1
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Yang S, Xu P. HemoDL: Hemolytic peptides prediction by double ensemble engines from Rich sequence-derived and transformer-enhanced information. Anal Biochem 2024; 690:115523. [PMID: 38552762 DOI: 10.1016/j.ab.2024.115523] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Revised: 03/20/2024] [Accepted: 03/22/2024] [Indexed: 04/02/2024]
Abstract
Hemolytic peptides can trigger hemolysis by rupturing red blood cells' membranes and triggering cell disruption. Due to the labor-intensive and time-consuming in-lab identification process, accurate, high-throughput hemolytic peptide prediction is crucial for the growth of peptide sequence data in proteomics and peptidomics. In this study, we offer the HemoDL ensemble learning model, which learns the distinct distribution of sequence characteristics for predicting the hemolytic activity of peptides using a double LightGBM framework. To determine the most informative encoding features, we compare 17 widely used features across four benchmark datasets. Our investigation reveals that CTD, BPF, Charge, AAC, GDPC, ATC, QSO, and transformer-based features exhibit more positive contributions to detecting the hemolytic activity of peptides. Comparison with eight state-of-the-art methods demonstrates that HemoDL outperforms other models, attaining higher Matthews Correlation Coefficient values on four test datasets, ranging from 6.30% to 16.04%, 6.63%-11.26%, 4.76%-9.92%, and 7.41%-15.03%, respectively. Additionally, we provide the HemoDL with a user-friendly graphical interface available at https://github.com/abcair/HemoDL. In summary, the HemoDL model, leveraging CTD, BPF, Charge, AAC, GDPC, ATC, QSO and transformer-based encoding features within a double LightGBM learning framework, achieves high accuracy in predicting the hemolytic activity of peptides.
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Affiliation(s)
- Sen Yang
- School of Computer Science and Artificial Intelligence Aliyun School of Big Data School of Software, Changzhou University, Changzhou, 213164, China; The Affiliated Changzhou No.2 People's Hospital of Nanjing Medical University, Changzhou, 213164, China
| | - Piao Xu
- College of Economics and Management, Nanjing Forestry University, China.
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2
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Zeng P, Wang H, Zhang P, Leung SSY. Unearthing naturally-occurring cyclic antibacterial peptides and their structural optimization strategies. Biotechnol Adv 2024; 73:108371. [PMID: 38704105 DOI: 10.1016/j.biotechadv.2024.108371] [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: 11/10/2023] [Revised: 03/08/2024] [Accepted: 04/29/2024] [Indexed: 05/06/2024]
Abstract
Natural products with antibacterial activity are highly desired globally to combat against multidrug-resistant (MDR) bacteria. Antibacterial peptide (ABP), especially cyclic ABP (CABP), is one of the abundant classes. Most of them were isolated from microbes, demonstrating excellent bactericidal effects. With the improved proteolytic stability, CABPs are normally considered to have better druggability than linear peptides. However, most clinically-used CABP-based antibiotics, such as colistin, also face the challenges of drug resistance soon after they reached the market, urgently requiring the development of next-generation succedaneums. We present here a detail review on the novel naturally-occurring CABPs discovered in the past decade and some of them are under clinical trials, exhibiting anticipated application potential. According to their chemical structures, they were broadly classified into five groups, including (i) lactam/lactone-based CABPs, (ii) cyclic lipopeptides, (iii) glycopeptides, (iv) cyclic sulfur-rich peptides and (v) multiple-modified CABPs. Their chemical structures, antibacterial spectrums and proposed mechanisms are discussed. Moreover, engineered analogs of these novel CABPs are also summarized to preliminarily analyze their structure-activity relationship. This review aims to provide a global perspective on research and development of novel CABPs to highlight the effectiveness of derivatives design in identifying promising antibacterial agents. Further research efforts in this area are believed to play important roles in fighting against the multidrug-resistance crisis.
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Affiliation(s)
- Ping Zeng
- School of Pharmacy, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, Hong Kong
| | - Honglan Wang
- School of Pharmacy, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, Hong Kong
| | - Pengfei Zhang
- School of Pharmacy, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, Hong Kong
| | - Sharon Shui Yee Leung
- School of Pharmacy, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, Hong Kong.
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3
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Yu D, Andersson-Li M, Maes S, Andersson-Li L, Neumann NF, Odlare M, Jonsson A. Development of a logic regression-based approach for the discovery of host- and niche-informative biomarkers in Escherichia coli and their application for microbial source tracking. Appl Environ Microbiol 2024:e0022724. [PMID: 38940567 DOI: 10.1128/aem.00227-24] [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: 02/07/2024] [Accepted: 06/07/2024] [Indexed: 06/29/2024] Open
Abstract
Microbial source tracking leverages a wide range of approaches designed to trace the origins of fecal contamination in aquatic environments. Although source tracking methods are typically employed within the laboratory setting, computational techniques can be leveraged to advance microbial source tracking methodology. Herein, we present a logic regression-based supervised learning approach for the discovery of source-informative genetic markers within intergenic regions across the Escherichia coli genome that can be used for source tracking. With just single intergenic loci, logic regression was able to identify highly source-specific (i.e., exceeding 97.00%) biomarkers for a wide range of host and niche sources, with sensitivities reaching as high as 30.00%-50.00% for certain source categories, including pig, sheep, mouse, and wastewater, depending on the specific intergenic locus analyzed. Restricting the source range to reflect the most prominent zoonotic sources of E. coli transmission (i.e., bovine, chicken, human, and pig) allowed for the generation of informative biomarkers for all host categories, with specificities of at least 90.00% and sensitivities between 12.50% and 70.00%, using the sequence data from key intergenic regions, including emrKY-evgAS, ibsB-(mdtABCD-baeSR), ompC-rcsDB, and yedS-yedR, that appear to be involved in antibiotic resistance. Remarkably, we were able to use this approach to classify 48 out of 113 river water E. coli isolates collected in Northwestern Sweden as either beaver, human, or reindeer in origin with a high degree of consensus-thus highlighting the potential of logic regression modeling as a novel approach for augmenting current source tracking efforts.IMPORTANCEThe presence of microbial contaminants, particularly from fecal sources, within water poses a serious risk to public health. The health and economic burden of waterborne pathogens can be substantial-as such, the ability to detect and identify the sources of fecal contamination in environmental waters is crucial for the control of waterborne diseases. This can be accomplished through microbial source tracking, which involves the use of various laboratory techniques to trace the origins of microbial pollution in the environment. Building on current source tracking methodology, we describe a novel workflow that uses logic regression, a supervised machine learning method, to discover genetic markers in Escherichia coli, a common fecal indicator bacterium, that can be used for source tracking efforts. Importantly, our research provides an example of how the rise in prominence of machine learning algorithms can be applied to improve upon current microbial source tracking methodology.
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Affiliation(s)
- Daniel Yu
- School of Public Health, University of Alberta, Edmonton, Alberta, Canada
| | | | - Sharon Maes
- Department of Natural Sciences, Design and Sustainable Development, Mid Sweden University, Östersund, Sweden
| | - Lili Andersson-Li
- Department of Microbiology, Tumor and Cell Biology, Karolinska Institutet, Solna, Sweden
| | - Norman F Neumann
- School of Public Health, University of Alberta, Edmonton, Alberta, Canada
| | - Monica Odlare
- Department of Natural Sciences, Design and Sustainable Development, Mid Sweden University, Östersund, Sweden
| | - Anders Jonsson
- Department of Natural Sciences, Design and Sustainable Development, Mid Sweden University, Östersund, Sweden
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4
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Almotairi S, Badr E, Abdelbaky I, Elhakeem M, Abdul Salam M. Hybrid transformer-CNN model for accurate prediction of peptide hemolytic potential. Sci Rep 2024; 14:14263. [PMID: 38902287 PMCID: PMC11190137 DOI: 10.1038/s41598-024-63446-5] [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: 04/02/2024] [Accepted: 05/29/2024] [Indexed: 06/22/2024] Open
Abstract
Hemolysis is a crucial factor in various biomedical and pharmaceutical contexts, driving our interest in developing advanced computational techniques for precise prediction. Our proposed approach takes advantage of the unique capabilities of convolutional neural networks (CNNs) and transformers to detect complex patterns inherent in the data. The integration of CNN and transformers' attention mechanisms allows for the extraction of relevant information, leading to accurate predictions of hemolytic potential. The proposed method was trained on three distinct data sets of peptide sequences known as recurrent neural network-hemolytic (RNN-Hem), Hlppredfuse, and Combined. Our computational results demonstrated the superior efficacy of our models compared to existing methods. The proposed approach demonstrated impressive Matthews correlation coefficients of 0.5962, 0.9111, and 0.7788 respectively, indicating its effectiveness in predicting hemolytic activity. With its potential to guide experimental efforts in peptide design and drug development, this method holds great promise for practical applications. Integrating CNNs and transformers proves to be a powerful tool in the fields of bioinformatics and therapeutic research, highlighting their potential to drive advancement in this area.
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Affiliation(s)
- Sultan Almotairi
- Department of Computer Science, Faculty of College of Computer and Information Sciences, Majmaah University, 11952, Majmaah, Saudi Arabia
- Department of Computer Science, Faculty of Computer and Information Systems, Islamic University of Madinah, 42351, Medinah, Saudi Arabia
| | - Elsayed Badr
- Scientific Computing Department, Faculty of Computers and Artificial Intelligence, Benha University, Benha, Egypt.
- The Egyptian School of Data Science (ESDS), Benha, Egypt.
| | - Ibrahim Abdelbaky
- Artificial Intelligence Department, Faculty of Computers and Artificial Intelligence, Benha University, Benha, Egypt
| | - Mohamed Elhakeem
- Artificial Intelligence Department, Faculty of Computers and Artificial Intelligence, Benha University, Benha, Egypt.
| | - Mustafa Abdul Salam
- Artificial Intelligence Department, Faculty of Computers and Artificial Intelligence, Benha University, Benha, Egypt
- Department of Computer Science, College of Arts and Science, Wadi Addawasir, Prince Sattam Bin Abdulaziz University, 16273, Al-Kharj, Saudi Arabia
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5
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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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6
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Wan F, Torres MDT, Peng J, de la Fuente-Nunez C. Deep-learning-enabled antibiotic discovery through molecular de-extinction. Nat Biomed Eng 2024:10.1038/s41551-024-01201-x. [PMID: 38862735 DOI: 10.1038/s41551-024-01201-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2023] [Accepted: 03/25/2024] [Indexed: 06/13/2024]
Abstract
Molecular de-extinction aims at resurrecting molecules to solve antibiotic resistance and other present-day biological and biomedical problems. Here we show that deep learning can be used to mine the proteomes of all available extinct organisms for the discovery of antibiotic peptides. We trained ensembles of deep-learning models consisting of a peptide-sequence encoder coupled with neural networks for the prediction of antimicrobial activity and used it to mine 10,311,899 peptides. The models predicted 37,176 sequences with broad-spectrum antimicrobial activity, 11,035 of which were not found in extant organisms. We synthesized 69 peptides and experimentally confirmed their activity against bacterial pathogens. Most peptides killed bacteria by depolarizing their cytoplasmic membrane, contrary to known antimicrobial peptides, which tend to target the outer membrane. Notably, lead compounds (including mammuthusin-2 from the woolly mammoth, elephasin-2 from the straight-tusked elephant, hydrodamin-1 from the ancient sea cow, mylodonin-2 from the giant sloth and megalocerin-1 from the extinct giant elk) showed anti-infective activity in mice with skin abscess or thigh infections. Molecular de-extinction aided by deep learning may accelerate the discovery of therapeutic molecules.
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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
- Department of Chemistry, School of Arts and Sciences, University of Pennsylvania, Philadelphia, PA, USA
- Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, PA, 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, USA
- Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA
- Department of Chemistry, School of Arts and Sciences, University of Pennsylvania, Philadelphia, PA, USA
- Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, PA, USA
| | - Jacqueline Peng
- Graduate Group in Genomics and Computational Biology, 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.
- Department of Chemistry, School of Arts and Sciences, University of Pennsylvania, Philadelphia, PA, USA.
- Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, PA, USA.
- Graduate Group in Genomics and Computational Biology, University of Pennsylvania, Philadelphia, PA, USA.
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7
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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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8
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Castillo-Mendieta K, Agüero-Chapin G, Marquez E, Perez-Castillo Y, Barigye SJ, Pérez-Cárdenas M, Peréz-Giménez F, Marrero-Ponce Y. Multiquery Similarity Searching Models: An Alternative Approach for Predicting Hemolytic Activity from Peptide Sequence. Chem Res Toxicol 2024; 37:580-589. [PMID: 38501392 DOI: 10.1021/acs.chemrestox.3c00408] [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: 03/20/2024]
Abstract
The desirable pharmacological properties and a broad number of therapeutic activities have made peptides promising drugs over small organic molecules and antibody drugs. Nevertheless, toxic effects, such as hemolysis, have hampered the development of such promising drugs. Hence, a reliable computational tool to predict peptide hemolytic toxicity is enormously useful before synthesis and experimental evaluation. Currently, four web servers that predict hemolytic activity using machine learning (ML) algorithms are available; however, they exhibit some limitations, such as the need for a reliable negative set and limited application domain. Hence, we developed a robust model based on a novel theoretical approach that combines network science and a multiquery similarity searching (MQSS) method. A total of 1152 initial models were constructed from 144 scaffolds generated in a previous report. These were evaluated on external data sets, and the best models were fused and improved. Our best MQSS model I1 outperformed all state-of-the-art ML-based models and was used to characterize the prevalence of hemolytic toxicity on therapeutic peptides. Based on our model's estimation, the number of hemolytic peptides might be 3.9-fold higher than the reported.
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Affiliation(s)
- Kevin Castillo-Mendieta
- School of Biological Sciences and Engineering, Yachay Tech University, Hda. San José s/n y Proyecto Yachay, Urcuquí 100119, Ecuador
| | - Guillermin Agüero-Chapin
- CIIMAR/CIMAR, Interdisciplinary Centre of Marine and Environmental Research, Terminal de Cruzeiros do Porto de Leixões, University of Porto, Av. General Norton de Matos s/n, 4450-208 Porto, Portugal
- Department of Biology, Faculty of Sciences, University of Porto, Rua do Campo Alegre, 4169-007 Porto, Portugal
| | - Edgar Marquez
- Grupo de Investigaciones en Química y Biología, Departamento de Química y Biología, Facultad de Ciencias Básicas, Universidad del Norte, Carrera 51B, Km 5, vía Puerto Colombia, Barranquilla 081007, Colombia
| | - Yunierkis Perez-Castillo
- Bio-Chemoinformatics Research Group and Escuela de Ciencias Físicas y Matemáticas. Universidad de Las Américas, Quito 170504, Ecuador
| | - Stephen J Barigye
- Departamento de Química Física Aplicada, Facultad de Ciencias, Universidad Autónoma de Madrid (UAM), 28049 Madrid, Spain
| | - Mariela Pérez-Cárdenas
- School of Biological Sciences and Engineering, Yachay Tech University, Hda. San José s/n y Proyecto Yachay, Urcuquí 100119, Ecuador
| | - Facundo Peréz-Giménez
- Unidad de Investigación de Diseño de Fármacos y Conectividad Molecular, Departamento de Química Física, Facultad de Farmacia, Universitat de València, Valencia 46100, Spain
| | - Yovani Marrero-Ponce
- Unidad de Investigación de Diseño de Fármacos y Conectividad Molecular, Departamento de Química Física, Facultad de Farmacia, Universitat de València, Valencia 46100, Spain
- Facultad de Ingeniería, Universidad Panamericana, Augusto Rodin No. 498, Insurgentes Mixcoac, Benito Juárez, CDMX, Mexico 03920, Mexico
- Grupo de Medicina Molecular y Traslacional (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, Universidad San Francisco de Quito (USFQ), Quito, Pichincha 170157, Ecuador
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9
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Paul S, Verma S, Chen YC. Peptide Dendrimer-Based Antibacterial Agents: Synthesis and Applications. ACS Infect Dis 2024; 10:1034-1055. [PMID: 38428037 PMCID: PMC11019562 DOI: 10.1021/acsinfecdis.3c00624] [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: 11/16/2023] [Revised: 02/15/2024] [Accepted: 02/16/2024] [Indexed: 03/03/2024]
Abstract
Pathogenic bacteria cause the deaths of millions of people every year. With the development of antibiotics, hundreds and thousands of people's lives have been saved. Nevertheless, bacteria can develop resistance to antibiotics, rendering them insensitive to antibiotics over time. Peptides containing specific amino acids can be used as antibacterial agents; however, they can be easily degraded by proteases in vivo. To address these issues, branched peptide dendrimers are now being considered as good antibacterial agents due to their high efficacy, resistance to protease degradation, and low cytotoxicity. The ease with which peptide dendrimers can be synthesized and modified makes them accessible for use in various biological and nonbiological fields. That is, peptide dendrimers hold a promising future as antibacterial agents with prolonged efficacy without bacterial resistance development. Their in vivo stability and multivalence allow them to effectively target multi-drug-resistant strains and prevent biofilm formation. Thus, it is interesting to have an overview of the development and applications of peptide dendrimers in antibacterial research, including the possibility of employing machine learning approaches for the design of AMPs and dendrimers. This review summarizes the synthesis and applications of peptide dendrimers as antibacterial agents. The challenges and perspectives of using peptide dendrimers as the antibacterial agents are also discussed.
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Affiliation(s)
- Suchita Paul
- Institute
of Semiconductor Technology, National Yang
Ming Chiao Tung University, Hsinchu 300, Taiwan
- Department
of Chemistry, Indian Institute of Technology
Kanpur, Kanpur 208016, Uttar Pradesh, India
| | - Sandeep Verma
- Department
of Chemistry, Indian Institute of Technology
Kanpur, Kanpur 208016, Uttar Pradesh, India
- Gangwal
School of Medical Sciences and Technology, Indian Institute of Technology Kanpur, Kanpur 208016, Uttar Pradesh, India
| | - Yu-Chie Chen
- Institute
of Semiconductor Technology, National Yang
Ming Chiao Tung University, Hsinchu 300, Taiwan
- Department
of Applied Chemistry, National Yang Ming
Chiao Tung University, Hsinchu 300, Taiwan
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10
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Hesamzadeh P, Seif A, Mahmoudzadeh K, Ganjali Koli M, Mostafazadeh A, Nayeri K, Mirjafary Z, Saeidian H. De novo antioxidant peptide design via machine learning and DFT studies. Sci Rep 2024; 14:6473. [PMID: 38499731 PMCID: PMC10948870 DOI: 10.1038/s41598-024-57247-z] [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: 12/29/2023] [Accepted: 03/15/2024] [Indexed: 03/20/2024] Open
Abstract
Antioxidant peptides (AOPs) are highly valued in food and pharmaceutical industries due to their significant role in human function. This study introduces a novel approach to identifying robust AOPs using a deep generative model based on sequence representation. Through filtration with a deep-learning classification model and subsequent clustering via the Butina cluster algorithm, twelve peptides (GP1-GP12) with potential antioxidant capacity were predicted. Density functional theory (DFT) calculations guided the selection of six peptides for synthesis and biological experiments. Molecular orbital representations revealed that the HOMO for these peptides is primarily localized on the indole segment, underscoring its pivotal role in antioxidant activity. All six synthesized peptides exhibited antioxidant activity in the DPPH assay, while the hydroxyl radical test showed suboptimal results. A hemolysis assay confirmed the non-hemolytic nature of the generated peptides. Additionally, an in silico investigation explored the potential inhibitory interaction between the peptides and the Keap1 protein. Analysis revealed that ligands GP3, GP4, and GP12 induced significant structural changes in proteins, affecting their stability and flexibility. These findings highlight the capability of machine learning approaches in generating novel antioxidant peptides.
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Affiliation(s)
- Parsa Hesamzadeh
- Department of Chemistry, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | - Abdolvahab Seif
- Dipartimento di Fisica, Universita' di Padova, Via Marzolo 8, 35131, Padua, Italy
- Department of Chemistry, University of Turin, Via Pietro Giuria 7, 10125, Turin, Italy
| | - Kazem Mahmoudzadeh
- Department of Organic Chemistry and Oil, Faculty of Chemistry, Shahid Beheshti University, Tehran, Iran
| | | | - Amrollah Mostafazadeh
- Cellular and Molecular Biology Research Center, Health Research Institute, Babol University of Medical Sciences, Babol, Iran
| | - Kosar Nayeri
- Student Research Committee, Babol University of Medical Sciences, Babol, Iran
| | - Zohreh Mirjafary
- Department of Chemistry, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | - Hamid Saeidian
- Department of Science, Payame Noor University (PNU), PO Box: 19395-4697, Tehran, Iran.
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11
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Wu X, Lin H, Bai R, Duan H. Deep learning for advancing peptide drug development: Tools and methods in structure prediction and design. Eur J Med Chem 2024; 268:116262. [PMID: 38387334 DOI: 10.1016/j.ejmech.2024.116262] [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: 01/04/2024] [Revised: 02/06/2024] [Accepted: 02/17/2024] [Indexed: 02/24/2024]
Abstract
Peptides can bind challenging disease targets with high affinity and specificity, offering enormous opportunities for addressing unmet medical needs. However, peptides' unique features, including smaller size, increased structural flexibility, and limited data availability, pose additional challenges to the design process compared to proteins. This review explores the dynamic field of peptide therapeutics, leveraging deep learning to enhance structure prediction and design. Our exploration encompasses various facets of peptide research, ranging from dataset curation handling to model development. As deep learning technologies become more refined, we channel our efforts into peptide structure prediction and design, aligning with the fundamental principles of structure-activity relationships in drug development. To guide researchers in harnessing the potential of deep learning to advance peptide drug development, our insights comprehensively explore current challenges and future directions of peptide therapeutics.
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Affiliation(s)
- Xinyi Wu
- College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou, 310014, PR China
| | - Huitian Lin
- College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou, 310014, PR China
| | - Renren Bai
- School of Pharmacy, Hangzhou Normal University, Hangzhou, 311121, PR China.
| | - Hongliang Duan
- Faculty of Applied Sciences, Macao Polytechnic University, Macao, 999078, PR China.
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12
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Orsi M, Shing Loh B, Weng C, Ang WH, Frei A. Using Machine Learning to Predict the Antibacterial Activity of Ruthenium Complexes. Angew Chem Int Ed Engl 2024; 63:e202317901. [PMID: 38088924 DOI: 10.1002/anie.202317901] [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] [Indexed: 01/26/2024]
Abstract
Rising antimicrobial resistance (AMR) and lack of innovation in the antibiotic pipeline necessitate novel approaches to discovering new drugs. Metal complexes have proven to be promising antimicrobial compounds, but the number of studied compounds is still low compared to the millions of organic molecules investigated so far. Lately, machine learning (ML) has emerged as a valuable tool for guiding the design of small organic molecules, potentially even in low-data scenarios. For the first time, we extend the application of ML to the discovery of metal-based medicines. Utilising 288 modularly synthesized ruthenium arene Schiff-base complexes and their antibacterial properties, a series of ML models were trained. The models perform well and are used to predict the activity of 54 new compounds. These displayed a 5.7x higher hit-rate (53.7 %) against methicillin-resistant Staphylococcus aureus (MRSA) compared to the original library (9.4 %), demonstrating that ML can be applied to improve the success-rates in the search of new metalloantibiotics. This work paves the way for more ambitious applications of ML in the field of metal-based drug discovery.
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Affiliation(s)
- Markus Orsi
- Department of Chemistry, Biochemistry & Pharmaceutical Sciences, University of Bern, Freiestrasse 3, 3012, Bern, Switzerland
| | - Boon Shing Loh
- Department of Chemistry, National University of Singapore, 3 Science Drive 3, Singapore, 117543, Singapore
| | - Cheng Weng
- Department of Chemistry, National University of Singapore, 3 Science Drive 3, Singapore, 117543, Singapore
| | - Wee Han Ang
- Department of Chemistry, National University of Singapore, 3 Science Drive 3, Singapore, 117543, Singapore
- NUS Graduate School - Integrated Science and Engineering Programme (ISEP), National University of Singapore, 21 Lower Kent Ridge Rd, Singapore, 119077, Singapore
| | - Angelo Frei
- Department of Chemistry, Biochemistry & Pharmaceutical Sciences, University of Bern, Freiestrasse 3, 3012, Bern, Switzerland
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13
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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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14
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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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15
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Alhhazmi AA, Alluhibi SS, Alhujaily R, Alenazi ME, Aljohani TL, Al-Jazzar AAT, Aljabri AD, Albaqami R, Almutairi D, Alhelali LK, Albasri HM, Almutawif YA, Alturkostani MA, Almutairi AZ. Novel antimicrobial peptides identified in legume plant, Medicago truncatula. Microbiol Spectr 2024; 12:e0182723. [PMID: 38236024 PMCID: PMC10845954 DOI: 10.1128/spectrum.01827-23] [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: 05/01/2023] [Accepted: 12/16/2023] [Indexed: 01/19/2024] Open
Abstract
One of the major issues in healthcare today is antibiotic resistance. Antimicrobial peptides (AMPs), a subclass of host defense peptides, have been suggested as a viable solution for the multidrug resistance problem. Legume plants express more than 700 nodule-specific cysteine-rich (NCR) peptides. Three NCR peptides (NCR094, NCR888, and NCR992) were predicted to have antimicrobial activity using in silico AMP prediction programs. This study focused on investigating the roles of the NCRs in antimicrobial activity and antibiofilm activity, followed by in vitro toxicity profiling. Different variants were synthesized, i.e., mutated and truncated derivatives. The effect on the growth of Klebsiella pneumoniae and methicillin-resistant Staphylococcus aureus (MRSA) was monitored post-treatment, and survived cells were counted using an in vitro and ex vivo killing assay. The antibiofilm assay was conducted using subinhibitory concentrations of the NCRs and monitoring K. pneumoniae biomass, followed by crystal violet staining. The cytotoxicity profile was evaluated using erythrocyte hemolysis and leukemia (K562) cell line toxicity assays. Out of the NCRs, NCR094 and NCR992 displayed mainly in vitro and ex vivo bactericidal activity on K. pneumoniae. NCR094 wild type (WT) and NCR992 eradicated K. pneumoniae at different potency; NCR094 and NCR992 killed K. pneumoniae completely at 25 and 50 µM, respectively. However, both peptides in the wild type showed negligible bactericidal effect on MRSA in vitro and ex vivo. NCR094 and its derivatives relatively retained the antimicrobial activity on K. pneumoniae in vitro and ex vivo. NCR992 WT lost its antimicrobial activity on K. pneumoniae ex vivo, yet the different truncated and mutated variants retained some of the antimicrobial role ex vivo. All the different variants of NCR094 had no effect on MRSA in vitro and ex vivo. Similarly, NCR992's variants had a negligible bactericidal role on MRSA in vitro, yet the truncated variants had a significantly high bactericidal effect on MRSA ex vivo. NCR094.3 (cystine replacement variant) and NCR992.1 displayed significant antibiofilm activity more than 90%. NCR992.3 and NCR992.2 displayed more than 50% of antibiofilm activity. All the NCR094 forms had no toxicity, except NCR094.1 (49.38%, SD ± 3.46) and all NCR992 forms (63%-93%), which were above the cutoff (20%). Only NCR992.2 showed low toxicity on K562 (24.8%, SD ± 3.40), yet above the 20% cutoff. This study provided preliminary antimicrobial and safety data for the potential use of these peptides for therapeutical applications.IMPORTANCEThe discovery of new antibiotics is urgently needed, given the global expansion of antibiotic-resistant bacteria and the rising mortality rate. One of the initial lines of defense against microbial infections is antimicrobial peptides (AMPs). Plants can express hundreds of such AMPs as defensins and defensin-like peptides. The nodule-specific cysteine-rich (NCR) peptides are a class of defensin-like peptides that have evolved in rhizobial-legume symbioses. This study screened the antimicrobial activity of a subset of NCR sequences using online computational AMP prediction algorithms. Two novel NCRs, NCR094 and NCR992, with different variants were identified to exhibit antimicrobial activity with various potency on two problematic pathogens, K. pneumoniae and MRSA, using in vitro and ex vivo killing assays. Yet, one variant, NCR094.3, had no toxicity toward human cells and displayed antibiofilm activity, which make it a promising lead for antimicrobial drug development.
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Affiliation(s)
- Areej A. Alhhazmi
- Medical Laboratories Technology Department, College of Applied Medical Sciences, Taibah University, Medina, Saudi Arabia
| | - Sarah S. Alluhibi
- Medical Laboratories Technology Department, College of Applied Medical Sciences, Taibah University, Medina, Saudi Arabia
| | - Rahaf Alhujaily
- Medical Laboratories Technology Department, College of Applied Medical Sciences, Taibah University, Medina, Saudi Arabia
| | - Maymona E. Alenazi
- Medical Laboratories Technology Department, College of Applied Medical Sciences, Taibah University, Medina, Saudi Arabia
| | - Taif L. Aljohani
- Medical Laboratories Technology Department, College of Applied Medical Sciences, Taibah University, Medina, Saudi Arabia
| | - Al-Anoud T. Al-Jazzar
- Medical Laboratories Technology Department, College of Applied Medical Sciences, Taibah University, Medina, Saudi Arabia
| | - Ahaad D. Aljabri
- Medical Laboratories Technology Department, College of Applied Medical Sciences, Taibah University, Medina, Saudi Arabia
| | - Razan Albaqami
- Medical Laboratories Technology Department, College of Applied Medical Sciences, Taibah University, Medina, Saudi Arabia
| | - Dalal Almutairi
- Medical Laboratories Technology Department, College of Applied Medical Sciences, Taibah University, Medina, Saudi Arabia
| | - Lujain K. Alhelali
- Medical Laboratories Technology Department, College of Applied Medical Sciences, Taibah University, Medina, Saudi Arabia
| | - Hibah M. Albasri
- Department of Biology, College of Science, Taibah University, Medina, Saudi Arabia
| | - Yahya A. Almutawif
- Medical Laboratories Technology Department, College of Applied Medical Sciences, Taibah University, Medina, Saudi Arabia
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16
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Xu B, Wang L, Yang C, Yan R, Zhang P, Jin M, Du H, Wang Y. Specifically targeted antimicrobial peptides synergize with bacterial-entrapping peptide against systemic MRSA infections. J Adv Res 2024:S2090-1232(24)00036-5. [PMID: 38266820 DOI: 10.1016/j.jare.2024.01.023] [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: 03/06/2023] [Revised: 12/03/2023] [Accepted: 01/20/2024] [Indexed: 01/26/2024] Open
Abstract
INTRODUCTION The design of precision antimicrobials aims to personalize the treatment of drug-resistant bacterial infections and avoid host microbiota dysbiosis. OBJECTIVES This study aimed to propose an efficient de novo design strategy to obtain specifically targeted antimicrobial peptides (STAMPs) against methicillin-resistant Staphylococcus aureus (MRSA). METHODS We evaluated three strategies designed to increase the selectivity of antimicrobial peptides (AMPs) for MRSA and mainly adopted an advanced hybrid peptide strategy. First, we proposed a traversal design to generate sequences, and constructed machine learning models to predict the anti-S. aureus activity levels of unknown peptides. Subsequently, six peptides were predicted to have high activity, among which, a broad-spectrum AMP (P18) was selected. Finally, the two targeting peptides from phage display libraries or lysostaphin were used to confer specific anti-S. aureus activity to P18. STAMPs were further screened out from hybrid peptides based on their in vitro and in vivo activities. RESULTS An advanced hybrid peptide strategy can enhance the specific and targeted properties of broad-spectrum AMPs. Among 25 assessed peptides, 10 passed in vitro tests, and two peptides containing one bacterial-entrapping peptide (BEP) and one STAMP passed an in vivo test. The lead STAMP (P18E6) disrupted MRSA cell walls and membranes, eliminated established biofilms, and exhibited desirable biocompatibility, systemic distribution and efficacy, and immunomodulatory activity in vivo. Furthermore, a bacterial-entrapping peptide (BEP, SP5) modified from P18, self-assembled into nanonetworks and rapidly entrapped MRSA. SP5 synergized with P18E6 to enhance antibacterial activity in vitro and reduced systemic MRSA infections. CONCLUSIONS This strategy may aid in the design of STAMPs against drug-resistant strains, and BEPs can serve as powerful STAMP adjuvants.
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Affiliation(s)
- Bocheng Xu
- National Engineering Research Center for Green Feed and Healthy Breeding, Key Laboratory of Molecular Animal Nutrition, Ministry of Education, Key Laboratory of Animal Nutrition and Feed Science (Eastern of China), Ministry of Agriculture and Rural Affairs, Key Laboratory of Animal Feed and Nutrition of Zhejiang Province, Institute of Feed Science, Zhejiang University, Hangzhou 310058, China
| | - Lin Wang
- National Engineering Research Center for Green Feed and Healthy Breeding, Key Laboratory of Molecular Animal Nutrition, Ministry of Education, Key Laboratory of Animal Nutrition and Feed Science (Eastern of China), Ministry of Agriculture and Rural Affairs, Key Laboratory of Animal Feed and Nutrition of Zhejiang Province, Institute of Feed Science, Zhejiang University, Hangzhou 310058, China
| | - Chen Yang
- Center for Drug Safety Evaluation and Research, Zhejiang Province Key Laboratory of Anti-Cancer Drug Research, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310007, China
| | - Rong Yan
- National Engineering Research Center for Green Feed and Healthy Breeding, Key Laboratory of Molecular Animal Nutrition, Ministry of Education, Key Laboratory of Animal Nutrition and Feed Science (Eastern of China), Ministry of Agriculture and Rural Affairs, Key Laboratory of Animal Feed and Nutrition of Zhejiang Province, Institute of Feed Science, Zhejiang University, Hangzhou 310058, China
| | - Pan Zhang
- College of Animal Science, Zhejiang University, Hangzhou 310058, China
| | - Mingliang Jin
- National Engineering Research Center for Green Feed and Healthy Breeding, Key Laboratory of Molecular Animal Nutrition, Ministry of Education, Key Laboratory of Animal Nutrition and Feed Science (Eastern of China), Ministry of Agriculture and Rural Affairs, Key Laboratory of Animal Feed and Nutrition of Zhejiang Province, Institute of Feed Science, Zhejiang University, Hangzhou 310058, China
| | - Huahua Du
- National Engineering Research Center for Green Feed and Healthy Breeding, Key Laboratory of Molecular Animal Nutrition, Ministry of Education, Key Laboratory of Animal Nutrition and Feed Science (Eastern of China), Ministry of Agriculture and Rural Affairs, Key Laboratory of Animal Feed and Nutrition of Zhejiang Province, Institute of Feed Science, Zhejiang University, Hangzhou 310058, China.
| | - Yizhen Wang
- National Engineering Research Center for Green Feed and Healthy Breeding, Key Laboratory of Molecular Animal Nutrition, Ministry of Education, Key Laboratory of Animal Nutrition and Feed Science (Eastern of China), Ministry of Agriculture and Rural Affairs, Key Laboratory of Animal Feed and Nutrition of Zhejiang Province, Institute of Feed Science, Zhejiang University, Hangzhou 310058, China.
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17
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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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18
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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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19
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Song P, Zhao L, Zhu L, Sha G, Dong W. BsR1, a broad-spectrum antibacterial peptide with potential for plant protection. Microbiol Spectr 2023; 11:e0257823. [PMID: 37948344 PMCID: PMC10714738 DOI: 10.1128/spectrum.02578-23] [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: 06/20/2023] [Accepted: 10/09/2023] [Indexed: 11/12/2023] Open
Abstract
IMPORTANCE This study addresses the critical need for new antibacterial drugs in the face of bacterial multidrug resistance resulting from antibiotic overuse. It highlights the significance of antimicrobial peptides as essential components of innate immunity in animals and plants, which have been proven effective against multidrug-resistant bacteria and are difficult to develop resistance against. This study successfully synthesizes a broad-spectrum antibacterial peptide, BsR1, with strong inhibitory activities against various Gram-positive and Gram-negative bacteria. BsR1 demonstrates favorable stability and a mode of action that damages bacterial cell membranes, leading to cell death. It also exhibits biological safety and shows potential in enhancing disease resistance in rice. This research offers a novel approach and potential medication for antibacterial drug development, presenting a valuable tool in combating pathogenic microorganisms, particularly in plants.
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Affiliation(s)
- Pei Song
- Department of Plant Pathology, College of Plant Science and Technology and the Key Lab of Crop Disease Monitoring & Safety Control in Hubei Province, Huazhong Agricultural University, Wuhan, China
| | - Li Zhao
- Department of Plant Pathology, College of Plant Science and Technology and the Key Lab of Crop Disease Monitoring & Safety Control in Hubei Province, Huazhong Agricultural University, Wuhan, China
| | - Li Zhu
- Department of Plant Pathology, College of Plant Science and Technology and the Key Lab of Crop Disease Monitoring & Safety Control in Hubei Province, Huazhong Agricultural University, Wuhan, China
| | - Gan Sha
- Department of Plant Pathology, College of Plant Science and Technology and the Key Lab of Crop Disease Monitoring & Safety Control in Hubei Province, Huazhong Agricultural University, Wuhan, China
| | - Wubei Dong
- Department of Plant Pathology, College of Plant Science and Technology and the Key Lab of Crop Disease Monitoring & Safety Control in Hubei Province, Huazhong Agricultural University, Wuhan, China
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20
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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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21
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Szymczak P, Szczurek E. Artificial intelligence-driven antimicrobial peptide discovery. Curr Opin Struct Biol 2023; 83:102733. [PMID: 37992451 DOI: 10.1016/j.sbi.2023.102733] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Revised: 10/06/2023] [Accepted: 10/30/2023] [Indexed: 11/24/2023]
Abstract
Antimicrobial peptides (AMPs) emerge as promising agents against antimicrobial resistance, providing an alternative to conventional antibiotics. Artificial intelligence (AI) revolutionized AMP discovery through both discrimination and generation approaches. The discriminators aid in the identification of promising candidates by predicting key peptide properties such as activity and toxicity, while the generators learn the distribution of peptides and enable sampling novel AMP candidates, either de novo or as analogs of a prototype peptide. Moreover, the controlled generation of AMPs with desired properties is achieved by discriminator-guided filtering, positive-only learning, latent space sampling, as well as conditional and optimized generation. Here we review recent achievements in AI-driven AMP discovery, highlighting the most exciting directions.
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Affiliation(s)
- Paulina Szymczak
- Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, Banacha 2, 02-097, Warsaw, Poland.
| | - Ewa Szczurek
- Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, Banacha 2, 02-097, Warsaw, Poland.
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22
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Guntuboina C, Das A, Mollaei P, Kim S, Barati Farimani A. PeptideBERT: A Language Model Based on Transformers for Peptide Property Prediction. J Phys Chem Lett 2023; 14:10427-10434. [PMID: 37956397 PMCID: PMC10683064 DOI: 10.1021/acs.jpclett.3c02398] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2023] [Revised: 11/04/2023] [Accepted: 11/07/2023] [Indexed: 11/15/2023]
Abstract
Recent advances in language models have enabled the protein modeling community with a powerful tool that uses transformers to represent protein sequences as text. This breakthrough enables a sequence-to-property prediction for peptides without relying on explicit structural data. Inspired by the recent progress in the field of large language models, we present PeptideBERT, a protein language model specifically tailored for predicting essential peptide properties such as hemolysis, solubility, and nonfouling. The PeptideBERT utilizes the ProtBERT pretrained transformer model with 12 attention heads and 12 hidden layers. Through fine-tuning the pretrained model for the three downstream tasks, our model is state of the art (SOTA) in predicting hemolysis, which is crucial for determining a peptide's potential to induce red blood cells as well as nonfouling properties. Leveraging primarily shorter sequences and a data set with negative samples predominantly associated with insoluble peptides, our model showcases remarkable performance.
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Affiliation(s)
- Chakradhar Guntuboina
- Department
of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States
| | - Adrita Das
- Department
of Biomedical Engineering, Carnegie Mellon
University, Pittsburgh, Pennsylvania 15213, United States
| | - Parisa Mollaei
- Department
of Mechanical Engineering, Carnegie Mellon
University, Pittsburgh, Pennsylvania 15213, United States
| | - Seongwon Kim
- Department
of Chemical Engineering, Carnegie Mellon
University, Pittsburgh, Pennsylvania 15213, United States
| | - Amir Barati Farimani
- Department
of Biomedical Engineering, Carnegie Mellon
University, Pittsburgh, Pennsylvania 15213, United States
- Department
of Chemical Engineering, Carnegie Mellon
University, Pittsburgh, Pennsylvania 15213, United States
- Machine
Learning Department, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States
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23
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Xu B, Shaoyong W, Wang L, Yang C, Chen T, Jiang X, Yan R, Jiang Z, Zhang P, Jin M, Wang Y. Gut-targeted nanoparticles deliver specifically targeted antimicrobial peptides against Clostridium perfringens infections. SCIENCE ADVANCES 2023; 9:eadf8782. [PMID: 37774026 PMCID: PMC10541502 DOI: 10.1126/sciadv.adf8782] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Accepted: 08/25/2023] [Indexed: 10/01/2023]
Abstract
Specifically targeted antimicrobial peptides (STAMPs) are novel alternatives to antibiotics, whereas the development of STAMPs for colonic infections is hindered by limited de novo design efficiency and colonic bioavailability. In this study, we report an efficient de novo STAMP design strategy that combines a traversal design, machine learning model, and phage display technology to identify STAMPs against Clostridium perfringens. STAMPs could physically damage C. perfringens, eliminate biofilms, and self-assemble into nanoparticles to entrap pathogens. Further, a gut-targeted engineering particle vaccine (EPV) was used for STAMPs delivery. In vivo studies showed that both STAMP and EPV@STAMP effectively limited C. perfringens infections and then reduced inflammatory response. Notably, EPV@STAMP exhibited stronger protection against colonic infections than STAMPs alone. Moreover, 16S ribosomal RNA sequencing showed that both STAMPs and EPV@STAMP facilitated the recovery of disturbed gut microflora. Collectively, our work may accelerate the development of the discovery and delivery of precise antimicrobials.
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Affiliation(s)
- Bocheng Xu
- National Engineering Research Center for Green Feed and Healthy Breeding, Key Laboratory of Molecular Animal Nutrition, Ministry of Education, Key Laboratory of Animal Nutrition and Feed Science (Eastern of China), Ministry of Agriculture and Rural Affairs, Key Laboratory of Animal Feed and Nutrition of Zhejiang Province, Institute of Feed Science, Zhejiang University, Hangzhou 310058, China
| | - Weike Shaoyong
- National Engineering Research Center for Green Feed and Healthy Breeding, Key Laboratory of Molecular Animal Nutrition, Ministry of Education, Key Laboratory of Animal Nutrition and Feed Science (Eastern of China), Ministry of Agriculture and Rural Affairs, Key Laboratory of Animal Feed and Nutrition of Zhejiang Province, Institute of Feed Science, Zhejiang University, Hangzhou 310058, China
| | - Lin Wang
- National Engineering Research Center for Green Feed and Healthy Breeding, Key Laboratory of Molecular Animal Nutrition, Ministry of Education, Key Laboratory of Animal Nutrition and Feed Science (Eastern of China), Ministry of Agriculture and Rural Affairs, Key Laboratory of Animal Feed and Nutrition of Zhejiang Province, Institute of Feed Science, Zhejiang University, Hangzhou 310058, China
| | - Chen Yang
- Center for Drug Safety Evaluation and Research, Zhejiang Province Key Laboratory of Anti-Cancer Drug Research, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310007, China
| | - Tingjun Chen
- College of Animal Science, Zhejiang University; Hangzhou 310058, China
| | - Xiao Jiang
- National Engineering Research Center for Green Feed and Healthy Breeding, Key Laboratory of Molecular Animal Nutrition, Ministry of Education, Key Laboratory of Animal Nutrition and Feed Science (Eastern of China), Ministry of Agriculture and Rural Affairs, Key Laboratory of Animal Feed and Nutrition of Zhejiang Province, Institute of Feed Science, Zhejiang University, Hangzhou 310058, China
| | - Rong Yan
- National Engineering Research Center for Green Feed and Healthy Breeding, Key Laboratory of Molecular Animal Nutrition, Ministry of Education, Key Laboratory of Animal Nutrition and Feed Science (Eastern of China), Ministry of Agriculture and Rural Affairs, Key Laboratory of Animal Feed and Nutrition of Zhejiang Province, Institute of Feed Science, Zhejiang University, Hangzhou 310058, China
| | - Zipeng Jiang
- National Engineering Research Center for Green Feed and Healthy Breeding, Key Laboratory of Molecular Animal Nutrition, Ministry of Education, Key Laboratory of Animal Nutrition and Feed Science (Eastern of China), Ministry of Agriculture and Rural Affairs, Key Laboratory of Animal Feed and Nutrition of Zhejiang Province, Institute of Feed Science, Zhejiang University, Hangzhou 310058, China
| | - Pan Zhang
- College of Animal Science, Zhejiang University; Hangzhou 310058, China
| | - Mingliang Jin
- National Engineering Research Center for Green Feed and Healthy Breeding, Key Laboratory of Molecular Animal Nutrition, Ministry of Education, Key Laboratory of Animal Nutrition and Feed Science (Eastern of China), Ministry of Agriculture and Rural Affairs, Key Laboratory of Animal Feed and Nutrition of Zhejiang Province, Institute of Feed Science, Zhejiang University, Hangzhou 310058, China
| | - Yizhen Wang
- National Engineering Research Center for Green Feed and Healthy Breeding, Key Laboratory of Molecular Animal Nutrition, Ministry of Education, Key Laboratory of Animal Nutrition and Feed Science (Eastern of China), Ministry of Agriculture and Rural Affairs, Key Laboratory of Animal Feed and Nutrition of Zhejiang Province, Institute of Feed Science, Zhejiang University, Hangzhou 310058, China
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24
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Dewey JA, Delalande C, Azizi SA, Lu V, Antonopoulos D, Babnigg G. Molecular Glue Discovery: Current and Future Approaches. J Med Chem 2023; 66:9278-9296. [PMID: 37437222 PMCID: PMC10805529 DOI: 10.1021/acs.jmedchem.3c00449] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/14/2023]
Abstract
The intracellular interactions of biomolecules can be maneuvered to redirect signaling, reprogram the cell cycle, or decrease infectivity using only a few dozen atoms. Such "molecular glues," which can drive both novel and known interactions between protein partners, represent an enticing therapeutic strategy. Here, we review the methods and approaches that have led to the identification of small-molecule molecular glues. We first classify current FDA-approved molecular glues to facilitate the selection of discovery methods. We then survey two broad discovery method strategies, where we highlight the importance of factors such as experimental conditions, software packages, and genetic tools for success. We hope that this curation of methodologies for directed discovery will inspire diverse research efforts targeting a multitude of human diseases.
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Affiliation(s)
- Jeffrey A Dewey
- Biosciences Division, Argonne National Laboratory, Lemont, Illinois 60439, United States
| | - Clémence Delalande
- Department of Chemistry, University of Chicago, Chicago, Illinois 60637, United States
| | - Saara-Anne Azizi
- Pritzker School of Medicine, University of Chicago, Chicago, Illinois 60637, United States
| | - Vivian Lu
- Department of Chemistry, University of Chicago, Chicago, Illinois 60637, United States
| | - Dionysios Antonopoulos
- Biosciences Division, Argonne National Laboratory, Lemont, Illinois 60439, United States
| | - Gyorgy Babnigg
- Biosciences Division, Argonne National Laboratory, Lemont, Illinois 60439, United States
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25
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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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26
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Fetse J, Kandel S, Mamani UF, Cheng K. Recent advances in the development of therapeutic peptides. Trends Pharmacol Sci 2023; 44:425-441. [PMID: 37246037 PMCID: PMC10330351 DOI: 10.1016/j.tips.2023.04.003] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Revised: 04/14/2023] [Accepted: 04/18/2023] [Indexed: 05/30/2023]
Abstract
Peptides have unique characteristics that make them highly desirable as therapeutic agents. The physicochemical and proteolytic stability profiles determine the therapeutic potential of peptides. Multiple strategies to enhance the therapeutic profile of peptides have emerged. They include chemical modifications, such as cyclization, substitution with d-amino acids, peptoid formation, N-methylation, and side-chain halogenation, and incorporation in delivery systems. There have been recent advances in approaches to discover peptides having these modifications to attain desirable therapeutic properties. We critically review these recent advancements in therapeutic peptide development.
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Affiliation(s)
- John Fetse
- Division of Pharmacology and Pharmaceutical Sciences, School of Pharmacy, University of Missouri-Kansas City, 2464 Charlotte Street, Kansas City, MO 64108, USA
| | - Sashi Kandel
- Division of Pharmacology and Pharmaceutical Sciences, School of Pharmacy, University of Missouri-Kansas City, 2464 Charlotte Street, Kansas City, MO 64108, USA
| | - Umar-Farouk Mamani
- Division of Pharmacology and Pharmaceutical Sciences, School of Pharmacy, University of Missouri-Kansas City, 2464 Charlotte Street, Kansas City, MO 64108, USA
| | - Kun Cheng
- Division of Pharmacology and Pharmaceutical Sciences, School of Pharmacy, University of Missouri-Kansas City, 2464 Charlotte Street, Kansas City, MO 64108, USA.
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27
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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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28
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Tučs A, Berenger F, Yumoto A, Tamura R, Uzawa T, Tsuda K. Quantum Annealing Designs Nonhemolytic Antimicrobial Peptides in a Discrete Latent Space. ACS Med Chem Lett 2023; 14:577-582. [PMID: 37197452 PMCID: PMC10184305 DOI: 10.1021/acsmedchemlett.2c00487] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Accepted: 04/10/2023] [Indexed: 05/19/2023] Open
Abstract
Increasing the variety of antimicrobial peptides is crucial in meeting the global challenge of multi-drug-resistant bacterial pathogens. While several deep-learning-based peptide design pipelines are reported, they may not be optimal in data efficiency. High efficiency requires a well-compressed latent space, where optimization is likely to fail due to numerous local minima. We present a multi-objective peptide design pipeline based on a discrete latent space and D-Wave quantum annealer with the aim of solving the local minima problem. To achieve multi-objective optimization, multiple peptide properties are encoded into a score using non-dominated sorting. Our pipeline is applied to design therapeutic peptides that are antimicrobial and non-hemolytic at the same time. From 200 000 peptides designed by our pipeline, four peptides proceeded to wet-lab validation. Three of them showed high anti-microbial activity, and two are non-hemolytic. Our results demonstrate how quantum-based optimizers can be taken advantage of in real-world medical studies.
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Affiliation(s)
- Andrejs Tučs
- Graduate
School of Frontier Sciences, The University
of Tokyo, 5-1-5 Kashiwa-no-ha, Kashiwa, Chiba 277-8561, Japan
| | - Francois Berenger
- Graduate
School of Frontier Sciences, The University
of Tokyo, 5-1-5 Kashiwa-no-ha, Kashiwa, Chiba 277-8561, Japan
| | - Akiko Yumoto
- Emergent
Bioengineering Materials Research Team, RIKEN Center for Emergent Matter Science, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan
| | - Ryo Tamura
- Graduate
School of Frontier Sciences, The University
of Tokyo, 5-1-5 Kashiwa-no-ha, Kashiwa, Chiba 277-8561, Japan
- International
Center for Materials Nanoarchitectonics (WPI-MANA), National Institute for Materials Science (NIMS), Tsukuba 305−0044, Japan
- Research
and Services Division of Materials Data and Integrated System, National Institute for Materials Science (NIMS), Tsukuba 305-0044, Japan
- RIKEN
Center for Advanced Intelligence Project, RIKEN, 1-4-1 Nihombashi, Chuo-ku, Tokyo 103-0027, Japan
| | - Takanori Uzawa
- Emergent
Bioengineering Materials Research Team, RIKEN Center for Emergent Matter Science, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan
- Nano Medical
Engineering Laboratory, RIKEN Cluster for
Pioneering Research, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan
- E-mail:
| | - Koji Tsuda
- Graduate
School of Frontier Sciences, The University
of Tokyo, 5-1-5 Kashiwa-no-ha, Kashiwa, Chiba 277-8561, Japan
- Research
and Services Division of Materials Data and Integrated System, National Institute for Materials Science (NIMS), Tsukuba 305-0044, Japan
- RIKEN
Center for Advanced Intelligence Project, RIKEN, 1-4-1 Nihombashi, Chuo-ku, Tokyo 103-0027, Japan
- E-mail:
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29
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Sowers A, Wang G, Xing M, Li B. Advances in Antimicrobial Peptide Discovery via Machine Learning and Delivery via Nanotechnology. Microorganisms 2023; 11:1129. [PMID: 37317103 PMCID: PMC10223199 DOI: 10.3390/microorganisms11051129] [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: 03/01/2023] [Revised: 04/13/2023] [Accepted: 04/19/2023] [Indexed: 06/16/2023] Open
Abstract
Antimicrobial peptides (AMPs) have been investigated for their potential use as an alternative to antibiotics due to the increased demand for new antimicrobial agents. AMPs, widely found in nature and obtained from microorganisms, have a broad range of antimicrobial protection, allowing them to be applied in the treatment of infections caused by various pathogenic microorganisms. Since these peptides are primarily cationic, they prefer anionic bacterial membranes due to electrostatic interactions. However, the applications of AMPs are currently limited owing to their hemolytic activity, poor bioavailability, degradation from proteolytic enzymes, and high-cost production. To overcome these limitations, nanotechnology has been used to improve AMP bioavailability, permeation across barriers, and/or protection against degradation. In addition, machine learning has been investigated due to its time-saving and cost-effective algorithms to predict AMPs. There are numerous databases available to train machine learning models. In this review, we focus on nanotechnology approaches for AMP delivery and advances in AMP design via machine learning. The AMP sources, classification, structures, antimicrobial mechanisms, their role in diseases, peptide engineering technologies, currently available databases, and machine learning techniques used to predict AMPs with minimal toxicity are discussed in detail.
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Affiliation(s)
- Alexa Sowers
- Department of Orthopaedics, School of Medicine, West Virginia University, Morgantown, WV 26506, USA
- School of Pharmacy, West Virginia University, Morgantown, WV 26506, USA
| | - Guangshun Wang
- Department of Pathology and Microbiology, College of Medicine, University of Nebraska Medical Center, 985900 Nebraska Medical Center, Omaha, NE 68198, USA
| | - Malcolm Xing
- Department of Mechanical Engineering, University of Manitoba, Winnipeg, MB R3T 2N2, Canada
| | - Bingyun Li
- Department of Orthopaedics, School of Medicine, West Virginia University, Morgantown, WV 26506, USA
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30
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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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31
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Kumar R, Yadav G, Kuddus M, Ashraf GM, Singh R. Unlocking the microbial studies through computational approaches: how far have we reached? ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:48929-48947. [PMID: 36920617 PMCID: PMC10016191 DOI: 10.1007/s11356-023-26220-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Accepted: 02/24/2023] [Indexed: 04/16/2023]
Abstract
The metagenomics approach accelerated the study of genetic information from uncultured microbes and complex microbial communities. In silico research also facilitated an understanding of protein-DNA interactions, protein-protein interactions, docking between proteins and phyto/biochemicals for drug design, and modeling of the 3D structure of proteins. These in silico approaches provided insight into analyzing pathogenic and nonpathogenic strains that helped in the identification of probable genes for vaccines and antimicrobial agents and comparing whole-genome sequences to microbial evolution. Artificial intelligence, more precisely machine learning (ML) and deep learning (DL), has proven to be a promising approach in the field of microbiology to handle, analyze, and utilize large data that are generated through nucleic acid sequencing and proteomics. This enabled the understanding of the functional and taxonomic diversity of microorganisms. ML and DL have been used in the prediction and forecasting of diseases and applied to trace environmental contaminants and environmental quality. This review presents an in-depth analysis of the recent application of silico approaches in microbial genomics, proteomics, functional diversity, vaccine development, and drug design.
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Affiliation(s)
- Rajnish Kumar
- Amity Institute of Biotechnology, Amity University Uttar Pradesh Lucknow Campus, Lucknow, Uttar Pradesh, India
- Department of Veterinary Medicine and Surgery, College of Veterinary Medicine, University of Missouri, Columbia, MO, USA
| | - Garima Yadav
- Amity Institute of Biotechnology, Amity University Uttar Pradesh Lucknow Campus, Lucknow, Uttar Pradesh, India
| | - Mohammed Kuddus
- Department of Biochemistry, College of Medicine, University of Hail, Hail, Saudi Arabia
| | - Ghulam Md Ashraf
- Department of Medical Laboratory Sciences, College of Health Sciences, and Sharjah Institute for Medical Research, University of Sharjah, Sharjah , 27272, United Arab Emirates
| | - Rachana Singh
- Amity Institute of Biotechnology, Amity University Uttar Pradesh Lucknow Campus, Lucknow, Uttar Pradesh, India.
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32
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Szymczak P, Możejko M, Grzegorzek T, Jurczak R, Bauer M, Neubauer D, Sikora K, Michalski M, Sroka J, Setny P, Kamysz W, Szczurek E. Discovering highly potent antimicrobial peptides with deep generative model HydrAMP. Nat Commun 2023; 14:1453. [PMID: 36922490 PMCID: PMC10017685 DOI: 10.1038/s41467-023-36994-z] [Citation(s) in RCA: 20] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Accepted: 02/28/2023] [Indexed: 03/17/2023] Open
Abstract
Antimicrobial peptides emerge as compounds that can alleviate the global health hazard of antimicrobial resistance, prompting a need for novel computational approaches to peptide generation. Here, we propose HydrAMP, a conditional variational autoencoder that learns lower-dimensional, continuous representation of peptides and captures their antimicrobial properties. The model disentangles the learnt representation of a peptide from its antimicrobial conditions and leverages parameter-controlled creativity. HydrAMP is the first model that is directly optimized for diverse tasks, including unconstrained and analogue generation and outperforms other approaches in these tasks. An additional preselection procedure based on ranking of generated peptides and molecular dynamics simulations increases experimental validation rate. Wet-lab experiments on five bacterial strains confirm high activity of nine peptides generated as analogues of clinically relevant prototypes, as well as six analogues of an inactive peptide. HydrAMP enables generation of diverse and potent peptides, making a step towards resolving the antimicrobial resistance crisis.
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Affiliation(s)
- Paulina Szymczak
- Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, Stefana Banacha 2, 02-097, Warsaw, Poland
| | - Marcin Możejko
- Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, Stefana Banacha 2, 02-097, Warsaw, Poland
| | - Tomasz Grzegorzek
- Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, Stefana Banacha 2, 02-097, Warsaw, Poland
- NVIDIA, 2788 San Tomas Expressway, Santa Clara, CA, 95051, USA
| | - Radosław Jurczak
- Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, Stefana Banacha 2, 02-097, Warsaw, Poland
| | - Marta Bauer
- Department of Inorganic Chemistry, Faculty of Pharmacy, Medical University of Gdańsk, Al. Gen. J. Hallera 107, 80-416, Gdańsk, Poland
| | - Damian Neubauer
- Department of Inorganic Chemistry, Faculty of Pharmacy, Medical University of Gdańsk, Al. Gen. J. Hallera 107, 80-416, Gdańsk, Poland
| | - Karol Sikora
- Department of Inorganic Chemistry, Faculty of Pharmacy, Medical University of Gdańsk, Al. Gen. J. Hallera 107, 80-416, Gdańsk, Poland
| | - Michał Michalski
- The Centre of New Technologies, University of Warsaw, Stefana Banacha 2c, 02-097, Warsaw, Poland
| | - Jacek Sroka
- Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, Stefana Banacha 2, 02-097, Warsaw, Poland
| | - Piotr Setny
- The Centre of New Technologies, University of Warsaw, Stefana Banacha 2c, 02-097, Warsaw, Poland
| | - Wojciech Kamysz
- Department of Inorganic Chemistry, Faculty of Pharmacy, Medical University of Gdańsk, Al. Gen. J. Hallera 107, 80-416, Gdańsk, Poland
| | - Ewa Szczurek
- Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, Stefana Banacha 2, 02-097, Warsaw, Poland.
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Zhang K, Teng D, Mao R, Yang N, Hao Y, Wang J. Thinking on the Construction of Antimicrobial Peptide Databases: Powerful Tools for the Molecular Design and Screening. Int J Mol Sci 2023; 24:ijms24043134. [PMID: 36834553 PMCID: PMC9960615 DOI: 10.3390/ijms24043134] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2022] [Revised: 01/29/2023] [Accepted: 02/02/2023] [Indexed: 02/08/2023] Open
Abstract
With the accelerating growth of antimicrobial resistance (AMR), there is an urgent need for new antimicrobial agents with low or no AMR. Antimicrobial peptides (AMPs) have been extensively studied as alternatives to antibiotics (ATAs). Coupled with the new generation of high-throughput technology for AMP mining, the number of derivatives has increased dramatically, but manual running is time-consuming and laborious. Therefore, it is necessary to establish databases that combine computer algorithms to summarize, analyze, and design new AMPs. A number of AMP databases have already been established, such as the Antimicrobial Peptides Database (APD), the Collection of Antimicrobial Peptides (CAMP), the Database of Antimicrobial Activity and Structure of Peptides (DBAASP), and the Database of Antimicrobial Peptides (dbAMPs). These four AMP databases are comprehensive and are widely used. This review aims to cover the construction, evolution, characteristic function, prediction, and design of these four AMP databases. It also offers ideas for the improvement and application of these databases based on merging the various advantages of these four peptide libraries. This review promotes research and development into new AMPs and lays their foundation in the fields of druggability and clinical precision treatment.
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Affiliation(s)
- Kun Zhang
- Gene Engineering Laboratory, Feed Research Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China
- Innovative Team of Antimicrobial Peptides and Alternatives to Antibiotics, Feed Research Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China
- Key Laboratory of Feed Biotechnology, Ministry of Agriculture and Rural Affairs, Beijing 100081, China
| | - Da Teng
- Gene Engineering Laboratory, Feed Research Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China
- Innovative Team of Antimicrobial Peptides and Alternatives to Antibiotics, Feed Research Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China
- Key Laboratory of Feed Biotechnology, Ministry of Agriculture and Rural Affairs, Beijing 100081, China
| | - Ruoyu Mao
- Gene Engineering Laboratory, Feed Research Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China
- Innovative Team of Antimicrobial Peptides and Alternatives to Antibiotics, Feed Research Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China
- Key Laboratory of Feed Biotechnology, Ministry of Agriculture and Rural Affairs, Beijing 100081, China
| | - Na Yang
- Gene Engineering Laboratory, Feed Research Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China
- Innovative Team of Antimicrobial Peptides and Alternatives to Antibiotics, Feed Research Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China
- Key Laboratory of Feed Biotechnology, Ministry of Agriculture and Rural Affairs, Beijing 100081, China
| | - Ya Hao
- Gene Engineering Laboratory, Feed Research Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China
- Innovative Team of Antimicrobial Peptides and Alternatives to Antibiotics, Feed Research Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China
- Key Laboratory of Feed Biotechnology, Ministry of Agriculture and Rural Affairs, Beijing 100081, China
| | - Jianhua Wang
- Gene Engineering Laboratory, Feed Research Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China
- Innovative Team of Antimicrobial Peptides and Alternatives to Antibiotics, Feed Research Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China
- Key Laboratory of Feed Biotechnology, Ministry of Agriculture and Rural Affairs, Beijing 100081, China
- Correspondence: ; Tel.: +86-10-82106081 or +86-10-82106079; Fax: +86-10-82106079
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Luo X, Chen H, Song Y, Qin Z, Xu L, He N, Tan Y, Dessie W. Advancements, challenges and future perspectives on peptide-based drugs: Focus on antimicrobial peptides. Eur J Pharm Sci 2023; 181:106363. [PMID: 36529161 DOI: 10.1016/j.ejps.2022.106363] [Citation(s) in RCA: 19] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Revised: 12/13/2022] [Accepted: 12/15/2022] [Indexed: 12/23/2022]
Abstract
Among other health related issues, the rising concerns on drug resistance led to look for alternative pharmaceutical drugs that are effective both against infectious and noninfectious diseases. Antimicrobial peptides (AMPs) emerged as potential therapeutic molecule with wide range of applications. With their limitations, AMPs have gained reputable attentions in research as well as in the pharmaceutical industry. This review highlighted the historical background, research trends, technological advancements, challenges, and future perspectives in the development and applications of peptide drugs. Some vital questions related with the need for pharmaceutical production, factors for the slow and steady journey, the importance of oral bioavailability, and the drug resistance possibilities of AMPs were raised and addressed accordingly. Therefore, the current study is believed to provide a profound understanding in the past and current scenarios and future directions on the therapeutic impacts of peptide drugs.
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Affiliation(s)
- Xiaofang Luo
- Hunan Key Laboratory of Biomedical Nanomaterials and Devices, Hunan University of Technology, Zhuzhou 412007, China; Hunan Engineering Technology Research Center for Comprehensive Development and Utilization of Biomass Resources, College of Chemistry and Bioengineering, Hunan University of Science and Engineering, 425199 Yongzhou, China
| | - Huifang Chen
- Hunan Key Laboratory of Biomedical Nanomaterials and Devices, Hunan University of Technology, Zhuzhou 412007, China; Hunan Engineering Technology Research Center for Comprehensive Development and Utilization of Biomass Resources, College of Chemistry and Bioengineering, Hunan University of Science and Engineering, 425199 Yongzhou, China
| | - Yannan Song
- Hunan Key Laboratory of Biomedical Nanomaterials and Devices, Hunan University of Technology, Zhuzhou 412007, China; Hunan Engineering Technology Research Center for Comprehensive Development and Utilization of Biomass Resources, College of Chemistry and Bioengineering, Hunan University of Science and Engineering, 425199 Yongzhou, China
| | - Zuodong Qin
- Hunan Key Laboratory of Biomedical Nanomaterials and Devices, Hunan University of Technology, Zhuzhou 412007, China; Hunan Engineering Technology Research Center for Comprehensive Development and Utilization of Biomass Resources, College of Chemistry and Bioengineering, Hunan University of Science and Engineering, 425199 Yongzhou, China
| | - Lijian Xu
- Hunan Key Laboratory of Biomedical Nanomaterials and Devices, Hunan University of Technology, Zhuzhou 412007, China
| | - Nongyue He
- Hunan Key Laboratory of Biomedical Nanomaterials and Devices, Hunan University of Technology, Zhuzhou 412007, China
| | - Yimin Tan
- Hunan Key Laboratory of Biomedical Nanomaterials and Devices, Hunan University of Technology, Zhuzhou 412007, China.
| | - Wubliker Dessie
- Hunan Engineering Technology Research Center for Comprehensive Development and Utilization of Biomass Resources, College of Chemistry and Bioengineering, Hunan University of Science and Engineering, 425199 Yongzhou, China.
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Specific Focus on Antifungal Peptides against Azole Resistant Aspergillus fumigatus: Current Status, Challenges, and Future Perspectives. J Fungi (Basel) 2022; 9:jof9010042. [PMID: 36675863 PMCID: PMC9864941 DOI: 10.3390/jof9010042] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Revised: 12/25/2022] [Accepted: 12/26/2022] [Indexed: 12/29/2022] Open
Abstract
The prevalence of fungal infections is increasing worldwide, especially that of aspergillosis, which previously only affected people with immunosuppression. Aspergillus fumigatus can cause allergic bronchopulmonary aspergillosis and endangers public health due to resistance to azole-type antimycotics such as fluconazole. Antifungal peptides are viable alternatives that combat infection by forming pores in membranes through electrostatic interactions with the phospholipids as well as cell death to peptides that inhibit protein synthesis and inhibit cell replication. Engineering antifungal peptides with nanotechnology can enhance the efficacy of these therapeutics at lower doses and reduce immune responses. This manuscript explains how antifungal peptides combat antifungal-resistant aspergillosis and also how rational peptide design with nanotechnology and artificial intelligence can engineer peptides to be a feasible antifungal alternative.
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Cheng Q, Zeng P, Chi Chan EW, Chen S. Development of Peptide-based Metallo-β-lactamase Inhibitors as a New Strategy to Combat Antimicrobial Resistance: A Mini-review. Curr Pharm Des 2022; 28:3538-3545. [PMID: 36177630 DOI: 10.2174/1381612828666220929154255] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Revised: 08/10/2022] [Accepted: 08/22/2022] [Indexed: 01/28/2023]
Abstract
Global dissemination of antimicrobial resistance (AMR) not only poses a significant threat to human health, food security, and social development but also results in millions of deaths each year. In Gram-negative bacteria, the primary mechanism of resistance to β-lactam antibiotics is the production of β-lactamases, one of which is carbapenem-hydrolyzing β-lactamases known as carbapenemases. As a general scheme, these enzymes are divided into Ambler class A, B, C, and D based on their protein sequence homology. Class B β-lactamases are also known as metallo-β-lactamases (MBLs). The incidence of recovery of bacteria expressing metallo-β- lactamases (MBLs) has increased dramatically in recent years, almost reaching a pandemic proportion. MBLs can be further divided into three subclasses (B1, B2, and B3) based on the homology of protein sequences as well as the differences in zinc coordination. The development of inhibitors is one effective strategy to suppress the activities of MBLs and restore the activity of β-lactam antibiotics. Although thousands of MBL inhibitors have been reported, none have been approved for clinical use. This review describes the clinical application potential of peptide-based drugs that exhibit inhibitory activity against MBLs identified in past decades. In this report, peptide-based inhibitors of MBLs are divided into several groups based on the mode of action, highlighting compounds of promising properties that are suitable for further advancement. We discuss how traditional computational tools, such as in silico screening and molecular docking, along with new methods, such as deep learning and machine learning, enable a more accurate and efficient design of peptide-based inhibitors of MBLs.
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Affiliation(s)
- Qipeng Cheng
- Anhui Provincial Key Laboratory of Molecular Enzymology and Mechanism of Major Diseases and Key Laboratory of Biomedicine in Gene Diseases and Health of Anhui Higher Education Institutes, College of Life Sciences, Anhui Normal University, Wuhu, Anhui, China
| | - Ping Zeng
- School of Pharmacy, The Chinese University of Hong Kong, Shatin, Hong Kong
| | - Edward Wai Chi Chan
- Department of Infectious Diseases and Public Health, Jockey Club College of Veterinary Medicine and Life Sciences, City University of Hong Kong, Kowloon, Hong Kong
| | - Sheng Chen
- Department of Infectious Diseases and Public Health, Jockey Club College of Veterinary Medicine and Life Sciences, City University of Hong Kong, Kowloon, Hong Kong
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Salem M, Keshavarzi Arshadi A, Yuan JS. AMPDeep: hemolytic activity prediction of antimicrobial peptides using transfer learning. BMC Bioinformatics 2022; 23:389. [PMID: 36163001 PMCID: PMC9511757 DOI: 10.1186/s12859-022-04952-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Accepted: 09/20/2022] [Indexed: 12/02/2022] Open
Abstract
Background Deep learning’s automatic feature extraction has proven to give superior performance in many sequence classification tasks. However, deep learning models generally require a massive amount of data to train, which in the case of Hemolytic Activity Prediction of Antimicrobial Peptides creates a challenge due to the small amount of available data. Results Three different datasets for hemolysis activity prediction of therapeutic and antimicrobial peptides are gathered and the AMPDeep pipeline is implemented for each. The result demonstrate that AMPDeep outperforms the previous works on all three datasets, including works that use physicochemical features to represent the peptides or those who solely rely on the sequence and use deep learning to learn representation for the peptides. Moreover, a combined dataset is introduced for hemolytic activity prediction to address the problem of sequence similarity in this domain. AMPDeep fine-tunes a large transformer based model on a small amount of peptides and successfully leverages the patterns learned from other protein and peptide databases to assist hemolysis activity prediction modeling. Conclusions In this work transfer learning is leveraged to overcome the challenge of small data and a deep learning based model is successfully adopted for hemolysis activity classification of antimicrobial peptides. This model is first initialized as a protein language model which is pre-trained on masked amino acid prediction on many unlabeled protein sequences in a self-supervised manner. Having done so, the model is fine-tuned on an aggregated dataset of labeled peptides in a supervised manner to predict secretion. Through transfer learning, hyper-parameter optimization and selective fine-tuning, AMPDeep is able to achieve state-of-the-art performance on three hemolysis datasets using only the sequence of the peptides. This work assists the adoption of large sequence-based models for peptide classification and modeling tasks in a practical manner.
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Affiliation(s)
- Milad Salem
- Electrical and Computer Engineering Department, University of Central Florida, Orlando, FL, USA.
| | | | - Jiann Shiun Yuan
- Electrical and Computer Engineering Department, University of Central Florida, Orlando, FL, USA
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Zakharova E, Orsi M, Capecchi A, Reymond JL. Machine Learning Guided Discovery of Non-Hemolytic Membrane Disruptive Anti-Cancer Peptides. ChemMedChem 2022; 17:e202200291. [PMID: 35880810 PMCID: PMC9541320 DOI: 10.1002/cmdc.202200291] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Revised: 06/29/2022] [Indexed: 12/05/2022]
Abstract
Most antimicrobial peptides (AMPs) and anticancer peptides (ACPs) fold into membrane disruptive cationic amphiphilic α‐helices, many of which are however also unpredictably hemolytic and toxic. Here we exploited the ability of recurrent neural networks (RNN) to distinguish active from inactive and non‐hemolytic from hemolytic AMPs and ACPs to discover new non‐hemolytic ACPs. Our discovery pipeline involved: 1) sequence generation using either a generative RNN or a genetic algorithm, 2) RNN classification for activity and hemolysis, 3) selection for sequence novelty, helicity and amphiphilicity, and 4) synthesis and testing. Experimental evaluation of thirty‐three peptides resulted in eleven active ACPs, four of which were non‐hemolytic, with properties resembling those of the natural ACP lasioglossin III. These experiments show the first example of direct machine learning guided discovery of non‐hemolytic ACPs.
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Affiliation(s)
- Elena Zakharova
- University of Bern: Universitat Bern, Departement of Chemistry, Biochemistry and Pharmaceutical Sciences, SWITZERLAND
| | - Markus Orsi
- University of Bern: Universitat Bern, Departement of Chemistry, Biochemistry and Pharmaceutical Sciences, SWITZERLAND
| | - Alice Capecchi
- University of Bern: Universitat Bern, Departement of Chemistry, Biochemistry and Pharmaceutical Sciences, SWITZERLAND
| | - Jean-Louis Reymond
- Universität Bern: Universitat Bern, Department of Chemistry and Biochemistry, Department of Chemistry and Biochemistry, Freiestrasse 3, 3012, Switzerland, 3012, Bern, SWITZERLAND
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Wan F, Kontogiorgos-Heintz D, de la Fuente-Nunez C. Deep generative models for peptide design. DIGITAL DISCOVERY 2022; 1:195-208. [PMID: 35769205 PMCID: PMC9189861 DOI: 10.1039/d1dd00024a] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Accepted: 03/19/2022] [Indexed: 12/13/2022]
Abstract
Computers can already be programmed for superhuman pattern recognition of images and text. For machines to discover novel molecules, they must first be trained to sort through the many characteristics of molecules and determine which properties should be retained, suppressed, or enhanced to optimize functions of interest. Machines need to be able to understand, read, write, and eventually create new molecules. Today, this creative process relies on deep generative models, which have gained popularity since powerful deep neural networks were introduced to generative model frameworks. In recent years, they have demonstrated excellent ability to model complex distribution of real-word data (e.g., images, audio, text, molecules, and biological sequences). Deep generative models can generate data beyond those provided in training samples, thus yielding an efficient and rapid tool for exploring the massive search space of high-dimensional data such as DNA/protein sequences and facilitating the design of biomolecules with desired functions. Here, we review the emerging field of deep generative models applied to peptide science. In particular, we discuss several popular deep generative model frameworks as well as their applications to generate peptides with various kinds of properties (e.g., antimicrobial, anticancer, cell penetration, etc). We conclude our review with a discussion of current limitations and future perspectives in this emerging field.
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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 Pennsylvania USA
- Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania Philadelphia Pennsylvania USA
- Penn Institute for Computational Science, University of Pennsylvania Philadelphia Pennsylvania USA
| | - Daphne Kontogiorgos-Heintz
- 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 USA
- Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania Philadelphia Pennsylvania USA
- Penn Institute for Computational Science, University of Pennsylvania Philadelphia Pennsylvania USA
- Department of Computer and Information Science, School of Engineering and Applied Science, University of Pennsylvania Philadelphia Pennsylvania 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 Pennsylvania USA
- Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania Philadelphia Pennsylvania USA
- Penn Institute for Computational Science, University of Pennsylvania Philadelphia Pennsylvania USA
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40
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Jiang Y, Luo J, Huang D, Liu Y, Li DD. Machine Learning Advances in Microbiology: A Review of Methods and Applications. Front Microbiol 2022; 13:925454. [PMID: 35711777 PMCID: PMC9196628 DOI: 10.3389/fmicb.2022.925454] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Accepted: 05/09/2022] [Indexed: 12/18/2022] Open
Abstract
Microorganisms play an important role in natural material and elemental cycles. Many common and general biology research techniques rely on microorganisms. Machine learning has been gradually integrated with multiple fields of study. Machine learning, including deep learning, aims to use mathematical insights to optimize variational functions to aid microbiology using various types of available data to help humans organize and apply collective knowledge of various research objects in a systematic and scaled manner. Classification and prediction have become the main achievements in the development of microbial community research in the direction of computational biology. This review summarizes the application and development of machine learning and deep learning in the field of microbiology and shows and compares the advantages and disadvantages of different algorithm tools in four fields: microbiome and taxonomy, microbial ecology, pathogen and epidemiology, and drug discovery.
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Gupta S, Azadvari N, Hosseinzadeh P. Design of Protein Segments and Peptides for Binding to Protein Targets. BIODESIGN RESEARCH 2022; 2022:9783197. [PMID: 37850124 PMCID: PMC10521657 DOI: 10.34133/2022/9783197] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2021] [Accepted: 03/16/2022] [Indexed: 10/19/2023] Open
Abstract
Recent years have witnessed a rise in methods for accurate prediction of structure and design of novel functional proteins. Design of functional protein fragments and peptides occupy a small, albeit unique, space within the general field of protein design. While the smaller size of these peptides allows for more exhaustive computational methods, flexibility in their structure and sparsity of data compared to proteins, as well as presence of noncanonical building blocks, add additional challenges to their design. This review summarizes the current advances in the design of protein fragments and peptides for binding to targets and discusses the challenges in the field, with an eye toward future directions.
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Affiliation(s)
- Suchetana Gupta
- Knight Campus Center for Accelerating Scientific Impact, University of Oregon, Eugene OR 97403, USA
| | - Noora Azadvari
- Knight Campus Center for Accelerating Scientific Impact, University of Oregon, Eugene OR 97403, USA
| | - Parisa Hosseinzadeh
- Knight Campus Center for Accelerating Scientific Impact, University of Oregon, Eugene OR 97403, USA
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Prediction of Linear Cationic Antimicrobial Peptides Active against Gram-Negative and Gram-Positive Bacteria Based on Machine Learning Models. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12073631] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Antimicrobial peptides (AMPs) are considered as promising alternatives to conventional antibiotics in order to overcome the growing problems of antibiotic resistance. Computational prediction approaches receive an increasing interest to identify and design the best candidate AMPs prior to the in vitro tests. In this study, we focused on the linear cationic peptides with non-hemolytic activity, which are downloaded from the Database of Antimicrobial Activity and Structure of Peptides (DBAASP). Referring to the MIC (Minimum inhibition concentration) values, we have assigned a positive label to a peptide if it shows antimicrobial activity; otherwise, the peptide is labeled as negative. Here, we focused on the peptides showing antimicrobial activity against Gram-negative and against Gram-positive bacteria separately, and we created two datasets accordingly. Ten different physico-chemical properties of the peptides are calculated and used as features in our study. Following data exploration and data preprocessing steps, a variety of classification algorithms are used with 100-fold Monte Carlo Cross-Validation to build models and to predict the antimicrobial activity of the peptides. Among the generated models, Random Forest has resulted in the best performance metrics for both Gram-negative dataset (Accuracy: 0.98, Recall: 0.99, Specificity: 0.97, Precision: 0.97, AUC: 0.99, F1: 0.98) and Gram-positive dataset (Accuracy: 0.95, Recall: 0.95, Specificity: 0.95, Precision: 0.90, AUC: 0.97, F1: 0.92) after outlier elimination is applied. This prediction approach might be useful to evaluate the antibacterial potential of a candidate peptide sequence before moving to the experimental studies.
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Traditional and Computational Screening of Non-Toxic Peptides and Approaches to Improving Selectivity. Pharmaceuticals (Basel) 2022; 15:ph15030323. [PMID: 35337121 PMCID: PMC8953747 DOI: 10.3390/ph15030323] [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: 01/26/2022] [Revised: 03/01/2022] [Accepted: 03/04/2022] [Indexed: 12/27/2022] Open
Abstract
Peptides have positively impacted the pharmaceutical industry as drugs, biomarkers, or diagnostic tools of high therapeutic value. However, only a handful have progressed to the market. Toxicity is one of the main obstacles to translating peptides into clinics. Hemolysis or hemotoxicity, the principal source of toxicity, is a natural or disease-induced event leading to the death of vital red blood cells. Initial screenings for toxicity have been widely evaluated using erythrocytes as the gold standard. More recently, many online databases filled with peptide sequences and their biological meta-data have paved the way toward hemolysis prediction using user-friendly, fast-access machine learning-driven programs. This review details the growing contributions of in silico approaches developed in the last decade for the large-scale prediction of erythrocyte lysis induced by peptides. After an overview of the pharmaceutical landscape of peptide therapeutics, we highlighted the relevance of early hemolysis studies in drug development. We emphasized the computational models and algorithms used to this end in light of historical and recent findings in this promising field. We benchmarked seven predictors using peptides from different data sets, having 7–35 amino acids in length. According to our predictions, the models have scored an accuracy over 50.42% and a minimal Matthew’s correlation coefficient over 0.11. The maximum values for these statistical parameters achieved 100.0% and 1.00, respectively. Finally, strategies for optimizing peptide selectivity were described, as well as prospects for future investigations. The development of in silico predictive approaches to peptide toxicity has just started, but their important contributions clearly demonstrate their potential for peptide science and computer-aided drug design. Methodology refinement and increasing use will motivate the timely and accurate in silico identification of selective, non-toxic peptide therapeutics.
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Qi L, Gao X, Pan D, Sun Y, Cai Z, Xiong Y, Dang Y. Research progress in the screening and evaluation of umami peptides. Compr Rev Food Sci Food Saf 2022; 21:1462-1490. [PMID: 35201672 DOI: 10.1111/1541-4337.12916] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Revised: 12/22/2021] [Accepted: 01/03/2022] [Indexed: 12/22/2022]
Abstract
Umami is an important element affecting food taste, and the development of umami peptides is a topic of interest in food-flavoring research. The existing technology used for traditional screening of umami peptides is time-consuming and labor-intensive, making it difficult to meet the requirements of high-throughput screening, which limits the rapid development of umami peptides. The difficulty in performing a standard measurement of umami intensity is another problem that restricts the development of umami peptides. The existing methods are not sensitive and specific, making it difficult to achieve a standard evaluation of umami taste. This review summarizes the umami receptors and umami peptides, focusing on the problems restricting the development of umami peptides, high-throughput screening, and establishment of evaluation standards. The rapid screening of umami peptides was realized based on molecular docking technology and a machine learning method, and the standard evaluation of umami could be realized with a bionic taste sensor. The progress of rapid screening and evaluation methods significantly promotes the study of umami peptides and increases its application in the seasoning industry.
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Affiliation(s)
- Lulu Qi
- State Key Laboratory for Managing Biotic and Chemical Threats to the Quality and Safety of AgroProducts, College of Food and Pharmaceutical Sciences, Ningbo University, Ningbo, Zhejiang, China
| | - Xinchang Gao
- Department of Chemistry, Tsinghua University, Beijing, China
| | - Daodong Pan
- State Key Laboratory for Managing Biotic and Chemical Threats to the Quality and Safety of AgroProducts, College of Food and Pharmaceutical Sciences, Ningbo University, Ningbo, Zhejiang, China.,National R&D Center for Freshwater Fish Processing, Jiangxi Normal University, Nanchang, Jiangxi, China
| | - Yangying Sun
- State Key Laboratory for Managing Biotic and Chemical Threats to the Quality and Safety of AgroProducts, College of Food and Pharmaceutical Sciences, Ningbo University, Ningbo, Zhejiang, China
| | - Zhendong Cai
- State Key Laboratory for Managing Biotic and Chemical Threats to the Quality and Safety of AgroProducts, College of Food and Pharmaceutical Sciences, Ningbo University, Ningbo, Zhejiang, China
| | - Yongzhao Xiong
- State Key Laboratory for Managing Biotic and Chemical Threats to the Quality and Safety of AgroProducts, College of Food and Pharmaceutical Sciences, Ningbo University, Ningbo, Zhejiang, China
| | - Yali Dang
- State Key Laboratory for Managing Biotic and Chemical Threats to the Quality and Safety of AgroProducts, College of Food and Pharmaceutical Sciences, Ningbo University, Ningbo, Zhejiang, China
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Zhang J, Yang L, Tian Z, Zhao W, Sun C, Zhu L, Huang M, Guo G, Liang G. Large-Scale Screening of Antifungal Peptides Based on Quantitative Structure-Activity Relationship. ACS Med Chem Lett 2021; 13:99-104. [PMID: 35059128 PMCID: PMC8762751 DOI: 10.1021/acsmedchemlett.1c00556] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2021] [Accepted: 12/06/2021] [Indexed: 01/16/2023] Open
Abstract
Antifungal peptides are effective, biocompatible, and biodegradable, and thus, they are promising to be the next generation of drugs for treating infections caused by fungi. The identification processes of highly active peptides, however, are still time-consuming and labor-intensive. Quantitative structure-activity relationships (QSARs) have dramatically facilitated the discovery of many bioactive drug molecules without a priori knowledge. In this study, we have established an effective QSAR protocol for screening antifungal peptides. The screening protocol integrates an accurate antifungal peptide classification model and four activity prediction models against specified target fungi. A demonstrative application was performed on more than three million candidate peptides, and three outstanding peptides were identified. The whole screening took only a few days, which was much faster than our previous experimental screening works. In conclusion, the protocol is useful and effective for reducing repetitive laboratory efforts in antifungal peptide discovery. The prediction server (antifungal Web server) is freely available at www.chemoinfolab.com/antifungal.
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Affiliation(s)
- Jin Zhang
- School
of Public Health, Guizhou Medical University, Guiyang 550025, China
| | - Longbing Yang
- School
of Basic Medical Sciences, Guizhou Medical
University, Guiyang 550025, China
| | - Zhuqing Tian
- School
of Basic Medical Sciences, Guizhou Medical
University, Guiyang 550025, China
| | - Wenjing Zhao
- School
of Basic Medical Sciences, Guizhou Medical
University, Guiyang 550025, China
| | - Chaoqin Sun
- School
of Basic Medical Sciences, Guizhou Medical
University, Guiyang 550025, China
| | - Lijuan Zhu
- School
of Basic Medical Sciences, Guizhou Medical
University, Guiyang 550025, China
| | - Mingjiao Huang
- School
of Basic Medical Sciences, Guizhou Medical
University, Guiyang 550025, China
| | - Guo Guo
- School
of Basic Medical Sciences, Guizhou Medical
University, Guiyang 550025, China,The
Key and Characteristic Laboratory of Modern Pathogen Biology, Guizhou Medical University, Guiyang 550025, China,Translational
Medicine Research Center, Guizhou Medical
University, Guiyang 550025, China,Guo
Guo: School of Basic Medical
Sciences, The Key and Characteristic Laboratory of Modern Pathogen
Biology, Translational Medicine Research Center, Guizhou Medical University,
Guiyang 550025, China.
| | - Guiyou Liang
- Translational
Medicine Research Center, Guizhou Medical
University, Guiyang 550025, China,Guiyou Liang
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Nasiri F, Atanaki FF, Behrouzi S, Kavousi K, Bagheri M. CpACpP: In Silico Cell-Penetrating Anticancer Peptide Prediction Using a Novel Bioinformatics Framework. ACS OMEGA 2021; 6:19846-19859. [PMID: 34368571 PMCID: PMC8340416 DOI: 10.1021/acsomega.1c02569] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Accepted: 07/13/2021] [Indexed: 05/12/2023]
Abstract
Cell-penetrating anticancer peptides (Cp-ACPs) are considered promising candidates in solid tumor and hematologic cancer therapies. Current approaches for the design and discovery of Cp-ACPs trust the expensive high-throughput screenings that often give rise to multiple obstacles, including instrumentation adaptation and experimental handling. The application of machine learning (ML) tools developed for peptide activity prediction is importantly of growing interest. In this study, we applied the random forest (RF)-, support vector machine (SVM)-, and eXtreme gradient boosting (XGBoost)-based algorithms to predict the active Cp-ACPs using an experimentally validated data set. The model, CpACpP, was developed on the basis of two independent cell-penetrating peptide (CPP) and anticancer peptide (ACP) subpredictors. Various compositional and physiochemical-based features were combined or selected using the multilayered recursive feature elimination (RFE) method for both data sets. Our results showed that the ACP subclassifiers obtain a mean performance accuracy (ACC) of 0.98 with an area under curve (AUC) ≈ 0.98 vis-à-vis the CPP predictors displaying relevant values of ∼0.94 and ∼0.95 via the hybrid-based features and independent data sets, respectively. Also, the predicting evaluation of Cp-ACPs gave accuracies of ∼0.79 and 0.89 on a series of independent sequences by applying our CPP and ACP classifiers, respectively, which leaves the performance of our predictors better than the earlier reported ACPred, mACPpred, MLCPP, and CPPred-RF. The described consensus-based fusion method additionally reached an AUC of 0.94 for the prediction of Cp-ACP (http://cbb1.ut.ac.ir/CpACpP/Index).
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Affiliation(s)
- Farid Nasiri
- Peptide
Chemistry Laboratory, Department of Biochemistry, Institute of Biochemistry
and Biophysics (IBB), University of Tehran, Tehran 14176-14335, Iran
| | - Fereshteh Fallah Atanaki
- Laboratory
of Complex Biological Systems and Bioinformatics (CBB), Department
of Bioinformatics, Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran 14176-14411, Iran
| | - Saman Behrouzi
- Laboratory
of Complex Biological Systems and Bioinformatics (CBB), Department
of Bioinformatics, Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran 14176-14411, Iran
| | - Kaveh Kavousi
- Laboratory
of Complex Biological Systems and Bioinformatics (CBB), Department
of Bioinformatics, Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran 14176-14411, Iran
| | - Mojtaba Bagheri
- Peptide
Chemistry Laboratory, Department of Biochemistry, Institute of Biochemistry
and Biophysics (IBB), University of Tehran, Tehran 14176-14335, Iran
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