1
|
Galatola E, Agrillo B, Gogliettino M, Palmieri G, Maccaroni S, Vicenza T, Proroga YTR, Mancusi A, Di Pasquale S, Suffredini E, Cozzi L. A Reliable Multifaceted Solution against Foodborne Viral Infections: The Case of RiLK1 Decapeptide. Molecules 2024; 29:2305. [PMID: 38792166 PMCID: PMC11124387 DOI: 10.3390/molecules29102305] [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: 04/10/2024] [Revised: 05/09/2024] [Accepted: 05/10/2024] [Indexed: 05/26/2024] Open
Abstract
Food-borne transmission is a recognized route for many viruses associated with gastrointestinal, hepatic, or neurological diseases. Therefore, it is essential to identify new bioactive compounds with broad-spectrum antiviral activity to exploit innovative solutions against these hazards. Recently, antimicrobial peptides (AMPs) have been recognized as promising antiviral agents. Indeed, while the antibacterial and antifungal effects of these molecules have been widely reported, their use as potential antiviral agents has not yet been fully investigated. Herein, the antiviral activity of previously identified or newly designed AMPs was evaluated against the non-enveloped RNA viruses, hepatitis A virus (HAV) and murine norovirus (MNV), a surrogate for human norovirus. Moreover, specific assays were performed to recognize at which stage of the viral infection cycle the peptides could function. The results showed that almost all peptides displayed virucidal effects, with about 90% of infectivity reduction in HAV or MNV. However, the decapeptide RiLK1 demonstrated, together with its antibacterial and antifungal properties, a notable reduction in viral infection for both HAV and MNV, possibly through direct interaction with viral particles causing their damage or hindering the recognition of cellular receptors. Hence, RiLK1 could represent a versatile antimicrobial agent effective against various foodborne pathogens including viruses, bacteria, and fungi.
Collapse
Affiliation(s)
- Emanuela Galatola
- Institute of Biosciences and BioResources (IBBR), National Research Council (CNR), 80131 Naples, Italy; (E.G.); (B.A.); (M.G.)
| | - Bruna Agrillo
- Institute of Biosciences and BioResources (IBBR), National Research Council (CNR), 80131 Naples, Italy; (E.G.); (B.A.); (M.G.)
| | - Marta Gogliettino
- Institute of Biosciences and BioResources (IBBR), National Research Council (CNR), 80131 Naples, Italy; (E.G.); (B.A.); (M.G.)
| | - Gianna Palmieri
- Institute of Biosciences and BioResources (IBBR), National Research Council (CNR), 80131 Naples, Italy; (E.G.); (B.A.); (M.G.)
- Materias Srl, 80146 Naples, Italy
| | - Serena Maccaroni
- National Reference Laboratory for Foodborne Viruses, Department of Food Safety, Nutrition and Veterinary Public Health, Istituto Superiore di Sanità, 00161 Rome, Italy; (S.M.); (T.V.); (S.D.P.); (E.S.); (L.C.)
| | - Teresa Vicenza
- National Reference Laboratory for Foodborne Viruses, Department of Food Safety, Nutrition and Veterinary Public Health, Istituto Superiore di Sanità, 00161 Rome, Italy; (S.M.); (T.V.); (S.D.P.); (E.S.); (L.C.)
| | - Yolande T. R. Proroga
- Department of Food Microbiology, Istituto Zooprofilattico Sperimentale del Mezzogiorno, 80055 Portici, Italy; (Y.T.R.P.); (A.M.)
| | - Andrea Mancusi
- Department of Food Microbiology, Istituto Zooprofilattico Sperimentale del Mezzogiorno, 80055 Portici, Italy; (Y.T.R.P.); (A.M.)
| | - Simona Di Pasquale
- National Reference Laboratory for Foodborne Viruses, Department of Food Safety, Nutrition and Veterinary Public Health, Istituto Superiore di Sanità, 00161 Rome, Italy; (S.M.); (T.V.); (S.D.P.); (E.S.); (L.C.)
| | - Elisabetta Suffredini
- National Reference Laboratory for Foodborne Viruses, Department of Food Safety, Nutrition and Veterinary Public Health, Istituto Superiore di Sanità, 00161 Rome, Italy; (S.M.); (T.V.); (S.D.P.); (E.S.); (L.C.)
| | - Loredana Cozzi
- National Reference Laboratory for Foodborne Viruses, Department of Food Safety, Nutrition and Veterinary Public Health, Istituto Superiore di Sanità, 00161 Rome, Italy; (S.M.); (T.V.); (S.D.P.); (E.S.); (L.C.)
| |
Collapse
|
2
|
Ullah M, Akbar S, Raza A, Zou Q. DeepAVP-TPPred: identification of antiviral peptides using transformed image-based localized descriptors and binary tree growth algorithm. Bioinformatics 2024; 40:btae305. [PMID: 38710482 DOI: 10.1093/bioinformatics/btae305] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2024] [Revised: 04/08/2024] [Accepted: 05/03/2024] [Indexed: 05/08/2024] Open
Abstract
MOTIVATION Despite the extensive manufacturing of antiviral drugs and vaccination, viral infections continue to be a major human ailment. Antiviral peptides (AVPs) have emerged as potential candidates in the pursuit of novel antiviral drugs. These peptides show vigorous antiviral activity against a diverse range of viruses by targeting different phases of the viral life cycle. Therefore, the accurate prediction of AVPs is an essential yet challenging task. Lately, many machine learning-based approaches have developed for this purpose; however, their limited capabilities in terms of feature engineering, accuracy, and generalization make these methods restricted. RESULTS In the present study, we aim to develop an efficient machine learning-based approach for the identification of AVPs, referred to as DeepAVP-TPPred, to address the aforementioned problems. First, we extract two new transformed feature sets using our designed image-based feature extraction algorithms and integrate them with an evolutionary information-based feature. Next, these feature sets were optimized using a novel feature selection approach called binary tree growth Algorithm. Finally, the optimal feature space from the training dataset was fed to the deep neural network to build the final classification model. The proposed model DeepAVP-TPPred was tested using stringent 5-fold cross-validation and two independent dataset testing methods, which achieved the maximum performance and showed enhanced efficiency over existing predictors in terms of both accuracy and generalization capabilities. AVAILABILITY AND IMPLEMENTATION https://github.com/MateeullahKhan/DeepAVP-TPPred.
Collapse
Affiliation(s)
- Matee Ullah
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, Sichuan 610054, China
| | - Shahid Akbar
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, Sichuan 610054, China
- Department of Computer Science, Abdul Wali Khan University Mardan, Mardan 23200, Pakistan
| | - Ali Raza
- Department of Computer Science, MY University, Islamabad 45750, Pakistan
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, Sichuan 610054, China
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, Zhejiang 324003, China
| |
Collapse
|
3
|
Kao HJ, Weng TH, Chen CH, Chen YC, Huang KY, Weng SL. iDVEIP: A computer-aided approach for the prediction of viral entry inhibitory peptides. Proteomics 2024; 24:e2300257. [PMID: 38263811 DOI: 10.1002/pmic.202300257] [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: 06/21/2023] [Revised: 01/03/2024] [Accepted: 01/05/2024] [Indexed: 01/25/2024]
Abstract
With the notable surge in therapeutic peptide development, various peptides have emerged as potential agents against virus-induced diseases. Viral entry inhibitory peptides (VEIPs), a subset of antiviral peptides (AVPs), offer a promising avenue as entry inhibitors (EIs) with distinct advantages over chemical counterparts. Despite this, a comprehensive analytical platform for characterizing these peptides and their effectiveness in blocking viral entry remains lacking. In this study, we introduce a groundbreaking in silico approach that leverages bioinformatics analysis and machine learning to characterize and identify novel VEIPs. Cross-validation results demonstrate the efficacy of a model combining sequence-based features in predicting VEIPs with high accuracy, validated through independent testing. Additionally, an EI type model has been developed to distinguish peptides specifically acting as Eis from AVPs with alternative activities. Notably, we present iDVEIP, a web-based tool accessible at http://mer.hc.mmh.org.tw/iDVEIP/, designed for automatic analysis and prediction of VEIPs. Emphasizing its capabilities, the tool facilitates comprehensive analyses of peptide characteristics, providing detailed amino acid composition data for each prediction. Furthermore, we showcase the tool's utility in identifying EIs against severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2).
Collapse
Affiliation(s)
- Hui-Ju Kao
- Department of Medical Research, Hsinchu MacKay Memorial Hospital, Hsinchu City, Taiwan
| | - Tzu-Hsiang Weng
- Department of Obstetrics and Gynecology, MacKay Memorial Hospital, Taipei City, Taiwan
| | - Chia-Hung Chen
- Department of Medical Research, Hsinchu MacKay Memorial Hospital, Hsinchu City, Taiwan
| | - Yu-Chi Chen
- Department of Medical Research, Hsinchu MacKay Memorial Hospital, Hsinchu City, Taiwan
| | - Kai-Yao Huang
- Department of Medical Research, Hsinchu MacKay Memorial Hospital, Hsinchu City, Taiwan
- Department of Medicine, MacKay Medical College, New Taipei City, Taiwan
- Institute of Biomedical Sciences, MacKay Medical College, New Taipei City, Taiwan
| | - Shun-Long Weng
- Department of Medicine, MacKay Medical College, New Taipei City, Taiwan
- Department of Obstetrics and Gynecology, Hsinchu MacKay Memorial Hospital, Hsinchu City, Taiwan
- MacKay Junior College of Medicine, Nursing and Management, Taipei City, Taiwan
| |
Collapse
|
4
|
Asseri AH, Islam MR, Alghamdi RM, Altayb HN. Identification of natural antimicrobial peptides mimetic to inhibit Ca 2+ influx DDX3X activity for blocking dengue viral infectivity. J Bioenerg Biomembr 2024; 56:125-139. [PMID: 38095733 DOI: 10.1007/s10863-023-09996-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Accepted: 11/16/2023] [Indexed: 04/06/2024]
Abstract
Viruses are microscopic biological entities that can quickly invade and multiply in a living organism. Each year, over 36,000 people die and nearly 400 million are infected with the dengue virus (DENV). Despite dengue being an endemic disease, no targeted and effective antiviral peptide resource is available against the dengue species. Antiviral peptides (AVPs) have shown tremendous ability to fight against different viruses. Accelerating antiviral drug discovery is crucial, particularly for RNA viruses. DDX3X, a vital cell component, supports viral translation and interacts with TRPV4, regulating viral RNA metabolism and infectivity. Its diverse signaling pathway makes it a potential therapeutic target. Our study focuses on inhibiting viral RNA translation by blocking the activity of the target gene and the TRPV4-mediated Ca2+ cation channel. Six major proteins from camel milk were first extracted and split with the enzyme pepsin. The antiviral properties were then analyzed using online bioinformatics programs, including AVPpred, Meta-iAVP, AMPfun, and ENNAVIA. The stability of the complex was assessed using MD simulation, MM/GBSA, and principal component analysis. Cytotoxicity evaluations were conducted using COPid and ToxinPred. The top ten AVPs, determined by optimal scores, were selected and saved for docking studies with the GalaxyPepDock tools. Bioinformatics analyses revealed that the peptides had very short hydrogen bond distances (1.8 to 3.6 Å) near the active site of the target protein. Approximately 76% of the peptide residues were 5-11 amino acids long. Additionally, the identified peptide candidates exhibited desirable properties for potential therapeutic agents, including a net positive charge, moderate toxicity, hydrophilicity, and selectivity. In conclusion, this computational study provides promising insights for discovering peptide-based therapeutic agents against DENV.
Collapse
Affiliation(s)
- Amer H Asseri
- Department of Biochemistry, Faculty of Science, King Abdulaziz University, Jeddah, 21589, Saudi Arabia.
- Centre for Artificial Intelligence in Precision Medicines, King Abdulaziz University, Jeddah, 21589, Saudi Arabia.
| | - Md Rashedul Islam
- Department of Biochemistry, Faculty of Science, King Abdulaziz University, Jeddah, 21589, Saudi Arabia
- Advanced Biological Invention Centre (Bioinventics), Rajshahi, 6204, Bangladesh
| | - Reem M Alghamdi
- Department of Radiology, Prince Sultan Military Medical City, Riyadh, Saudi Arabia
| | - Hisham N Altayb
- Department of Biochemistry, Faculty of Science, King Abdulaziz University, Jeddah, 21589, Saudi Arabia
- Centre for Artificial Intelligence in Precision Medicines, King Abdulaziz University, Jeddah, 21589, Saudi Arabia
| |
Collapse
|
5
|
Guan J, Yao L, Xie P, Chung CR, Huang Y, Chiang YC, Lee TY. A two-stage computational framework for identifying antiviral peptides and their functional types based on contrastive learning and multi-feature fusion strategy. Brief Bioinform 2024; 25:bbae208. [PMID: 38706321 PMCID: PMC11070730 DOI: 10.1093/bib/bbae208] [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: 02/04/2024] [Revised: 03/14/2024] [Accepted: 04/17/2024] [Indexed: 05/07/2024] Open
Abstract
Antiviral peptides (AVPs) have shown potential in inhibiting viral attachment, preventing viral fusion with host cells and disrupting viral replication due to their unique action mechanisms. They have now become a broad-spectrum, promising antiviral therapy. However, identifying effective AVPs is traditionally slow and costly. This study proposed a new two-stage computational framework for AVP identification. The first stage identifies AVPs from a wide range of peptides, and the second stage recognizes AVPs targeting specific families or viruses. This method integrates contrastive learning and multi-feature fusion strategy, focusing on sequence information and peptide characteristics, significantly enhancing predictive ability and interpretability. The evaluation results of the model show excellent performance, with accuracy of 0.9240 and Matthews correlation coefficient (MCC) score of 0.8482 on the non-AVP independent dataset, and accuracy of 0.9934 and MCC score of 0.9869 on the non-AMP independent dataset. Furthermore, our model can predict antiviral activities of AVPs against six key viral families (Coronaviridae, Retroviridae, Herpesviridae, Paramyxoviridae, Orthomyxoviridae, Flaviviridae) and eight viruses (FIV, HCV, HIV, HPIV3, HSV1, INFVA, RSV, SARS-CoV). Finally, to facilitate user accessibility, we built a user-friendly web interface deployed at https://awi.cuhk.edu.cn/∼dbAMP/AVP/.
Collapse
Affiliation(s)
- Jiahui Guan
- School of Medicine, The Chinese University of Hong Kong, Shenzhen, 2001 Longxiang Road, 518172 Shenzhen, China
- Kobilka Institute of Innovative Drug Discovery, School of Medicine, The Chinese University of Hong Kong, 2001 Longxiang Road, 518172 Shenzhen, China
| | - Lantian Yao
- School of Medicine, The Chinese University of Hong Kong, Shenzhen, 2001 Longxiang Road, 518172 Shenzhen, China
- School of Science and Engineering, The Chinese University of Hong Kong, 2001 Longxiang Road, 518172 Shenzhen, China
| | - Peilin Xie
- Kobilka Institute of Innovative Drug Discovery, School of Medicine, The Chinese University of Hong Kong, 2001 Longxiang Road, 518172 Shenzhen, China
| | - Chia-Ru Chung
- Department of Computer Science and Information Engineering, National Central University, 320317 Taoyuan, Taiwan
| | - Yixian Huang
- School of Medicine, The Chinese University of Hong Kong, Shenzhen, 2001 Longxiang Road, 518172 Shenzhen, China
| | - Ying-Chih Chiang
- School of Medicine, The Chinese University of Hong Kong, Shenzhen, 2001 Longxiang Road, 518172 Shenzhen, China
- Kobilka Institute of Innovative Drug Discovery, School of Medicine, The Chinese University of Hong Kong, 2001 Longxiang Road, 518172 Shenzhen, China
| | - Tzong-Yi Lee
- Institute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, 300093 Hsinchu, Taiwan
- Center for Intelligent Drug Systems and Smart Bio-devices (IDS2B), National Yang Ming Chiao Tung University, 300093 Hsinchu, Taiwan
| |
Collapse
|
6
|
Akbar S, Raza A, Zou Q. Deepstacked-AVPs: predicting antiviral peptides using tri-segment evolutionary profile and word embedding based multi-perspective features with deep stacking model. BMC Bioinformatics 2024; 25:102. [PMID: 38454333 PMCID: PMC10921744 DOI: 10.1186/s12859-024-05726-5] [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: 11/01/2023] [Accepted: 03/01/2024] [Indexed: 03/09/2024] Open
Abstract
BACKGROUND Viral infections have been the main health issue in the last decade. Antiviral peptides (AVPs) are a subclass of antimicrobial peptides (AMPs) with substantial potential to protect the human body against various viral diseases. However, there has been significant production of antiviral vaccines and medications. Recently, the development of AVPs as an antiviral agent suggests an effective way to treat virus-affected cells. Recently, the involvement of intelligent machine learning techniques for developing peptide-based therapeutic agents is becoming an increasing interest due to its significant outcomes. The existing wet-laboratory-based drugs are expensive, time-consuming, and cannot effectively perform in screening and predicting the targeted motif of antiviral peptides. METHODS In this paper, we proposed a novel computational model called Deepstacked-AVPs to discriminate AVPs accurately. The training sequences are numerically encoded using a novel Tri-segmentation-based position-specific scoring matrix (PSSM-TS) and word2vec-based semantic features. Composition/Transition/Distribution-Transition (CTDT) is also employed to represent the physiochemical properties based on structural features. Apart from these, the fused vector is formed using PSSM-TS features, semantic information, and CTDT descriptors to compensate for the limitations of single encoding methods. Information gain (IG) is applied to choose the optimal feature set. The selected features are trained using a stacked-ensemble classifier. RESULTS The proposed Deepstacked-AVPs model achieved a predictive accuracy of 96.60%%, an area under the curve (AUC) of 0.98, and a precision-recall (PR) value of 0.97 using training samples. In the case of the independent samples, our model obtained an accuracy of 95.15%, an AUC of 0.97, and a PR value of 0.97. CONCLUSION Our Deepstacked-AVPs model outperformed existing models with a ~ 4% and ~ 2% higher accuracy using training and independent samples, respectively. The reliability and efficacy of the proposed Deepstacked-AVPs model make it a valuable tool for scientists and may perform a beneficial role in pharmaceutical design and research academia.
Collapse
Affiliation(s)
- Shahid Akbar
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China
- Department of Computer Science, Abdul Wali Khan University Mardan, Mardan, 23200, KP, Pakistan
| | - Ali Raza
- Department of Physical and Numerical Sciences, Qurtuba University of Science and Information Technology, Peshawar, 25124, KP, Pakistan
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China.
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, 324000, People's Republic of China.
| |
Collapse
|
7
|
Medvedeva A, Domakhina S, Vasnetsov C, Vasnetsov V, Kolomeisky A. Physical-Chemical Approach to Designing Drugs with Multiple Targets. J Phys Chem Lett 2024; 15:1828-1835. [PMID: 38330920 DOI: 10.1021/acs.jpclett.3c03624] [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: 02/10/2024]
Abstract
Many people simultaneously exhibit multiple diseases, which complicates efficient medical treatments. For example, patients with cancer are frequently susceptible to infections. However, developing drugs that could simultaneously target several diseases is challenging. We present a novel theoretical method to assist in selecting compounds with multiple therapeutic targets. The idea is to find correlations between the physical and chemical properties of drug molecules and their abilities to work against multiple targets. As a first step, we investigated potential drugs against cancer and viral infections. Specifically, we investigated antimicrobial peptides (AMPs), which are short positively charged biomolecules produced by living systems as a part of their immune defense. AMPs show anticancer and antiviral activity. We use chemoinformatics and correlation analysis as a part of the machine-learning method to identify the specific properties that distinguish AMPs with dual anticancer and antiviral activities. Physical-chemical arguments to explain these observations are presented.
Collapse
Affiliation(s)
- Angela Medvedeva
- Department of Chemistry, Rice University, Houston, Texas 77005, United States
- Center for Theoretical Biological Physics, Rice University, Houston, Texas 77005, United States
| | - Sofya Domakhina
- Department of Chemistry, Rice University, Houston, Texas 77005, United States
| | - Catherine Vasnetsov
- Department of Chemistry, Rice University, Houston, Texas 77005, United States
| | - Victor Vasnetsov
- Department of Chemistry, Rice University, Houston, Texas 77005, United States
| | - Anatoly Kolomeisky
- Department of Chemistry, Rice University, Houston, Texas 77005, United States
- Center for Theoretical Biological Physics, Rice University, Houston, Texas 77005, United States
- Department of Chemical and Biomolecular Engineering, Rice University, Houston, Texas 77005, United States
- Department of Physics and Astronomy, Rice University, Houston, Texas 77005, United States
| |
Collapse
|
8
|
Iwaniak A, Minkiewicz P, Darewicz M. Bioinformatics and bioactive peptides from foods: Do they work together? ADVANCES IN FOOD AND NUTRITION RESEARCH 2024; 108:35-111. [PMID: 38461003 DOI: 10.1016/bs.afnr.2023.09.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/11/2024]
Abstract
We live in the Big Data Era which affects many aspects of science, including research on bioactive peptides derived from foods, which during the last few decades have been a focus of interest for scientists. These two issues, i.e., the development of computer technologies and progress in the discovery of novel peptides with health-beneficial properties, are closely interrelated. This Chapter presents the example applications of bioinformatics for studying biopeptides, focusing on main aspects of peptide analysis as the starting point, including: (i) the role of peptide databases; (ii) aspects of bioactivity prediction; (iii) simulation of peptide release from proteins. Bioinformatics can also be used for predicting other features of peptides, including ADMET, QSAR, structure, and taste. To answer the question asked "bioinformatics and bioactive peptides from foods: do they work together?", currently it is almost impossible to find examples of peptide research with no bioinformatics involved. However, theoretical predictions are not equivalent to experimental work and always require critical scrutiny. The aspects of compatibility of in silico and in vitro results are also summarized herein.
Collapse
Affiliation(s)
- Anna Iwaniak
- Chair of Food Biochemistry, Faculty of Food Science, University of Warmia and Mazury in Olsztyn, Olsztyn-Kortowo, Poland.
| | - Piotr Minkiewicz
- Chair of Food Biochemistry, Faculty of Food Science, University of Warmia and Mazury in Olsztyn, Olsztyn-Kortowo, Poland
| | - Małgorzata Darewicz
- Chair of Food Biochemistry, Faculty of Food Science, University of Warmia and Mazury in Olsztyn, Olsztyn-Kortowo, Poland
| |
Collapse
|
9
|
Vincenzi M, Mercurio FA, Leone M. Virtual Screening of Peptide Libraries: The Search for Peptide-Based Therapeutics Using Computational Tools. Int J Mol Sci 2024; 25:1798. [PMID: 38339078 PMCID: PMC10855943 DOI: 10.3390/ijms25031798] [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: 12/22/2023] [Revised: 01/26/2024] [Accepted: 01/30/2024] [Indexed: 02/12/2024] Open
Abstract
Over the last few decades, we have witnessed growing interest from both academic and industrial laboratories in peptides as possible therapeutics. Bioactive peptides have a high potential to treat various diseases with specificity and biological safety. Compared to small molecules, peptides represent better candidates as inhibitors (or general modulators) of key protein-protein interactions. In fact, undruggable proteins containing large and smooth surfaces can be more easily targeted with the conformational plasticity of peptides. The discovery of bioactive peptides, working against disease-relevant protein targets, generally requires the high-throughput screening of large libraries, and in silico approaches are highly exploited for their low-cost incidence and efficiency. The present review reports on the potential challenges linked to the employment of peptides as therapeutics and describes computational approaches, mainly structure-based virtual screening (SBVS), to support the identification of novel peptides for therapeutic implementations. Cutting-edge SBVS strategies are reviewed along with examples of applications focused on diverse classes of bioactive peptides (i.e., anticancer, antimicrobial/antiviral peptides, peptides blocking amyloid fiber formation).
Collapse
Affiliation(s)
| | | | - Marilisa Leone
- Institute of Biostructures and Bioimaging, Via Pietro Castellino 111, 80131 Naples, Italy; (M.V.); (F.A.M.)
| |
Collapse
|
10
|
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.
Collapse
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.
| |
Collapse
|
11
|
Ma X, Liang Y, Zhang S. iAVPs-ResBi: Identifying antiviral peptides by using deep residual network and bidirectional gated recurrent unit. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:21563-21587. [PMID: 38124610 DOI: 10.3934/mbe.2023954] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/23/2023]
Abstract
Human history is also the history of the fight against viral diseases. From the eradication of viruses to coexistence, advances in biomedicine have led to a more objective understanding of viruses and a corresponding increase in the tools and methods to combat them. More recently, antiviral peptides (AVPs) have been discovered, which due to their superior advantages, have achieved great impact as antiviral drugs. Therefore, it is very necessary to develop a prediction model to accurately identify AVPs. In this paper, we develop the iAVPs-ResBi model using k-spaced amino acid pairs (KSAAP), encoding based on grouped weight (EBGW), enhanced grouped amino acid composition (EGAAC) based on the N5C5 sequence, composition, transition and distribution (CTD) based on physicochemical properties for multi-feature extraction. Then we adopt bidirectional long short-term memory (BiLSTM) to fuse features for obtaining the most differentiated information from multiple original feature sets. Finally, the deep model is built by combining improved residual network and bidirectional gated recurrent unit (BiGRU) to perform classification. The results obtained are better than those of the existing methods, and the accuracies are 95.07, 98.07, 94.29 and 97.50% on the four datasets, which show that iAVPs-ResBi can be used as an effective tool for the identification of antiviral peptides. The datasets and codes are freely available at https://github.com/yunyunliang88/iAVPs-ResBi.
Collapse
Affiliation(s)
- Xinyan Ma
- School of Science, Xi'an Polytechnic University, Xi'an 710048, China
| | - Yunyun Liang
- School of Science, Xi'an Polytechnic University, Xi'an 710048, China
| | - Shengli Zhang
- School of Mathematics and Statistics, Xidian University, Xi'an 710071, China
| |
Collapse
|
12
|
Beltrán JF, Belén LH, Farias JG, Zamorano M, Lefin N, Miranda J, Parraguez-Contreras F. VirusHound-I: prediction of viral proteins involved in the evasion of host adaptive immune response using the random forest algorithm and generative adversarial network for data augmentation. Brief Bioinform 2023; 25:bbad434. [PMID: 38033292 PMCID: PMC10753651 DOI: 10.1093/bib/bbad434] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Revised: 10/18/2023] [Accepted: 11/05/2023] [Indexed: 12/02/2023] Open
Abstract
Throughout evolution, pathogenic viruses have developed different strategies to evade the response of the adaptive immune system. To carry out successful replication, some pathogenic viruses encode different proteins that manipulate the molecular mechanisms of host cells. Currently, there are different bioinformatics tools for virus research; however, none of them focus on predicting viral proteins that evade the adaptive system. In this work, we have developed a novel tool based on machine and deep learning for predicting this type of viral protein named VirusHound-I. This tool is based on a model developed with the multilayer perceptron algorithm using the dipeptide composition molecular descriptor. In this study, we have also demonstrated the robustness of our strategy for data augmentation of the positive dataset based on generative antagonistic networks. During the 10-fold cross-validation step in the training dataset, the predictive model showed 0.947 accuracy, 0.994 precision, 0.943 F1 score, 0.995 specificity, 0.896 sensitivity, 0.894 kappa, 0.898 Matthew's correlation coefficient and 0.989 AUC. On the other hand, during the testing step, the model showed 0.964 accuracy, 1.0 precision, 0.967 F1 score, 1.0 specificity, 0.936 sensitivity, 0.929 kappa, 0.931 Matthew's correlation coefficient and 1.0 AUC. Taking this model into account, we have developed a tool called VirusHound-I that makes it possible to predict viral proteins that evade the host's adaptive immune system. We believe that VirusHound-I can be very useful in accelerating studies on the molecular mechanisms of evasion of pathogenic viruses, as well as in the discovery of therapeutic targets.
Collapse
Affiliation(s)
- Jorge F Beltrán
- Department of Chemical Engineering, Faculty of Engineering and Science, Universidad de La Frontera, Ave. Francisco Salazar 01145, Temuco, Chile
| | | | - Jorge G Farias
- Department of Chemical Engineering, Faculty of Engineering and Science, Universidad de La Frontera, Ave. Francisco Salazar 01145, Temuco, Chile
| | - Mauricio Zamorano
- Department of Chemical Engineering, Faculty of Engineering and Science, Universidad de La Frontera, Ave. Francisco Salazar 01145, Temuco, Chile
| | - Nicolás Lefin
- Department of Chemical Engineering, Faculty of Engineering and Science, Universidad de La Frontera, Ave. Francisco Salazar 01145, Temuco, Chile
| | - Javiera Miranda
- Department of Chemical Engineering, Faculty of Engineering and Science, Universidad de La Frontera, Ave. Francisco Salazar 01145, Temuco, Chile
| | - Fernanda Parraguez-Contreras
- Department of Chemical Engineering, Faculty of Engineering and Science, Universidad de La Frontera, Ave. Francisco Salazar 01145, Temuco, Chile
| |
Collapse
|
13
|
Ayusso GM, da Silva Sanches PR, Carvalho T, Santos IA, Martins DOS, Lima MLD, da Conceição PJP, Bittar C, Merits A, Cilli EM, Jardim ACG, Rahal P, Calmon MF. The Synthetic Peptide GA-Hecate and Its Analogs Inhibit Multiple Steps of the Chikungunya Virus Infection Cycle In Vitro. Pharmaceuticals (Basel) 2023; 16:1389. [PMID: 37895860 PMCID: PMC10610090 DOI: 10.3390/ph16101389] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2023] [Revised: 09/08/2023] [Accepted: 09/27/2023] [Indexed: 10/29/2023] Open
Abstract
Chikungunya virus (CHIKV) belongs to the Alphavirus genus and is responsible for significant outbreaks worldwide. Currently, there is no approved antiviral therapy against CHIKV. Bioactive peptides have great potential for new drug development. Here, we evaluated the antiviral activity of the synthetic peptide GA-Hecate and its analogs PSSct1905 and PSSct1910 against CHIKV infection. Initial screening showed that all three peptides inhibited the CHIKV replication cycle in baby hamster kidney fibroblast cells (BHK-21) and human hepatocarcinoma epithelial cells (Huh-7). GA-Hecate and its analog PSSct1905 were the most active, demonstrating suppression of viral infection by more than 91%. The analog PSSct1905 exhibited a protective effect in cells against CHIKV infection. We also observed that the analogs PSSct1905 and PSSct1910 affected CHIKV entry into both cell lines, inhibiting viral attachment and internalization. Finally, all tested compounds presented antiviral activity on the post-entry steps of CHIKV infection in all cells evaluated. In conclusion, this study highlights the potential of the peptide GA-Hecate and its analogs as novel anti-CHIKV compounds targeting different stages of the viral replication cycle, warranting the development of GA-Hecate-based compounds with broad antiviral activity.
Collapse
Affiliation(s)
- Gabriela Miranda Ayusso
- Institute of Biosciences, Letters and Exact Sciences, São Paulo State University, São José do Rio Preto 15054-000, SP, Brazil; (G.M.A.); (T.C.); (D.O.S.M.); (M.L.D.L.); (P.J.P.d.C.); (C.B.); (A.C.G.J.); (P.R.)
| | | | - Tamara Carvalho
- Institute of Biosciences, Letters and Exact Sciences, São Paulo State University, São José do Rio Preto 15054-000, SP, Brazil; (G.M.A.); (T.C.); (D.O.S.M.); (M.L.D.L.); (P.J.P.d.C.); (C.B.); (A.C.G.J.); (P.R.)
| | - Igor Andrade Santos
- Institute of Biomedical Sciences, Federal University of Uberlândia, Uberlândia 38408-100, MG, Brazil;
| | - Daniel Oliveira Silva Martins
- Institute of Biosciences, Letters and Exact Sciences, São Paulo State University, São José do Rio Preto 15054-000, SP, Brazil; (G.M.A.); (T.C.); (D.O.S.M.); (M.L.D.L.); (P.J.P.d.C.); (C.B.); (A.C.G.J.); (P.R.)
- Institute of Biomedical Sciences, Federal University of Uberlândia, Uberlândia 38408-100, MG, Brazil;
| | - Maria Letícia Duarte Lima
- Institute of Biosciences, Letters and Exact Sciences, São Paulo State University, São José do Rio Preto 15054-000, SP, Brazil; (G.M.A.); (T.C.); (D.O.S.M.); (M.L.D.L.); (P.J.P.d.C.); (C.B.); (A.C.G.J.); (P.R.)
| | - Pâmela Jóyce Previdelli da Conceição
- Institute of Biosciences, Letters and Exact Sciences, São Paulo State University, São José do Rio Preto 15054-000, SP, Brazil; (G.M.A.); (T.C.); (D.O.S.M.); (M.L.D.L.); (P.J.P.d.C.); (C.B.); (A.C.G.J.); (P.R.)
| | - Cíntia Bittar
- Institute of Biosciences, Letters and Exact Sciences, São Paulo State University, São José do Rio Preto 15054-000, SP, Brazil; (G.M.A.); (T.C.); (D.O.S.M.); (M.L.D.L.); (P.J.P.d.C.); (C.B.); (A.C.G.J.); (P.R.)
- Laboratory of Molecular Immunology, The Rockefeller University, New York, NY 10065, USA
| | - Andres Merits
- Institute of Technology, University of Tartu, 50090 Tartu, Estonia;
| | - Eduardo Maffud Cilli
- Institute of Chemistry, São Paulo State University, Araraquara 14800-060, SP, Brazil;
| | - Ana Carolina Gomes Jardim
- Institute of Biosciences, Letters and Exact Sciences, São Paulo State University, São José do Rio Preto 15054-000, SP, Brazil; (G.M.A.); (T.C.); (D.O.S.M.); (M.L.D.L.); (P.J.P.d.C.); (C.B.); (A.C.G.J.); (P.R.)
- Institute of Biomedical Sciences, Federal University of Uberlândia, Uberlândia 38408-100, MG, Brazil;
| | - Paula Rahal
- Institute of Biosciences, Letters and Exact Sciences, São Paulo State University, São José do Rio Preto 15054-000, SP, Brazil; (G.M.A.); (T.C.); (D.O.S.M.); (M.L.D.L.); (P.J.P.d.C.); (C.B.); (A.C.G.J.); (P.R.)
| | - Marilia Freitas Calmon
- Institute of Biosciences, Letters and Exact Sciences, São Paulo State University, São José do Rio Preto 15054-000, SP, Brazil; (G.M.A.); (T.C.); (D.O.S.M.); (M.L.D.L.); (P.J.P.d.C.); (C.B.); (A.C.G.J.); (P.R.)
| |
Collapse
|
14
|
Chen S, Liao Y, Zhao J, Bin Y, Zheng C. PACVP: Prediction of Anti-Coronavirus Peptides Using a Stacking Learning Strategy With Effective Feature Representation. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:3106-3116. [PMID: 37022025 DOI: 10.1109/tcbb.2023.3238370] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Due to the global outbreak of COVID-19 and its variants, antiviral peptides with anti-coronavirus activity (ACVPs) represent a promising new drug candidate for the treatment of coronavirus infection. At present, several computational tools have been developed to identify ACVPs, but the overall prediction performance is still not enough to meet the actual therapeutic application. In this study, we constructed an efficient and reliable prediction model PACVP (Prediction of Anti-CoronaVirus Peptides) for identifying ACVPs based on effective feature representation and a two-layer stacking learning framework. In the first layer, we use nine feature encoding methods with different feature representation angles to characterize the rich sequence information and fuse them into a feature matrix. Secondly, data normalization and unbalanced data processing are carried out. Next, 12 baseline models are constructed by combining three feature selection methods and four machine learning classification algorithms. In the second layer, we input the optimal probability features into the logistic regression algorithm (LR) to train the final model PACVP. The experiments show that PACVP achieves favorable prediction performance on independent test dataset, with ACC of 0.9208 and AUC of 0.9465. We hope that PACVP will become a useful method for identifying, annotating and characterizing novel ACVPs.
Collapse
|
15
|
Lefin N, Herrera-Belén L, Farias JG, Beltrán JF. Review and perspective on bioinformatics tools using machine learning and deep learning for predicting antiviral peptides. Mol Divers 2023:10.1007/s11030-023-10718-3. [PMID: 37626205 DOI: 10.1007/s11030-023-10718-3] [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: 05/02/2023] [Accepted: 08/15/2023] [Indexed: 08/27/2023]
Abstract
Viruses constitute a constant threat to global health and have caused millions of human and animal deaths throughout human history. Despite advances in the discovery of antiviral compounds that help fight these pathogens, finding a solution to this problem continues to be a task that consumes time and financial resources. Currently, artificial intelligence (AI) has revolutionized many areas of the biological sciences, making it possible to decipher patterns in amino acid sequences that encode different functions and activities. Within the field of AI, machine learning, and deep learning algorithms have been used to discover antimicrobial peptides. Due to their effectiveness and specificity, antimicrobial peptides (AMPs) hold excellent promise for treating various infections caused by pathogens. Antiviral peptides (AVPs) are a specific type of AMPs that have activity against certain viruses. Unlike the research focused on the development of tools and methods for the prediction of antimicrobial peptides, those related to the prediction of AVPs are still scarce. Given the significance of AVPs as potential pharmaceutical options for human and animal health and the ongoing AI revolution, we have reviewed and summarized the current machine learning and deep learning-based tools and methods available for predicting these types of peptides.
Collapse
Affiliation(s)
- Nicolás Lefin
- Department of Chemical Engineering, Faculty of Engineering and Science, University of La Frontera, Ave. Francisco Salazar, 01145, Temuco, Chile
| | - Lisandra Herrera-Belén
- Departamento de Ciencias Básicas, Facultad de Ciencias, Universidad Santo Tomás, Temuco, Chile
| | - Jorge G Farias
- Department of Chemical Engineering, Faculty of Engineering and Science, University of La Frontera, Ave. Francisco Salazar, 01145, Temuco, Chile
| | - Jorge F Beltrán
- Department of Chemical Engineering, Faculty of Engineering and Science, University of La Frontera, Ave. Francisco Salazar, 01145, Temuco, Chile.
| |
Collapse
|
16
|
Singh V, Singh SK. A separable temporal convolutional networks based deep learning technique for discovering antiviral medicines. Sci Rep 2023; 13:13722. [PMID: 37608092 PMCID: PMC10444765 DOI: 10.1038/s41598-023-40922-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2023] [Accepted: 08/18/2023] [Indexed: 08/24/2023] Open
Abstract
An alarming number of fatalities caused by the COVID-19 pandemic has forced the scientific community to accelerate the process of therapeutic drug discovery. In this regard, the collaboration between biomedical scientists and experts in artificial intelligence (AI) has led to a number of in silico tools being developed for the initial screening of therapeutic molecules. All living organisms produce antiviral peptides (AVPs) as a part of their first line of defense against invading viruses. The Deep-AVPiden model proposed in this paper and its corresponding web app, deployed at https://deep-avpiden.anvil.app , is an effort toward discovering novel AVPs in proteomes of living organisms. Apart from Deep-AVPiden, a computationally efficient model called Deep-AVPiden (DS) has also been developed using the same underlying network but with point-wise separable convolutions. The Deep-AVPiden and Deep-AVPiden (DS) models show an accuracy of 90% and 88%, respectively, and both have a precision of 90%. Also, the proposed models were statistically compared using the Student's t-test. On comparing the proposed models with the state-of-the-art classifiers, it was found that they are much better than them. To test the proposed model, we identified some AVPs in the natural defense proteins of plants, mammals, and fishes and found them to have appreciable sequence similarity with some experimentally validated antimicrobial peptides. These AVPs can be chemically synthesized and tested for their antiviral activity.
Collapse
Affiliation(s)
- Vishakha Singh
- Department of Computer Science and Engineering, Indian Institute of Technology (BHU) Varanasi, Varanasi, Uttar Pradesh, 221005, India.
| | - Sanjay Kumar Singh
- Department of Computer Science and Engineering, Indian Institute of Technology (BHU) Varanasi, Varanasi, Uttar Pradesh, 221005, India.
| |
Collapse
|
17
|
Pritam M, Dutta S, Medicherla KM, Kumar R, Singh SP. Computational analysis of spike protein of SARS-CoV-2 (Omicron variant) for development of peptide-based therapeutics and diagnostics. J Biomol Struct Dyn 2023:1-19. [PMID: 37498146 DOI: 10.1080/07391102.2023.2239932] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Accepted: 07/17/2023] [Indexed: 07/28/2023]
Abstract
In the last few years, the worldwide population has suffered from the SARS-CoV-2 pandemic. The WHO dashboard indicated that around 504,079,039 people were infected and 6,204,155 died from COVID-19 caused by different variants of SARS-CoV-2. Recently, a new variant of SARS-CoV-2 (B.1.1.529) was reported by South Africa known as Omicron. The high transmissibility rate and resistance towards available anti-SARS-CoV-2 drugs/vaccines/monoclonal antibodies, make Omicron a variant of concern. Because of various mutations in spike protein, available diagnostic and therapeutic treatments are not reliable. Therefore, the present study explored the development of some therapeutic peptides that can inhibit the SARS-CoV-2 virus interaction with host ACE2 receptors and can also be used for diagnostic purposes. The screened linear B cell epitopes derived from receptor-binding domain of spike protein of Omicron variant were evaluated as peptide inhibitor/vaccine candidates through different bioinformatics tools including molecular docking and simulation to analyze the interaction between Omicron peptide and human ACE2 receptor. Overall, in-silico studies revealed that Omicron peptides OP1-P12, OP14, OP20, OP23, OP24, OP25, OP26, OP27, OP28, OP29, and OP30 have the potential to inhibit Omicron interaction with ACE2 receptor. Moreover, Omicron peptides OP20, OP22, OP23, OP24, OP25, OP26, OP27, and OP30 have shown potential antigenic and immunogenic properties that can be used in design and development vaccines against Omicron. Although the in-silico validation was performed by comparative analysis with the control peptide inhibitor, further validation through wet lab experimentation is required before its use as therapeutic peptides.Communicated by Ramaswamy H. Sarma.
Collapse
Affiliation(s)
- Manisha Pritam
- Department of Biotechnology and Bioinformatics, Birla Institute of Scientific Research, Jaipur, India
- Amity Institute of Biotechnology, Amity University Uttar Pradesh, Lucknow, India
| | - Somenath Dutta
- Department of Biotechnology and Bioinformatics, Birla Institute of Scientific Research, Jaipur, India
- Department of Bioinformatics, Pondicherry Central University, Puducherry, India
| | - Krishna Mohan Medicherla
- Department of Biotechnology and Bioinformatics, Birla Institute of Scientific Research, Jaipur, India
| | - Rajnish Kumar
- Amity Institute of Biotechnology, Amity University Uttar Pradesh, Lucknow, India
- Department of Veterinary Medicine and Surgery, College of Veterinary Medicine, University of Missouri, Columbia, Missouri, USA
| | | |
Collapse
|
18
|
Li Y, Ma D, Chen D, Chen Y. ACP-GBDT: An improved anticancer peptide identification method with gradient boosting decision tree. Front Genet 2023; 14:1165765. [PMID: 37065496 PMCID: PMC10090421 DOI: 10.3389/fgene.2023.1165765] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Accepted: 03/09/2023] [Indexed: 03/31/2023] Open
Abstract
Cancer is one of the most dangerous diseases in the world, killing millions of people every year. Drugs composed of anticancer peptides have been used to treat cancer with low side effects in recent years. Therefore, identifying anticancer peptides has become a focus of research. In this study, an improved anticancer peptide predictor named ACP-GBDT, based on gradient boosting decision tree (GBDT) and sequence information, is proposed. To encode the peptide sequences included in the anticancer peptide dataset, ACP-GBDT uses a merged-feature composed of AAIndex and SVMProt-188D. A GBDT is adopted to train the prediction model in ACP-GBDT. Independent testing and ten-fold cross-validation show that ACP-GBDT can effectively distinguish anticancer peptides from non-anticancer ones. The comparison results of the benchmark dataset show that ACP-GBDT is simpler and more effective than other existing anticancer peptide prediction methods.
Collapse
Affiliation(s)
- Yanjuan Li
- College of Electrical and Information Engineering, Quzhou University, Quzhou, China
| | - Di Ma
- College of Computer, Hangzhou Dianzi University, Hangzhou, China
| | - Dong Chen
- College of Electrical and Information Engineering, Quzhou University, Quzhou, China
- *Correspondence: Dong Chen, ; Yu Chen,
| | - Yu Chen
- College of Information and Computer Engineering, Northeast Forestry University, Harbin, China
- *Correspondence: Dong Chen, ; Yu Chen,
| |
Collapse
|
19
|
Liu L, Wang D. Four antimicrobial peptides of Asian gypsy moth respond to infection of its viral pathogen, nucleopolyhedrovirus (LdMNPV). PESTICIDE BIOCHEMISTRY AND PHYSIOLOGY 2023; 190:105335. [PMID: 36740343 DOI: 10.1016/j.pestbp.2022.105335] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/04/2022] [Revised: 12/12/2022] [Accepted: 12/30/2022] [Indexed: 06/18/2023]
Abstract
Antimicrobial peptides (AMPs) play essential roles in defending against various invading pathogens. Although antibacterial or antifungal properties of AMPs have been well characterized, the contribution of AMPs to immune defenses against viruses especially baculoviruses is still unclear. In this study, four full-length AMP genes (Ldcec, Ldatt, Ldglo and Ldmor) that encode the cecropin, attacin, gloverin and moricin, respectively, were characterized in Lymantria dispar (Asian gypsy moth). All four AMPs were cationic peptides and exhibited hydrophilicity. Structural analysis showed that the Ldcec and Ldmor were α-helical peptides. Tissue-specific Ldcec expression was the highest in fat body, while expression of Ldatt, Ldglo and Ldmor was the highest in epidermis. All four AMP genes were expressed during all developmental stages with the highest expression in the pupa and adult. Compared to mock infection, expression of these four AMP genes were significantly induced following Lymantria dispar multiple nucleopolyhedrovirus (LdMNPV) challenge and sharply increased at 72 h post infection. After Ldglo gene silencing, the DNA replication levels of LdMNPV in L. dispar larvae significantly increased at 48 and 72 h post infection, indicating that the Ldglo could suppress the DNA replication of LdMNPV. Our results suggest that four AMPs of L. dispar may play important roles in antiviral immunity against LdMNPV.
Collapse
Affiliation(s)
- Long Liu
- State Key Laboratory of Crop Stress Biology for Arid Areas, College of Plant Protection, Northwest A&F University, Yangling 712100, China
| | - Dun Wang
- State Key Laboratory of Crop Stress Biology for Arid Areas, College of Plant Protection, Northwest A&F University, Yangling 712100, China.
| |
Collapse
|
20
|
Fathi F, Ghobeh M, H Shirazi F, Tabarzad M. Design and Evaluation of a Novel Anti-microbial Peptide from Cathelicidin-2: Selectively Active Against Acinetobacter baumannii. IRANIAN JOURNAL OF PHARMACEUTICAL RESEARCH : IJPR 2023; 22:e141920. [PMID: 38435443 PMCID: PMC10909124 DOI: 10.5812/ijpr-141920] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Revised: 11/05/2023] [Accepted: 11/16/2023] [Indexed: 03/05/2024]
Abstract
Background Infections caused by pathogenic microorganisms have increased the need for hospital care and have thus represented a public health problem and a significant financial burden. Classical treatments consisting of traditional antibiotics face several challenges today. Anti-microbial peptides (AMPs) are a conserved characteristic of the innate immune response among different animal species to defend against pathogenic microorganisms. Objectives In this study, a new peptide sequence (mCHTL131-140) was designed using the in silico approach. Methods Cathelicidin-2 (UniprotID: Q2IAL7) was used as a potential antimicrobial protein, and a novel 10 - 12 amino acids sequence AMP was designed using bioinformatics tools and the AMP databases. Then, the anti-bacterial, anti-biofilm, and anti-fungal properties of the peptide, as well as its hemolytic activity and cytotoxicity towards human fibroblast (HDF) cells, were investigated in vitro. Results Online bioinformatics tools indicated that the peptide sequence could have anti-bacterial, anti-viral, anti-fungal, and anti-biofilm properties with little hemolytic properties. The experimental tests confirmed that mCHTL131-140 exhibited the best anti-bacterial properties against Acinetobacter baumannii and had fair anti-fungal properties. Besides, it did not cause red blood cell lysis and showed no cytotoxicity towards HDF cells. Conclusions In general, the designed peptide can be considered a promising AMP to control hospital-acquired infections by A. baumannii.
Collapse
Affiliation(s)
- Fariba Fathi
- Department of Biology, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | - Maryam Ghobeh
- Department of Biology, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | - Farshad H Shirazi
- Department of Toxicology and Pharmacology, School of Pharmacy, Shahid Beheshti University of Medical Sciences, Tehran, Iran
- Pharmaceutical Sciences Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Maryam Tabarzad
- Protein Technology Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| |
Collapse
|
21
|
Computer-Aided Screening for Potential Coronavirus 3-Chymotrypsin-like Protease (3CLpro) Inhibitory Peptides from Putative Hemp Seed Trypsinized Peptidome. MOLECULES (BASEL, SWITZERLAND) 2022; 28:molecules28010050. [PMID: 36615263 PMCID: PMC9822321 DOI: 10.3390/molecules28010050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 12/05/2022] [Accepted: 12/13/2022] [Indexed: 12/24/2022]
Abstract
To control the COVID-19 pandemic, antivirals that specifically target the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are urgently required. The 3-chymotrypsin-like protease (3CLpro) is a promising drug target since it functions as a catalytic dyad in hydrolyzing polyprotein during the viral life cycle. Bioactive peptides, especially food-derived peptides, have a variety of functional activities, including antiviral activity, and also have a potential therapeutic effect against COVID-19. In this study, the hemp seed trypsinized peptidome was subjected to computer-aided screening against the 3CLpro of SARS-CoV-2. Using predictive trypsinized products of the five major proteins in hemp seed (i.e., edestin 1, edestin 2, edestin 3, albumin, and vicilin), the putative hydrolyzed peptidome was established and used as the input dataset. To select the Cannabis sativa antiviral peptides (csAVPs), a predictive bioinformatic analysis was performed by three webserver screening programs: iAMPpred, AVPpred, and Meta-iAVP. The amino acid composition profile comparison was performed by COPid to screen for the non-toxic and non-allergenic candidates, ToxinPred and AllerTOP and AllergenFP, respectively. GalaxyPepDock and HPEPDOCK were employed to perform the molecular docking of all selected csAVPs to the 3CLpro of SARS-CoV-2. Only the top docking-scored candidate (csAVP4) was further analyzed by molecular dynamics simulation for 150 nanoseconds. Molecular docking and molecular dynamics revealed the potential ability and stability of csAVP4 to inhibit the 3CLpro catalytic domain with hydrogen bond formation in domain 2 with short bonding distances. In addition, these top ten candidate bioactive peptides contained hydrophilic amino acid residues and exhibited a positive net charge. We hope that our results may guide the future development of alternative therapeutics against COVID-19.
Collapse
|
22
|
Hemmati S, Rasekhi Kazerooni H. Polypharmacological Cell-Penetrating Peptides from Venomous Marine Animals Based on Immunomodulating, Antimicrobial, and Anticancer Properties. Mar Drugs 2022; 20:md20120763. [PMID: 36547910 PMCID: PMC9787916 DOI: 10.3390/md20120763] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Revised: 11/25/2022] [Accepted: 11/30/2022] [Indexed: 12/09/2022] Open
Abstract
Complex pathological diseases, such as cancer, infection, and Alzheimer's, need to be targeted by multipronged curative. Various omics technologies, with a high rate of data generation, demand artificial intelligence to translate these data into druggable targets. In this study, 82 marine venomous animal species were retrieved, and 3505 cryptic cell-penetrating peptides (CPPs) were identified in their toxins. A total of 279 safe peptides were further analyzed for antimicrobial, anticancer, and immunomodulatory characteristics. Protease-resistant CPPs with endosomal-escape ability in Hydrophis hardwickii, nuclear-localizing peptides in Scorpaena plumieri, and mitochondrial-targeting peptides from Synanceia horrida were suitable for compartmental drug delivery. A broad-spectrum S. horrida-derived antimicrobial peptide with a high binding-affinity to bacterial membranes was an antigen-presenting cell (APC) stimulator that primes cytokine release and naïve T-cell maturation simultaneously. While antibiofilm and wound-healing peptides were detected in Synanceia verrucosa, APC epitopes as universal adjuvants for antiviral vaccination were in Pterois volitans and Conus monile. Conus pennaceus-derived anticancer peptides showed antiangiogenic and IL-2-inducing properties with moderate BBB-permeation and were defined to be a tumor-homing peptide (THP) with the ability to inhibit programmed death ligand-1 (PDL-1). Isoforms of RGD-containing peptides with innate antiangiogenic characteristics were in Conus tessulatus for tumor targeting. Inhibitors of neuropilin-1 in C. pennaceus are proposed for imaging probes or therapeutic delivery. A Conus betulinus cryptic peptide, with BBB-permeation, mitochondrial-targeting, and antioxidant capacity, was a stimulator of anti-inflammatory cytokines and non-inducer of proinflammation proposed for Alzheimer's. Conclusively, we have considered the dynamic interaction of cells, their microenvironment, and proportional-orchestrating-host- immune pathways by multi-target-directed CPPs resembling single-molecule polypharmacology. This strategy might fill the therapeutic gap in complex resistant disorders and increase the candidates' clinical-translation chance.
Collapse
Affiliation(s)
- Shiva Hemmati
- Department of Pharmaceutical Biotechnology, School of Pharmacy, Shiraz University of Medical Sciences, Shiraz 71345-1583, Iran
- Department of Pharmaceutical Biology, Faculty of Pharmaceutical Sciences, UCSI University, Cheras, Kuala Lumpur 56000, Malaysia
- Biotechnology Research Center, Shiraz University of Medical Sciences, Shiraz 71345-1583, Iran
- Correspondence: ; Tel.: +98-7132-424-128
| | | |
Collapse
|
23
|
Hasegawa K, Moriwaki Y, Terada T, Wei C, Shimizu K. Feedback-AVPGAN: Feedback-guided generative adversarial network for generating antiviral peptides. J Bioinform Comput Biol 2022; 20:2250026. [PMID: 36514872 DOI: 10.1142/s0219720022500263] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
In this study, we propose Feedback-AVPGAN, a system that aims to computationally generate novel antiviral peptides (AVPs). This system relies on the key premise of the Generative Adversarial Network (GAN) model and the Feedback method. GAN, a generative modeling approach that uses deep learning methods, comprises a generator and a discriminator. The generator is used to generate peptides; the generated proteins are fed to the discriminator to distinguish between the AVPs and non-AVPs. The original GAN design uses actual data to train the discriminator. However, not many AVPs have been experimentally obtained. To solve this problem, we used the Feedback method to allow the discriminator to learn from the existing as well as generated synthetic data. We implemented this method using a classifier module that classifies each peptide sequence generated by the GAN generator as AVP or non-AVP. The classifier uses the transformer network and achieves high classification accuracy. This mechanism enables the efficient generation of peptides with a high probability of exhibiting antiviral activity. Using the Feedback method, we evaluated various algorithms and their performance. Moreover, we modeled the structure of the generated peptides using AlphaFold2 and determined the peptides having similar physicochemical properties and structures to those of known AVPs, although with different sequences.
Collapse
Affiliation(s)
- Kano Hasegawa
- Department of Biotechnology, Graduate School of Agricultural and Life Sciences, Faculty of Agriculture The University of Tokyo, 1-1-1, Yayoi, Bunkyo-ku, Tokyo 113-8657, Japan
| | - Yoshitaka Moriwaki
- Department of Biotechnology, Graduate School of Agricultural and Life Sciences, Faculty of Agriculture The University of Tokyo, 1-1-1, Yayoi, Bunkyo-ku, Tokyo 113-8657, Japan.,Collaborative Research Institute for Innovative Microbiology, The Institute of Medical Science The University of Tokyo, 1-1-1, Yayoi, Bunkyo-ku, Tokyo 113-8657, Japan
| | - Tohru Terada
- Department of Biotechnology, Graduate School of Agricultural and Life Sciences, Faculty of Agriculture The University of Tokyo, 1-1-1, Yayoi, Bunkyo-ku, Tokyo 113-8657, Japan.,Collaborative Research Institute for Innovative Microbiology, The Institute of Medical Science The University of Tokyo, 1-1-1, Yayoi, Bunkyo-ku, Tokyo 113-8657, Japan
| | - Cao Wei
- Research Center for Agricultural Information Technology, National Agriculture and Food Research Organization, Tsukuba, Ibaraki 305-8517, Japan
| | - Kentaro Shimizu
- Department of Biotechnology, Graduate School of Agricultural and Life Sciences, Faculty of Agriculture The University of Tokyo, 1-1-1, Yayoi, Bunkyo-ku, Tokyo 113-8657, Japan.,Collaborative Research Institute for Innovative Microbiology, The Institute of Medical Science The University of Tokyo, 1-1-1, Yayoi, Bunkyo-ku, Tokyo 113-8657, Japan
| |
Collapse
|
24
|
Huang KY, Kao HJ, Weng TH, Chen CH, Weng SL. iDVIP: identification and characterization of viral integrase inhibitory peptides. Brief Bioinform 2022; 23:6754756. [PMID: 36215051 DOI: 10.1093/bib/bbac406] [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: 05/26/2022] [Revised: 08/12/2022] [Accepted: 08/22/2022] [Indexed: 12/14/2022] Open
Abstract
Antiretroviral peptides are a kind of bioactive peptides that present inhibitory activity against retroviruses through various mechanisms. Among them, viral integrase inhibitory peptides (VINIPs) are a class of antiretroviral peptides that have the ability to block the action of integrase proteins, which is essential for retroviral replication. As the number of experimentally verified bioactive peptides has increased significantly, the lack of in silico machine learning approaches can effectively predict the peptides with the integrase inhibitory activity. Here, we have developed the first prediction model for identifying the novel VINIPs using the sequence characteristics, and the hybrid feature set was considered to improve the predictive ability. The performance was evaluated by 5-fold cross-validation based on the training dataset, and the result indicates the proposed model is capable of predicting the VINIPs, with a sensitivity of 85.82%, a specificity of 88.81%, an accuracy of 88.37%, a balanced accuracy of 87.32% and a Matthews correlation coefficient value of 0.64. Most importantly, the model also consistently provides effective performance in independent testing. To sum up, we propose the first computational approach for identifying and characterizing the VINIPs, which can be considered novel antiretroviral therapy agents. Ultimately, to facilitate further research and development, iDVIP, an automatic computational tool that predicts the VINIPs has been developed, which is now freely available at http://mer.hc.mmh.org.tw/iDVIP/.
Collapse
Affiliation(s)
- Kai-Yao Huang
- Department of Medical Research, Hsinchu MacKay Memorial Hospital, Hsinchu city 300, Taiwan.,Department of Medicine, MacKay Medical College, New Taipei City 252, Taiwan
| | - Hui-Ju Kao
- Department of Medical Research, Hsinchu MacKay Memorial Hospital, Hsinchu city 300, Taiwan
| | - Tzu-Hsiang Weng
- Department of Obstetrics and Gynecology, MacKay Memorial Hospital, Taipei city 104, Taiwan
| | - Chia-Hung Chen
- Department of Medical Research, Hsinchu MacKay Memorial Hospital, Hsinchu city 300, Taiwan
| | - Shun-Long Weng
- Department of Medicine, MacKay Medical College, New Taipei City 252, Taiwan.,Department of Obstetrics and Gynecology, Hsinchu MacKay Memorial Hospital, Hsinchu city 300, Taiwan.,MacKay Junior College of Medicine, Nursing and Management, Taipei 112, Taiwan
| |
Collapse
|
25
|
Lin TT, Sun YY, Wang CT, Cheng WC, Lu IH, Lin CY, Chen SH. AI4AVP: an antiviral peptides predictor in deep learning approach with generative adversarial network data augmentation. BIOINFORMATICS ADVANCES 2022; 2:vbac080. [PMID: 36699402 PMCID: PMC9710571 DOI: 10.1093/bioadv/vbac080] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Revised: 09/14/2022] [Accepted: 10/25/2022] [Indexed: 11/05/2022]
Abstract
Motivation Antiviral peptides (AVPs) from various sources suggest the possibility of developing peptide drugs for treating viral diseases. Because of the increasing number of identified AVPs and the advances in deep learning theory, it is reasonable to experiment with peptide drug design using in silico methods. Results We collected the most up-to-date AVPs and used deep learning to construct a sequence-based binary classifier. A generative adversarial network was employed to augment the number of AVPs in the positive training dataset and enable our deep learning convolutional neural network (CNN) model to learn from the negative dataset. Our classifier outperformed other state-of-the-art classifiers when using the testing dataset. We have placed the trained classifiers on a user-friendly web server, AI4AVP, for the research community. Availability and implementation AI4AVP is freely accessible at http://axp.iis.sinica.edu.tw/AI4AVP/; codes and datasets for the peptide GAN and the AVP predictor CNN are available at https://github.com/lsbnb/amp_gan and https://github.com/LinTzuTang/AI4AVP_predictor. Supplementary information Supplementary data are available at Bioinformatics Advances online.
Collapse
Affiliation(s)
- Tzu-Tang Lin
- Institute of Information Science, Academia Sinica, Taipei 115, Taiwan
| | - Yih-Yun Sun
- Institute of Information Science, Academia Sinica, Taipei 115, Taiwan.,Graduate Institute of Biomedical Electronics and Bioinformatics, College of Electrical Engineering and Computer Science, National Taiwan University, Taipei 106, Taiwan
| | - Ching-Tien Wang
- Institute of Information Science, Academia Sinica, Taipei 115, Taiwan
| | - Wen-Chih Cheng
- Institute of Information Science, Academia Sinica, Taipei 115, Taiwan
| | - I-Hsuan Lu
- Institute of Information Science, Academia Sinica, Taipei 115, Taiwan
| | - Chung-Yen Lin
- Institute of Information Science, Academia Sinica, Taipei 115, Taiwan.,Institute of Fisheries Science, National Taiwan University, Taipei 106, Taiwan.,Genome and Systems Biology Degree Program, National Taiwan University, Taipei 106, Taiwan
| | - Shu-Hwa Chen
- TMU Research Center of Cancer Translational Medicine, Taipei Medical University, Taipei 110, Taiwan
| |
Collapse
|
26
|
Porosk L, Langel Ü. Approaches for evaluation of novel CPP-based cargo delivery systems. Front Pharmacol 2022; 13:1056467. [PMID: 36339538 PMCID: PMC9634181 DOI: 10.3389/fphar.2022.1056467] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Accepted: 10/10/2022] [Indexed: 08/05/2023] Open
Abstract
Cell penetrating peptides (CPPs) can be broadly defined as relatively short synthetic, protein derived or chimeric peptides. Their most remarkable property is their ability to cross cell barriers and facilitate the translocation of cargo, such as drugs, nucleic acids, peptides, small molecules, dyes, and many others across the plasma membrane. Over the years there have been several approaches used, adapted, and developed for the evaluation of CPP efficacies as delivery systems, with the fluorophore attachment as the most widely used approach. It has become progressively evident, that the evaluation method, in order to lead to successful outcome, should concede with the specialties of the delivery. For characterization and assessment of CPP-cargo a combination of research tools of chemistry, physics, molecular biology, engineering, and other fields have been applied. In this review, we summarize the diverse, in silico, in vitro and in vivo approaches used for evaluation and characterization of CPP-based cargo delivery systems.
Collapse
Affiliation(s)
- Ly Porosk
- Laboratory of Drug Delivery, Institute of Technology, Faculty of Science and Technology, University of Tartu, Tartu, Estonia
| | - Ülo Langel
- Laboratory of Drug Delivery, Institute of Technology, Faculty of Science and Technology, University of Tartu, Tartu, Estonia
- Department of Biochemistry and Biophysics, Stockholm University, Stockholm, Sweden
| |
Collapse
|
27
|
Harnkit N, Khongsonthi T, Masuwan N, Prasartkul P, Noikaew T, Chumnanpuen P. Virtual Screening for SARS-CoV-2 Main Protease Inhibitory Peptides from the Putative Hydrolyzed Peptidome of Rice Bran. Antibiotics (Basel) 2022; 11:antibiotics11101318. [PMID: 36289976 PMCID: PMC9598432 DOI: 10.3390/antibiotics11101318] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Revised: 09/21/2022] [Accepted: 09/26/2022] [Indexed: 11/16/2022] Open
Abstract
The Coronavirus Disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has led to the loss of life and has affected the life quality, economy, and lifestyle. The SARS-CoV-2 main protease (Mpro), which hydrolyzes the polyprotein, is an interesting antiviral target to inhibit the spreading mechanism of COVID-19. Through predictive digestion, the peptidomes of the four major proteins in rice bran, albumin, glutelin, globulin, and prolamin, with three protease enzymes (pepsin, trypsin, and chymotrypsin), the putative hydrolyzed peptidome was established and used as the input dataset. Then, the prediction of the antiviral peptides (AVPs) was performed by online bioinformatics tools, i.e., AVPpred, Meta-iAVP, AMPfun, and ENNAVIA programs. The amino acid composition and cytotoxicity of candidate AVPs were analyzed by COPid and ToxinPred, respectively. The ten top-ranked antiviral peptides were selected and docked to the SARS-CoV-2 main protease using GalaxyPepDock. Only the top docking scored candidate (AVP4) was further analyzed by molecular dynamics simulation for one nanosecond. According to the bioinformatic analysis results, the candidate SARS-CoV-2 main protease inhibitory peptides were 7–33 amino acid residues and formed hydrogen bonds at Thr22–24, Glu154, and Thr178 in domain 2 with short bonding distances. In addition, these top-ten candidate bioactive peptides contain hydrophilic amino acid residues and have a positive net charge. We hope that this study will provide a potential starting point for peptide-based therapeutic agents against COVID-19.
Collapse
Affiliation(s)
- Nathaphat Harnkit
- Medicinal Plant Research Institute, Department of Medical Sciences, Ministry of Public Health, Nonthaburi 11000, Thailand
| | - Thanakamol Khongsonthi
- Mahidol Wittayanusorn School, 364 Salaya, Phuttamonthon District, Nakhon Prathom 73170, Thailand
| | - Noprada Masuwan
- Mahidol Wittayanusorn School, 364 Salaya, Phuttamonthon District, Nakhon Prathom 73170, Thailand
| | - Pornpinit Prasartkul
- Mahidol Wittayanusorn School, 364 Salaya, Phuttamonthon District, Nakhon Prathom 73170, Thailand
| | - Tipanart Noikaew
- Department of Biology and Health Science, Mahidol Wittayanusorn School, 364 Salaya, Phuttamonthon District, Nakhon Prathom 73170, Thailand
| | - Pramote Chumnanpuen
- Omics Center for Agriculture, Bioresources, Food and Health, Kasetsart University (OmiKU), Bangkok 10900, Thailand
- Department of Zoology, Faculty of Science, Kasetsart University, Bangkok 10900, Thailand
- Correspondence:
| |
Collapse
|
28
|
Herrera-Bravo J, Farías JG, Sandoval C, Herrera-Belén L, Quiñones J, Díaz R, Beltrán JF. nAChR-PEP-PRED: A Robust Tool for Predicting Peptide Inhibitors of Acetylcholine Receptors Using the Random Forest Classifier. Int J Pept Res Ther 2022. [DOI: 10.1007/s10989-022-10460-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
|
29
|
In Silico Prediction of Anti-Infective and Cell-Penetrating Peptides from Thalassophryne nattereri Natterin Toxins. Pharmaceuticals (Basel) 2022; 15:ph15091141. [PMID: 36145362 PMCID: PMC9501638 DOI: 10.3390/ph15091141] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2022] [Revised: 09/01/2022] [Accepted: 09/06/2022] [Indexed: 12/14/2022] Open
Abstract
The therapeutic potential of venom-derived peptides, such as bioactive peptides (BAPs), is determined by specificity, stability, and pharmacokinetics properties. BAPs, including anti-infective or antimicrobial peptides (AMPs) and cell-penetrating peptides (CPPs), share several physicochemical characteristics and are potential alternatives to antibiotic-based therapies and drug delivery systems, respectively. This study used in silico methods to predict AMPs and CPPs derived from natterins from the venomous fish Thalassophryne nattereri. Fifty-seven BAPs (19 AMPs, 8 CPPs, and 30 AMPs/CPPs) were identified using the web servers CAMP, AMPA, AmpGram, C2Pred, and CellPPD. The physicochemical properties were analyzed using ProtParam, PepCalc, and DispHred tools. The membrane-binding potential and cellular location of each peptide were analyzed using the Boman index by APD3, and TMHMM web servers. All CPPs and two AMPs showed high membrane-binding potential. Fifty-four peptides were located in the plasma membrane. Peptide immunogenicity, toxicity, allergenicity, and ADMET parameters were evaluated using several web servers. Sixteen antiviral peptides and 37 anticancer peptides were predicted using the web servers Meta-iAVP and ACPred. Secondary structures and helical wheel projections were predicted using the PEP-FOLD3 and Heliquest web servers. Fifteen peptides are potential lead compounds and were selected to be further synthesized and tested experimentally in vitro to validate the in silico screening. The use of computer-aided design for predicting peptide structure and activity is fast and cost-effective and facilitates the design of potent therapeutic peptides. The results demonstrate that toxins form a natural biotechnological platform in drug discovery, and the presence of CPP and AMP sequences in toxin families opens new possibilities in toxin biochemistry research.
Collapse
|
30
|
Juretić D. Designed Multifunctional Peptides for Intracellular Targets. Antibiotics (Basel) 2022; 11:antibiotics11091196. [PMID: 36139975 PMCID: PMC9495127 DOI: 10.3390/antibiotics11091196] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Revised: 08/25/2022] [Accepted: 08/29/2022] [Indexed: 11/25/2022] Open
Abstract
Nature’s way for bioactive peptides is to provide them with several related functions and the ability to cooperate in performing their job. Natural cell-penetrating peptides (CPP), such as penetratins, inspired the design of multifunctional constructs with CPP ability. This review focuses on known and novel peptides that can easily reach intracellular targets with little or no toxicity to mammalian cells. All peptide candidates were evaluated and ranked according to the predictions of low toxicity to mammalian cells and broad-spectrum activity. The final set of the 20 best peptide candidates contains the peptides optimized for cell-penetrating, antimicrobial, anticancer, antiviral, antifungal, and anti-inflammatory activity. Their predicted features are intrinsic disorder and the ability to acquire an amphipathic structure upon contact with membranes or nucleic acids. In conclusion, the review argues for exploring wide-spectrum multifunctionality for novel nontoxic hybrids with cell-penetrating peptides.
Collapse
Affiliation(s)
- Davor Juretić
- Mediterranean Institute for Life Sciences, 21000 Split, Croatia;
- Faculty of Science, University of Split, 21000 Split, Croatia;
| |
Collapse
|
31
|
Antimicrobial peptides with cell-penetrating activity as prophylactic and treatment drugs. Biosci Rep 2022; 42:231731. [PMID: 36052730 PMCID: PMC9508529 DOI: 10.1042/bsr20221789] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2022] [Revised: 08/31/2022] [Accepted: 09/01/2022] [Indexed: 01/18/2023] Open
Abstract
Health is fundamental for the development of individuals and evolution of species. In that sense, for human societies is relevant to understand how the human body has developed molecular strategies to maintain health. In the present review, we summarize diverse evidence that support the role of peptides in this endeavor. Of particular interest to the present review are antimicrobial peptides (AMP) and cell-penetrating peptides (CPP). Different experimental evidence indicates that AMP/CPP are able to regulate autophagy, which in turn regulates the immune system response. AMP also assists in the establishment of the microbiota, which in turn is critical for different behavioral and health aspects of humans. Thus, AMP and CPP are multifunctional peptides that regulate two aspects of our bodies that are fundamental to our health: autophagy and microbiota. While it is now clear the multifunctional nature of these peptides, we are still in the early stages of the development of computational strategies aimed to assist experimentalists in identifying selective multifunctional AMP/CPP to control nonhealthy conditions. For instance, both AMP and CPP are computationally characterized as amphipatic and cationic, yet none of these features are relevant to differentiate these peptides from non-AMP or non-CPP. The present review aims to highlight current knowledge that may facilitate the development of AMP’s design tools for preventing or treating illness.
Collapse
|
32
|
Kurata H, Tsukiyama S, Manavalan B. iACVP: markedly enhanced identification of anti-coronavirus peptides using a dataset-specific word2vec model. Brief Bioinform 2022; 23:6623727. [PMID: 35772910 DOI: 10.1093/bib/bbac265] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Revised: 05/23/2022] [Accepted: 06/06/2022] [Indexed: 01/22/2023] Open
Abstract
The COVID-19 pandemic caused several million deaths worldwide. Development of anti-coronavirus drugs is thus urgent. Unlike conventional non-peptide drugs, antiviral peptide drugs are highly specific, easy to synthesize and modify, and not highly susceptible to drug resistance. To reduce the time and expense involved in screening thousands of peptides and assaying their antiviral activity, computational predictors for identifying anti-coronavirus peptides (ACVPs) are needed. However, few experimentally verified ACVP samples are available, even though a relatively large number of antiviral peptides (AVPs) have been discovered. In this study, we attempted to predict ACVPs using an AVP dataset and a small collection of ACVPs. Using conventional features, a binary profile and a word-embedding word2vec (W2V), we systematically explored five different machine learning methods: Transformer, Convolutional Neural Network, bidirectional Long Short-Term Memory, Random Forest (RF) and Support Vector Machine. Via exhaustive searches, we found that the RF classifier with W2V consistently achieved better performance on different datasets. The two main controlling factors were: (i) the dataset-specific W2V dictionary was generated from the training and independent test datasets instead of the widely used general UniProt proteome and (ii) a systematic search was conducted and determined the optimal k-mer value in W2V, which provides greater discrimination between positive and negative samples. Therefore, our proposed method, named iACVP, consistently provides better prediction performance compared with existing state-of-the-art methods. To assist experimentalists in identifying putative ACVPs, we implemented our model as a web server accessible via the following link: http://kurata35.bio.kyutech.ac.jp/iACVP.
Collapse
Affiliation(s)
- Hiroyuki Kurata
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan
| | - Sho Tsukiyama
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan
| | - Balachandran Manavalan
- Computational Biology and Bioinformatics Laboratory, Department of Integrative Biotechnology, College of Biotechnology and Bioengineering, Sungkyunkwan University, Suwon 16419, Gyeonggi-do, Republic of Korea
| |
Collapse
|
33
|
Otović E, Njirjak M, Kalafatovic D, Mauša G. Sequential Properties Representation Scheme for Recurrent Neural Network-Based Prediction of Therapeutic Peptides. J Chem Inf Model 2022; 62:2961-2972. [PMID: 35704881 DOI: 10.1021/acs.jcim.2c00526] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
The discovery of therapeutic peptides is often accelerated by means of virtual screening supported by machine learning-based predictive models. The predictive performance of such models is sensitive to the choice of data and its representation scheme. While the peptide physicochemical and compositional representations fail to distinguish sequence permutations, the amino acid arrangement within the sequence lacks the important information contained in physicochemical, conformational, topological, and geometrical properties. In this paper, we propose a solution to the identified information gap by implementing a hybrid scheme that complements the best traits from both approaches with the aim of predicting antimicrobial and antiviral activities based on experimental data from DRAMP 2.0, AVPdb, and Uniprot data repositories. Using the Friedman test of statistical significance, we compared our hybrid, sequential properties approach to peptide properties, one-hot vector encoding, and word embedding schemes in the 10-fold cross-validation setting, with respect to the F1 score, Matthews correlation coefficient, geometric mean, recall, and precision evaluation metrics. Moreover, the sequence modeling neural network was employed to gain insight into the synergic effect of both properties- and amino acid order-based predictions. The results suggest that sequential properties significantly (P < 0.01) surpasses the aforementioned state-of-the-art representation schemes. This makes it a strong candidate for increasing the predictive power of screening methods based on machine learning, applicable to any category of peptides.
Collapse
Affiliation(s)
- Erik Otović
- University of Rijeka, Faculty of Engineering, 51000 Rijeka, Croatia
| | - Marko Njirjak
- University of Rijeka, Faculty of Engineering, 51000 Rijeka, Croatia
| | - Daniela Kalafatovic
- University of Rijeka, Department of Biotechnology, 51000 Rijeka, Croatia.,University of Rijeka, Center for Artificial Intelligence and Cybersecurity, 51000 Rijeka, Croatia
| | - Goran Mauša
- University of Rijeka, Faculty of Engineering, 51000 Rijeka, Croatia.,University of Rijeka, Center for Artificial Intelligence and Cybersecurity, 51000 Rijeka, Croatia
| |
Collapse
|
34
|
Charoenkwan P, Ahmed S, Nantasenamat C, Quinn JMW, Moni MA, Lio' P, Shoombuatong W. AMYPred-FRL is a novel approach for accurate prediction of amyloid proteins by using feature representation learning. Sci Rep 2022; 12:7697. [PMID: 35546347 PMCID: PMC9095707 DOI: 10.1038/s41598-022-11897-z] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2021] [Accepted: 05/03/2022] [Indexed: 12/13/2022] Open
Abstract
Amyloid proteins have the ability to form insoluble fibril aggregates that have important pathogenic effects in many tissues. Such amyloidoses are prominently associated with common diseases such as type 2 diabetes, Alzheimer's disease, and Parkinson's disease. There are many types of amyloid proteins, and some proteins that form amyloid aggregates when in a misfolded state. It is difficult to identify such amyloid proteins and their pathogenic properties, but a new and effective approach is by developing effective bioinformatics tools. While several machine learning (ML)-based models for in silico identification of amyloid proteins have been proposed, their predictive performance is limited. In this study, we present AMYPred-FRL, a novel meta-predictor that uses a feature representation learning approach to achieve more accurate amyloid protein identification. AMYPred-FRL combined six well-known ML algorithms (extremely randomized tree, extreme gradient boosting, k-nearest neighbor, logistic regression, random forest, and support vector machine) with ten different sequence-based feature descriptors to generate 60 probabilistic features (PFs), as opposed to state-of-the-art methods developed by a single feature-based approach. A logistic regression recursive feature elimination (LR-RFE) method was used to find the optimal m number of 60 PFs in order to improve the predictive performance. Finally, using the meta-predictor approach, the 20 selected PFs were fed into a logistic regression method to create the final hybrid model (AMYPred-FRL). Both cross-validation and independent tests showed that AMYPred-FRL achieved superior predictive performance than its constituent baseline models. In an extensive independent test, AMYPred-FRL outperformed the existing methods by 5.5% and 16.1%, respectively, with accuracy and MCC of 0.873 and 0.710. To expedite high-throughput prediction, a user-friendly web server of AMYPred-FRL is freely available at http://pmlabstack.pythonanywhere.com/AMYPred-FRL. It is anticipated that AMYPred-FRL will be a useful tool in helping researchers to identify new amyloid proteins.
Collapse
Affiliation(s)
- Phasit Charoenkwan
- Modern Management and Information Technology, College of Arts, Media and Technology, Chiang Mai University, Chiang Mai, 50200, Thailand
| | - Saeed Ahmed
- Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, 10700, Thailand
| | - Chanin Nantasenamat
- Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, 10700, Thailand
| | - Julian M W Quinn
- Bone Biology Division, Garvan Institute of Medical Research, 384 Victoria Street, Darlinghurst, NSW, 2010, Australia
| | - Mohammad Ali Moni
- Artificial Intelligence and Digital Health Data Science, School of Health and Rehabilitation Sciences, Faculty of Health and Behavioural Sciences, The University of Queensland, St Lucia, QLD, 4072, Australia
| | - Pietro Lio'
- Department of Computer Science and Technology, University of Cambridge, Cambridge, CB3 0FD, UK
| | - Watshara Shoombuatong
- Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, 10700, Thailand.
| |
Collapse
|
35
|
Delaunay M, Ha-Duong T. Computational Tools and Strategies to Develop Peptide-Based Inhibitors of Protein-Protein Interactions. METHODS IN MOLECULAR BIOLOGY (CLIFTON, N.J.) 2022; 2405:205-230. [PMID: 35298816 DOI: 10.1007/978-1-0716-1855-4_11] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Protein-protein interactions play crucial and subtle roles in many biological processes and modifications of their fine mechanisms generally result in severe diseases. Peptide derivatives are very promising therapeutic agents for modulating protein-protein associations with sizes and specificities between those of small compounds and antibodies. For the same reasons, rational design of peptide-based inhibitors naturally borrows and combines computational methods from both protein-ligand and protein-protein research fields. In this chapter, we aim to provide an overview of computational tools and approaches used for identifying and optimizing peptides that target protein-protein interfaces with high affinity and specificity. We hope that this review will help to implement appropriate in silico strategies for peptide-based drug design that builds on available information for the systems of interest.
Collapse
Affiliation(s)
| | - Tâp Ha-Duong
- Université Paris-Saclay, CNRS, BioCIS, Châtenay-Malabry, France.
| |
Collapse
|
36
|
Manavalan B, Basith S, Lee G. Comparative analysis of machine learning-based approaches for identifying therapeutic peptides targeting SARS-CoV-2. Brief Bioinform 2022; 23:bbab412. [PMID: 34595489 PMCID: PMC8500067 DOI: 10.1093/bib/bbab412] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Revised: 08/27/2021] [Accepted: 09/07/2021] [Indexed: 01/08/2023] Open
Abstract
Coronavirus disease 2019 (COVID-19) has impacted public health as well as societal and economic well-being. In the last two decades, various prediction algorithms and tools have been developed for predicting antiviral peptides (AVPs). The current COVID-19 pandemic has underscored the need to develop more efficient and accurate machine learning (ML)-based prediction algorithms for the rapid identification of therapeutic peptides against severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). Several peptide-based ML approaches, including anti-coronavirus peptides (ACVPs), IL-6 inducing epitopes and other epitopes targeting SARS-CoV-2, have been implemented in COVID-19 therapeutics. Owing to the growing interest in the COVID-19 field, it is crucial to systematically compare the existing ML algorithms based on their performances. Accordingly, we comprehensively evaluated the state-of-the-art IL-6 and AVP predictors against coronaviruses in terms of core algorithms, feature encoding schemes, performance evaluation metrics and software usability. A comprehensive performance assessment was then conducted to evaluate the robustness and scalability of the existing predictors using well-constructed independent validation datasets. Additionally, we discussed the advantages and disadvantages of the existing methods, providing useful insights into the development of novel computational tools for characterizing and identifying epitopes or ACVPs. The insights gained from this review are anticipated to provide critical guidance to the scientific community in the rapid design and development of accurate and efficient next-generation in silico tools against SARS-CoV-2.
Collapse
Affiliation(s)
| | - Shaherin Basith
- Department of Physiology, Ajou University School of Medicine, Suwon 16499, Korea
| | - Gwang Lee
- Department of Physiology, Ajou University School of Medicine, Suwon 16499, Korea
| |
Collapse
|
37
|
Qiu WR, Guan MY, Wang QK, Lou LL, Xiao X. Identifying Pupylation Proteins and Sites by Incorporating Multiple Methods. Front Endocrinol (Lausanne) 2022; 13:849549. [PMID: 35557849 PMCID: PMC9088680 DOI: 10.3389/fendo.2022.849549] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/06/2022] [Accepted: 03/07/2022] [Indexed: 11/20/2022] Open
Abstract
Pupylation is an important posttranslational modification in proteins and plays a key role in the cell function of microorganisms; an accurate prediction of pupylation proteins and specified sites is of great significance for the study of basic biological processes and development of related drugs since it would greatly save experimental costs and improve work efficiency. In this work, we first constructed a model for identifying pupylation proteins. To improve the pupylation protein prediction model, the KNN scoring matrix model based on functional domain GO annotation and the Word Embedding model were used to extract the features and Random Under-sampling (RUS) and Synthetic Minority Over-sampling Technique (SMOTE) were applied to balance the dataset. Finally, the balanced data sets were input into Extreme Gradient Boosting (XGBoost). The performance of 10-fold cross-validation shows that accuracy (ACC), Matthew's correlation coefficient (MCC), and area under the ROC curve (AUC) are 95.23%, 0.8100, and 0.9864, respectively. For the pupylation site prediction model, six feature extraction codes (i.e., TPC, AAI, One-hot, PseAAC, CKSAAP, and Word Embedding) served to extract protein sequence features, and the chi-square test was employed for feature selection. Rigorous 10-fold cross-validations indicated that the accuracies are very high and outperformed its existing counterparts. Finally, for the convenience of researchers, PUP-PS-Fuse has been established at https://bioinfo.jcu.edu.cn/PUP-PS-Fuse and http://121.36.221.79/PUP-PS-Fuse/as a backup.
Collapse
Affiliation(s)
| | | | | | | | - Xuan Xiao
- *Correspondence: Wang-Ren Qiu, ; Xuan Xiao,
| |
Collapse
|
38
|
Liou JW, Chang CC, Hsu HJ, Wu TY. Computer-aided discovery, design, and investigation of COVID-19 therapeutics. Tzu Chi Med J 2022; 34:276-286. [PMID: 35912059 PMCID: PMC9333103 DOI: 10.4103/tcmj.tcmj_318_21] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Revised: 12/20/2021] [Accepted: 12/30/2021] [Indexed: 11/22/2022] Open
Abstract
Coronavirus disease 2019 (COVID-19) pandemic is currently the most serious public health threat faced by mankind. Thus, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes COVID-19, is being intensively investigated. Several vaccines are now available for clinical use. However, owing to the highly mutated nature of RNA viruses, the SARS-CoV-2 is changing at a rapid speed. Breakthrough infections by SARS-CoV-2 variants have been seen in vaccinated individuals. As a result, effective therapeutics for treating COVID-19 patients is urgently required. With the advance of computer technology, computational methods have become increasingly powerful in the biomedical research and pharmaceutical drug discovery. The applications of these techniques have largely reduced the costs and simplified processes of pharmaceutical drug developments. Intensive and extensive studies on SARS-CoV-2 proteins have been carried out and three-dimensional structures of the major SARS-CoV-2 proteins have been resolved and deposited in the Protein Data Bank. These structures provide the foundations for drug discovery and design using the structure-based computations, such as molecular docking and molecular dynamics simulations. In this review, introduction to the applications of computational methods in the discovery and design of novel drugs and repurposing of existing drugs for the treatments of COVID-19 is given. The examples of computer-aided investigations and screening of COVID-19 effective therapeutic compounds, functional peptides, as well as effective molecules from the herb medicines are discussed.
Collapse
|
39
|
Dutta K, Elmezayen AD, Al-Obaidi A, Zhu W, Morozova OV, Shityakov S, Khalifa I. Seq12, Seq12m, and Seq13m, peptide analogues of the spike glycoprotein shows antiviral properties against SARS-CoV-2: An in silico study through molecular docking, molecular dynamics simulation, and MM-PB/GBSA calculations. J Mol Struct 2021; 1246:131113. [PMID: 34305174 PMCID: PMC8283670 DOI: 10.1016/j.molstruc.2021.131113] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Revised: 07/10/2021] [Accepted: 07/12/2021] [Indexed: 02/07/2023]
Abstract
At the very beginning of the new decade, the COVID-19 pandemic has badly hit modern human societies. SARS-CoV-2, the causative agent of COVID-19 acquiring mutations and circulating as new variants. Herein, we have found three new antiviral peptides (AVPs) against the SARS-CoV-2. These AVPs are analogous to the spike glycoprotein of the SARS-CoV-2. Antiviral peptides, i.e., Seq12, Seq12m, and Seq13m, can block the receptor-binding domain (RBD) of the SARS-CoV-2, which is necessary for communicating with the angiotensin-converting enzyme 2 (ACE2). Also, these AVPs sustain their antiviral properties, even after the insertion of 25 mutations in the RBD (Rosetta and FoldX based). Further, Seq12 and Seq12m showed negligible cytotoxicity. Besides, the binding free energies calculated using MM-PB/GBSA method are also in agreement with the molecular docking studies. The molecular interactions between AVPs and the viral membrane protein (M) also showed a favorable interaction suggesting it could inhibit the viral re-packaging process. In conclusion, this study suggests Seq12, Seq12m, and Seq13m could be helpful to fight against SARS-CoV-2. These AVPs could also aid virus diagnostic tools and nasal spray against SARS-CoV-2 in the future.
Collapse
Affiliation(s)
- Kunal Dutta
- Department of Human Physiology, Vidyasagar University, Midnapore 721102, West Bengal, India
| | - Ammar D Elmezayen
- Department of Bioinformatics and Genetics, Faculty of Engineering and Natural Sciences, Kadir Has University, Cibali 34083, Istanbul, Turkey
| | - Anas Al-Obaidi
- Department of Bioinformatics and Genetics, Faculty of Engineering and Natural Sciences, Kadir Has University, Cibali 34083, Istanbul, Turkey
| | - Wei Zhu
- College of Food Science and Technology, Huazhong Agricultural University, Key Laboratory of Environment Correlative Food Science, Ministry of Education, Wuhan 430070, China
| | - Olga V Morozova
- I.N. Blokhina Nizhny Novgorod Research Institute of Epidemiology and Microbiology, 71 Malaya Yamskaya Str., Nizhny Novgorod 603950, Russian Federation
| | - Sergey Shityakov
- Laboratory of Chemoinformatics, Infochemistry Scientific Center, ITMO University, 191002 Saint-Petersburg, Russian Federation
| | - Ibrahim Khalifa
- Food Technology Department, Faculty of Agriculture, Moshtohor 13736, Benha University, Egypt
| |
Collapse
|
40
|
He W, Jiang Y, Jin J, Li Z, Zhao J, Manavalan B, Su R, Gao X, Wei L. Accelerating bioactive peptide discovery via mutual information-based meta-learning. Brief Bioinform 2021; 23:6457168. [PMID: 34882225 DOI: 10.1093/bib/bbab499] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Revised: 10/07/2021] [Accepted: 10/30/2021] [Indexed: 12/28/2022] Open
Abstract
Recently, machine learning methods have been developed to identify various peptide bio-activities. However, due to the lack of experimentally validated peptides, machine learning methods cannot provide a sufficiently trained model, easily resulting in poor generalizability. Furthermore, there is no generic computational framework to predict the bioactivities of different peptides. Thus, a natural question is whether we can use limited samples to build an effective predictive model for different kinds of peptides. To address this question, we propose Mutual Information Maximization Meta-Learning (MIMML), a novel meta-learning-based predictive model for bioactive peptide discovery. Using few samples from various functional peptides, MIMML can sufficiently learn the discriminative information amongst various functions and characterize functional differences. Experimental results show excellent performance of MIMML though using far fewer training samples as compared to the state-of-the-art methods. We also decipher the latent relationships among different kinds of functions to understand what meta-model learned to improve a specific task. In summary, this study is a pioneering work in the field of functional peptide mining and provides the first-of-its-kind solution for few-sample learning problems in biological sequence analysis, accelerating the new functional peptide discovery. The source codes and datasets are available on https://github.com/TearsWaiting/MIMML.
Collapse
Affiliation(s)
- Wenjia He
- School of Software, Shandong University, Jinan, China.,Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan, China.,BioMap, Beijing, China
| | - Yi Jiang
- School of Software, Shandong University, Jinan, China.,Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan, China
| | - Junru Jin
- School of Software, Shandong University, Jinan, China.,Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan, China
| | - Zhongshen Li
- School of Software, Shandong University, Jinan, China.,Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan, China
| | - Jiaojiao Zhao
- School of Software, Shandong University, Jinan, China.,Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan, China
| | | | - Ran Su
- College of Intelligence and Computing, Tianjin University, Tianjin, China
| | - Xin Gao
- King Abdullah University of Science and Technology (KAUST), Computational Bioscience Research Center (CBRC), Computer, Electrical, and Mathematical Sciences and Engineering (CEMSE) Division, Thuwal, 23955-6900, Saudi Arabia
| | - Leyi Wei
- School of Software, Shandong University, Jinan, China.,Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan, China
| |
Collapse
|
41
|
Charoenkwan P, Nantasenamat C, Hasan MM, Moni MA, Manavalan B, Shoombuatong W. UMPred-FRL: A New Approach for Accurate Prediction of Umami Peptides Using Feature Representation Learning. Int J Mol Sci 2021; 22:ijms222313124. [PMID: 34884927 PMCID: PMC8658322 DOI: 10.3390/ijms222313124] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2021] [Revised: 12/01/2021] [Accepted: 12/02/2021] [Indexed: 11/16/2022] Open
Abstract
Umami ingredients have been identified as important factors in food seasoning and production. Traditional experimental methods for characterizing peptides exhibiting umami sensory properties (umami peptides) are time-consuming, laborious, and costly. As a result, it is preferable to develop computational tools for the large-scale identification of available sequences in order to identify novel peptides with umami sensory properties. Although a computational tool has been developed for this purpose, its predictive performance is still insufficient. In this study, we use a feature representation learning approach to create a novel machine-learning meta-predictor called UMPred-FRL for improved umami peptide identification. We combined six well-known machine learning algorithms (extremely randomized trees, k-nearest neighbor, logistic regression, partial least squares, random forest, and support vector machine) with seven different feature encodings (amino acid composition, amphiphilic pseudo-amino acid composition, dipeptide composition, composition-transition-distribution, and pseudo-amino acid composition) to develop the final meta-predictor. Extensive experimental results demonstrated that UMPred-FRL was effective and achieved more accurate performance on the benchmark dataset compared to its baseline models, and consistently outperformed the existing method on the independent test dataset. Finally, to aid in the high-throughput identification of umami peptides, the UMPred-FRL web server was established and made freely available online. It is expected that UMPred-FRL will be a powerful tool for the cost-effective large-scale screening of candidate peptides with potential umami sensory properties.
Collapse
Affiliation(s)
- Phasit Charoenkwan
- Modern Management and Information Technology, College of Arts, Media and Technology, Chiang Mai University, Chiang Mai 50200, Thailand;
| | - Chanin Nantasenamat
- Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand;
| | - Md Mehedi Hasan
- Tulane Center for Biomedical Informatics and Genomics, Division of Biomedical Informatics and Genomics, John W. Deming Department of Medicine, School of Medicine, Tulane University, New Orleans, LA 70112, USA;
| | - Mohammad Ali Moni
- Artificial Intelligence & Digital Health Data Science, School of Health and Rehabilitation Sciences, Faculty of Health and Behavioural Sciences, The University of Queensland, St Lucia, QLD 4072, Australia;
| | - Balachandran Manavalan
- Department of Physiology, Ajou University School of Medicine, Suwon 16499, Korea
- Correspondence: (B.M.); (W.S.)
| | - Watshara Shoombuatong
- Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand;
- Correspondence: (B.M.); (W.S.)
| |
Collapse
|
42
|
Li Y, Cai J, Du C, Lin Y, Li S, Ma A, Qin Y. Bioinformatic analysis and antiviral effect of Periplaneta americana defensins. Virus Res 2021; 308:198627. [PMID: 34785275 DOI: 10.1016/j.virusres.2021.198627] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Revised: 11/01/2021] [Accepted: 11/06/2021] [Indexed: 01/08/2023]
Abstract
Due to the lack of an adaptive immune system, insects rely on innate immune mechanisms to fight against pathogenic infections. Two major innate immune pathways, Toll and IMD, orchestrate anti-pathogen responses by regulating the expression of antimicrobial peptide (AMP) genes. Although the antifungal or antibacterial function of AMPs has been well characterized, the antiviral role of AMPs in insects remains largely unclear. Periplaneta americana (P. americana), or the American cockroach, is used in traditional Chinese medicine as an antiviral agent; however, the underlying mechanism of action of P. americana extracts is unclear. Our previous study showed that the P. americana genome encodes multiple antimicrobial peptide genes. Based on these data, we predicted five novel P. americana defensins (PaDefensins) and analyzed their primary structure, secondary structure, and physicochemical properties. The putative antiviral, antifungal, antibacterial, and anticancer activities suggested that PaDefensin5 is a desirable therapeutic candidate against viral diseases. As the first experimental evidence of the antiviral effects of insect defensins, we also showed the antiviral effect of PaDefensin5 in Drosophila Kc cells and Drosophila embryos in vivo . In conclusion, results of both in silico predictions and subsequent antiviral experiments suggested PaDefensin5 a promising antiviral drug.
Collapse
Affiliation(s)
- Ying Li
- Guangdong Provincial Key Laboratory of Insect Developmental Biology and Applied Technology, Institute of Insect Science and Technology & School of Life Sciences, South China Normal University, Guangzhou, China; Guangmeiyuan R&D Center, Guangdong Provincial Key Laboratory of Insect Developmental Biology and Applied Technology, South China Normal University, Meizhou, China
| | - Jie Cai
- Guangdong Provincial Key Laboratory of Insect Developmental Biology and Applied Technology, Institute of Insect Science and Technology & School of Life Sciences, South China Normal University, Guangzhou, China; Guangmeiyuan R&D Center, Guangdong Provincial Key Laboratory of Insect Developmental Biology and Applied Technology, South China Normal University, Meizhou, China
| | - Chunyu Du
- Guangdong Provincial Key Laboratory of Insect Developmental Biology and Applied Technology, Institute of Insect Science and Technology & School of Life Sciences, South China Normal University, Guangzhou, China; Guangmeiyuan R&D Center, Guangdong Provincial Key Laboratory of Insect Developmental Biology and Applied Technology, South China Normal University, Meizhou, China
| | - Yuhua Lin
- Guangdong Provincial Key Laboratory of Insect Developmental Biology and Applied Technology, Institute of Insect Science and Technology & School of Life Sciences, South China Normal University, Guangzhou, China
| | - Sheng Li
- Guangdong Provincial Key Laboratory of Insect Developmental Biology and Applied Technology, Institute of Insect Science and Technology & School of Life Sciences, South China Normal University, Guangzhou, China; Guangmeiyuan R&D Center, Guangdong Provincial Key Laboratory of Insect Developmental Biology and Applied Technology, South China Normal University, Meizhou, China
| | - Anping Ma
- Insititution of chemical surveillance, Guangdong Province Hospital for Occupational Disease Prevention and Treatment, Guangzhou, Guangdong, China
| | - Yiru Qin
- Guangdong Provincial Key Laboratory of Insect Developmental Biology and Applied Technology, Institute of Insect Science and Technology & School of Life Sciences, South China Normal University, Guangzhou, China; Insititution of chemical surveillance, Guangdong Province Hospital for Occupational Disease Prevention and Treatment, Guangzhou, Guangdong, China.
| |
Collapse
|
43
|
Timmons PB, Hewage CM. ENNAVIA is a novel method which employs neural networks for antiviral and anti-coronavirus activity prediction for therapeutic peptides. Brief Bioinform 2021; 22:bbab258. [PMID: 34297817 PMCID: PMC8575049 DOI: 10.1093/bib/bbab258] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2021] [Revised: 06/09/2021] [Accepted: 06/18/2021] [Indexed: 11/14/2022] Open
Abstract
Viruses represent one of the greatest threats to human health, necessitating the development of new antiviral drug candidates. Antiviral peptides often possess excellent biological activity and a favourable toxicity profile, and therefore represent a promising field of novel antiviral drugs. As the quantity of sequencing data grows annually, the development of an accurate in silico method for the prediction of peptide antiviral activities is important. This study leverages advances in deep learning and cheminformatics to produce a novel sequence-based deep neural network classifier for the prediction of antiviral peptide activity. The method outperforms the existent best-in-class, with an external test accuracy of 93.9%, Matthews correlation coefficient of 0.87 and an Area Under the Curve of 0.93 on the dataset of experimentally validated peptide activities. This cutting-edge classifier is available as an online web server at https://research.timmons.eu/ennavia, facilitating in silico screening and design of peptide antiviral drugs by the wider research community.
Collapse
Affiliation(s)
- Patrick Brendan Timmons
- UCD School of Biomolecular and Biomedical Science, UCD Centre for Synthesis and Chemical Biology, UCD Conway Institute, University College Dublin, Dublin 4, Ireland
| | - Chandralal M Hewage
- UCD School of Biomolecular and Biomedical Science, UCD Centre for Synthesis and Chemical Biology, UCD Conway Institute, University College Dublin, Dublin 4, Ireland
| |
Collapse
|
44
|
Bin Hafeez A, Jiang X, Bergen PJ, Zhu Y. Antimicrobial Peptides: An Update on Classifications and Databases. Int J Mol Sci 2021; 22:11691. [PMID: 34769122 PMCID: PMC8583803 DOI: 10.3390/ijms222111691] [Citation(s) in RCA: 87] [Impact Index Per Article: 29.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Revised: 10/24/2021] [Accepted: 10/25/2021] [Indexed: 02/06/2023] Open
Abstract
Antimicrobial peptides (AMPs) are distributed across all kingdoms of life and are an indispensable component of host defenses. They consist of predominantly short cationic peptides with a wide variety of structures and targets. Given the ever-emerging resistance of various pathogens to existing antimicrobial therapies, AMPs have recently attracted extensive interest as potential therapeutic agents. As the discovery of new AMPs has increased, many databases specializing in AMPs have been developed to collect both fundamental and pharmacological information. In this review, we summarize the sources, structures, modes of action, and classifications of AMPs. Additionally, we examine current AMP databases, compare valuable computational tools used to predict antimicrobial activity and mechanisms of action, and highlight new machine learning approaches that can be employed to improve AMP activity to combat global antimicrobial resistance.
Collapse
Affiliation(s)
- Ahmer Bin Hafeez
- Centre of Biotechnology and Microbiology, University of Peshawar, Peshawar 25120, Pakistan;
| | - Xukai Jiang
- Infection and Immunity Program, Department of Microbiology, Biomedicine Discovery Institute, Monash University, Clayton, VIC 3800, Australia; (X.J.); (P.J.B.)
- National Glycoengineering Research Center, Shandong University, Qingdao 266237, China
| | - Phillip J. Bergen
- Infection and Immunity Program, Department of Microbiology, Biomedicine Discovery Institute, Monash University, Clayton, VIC 3800, Australia; (X.J.); (P.J.B.)
| | - Yan Zhu
- Infection and Immunity Program, Department of Microbiology, Biomedicine Discovery Institute, Monash University, Clayton, VIC 3800, Australia; (X.J.); (P.J.B.)
| |
Collapse
|
45
|
iBitter-Fuse: A Novel Sequence-Based Bitter Peptide Predictor by Fusing Multi-View Features. Int J Mol Sci 2021; 22:ijms22168958. [PMID: 34445663 PMCID: PMC8396555 DOI: 10.3390/ijms22168958] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2021] [Revised: 08/08/2021] [Accepted: 08/17/2021] [Indexed: 12/19/2022] Open
Abstract
Accurate identification of bitter peptides is of great importance for better understanding their biochemical and biophysical properties. To date, machine learning-based methods have become effective approaches for providing a good avenue for identifying potential bitter peptides from large-scale protein datasets. Although few machine learning-based predictors have been developed for identifying the bitterness of peptides, their prediction performances could be improved. In this study, we developed a new predictor (named iBitter-Fuse) for achieving more accurate identification of bitter peptides. In the proposed iBitter-Fuse, we have integrated a variety of feature encoding schemes for providing sufficient information from different aspects, namely consisting of compositional information and physicochemical properties. To enhance the predictive performance, the customized genetic algorithm utilizing self-assessment-report (GA-SAR) was employed for identifying informative features followed by inputting optimal ones into a support vector machine (SVM)-based classifier for developing the final model (iBitter-Fuse). Benchmarking experiments based on both 10-fold cross-validation and independent tests indicated that the iBitter-Fuse was able to achieve more accurate performance as compared to state-of-the-art methods. To facilitate the high-throughput identification of bitter peptides, the iBitter-Fuse web server was established and made freely available online. It is anticipated that the iBitter-Fuse will be a useful tool for aiding the discovery and de novo design of bitter peptides.
Collapse
|
46
|
Jiang M, Zhao B, Luo S, Wang Q, Chu Y, Chen T, Mao X, Liu Y, Wang Y, Jiang X, Wei DQ, Xiong Y. NeuroPpred-Fuse: an interpretable stacking model for prediction of neuropeptides by fusing sequence information and feature selection methods. Brief Bioinform 2021; 22:6350884. [PMID: 34396388 DOI: 10.1093/bib/bbab310] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Revised: 07/01/2021] [Accepted: 07/18/2021] [Indexed: 12/13/2022] Open
Abstract
Neuropeptides acting as signaling molecules in the nervous system of various animals play crucial roles in a wide range of physiological functions and hormone regulation behaviors. Neuropeptides offer many opportunities for the discovery of new drugs and targets for the treatment of neurological diseases. In recent years, there have been several data-driven computational predictors of various types of bioactive peptides, but the relevant work about neuropeptides is little at present. In this work, we developed an interpretable stacking model, named NeuroPpred-Fuse, for the prediction of neuropeptides through fusing a variety of sequence-derived features and feature selection methods. Specifically, we used six types of sequence-derived features to encode the peptide sequences and then combined them. In the first layer, we ensembled three base classifiers and four feature selection algorithms, which select non-redundant important features complementarily. In the second layer, the output of the first layer was merged and fed into logistic regression (LR) classifier to train the model. Moreover, we analyzed the selected features and explained the feasibility of the selected features. Experimental results show that our model achieved 90.6% accuracy and 95.8% AUC on the independent test set, outperforming the state-of-the-art models. In addition, we exhibited the distribution of selected features by these tree models and compared the results on the training set to that on the test set. These results fully showed that our model has a certain generalization ability. Therefore, we expect that our model would provide important advances in the discovery of neuropeptides as new drugs for the treatment of neurological diseases.
Collapse
Affiliation(s)
- Mingming Jiang
- State Key Laboratory of Microbial Metabolism, and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Bowen Zhao
- State Key Laboratory of Microbial Metabolism, and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Shenggan Luo
- State Key Laboratory of Microbial Metabolism, and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Qiankun Wang
- State Key Laboratory of Microbial Metabolism, and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Yanyi Chu
- State Key Laboratory of Microbial Metabolism, and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Tianhang Chen
- State Key Laboratory of Microbial Metabolism, and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Xueying Mao
- State Key Laboratory of Microbial Metabolism, and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Yatong Liu
- State Key Laboratory of Microbial Metabolism, and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Yanjing Wang
- State Key Laboratory of Microbial Metabolism, and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Xue Jiang
- State Key Laboratory of Microbial Metabolism, and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Dong-Qing Wei
- State Key Laboratory of Microbial Metabolism, and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Yi Xiong
- State Key Laboratory of Microbial Metabolism, and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| |
Collapse
|
47
|
Li Y, Pu F, Wang J, Zhou Z, Zhang C, He F, Ma Z, Zhang J. Machine Learning Methods in Prediction of Protein Palmitoylation Sites: A Brief Review. Curr Pharm Des 2021; 27:2189-2198. [PMID: 33183190 DOI: 10.2174/1381612826666201112142826] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2020] [Accepted: 07/27/2020] [Indexed: 11/22/2022]
Abstract
Protein palmitoylation is a fundamental and reversible post-translational lipid modification that involves a series of biological processes. Although a large number of experimental studies have explored the molecular mechanism behind the palmitoylation process, the computational methods has attracted much attention for its good performance in predicting palmitoylation sites compared with expensive and time-consuming biochemical experiments. The prediction of protein palmitoylation sites is helpful to reveal its biological mechanism. Therefore, the research on the application of machine learning methods to predict palmitoylation sites has become a hot topic in bioinformatics and promoted the development in the related fields. In this review, we briefly introduced the recent development in predicting protein palmitoylation sites by using machine learningbased methods and discussed their benefits and drawbacks. The perspective of machine learning-based methods in predicting palmitoylation sites was also provided. We hope the review could provide a guide in related fields.
Collapse
Affiliation(s)
- Yanwen Li
- School of Information Science and Technology, Northeast Normal University, Changchun 130117, China
| | - Feng Pu
- School of Information Science and Technology, Northeast Normal University, Changchun 130117, China
| | - Jingru Wang
- School of Information Science and Technology, Northeast Normal University, Changchun 130117, China
| | - Zhiguo Zhou
- School of Information Science and Technology, Northeast Normal University, Changchun 130117, China
| | - Chunhua Zhang
- School of Information Science and Technology, Northeast Normal University, Changchun 130117, China
| | - Fei He
- School of Information Science and Technology, Northeast Normal University, Changchun 130117, China
| | - Zhiqiang Ma
- School of Information Science and Technology, Northeast Normal University, Changchun 130117, China
| | - Jingbo Zhang
- School of Information Science and Technology, Northeast Normal University, Changchun 130117, China
| |
Collapse
|
48
|
Charoenkwan P, Anuwongcharoen N, Nantasenamat C, Hasan MM, Shoombuatong W. In Silico Approaches for the Prediction and Analysis of Antiviral Peptides: A Review. Curr Pharm Des 2021; 27:2180-2188. [PMID: 33138759 DOI: 10.2174/1381612826666201102105827] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2020] [Accepted: 08/20/2020] [Indexed: 11/22/2022]
Abstract
In light of the growing resistance toward current antiviral drugs, efforts to discover novel and effective antiviral therapeutic agents remain a pressing scientific effort. Antiviral peptides (AVPs) represent promising therapeutic agents due to their extraordinary advantages in terms of potency, efficacy and pharmacokinetic properties. The growing volume of newly discovered peptide sequences in the post-genomic era requires computational approaches for timely and accurate identification of AVPs. Machine learning (ML) methods such as random forest and support vector machine represent robust learning algorithms that are instrumental in successful peptide-based drug discovery. Therefore, this review summarizes the current state-of-the-art application of ML methods for identifying AVPs directly from the sequence information. We compare the efficiency of these methods in terms of the underlying characteristics of the dataset used along with feature encoding methods, ML algorithms, cross-validation methods and prediction performance. Finally, guidelines for the development of robust AVP models are also discussed. It is anticipated that this review will serve as a useful guide for the design and development of robust AVP and related therapeutic peptide predictors in the future.
Collapse
Affiliation(s)
- Phasit Charoenkwan
- Modern Management and Information Technology, College of Arts, Media and Technology, Chiang Mai University, Chiang Mai 50200, Thailand
| | - Nuttapat Anuwongcharoen
- Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, 10700, Thailand
| | - Chanin Nantasenamat
- Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, 10700, Thailand
| | - Md Mehedi Hasan
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan
| | - Watshara Shoombuatong
- Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, 10700, Thailand
| |
Collapse
|
49
|
Pang Y, Yao L, Jhong JH, Wang Z, Lee TY. AVPIden: a new scheme for identification and functional prediction of antiviral peptides based on machine learning approaches. Brief Bioinform 2021; 22:6323205. [PMID: 34279599 DOI: 10.1093/bib/bbab263] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2021] [Revised: 06/07/2021] [Accepted: 06/21/2021] [Indexed: 02/06/2023] Open
Abstract
Antiviral peptide (AVP) is a kind of antimicrobial peptide (AMP) that has the potential ability to fight against virus infection. Machine learning-based prediction with a computational biology approach can facilitate the development of the novel therapeutic agents. In this study, we proposed a double-stage classification scheme, named AVPIden, for predicting the AVPs and their functional activities against different viruses. The first stage is to distinguish the AVP from a broad-spectrum peptide collection, including not only the regular peptides (non-AMP) but also the AMPs without antiviral functions (non-AVP). The second stage is responsible for characterizing one or more virus families or species that the AVP targets. Imbalanced learning is utilized to improve the performance of prediction. The AVPIden uses multiple descriptors to precisely demonstrate the peptide properties and adopts explainable machine learning strategies based on Shapley value to exploit how the descriptors impact the antiviral activities. Finally, the evaluation performance of the proposed model suggests its ability to predict the antivirus activities and their potential functions against six virus families (Coronaviridae, Retroviridae, Herpesviridae, Paramyxoviridae, Orthomyxoviridae, Flaviviridae) and eight kinds of virus (FIV, HCV, HIV, HPIV3, HSV1, INFVA, RSV, SARS-CoV). The AVPIden gives an option for reinforcing the development of AVPs with the computer-aided method and has been deployed at http://awi.cuhk.edu.cn/AVPIden/.
Collapse
Affiliation(s)
- Yuxuan Pang
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, PR China
| | - Lantian Yao
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, PR China
| | - Jhih-Hua Jhong
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, PR China
| | - Zhuo Wang
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, PR China
| | - Tzong-Yi Lee
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, PR China
| |
Collapse
|
50
|
Pinacho-Castellanos SA, García-Jacas CR, Gilson MK, Brizuela CA. Alignment-Free Antimicrobial Peptide Predictors: Improving Performance by a Thorough Analysis of the Largest Available Data Set. J Chem Inf Model 2021; 61:3141-3157. [PMID: 34081438 DOI: 10.1021/acs.jcim.1c00251] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
In the last two decades, a large number of machine-learning-based predictors for the activities of antimicrobial peptides (AMPs) have been proposed. These predictors differ from one another in the learning method and in the training and testing data sets used. Unfortunately, the training data sets present several drawbacks, such as a low representativeness regarding the experimentally validated AMP space, and duplicated peptide sequences between negative and positive data sets. These limitations give a low confidence to most of the approaches to be used in prospective studies. To address these weaknesses, we propose novel modeling and assessing data sets from the largest experimentally validated nonredundant peptide data set reported to date. From these novel data sets, alignment-free quantitative sequence-activity models (AF-QSAMs) based on Random Forest are created to identify general AMPs and their antibacterial, antifungal, antiparasitic, and antiviral functional types. An applicability domain analysis is carried out to determine the reliability of the predictions obtained, which, to the best of our knowledge, is performed for the first time for AMP recognition. A benchmarking is undertaken between the models proposed and several models from the literature that are freely available in 13 programs (ClassAMP, iAMP-2L, ADAM, MLAMP, AMPScanner v2.0, AntiFP, AMPfun, PEPred-suite, AxPEP, CAMPR3, iAMPpred, APIN, and Meta-iAVP). The models proposed are those with the best performance in all of the endpoints modeled, while most of the methods from the literature have weak-to-random predictive agreements. The models proposed are also assessed through Y-scrambling and repeated k-fold cross-validation tests, demonstrating that the outcomes obtained by them are not given by chance. Three chemometric analyses also confirmed the relevance of the peptides descriptors used in the modeling. Therefore, it can be concluded that the models built by fixing the drawbacks existing in the literature contribute to identifying antibacterial, antifungal, antiparasitic, and antiviral peptides with high effectivity and reliability. Models are freely available via the AMPDiscover tool at https://biocom-ampdiscover.cicese.mx/.
Collapse
Affiliation(s)
- Sergio A Pinacho-Castellanos
- Departamento de Ciencias de la Computación, Centro de Investigación Científica y de Educación Superior de Ensenada (CICESE), 22860 Ensenada, Baja California, México.,Centro de Investigación y Desarrollo de Tecnología Digital (CITEDI), Instituto Politécnico Nacional (IPN), 22435 Tijuana, Baja California, México
| | - César R García-Jacas
- Cátedras CONACYT-Departamento de Ciencias de la Computación, Centro de Investigación Científica y de Educación Superior de Ensenada (CICESE), 22860 Ensenada, Baja California, México
| | - Michael K Gilson
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, California 92093, United States
| | - Carlos A Brizuela
- Departamento de Ciencias de la Computación, Centro de Investigación Científica y de Educación Superior de Ensenada (CICESE), 22860 Ensenada, Baja California, México
| |
Collapse
|