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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 2024; 42:7321-7339. [PMID: 37498146 DOI: 10.1080/07391102.2023.2239932] [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: 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.
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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
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Medina-Ortiz D, Contreras S, Fernández D, Soto-García N, Moya I, Cabas-Mora G, Olivera-Nappa Á. Protein Language Models and Machine Learning Facilitate the Identification of Antimicrobial Peptides. Int J Mol Sci 2024; 25:8851. [PMID: 39201537 DOI: 10.3390/ijms25168851] [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: 06/19/2024] [Revised: 08/05/2024] [Accepted: 08/08/2024] [Indexed: 09/02/2024] Open
Abstract
Peptides are bioactive molecules whose functional versatility in living organisms has led to successful applications in diverse fields. In recent years, the amount of data describing peptide sequences and function collected in open repositories has substantially increased, allowing the application of more complex computational models to study the relations between the peptide composition and function. This work introduces AMP-Detector, a sequence-based classification model for the detection of peptides' functional biological activity, focusing on accelerating the discovery and de novo design of potential antimicrobial peptides (AMPs). AMP-Detector introduces a novel sequence-based pipeline to train binary classification models, integrating protein language models and machine learning algorithms. This pipeline produced 21 models targeting antimicrobial, antiviral, and antibacterial activity, achieving average precision exceeding 83%. Benchmark analyses revealed that our models outperformed existing methods for AMPs and delivered comparable results for other biological activity types. Utilizing the Peptide Atlas, we applied AMP-Detector to discover over 190,000 potential AMPs and demonstrated that it is an integrative approach with generative learning to aid in de novo design, resulting in over 500 novel AMPs. The combination of our methodology, robust models, and a generative design strategy offers a significant advancement in peptide-based drug discovery and represents a pivotal tool for therapeutic applications.
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Affiliation(s)
- David Medina-Ortiz
- Departamento de Ingeniería en Computación, Universidad de Magallanes, Punta Arenas 6210005, Chile
- Centre for Biotechnology and Bioengineering, CeBiB, Universidad de Chile, Santiago 8370456, Chile
| | - Seba Contreras
- Max Planck Institute for Dynamics and Self-Organization, Am Faßberg 17, 37077 Göttingen, Germany
| | - Diego Fernández
- Departamento de Ingeniería en Computación, Universidad de Magallanes, Punta Arenas 6210005, Chile
| | - Nicole Soto-García
- Departamento de Ingeniería en Computación, Universidad de Magallanes, Punta Arenas 6210005, Chile
| | - Iván Moya
- Departamento de Ingeniería en Computación, Universidad de Magallanes, Punta Arenas 6210005, Chile
- Departamento de Ingeniería Química, Universidad de Magallanes, Punta Arenas 6210005, Chile
| | - Gabriel Cabas-Mora
- Departamento de Ingeniería en Computación, Universidad de Magallanes, Punta Arenas 6210005, Chile
| | - Álvaro Olivera-Nappa
- Centre for Biotechnology and Bioengineering, CeBiB, Universidad de Chile, Santiago 8370456, Chile
- Departamento de Ingeniería Química, Biotecnología y Materiales, Universidad de Chile, Santiago 8370456, Chile
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de Llano García D, Marrero-Ponce Y, Agüero-Chapin G, Ferri FJ, Antunes A, Martinez-Rios F, Rodríguez H. Innovative Alignment-Based Method for Antiviral Peptide Prediction. Antibiotics (Basel) 2024; 13:768. [PMID: 39200068 PMCID: PMC11350826 DOI: 10.3390/antibiotics13080768] [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: 07/14/2024] [Revised: 08/08/2024] [Accepted: 08/09/2024] [Indexed: 09/01/2024] Open
Abstract
Antiviral peptides (AVPs) represent a promising strategy for addressing the global challenges of viral infections and their growing resistances to traditional drugs. Lab-based AVP discovery methods are resource-intensive, highlighting the need for efficient computational alternatives. In this study, we developed five non-trained but supervised multi-query similarity search models (MQSSMs) integrated into the StarPep toolbox. Rigorous testing and validation across diverse AVP datasets confirmed the models' robustness and reliability. The top-performing model, M13+, demonstrated impressive results, with an accuracy of 0.969 and a Matthew's correlation coefficient of 0.71. To assess their competitiveness, the top five models were benchmarked against 14 publicly available machine-learning and deep-learning AVP predictors. The MQSSMs outperformed these predictors, highlighting their efficiency in terms of resource demand and public accessibility. Another significant achievement of this study is the creation of the most comprehensive dataset of antiviral sequences to date. In general, these results suggest that MQSSMs are promissory tools to develop good alignment-based models that can be successfully applied in the screening of large datasets for new AVP discovery.
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Affiliation(s)
- Daniela de Llano García
- School of Chemical Sciences and Engineering, Yachay Tech University, Hda. San José s/n y Proyecto Yachay, Urcuquí 100119, Imbabura, Ecuador; (D.d.L.G.); (H.R.)
| | - Yovani Marrero-Ponce
- Universidad San Francisco de Quito (USFQ), Grupo de Medicina Molecular y Traslacional (MeM&T), Colegio de Ciencias de la Salud (COCSA), Escuela de Medicina, Edificio de Especialidades Médicas, Instituto de Simulación Computacional (ISC-USFQ), Diego de Robles y vía Interoceánica, Quito 170157, Pichincha, Ecuador
- Facultad de Ingeniería, Universidad Panamericana, Augusto Rodin 498, Benito Juárez 03920, Ciudad de México, Mexico;
- Computer Science Department, Universitat de València, 46100 Valencia, Burjassot, Spain;
| | - Guillermin Agüero-Chapin
- CIIMAR—Centro Interdisciplinar de Investigação Marinha e Ambiental, Universidade do Porto, Terminal de Cruzeiros do Porto de Leixões, Av. General Norton de Matos, s/n, 4450-208 Porto, Portugal;
- Departamento de Biologia, Faculdade de Ciências, Universidade do Porto, Rua do Campo Alegre, 4169-007 Porto, Portugal
| | - Francesc J. Ferri
- Computer Science Department, Universitat de València, 46100 Valencia, Burjassot, Spain;
| | - Agostinho Antunes
- CIIMAR—Centro Interdisciplinar de Investigação Marinha e Ambiental, Universidade do Porto, Terminal de Cruzeiros do Porto de Leixões, Av. General Norton de Matos, s/n, 4450-208 Porto, Portugal;
- Departamento de Biologia, Faculdade de Ciências, Universidade do Porto, Rua do Campo Alegre, 4169-007 Porto, Portugal
| | - Felix Martinez-Rios
- Facultad de Ingeniería, Universidad Panamericana, Augusto Rodin 498, Benito Juárez 03920, Ciudad de México, Mexico;
| | - Hortensia Rodríguez
- School of Chemical Sciences and Engineering, Yachay Tech University, Hda. San José s/n y Proyecto Yachay, Urcuquí 100119, Imbabura, Ecuador; (D.d.L.G.); (H.R.)
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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 PMCID: PMC11256913 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.
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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
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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.
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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.
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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.
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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
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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.
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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
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Urmi UL, Attard S, Vijay AK, Willcox MDP, Kumar N, Islam S, Kuppusamy R. Antiviral Activity of Anthranilamide Peptidomimetics against Herpes Simplex Virus 1 and a Coronavirus. Antibiotics (Basel) 2023; 12:1436. [PMID: 37760732 PMCID: PMC10525570 DOI: 10.3390/antibiotics12091436] [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: 08/27/2023] [Revised: 09/08/2023] [Accepted: 09/10/2023] [Indexed: 09/29/2023] Open
Abstract
The development of potent antiviral agents is of utmost importance to combat the global burden of viral infections. Traditional antiviral drug development involves targeting specific viral proteins, which may lead to the emergence of resistant strains. To explore alternative strategies, we investigated the antiviral potential of antimicrobial peptidomimetic compounds. In this study, we evaluated the antiviral potential of 17 short anthranilamide-based peptidomimetic compounds against two viruses: Murine hepatitis virus 1 (MHV-1) which is a surrogate of human coronaviruses and herpes simplex virus 1 (HSV-1). The half-maximal inhibitory concentration (IC50) values of these compounds were determined in vitro to assess their potency as antiviral agents. Compounds 11 and 14 displayed the most potent inhibitory effects with IC50 values of 2.38 μM, and 6.3 μM against MHV-1 while compounds 9 and 14 showed IC50 values of 14.8 μM and 13 μM against HSV-1. Multiple antiviral assessments and microscopic images obtained through transmission electron microscopy (TEM) collectively demonstrated that these compounds exert a direct influence on the viral envelope. Based on this outcome, it can be concluded that peptidomimetic compounds could offer a new approach for the development of potent antiviral agents.
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Affiliation(s)
- Umme Laila Urmi
- School of Optometry and Vision Science, University of New South Wales, Sydney, NSW 2052, Australia; (A.K.V.); (S.I.); (R.K.)
| | - Samuel Attard
- School of Chemistry, University of New South Wales, Sydney, NSW 2052, Australia; (S.A.); (N.K.)
| | - Ajay Kumar Vijay
- School of Optometry and Vision Science, University of New South Wales, Sydney, NSW 2052, Australia; (A.K.V.); (S.I.); (R.K.)
| | - Mark D. P. Willcox
- School of Optometry and Vision Science, University of New South Wales, Sydney, NSW 2052, Australia; (A.K.V.); (S.I.); (R.K.)
| | - Naresh Kumar
- School of Chemistry, University of New South Wales, Sydney, NSW 2052, Australia; (S.A.); (N.K.)
| | - Salequl Islam
- School of Optometry and Vision Science, University of New South Wales, Sydney, NSW 2052, Australia; (A.K.V.); (S.I.); (R.K.)
- Department of Microbiology, Jahangirnagar University, Savar 1342, Bangladesh
| | - Rajesh Kuppusamy
- School of Optometry and Vision Science, University of New South Wales, Sydney, NSW 2052, Australia; (A.K.V.); (S.I.); (R.K.)
- School of Chemistry, University of New South Wales, Sydney, NSW 2052, Australia; (S.A.); (N.K.)
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Liu M, Liu H, Wu T, Zhu Y, Zhou Y, Huang Z, Xiang C, Huang J. ACP-Dnnel: anti-coronavirus peptides' prediction based on deep neural network ensemble learning. Amino Acids 2023; 55:1121-1136. [PMID: 37402073 DOI: 10.1007/s00726-023-03300-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Accepted: 06/25/2023] [Indexed: 07/05/2023]
Abstract
The ongoing COVID-19 pandemic has caused dramatic loss of human life. There is an urgent need for safe and efficient anti-coronavirus infection drugs. Anti-coronavirus peptides (ACovPs) can inhibit coronavirus infection. With high-efficiency, low-toxicity, and broad-spectrum inhibitory effects on coronaviruses, they are promising candidates to be developed into a new type of anti-coronavirus drug. Experiment is the traditional way of ACovPs' identification, which is less efficient and more expensive. With the accumulation of experimental data on ACovPs, computational prediction provides a cheaper and faster way to find anti-coronavirus peptides' candidates. In this study, we ensemble several state-of-the-art machine learning methodologies to build nine classification models for the prediction of ACovPs. These models were pre-trained using deep neural networks, and the performance of our ensemble model, ACP-Dnnel, was evaluated across three datasets and independent dataset. We followed Chou's 5-step rules. (1) we constructed the benchmark datasets data1, data2, and data3 for training and testing, and introduced the independent validation dataset ACVP-M; (2) we analyzed the peptides sequence composition feature of the benchmark dataset; (3) we constructed the ACP-Dnnel model with deep convolutional neural network (DCNN) merged the bi-directional long short-term memory (BiLSTM) as the base model for pre-training to extract the features embedded in the benchmark dataset, and then, nine classification algorithms were introduced to ensemble together for classification prediction and voting together; (4) tenfold cross-validation was introduced during the training process, and the final model performance was evaluated; (5) finally, we constructed a user-friendly web server accessible to the public at http://150.158.148.228:5000/ . The highest accuracy (ACC) of ACP-Dnnel reaches 97%, and the Matthew's correlation coefficient (MCC) value exceeds 0.9. On three different datasets, its average accuracy is 96.0%. After the latest independent dataset validation, ACP-Dnnel improved at MCC, SP, and ACC values 6.2%, 7.5% and 6.3% greater, respectively. It is suggested that ACP-Dnnel can be helpful for the laboratory identification of ACovPs, speeding up the anti-coronavirus peptide drug discovery and development. We constructed the web server of anti-coronavirus peptides' prediction and it is available at http://150.158.148.228:5000/ .
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Affiliation(s)
- Mingyou Liu
- School of Biology and Engineering, Guizhou Medical University, Guiyang, Guizhou, China
- School of Life Science and Technology, University of Electronic Science and Technology, Chengdu, Sichuan, China
| | - Hongmei Liu
- School of Biology and Engineering, Guizhou Medical University, Guiyang, Guizhou, China
| | - Tao Wu
- School of Biology and Engineering, Guizhou Medical University, Guiyang, Guizhou, China
| | - Yingxue Zhu
- School of Biology and Engineering, Guizhou Medical University, Guiyang, Guizhou, China
| | - Yuwei Zhou
- School of Life Science and Technology, University of Electronic Science and Technology, Chengdu, Sichuan, China
| | - Ziru Huang
- School of Life Science and Technology, University of Electronic Science and Technology, Chengdu, Sichuan, China
| | - Changcheng Xiang
- School of Computer Science and Technology, Aba Teachers University, Aba, Sichuan, China.
| | - Jian Huang
- School of Life Science and Technology, University of Electronic Science and Technology, Chengdu, Sichuan, China.
- School of Healthcare Technology, Chengdu Neusoft University, Chengdu, Sichuan, China.
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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.
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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.
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11
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Kaygisiz K, Rauch-Wirth L, Dutta A, Yu X, Nagata Y, Bereau T, Münch J, Synatschke CV, Weil T. Data-mining unveils structure-property-activity correlation of viral infectivity enhancing self-assembling peptides. Nat Commun 2023; 14:5121. [PMID: 37612273 PMCID: PMC10447463 DOI: 10.1038/s41467-023-40663-6] [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: 02/27/2023] [Accepted: 08/01/2023] [Indexed: 08/25/2023] Open
Abstract
Gene therapy via retroviral vectors holds great promise for treating a variety of serious diseases. It requires the use of additives to boost infectivity. Amyloid-like peptide nanofibers (PNFs) were shown to efficiently enhance retroviral gene transfer. However, the underlying mode of action of these peptides remains largely unknown. Data-mining is an efficient method to systematically study structure-function relationship and unveil patterns in a database. This data-mining study elucidates the multi-scale structure-property-activity relationship of transduction enhancing peptides for retroviral gene transfer. In contrast to previous reports, we find that not the amyloid fibrils themselves, but rather µm-sized β-sheet rich aggregates enhance infectivity. Specifically, microscopic aggregation of β-sheet rich amyloid structures with a hydrophobic surface pattern and positive surface charge are identified as key material properties. We validate the reliability of the amphiphilic sequence pattern and the general applicability of the key properties by rationally creating new active sequences and identifying short amyloidal peptides from various pathogenic and functional origin. Data-mining-even for small datasets-enables the development of new efficient retroviral transduction enhancers and provides important insights into the diverse bioactivity of the functional material class of amyloids.
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Affiliation(s)
- Kübra Kaygisiz
- Department Synthesis of Macromolecules, Max Planck Institute for Polymer Research, Ackermannweg 10, 55128, Mainz, Germany
| | - Lena Rauch-Wirth
- Institute of Molecular Virology, Ulm University Medical Center, Meyerhofstraße 1, 89081, Ulm, Germany
| | - Arghya Dutta
- Department Polymer Theory, Max Planck Institute for Polymer Research, Ackermannweg 10, 55128, Mainz, Germany
- Institute of Biochemistry II, Faculty of Medicine, Goethe University, Theodor-Stern-Kai 7, 60590, Frankfurt, Germany
| | - Xiaoqing Yu
- Department Molecular Spectroscopy, Max Planck Institute for Polymer Research, Ackermannweg 10, 55128, Mainz, Germany
| | - Yuki Nagata
- Department Molecular Spectroscopy, Max Planck Institute for Polymer Research, Ackermannweg 10, 55128, Mainz, Germany
| | - Tristan Bereau
- Department Polymer Theory, Max Planck Institute for Polymer Research, Ackermannweg 10, 55128, Mainz, Germany
- Institute for Theoretical Physics, Heidelberg University, Philosophenweg 19, 69120, Heidelberg, Germany
| | - Jan Münch
- Institute of Molecular Virology, Ulm University Medical Center, Meyerhofstraße 1, 89081, Ulm, Germany
| | - Christopher V Synatschke
- Department Synthesis of Macromolecules, Max Planck Institute for Polymer Research, Ackermannweg 10, 55128, Mainz, Germany.
| | - Tanja Weil
- Department Synthesis of Macromolecules, Max Planck Institute for Polymer Research, Ackermannweg 10, 55128, Mainz, Germany.
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12
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Singh V, Singh SK. A separable temporal convolutional networks based deep learning technique for discovering antiviral medicines. Sci Rep 2023; 13:13722. [PMID: 37608092 PMCID: PMC10444765 DOI: 10.1038/s41598-023-40922-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2023] [Accepted: 08/18/2023] [Indexed: 08/24/2023] Open
Abstract
An alarming number of fatalities caused by the COVID-19 pandemic has forced the scientific community to accelerate the process of therapeutic drug discovery. In this regard, the collaboration between biomedical scientists and experts in artificial intelligence (AI) has led to a number of in silico tools being developed for the initial screening of therapeutic molecules. All living organisms produce antiviral peptides (AVPs) as a part of their first line of defense against invading viruses. The Deep-AVPiden model proposed in this paper and its corresponding web app, deployed at https://deep-avpiden.anvil.app , is an effort toward discovering novel AVPs in proteomes of living organisms. Apart from Deep-AVPiden, a computationally efficient model called Deep-AVPiden (DS) has also been developed using the same underlying network but with point-wise separable convolutions. The Deep-AVPiden and Deep-AVPiden (DS) models show an accuracy of 90% and 88%, respectively, and both have a precision of 90%. Also, the proposed models were statistically compared using the Student's t-test. On comparing the proposed models with the state-of-the-art classifiers, it was found that they are much better than them. To test the proposed model, we identified some AVPs in the natural defense proteins of plants, mammals, and fishes and found them to have appreciable sequence similarity with some experimentally validated antimicrobial peptides. These AVPs can be chemically synthesized and tested for their antiviral activity.
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Affiliation(s)
- Vishakha Singh
- Department of Computer Science and Engineering, Indian Institute of Technology (BHU) Varanasi, Varanasi, Uttar Pradesh, 221005, India.
| | - Sanjay Kumar Singh
- Department of Computer Science and Engineering, Indian Institute of Technology (BHU) Varanasi, Varanasi, Uttar Pradesh, 221005, India.
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13
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Akhter S, Miller JH. BaPreS: a software tool for predicting bacteriocins using an optimal set of features. BMC Bioinformatics 2023; 24:313. [PMID: 37592230 PMCID: PMC10433575 DOI: 10.1186/s12859-023-05330-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Accepted: 05/09/2023] [Indexed: 08/19/2023] Open
Abstract
BACKGROUND Antibiotic resistance is a major public health concern around the globe. As a result, researchers always look for new compounds to develop new antibiotic drugs for combating antibiotic-resistant bacteria. Bacteriocin becomes a promising antimicrobial agent to fight against antibiotic resistance, due to cases of both broad and narrow killing spectra. Sequence matching methods are widely used to identify bacteriocins by comparing them with the known bacteriocin sequences; however, these methods often fail to detect new bacteriocin sequences due to their high diversity. The ability to use a machine learning approach can help find new highly dissimilar bacteriocins for developing highly effective antibiotic drugs. The aim of this work is to develop a machine learning-based software tool called BaPreS (Bacteriocin Prediction Software) using an optimal set of features for detecting bacteriocin protein sequences with high accuracy. We extracted potential features from known bacteriocin and non-bacteriocin sequences by considering the physicochemical and structural properties of the protein sequences. Then we reduced the feature set using statistical justifications and recursive feature elimination technique. Finally, we built support vector machine (SVM) and random forest (RF) models using the selected features and utilized the best machine learning model to implement the software tool. RESULTS We applied BaPreS to an established dataset and evaluated its prediction performance. Acquired results show that the software tool can achieve a prediction accuracy of 95.54% for testing protein sequences. This tool allows users to add new bacteriocin or non-bacteriocin sequences in the training dataset to further enhance the predictive power of the tool. We compared the prediction performance of the BaPreS with a popular sequence matching-based tool and a deep learning-based method, and our software tool outperformed both. CONCLUSIONS BaPreS is a bacteriocin prediction tool that can be used to discover new highly dissimilar bacteriocins for developing highly effective antibiotic drugs. This software tool can be used with Windows, Linux and macOS operating systems. The open-source software package and its user manual are available at https://github.com/suraiya14/BaPreS .
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Affiliation(s)
- Suraiya Akhter
- School of Electrical Engineering and Computer Science, Washington State University, Pullman, WA, USA.
- School of Engineering and Applied Sciences, Washington State University Tri-Cities, Richland, WA, USA.
| | - John H Miller
- School of Engineering and Applied Sciences, Washington State University Tri-Cities, Richland, WA, USA.
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14
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Zhao X, Cai B, Chen H, Wan P, Chen D, Ye Z, Duan A, Chen X, Sun H, Pan J. Tuna trimmings (Thunnas albacares) hydrolysate alleviates immune stress and intestinal mucosal injury during chemotherapy on mice and identification of potentially active peptides. Curr Res Food Sci 2023; 7:100547. [PMID: 37522134 PMCID: PMC10371818 DOI: 10.1016/j.crfs.2023.100547] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Revised: 07/03/2023] [Accepted: 07/03/2023] [Indexed: 08/01/2023] Open
Abstract
In this study, Tuna trimmings (Thunnas albacares) protein hydrolysate (TPA) was produced by alcalase. The anti-tumor synergistic effect and intestinal mucosa protective effect of TPA on S180 tumor-bearing mice treated with 5-fluorouracil (5-FU) chemotherapy were investigated. The results showed that TPA can enhance the anti-tumor effect of 5-FU chemotherapy, as evident by a significant reduction in tumor volume observed in the medium and high dose TPA+5-FU groups compared to the 5-FU group (p < 0.001). Moreover, TPA significantly elevated the content of total protein and albumin in all TPA dose groups (p < 0.01, p < 0.001), indicating its ability to regulate the nutritional status of the mice. Furthermore, histopathological studies revealed a significant increase in the height of small intestinal villi, crypt depth, mucosal thickness, and villi area in the TPA+5-FU groups compared to the 5-FU group (p < 0.05), suggesting that TPA has a protective effect on the intestinal mucosa. Amino acid analysis revealed that TPA had a total amino acid content of 66.30 g/100 g, with essential amino acids accounting for 30.36 g/100 g. Peptide molecular weight distribution analysis of TPA indicated that peptides ranging from 0.25 to 1 kDa constituted 64.54%. LC-MS/MS analysis identified 109 peptide sequences, which were predicted to possess anti-cancer and anti-inflammatory activities through database prediction. Therefore, TPA has the potential to enhance the antitumor effects of 5-FU, mitigate immune depression and intestinal mucosal damage induced by 5-FU. Thus, TPA could be serve as an adjuvant nutritional support for malnourished patients undergoing chemotherapy.
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Affiliation(s)
- Xiangtan Zhao
- Key Laboratory of Tropical Marine Bio-Resources and Ecology, Guangdong Key Laboratory of Marine Materia Medica, South China Sea Institute of Oceanology, Chinese Academy of Sciences, 164 West Xingang Road, Guangzhou, 510301, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
- Innovation Academy of South China Sea Ecology and Environmental Engineering (ISEE), Chinese Academy of Sciences, China
| | - Bingna Cai
- Key Laboratory of Tropical Marine Bio-Resources and Ecology, Guangdong Key Laboratory of Marine Materia Medica, South China Sea Institute of Oceanology, Chinese Academy of Sciences, 164 West Xingang Road, Guangzhou, 510301, China
- Southern Marine Science and Engineering Guangdong Laboratory(Guangzhou), Guangzhou, 511458, China
| | - Hua Chen
- Key Laboratory of Tropical Marine Bio-Resources and Ecology, Guangdong Key Laboratory of Marine Materia Medica, South China Sea Institute of Oceanology, Chinese Academy of Sciences, 164 West Xingang Road, Guangzhou, 510301, China
- Southern Marine Science and Engineering Guangdong Laboratory(Guangzhou), Guangzhou, 511458, China
| | - Peng Wan
- Key Laboratory of Tropical Marine Bio-Resources and Ecology, Guangdong Key Laboratory of Marine Materia Medica, South China Sea Institute of Oceanology, Chinese Academy of Sciences, 164 West Xingang Road, Guangzhou, 510301, China
- Southern Marine Science and Engineering Guangdong Laboratory(Guangzhou), Guangzhou, 511458, China
| | - Deke Chen
- Key Laboratory of Tropical Marine Bio-Resources and Ecology, Guangdong Key Laboratory of Marine Materia Medica, South China Sea Institute of Oceanology, Chinese Academy of Sciences, 164 West Xingang Road, Guangzhou, 510301, China
- Southern Marine Science and Engineering Guangdong Laboratory(Guangzhou), Guangzhou, 511458, China
| | - Ziqing Ye
- Key Laboratory of Tropical Marine Bio-Resources and Ecology, Guangdong Key Laboratory of Marine Materia Medica, South China Sea Institute of Oceanology, Chinese Academy of Sciences, 164 West Xingang Road, Guangzhou, 510301, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
- Innovation Academy of South China Sea Ecology and Environmental Engineering (ISEE), Chinese Academy of Sciences, China
| | - Ailing Duan
- Key Laboratory of Tropical Marine Bio-Resources and Ecology, Guangdong Key Laboratory of Marine Materia Medica, South China Sea Institute of Oceanology, Chinese Academy of Sciences, 164 West Xingang Road, Guangzhou, 510301, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
- Innovation Academy of South China Sea Ecology and Environmental Engineering (ISEE), Chinese Academy of Sciences, China
| | - Xin Chen
- Foshan University, School of Environment and Chemical Engineering, Foshan, 528000, China
| | - Huili Sun
- Key Laboratory of Tropical Marine Bio-Resources and Ecology, Guangdong Key Laboratory of Marine Materia Medica, South China Sea Institute of Oceanology, Chinese Academy of Sciences, 164 West Xingang Road, Guangzhou, 510301, China
| | - Jianyu Pan
- Key Laboratory of Tropical Marine Bio-Resources and Ecology, Guangdong Key Laboratory of Marine Materia Medica, South China Sea Institute of Oceanology, Chinese Academy of Sciences, 164 West Xingang Road, Guangzhou, 510301, China
- Southern Marine Science and Engineering Guangdong Laboratory(Guangzhou), Guangzhou, 511458, China
- Innovation Academy of South China Sea Ecology and Environmental Engineering (ISEE), Chinese Academy of Sciences, China
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15
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Ali F, Kumar H, Alghamdi W, Kateb FA, Alarfaj FK. Recent Advances in Machine Learning-Based Models for Prediction of Antiviral Peptides. ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING : STATE OF THE ART REVIEWS 2023; 30:1-12. [PMID: 37359746 PMCID: PMC10148704 DOI: 10.1007/s11831-023-09933-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Accepted: 04/19/2023] [Indexed: 06/28/2023]
Abstract
Viruses have killed and infected millions of people across the world. It causes several chronic diseases like COVID-19, HIV, and hepatitis. To cope with such diseases and virus infections, antiviral peptides (AVPs) have been applied in the design of drugs. Keeping in view the significant role in pharmaceutical industry and other research fields, identification of AVPs is highly indispensable. In this connection, experimental and computational methods were proposed to identify AVPs. However, more accurate predictors for boosting AVPs identification are highly desirable. This work presents a thorough study and reports the available predictors of AVPs. We explained applied datasets, feature representation approaches, classification algorithms, and evaluation parameters of performance. In this study, the limitations of the existing studies and the best methods were emphasized. Provided the pros and cons of the applied classifiers. The future insights demonstrate efficient feature encoding approaches, best feature optimization schemes, and effective classification techniques that can improve the performance of novel method for accurate prediction of AVPs.
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Affiliation(s)
- Farman Ali
- Sarhad University of Science and Information Technology Peshawar, Mardan Campus, Khyber Pakhtunkhwa, Pakistan
| | - Harish Kumar
- Department of Computer Science, College of Computer Science, King Khalid University, Abha, Saudi Arabia
| | - Wajdi Alghamdi
- Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, 21589 Saudi Arabia
| | - Faris A. Kateb
- Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, 21589 Saudi Arabia
| | - Fawaz Khaled Alarfaj
- Department of Management Information Systems, King Faisal University, Hufof, Saudi Arabia
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16
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Jiang Y, Wang R, Feng J, Jin J, Liang S, Li Z, Yu Y, Ma A, Su R, Zou Q, Ma Q, Wei L. Explainable Deep Hypergraph Learning Modeling the Peptide Secondary Structure Prediction. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2023; 10:e2206151. [PMID: 36794291 PMCID: PMC10104664 DOI: 10.1002/advs.202206151] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Revised: 01/20/2023] [Indexed: 06/18/2023]
Abstract
Accurately predicting peptide secondary structures remains a challenging task due to the lack of discriminative information in short peptides. In this study, PHAT is proposed, a deep hypergraph learning framework for the prediction of peptide secondary structures and the exploration of downstream tasks. The framework includes a novel interpretable deep hypergraph multi-head attention network that uses residue-based reasoning for structure prediction. The algorithm can incorporate sequential semantic information from large-scale biological corpus and structural semantic information from multi-scale structural segmentation, leading to better accuracy and interpretability even with extremely short peptides. The interpretable models are able to highlight the reasoning of structural feature representations and the classification of secondary substructures. The importance of secondary structures in peptide tertiary structure reconstruction and downstream functional analysis is further demonstrated, highlighting the versatility of our models. To facilitate the use of the model, an online server is established which is accessible via http://inner.wei-group.net/PHAT/. The work is expected to assist in the design of functional peptides and contribute to the advancement of structural biology research.
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Affiliation(s)
- Yi Jiang
- School of SoftwareShandong UniversityJinanShandong250101China
- Joint SDU‐NTU Centre for Artificial Intelligence Research (C‐FAIR)Shandong UniversityJinanShandong250101China
| | - Ruheng Wang
- School of SoftwareShandong UniversityJinanShandong250101China
- Joint SDU‐NTU Centre for Artificial Intelligence Research (C‐FAIR)Shandong UniversityJinanShandong250101China
| | - Jiuxin Feng
- School of SoftwareShandong UniversityJinanShandong250101China
- Joint SDU‐NTU Centre for Artificial Intelligence Research (C‐FAIR)Shandong UniversityJinanShandong250101China
| | - Junru Jin
- School of SoftwareShandong UniversityJinanShandong250101China
- Joint SDU‐NTU Centre for Artificial Intelligence Research (C‐FAIR)Shandong UniversityJinanShandong250101China
| | - Sirui Liang
- School of SoftwareShandong UniversityJinanShandong250101China
- Joint SDU‐NTU Centre for Artificial Intelligence Research (C‐FAIR)Shandong UniversityJinanShandong250101China
| | - Zhongshen Li
- School of SoftwareShandong UniversityJinanShandong250101China
- Joint SDU‐NTU Centre for Artificial Intelligence Research (C‐FAIR)Shandong UniversityJinanShandong250101China
| | - Yingying Yu
- School of SoftwareShandong UniversityJinanShandong250101China
- Joint SDU‐NTU Centre for Artificial Intelligence Research (C‐FAIR)Shandong UniversityJinanShandong250101China
| | - Anjun Ma
- Department of Biomedical InformaticsCollege of MedicineThe Ohio State UniversityColumbusOH43210USA
| | - Ran Su
- College of Intelligence and ComputingTianjin UniversityTianjin300350China
| | - Quan Zou
- Institute of Fundamental and Frontier SciencesUniversity of Electronic Science and Technology of ChinaChengduSichuan610054China
| | - Qin Ma
- Department of Biomedical InformaticsCollege of MedicineThe Ohio State UniversityColumbusOH43210USA
| | - Leyi Wei
- School of SoftwareShandong UniversityJinanShandong250101China
- Joint SDU‐NTU Centre for Artificial Intelligence Research (C‐FAIR)Shandong UniversityJinanShandong250101China
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17
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Information entropy-based differential evolution with extremely randomized trees and LightGBM for protein structural class prediction. Appl Soft Comput 2023. [DOI: 10.1016/j.asoc.2023.110064] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
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18
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Characterisation of a novel crustin isoform from mud crab, Scylla serrata (Forsskål, 1775) and its functional analysis in silico. In Silico Pharmacol 2022; 11:2. [PMID: 36582926 PMCID: PMC9795441 DOI: 10.1007/s40203-022-00138-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Accepted: 12/18/2022] [Indexed: 12/29/2022] Open
Abstract
A 336-base pair (bp) sized mRNA sequence encoding 111 amino acid size crustin isoform (MC-crustin) was obtained from the gill sample of the green mud crab, Scylla serrata. MC-crustin possessed an N-terminal signal peptide region comprising of 21 amino acid residues, followed by a 90 amino acid mature peptide region having a molecular weight of 10.164 kDa, charge + 4.25 and theoretical pI of 8.27. Sequence alignment and phylogenetic tree analyses revealed the peptide to be a Type I crustin, with four conserved cysteine residues forming the cysteine rich region, followed by WAP domain. MC-crustin was cationic with cysteine/proline rich structure and was predicted with antimicrobial, anti-inflammatory, anti-angiogenic and anti-hypertensive property making it a potential molecule for possible therapeutic applications.
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19
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Prasertsuk K, Prongfa K, Suttiwanich P, Harnkit N, Sangkhawasi M, Promta P, Chumnanpuen P. Computer-Aided Screening for Potential Coronavirus 3-Chymotrypsin-like Protease (3CLpro) Inhibitory Peptides from Putative Hemp Seed Trypsinized Peptidome. Molecules 2022; 28:50. [PMID: 36615263 PMCID: PMC9822321 DOI: 10.3390/molecules28010050] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 12/05/2022] [Accepted: 12/13/2022] [Indexed: 12/24/2022] Open
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.
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Affiliation(s)
- Kansate Prasertsuk
- Pibulwitthayalai School, 777 Naraimaharach, Talaychoopsorn, Lopburi District, Lopburi 15000, Thailand
| | - Kasidit Prongfa
- Pibulwitthayalai School, 777 Naraimaharach, Talaychoopsorn, Lopburi District, Lopburi 15000, Thailand
| | - Piyapach Suttiwanich
- Pibulwitthayalai School, 777 Naraimaharach, Talaychoopsorn, Lopburi District, Lopburi 15000, Thailand
| | - Nathaphat Harnkit
- Medicinal Plant Research Institute, Department of Medical Sciences, Ministry of Public Health, Nonthaburi 11000, Thailand
| | - Mattanun Sangkhawasi
- Program in Biotechnology, Faculty of Science, Chulalongkorn University, Bangkok 10330, Thailand
| | - Pongsakorn Promta
- Pibulwitthayalai School, 777 Naraimaharach, Talaychoopsorn, Lopburi District, Lopburi 15000, 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
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20
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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.
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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
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21
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Rodrigues CHM, Garg A, Keizer D, Pires DEV, Ascher DB. CSM-peptides: A computational approach to rapid identification of therapeutic peptides. Protein Sci 2022; 31:e4442. [PMID: 36173168 PMCID: PMC9518225 DOI: 10.1002/pro.4442] [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: 07/17/2022] [Revised: 08/29/2022] [Accepted: 08/30/2022] [Indexed: 11/25/2022]
Abstract
Peptides are attractive alternatives for the development of new therapeutic strategies due to their versatility and low complexity of synthesis. Increasing interest in these molecules has led to the creation of large collections of experimentally characterized therapeutic peptides, which greatly contributes to development of data-driven computational approaches. Here we propose CSM-peptides, a novel machine learning method for rapid identification of eight different types of therapeutic peptides: anti-angiogenic, anti-bacterial, anti-cancer, anti-inflammatory, anti-viral, cell-penetrating, quorum sensing, and surface binding. Our method has shown to outperform existing approaches, achieving an AUC of up to 0.92 on independent blind tests, and consistent performance on cross-validation. We anticipate CSM-peptides to be of great value in helping screening large libraries to identify novel peptides with therapeutic potential and have made it freely available as a user-friendly web server and Application Programming Interface at https://biosig.lab.uq.edu.au/csm_peptides.
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Affiliation(s)
- Carlos H. M. Rodrigues
- Structural Biology and Bioinformatics, Department of BiochemistryUniversity of MelbourneMelbourneVictoriaAustralia
- Systems and Computational Biology, Bio21 Institute, University of MelbourneMelbourneVictoriaAustralia
- Computational Biology and Clinical InformaticsBaker Heart and Diabetes InstituteMelbourneVictoriaAustralia
- School of Chemistry and Molecular BiosciencesUniversity of QueenslandSt LuciaQueenslandAustralia
| | - Anjali Garg
- Structural Biology and Bioinformatics, Department of BiochemistryUniversity of MelbourneMelbourneVictoriaAustralia
- Systems and Computational Biology, Bio21 Institute, University of MelbourneMelbourneVictoriaAustralia
| | - David Keizer
- Structural Biology and Bioinformatics, Department of BiochemistryUniversity of MelbourneMelbourneVictoriaAustralia
- Systems and Computational Biology, Bio21 Institute, University of MelbourneMelbourneVictoriaAustralia
| | - Douglas E. V. Pires
- Systems and Computational Biology, Bio21 Institute, University of MelbourneMelbourneVictoriaAustralia
- Computational Biology and Clinical InformaticsBaker Heart and Diabetes InstituteMelbourneVictoriaAustralia
- School of Computing and Information SystemsUniversity of MelbourneMelbourneVictoriaAustralia
| | - David B. Ascher
- Structural Biology and Bioinformatics, Department of BiochemistryUniversity of MelbourneMelbourneVictoriaAustralia
- Systems and Computational Biology, Bio21 Institute, University of MelbourneMelbourneVictoriaAustralia
- Computational Biology and Clinical InformaticsBaker Heart and Diabetes InstituteMelbourneVictoriaAustralia
- School of Chemistry and Molecular BiosciencesUniversity of QueenslandSt LuciaQueenslandAustralia
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22
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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.
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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:
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23
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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.
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24
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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.
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Affiliation(s)
- Davor Juretić
- Mediterranean Institute for Life Sciences, 21000 Split, Croatia;
- Faculty of Science, University of Split, 21000 Split, Croatia;
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25
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de Amaral M, Ienes-Lima J. Anurans against SARS-CoV-2: A review of the potential antiviral action of anurans cutaneous peptides. Virus Res 2022; 315:198769. [PMID: 35430319 PMCID: PMC9008983 DOI: 10.1016/j.virusres.2022.198769] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Revised: 04/05/2022] [Accepted: 04/06/2022] [Indexed: 01/17/2023]
Abstract
At the end of 2019, in China, clinical signs and symptoms of unknown etiology have been reported in several patients whose sample sequencing revealed pneumonia caused by the SARS-CoV-2 virus. COVID-19 is a disease triggered by this virus, and in 2020, the World Health Organization declared it a pandemic. Since then, efforts have been made to find effective therapeutic agents against this disease. Identifying novel natural antiviral drugs can be an alternative to treatment. For this reason, antimicrobial peptides secreted by anurans' skin have gained attention for showing a promissory antiviral effect. Hence, this review aimed to elucidate how and which peptides secreted by anurans' skin can be considered therapeutic agents to treat or prevent human viral infectious diseases. Through a literature review, we attempted to identify potential antiviral frogs' peptides to combat COVID-19. As a result, the Magainin-1 and -2 peptides, from the Magainin family, the Dermaseptin-S9, from the Dermaseptin family, and Caerin 1.6 and 1.10, from the Caerin family, are molecules that already showed antiviral effects against SARS-CoV-2 in silico. In addition to these peptides, this review suggests that future studies should use other families that already have antiviral action against other viruses, such as Brevinins, Maculatins, Esculentins, Temporins, and Urumins. To apply these peptides as therapeutic agents, experimental studies with peptides already tested in silico and new studies with other families not tested yet should be considered.
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Affiliation(s)
- Marjoriane de Amaral
- Comparative Metabolism and Endocrinology Laboratory, Department of Physiology, Federal University of Rio Grande do Sul (UFRGS), Sarmento Leite, 500, Porto Alegre, Rio Grande do Sul 90050-170, Brazil.
| | - Julia Ienes-Lima
- Department of Population Health, College of Veterinary Medicine, University of Georgia, Athens, GA 30602, United States
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26
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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: 16] [Impact Index Per Article: 8.0] [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.
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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
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27
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Chang CC, Hsu HJ, Wu TY, Liou JW. 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] [Key Words] [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.
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Affiliation(s)
- Chun-Chun Chang
- Department of Laboratory Medicine, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Hualien, Taiwan
- Department of Laboratory Medicine and Biotechnology, Tzu Chi University, Hualien, Taiwan
| | - Hao-Jen Hsu
- Department of Life Sciences, Tzu Chi University, Hualien, Taiwan
| | - Tien-Yuan Wu
- Department of Pharmacology, School of Medicine, Tzu Chi University, Hualien, Taiwan
| | - Je-Wen Liou
- Department of Laboratory Medicine and Biotechnology, Tzu Chi University, Hualien, Taiwan
- Department of Biochemistry, School of Medicine, Tzu Chi University, Hualien, Taiwan
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28
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Chen X, Huang J, He B. AntiDMPpred: a web service for identifying anti-diabetic peptides. PeerJ 2022; 10:e13581. [PMID: 35722269 PMCID: PMC9205309 DOI: 10.7717/peerj.13581] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2022] [Accepted: 05/23/2022] [Indexed: 01/17/2023] Open
Abstract
Diabetes mellitus (DM) is a chronic metabolic disease that has been a major threat to human health globally, causing great economic and social adversities. The oral administration of anti-diabetic peptide drugs has become a novel route for diabetes therapy. Numerous bioactive peptides have demonstrated potential anti-diabetic properties and are promising as alternative treatment measures to prevent and manage diabetes. The computational prediction of anti-diabetic peptides can help promote peptide-based drug discovery in the process of searching newly effective therapeutic peptide agents for diabetes treatment. Here, we resorted to random forest to develop a computational model, named AntiDMPpred, for predicting anti-diabetic peptides. A benchmark dataset with 236 anti-diabetic and 236 non-anti-diabetic peptides was first constructed. Four types of sequence-derived descriptors were used to represent the peptide sequences. We then combined four machine learning methods and six feature scoring methods to select the non-redundant features, which were fed into diverse machine learning classifiers to train the models. Experimental results show that AntiDMPpred reached an accuracy of 77.12% and area under the receiver operating curve (AUCROC) of 0.8193 in the nested five-fold cross-validation, yielding a satisfactory performance and surpassing other classifiers implemented in the study. The web service is freely accessible at http://i.uestc.edu.cn/AntiDMPpred/cgi-bin/AntiDMPpred.pl. We hope AntiDMPpred could improve the discovery of anti-diabetic bioactive peptides.
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Affiliation(s)
- Xue Chen
- Medical College, Guizhou University, Guiyang, China
| | - Jian Huang
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Bifang He
- Medical College, Guizhou University, Guiyang, China
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29
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Ripperda T, Yu Y, Verma A, Klug E, Thurman M, Reid SP, Wang G. Improved Database Filtering Technology Enables More Efficient Ab Initio Design of Potent Peptides against Ebola Viruses. Pharmaceuticals (Basel) 2022; 15:ph15050521. [PMID: 35631348 PMCID: PMC9143221 DOI: 10.3390/ph15050521] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Revised: 04/16/2022] [Accepted: 04/22/2022] [Indexed: 02/07/2023] Open
Abstract
The rapid mutations of viruses such as SARS-CoV-2 require vaccine updates and the development of novel antiviral drugs. This article presents an improved database filtering technology for a more effective design of novel antiviral agents. Different from the previous approach, where the most probable parameters were obtained stepwise from the antimicrobial peptide database, we found it possible to accelerate the design process by deriving multiple parameters in a single step during the peptide amino acid analysis. The resulting peptide DFTavP1 displays the ability to inhibit Ebola virus. A deviation from the most probable peptide parameters reduces antiviral activity. The designed peptides appear to block viral entry. In addition, the amino acid signature provides a clue to peptide engineering to gain cell selectivity. Like human cathelicidin LL-37, our engineered peptide DDIP1 inhibits both Ebola and SARS-CoV-2 viruses. These peptides, with broad antiviral activity, may selectively disrupt viral envelopes and offer the lasting efficacy required to treat various RNA viruses, including their emerging mutants.
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Affiliation(s)
| | | | | | | | | | - St Patrick Reid
- Correspondence: (S.P.R.); (G.W.); Tel.: +1-(402)-559-3644 (S.P.R.); +1-(402)-559-4176 (G.W.)
| | - Guangshun Wang
- Correspondence: (S.P.R.); (G.W.); Tel.: +1-(402)-559-3644 (S.P.R.); +1-(402)-559-4176 (G.W.)
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30
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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.
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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
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31
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Sureram S, Arduino I, Ueoka R, Rittà M, Francese R, Srivibool R, Darshana D, Piel J, Ruchirawat S, Muratori L, Lembo D, Kittakoop P, Donalisio M. The Peptide A-3302-B Isolated from a Marine Bacterium Micromonospora sp. Inhibits HSV-2 Infection by Preventing the Viral Egress from Host Cells. Int J Mol Sci 2022; 23:947. [PMID: 35055133 PMCID: PMC8778767 DOI: 10.3390/ijms23020947] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Revised: 01/07/2022] [Accepted: 01/13/2022] [Indexed: 02/04/2023] Open
Abstract
Herpesviruses are highly prevalent in the human population, and frequent reactivations occur throughout life. Despite antiviral drugs against herpetic infections, the increasing appearance of drug-resistant viral strains and their adverse effects prompt the research of novel antiherpetic drugs for treating lesions. Peptides obtained from natural sources have recently become of particular interest for antiviral therapy applications. In this work, we investigated the antiviral activity of the peptide A-3302-B, isolated from a marine bacterium, Micromonospora sp., strain MAG 9-7, against herpes simplex virus type 1, type 2, and human cytomegalovirus. Results showed that the peptide exerted a specific inhibitory activity against HSV-2 with an EC50 value of 14 μM. Specific antiviral assays were performed to investigate the mechanism of action of A-3302-B. We demonstrated that the peptide did not affect the expression of viral proteins, but it inhibited the late events of the HSV-2 replicative cycle. In detail, it reduced the cell-to-cell virus spread and the transmission of the extracellular free virus by preventing the egress of HSV-2 progeny from the infected cells. The dual antiviral and previously reported anti-inflammatory activities of A-3302-B, and its effect against an acyclovir-resistant HSV-2 strain are attractive features for developing a therapeutic to reduce the transmission of HSV-2 infections.
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Affiliation(s)
- Sanya Sureram
- Chulabhorn Research Institute, Kamphaeng Phet 6 Road, Laksi, Bangkok 10210, Thailand; (S.S.); (S.R.)
| | - Irene Arduino
- Department of Clinical and Biological Sciences, University of Turin, Regione Gonzole 10, 10043 Orbassano, Italy; (I.A.); (M.R.); (R.F.); (D.L.)
| | - Reiko Ueoka
- Institute of Microbiology, ETH Zurich, Vladimir-Prelog-Weg 4, 8093 Zurich, Switzerland; (R.U.); (J.P.)
| | - Massimo Rittà
- Department of Clinical and Biological Sciences, University of Turin, Regione Gonzole 10, 10043 Orbassano, Italy; (I.A.); (M.R.); (R.F.); (D.L.)
| | - Rachele Francese
- Department of Clinical and Biological Sciences, University of Turin, Regione Gonzole 10, 10043 Orbassano, Italy; (I.A.); (M.R.); (R.F.); (D.L.)
| | | | - Dhanushka Darshana
- Program in Chemical Sciences, Chulabhorn Graduate Institute, Chulabhorn Royal Academy, Kamphaeng Phet 6 Road, Laksi, Bangkok 10210, Thailand;
| | - Jörn Piel
- Institute of Microbiology, ETH Zurich, Vladimir-Prelog-Weg 4, 8093 Zurich, Switzerland; (R.U.); (J.P.)
| | - Somsak Ruchirawat
- Chulabhorn Research Institute, Kamphaeng Phet 6 Road, Laksi, Bangkok 10210, Thailand; (S.S.); (S.R.)
- Program in Chemical Sciences, Chulabhorn Graduate Institute, Chulabhorn Royal Academy, Kamphaeng Phet 6 Road, Laksi, Bangkok 10210, Thailand;
- Center of Excellence on Environmental Health and Toxicology (EHT), OPS, Ministry of Higher Education, Science, Research and Innovation, Bangkok 10210, Thailand
| | - Luisa Muratori
- Department of Clinical and Biological Sciences, Neuroscience Institute of the “Cavalieri Ottolenghi” Foundation (NICO), University of Turin, 10043 Orbassano, Italy;
| | - David Lembo
- Department of Clinical and Biological Sciences, University of Turin, Regione Gonzole 10, 10043 Orbassano, Italy; (I.A.); (M.R.); (R.F.); (D.L.)
| | - Prasat Kittakoop
- Chulabhorn Research Institute, Kamphaeng Phet 6 Road, Laksi, Bangkok 10210, Thailand; (S.S.); (S.R.)
- Program in Chemical Sciences, Chulabhorn Graduate Institute, Chulabhorn Royal Academy, Kamphaeng Phet 6 Road, Laksi, Bangkok 10210, Thailand;
- Center of Excellence on Environmental Health and Toxicology (EHT), OPS, Ministry of Higher Education, Science, Research and Innovation, Bangkok 10210, Thailand
| | - Manuela Donalisio
- Department of Clinical and Biological Sciences, University of Turin, Regione Gonzole 10, 10043 Orbassano, Italy; (I.A.); (M.R.); (R.F.); (D.L.)
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32
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Abstract
Antibiotic resistance constitutes a global threat and could lead to a future pandemic. One strategy is to develop a new generation of antimicrobials. Naturally occurring antimicrobial peptides (AMPs) are recognized templates and some are already in clinical use. To accelerate the discovery of new antibiotics, it is useful to predict novel AMPs from the sequenced genomes of various organisms. The antimicrobial peptide database (APD) provided the first empirical peptide prediction program. It also facilitated the testing of the first machine-learning algorithms. This chapter provides an overview of machine-learning predictions of AMPs. Most of the predictors, such as AntiBP, CAMP, and iAMPpred, involve a single-label prediction of antimicrobial activity. This type of prediction has been expanded to antifungal, antiviral, antibiofilm, anti-TB, hemolytic, and anti-inflammatory peptides. The multiple functional roles of AMPs annotated in the APD also enabled multi-label predictions (iAMP-2L, MLAMP, and AMAP), which include antibacterial, antiviral, antifungal, antiparasitic, antibiofilm, anticancer, anti-HIV, antimalarial, insecticidal, antioxidant, chemotactic, spermicidal activities, and protease inhibiting activities. Also considered in predictions are peptide posttranslational modification, 3D structure, and microbial species-specific information. We compare important amino acids of AMPs implied from machine learning with the frequently occurring residues of the major classes of natural peptides. Finally, we discuss advances, limitations, and future directions of machine-learning predictions of antimicrobial peptides. Ultimately, we may assemble a pipeline of such predictions beyond antimicrobial activity to accelerate the discovery of novel AMP-based antimicrobials.
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Affiliation(s)
- Guangshun Wang
- Department of Pathology and Microbiology, College of Medicine, University of Nebraska Medical Center, 985900 Nebraska Medical Center, Omaha, NE 68198-5900, USA;,Corresponding to: Dr. Monique van Hoek: ; Dr. Iosif Vaisman: ; Dr. Guangshun Wang:
| | - Iosif I. Vaisman
- School of Systems Biology, George Mason University, 10920 George Mason Circle, Manassas, VA, 20110, USA.,Corresponding to: Dr. Monique van Hoek: ; Dr. Iosif Vaisman: ; Dr. Guangshun Wang:
| | - Monique L. van Hoek
- School of Systems Biology, George Mason University, 10920 George Mason Circle, Manassas, VA, 20110, USA.,Corresponding to: Dr. Monique van Hoek: ; Dr. Iosif Vaisman: ; Dr. Guangshun Wang:
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33
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Rabbani G, Ahn SN, Kwon H, Ahmad K, Choi I. Penta-peptide ATN-161 based neutralization mechanism of SARS-CoV-2 spike protein. Biochem Biophys Rep 2021; 28:101170. [PMID: 34778573 PMCID: PMC8578017 DOI: 10.1016/j.bbrep.2021.101170] [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: 06/07/2021] [Revised: 10/25/2021] [Accepted: 11/08/2021] [Indexed: 12/12/2022] Open
Abstract
SARS-CoV-2 has become a big challenge for the scientific community worldwide. SARS-CoV-2 enters into the host cell by the spike protein binding with an ACE2 receptor present on the host cell. Developing safe and effective inhibitor appears an urgent need to interrupt the binding of SARS-CoV-2 spike protein with ACE2 receptor in order to reduce the SARS-CoV-2 infection. We have examined the penta-peptide ATN-161 as potential inhibitor of ACE2 and SARS-CoV-2 spike protein binding, where ATN-161 has been commercially approved for the safety and possess high affinity and specificity towards the receptor binding domain (RBD) of S1 subunit in SARS-CoV-2 spike protein. We carried out experiments and confirmed these phenomena that the virus bindings were indeed minimized. ATN-161 peptide can be used as an inhibitor of protein-protein interaction (PPI) stands as a crucial interaction in biological systems. The molecular docking finding suggests that the binding energy of the ACE2-spike protein complex is reduced in the presence of ATN-161. Protein-protein docking binding energy (-40.50 kcal/mol) of the spike glycoprotein toward the human ACE2 and binding of ATN-161 at their binding interface reduced the biding energy (-26.25 kcal/mol). The finding of this study suggests that ATN-161 peptide can mask the RBD of the spike protein and be considered as a neutralizing candidate by binding with the ACE2 receptor. Peptide-based masking of spike S1 protein (RBD) and its neutralization is a highly promising strategy to prevent virus penetration into the host cell. Thus masking of the RBD leads to the loss of receptor recognition property which can reduce the chance of infection host cells.
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Affiliation(s)
- Gulam Rabbani
- Nano Diagnostics & Devices (NDD), IT-Medical Fusion Center, 350-27 Gumidae-ro, Gumi-si, Gyeongbuk, 39253, Republic of Korea
| | - Saeyoung Nate Ahn
- Nano Diagnostics & Devices (NDD), IT-Medical Fusion Center, 350-27 Gumidae-ro, Gumi-si, Gyeongbuk, 39253, Republic of Korea
- Fuzbien Technology Institute, 13 Taft Court, Rockville, MD, 20850, USA
| | - Hyunhwa Kwon
- Nano Diagnostics & Devices (NDD), IT-Medical Fusion Center, 350-27 Gumidae-ro, Gumi-si, Gyeongbuk, 39253, Republic of Korea
| | - Khurshid Ahmad
- Department of Medical Biotechnology, Yeungnam University, Gyeongsan, 38541, Republic of Korea
| | - Inho Choi
- Department of Medical Biotechnology, Yeungnam University, Gyeongsan, 38541, Republic of Korea
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34
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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: 34] [Impact Index Per Article: 11.3] [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.
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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
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35
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Prediction for understanding the effectiveness of antiviral peptides. Comput Biol Chem 2021; 95:107588. [PMID: 34655913 DOI: 10.1016/j.compbiolchem.2021.107588] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2021] [Revised: 10/01/2021] [Accepted: 10/02/2021] [Indexed: 11/20/2022]
Abstract
The low efficacy of current antivirals in conjunction with the resistance of viruses against existing antiviral drugs has resulted in the demand for the development of novel antiviral agents. Antiviral peptides (AVPs) are those bioactive peptides having virucidal activity and they can be developed into promising antiviral drugs. They are shorter length peptides having the ability to cease the progression of viral infections. The use of antiviral peptides in therapeutics has recently attracted the attention of the research community. The development and identification of AVPs is imperative for the discovery of novel therapeutics for viral infections. In the present work, a meta classifier (stacking) based approach is implemented for the prediction of IC50 (half maximal inhibitory concentration) and pIC50 (negative log of half maximal inhibitory concentration) values. The best prediction model with evolutionary information and local alignment scores as features achieved a correlation coefficient values of 0.670 and 0.753 on the training and testing sets respectively for IC50. Further, the prediction of pIC50 reached a correlation coefficient value of 0.797 and 0.789 for training and testing sets respectively. For the development of machine learning models involved in the prediction of IC50, the use of pIC50 over IC50 is recommended as the target variable. Further on a systematic comparison of AVPs with high IC50 values and Low IC50 values, it is revealed that higher mean charge and tiny amino acids are preferred and higher length and consecutive hydrophilic amino acids are avoided in the former.
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The Peptide TAT-I24 with Antiviral Activity against DNA Viruses Binds Double-Stranded DNA with High Affinity. Biologics 2021. [DOI: 10.3390/biologics1010003] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
The peptide TAT-I24, composed of the 9-mer peptide I24 and the TAT (48-60) peptide, exerts broad-spectrum antiviral activity against several DNA viruses. The current model of the mode of action suggests a reduction of viral entry and also a possible interaction with the viral DNA upon virus entry. To further support this model, the present study investigates the DNA binding properties of TAT-I24. DNA binding was analysed by gel retardation of a peptide-complexed DNA, fluorescence reduction of DNA labelled with intercalating dyes and determination of binding kinetics by surface plasmon resonance. Molecular dynamics simulations of DNA-peptide complexes predict high-affinity binding and destabilization of the DNA by TAT-I24. The effect on viral DNA levels of infected cells were studied by real-time PCR and staining of viral DNA by bromodeoxyuridine. TAT-I24 binds double-stranded DNA with high affinity, leading to inhibition of polymerase binding and thereby blocking of de novo nucleic acid synthesis. Analysis of early steps of virus entry using a bromodeoxyuridine-labelled virus as well as quantification of viral genomes in the cells indicate direct binding of the peptide to the viral DNA. Saturation of the peptide with exogenous DNA can fully neutralize the inhibitory effect. The antiviral activity of TAT-I24 is linked to its ability to bind DNA with high affinity. This mechanism could be the basis for the development of novel antiviral agents.
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Serafim MSM, Dos Santos Júnior VS, Gertrudes JC, Maltarollo VG, Honorio KM. Machine learning techniques applied to the drug design and discovery of new antivirals: a brief look over the past decade. Expert Opin Drug Discov 2021; 16:961-975. [PMID: 33957833 DOI: 10.1080/17460441.2021.1918098] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Introduction: Drug design and discovery of new antivirals will always be extremely important in medicinal chemistry, taking into account known and new viral diseases that are yet to come. Although machine learning (ML) have shown to improve predictions on the biological potential of chemicals and accelerate the discovery of drugs over the past decade, new methods and their combinations have improved their performance and established promising perspectives regarding ML in the search for new antivirals.Areas covered: The authors consider some interesting areas that deal with different ML techniques applied to antivirals. Recent innovative studies on ML and antivirals were selected and analyzed in detail. Also, the authors provide a brief look at the past to the present to detect advances and bottlenecks in the area.Expert opinion: From classical ML techniques, it was possible to boost the searches for antivirals. However, from the emergence of new algorithms and the improvement in old approaches, promising results will be achieved every day, as we have observed in the case of SARS-CoV-2. Recent experience has shown that it is possible to use ML to discover new antiviral candidates from virtual screening and drug repurposing.
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Affiliation(s)
- Mateus Sá Magalhães Serafim
- Departamento de Microbiologia, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, Brazil
| | | | - Jadson Castro Gertrudes
- Departamento de Computação, Instituto de Ciências Exatas e Biológicas, Universidade Federal de Ouro Preto (UFOP), Ouro Preto, Brazil
| | - Vinícius Gonçalves Maltarollo
- Departamento de Produtos Farmacêuticos, Faculdade de Farmácia, Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, Brazil
| | - Kathia Maria Honorio
- Escola de Artes, Ciências e Humanidades, Universidade de São Paulo (USP), São Paulo, Brazil.,Centro de Ciências Naturais e Humanas, Universidade Federal do ABC (UFABC), Santo André, Brazil
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