyzed all k-mers between 10 and 60 amino acids in length to predict all AMPs in less than 20 min. As an application example we predicted AMPs in different rodents (common cuy, common rat, and naked mole rat) with different reported longevities and found a relation between species longevity and the number of predicted AMPs. The analysis shows as the longevity of the species is higher, the number of predicted AMPs is also higher. The pipeline is available as a web service.
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Affiliation(s)
| | - Karen Guerrero Vázquez
- Computer Science Department, CICESE Research Center, Ensenada 22860, Mexico
- School of Mathematical & Statistical Sciences, University of Galway, H91 TK33 Galway, Ireland
| | | | - Gabriel Del Rio
- Department of Biochemistry and Structural Biology, Instituto de Fisiologia Celular, UNAM, Mexico City 04510, Mexico
- Correspondence: (G.D.R.); (C.A.B.)
| | - Carlos A. Brizuela
- Computer Science Department, CICESE Research Center, Ensenada 22860, Mexico
- Correspondence: (G.D.R.); (C.A.B.)
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11
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ABP-Finder: A Tool to Identify Antibacterial Peptides and the Gram-Staining Type of Targeted Bacteria. Antibiotics (Basel) 2022; 11:antibiotics11121708. [PMID: 36551365 PMCID: PMC9774453 DOI: 10.3390/antibiotics11121708] [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: 10/20/2022] [Revised: 11/16/2022] [Accepted: 11/17/2022] [Indexed: 11/29/2022] Open
Abstract
Multi-drug resistance in bacteria is a major health problem worldwide. To overcome this issue, new approaches allowing for the identification and development of antibacterial agents are urgently needed. Peptides, due to their binding specificity and low expected side effects, are promising candidates for a new generation of antibiotics. For over two decades, a large diversity of antimicrobial peptides (AMPs) has been discovered and annotated in public databases. The AMP family encompasses nearly 20 biological functions, thus representing a potentially valuable resource for data mining analyses. Nonetheless, despite the availability of machine learning-based approaches focused on AMPs, these tools lack evidence of successful application for AMPs' discovery, and many are not designed to predict a specific function for putative AMPs, such as antibacterial activity. Consequently, among the apparent variety of data mining methods to screen peptide sequences for antibacterial activity, only few tools can deal with such task consistently, although with limited precision and generally no information about the possible targets. Here, we addressed this gap by introducing a tool specifically designed to identify antibacterial peptides (ABPs) with an estimation of which type of bacteria is susceptible to the action of these peptides, according to their response to the Gram-staining assay. Our tool is freely available via a web server named ABP-Finder. This new method ranks within the top state-of-the-art ABP predictors, particularly in terms of precision. Importantly, we showed the successful application of ABP-Finder for the screening of a large peptide library from the human urine peptidome and the identification of an antibacterial peptide.
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García-Jacas CR, García-González LA, Martinez-Rios F, Tapia-Contreras IP, Brizuela CA. Handcrafted versus non-handcrafted (self-supervised) features for the classification of antimicrobial peptides: complementary or redundant? Brief Bioinform 2022; 23:6754757. [PMID: 36215083 DOI: 10.1093/bib/bbac428] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Revised: 08/28/2022] [Accepted: 09/02/2022] [Indexed: 12/14/2022] Open
Abstract
Antimicrobial peptides (AMPs) have received a great deal of attention given their potential to become a plausible option to fight multi-drug resistant bacteria as well as other pathogens. Quantitative sequence-activity models (QSAMs) have been helpful to discover new AMPs because they allow to explore a large universe of peptide sequences and help reduce the number of wet lab experiments. A main aspect in the building of QSAMs based on shallow learning is to determine an optimal set of protein descriptors (features) required to discriminate between sequences with different antimicrobial activities. These features are generally handcrafted from peptide sequence datasets that are labeled with specific antimicrobial activities. However, recent developments have shown that unsupervised approaches can be used to determine features that outperform human-engineered (handcrafted) features. Thus, knowing which of these two approaches contribute to a better classification of AMPs, it is a fundamental question in order to design more accurate models. Here, we present a systematic and rigorous study to compare both types of features. Experimental outcomes show that non-handcrafted features lead to achieve better performances than handcrafted features. However, the experiments also prove that an improvement in performance is achieved when both types of features are merged. A relevance analysis reveals that non-handcrafted features have higher information content than handcrafted features, while an interaction-based importance analysis reveals that handcrafted features are more important. These findings suggest that there is complementarity between both types of features. Comparisons regarding state-of-the-art deep models show that shallow models yield better performances both when fed with non-handcrafted features alone and when fed with non-handcrafted and handcrafted features together.
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Affiliation(s)
- César R García-Jacas
- Cátedras CONACYT - Departamento de Ciencias de la Computación, Centro de Investigación Científica y de Educación Superior de Ensenada (CICESE), 22860 Ensenada, Baja California, México
| | - Luis A García-González
- Departamento de Ciencias de la Computación, Centro de Investigación Científica y de Educación Superior de Ensenada (CICESE), 22860 Ensenada, Baja California, México
| | | | - Issac P Tapia-Contreras
- Departamento de Ciencias de la Computación, Centro de Investigación Científica y de Educación Superior de Ensenada (CICESE), 22860 Ensenada, Baja California, México
| | - Carlos A Brizuela
- 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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The dynamic landscape of peptide activity prediction. Comput Struct Biotechnol J 2022; 20:6526-6533. [DOI: 10.1016/j.csbj.2022.11.043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Revised: 11/21/2022] [Accepted: 11/21/2022] [Indexed: 11/27/2022] Open
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Antimicrobial peptides with cell-penetrating activity as prophylactic and treatment drugs. Biosci Rep 2022; 42:231731. [PMID: 36052730 PMCID: PMC9508529 DOI: 10.1042/bsr20221789] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2022] [Revised: 08/31/2022] [Accepted: 09/01/2022] [Indexed: 01/18/2023] Open
Abstract
Health is fundamental for the development of individuals and evolution of species. In that sense, for human societies is relevant to understand how the human body has developed molecular strategies to maintain health. In the present review, we summarize diverse evidence that support the role of peptides in this endeavor. Of particular interest to the present review are antimicrobial peptides (AMP) and cell-penetrating peptides (CPP). Different experimental evidence indicates that AMP/CPP are able to regulate autophagy, which in turn regulates the immune system response. AMP also assists in the establishment of the microbiota, which in turn is critical for different behavioral and health aspects of humans. Thus, AMP and CPP are multifunctional peptides that regulate two aspects of our bodies that are fundamental to our health: autophagy and microbiota. While it is now clear the multifunctional nature of these peptides, we are still in the early stages of the development of computational strategies aimed to assist experimentalists in identifying selective multifunctional AMP/CPP to control nonhealthy conditions. For instance, both AMP and CPP are computationally characterized as amphipatic and cationic, yet none of these features are relevant to differentiate these peptides from non-AMP or non-CPP. The present review aims to highlight current knowledge that may facilitate the development of AMP’s design tools for preventing or treating illness.
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Sidorczuk K, Gagat P, Pietluch F, Kała J, Rafacz D, Bąkała L, Słowik J, Kolenda R, Rödiger S, Fingerhut LCHW, Cooke IR, Mackiewicz P, Burdukiewicz M. Benchmarks in antimicrobial peptide prediction are biased due to the selection of negative data. Brief Bioinform 2022; 23:6672903. [PMID: 35988923 PMCID: PMC9487607 DOI: 10.1093/bib/bbac343] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Revised: 07/07/2022] [Accepted: 07/25/2022] [Indexed: 12/29/2022] Open
Abstract
Antimicrobial peptides (AMPs) are a heterogeneous group of short polypeptides that target not only microorganisms but also viruses and cancer cells. Due to their lower selection for resistance compared with traditional antibiotics, AMPs have been attracting the ever-growing attention from researchers, including bioinformaticians. Machine learning represents the most cost-effective method for novel AMP discovery and consequently many computational tools for AMP prediction have been recently developed. In this article, we investigate the impact of negative data sampling on model performance and benchmarking. We generated 660 predictive models using 12 machine learning architectures, a single positive data set and 11 negative data sampling methods; the architectures and methods were defined on the basis of published AMP prediction software. Our results clearly indicate that similar training and benchmark data set, i.e. produced by the same or a similar negative data sampling method, positively affect model performance. Consequently, all the benchmark analyses that have been performed for AMP prediction models are significantly biased and, moreover, we do not know which model is the most accurate. To provide researchers with reliable information about the performance of AMP predictors, we also created a web server AMPBenchmark for fair model benchmarking. AMPBenchmark is available at http://BioGenies.info/AMPBenchmark.
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Affiliation(s)
| | | | | | - Jakub Kała
- Warsaw University of Technology, Faculty of Mathematics and Information Science, Poland
| | - Dominik Rafacz
- Warsaw University of Technology, Faculty of Mathematics and Information Science, Poland
| | - Laura Bąkała
- Warsaw University of Technology, Faculty of Mathematics and Information Science, Poland
| | - Jadwiga Słowik
- Warsaw University of Technology, Faculty of Mathematics and Information Science, Poland
| | - Rafał Kolenda
- Quadram Institute Biosciences, Norwich Research Park, Norwich, United Kingdom,Wrocław University of Environmental and Life Sciences, Faculty of Veterinary Medicine, Poland
| | - Stefan Rödiger
- Brandenburg University of Technology Cottbus-Senftenberg, Faculty of Natural Sciences, Germany
| | - Legana C H W Fingerhut
- Department of Molecular and Cell Biology, Centre for Tropical Bioinformatics and Molecular Biology, James Cook University, Australia
| | - Ira R Cooke
- Department of Molecular and Cell Biology, Centre for Tropical Bioinformatics and Molecular Biology, James Cook University, Australia
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Agüero-Chapin G, Galpert-Cañizares D, Domínguez-Pérez D, Marrero-Ponce Y, Pérez-Machado G, Teijeira M, Antunes A. Emerging Computational Approaches for Antimicrobial Peptide Discovery. Antibiotics (Basel) 2022; 11:antibiotics11070936. [PMID: 35884190 PMCID: PMC9311958 DOI: 10.3390/antibiotics11070936] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Revised: 07/01/2022] [Accepted: 07/08/2022] [Indexed: 02/05/2023] Open
Abstract
In the last two decades many reports have addressed the application of artificial intelligence (AI) in the search and design of antimicrobial peptides (AMPs). AI has been represented by machine learning (ML) algorithms that use sequence-based features for the discovery of new peptidic scaffolds with promising biological activity. From AI perspective, evolutionary algorithms have been also applied to the rational generation of peptide libraries aimed at the optimization/design of AMPs. However, the literature has scarcely dedicated to other emerging non-conventional in silico approaches for the search/design of such bioactive peptides. Thus, the first motivation here is to bring up some non-standard peptide features that have been used to build classical ML predictive models. Secondly, it is valuable to highlight emerging ML algorithms and alternative computational tools to predict/design AMPs as well as to explore their chemical space. Another point worthy of mention is the recent application of evolutionary algorithms that actually simulate sequence evolution to both the generation of diversity-oriented peptide libraries and the optimization of hit peptides. Last but not least, included here some new considerations in proteogenomic analyses currently incorporated into the computational workflow for unravelling AMPs in natural sources.
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Affiliation(s)
- Guillermin Agüero-Chapin
- CIIMAR—Centro Interdisciplinar de Investigação Marinha e Ambiental, Universidade do Porto, Terminal de Cruzeiros do Porto de Leixões, Av. General Norton de Matos, s/n, 4450-208 Porto, Portugal;
- Departamento de Biologia, Faculdade de Ciências, Universidade do Porto, Rua do Campo Alegre, 4169-007 Porto, Portugal
- Correspondence: (G.A.-C.); (A.A.); Tel.: +351-22-340-1813 (G.A.-C. & A.A.)
| | - Deborah Galpert-Cañizares
- Departamento de Ciencia de la Computación, Universidad Central Marta Abreu de Las Villas (UCLV), Santa Clara 54830, Cuba;
| | - Dany Domínguez-Pérez
- CIIMAR—Centro Interdisciplinar de Investigação Marinha e Ambiental, Universidade do Porto, Terminal de Cruzeiros do Porto de Leixões, Av. General Norton de Matos, s/n, 4450-208 Porto, Portugal;
- Proquinorte, Unipessoal, Lda, Avenida 5 de Outubro, 124, 7º Piso, Avenidas Novas, 1050-061 Lisboa, Portugal
| | - Yovani Marrero-Ponce
- Universidad San Francisco de Quito (USFQ), Grupo de Medicina Molecular y Translacional (MeM&T), Colegio de Ciencias de la Salud (COCSA), Escuela de Medicina, Edificio de Especialidades Médicas and Instituto de Simulación Computacional (ISC-USFQ), Diego de Robles y vía Interoceánica, Quito 170157, Ecuador;
| | - Gisselle Pérez-Machado
- EpiDisease S.L—Spin-Off of Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), 46980 Valencia, Spain;
| | - Marta Teijeira
- Departamento de Química Orgánica, Facultade de Química, Universidade de Vigo, 36310 Vigo, Spain;
- Instituto de Investigación Sanitaria Galicia Sur, Hospital Álvaro Cunqueiro, 36213 Vigo, Spain
| | - Agostinho Antunes
- CIIMAR—Centro Interdisciplinar de Investigação Marinha e Ambiental, Universidade do Porto, Terminal de Cruzeiros do Porto de Leixões, Av. General Norton de Matos, s/n, 4450-208 Porto, Portugal;
- Departamento de Biologia, Faculdade de Ciências, Universidade do Porto, Rua do Campo Alegre, 4169-007 Porto, Portugal
- Correspondence: (G.A.-C.); (A.A.); Tel.: +351-22-340-1813 (G.A.-C. & A.A.)
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