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Bajiya N, Choudhury S, Dhall A, Raghava GPS. AntiBP3: A Method for Predicting Antibacterial Peptides against Gram-Positive/Negative/Variable Bacteria. Antibiotics (Basel) 2024; 13:168. [PMID: 38391554 PMCID: PMC10885866 DOI: 10.3390/antibiotics13020168] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2023] [Revised: 02/03/2024] [Accepted: 02/06/2024] [Indexed: 02/24/2024] Open
Abstract
Most of the existing methods developed for predicting antibacterial peptides (ABPs) are mostly designed to target either gram-positive or gram-negative bacteria. In this study, we describe a method that allows us to predict ABPs against gram-positive, gram-negative, and gram-variable bacteria. Firstly, we developed an alignment-based approach using BLAST to identify ABPs and achieved poor sensitivity. Secondly, we employed a motif-based approach to predict ABPs and obtained high precision with low sensitivity. To address the issue of poor sensitivity, we developed alignment-free methods for predicting ABPs using machine/deep learning techniques. In the case of alignment-free methods, we utilized a wide range of peptide features that include different types of composition, binary profiles of terminal residues, and fastText word embedding. In this study, a five-fold cross-validation technique has been used to build machine/deep learning models on training datasets. These models were evaluated on an independent dataset with no common peptide between training and independent datasets. Our machine learning-based model developed using the amino acid binary profile of terminal residues achieved maximum AUC 0.93, 0.98, and 0.94 for gram-positive, gram-negative, and gram-variable bacteria, respectively, on an independent dataset. Our method performs better than existing methods when compared with existing approaches on an independent dataset. A user-friendly web server, standalone package and pip package have been developed to facilitate peptide-based therapeutics.
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Affiliation(s)
- Nisha Bajiya
- Department of Computational Biology, Indraprastha Institute of Information Technology, Okhla Phase 3, New Delhi 110020, India
| | - Shubham Choudhury
- Department of Computational Biology, Indraprastha Institute of Information Technology, Okhla Phase 3, New Delhi 110020, India
| | - Anjali Dhall
- Department of Computational Biology, Indraprastha Institute of Information Technology, Okhla Phase 3, New Delhi 110020, India
| | - Gajendra P S Raghava
- Department of Computational Biology, Indraprastha Institute of Information Technology, Okhla Phase 3, New Delhi 110020, India
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Shah M, Jaan S, Shehroz M, Sarfraz A, Asad K, Wara TU, Zaman A, Ullah R, Ali EA, Nishan U, Ojha SC. Deciphering the Immunogenicity of Monkeypox Proteins for Designing the Potential mRNA Vaccine. ACS OMEGA 2023; 8:43341-43355. [PMID: 38024731 PMCID: PMC10652822 DOI: 10.1021/acsomega.3c07866] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Revised: 10/17/2023] [Accepted: 10/18/2023] [Indexed: 12/01/2023]
Abstract
The Monkeypox virus (MPXV), an orthopox virus, is responsible for monkeypox in humans, a zoonotic disease similar to smallpox. This infection first appeared in the 1970s in humans and then in 2003, after which it kept on spreading all around the world. To date, various antivirals have been used to cure this disease, but now, MPXV has developed resistance against these, thus increasing the need for an alternative cure for this deadly disease. In this study, we devised a reverse vaccinology approach against MPXV using a messenger RNA (mRNA) vaccine by pinning down the antigenic proteins of this virus. By using bioinformatic tools, we predicted prospective immunogenic B and T lymphocyte epitopes. Based on cytokine inducibility score, nonallergenicity, nontoxicity, antigenicity, and conservancy, the final epitopes were selected. Our analysis revealed the stable structure of the mRNA vaccine and its efficient expression in host cells. Furthermore, strong interactions were demonstrated with toll-like receptors 2 (TLR2) and 4 (TLR4) according to the molecular dynamic simulation studies. The in silico immune simulation analyses revealed an overall increase in the immune responses following repeated exposure to the designed vaccine. Based on our findings, the vaccine candidate designed in this study has the potential to be tested as a promising novel mRNA therapeutic vaccine against MPXV infection.
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Affiliation(s)
- Mohibullah Shah
- Department
of Biochemistry, Bahauddin Zakariya University, Multan 66000, Pakistan
| | - Samavia Jaan
- Department
of Biochemistry, Bahauddin Zakariya University, Multan 66000, Pakistan
- School
of Biochemistry and Biotechnology, University
of the Punjab, Lahore 54590, Pakistan
| | - Muhammad Shehroz
- Department
of Bioinformatics, Kohsar University Murree, Murree 47150 Pakistan
| | - Asifa Sarfraz
- Department
of Biochemistry, Bahauddin Zakariya University, Multan 66000, Pakistan
| | - Khamna Asad
- School
of Biochemistry and Biotechnology, University
of the Punjab, Lahore 54590, Pakistan
| | - Tehreem Ul Wara
- Department
of Biochemistry, Bahauddin Zakariya University, Multan 66000, Pakistan
| | - Aqal Zaman
- Department
of Microbiology & Molecular Genetics, Bahauddin Zakariya University, Multan 66000, Pakistan
| | - Riaz Ullah
- Department
of Pharmacognosy, College of Pharmacy, King
Saud University Riyadh 11451, Saudi Arabia
| | - Essam A. Ali
- Department
of Pharmaceutical Chemistry, College of Pharmacy, King Saud University, Riyadh 11451, Saudi Arabia
| | - Umar Nishan
- Department
of Chemistry, Kohat University of Science
& Technology, Kohat 26000, Pakistan
| | - Suvash Chandra Ojha
- Department
of Infectious Diseases, The Affiliated Hospital
of Southwest Medical University, 646000 Luzhou, China
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Naorem LD, Sharma N, Raghava GPS. A web server for predicting and scanning of IL-5 inducing peptides using alignment-free and alignment-based method. Comput Biol Med 2023; 158:106864. [PMID: 37058758 DOI: 10.1016/j.compbiomed.2023.106864] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Revised: 03/06/2023] [Accepted: 03/30/2023] [Indexed: 04/16/2023]
Abstract
Interleukin-5 (IL-5) can act as an enticing therapeutic target due to its pivotal role in several eosinophil-mediated diseases. The aim of this study is to develop a model for predicting IL-5 inducing antigenic regions in a protein with high precision. All models in this study have been trained, tested and validated on experimentally validated 1907 IL-5 inducing and 7759 non-IL-5 inducing peptides obtained from IEDB. Our primary analysis indicates that IL-5 inducing peptides are dominated by certain residues like Ile, Asn, and Tyr. It was also observed that binders of a wide range of HLA alleles can induce IL-5. Initially, alignment-based methods have been developed using similarity and motif search. These alignment-based methods provide high precision but poor coverage. In order to overcome this limitation, we explore alignment-free methods which are mainly machine learning-based models. Firstly, models have been developed using binary profiles and eXtreme Gradient Boosting-based model achieved a maximum AUC of 0.59. Secondly, composition-based models have been developed and our dipeptide-based random forest model achieved a maximum AUC of 0.74. Thirdly, random forest model developed using selected 250 dipeptides and achieved AUC 0.75 and MCC 0.29 on validation dataset; best among alignment-free models. In order to improve the performance, we developed an ensemble or hybrid method that combined alignment-based and alignment-free methods. Our hybrid method achieved AUC 0.94 with MCC 0.60 on a validation/independent dataset. The best hybrid model developed in this study has been incorporated into the user-friendly web server and a standalone package named 'IL5pred' (https://webs.iiitd.edu.in/raghava/il5pred/).
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Affiliation(s)
- Leimarembi Devi Naorem
- Department of Computational Biology, Indraprastha Institute of Information Technology, Okhla Phase 3, New Delhi, 110020, India.
| | - Neelam Sharma
- Department of Computational Biology, Indraprastha Institute of Information Technology, Okhla Phase 3, New Delhi, 110020, India.
| | - Gajendra P S Raghava
- Department of Computational Biology, Indraprastha Institute of Information Technology, Okhla Phase 3, New Delhi, 110020, India.
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Malik A, Mahajan N, Dar TA, Kim CB. C10Pred: A First Machine Learning Based Tool to Predict C10 Family Cysteine Peptidases Using Sequence-Derived Features. Int J Mol Sci 2022; 23:ijms23179518. [PMID: 36076915 PMCID: PMC9455582 DOI: 10.3390/ijms23179518] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Revised: 08/17/2022] [Accepted: 08/20/2022] [Indexed: 12/02/2022] Open
Abstract
Streptococcus pyogenes, or group A Streptococcus (GAS), a gram-positive bacterium, is implicated in a wide range of clinical manifestations and life-threatening diseases. One of the key virulence factors of GAS is streptopain, a C10 family cysteine peptidase. Since its discovery, various homologs of streptopain have been reported from other bacterial species. With the increased affordability of sequencing, a significant increase in the number of potential C10 family-like sequences in the public databases is anticipated, posing a challenge in classifying such sequences. Sequence-similarity-based tools are the methods of choice to identify such streptopain-like sequences. However, these methods depend on some level of sequence similarity between the existing C10 family and the target sequences. Therefore, in this work, we propose a novel predictor, C10Pred, for the prediction of C10 peptidases using sequence-derived optimal features. C10Pred is a support vector machine (SVM) based model which is efficient in predicting C10 enzymes with an overall accuracy of 92.7% and Matthews’ correlation coefficient (MCC) value of 0.855 when tested on an independent dataset. We anticipate that C10Pred will serve as a handy tool to classify novel streptopain-like proteins belonging to the C10 family and offer essential information.
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Affiliation(s)
- Adeel Malik
- Institute of Intelligence Informatics Technology, Sangmyung University, Seoul 03016, Korea
- Correspondence: (A.M.); (C.-B.K.)
| | - Nitin Mahajan
- Department of Pediatrics, Washington University in St. Louis, St. Louis, MO 63110, USA
| | - Tanveer Ali Dar
- Department of Clinical Biochemistry, University of Kashmir, Srinagar 190006, India
| | - Chang-Bae Kim
- Department of Biotechnology, Sangmyung University, Seoul 03016, Korea
- Correspondence: (A.M.); (C.-B.K.)
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