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Wu Q, Ong L, Aldalur A, Nie S, Kentish SE, Gras SL. Modulation of cream cheese physicochemical and functional properties with ultrafiltration and calcium reduction. Food Chem 2024; 457:140010. [PMID: 38908254 DOI: 10.1016/j.foodchem.2024.140010] [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: 03/07/2024] [Revised: 05/14/2024] [Accepted: 06/06/2024] [Indexed: 06/24/2024]
Abstract
The production of cream cheese from ultrafiltered (UF) milk can reduce acid whey generation but the effect of altered protein and calcium concentration on the physicochemical properties of cream cheese is not well understood. In this study, the effect of skim milk concentration by UF (2.5 and 5 fold) was assessed both with and without calcium reduction using 2% (w/v) cation resin treatment. UF concentration increased the concentration of peptides and free amino acids and led to a more heterogeneous and porous microstructure, resulting in a softer, less viscous and less thermally stable cream cheese. Calcium reduction decreased peptide generation, increased the size of corpuscular structures, decreased porosity and increased thermal stability but did not significantly decrease cheese hardness or viscosity. The study illustrates how protein or calcium concentration, can be used to alter functional properties.
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Affiliation(s)
- Qihui Wu
- Department of Chemical Engineering, The University of Melbourne, Parkville, Victoria 3010, Australia; The Bio21 Molecular Science and Biotechnology Institute, The University of Melbourne, Parkville, Victoria 3010, Australia
| | - Lydia Ong
- Department of Chemical Engineering, The University of Melbourne, Parkville, Victoria 3010, Australia; The Bio21 Molecular Science and Biotechnology Institute, The University of Melbourne, Parkville, Victoria 3010, Australia
| | - Ane Aldalur
- Department of Chemical Engineering, The University of Melbourne, Parkville, Victoria 3010, Australia; The Bio21 Molecular Science and Biotechnology Institute, The University of Melbourne, Parkville, Victoria 3010, Australia
| | - Shuai Nie
- Mass Spectrometry and Proteomics Facility, The Bio21 Molecular Science and Biotechnology Institute, The University of Melbourne, Parkville, Victoria 3010, Australia
| | - Sandra Elizabeth Kentish
- Department of Chemical Engineering, The University of Melbourne, Parkville, Victoria 3010, Australia
| | - Sally Louise Gras
- Department of Chemical Engineering, The University of Melbourne, Parkville, Victoria 3010, Australia; The Bio21 Molecular Science and Biotechnology Institute, The University of Melbourne, Parkville, Victoria 3010, Australia.
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Ureiro-Cueto G, Rodil SE, Santana-Vázquez M, Hoz-Rodriguez L, Arzate H, Montoya-Ayala G. Characterization of aTiO 2 surfaces functionalized with CAP-p15 peptide. J Biomed Mater Res A 2024; 112:1399-1411. [PMID: 38284510 DOI: 10.1002/jbm.a.37676] [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: 08/17/2023] [Revised: 01/10/2024] [Accepted: 01/15/2024] [Indexed: 01/30/2024]
Abstract
Functionalization of Titanium implants using adequate organic molecules is a proposed method to accelerate the osteointegration process, which relates to topographical, chemical, mechanical, and physical features. This study aimed to assess the potential of a peptide derived from cementum attachment protein (CAP-p15) adsorbed onto aTiO2 surfaces to promote the deposition of calcium phosphate (CaP) minerals and its impact on the adhesion and viability of human periodontal ligament cells (hPDLCs). aTiO2 surfaces were synthesized by magnetron sputtering technique. The CAP-p15 peptide was physically attached to aTiO2 surfaces and characterized by atomic force microscopy, fluorescence microscopy, and water contact angle measurement. We performed in vitro calcium phosphate nucleation assays using an artificial saliva solution (pH 7.4) to simulate the oral environment. morphological and chemical characterization of the deposits were evaluated by scanning electronic microscopy (SEM) and spectroscopy molecular techniques (Raman Spectroscopy, ATR-FTIR). The aTiO2 surfaces biofunctionalized with CAP-p15 were also analyzed for hPDLCs attachment, proliferation, and in vitro scratch-healing assay. The results let us see that the homogeneous amorphous titanium oxide coating was 70 nanometers thick. The CAP-p15 (1 μg/mL) displayed the ability to adsorb onto the aTiO2 surface, increasing the roughness and maintaining the hydrophilicity of the aTiO2 surfaces. The physical adsorption of CAP-p15 onto the aTiO2 surfaces promoted the precipitation of a uniform layer of crystals with a flake-like morphology and a Ca/P ratio of 1.79. According to spectroscopy molecular analysis, these crystalline deposits correspond to carbonated hydroxyapatite. Regarding cell behavior, the biofunctionalized aTiO2 surfaces improved the adhesion of hPDLCs after 24 h of cell culture, achieving 3.4-fold when compared to pristine surfaces. Moreover, there was an increase in cell proliferation and cell migration processes. Physical adsorption of CAP-p15 onto aTiO2 surfaces enhanced the formation of carbonate hydroxyapatite crystals and promoted the proliferation and migration of human periodontal ligament-derived cells in in vitro studies. This experimental model using the novel bioactive peptide CAP-p15 could be used as an alternative to increasing the osseointegration process of implants.
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Affiliation(s)
- Guadalupe Ureiro-Cueto
- Laboratorio de Biología Periodontal y Tejidos Mineralizados, División de Estudios de Posgrado e Investigación Facultad de Odontología, Universidad Nacional Autónoma de, México city, Mexico
| | - Sandra E Rodil
- Instituto de Investigaciones en Materiales, Universidad Nacional Autónoma de, México city, Mexico
| | - Maricela Santana-Vázquez
- Laboratorio de Biología Periodontal y Tejidos Mineralizados, División de Estudios de Posgrado e Investigación Facultad de Odontología, Universidad Nacional Autónoma de, México city, Mexico
| | - Lia Hoz-Rodriguez
- Laboratorio de Biología Periodontal y Tejidos Mineralizados, División de Estudios de Posgrado e Investigación Facultad de Odontología, Universidad Nacional Autónoma de, México city, Mexico
| | - Higinio Arzate
- Laboratorio de Biología Periodontal y Tejidos Mineralizados, División de Estudios de Posgrado e Investigación Facultad de Odontología, Universidad Nacional Autónoma de, México city, Mexico
| | - Gonzalo Montoya-Ayala
- Laboratorio de Biología Periodontal y Tejidos Mineralizados, División de Estudios de Posgrado e Investigación Facultad de Odontología, Universidad Nacional Autónoma de, México city, Mexico
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Nugraha R, Kurniawan F, Abdullah A, Lopata AL, Ruethers T. Antihypertensive and Antidiabetic Drug Candidates from Milkfish ( Chanos chanos)-Identification and Characterization through an Integrated Bioinformatic Approach. Foods 2024; 13:2594. [PMID: 39200521 PMCID: PMC11353658 DOI: 10.3390/foods13162594] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2024] [Revised: 08/11/2024] [Accepted: 08/13/2024] [Indexed: 09/02/2024] Open
Abstract
Integrated bioinformatics tools have created more efficient and robust methods to overcome in vitro challenges and have been widely utilized for the investigation of food proteins and the generation of peptide sequences. This study aimed to analyze the physicochemical properties and bioactivities of novel peptides derived from hydrolyzed milkfish (Chanos chanos) protein sequences and to discover their potential angiotensin-converting enzyme (ACE)- and dipeptidyl peptidase-4 (DPPIV)-inhibitory activities using machine learning-based tools, including BIOPEP-UWM, PeptideRanker, and the molecular docking software HADDOCK 2.4. Nine and three peptides were predicted to have ACE- and DPPIV-inhibitory activities, respectively. The DPPIV-inhibitory peptides were predicted to inhibit the compound with no known specific mode. Meanwhile, two tetrapeptides (MVWH and PPPS) were predicted to possess a competitive mode of ACE inhibition by directly binding to the tetra-coordinated Zn ion. Among all nine discovered ACE-inhibitory peptides, only the PPPS peptide satisfied the drug-likeness analysis requirements with no violations of the Lipinski rule of five and should be further investigated in vitro.
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Affiliation(s)
- Roni Nugraha
- Department of Aquatic Product Technology, Faculty of Fisheries and Marine Science, IPB University, Dramaga 16680, Indonesia; (F.K.); (A.A.)
- Tropical Futures Institute, James Cook University, Singapore 387380, Singapore; (A.L.L.); (T.R.)
| | - Fahmi Kurniawan
- Department of Aquatic Product Technology, Faculty of Fisheries and Marine Science, IPB University, Dramaga 16680, Indonesia; (F.K.); (A.A.)
| | - Asadatun Abdullah
- Department of Aquatic Product Technology, Faculty of Fisheries and Marine Science, IPB University, Dramaga 16680, Indonesia; (F.K.); (A.A.)
| | - Andreas L. Lopata
- Tropical Futures Institute, James Cook University, Singapore 387380, Singapore; (A.L.L.); (T.R.)
- Molecular Allergy Research Laboratory, Australian Institute of Tropical Health and Medicine, James Cook University, Douglas, QLD 4811, Australia
| | - Thimo Ruethers
- Tropical Futures Institute, James Cook University, Singapore 387380, Singapore; (A.L.L.); (T.R.)
- Molecular Allergy Research Laboratory, Australian Institute of Tropical Health and Medicine, James Cook University, Douglas, QLD 4811, Australia
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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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Hasnat S, Hoque MN, Mahbub MM, Sakif TI, Shahinuzzaman A, Islam T. Pantothenate kinase: A promising therapeutic target against pathogenic Clostridium species. Heliyon 2024; 10:e34544. [PMID: 39130480 PMCID: PMC11315101 DOI: 10.1016/j.heliyon.2024.e34544] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2024] [Revised: 07/08/2024] [Accepted: 07/11/2024] [Indexed: 08/13/2024] Open
Abstract
Current treatment of clostridial infections includes broad-spectrum antibiotics and antitoxins, yet antitoxins are ineffective against all Clostridiumspecies. Moreover, rising antimicrobial resistance (AMR) threatens treatment effectiveness and public health. This study therefore aimed to discover a common drug target for four pathogenic clostridial species, Clostridium botulinum, C. difficile, C. tetani, and C. perfringens through an in-silico core genomic approach. Using four reference genomes of C. botulinum, C. difficile, C. tetani, and C. perfringens, we identified 1484 core genomic proteins (371/genome) and screened them for potential drug targets. Through a subtractive approach, four core proteins were finally identified as drug targets, represented by type III pantothenate kinase (CoaX) and, selected for further analyses. Interestingly, the CoaX is involved in the phosphorylation of pantothenate (vitamin B5), which is a critical precursor for coenzyme A (CoA) biosynthesis. Investigation of druggability analysis on the identified drug target reinforces CoaX as a promising novel drug target for the selected Clostridium species. During the molecular screening of 1201 compounds, a known agonist drug compound (Vibegron) showed strong inhibitory activity against targeted clostridial CoaX. Additionally, we identified tazobactam, a beta-lactamase inhibitor, as effective against the newly proposed target, CoaX. Therefore, identifying CoaX as a single drug target effective against all four clostridial pathogens presents a valuable opportunity to develop a cost-effective treatment for multispecies clostridial infections.
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Affiliation(s)
- Soharth Hasnat
- Institute of Biotechnology and Genetic Engineering (IBGE), Bangabandhu Sheikh Mujibur Rahman Agricultural University (BSMRAU), Gazipur, 1706, Bangladesh
- Molecular Biology and Bioinformatics Laboratory (MBBL), Department of Gynecology, Obstetrics and Reproductive Health, BSMRAU, Gazipur, 1706, Bangladesh
- Department of Genetic Engineering and Biotechnology, East West University, Dhaka, 1212, Bangladesh
| | - M. Nazmul Hoque
- Molecular Biology and Bioinformatics Laboratory (MBBL), Department of Gynecology, Obstetrics and Reproductive Health, BSMRAU, Gazipur, 1706, Bangladesh
| | - M Murshida Mahbub
- Department of Genetic Engineering and Biotechnology, East West University, Dhaka, 1212, Bangladesh
| | - Tahsin Islam Sakif
- Lane Department of Computer Science and Electrical Engineering, West Virginia University, Morgantown, WV, WV 26506, USA
| | - A.D.A. Shahinuzzaman
- Pharmaceutical Sciences Research Division, Bangladesh Council of Scientific and Industrial Research (BCSIR), Dhaka, 1205, Bangladesh
| | - Tofazzal Islam
- Institute of Biotechnology and Genetic Engineering (IBGE), Bangabandhu Sheikh Mujibur Rahman Agricultural University (BSMRAU), Gazipur, 1706, Bangladesh
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Huda TI, Nguyen D, Sahoo A, Song JJ, Gutierrez AF, Chobrutskiy BI, Blanck G. Adaptive Immune Receptor Distinctions Along the Colorectal Polyp-Tumor Timelapse. Clin Colorectal Cancer 2024:S1533-0028(24)00064-1. [PMID: 39174387 DOI: 10.1016/j.clcc.2024.07.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2024] [Revised: 07/19/2024] [Accepted: 07/20/2024] [Indexed: 08/24/2024]
Abstract
INTRODUCTION Colorectal cancer (CRC) is the third-most common cancer diagnosed worldwide, with 1.85 million new cases per year. While mortality has significantly decreased due to preventive colonoscopy, only 5% of polyps identified progress to cancer. Studies have found that immunological alterations in other solid tumor microenvironments are associated with worse prognoses. METHODS We applied an immunogenomics approach to assess adaptive immune receptor gene expression changes that were associated with development of adenocarcinoma, utilizing 79 samples that represented normal, tubular, villous, and tumor colorectal tissue for 32 patients. RESULTS Results indicated that the number of productive TRD and TRG recombination reads, representing gamma-delta (γδ) T-cells, significantly decreased with progression from normal to tumor tissue. A further assessment of two independent CRC datasets was consistent with a decrease in TRD recombination reads with progression to CRC. Further, we identified three physicochemical parameters for immunoglobulin, complementarity determining region-3 (CDR3) amino acids associated with progression from normal to tumor tissue. CONCLUSIONS Overall, this study points towards a need for further investigation of γδ T-cells in relation to CRC development; and indicates immunoglobulin CDR3 physicochemical features as potential CRC biomarkers.
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Affiliation(s)
- Taha I Huda
- Department of Internal Medicine, HCA Healthcare/University of South Florida Morsani College of Medicine, Graduate Medical Education, HCA Florida Bayonet Point Hospital, Hudson, FL; Department of Molecular Medicine, Morsani College of Medicine, University of South Florida, Tampa, FL
| | - Diep Nguyen
- Department of Child and Family Studies, College of Behavioral and Community Sciences, University of South Florida, Tampa, FL
| | - Arpan Sahoo
- Department of Molecular Medicine, Morsani College of Medicine, University of South Florida, Tampa, FL
| | - Joanna J Song
- Department of Molecular Medicine, Morsani College of Medicine, University of South Florida, Tampa, FL
| | - Alexander F Gutierrez
- Department of Internal Medicine, HCA Healthcare/University of South Florida Morsani College of Medicine, Graduate Medical Education, HCA Florida Bayonet Point Hospital, Hudson, FL
| | - Boris I Chobrutskiy
- Department of Internal Medicine, Oregon Health and Sciences University Hospital, Portland, OR
| | - George Blanck
- Department of Molecular Medicine, Morsani College of Medicine, University of South Florida, Tampa, FL; Department of Immunology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL.
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Naskar S, Harsukhbhai Chandpa H, Agarwal S, Meena J. Super epitope dengue vaccine instigated serotype independent immune protection in-silico. Vaccine 2024; 42:3857-3873. [PMID: 38616437 DOI: 10.1016/j.vaccine.2024.04.009] [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: 02/23/2024] [Revised: 03/21/2024] [Accepted: 04/04/2024] [Indexed: 06/14/2024]
Abstract
Dengue becomes the most common life-threatening infectious arbovirus disease globally, with prevalence in the tropical and subtropical areas. The major clinical features include dengue haemorrhagic fever (DHF) and dengue shock syndrome (DSS), a condition of hypovolemic shock. Four different serotypes of the dengue virus, known as dengue virus serotype (DENV)- 1, 2, 3 and 4 can infect humans. Only one vaccine is available in the market, named Dengvaxia by Sanofi Pasteur, but there is no desired outcome of this treatment due the antibody dependent enhancement (ADE) of the multiple dengue serotypes. As of now, there is no cure against dengue disease. Our goal in this work was to create a subunit vaccine based on several epitopes that would be effective against every serotype of the dengue virus. Here, computational methods like- immunoinformatics and bioinformatics were implemented to find out possible dominant epitopes. A total of 21 epitopes were chosen using various in-silico techniques from the expected 133 major histocompatibility complex (MHC)- I and major histocompatibility complex (MHC)- II epitopes, along with 95 B-cell epitopes which were greatly conserved. Immune stimulant, non-allergenic and non-toxic immunodominant epitopes (super epitopes) with a suitable adjuvant (Heparin-Binding Hemagglutinin Adhesin, HBHA) were used to construct the vaccine. Following the physicochemical analysis, vaccine construct was docked with Toll-like receptors (TLRs) to predict the immune stimulation. Consequently, the optimal docked complex that demonstrated the least amount of ligand-receptor complex deformability was used to conduct the molecular dynamics analysis. By following the codon optimization, the final vaccine molecule was administered into an expressing vector to perform in-silico cloning. The robust immune responses were generated in the in-silico immune simulation analysis. Hence, this study provides a hope to control the dengue infections. For validation of the immune outcomes, in-vitro as well as in-vivo investigations are essential.
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Affiliation(s)
- Shovan Naskar
- ImmunoEngineering and Therapeutics Laboratory, Department of Pharmaceutical Engineering and Technology, Indian Institute of Technology (Banaras Hindu University), Varanasi 221005, India
| | - Hitesh Harsukhbhai Chandpa
- ImmunoEngineering and Therapeutics Laboratory, Department of Pharmaceutical Engineering and Technology, Indian Institute of Technology (Banaras Hindu University), Varanasi 221005, India
| | - Shalini Agarwal
- ImmunoEngineering and Therapeutics Laboratory, Department of Pharmaceutical Engineering and Technology, Indian Institute of Technology (Banaras Hindu University), Varanasi 221005, India
| | - Jairam Meena
- ImmunoEngineering and Therapeutics Laboratory, Department of Pharmaceutical Engineering and Technology, Indian Institute of Technology (Banaras Hindu University), Varanasi 221005, India.
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Dwivedi M, Jose S, Gupta M, Devi SS, Raj R, Kumar D. Copper transporter protein (MctB) as a therapeutic target to elicit antimycobacterial activity against tuberculosis. J Biomol Struct Dyn 2024; 42:5334-5348. [PMID: 37340670 DOI: 10.1080/07391102.2023.2226728] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2022] [Accepted: 06/10/2023] [Indexed: 06/22/2023]
Abstract
Tuberculosis (TB) is a prehistoric infection and major etiologic agent of TB, Mycobacterium tuberculosis, which is considered to have advanced from an early progenitor species found in Eastern Africa. By the 1800s, there were approximately 800 to 1000 fatality case reports per 100,000 people in Europe and North America. This research suggests an In-silico study to identify potential inhibitory compounds for the target Mycobacterial copper transport protein (Mctb). ADME-based virtual screening, molecular docking, and molecular dynamics simulations were conducted to find promising compounds to modulate the function of the target protein. Four chemical compounds, namely Anti-MCT1, Anti-MCT2, Anti-MCT3 and Anti-MCT4 out of 1500 small molecules from the Diverse-lib of MTiOpenScreen were observed to completely satisfy Lipinski rule of five and Veber's rule. Further, significantly steady interactions with the MctB target protein were observed. Docking experiments have presented 9 compounds with less than -9.0 kcal/mol free binding energies and further MD simulation eventually gave 4 compounds having potential interactions and affinity with target protein and favorable binding energy ranging from -9.2 to -9.3 kcal/mol. We may propose these compounds as an effective candidate to reduce the growth of M. tuberculosis and may also assist present a novel therapeutic approach for Tuberculosis. In vivo and In vitro validation would be needed to proceed further in this direction.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Manish Dwivedi
- Amity Institute of Biotechnology, Amity University Uttar Pradesh, Lucknow, India
| | - Sandra Jose
- Technology and Advanced Studies, Vels Institute of Science, Chennai, India
| | - Megha Gupta
- Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology, Chennai, India
| | - Sreevidya S Devi
- Mar Athanasios College for Advanced Studies, Thiruvalla, Kerala, India
| | - Ritu Raj
- Centre of Biomedical Research (CBMR), SGPGIMS Campus, Lucknow, Uttar Pradesh, India
| | - Dinesh Kumar
- Centre of Biomedical Research (CBMR), SGPGIMS Campus, Lucknow, Uttar Pradesh, India
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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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Roy A, Ray S. Traversing DNA-Protein Interactions Between Mesophilic and Thermophilic Bacteria: Implications from Their Cold Shock Response. Mol Biotechnol 2024; 66:824-844. [PMID: 36905463 DOI: 10.1007/s12033-023-00711-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2022] [Accepted: 02/25/2023] [Indexed: 03/12/2023]
Abstract
Cold shock proteins (CSPs) are small, acidic proteins which contain a conserved nucleic acid-binding domain. These perform mRNA translation acting as "RNA chaperones" when triggered by low temperatures initiating their cold shock response. CSP- RNA interactions have been predominantly studied. Our focus will be CSP-DNA interaction examination, to analyse the diverse interaction patterns such as electrostatic, hydrogen and hydrophobic bonding in both thermophilic and mesophilic bacteria. The differences in the molecular mechanism of these contrasting bacterial proteins are studied. Computational techniques such as modelling, energy refinement, simulation and docking were operated to obtain data for comparative analysis. The thermostability factors which stabilise a thermophilic bacterium and their effect on their molecular regulation is investigated. Conformational deviation, atomic residual fluctuations, binding affinity, Electrostatic energy and Solvent Accessibility energy were determined during stimulation along with their conformational study. The study revealed that mesophilic bacteria E. coli CSP have higher binding affinity to DNA than thermophilic G. stearothermophilus. This was further evident by low conformation deviation and atomic fluctuations during simulation.
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Affiliation(s)
- Alankar Roy
- Amity Institute of Biotechnology, Amity University, Kolkata, India
| | - Sujay Ray
- Amity Institute of Biotechnology, Amity University, Kolkata, India.
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Asseri AH, Islam MR, Alghamdi RM, Altayb HN. Identification of natural antimicrobial peptides mimetic to inhibit Ca 2+ influx DDX3X activity for blocking dengue viral infectivity. J Bioenerg Biomembr 2024; 56:125-139. [PMID: 38095733 DOI: 10.1007/s10863-023-09996-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Accepted: 11/16/2023] [Indexed: 04/06/2024]
Abstract
Viruses are microscopic biological entities that can quickly invade and multiply in a living organism. Each year, over 36,000 people die and nearly 400 million are infected with the dengue virus (DENV). Despite dengue being an endemic disease, no targeted and effective antiviral peptide resource is available against the dengue species. Antiviral peptides (AVPs) have shown tremendous ability to fight against different viruses. Accelerating antiviral drug discovery is crucial, particularly for RNA viruses. DDX3X, a vital cell component, supports viral translation and interacts with TRPV4, regulating viral RNA metabolism and infectivity. Its diverse signaling pathway makes it a potential therapeutic target. Our study focuses on inhibiting viral RNA translation by blocking the activity of the target gene and the TRPV4-mediated Ca2+ cation channel. Six major proteins from camel milk were first extracted and split with the enzyme pepsin. The antiviral properties were then analyzed using online bioinformatics programs, including AVPpred, Meta-iAVP, AMPfun, and ENNAVIA. The stability of the complex was assessed using MD simulation, MM/GBSA, and principal component analysis. Cytotoxicity evaluations were conducted using COPid and ToxinPred. The top ten AVPs, determined by optimal scores, were selected and saved for docking studies with the GalaxyPepDock tools. Bioinformatics analyses revealed that the peptides had very short hydrogen bond distances (1.8 to 3.6 Å) near the active site of the target protein. Approximately 76% of the peptide residues were 5-11 amino acids long. Additionally, the identified peptide candidates exhibited desirable properties for potential therapeutic agents, including a net positive charge, moderate toxicity, hydrophilicity, and selectivity. In conclusion, this computational study provides promising insights for discovering peptide-based therapeutic agents against DENV.
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Affiliation(s)
- Amer H Asseri
- Department of Biochemistry, Faculty of Science, King Abdulaziz University, Jeddah, 21589, Saudi Arabia.
- Centre for Artificial Intelligence in Precision Medicines, King Abdulaziz University, Jeddah, 21589, Saudi Arabia.
| | - Md Rashedul Islam
- Department of Biochemistry, Faculty of Science, King Abdulaziz University, Jeddah, 21589, Saudi Arabia
- Advanced Biological Invention Centre (Bioinventics), Rajshahi, 6204, Bangladesh
| | - Reem M Alghamdi
- Department of Radiology, Prince Sultan Military Medical City, Riyadh, Saudi Arabia
| | - Hisham N Altayb
- Department of Biochemistry, Faculty of Science, King Abdulaziz University, Jeddah, 21589, Saudi Arabia
- Centre for Artificial Intelligence in Precision Medicines, King Abdulaziz University, Jeddah, 21589, Saudi Arabia
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12
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Guan J, Yao L, Xie P, Chung CR, Huang Y, Chiang YC, Lee TY. A two-stage computational framework for identifying antiviral peptides and their functional types based on contrastive learning and multi-feature fusion strategy. Brief Bioinform 2024; 25:bbae208. [PMID: 38706321 PMCID: PMC11070730 DOI: 10.1093/bib/bbae208] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2024] [Revised: 03/14/2024] [Accepted: 04/17/2024] [Indexed: 05/07/2024] Open
Abstract
Antiviral peptides (AVPs) have shown potential in inhibiting viral attachment, preventing viral fusion with host cells and disrupting viral replication due to their unique action mechanisms. They have now become a broad-spectrum, promising antiviral therapy. However, identifying effective AVPs is traditionally slow and costly. This study proposed a new two-stage computational framework for AVP identification. The first stage identifies AVPs from a wide range of peptides, and the second stage recognizes AVPs targeting specific families or viruses. This method integrates contrastive learning and multi-feature fusion strategy, focusing on sequence information and peptide characteristics, significantly enhancing predictive ability and interpretability. The evaluation results of the model show excellent performance, with accuracy of 0.9240 and Matthews correlation coefficient (MCC) score of 0.8482 on the non-AVP independent dataset, and accuracy of 0.9934 and MCC score of 0.9869 on the non-AMP independent dataset. Furthermore, our model can predict antiviral activities of AVPs against six key viral families (Coronaviridae, Retroviridae, Herpesviridae, Paramyxoviridae, Orthomyxoviridae, Flaviviridae) and eight viruses (FIV, HCV, HIV, HPIV3, HSV1, INFVA, RSV, SARS-CoV). Finally, to facilitate user accessibility, we built a user-friendly web interface deployed at https://awi.cuhk.edu.cn/∼dbAMP/AVP/.
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Affiliation(s)
- Jiahui Guan
- School of Medicine, The Chinese University of Hong Kong, Shenzhen, 2001 Longxiang Road, 518172 Shenzhen, China
- Kobilka Institute of Innovative Drug Discovery, School of Medicine, The Chinese University of Hong Kong, 2001 Longxiang Road, 518172 Shenzhen, China
| | - Lantian Yao
- School of Medicine, The Chinese University of Hong Kong, Shenzhen, 2001 Longxiang Road, 518172 Shenzhen, China
- School of Science and Engineering, The Chinese University of Hong Kong, 2001 Longxiang Road, 518172 Shenzhen, China
| | - Peilin Xie
- Kobilka Institute of Innovative Drug Discovery, School of Medicine, The Chinese University of Hong Kong, 2001 Longxiang Road, 518172 Shenzhen, China
| | - Chia-Ru Chung
- Department of Computer Science and Information Engineering, National Central University, 320317 Taoyuan, Taiwan
| | - Yixian Huang
- School of Medicine, The Chinese University of Hong Kong, Shenzhen, 2001 Longxiang Road, 518172 Shenzhen, China
| | - Ying-Chih Chiang
- School of Medicine, The Chinese University of Hong Kong, Shenzhen, 2001 Longxiang Road, 518172 Shenzhen, China
- Kobilka Institute of Innovative Drug Discovery, School of Medicine, The Chinese University of Hong Kong, 2001 Longxiang Road, 518172 Shenzhen, China
| | - Tzong-Yi Lee
- Institute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, 300093 Hsinchu, Taiwan
- Center for Intelligent Drug Systems and Smart Bio-devices (IDS2B), National Yang Ming Chiao Tung University, 300093 Hsinchu, Taiwan
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13
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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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14
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Ghassemi Z, Leach JB. Impact of Confinement within a Hydrogel Mesh on Protein Thermodynamic Stability and Aggregation Kinetics. Mol Pharm 2024; 21:1137-1148. [PMID: 38277273 DOI: 10.1021/acs.molpharmaceut.3c00677] [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] [Indexed: 01/28/2024]
Abstract
Though protein stability and aggregation have been well characterized in dilute solutions, the influence of a confining environment that exists (e.g., in intercellular and tissue spaces and therapeutic formulations) on the protein structure is largely unknown. Herein, the effects of confinement on stability and aggregation were explored for proteins of different sizes, stability, and hydrophobicity when encapsulated in hydrophilic poly(ethylene glycol) hydrogels. Denaturation curves show linear correlations between confinement size (mesh size) and thermodynamic stability, i.e., unfolding free energy and surface area accessible for solvation (represented by m-value). Two clusters of protein types are identifiable from these correlations; the clusters are defined by differences in protein stability, surface area, and aggregation propensity. Proteins with higher stability, larger surface area, and lower aggregation propensity (e.g., lysozyme and myoglobin) are less affected by the confinement imposed by mesh size than proteins with lower stability, smaller surface area, and higher aggregation propensity (e.g., growth hormone and aldehyde dehydrogenase). According to aggregation kinetics measured by thioflavin T fluorescence, confinement in smaller mesh sizes resulted in slower aggregation rates than that in larger mesh sizes. Compared to that in buffer solution, the confinement of a hydrophobic protein (e.g., human insulin) in the hydrogels accelerates protein aggregation. Conversely, the confinement of a hydrophilic protein (e.g., human amylin) in the hydrogels decelerates or prevents aggregation, with the rates of aggregation inversely proportional to mesh size. These findings provide new insights into protein conformational stability in confined microenvironments relevant to various cellular, tissue, and therapeutics scenarios.
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Affiliation(s)
- Zahra Ghassemi
- Department of Chemical, Biochemical and Environmental Engineering, University of Maryland, Baltimore County, ECS 314, 1000 Hilltop Circle, Baltimore, Maryland 21250, United States
| | - Jennie B Leach
- Department of Chemical, Biochemical and Environmental Engineering, University of Maryland, Baltimore County, ECS 314, 1000 Hilltop Circle, Baltimore, Maryland 21250, United States
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15
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Medvedeva A, Domakhina S, Vasnetsov C, Vasnetsov V, Kolomeisky A. Physical-Chemical Approach to Designing Drugs with Multiple Targets. J Phys Chem Lett 2024; 15:1828-1835. [PMID: 38330920 DOI: 10.1021/acs.jpclett.3c03624] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/10/2024]
Abstract
Many people simultaneously exhibit multiple diseases, which complicates efficient medical treatments. For example, patients with cancer are frequently susceptible to infections. However, developing drugs that could simultaneously target several diseases is challenging. We present a novel theoretical method to assist in selecting compounds with multiple therapeutic targets. The idea is to find correlations between the physical and chemical properties of drug molecules and their abilities to work against multiple targets. As a first step, we investigated potential drugs against cancer and viral infections. Specifically, we investigated antimicrobial peptides (AMPs), which are short positively charged biomolecules produced by living systems as a part of their immune defense. AMPs show anticancer and antiviral activity. We use chemoinformatics and correlation analysis as a part of the machine-learning method to identify the specific properties that distinguish AMPs with dual anticancer and antiviral activities. Physical-chemical arguments to explain these observations are presented.
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Affiliation(s)
- Angela Medvedeva
- Department of Chemistry, Rice University, Houston, Texas 77005, United States
- Center for Theoretical Biological Physics, Rice University, Houston, Texas 77005, United States
| | - Sofya Domakhina
- Department of Chemistry, Rice University, Houston, Texas 77005, United States
| | - Catherine Vasnetsov
- Department of Chemistry, Rice University, Houston, Texas 77005, United States
| | - Victor Vasnetsov
- Department of Chemistry, Rice University, Houston, Texas 77005, United States
| | - Anatoly Kolomeisky
- Department of Chemistry, Rice University, Houston, Texas 77005, United States
- Center for Theoretical Biological Physics, Rice University, Houston, Texas 77005, United States
- Department of Chemical and Biomolecular Engineering, Rice University, Houston, Texas 77005, United States
- Department of Physics and Astronomy, Rice University, Houston, Texas 77005, United States
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16
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Thathapudi NC, Callai-Silva N, Malhotra K, Basu S, Aghajanzadeh-Kiyaseh M, Zamani-Roudbaraki M, Groleau M, Lombard-Vadnais F, Lesage S, Griffith M. Modified host defence peptide GF19 slows TNT-mediated spread of corneal herpes simplex virus serotype I infection. Sci Rep 2024; 14:4096. [PMID: 38374240 PMCID: PMC10876564 DOI: 10.1038/s41598-024-53662-4] [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: 12/11/2023] [Accepted: 02/03/2024] [Indexed: 02/21/2024] Open
Abstract
Corneal HSV-1 infections are a leading cause of infectious blindness globally by triggering tissue damage due to the intense inflammation. HSV-1 infections are treated mainly with antiviral drugs that clear the infections but are inefficient as prophylactics. The body produces innate cationic host defence peptides (cHDP), such as the cathelicidin LL37. Various epithelia, including the corneal epithelium, express LL37. cHDPs can cause disintegration of pathogen membranes, stimulate chemokine production, and attract immune cells. Here, we selected GF17, a peptide containing the LL37 fragment with bioactivity but with minimal cytotoxicity, and added two cell-penetrating amino acids to enhance its activity. The resulting GF19 was relatively cell-friendly, inducing only partial activation of antigen presenting immune cells in vitro. We showed that HSV-1 spreads by tunneling nanotubes in cultured human corneal epithelial cells. GF19 given before infection was able to block infection, most likely by blocking viral entry. When cells were sequentially exposed to viruses and GF19, the infection was attenuated but not arrested, supporting the contention that the GF19 mode of action was to block viral entry. Encapsulation into silica nanoparticles allowed a more sustained release of GF19, enhancing its activity. GF19 is most likely suitable as a prevention rather than a virucidal treatment.
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Affiliation(s)
- Neethi C Thathapudi
- Maisonneuve-Rosemont Hospital Research Centre, Montreal, QC, H1T 2M4, Canada
- Department of Ophthalmology, Université de Montréal, Montreal, QC, H3C 3J7, Canada
- Institute of Biomedical Engineering, Université de Montréal, Montreal, QC, H3T 1J4, Canada
| | - Natalia Callai-Silva
- Maisonneuve-Rosemont Hospital Research Centre, Montreal, QC, H1T 2M4, Canada
- Department of Ophthalmology, Université de Montréal, Montreal, QC, H3C 3J7, Canada
- Institute of Biomedical Engineering, Université de Montréal, Montreal, QC, H3T 1J4, Canada
| | - Kamal Malhotra
- Maisonneuve-Rosemont Hospital Research Centre, Montreal, QC, H1T 2M4, Canada
- Department of Ophthalmology, Université de Montréal, Montreal, QC, H3C 3J7, Canada
- Division of Cardiology, Department of Medicine, University of Ottawa Heart Institute, University of Ottawa, Ottawa, K1Y 4W7, Canada
- Department of Cellular and Molecular Medicine, University of Ottawa, Ottawa, K1H 8M5, Canada
| | - Sankar Basu
- Department of Microbiology, Asutosh College, (Affiliated With University of Calcutta), Kolkata, 700026, India
| | - Mozhgan Aghajanzadeh-Kiyaseh
- Maisonneuve-Rosemont Hospital Research Centre, Montreal, QC, H1T 2M4, Canada
- Department of Ophthalmology, Université de Montréal, Montreal, QC, H3C 3J7, Canada
- Institute of Biomedical Engineering, Université de Montréal, Montreal, QC, H3T 1J4, Canada
| | - Mostafa Zamani-Roudbaraki
- Maisonneuve-Rosemont Hospital Research Centre, Montreal, QC, H1T 2M4, Canada
- Department of Ophthalmology, Université de Montréal, Montreal, QC, H3C 3J7, Canada
- Institute of Biomedical Engineering, Université de Montréal, Montreal, QC, H3T 1J4, Canada
| | - Marc Groleau
- Maisonneuve-Rosemont Hospital Research Centre, Montreal, QC, H1T 2M4, Canada
- Department of Ophthalmology, Université de Montréal, Montreal, QC, H3C 3J7, Canada
- Département de Microbiologie, Infectiologie et Immunologie, Université de Montréal, Montréal, QC, H3T 1J4, Canada
| | | | - Sylvie Lesage
- Maisonneuve-Rosemont Hospital Research Centre, Montreal, QC, H1T 2M4, Canada
- Département de Microbiologie, Infectiologie et Immunologie, Université de Montréal, Montréal, QC, H3T 1J4, Canada
| | - May Griffith
- Maisonneuve-Rosemont Hospital Research Centre, Montreal, QC, H1T 2M4, Canada.
- Department of Ophthalmology, Université de Montréal, Montreal, QC, H3C 3J7, Canada.
- Institute of Biomedical Engineering, Université de Montréal, Montreal, QC, H3T 1J4, Canada.
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17
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Habib A, Liang Y, Xu X, Zhu N, Xie J. Immunoinformatic Identification of Multiple Epitopes of gp120 Protein of HIV-1 to Enhance the Immune Response against HIV-1 Infection. Int J Mol Sci 2024; 25:2432. [PMID: 38397105 PMCID: PMC10889372 DOI: 10.3390/ijms25042432] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2024] [Revised: 02/10/2024] [Accepted: 02/13/2024] [Indexed: 02/25/2024] Open
Abstract
Acquired Immunodeficiency Syndrome is caused by the Human Immunodeficiency Virus (HIV), and a significant number of fatalities occur annually. There is a dire need to develop an effective vaccine against HIV-1. Understanding the structural proteins of viruses helps in designing a vaccine based on immunogenic peptides. In the current experiment, we identified gp120 epitopes using bioinformatic epitope prediction tools, molecular docking, and MD simulations. The Gb-1 peptide was considered an adjuvant. Consecutive sequences of GTG, GSG, GGTGG, and GGGGS linkers were used to bind the B cell, Cytotoxic T Lymphocytes (CTL), and Helper T Lymphocytes (HTL) epitopes. The final vaccine construct consisted of 315 amino acids and is expected to be a recombinant protein of approximately 35.49 kDa. Based on docking experiments, molecular dynamics simulations, and tertiary structure validation, the analysis of the modeled protein indicates that it possesses a stable structure and can interact with Toll-like receptors. The analysis demonstrates that the proposed vaccine can provoke an immunological response by activating T and B cells, as well as stimulating the release of IgA and IgG antibodies. This vaccine shows potential for HIV-1 prophylaxis. The in-silico design suggests that multiple-epitope constructs can be used as potentially effective immunogens for HIV-1 vaccine development.
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Affiliation(s)
- Arslan Habib
- Laboratory of Molecular Immunology, State Key Laboratory of Genetic Engineering, School of Life Sciences, Fudan University, Shanghai 200433, China; (A.H.); (X.X.); (N.Z.)
| | - Yulai Liang
- Laboratory of Molecular Immunology, State Key Laboratory of Genetic Engineering, School of Life Sciences, Fudan University, Shanghai 200433, China; (A.H.); (X.X.); (N.Z.)
| | - Xinyi Xu
- Laboratory of Molecular Immunology, State Key Laboratory of Genetic Engineering, School of Life Sciences, Fudan University, Shanghai 200433, China; (A.H.); (X.X.); (N.Z.)
| | - Naishuo Zhu
- Laboratory of Molecular Immunology, State Key Laboratory of Genetic Engineering, School of Life Sciences, Fudan University, Shanghai 200433, China; (A.H.); (X.X.); (N.Z.)
- Institute of Biomedical Sciences, School of Life Sciences, Fudan University, Shanghai 200438, China
| | - Jun Xie
- Laboratory of Molecular Immunology, State Key Laboratory of Genetic Engineering, School of Life Sciences, Fudan University, Shanghai 200433, China; (A.H.); (X.X.); (N.Z.)
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18
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Salahlou R, Farajnia S, Bargahi N, Bakhtiyari N, Elmi F, Shahgolzari M, Fiering S, Venkataraman S. Development of a novel multi‑epitope vaccine against the pathogenic human polyomavirus V6/7 using reverse vaccinology. BMC Infect Dis 2024; 24:177. [PMID: 38336665 PMCID: PMC10854057 DOI: 10.1186/s12879-024-09046-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Accepted: 01/22/2024] [Indexed: 02/12/2024] Open
Abstract
BACKGROUND Human polyomaviruses contribute to human oncogenesis through persistent infections, but currently there is no effective preventive measure against the malignancies caused by this virus. Therefore, the development of a safe and effective vaccine against HPyV is of high priority. METHODS First, the proteomes of 2 polyomavirus species (HPyV6 and HPyV7) were downloaded from the NCBI database for the selection of the target proteins. The epitope identification process focused on selecting proteins that were crucial, associated with virulence, present on the surface, antigenic, non-toxic, and non-homologous with the human proteome. Then, the immunoinformatic methods were used to identify cytotoxic T-lymphocyte (CTL), helper T-lymphocyte (HTL), and B-cell epitopes from the target antigens, which could be used to create epitope-based vaccine. The physicochemical features of the designed vaccine were predicted through various online servers. The binding pattern and stability between the vaccine candidate and Toll-like receptors were analyzed through molecular docking and molecular dynamics (MD) simulation, while the immunogenicity of the designed vaccines was assessed using immune simulation. RESULTS Online tools were utilized to forecast the most optimal epitope from the immunogenic targets, including LTAg, VP1, and VP1 antigens of HPyV6 and HPyV7. A multi-epitope vaccine was developed by combining 10 CTL, 7 HTL, and 6 LBL epitopes with suitable linkers and adjuvant. The vaccine displayed 98.35% of the world's population coverage. The 3D model of the vaccine structure revealed that the majority of residues (87.7%) were located in favored regions of the Ramachandran plot. The evaluation of molecular docking and MD simulation revealed that the constructed vaccine exhibits a strong binding (-1414.0 kcal/mol) towards the host's TLR4. Moreover, the vaccine-TLR complexes remained stable throughout the dynamic conditions present in the natural environment. The immune simulation results demonstrated that the vaccine design had the capacity to elicit robust immune responses in the host. CONCLUSION The multi-parametric analysis revealed that the designed vaccine is capable of inducing sustained immunity against the selected polyomaviruses, although further in-vivo investigations are needed to verify its effectiveness.
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Affiliation(s)
- Reza Salahlou
- Biotechnology Research Center, Tabriz University of Medical Sciences, Tabriz, Iran
- Student Research Committee, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Safar Farajnia
- Biotechnology Research Center, Tabriz University of Medical Sciences, Tabriz, Iran.
- Drug Applied Research Center, Tabriz University of Medical Sciences, Tabriz, Iran.
| | - Nasrin Bargahi
- Biotechnology Research Center, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Nasim Bakhtiyari
- Student Research Committee, Tabriz University of Medical Sciences, Tabriz, Iran
- Drug Applied Research Center, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Faranak Elmi
- Department of Medical Biotechnology, Faculty of Advanced Medical Sciences, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Mehdi Shahgolzari
- Drug Applied Research Center, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Steven Fiering
- Department of Microbiology and Immunology, Geisel School of Medicine, and Dartmouth Cancer Center, Lebanon, NH, USA
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19
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Malik A, Jayarathna DK, Fisher M, Barbhuiya TK, Gandhi NS, Batra J. Dynamics and recognition of homeodomain containing protein-DNA complex of IRX4. Proteins 2024; 92:282-301. [PMID: 37861198 DOI: 10.1002/prot.26604] [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: 01/15/2022] [Revised: 09/15/2023] [Accepted: 09/25/2023] [Indexed: 10/21/2023]
Abstract
Iroquois Homeobox 4 (IRX4) belongs to a family of homeobox TFs having roles in embryogenesis, cell specification, and organ development. Recently, large scale genome-wide association studies and epigenetic studies have highlighted the role of IRX4 and its associated variants in prostate cancer. No studies have investigated and characterized the structural aspect of the IRX4 homeodomain and its potential to bind to DNA. The current study uses sequence analysis, homology modeling, and molecular dynamics simulations to explore IRX4 homeodomain-DNA recognition mechanisms and the role of somatic mutations affecting these interactions. Using publicly available databases, gene expression of IRX4 was found in different tissues, including prostate, heart, skin, vagina, and the protein expression was found in cancer cell lines (HCT166, HEK293), B cells, ascitic fluid, and brain. Sequence conservation of the homeodomain shed light on the importance of N- and C-terminal residues involved in DNA binding. The specificity of IRX4 homodimer bound to consensus human DNA sequence was confirmed by molecular dynamics simulations, representing the role of conserved amino acids including R145, A194, N195, S190, R198, and R199 in binding to DNA. Additional N-terminal residues like T144 and G143 were also found to have specific interactions highlighting the importance of N-terminus of the homeodomain in DNA recognition. Additionally, the effects of somatic mutations, including the conserved Arginine (R145, R198, and R199) residues on DNA binding elucidated the importance of these residues in stabilizing the protein-DNA complex. Secondary structure and hydrogen bonding analysis showed the roles of specific residues (R145, T191, A194, N195, R198, and R199) in maintaining the homogeneity of the structure and its interaction with DNA. The differences in relative binding free energies of all the mutants shed light on the structural modularity of this protein and the dynamics behind protein-DNA interaction. We also have predicted that the C-terminal sequence of the IRX4 homeodomain could act as a potential cell-penetrating peptide, emphasizing the role these small peptides could play in targeting homeobox TFs.
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Affiliation(s)
- Adil Malik
- School of Biomedical Sciences, Faculty of Health, Queensland University of Technology, Brisbane, Queensland, Australia
- Translational Research Institute, Woolloongabba, Queensland, Australia
| | - Dulari K Jayarathna
- Centre for Genomics and Personalised Health, Queensland University of Technology, Brisbane, Queensland, Australia
- School of Chemistry and Physics, Faculty of Science, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Mark Fisher
- School of Biomedical Sciences, Faculty of Health, Queensland University of Technology, Brisbane, Queensland, Australia
- Centre for Genomics and Personalised Health, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Tabassum Khair Barbhuiya
- Centre for Genomics and Personalised Health, Queensland University of Technology, Brisbane, Queensland, Australia
- School of Chemistry and Physics, Faculty of Science, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Neha S Gandhi
- Centre for Genomics and Personalised Health, Queensland University of Technology, Brisbane, Queensland, Australia
- School of Chemistry and Physics, Faculty of Science, Queensland University of Technology, Brisbane, Queensland, Australia
- Department of Computer Science and Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Udupi, Karnataka, India
| | - Jyotsna Batra
- School of Biomedical Sciences, Faculty of Health, Queensland University of Technology, Brisbane, Queensland, Australia
- Translational Research Institute, Woolloongabba, Queensland, Australia
- Centre for Genomics and Personalised Health, Queensland University of Technology, Brisbane, Queensland, Australia
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20
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Zhuang J, Gao W, Su R. EnAMP: A novel deep learning ensemble antibacterial peptide recognition algorithm based on multi-features. J Bioinform Comput Biol 2024; 22:2450001. [PMID: 38406833 DOI: 10.1142/s021972002450001x] [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] [Indexed: 02/27/2024]
Abstract
Antimicrobial peptides (AMPs), as the preferred alternatives to antibiotics, have wide application with good prospects. Identifying AMPs through wet lab experiments remains expensive, time-consuming and challenging. Many machine learning methods have been proposed to predict AMPs and achieved good results. In this work, we combine two kinds of word embedding features with the statistical features of peptide sequences to develop an ensemble classifier, named EnAMP, in which, two deep neural networks are trained based on Word2vec and Glove word embedding features of peptide sequences, respectively, meanwhile, we utilize statistical features of peptide sequences to train random forest and support vector machine classifiers. The average of four classifiers is the final prediction result. Compared with other state-of-the-art algorithms on six datasets, EnAMP outperforms most existing models with similar computational costs, even when compared with high computational cost algorithms based on Bidirectional Encoder Representation from Transformers (BERT), the performance of our model is comparable. EnAMP source code and the data are available at https://github.com/ruisue/EnAMP.
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Affiliation(s)
- Jujuan Zhuang
- School of Science, Dalian Maritime University, Dalian, Liaoning, P. R. China
| | - Wanquan Gao
- School of Science, Dalian Maritime University, Dalian, Liaoning, P. R. China
| | - Rui Su
- School of Science, Dalian Maritime University, Dalian, Liaoning, P. R. China
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21
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Jiao L, Jing Z, Zhang W, Su X, Yan H, Tian S. Codon Pattern and Context Analysis in Genes Triggering Alzheimer's Disease and Latent Tau Protein Aggregation Post-Anesthesia Exhibited Unique Molecular Patterns Associated with Functional Aspects. J Alzheimers Dis 2024; 97:1645-1660. [PMID: 38306048 DOI: 10.3233/jad-231142] [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] [Indexed: 02/03/2024]
Abstract
Background Previous reports have demonstrated post-operative dementia and Alzheimer's disease (AD), and increased amyloid-β levels and tau hyperphosphorylation have been observed in animal models post-anesthesia. Objective After surgical interventions, loss in memory has been observed that has been found linked with genes modulated after anesthesia. Present study aimed to study molecular pattern present in genes modulated post anesthesia and involved in characters progressing towards AD. Methods In the present study, 17 transcript variants belonging to eight genes, which have been found to modulate post-anesthesia and contribute to AD progression, were envisaged for their compositional features, molecular patterns, and codon and codon context-associated studies. Results The sequences' composition was G/C rich, influencing dinucleotide preference, codon preference, codon usage, and codon context. The G/C nucleotides being highly occurring nucleotides, CpGdinucleotides were also preferred; however, CpG was highly disfavored at p3-1 at the codon junction. The nucleotide composition of Cytosine exhibited a unique feature, and unlike other nucleotides, it did not correlate with codon bias. Contrarily, it correlated with the sequence lengths. The sequences were leucine-rich, and multiple leucine repeats were present, exhibiting the functional role of neuroprotection from neuroinflammation post-anesthesia. Conclusions The analysis pave the way to elucidate unique molecular patterns in genes modulated during anesthetic treatment and might help ameliorate the ill effects of anesthetics in the future.
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Affiliation(s)
- Liyuan Jiao
- Department of Anesthesiology, First Hospital of Shanxi Medical University, Taiyuan, China
| | - Ziye Jing
- Department of Anesthesiology, First Hospital of Shanxi Medical University, Taiyuan, China
| | - Wenjie Zhang
- Department of Anesthesiology, First Hospital of Shanxi Medical University, Taiyuan, China
| | - Xuesen Su
- Department of Anesthesiology, First Hospital of Shanxi Medical University, Taiyuan, China
| | - Hualei Yan
- Department of Anesthesiology, First Hospital of Shanxi Medical University, Taiyuan, China
| | - Shouyuan Tian
- Shanxi Province Cancer Hospital/Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences, Taiyuan, China
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22
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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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23
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Putri RA, Rohman MS, Swasono RT, Raharjo TJ. A novel synthetic peptide analog enhanced antibacterial activity of the frog-derived skin peptide wuchuanin-A1. J Biomol Struct Dyn 2023:1-11. [PMID: 37968993 DOI: 10.1080/07391102.2023.2281633] [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: 09/05/2023] [Accepted: 11/04/2023] [Indexed: 11/17/2023]
Abstract
In recent years, there has been a growing focus on the development of novel antibacterial compounds for clinical applications, such as antimicrobial peptide (AMP). Among the developed AMP, wuchuanin-A1, a coil-shaped bioactive peptide derived from Odorrana wuchuanensis frog skin, has been reported to exhibit antibacterial, antifungal, and antioxidant activity, but there are limited studies on its potential as an antibacterial agent. Therefore, this study aims to molecularly modify the sequence of wuchuanin-A1 to enhance its antibacterial properties. The interaction of both the native and analog peptide with bacterial inner membranes was initially assessed using computational methods. Specific amino acid substitutions were then used to enhance the modified peptide's antibacterial efficacy, followed by several preliminary tests to evaluate its activity. This study bridges the gap in exploring the potential of wuchuanin-A1 for antibacterial purposes, providing insights into the design of effective antimicrobial agents.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
| | | | | | - Tri Joko Raharjo
- Department of Chemistry, Universitas Gadjah Mada, Bulaksumur, Indonesia
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24
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Cao R, Hu W, Wei P, Ding Y, Bin Y, Zheng C. FFMAVP: a new classifier based on feature fusion and multitask learning for identifying antiviral peptides and their subclasses. Brief Bioinform 2023; 24:bbad353. [PMID: 37861174 DOI: 10.1093/bib/bbad353] [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: 07/13/2023] [Revised: 08/25/2023] [Accepted: 09/06/2023] [Indexed: 10/21/2023] Open
Abstract
Antiviral peptides (AVPs) are widely found in animals and plants, with high specificity and strong sensitivity to drug-resistant viruses. However, due to the great heterogeneity of different viruses, most of the AVPs have specific antiviral activities. Therefore, it is necessary to identify the specific activities of AVPs on virus types. Most existing studies only identify AVPs, with only a few studies identifying subclasses by training multiple binary classifiers. We develop a two-stage prediction tool named FFMAVP that can simultaneously predict AVPs and their subclasses. In the first stage, we identify whether a peptide is AVP or not. In the second stage, we predict the six virus families and eight species specifically targeted by AVPs based on two multiclass tasks. Specifically, the feature extraction module in the two-stage task of FFMAVP adopts the same neural network structure, in which one branch extracts features based on amino acid feature descriptors and the other branch extracts sequence features. Then, the two types of features are fused for the following task. Considering the correlation between the two tasks of the second stage, a multitask learning model is constructed to improve the effectiveness of the two multiclass tasks. In addition, to improve the effectiveness of the second stage, the network parameters trained through the first-stage data are used to initialize the network parameters in the second stage. As a demonstration, the cross-validation results, independent test results and visualization results show that FFMAVP achieves great advantages in both stages.
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Affiliation(s)
- Ruifen Cao
- Information Materials and Intelligent Sensing Laboratory of Anhui Province, School of Computer Science and Technology, Anhui University
| | - Weiling Hu
- Information Materials and Intelligent Sensing Laboratory of Anhui Province, School of Computer Science and Technology, Anhui University
| | - Pijing Wei
- Institutes of Physical Science and Information Technology, Anhui University
| | - Yun Ding
- Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, School of Artificial Intelligence, Anhui University
| | - Yannan Bin
- Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education and Information Materials and Intelligent Sensing Laboratory of Anhui Province, Institutes of Physical Science and Information Technology, Anhui University
| | - Chunhou Zheng
- Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, School of Artificial Intelligence, Anhui University
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25
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Ho C, Nazarie WFWM, Lee PC. An In Silico Design of Peptides Targeting the S1/S2 Cleavage Site of the SARS-CoV-2 Spike Protein. Viruses 2023; 15:1930. [PMID: 37766336 PMCID: PMC10536081 DOI: 10.3390/v15091930] [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: 08/16/2023] [Accepted: 08/23/2023] [Indexed: 09/29/2023] Open
Abstract
SARS-CoV-2, responsible for the COVID-19 pandemic, invades host cells via its spike protein, which includes critical binding regions, such as the receptor-binding domain (RBD), the S1/S2 cleavage site, the S2 cleavage site, and heptad-repeat (HR) sections. Peptides targeting the RBD and HR1 inhibit binding to host ACE2 receptors and the formation of the fusion core. Other peptides target proteases, such as TMPRSS2 and cathepsin L, to prevent the cleavage of the S protein. However, research has largely ignored peptides targeting the S1/S2 cleavage site. In this study, bioinformatics was used to investigate the binding of the S1/S2 cleavage site to host proteases, including furin, trypsin, TMPRSS2, matriptase, cathepsin B, and cathepsin L. Peptides targeting the S1/S2 site were designed by identifying binding residues. Peptides were docked to the S1/S2 site using HADDOCK (High-Ambiguity-Driven protein-protein DOCKing). Nine peptides with the lowest HADDOCK scores and strong binding affinities were selected, which was followed by molecular dynamics simulations (MDSs) for further investigation. Among these peptides, BR582 and BR599 stand out. They exhibited relatively high interaction energies with the S protein at -1004.769 ± 21.2 kJ/mol and -1040.334 ± 24.1 kJ/mol, respectively. It is noteworthy that the binding of these peptides to the S protein remained stable during the MDSs. In conclusion, this research highlights the potential of peptides targeting the S1/S2 cleavage site as a means to prevent SARS-CoV-2 from entering cells, and contributes to the development of therapeutic interventions against COVID-19.
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Affiliation(s)
- Chian Ho
- Faculty of Science and Natural Resources, Universiti Malaysia Sabah, Kota Kinabalu 88400, Sabah, Malaysia; (C.H.); (W.F.W.M.N.)
| | - Wan Fahmi Wan Mohamad Nazarie
- Faculty of Science and Natural Resources, Universiti Malaysia Sabah, Kota Kinabalu 88400, Sabah, Malaysia; (C.H.); (W.F.W.M.N.)
| | - Ping-Chin Lee
- Faculty of Science and Natural Resources, Universiti Malaysia Sabah, Kota Kinabalu 88400, Sabah, Malaysia; (C.H.); (W.F.W.M.N.)
- Biotechnology Research Institute, Universiti Malaysia Sabah, Kota Kinabalu 88400, Sabah, Malaysia
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26
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Alguridi HI, Alzahrani F, Almalki S, Zamzami MA, Altayb HN. Identification and molecular docking of novel chikungunya virus NSP4 inhibitory peptides from camel milk proteins. J Biomol Struct Dyn 2023:1-16. [PMID: 37668009 DOI: 10.1080/07391102.2023.2254398] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Accepted: 08/28/2023] [Indexed: 09/06/2023]
Abstract
The chikungunya (CHIK) virus is an arbovirus belonging to the alphavirus (Togaviridae family). Around 85% of infected individuals suffer from symptoms such as high fever and severe joint pain; about 30 to 40% will develop a chronic joint illness. The Nsp4 protease is the most conserved protein in the alphavirus family and serves as an RNA-dependent RNA polymerase (RdRp). Targeting this enzyme might inhibit the CHIKV replication cycle. This work aims to in silico study the CHIKV RdRp inhibitory effect of peptides derived from camel milk protein as antiviral peptides. Various bioinformatics tools were recruited to identify, screen, predict and assess peptides obtained from camel milk as antiviral peptides (AVPs). During this study, CHIKV Nsp4 (polymerase) was used as a target to be inhibited by interaction with peptides derived from camel milk protein. Among 91 putative bioactive peptides, the best predicted 5 were further evaluated. Molecular docking showed that the top 5 AVPs generated better docking scores and interacted well with active sites of Nsp4 by the formation of different hydrogen bonds as well as other bonds. AVP63 and AVP20 showed the best Molecular docking and MD simulation results. The residue 315ASP of the GDD motif (catalytic core) exhibited a favorable interaction with the AVPs. The findings of this study suggest that the AVP20 derived from camel milk protein can be a potential novel CHIKV polymerase inhibitor.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Hassan I Alguridi
- Molecular Biology Department, Jeddah Regional Laboratory, Ministry of Health, Jeddah, Saudi Arabia
- Department of Biochemistry, Faculty of Sciences, King Abdulaziz University Jeddah, Saudi Arabia
- Research Unit, Jeddah Regional Laboratory, Ministry of Health, Jeddah, Saudi Arabia
| | - Faisal Alzahrani
- Department of Biochemistry, Faculty of Sciences, King Abdulaziz University Jeddah, Saudi Arabia
- Centre for Artificial Intelligence in Precision Medicine, King Abdulaziz University, Jeddah, Saudi Arabia
- King Fahd Medical Research Center, Embryonic Stem Cells Unit, King Abdulaziz University Jeddah, Saudi Arabia
| | - Safar Almalki
- Molecular Biology Department, Jeddah Regional Laboratory, Ministry of Health, Jeddah, Saudi Arabia
- Laboratories and Blood Banks Administration, Ministry of Health, Jeddah, Saudi Arabia
| | - Mazin A Zamzami
- Department of Biochemistry, Faculty of Sciences, King Abdulaziz University Jeddah, Saudi Arabia
- Centre for Artificial Intelligence in Precision Medicine, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Hisham N Altayb
- Department of Biochemistry, Faculty of Sciences, King Abdulaziz University Jeddah, Saudi Arabia
- Centre for Artificial Intelligence in Precision Medicine, King Abdulaziz University, Jeddah, Saudi Arabia
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27
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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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28
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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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29
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Imon RR, Samad A, Alam R, Alsaiari AA, Talukder MEK, Almehmadi M, Ahammad F, Mohammad F. Computational formulation of a multiepitope vaccine unveils an exceptional prophylactic candidate against Merkel cell polyomavirus. Front Immunol 2023; 14:1160260. [PMID: 37441076 PMCID: PMC10333698 DOI: 10.3389/fimmu.2023.1160260] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Accepted: 05/30/2023] [Indexed: 07/15/2023] Open
Abstract
Merkel cell carcinoma (MCC) is a rare neuroendocrine skin malignancy caused by human Merkel cell polyomavirus (MCV), leading to the most aggressive skin cancer in humans. MCV has been identified in approximately 43%-100% of MCC cases, contributing to the highly aggressive nature of primary cutaneous carcinoma and leading to a notable mortality rate. Currently, no existing vaccines or drug candidates have shown efficacy in addressing the ailment caused by this specific pathogen. Therefore, this study aimed to design a novel multiepitope vaccine candidate against the virus using integrated immunoinformatics and vaccinomics approaches. Initially, the highest antigenic, immunogenic, and non-allergenic epitopes of cytotoxic T lymphocytes, helper T lymphocytes, and linear B lymphocytes corresponding to the virus whole protein sequences were identified and retrieved for vaccine construction. Subsequently, the selected epitopes were linked with appropriate linkers and added an adjuvant in front of the construct to enhance the immunogenicity of the vaccine candidates. Additionally, molecular docking and dynamics simulations identified strong and stable binding interactions between vaccine candidates and human Toll-like receptor 4. Furthermore, computer-aided immune simulation found the real-life-like immune response of vaccine candidates upon administration to the human body. Finally, codon optimization was conducted on the vaccine candidates to facilitate the in silico cloning of the vaccine into the pET28+(a) cloning vector. In conclusion, the vaccine candidate developed in this study is anticipated to augment the immune response in humans and effectively combat the virus. Nevertheless, it is imperative to conduct in vitro and in vivo assays to evaluate the efficacy of these vaccine candidates thoroughly. These evaluations will provide critical insights into the vaccine's effectiveness and potential for further development.
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Affiliation(s)
- Raihan Rahman Imon
- Laboratory of Computational Biology, Biological Solution Centre (BioSol Centre), Jashore, Bangladesh
- Department of Genetic Engineering and Biotechnology, Jashore University of Science and Technology, Jashore, Bangladesh
| | - Abdus Samad
- Laboratory of Computational Biology, Biological Solution Centre (BioSol Centre), Jashore, Bangladesh
- Department of Genetic Engineering and Biotechnology, Jashore University of Science and Technology, Jashore, Bangladesh
| | - Rahat Alam
- Laboratory of Computational Biology, Biological Solution Centre (BioSol Centre), Jashore, Bangladesh
- Department of Genetic Engineering and Biotechnology, Jashore University of Science and Technology, Jashore, Bangladesh
| | - Ahad Amer Alsaiari
- Clinical Laboratories Science Department, College of Applied Medical Science, Taif University, Taif, Saudi Arabia
| | - Md. Enamul Kabir Talukder
- Laboratory of Computational Biology, Biological Solution Centre (BioSol Centre), Jashore, Bangladesh
- Department of Genetic Engineering and Biotechnology, Jashore University of Science and Technology, Jashore, Bangladesh
| | - Mazen Almehmadi
- Clinical Laboratories Science Department, College of Applied Medical Science, Taif University, Taif, Saudi Arabia
| | - Foysal Ahammad
- Laboratory of Computational Biology, Biological Solution Centre (BioSol Centre), Jashore, Bangladesh
- Division of Biological and Biomedical Sciences (BBS), College of Health and Life Sciences (CHLS), Hamad Bin Khalifa University (HBKU), Doha, Qatar
| | - Farhan Mohammad
- Division of Biological and Biomedical Sciences (BBS), College of Health and Life Sciences (CHLS), Hamad Bin Khalifa University (HBKU), Doha, Qatar
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30
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Tîrziu A, Avram S, Madă L, Crișan-Vida M, Popovici C, Popovici D, Faur C, Duda-Seiman C, Păunescu V, Vernic C. Design of a Synthetic Long Peptide Vaccine Targeting HPV-16 and -18 Using Immunoinformatic Methods. Pharmaceutics 2023; 15:1798. [PMID: 37513985 PMCID: PMC10384861 DOI: 10.3390/pharmaceutics15071798] [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: 04/07/2023] [Revised: 06/19/2023] [Accepted: 06/21/2023] [Indexed: 07/30/2023] Open
Abstract
Human papillomavirus types 16 and 18 cause the majority of cervical cancers worldwide. Despite the availability of three prophylactic vaccines based on virus-like particles (VLP) of the major capsid protein (L1), these vaccines are unable to clear an existing infection. Such infected persons experience an increased risk of neoplastic transformation. To overcome this problem, this study proposes an alternative synthetic long peptide (SLP)-based vaccine for persons already infected, including those with precancerous lesions. This new vaccine was designed to stimulate both CD8+ and CD4+ T cells, providing a robust and long-lasting immune response. The SLP construct includes both HLA class I- and class II-restricted epitopes, identified from IEDB or predicted using NetMHCPan and NetMHCIIPan. None of the SLPs were allergenic nor toxic, based on in silico studies. Population coverage studies provided 98.18% coverage for class I epitopes and 99.81% coverage for class II peptides in the IEDB world population's allele set. Three-dimensional structure ab initio prediction using Rosetta provided good quality models, which were assessed using PROCHECK and QMEAN4. Molecular docking with toll-like receptor 2 identified potential intrinsic TLR2 agonist activity, while molecular dynamics studies of SLPs in water suggested good stability, with favorable thermodynamic properties.
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Affiliation(s)
- Alexandru Tîrziu
- Department of Functional Sciences, "Victor Babes" University of Medicine and Pharmacy, Eftimie Murgu Square, No. 2, 300041 Timisoara, Romania
| | - Speranța Avram
- Department of Anatomy, Animal Physiology and Biophysics, Faculty of Biology, University of Bucharest, 91-95 Splaiul Independentei, 050095 Bucharest, Romania
| | - Leonard Madă
- Syonic SRL, Grigore T Popa Street, No. 81, 300254 Timisoara, Romania
| | - Mihaela Crișan-Vida
- Department of Automation and Computers, Politehnica University of Timisoara, 300006 Timisoara, Romania
| | - Casiana Popovici
- Section of Bioinformatics, Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College London, South Kensington Campus, London SW7 2AZ, UK
| | - Dan Popovici
- Department of Mathematics, University of the West Timişoara, Bd. Vasile Pârvan No. 4, 300223 Timişoara, Romania
| | - Cosmin Faur
- Department of Orthopaedic Surgery, University of Medicine and Pharmacy "Victor Babes", Dropiei Street, No. 7, sc B, ap 8, 300661 Timisoara, Romania
| | - Corina Duda-Seiman
- Department of Chemistry and Biology, Faculty of Chemistry, Biology, Geography, West University of Timisoara, 16 Pestalozzi, 300115 Timisoara, Romania
| | - Virgil Păunescu
- Department of Functional Sciences, "Victor Babes" University of Medicine and Pharmacy, Eftimie Murgu Square, No. 2, 300041 Timisoara, Romania
- Center for Gene and Cellular Therapies in the Treatment of Cancer Timisoara-OncoGen, Clinical Emergency County Hospital "Pius Brinzeu" Timisoara, No. 156 Liviu Rebreanu, 300723 Timisoara, Romania
- Immuno-Physiology and Biotechnologies Center, Department of Functional Sciences, "Victor Babes" University of Medicine and Pharmacy, No. 2 Eftimie Murgu Square, 300041 Timisoara, Romania
| | - Corina Vernic
- Department of Functional Sciences, "Victor Babes" University of Medicine and Pharmacy, Eftimie Murgu Square, No. 2, 300041 Timisoara, Romania
- Discipline of Medical Informatics and Biostatistics, "Victor Babes" University of Medicine and Pharmacy, 300041 Timisoara, Romania
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31
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Guan J, Yao L, Chung CR, Chiang YC, Lee TY. StackTHPred: Identifying Tumor-Homing Peptides through GBDT-Based Feature Selection with Stacking Ensemble Architecture. Int J Mol Sci 2023; 24:10348. [PMID: 37373494 DOI: 10.3390/ijms241210348] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Revised: 05/31/2023] [Accepted: 06/02/2023] [Indexed: 06/29/2023] Open
Abstract
One of the major challenges in cancer therapy lies in the limited targeting specificity exhibited by existing anti-cancer drugs. Tumor-homing peptides (THPs) have emerged as a promising solution to this issue, due to their capability to specifically bind to and accumulate in tumor tissues while minimally impacting healthy tissues. THPs are short oligopeptides that offer a superior biological safety profile, with minimal antigenicity, and faster incorporation rates into target cells/tissues. However, identifying THPs experimentally, using methods such as phage display or in vivo screening, is a complex, time-consuming task, hence the need for computational methods. In this study, we proposed StackTHPred, a novel machine learning-based framework that predicts THPs using optimal features and a stacking architecture. With an effective feature selection algorithm and three tree-based machine learning algorithms, StackTHPred has demonstrated advanced performance, surpassing existing THP prediction methods. It achieved an accuracy of 0.915 and a 0.831 Matthews Correlation Coefficient (MCC) score on the main dataset, and an accuracy of 0.883 and a 0.767 MCC score on the small dataset. StackTHPred also offers favorable interpretability, enabling researchers to better understand the intrinsic characteristics of THPs. Overall, StackTHPred is beneficial for both the exploration and identification of THPs and facilitates the development of innovative cancer therapies.
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Affiliation(s)
- Jiahui Guan
- School of Medicine, The Chinese University of Hong Kong (Shenzhen) 2001 Longxiang Road, Shenzhen 518172, China
| | - Lantian Yao
- Kobilka Institute of Innovative Drug Discovery, School of Medicine, The Chinese University of Hong Kong (Shenzhen), 2001 Longxiang Road, Shenzhen 518172, China
- School of Science and Engineering, The Chinese University of Hong Kong (Shenzhen), 2001 Longxiang Road, Shenzhen 518172, China
| | - Chia-Ru Chung
- Kobilka Institute of Innovative Drug Discovery, School of Medicine, The Chinese University of Hong Kong (Shenzhen), 2001 Longxiang Road, Shenzhen 518172, China
| | - Ying-Chih Chiang
- School of Medicine, The Chinese University of Hong Kong (Shenzhen) 2001 Longxiang Road, Shenzhen 518172, China
- Kobilka Institute of Innovative Drug Discovery, School of Medicine, The Chinese University of Hong Kong (Shenzhen), 2001 Longxiang Road, Shenzhen 518172, China
| | - Tzong-Yi Lee
- Institute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, Hsinchu 300, Taiwan
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32
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Moin AT, Ullah MA, Patil RB, Faruqui NA, Araf Y, Das S, Uddin KMK, Hossain MS, Miah MF, Moni MA, Chowdhury DUS, Islam S. A computational approach to design a polyvalent vaccine against human respiratory syncytial virus. Sci Rep 2023; 13:9702. [PMID: 37322049 PMCID: PMC10272159 DOI: 10.1038/s41598-023-35309-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: 11/14/2022] [Accepted: 05/16/2023] [Indexed: 06/17/2023] Open
Abstract
Human Respiratory Syncytial Virus (RSV) is one of the leading causes of lower respiratory tract infections (LRTI), responsible for infecting people from all age groups-a majority of which comprises infants and children. Primarily, severe RSV infections are accountable for multitudes of deaths worldwide, predominantly of children, every year. Despite several efforts to develop a vaccine against RSV as a potential countermeasure, there has been no approved or licensed vaccine available yet, to control the RSV infection effectively. Therefore, through the utilization of immunoinformatics tools, a computational approach was taken in this study, to design a multi-epitope polyvalent vaccine against two major antigenic subtypes of RSV, RSV-A and RSV-B. Potential predictions of the T-cell and B-cell epitopes were followed by extensive tests of antigenicity, allergenicity, toxicity, conservancy, homology to human proteome, transmembrane topology, and cytokine-inducing ability. The peptide vaccine was modeled, refined, and validated. Molecular docking analysis with specific Toll-like receptors (TLRs) revealed excellent interactions with suitable global binding energies. Additionally, molecular dynamics (MD) simulation ensured the stability of the docking interactions between the vaccine and TLRs. Mechanistic approaches to imitate and predict the potential immune response generated by the administration of vaccines were determined through immune simulations. Subsequent mass production of the vaccine peptide was evaluated; however, there remains a necessity for further in vitro and in vivo experiments to validate its efficacy against RSV infections.
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Affiliation(s)
- Abu Tayab Moin
- Department of Genetic Engineering and Biotechnology, Faculty of Biological Sciences, University of Chittagong, Chattogram, Bangladesh.
| | - Md Asad Ullah
- Department of Biotechnology and Genetic Engineering, Faculty of Biological Sciences, Jahangirnagar University, Savar, Dhaka, Bangladesh
| | - Rajesh B Patil
- Department of Pharmaceutical Chemistry, Sinhgad Technical Education Society's, Sinhgad College of Pharmacy, Pune, Maharashtra, India
| | - Nairita Ahsan Faruqui
- Biotechnology Program, Department of Mathematics and Natural Sciences, School of Data and Sciences, BRAC University, Dhaka, Bangladesh
| | - Yusha Araf
- Department of Genetic Engineering and Biotechnology, School of Life Sciences, Shahjalal University of Science and Technology, Sylhet, Bangladesh
| | - Sowmen Das
- Department of Computer Science and Engineering, School of Physical Sciences, Shahjalal University of Science and Technology, Sylhet, Bangladesh
| | - Khaza Md Kapil Uddin
- Department of Genetic Engineering and Biotechnology, Faculty of Biological Sciences, University of Chittagong, Chattogram, Bangladesh
| | - Md Shakhawat Hossain
- Department of Genetic Engineering and Biotechnology, Faculty of Biological Sciences, University of Chittagong, Chattogram, Bangladesh
| | - Md Faruque Miah
- Department of Genetic Engineering and Biotechnology, School of Life Sciences, Shahjalal University of Science and Technology, Sylhet, Bangladesh
| | - Mohammad Ali Moni
- Bone Biology Division, The Garvan Institute of Medical Research, Darlinghurst, New South Wales, Australia
- WHO Collaborating Centre on eHealth, UNSW Digital Health, School of Public Health and Community Medicine, Faculty of Medicine, UNSW Sydney, Sydney, Australia
- Artificial Intelligence and Data Science, Faculty of Health and Behavioural Sciences, School of Health and Rehabilitation Sciences, The University of Queensland, Brisbane, Australia
| | - Dil Umme Salma Chowdhury
- Department of Genetic Engineering and Biotechnology, Faculty of Biological Sciences, University of Chittagong, Chattogram, Bangladesh.
| | - Saiful Islam
- Bangladesh Council of Scientific and Industrial Research (BCSIR), Chattogram Laboratories, Chattogram, Bangladesh.
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33
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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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34
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Ozgul M, Nesburn AB, Nasralla N, Katz B, Taylan E, Kuppermann BD, Kenney MC. Stability Determination of Intact Humanin-G with Characterizations of Oxidation and Dimerization Patterns. Biomolecules 2023; 13:biom13030515. [PMID: 36979450 PMCID: PMC10046509 DOI: 10.3390/biom13030515] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Revised: 02/23/2023] [Accepted: 02/25/2023] [Indexed: 03/14/2023] Open
Abstract
Humanin is the first identified mitochondrial-derived peptide. Humanin-G (HNG) is a variant of Humanin that has significantly higher cytoprotective properties. Here, we describe the stability features of HNG in different conditions and characterize HNG degradation, oxidation, and dimerization patterns over short-term and long-term periods. HNG solutions were prepared in high-performance liquid chromatography (HPLC) water or MO formulation and stored at either 4 °C or 37 °C. Stored HNG samples were analyzed using HPLC and high-resolution mass spectrometry (HRMS). Using HPLC, full-length HNG peptides in HPLC water decreased significantly with time and higher temperature, while HNG in MO formulation remained stable up to 95% at 4 °C on day 28. HNG peptides in HPLC water, phosphate-buffered saline (PBS) and MO formulation were incubated at 37 °C and analyzed at day 1, day 7 and day 14 using HRMS. Concentrations of full-length HNG peptide in HPLC water and PBS declined over time with a corresponding appearance of new peaks that increased over time. These new peaks were identified to be singly oxidized HNG, doubly oxidized HNG, homodimerized HNG, singly oxidized homodimerized HNG, and doubly oxidized homodimerized HNG. Our results may help researchers improve the experimental design to further understand the critical role of HNG in human diseases.
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Affiliation(s)
- Mustafa Ozgul
- Department of Ophthalmology, Gavin Herbert Eye Institute, University of California Irvine, Irvine, CA 92617, USA
- Correspondence: (M.O.); (M.C.K.)
| | - Anthony B. Nesburn
- Department of Ophthalmology, Gavin Herbert Eye Institute, University of California Irvine, Irvine, CA 92617, USA
- Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
| | | | - Benjamin Katz
- Department of Chemistry, University of California Irvine, Irvine, CA 92697, USA
| | - Enes Taylan
- Department of Obstetrics and Gynecology, University of California Los Angeles, Los Angeles, CA 90095, USA
| | - Baruch D. Kuppermann
- Department of Ophthalmology, Gavin Herbert Eye Institute, University of California Irvine, Irvine, CA 92617, USA
| | - Maria Cristina Kenney
- Department of Ophthalmology, Gavin Herbert Eye Institute, University of California Irvine, Irvine, CA 92617, USA
- Department of Pathology and Laboratory Medicine, University of California Irvine, Irvine, CA 92617, USA
- Correspondence: (M.O.); (M.C.K.)
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35
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Moin AT, Singh G, Ahmed N, Saiara SA, Timofeev VI, Ahsan Faruqui N, Sharika Ahsan S, Tabassum A, Nebir SS, Andalib KMS, Araf Y, Ullah MA, Sarkar B, Islam NN, Zohora US. Computational designing of a novel subunit vaccine for human cytomegalovirus by employing the immunoinformatics framework. J Biomol Struct Dyn 2023; 41:833-855. [PMID: 36617426 DOI: 10.1080/07391102.2021.2014969] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
Human cytomegalovirus (HCMV) is a widespread virus that can cause serious and irreversible neurological damage in newborns and even death in children who do not have the access to much-needed medications. While some vaccines and drugs are found to be effective against HCMV, their extended use has given rise to dose-limiting toxicities and the development of drug-resistant mutants among patients. Despite half a century's worth of research, the lack of a licensed HCMV vaccine heightens the need to develop newer antiviral therapies and vaccine candidates with improved effectiveness and reduced side effects. In this study, the immunoinformatics approach was utilized to design a potential polyvalent epitope-based vaccine effective against the four virulent strains of HCMV. The vaccine was constructed using seven CD8+ cytotoxic T lymphocytes epitopes, nine CD4+ helper T lymphocyte epitopes, and twelve linear B-cell lymphocyte epitopes that were predicted to be antigenic, non-allergenic, non-toxic, fully conserved, and non-human homologous. Subsequently, molecular docking study, protein-protein interaction analysis, molecular dynamics simulation (including the root mean square fluctuation (RMSF) and root mean square deviation (RMSD)), and immune simulation study rendered promising results assuring the vaccine to be stable, safe, and effective. Finally, in silico cloning was conducted to develop an efficient mass production strategy of the vaccine. However, further in vitro and in vivo research studies on the proposed vaccine are required to confirm its safety and efficacy.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Abu Tayab Moin
- Department of Genetic Engineering and Biotechnology, Faculty of Biological Sciences, University of Chittagong, Chattogram, Bangladesh
| | - Gagandeep Singh
- Section of Microbiology, Central Ayurveda Research Institute, Jhansi, Uttar Pradesh, India.,Kusuma School of Biological Sciences, Indian Institute of Technology, Delhi, India
| | - Nafisa Ahmed
- Biotechnology Program, Department of Mathematics and Natural Sciences, BRAC University, Dhaka, Bangladesh
| | | | - Vladimir I Timofeev
- Shubnikov Institute of Crystallography of Federal Scientific Research Centre "Crystallography and Photonics" of Russian Academy of Sciences, Moscow, Russian Federation
| | - Nairita Ahsan Faruqui
- Biotechnology Program, Department of Mathematics and Natural Sciences, BRAC University, Dhaka, Bangladesh
| | | | - Afrida Tabassum
- Department of Genetic Engineering and Biotechnology, Jagannath University, Dhaka, Bangladesh
| | - Sadman Sakib Nebir
- Department of Microbiology and Immunology, Bangladesh University of Health Sciences, Dhaka, Bangladesh
| | | | - Yusha Araf
- Community of Biotechnology, Dhaka, Bangladesh.,Department of Genetic Engineering and Biotechnology, School of Life Sciences, Shahjalal University of Science and Technology, Sylhet, Bangladesh
| | - Md Asad Ullah
- Community of Biotechnology, Dhaka, Bangladesh.,Department of Biotechnology and Genetic Engineering, Faculty of Biological Sciences, Jahangirnagar University, Dhaka, Bangladesh
| | - Bishajit Sarkar
- Community of Biotechnology, Dhaka, Bangladesh.,Department of Biotechnology and Genetic Engineering, Faculty of Biological Sciences, Jahangirnagar University, Dhaka, Bangladesh
| | - Nafisa Nawal Islam
- Department of Biotechnology and Genetic Engineering, Faculty of Biological Sciences, Jahangirnagar University, Dhaka, Bangladesh
| | - Umme Salma Zohora
- Department of Biotechnology and Genetic Engineering, Faculty of Biological Sciences, Jahangirnagar University, Dhaka, Bangladesh
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36
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Chowdhury AT, Hasan MN, Bhuiyan FH, Islam MQ, Nayon MRW, Rahaman MM, Hoque H, Jewel NA, Ashrafuzzaman M, Prodhan SH. Identification, characterization of Apyrase (APY) gene family in rice (Oryza sativa) and analysis of the expression pattern under various stress conditions. PLoS One 2023; 18:e0273592. [PMID: 37163561 PMCID: PMC10171694 DOI: 10.1371/journal.pone.0273592] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Accepted: 02/27/2023] [Indexed: 05/12/2023] Open
Abstract
Apyrase (APY) is a nucleoside triphosphate (NTP) diphosphohydrolase (NTPDase) which is a member of the superfamily of guanosine diphosphatase 1 (GDA1)-cluster of differentiation 39 (CD39) nucleoside phosphatase. Under various circumstances like stress, cell growth, the extracellular adenosine triphosphate (eATP) level increases, causing a detrimental influence on cells such as cell growth retardation, ROS production, NO burst, and apoptosis. Apyrase hydrolyses eATP accumulated in the extracellular membrane during stress, wounds, into adenosine diphosphate (ADP) and adenosine monophosphate (AMP) and regulates the stress-responsive pathway in plants. This study was designed for the identification, characterization, and for analysis of APY gene expression in Oryza sativa. This investigation discovered nine APYs in rice, including both endo- and ecto-apyrase. According to duplication event analysis, in the evolution of OsAPYs, a significant role is performed by segmental duplication. Their role in stress control, hormonal responsiveness, and the development of cells is supported by the corresponding cis-elements present in their promoter regions. According to expression profiling by RNA-seq data, the genes were expressed in various tissues. Upon exposure to a variety of biotic as well as abiotic stimuli, including anoxia, drought, submergence, alkali, heat, dehydration, salt, and cold, they showed a differential expression pattern. The expression analysis from the RT-qPCR data also showed expression under various abiotic stress conditions, comprising cold, salinity, cadmium, drought, submergence, and especially heat stress. This finding will pave the way for future in-vivo analysis, unveil the molecular mechanisms of APY genes in stress response, and contribute to the development of stress-tolerant rice varieties.
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Affiliation(s)
- Aniqua Tasnim Chowdhury
- Department of Genetic Engineering and Biotechnology, School of Life Sciences, Shahjalal University of Science and Technology, Sylhet, Bangladesh
| | - Md Nazmul Hasan
- Department of Genetic Engineering and Biotechnology, School of Life Sciences, Shahjalal University of Science and Technology, Sylhet, Bangladesh
| | - Fahmid H Bhuiyan
- Plant Biotechnology Division, National Institute of Biotechnology, Ganakbari, Ashulia, Savar, Dhaka, Bangladesh
| | - Md Qamrul Islam
- Department of Genetic Engineering and Biotechnology, School of Life Sciences, Shahjalal University of Science and Technology, Sylhet, Bangladesh
| | - Md Rakib Wazed Nayon
- Department of Genetic Engineering and Biotechnology, School of Life Sciences, Shahjalal University of Science and Technology, Sylhet, Bangladesh
| | - Md Mashiur Rahaman
- Department of Genetic Engineering and Biotechnology, School of Life Sciences, Shahjalal University of Science and Technology, Sylhet, Bangladesh
- Institute of Epidemiology, Disease Control and Research (IEDCR), Dhaka, Bangladesh
| | - Hammadul Hoque
- Department of Genetic Engineering and Biotechnology, School of Life Sciences, Shahjalal University of Science and Technology, Sylhet, Bangladesh
| | - Nurnabi Azad Jewel
- Department of Genetic Engineering and Biotechnology, School of Life Sciences, Shahjalal University of Science and Technology, Sylhet, Bangladesh
| | - Md Ashrafuzzaman
- Department of Genetic Engineering and Biotechnology, School of Life Sciences, Shahjalal University of Science and Technology, Sylhet, Bangladesh
| | - Shamsul H Prodhan
- Department of Genetic Engineering and Biotechnology, School of Life Sciences, Shahjalal University of Science and Technology, Sylhet, Bangladesh
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37
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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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38
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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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39
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Hasegawa K, Moriwaki Y, Terada T, Wei C, Shimizu K. Feedback-AVPGAN: Feedback-guided generative adversarial network for generating antiviral peptides. J Bioinform Comput Biol 2022; 20:2250026. [PMID: 36514872 DOI: 10.1142/s0219720022500263] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
In this study, we propose Feedback-AVPGAN, a system that aims to computationally generate novel antiviral peptides (AVPs). This system relies on the key premise of the Generative Adversarial Network (GAN) model and the Feedback method. GAN, a generative modeling approach that uses deep learning methods, comprises a generator and a discriminator. The generator is used to generate peptides; the generated proteins are fed to the discriminator to distinguish between the AVPs and non-AVPs. The original GAN design uses actual data to train the discriminator. However, not many AVPs have been experimentally obtained. To solve this problem, we used the Feedback method to allow the discriminator to learn from the existing as well as generated synthetic data. We implemented this method using a classifier module that classifies each peptide sequence generated by the GAN generator as AVP or non-AVP. The classifier uses the transformer network and achieves high classification accuracy. This mechanism enables the efficient generation of peptides with a high probability of exhibiting antiviral activity. Using the Feedback method, we evaluated various algorithms and their performance. Moreover, we modeled the structure of the generated peptides using AlphaFold2 and determined the peptides having similar physicochemical properties and structures to those of known AVPs, although with different sequences.
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Affiliation(s)
- Kano Hasegawa
- Department of Biotechnology, Graduate School of Agricultural and Life Sciences, Faculty of Agriculture The University of Tokyo, 1-1-1, Yayoi, Bunkyo-ku, Tokyo 113-8657, Japan
| | - Yoshitaka Moriwaki
- Department of Biotechnology, Graduate School of Agricultural and Life Sciences, Faculty of Agriculture The University of Tokyo, 1-1-1, Yayoi, Bunkyo-ku, Tokyo 113-8657, Japan.,Collaborative Research Institute for Innovative Microbiology, The Institute of Medical Science The University of Tokyo, 1-1-1, Yayoi, Bunkyo-ku, Tokyo 113-8657, Japan
| | - Tohru Terada
- Department of Biotechnology, Graduate School of Agricultural and Life Sciences, Faculty of Agriculture The University of Tokyo, 1-1-1, Yayoi, Bunkyo-ku, Tokyo 113-8657, Japan.,Collaborative Research Institute for Innovative Microbiology, The Institute of Medical Science The University of Tokyo, 1-1-1, Yayoi, Bunkyo-ku, Tokyo 113-8657, Japan
| | - Cao Wei
- Research Center for Agricultural Information Technology, National Agriculture and Food Research Organization, Tsukuba, Ibaraki 305-8517, Japan
| | - Kentaro Shimizu
- Department of Biotechnology, Graduate School of Agricultural and Life Sciences, Faculty of Agriculture The University of Tokyo, 1-1-1, Yayoi, Bunkyo-ku, Tokyo 113-8657, Japan.,Collaborative Research Institute for Innovative Microbiology, The Institute of Medical Science The University of Tokyo, 1-1-1, Yayoi, Bunkyo-ku, Tokyo 113-8657, Japan
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40
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Medina L, Guzmán F, Álvarez C, Delgado JP, Carbonell-M B. Ramosin: The First Antibacterial Peptide Identified on Bolitoglossa ramosi Colombian Salamander. Pharmaceutics 2022; 14:pharmaceutics14122579. [PMID: 36559073 PMCID: PMC9782819 DOI: 10.3390/pharmaceutics14122579] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Revised: 11/16/2022] [Accepted: 11/18/2022] [Indexed: 11/25/2022] Open
Abstract
The discovery and improvements of antimicrobial peptides (AMPs) have become an alternative to conventional antibiotics. They are usually small and heat-stable peptides, exhibiting inhibitory activity against Gram-negative and Gram-positive bacteria. In this way, studies on broad-spectrum AMPs found in amphibians with the remarkable capability to regenerate a wide array of tissues are of particular interest in the search for new strategies to treat multidrug-resistant bacterial strains. In this work, the use of bioinformatic approaches such as sequence alignment with Fasta36 and prediction of antimicrobial activity allowed the identification of the Ramosin peptide from the de novo assembled transcriptome of the plethodontid salamander Bolitoglossa ramosi obtained from post-amputation of the upper limb tissue, heart, and intestine samples. BLAST analysis revealed that the Ramosin peptide sequence is unique in Bolitoglossa ramosi. The peptide was chemically synthesized, and physicochemical properties were characterized. Furthermore, the in vitro antimicrobial activity against relevant Gram-positive and Gram-negative human pathogenic bacteria was demonstrated. Finally, no effect against eukaryotic cells or human red blood cells was evidenced. This is the first antibacterial peptide identified from a Colombian endemic salamander with interesting antimicrobial properties and no hemolytic activity.
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Affiliation(s)
- Laura Medina
- Grupo Genética, Regeneración y Cáncer, Facultad de Ciencias Exactas y Naturales, Instituto de Biología, Universidad de Antioquia, Medellín 050010, Colombia
- Correspondence:
| | - Fanny Guzmán
- Núcleo de Biotecnología Curauma (NBC), Pontificia Universidad Católica de Valparaíso, Valparaíso 2373223, Chile
| | - Claudio Álvarez
- Laboratorio de Fisiología y Genética Marina (FIGEMA), Centro de Estudios Avanzados en Zonas Áridas (CEAZA), Coquimbo 1781421, Chile
- Facultad de Ciencias del Mar, Universidad Católica del Norte, Coquimbo 1781421, Chile
| | - Jean Paul Delgado
- Grupo Genética, Regeneración y Cáncer, Facultad de Ciencias Exactas y Naturales, Instituto de Biología, Universidad de Antioquia, Medellín 050010, Colombia
| | - Belfran Carbonell-M
- Grupo Genética, Regeneración y Cáncer, Facultad de Ciencias Exactas y Naturales, Instituto de Biología, Universidad de Antioquia, Medellín 050010, Colombia
- Departamento de Estudios Básicos Integrados, Facultad de Odontología, Universidad de Antioquia, Medellín 050010, Colombia
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Vivekanandan S, Vetrivel U, Hanna LE. Design of human immunodeficiency virus-1 neutralizing peptides targeting CD4-binding site: An integrative computational biologics approach. Front Med (Lausanne) 2022; 9:1036874. [DOI: 10.3389/fmed.2022.1036874] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Accepted: 10/26/2022] [Indexed: 11/19/2022] Open
Abstract
Peptide therapeutics have recently gained momentum in antiviral therapy due to their increased potency and cost-effectiveness. Interaction of the HIV-1 envelope gp120 with the host CD4 receptor is a critical step for viral entry, and therefore the CD4-binding site (CD4bs) of gp120 is a potential hotspot for blocking HIV-1 infection. The present study aimed to design short peptides from well-characterized CD4bs targeting broadly neutralizing antibodies (bNAbs), which could be utilized as bNAb mimetics for viral neutralization. Co-crystallized structures of HIV-1 gp120 in complex with CD4bs-directed bNAbs were used to derive hexameric peptides using the Rosetta Peptiderive protocol. Based on empirical insights into co-crystallized structures, peptides derived from the heavy chain alone were considered. The peptides were docked with both HIV-1 subtype B and C gp120, and the stability of the peptide–antigen complexes was validated using extensive Molecular Dynamics (MD) simulations. Two peptides identified in the study demonstrated stable intermolecular interactions with SER365, GLY366, and GLY367 of the PHE43 cavity in the CD4 binding pocket, and with ASP368 of HIV-1 gp120, thereby mimicking the natural interaction between ASP368gp120 and ARG59CD4–RECEPTOR. Furthermore, the peptides featured favorable physico-chemical properties for virus neutralization suggesting that these peptides may be highly promising bNAb mimetic candidates that may be taken up for experimental validation.
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Shey RA, Ghogomu SM, Nebangwa DN, Shintouo CM, Yaah NE, Yengo BN, Nkemngo FN, Esoh KK, Tchatchoua NMT, Mbachick TT, Dede AF, Lemoge AA, Ngwese RA, Asa BF, Ayong L, Njemini R, Vanhamme L, Souopgui J. Rational design of a novel multi-epitope peptide-based vaccine against Onchocerca volvulus using transmembrane proteins. FRONTIERS IN TROPICAL DISEASES 2022. [DOI: 10.3389/fitd.2022.1046522] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022] Open
Abstract
Almost a decade ago, it was recognized that the global elimination of onchocerciasis by 2030 will not be feasible without, at least, an effective prophylactic and/or therapeutic vaccine to complement chemotherapy and vector control strategies. Recent advances in computational immunology (immunoinformatics) have seen the design of novel multi-epitope onchocerciasis vaccine candidates which are however yet to be evaluated in clinical settings. Still, continued research to increase the pool of vaccine candidates, and therefore the chance of success in a clinical trial remains imperative. Here, we designed a multi-epitope vaccine candidate by assembling peptides from 14 O. volvulus (Ov) proteins using an immunoinformatics approach. An initial 126 Ov proteins, retrieved from the Wormbase database, and at least 90% similar to orthologs in related nematode species of economic importance, were screened for localization, presence of transmembrane domain, and antigenicity using different web servers. From the 14 proteins retained after the screening, 26 MHC-1 and MHC-II (T-cell) epitopes, and linear B-lymphocytes epitopes were predicted and merged using suitable linkers. The Mycobacterium tuberculosis Resuscitation-promoting factor E (RPFE_MYCTU), which is an agonist of TLR4, was then added to the N-terminal of the vaccine candidate as a built-in adjuvant. Immune simulation analyses predicted strong B-cell and IFN-γ based immune responses which are necessary for protection against O. volvulus infection. Protein-protein docking and molecular dynamic simulation predicted stable interactions between the 3D structure of the vaccine candidate and human TLR4. These results show that the designed vaccine candidate has the potential to stimulate both humoral and cellular immune responses and should therefore be subject to further laboratory investigation.
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Abstract
Epstein-Barr virus (EBV) is a lymphotropic virus responsible for numerous epithelial and lymphoid cell malignancies, including gastric carcinoma, Hodgkin's lymphoma, nasopharyngeal carcinoma, and Burkitt lymphoma. Hundreds of thousands of people worldwide get infected with this virus, and in most cases, this viral infection leads to cancer. Although researchers are trying to develop potential vaccines and drug therapeutics, there is still no effective vaccine to combat this virus. In this study, the immunoinformatics approach was utilized to develop a potential multiepitope subunit vaccine against the two most common subtypes of EBV, targeting three of their virulent envelope glycoproteins. Eleven cytotoxic T lymphocyte (CTL) epitopes, 11 helper T lymphocyte (HTL) epitopes, and 10 B-cell lymphocyte (BCL) epitopes were predicted to be antigenic, nonallergenic, nontoxic, and fully conserved among the two subtypes, and nonhuman homologs were used for constructing the vaccine after much analysis. Later, further validation experiments, including molecular docking with different immune receptors (e.g., Toll-like receptors [TLRs]), molecular dynamics simulation analyses (including root means square deviation [RMSD], root mean square fluctuation [RMSF], radius of gyration [Rg], principal-component analysis [PCA], dynamic cross-correlation [DCC], definition of the secondary structure of proteins [DSSP], and Molecular Mechanics Poisson-Boltzmann Surface Area [MM-PBSA]), and immune simulation analyses generated promising results, ensuring the safe and stable response of the vaccine with specific immune receptors after potential administration within the human body. The vaccine's high binding affinity with TLRs was revealed in the docking study, and a very stable interaction throughout the simulation proved the potential high efficacy of the proposed vaccine. Further, in silico cloning was also conducted to design an efficient mass production strategy for future bulk industrial vaccine production. IMPORTANCE Epstein-Barr virus (EBV) vaccines have been developing for over 30 years, but polyphyletic and therapeutic vaccines have failed to get licensed. Our vaccine surpasses the limitations of many such vaccines and remains very promising, which is crucial because the infection rate is higher than most viral infections, affecting a whopping 90% of the adult population. One of the major identifications covers a holistic analysis of populations worldwide, giving us crucial information about its effectiveness for everyone's unique immunological system. We targeted three glycoproteins that enhance the virulence of the virus to design an epitope-based polyvalent vaccine against two different strains of EBV, type 1 and 2. Our methodology in this study is nonconventional yet swift to show effective results while designing vaccines.
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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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Stability of antioxidant and hypoglycemic activities of peptide fractions of Maize (Zea mays L.) under different processes. JOURNAL OF FOOD MEASUREMENT AND CHARACTERIZATION 2022. [DOI: 10.1007/s11694-022-01618-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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46
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Singh D, Mahadik A, Surana S, Arora P. Proteochemometric Method for pIC50 Prediction of Flaviviridae. BIOMED RESEARCH INTERNATIONAL 2022; 2022:7901791. [PMID: 36158882 PMCID: PMC9499780 DOI: 10.1155/2022/7901791] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Accepted: 07/18/2022] [Indexed: 11/17/2022]
Abstract
Viruses remain an area of concern despite constant development of antiviral drugs and therapies. One of the contributors is the Flaviviridae family of viruses causing diseases that need attention. Among other anitviral methods, antiviral peptides are being studied as viable candidates. Although antiviral peptides (AVPs) are emerging as potential therapeutics, it is important to assess the efficacy of a given peptide in terms of its bioactivity. Experimental identification of the bioactivity of each potential peptide is an expensive and time consuming task. Computational methods like proteochemometric modeling (PCM) is a promising method for prediction of bioactivity (pIC50) based on peptide and target sequence pair. In this study, we propose a prediction of pIC50 of AVP against the Flaviviridae family that may help make the decision to choose a peptide with desired efficacy. The peptides data was collected from a public database and target sequences were manually curated from literature. Features are calculated using peptide and target sequence PCM descriptors which consist of individual and cross-term features of peptide and respective target. The resultant R 2 and MAPE values are 0.85 and 8.44%, respectively, for prediction of pIC50 value of AVPs.
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Affiliation(s)
- Divye Singh
- Engineering for Research, Thoughtworks Technologies, Pune, Maharashtra 411006, India
| | - Avani Mahadik
- Engineering for Research, Thoughtworks Technologies, Pune, Maharashtra 411006, India
| | - Shraddha Surana
- Engineering for Research, Thoughtworks Technologies, Pune, Maharashtra 411006, India
| | - Pooja Arora
- Engineering for Research, Thoughtworks Technologies, Pune, Maharashtra 411006, India
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Dey S, Guchhait KC, Manna T, Panda AK, Patra A, Mondal SK, Ghosh C. Evolutionary and compositional analysis of streptokinase including its interaction with plasminogen: An in silico approach. GENE REPORTS 2022. [DOI: 10.1016/j.genrep.2022.101689] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Abbasi BA, Dharan A, Mishra A, Saraf D, Ahamad I, Suravajhala P, Valadi J. In Silico Characterization of Uncharacterized Proteins From Multiple Strains of Clostridium Difficile. Front Genet 2022; 13:878012. [PMID: 36035185 PMCID: PMC9403866 DOI: 10.3389/fgene.2022.878012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Accepted: 06/22/2022] [Indexed: 11/13/2022] Open
Abstract
Clostridium difficile (C. difficile) is a multi-strain, spore-forming, Gram-positive, opportunistic enteropathogen bacteria, majorly associated with nosocomial infections, resulting in severe diarrhoea and colon inflammation. Several antibiotics including penicillin, tetracycline, and clindamycin have been employed to control C. difficile infection, but studies have suggested that injudicious use of antibiotics has led to the development of resistance in C. difficile strains. However, many proteins from its genome are still considered uncharacterized proteins that might serve crucial functions and assist in the biological understanding of the organism. In this study, we aimed to annotate and characterise the 6 C. difficile strains using in silico approaches. We first analysed the complete genome of 6 C. difficile strains using standardised approaches and analysed hypothetical proteins (HPs) employing various bioinformatics approaches coalescing, including identifying contigs, coding sequences, phage sequences, CRISPR-Cas9 systems, antimicrobial resistance determination, membrane helices, instability index, secretory nature, conserved domain, and vaccine target properties like comparative homology analysis, allergenicity, antigenicity determination along with structure prediction and binding-site analysis. This study provides crucial supporting information about the functional characterization of the HPs involved in the pathophysiology of the disease. Moreover, this information also aims to assist in mechanisms associated with bacterial pathogenesis and further design candidate inhibitors and bona fide pharmaceutical targets.
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Affiliation(s)
| | | | | | | | | | - Prashanth Suravajhala
- Bioclues.org, Hyderabad, India
- Amrita School of Biotechnology, Amrita Vishwa Vidyapeetham, Clappana, India
- *Correspondence: Prashanth Suravajhala, ; Jayaraman Valadi,
| | - Jayaraman Valadi
- Bioclues.org, Hyderabad, India
- School of Computational and Data Sciences, Vidyashilp University, Bengaluru, India
- Department of Computer Science, FLAME University, Pune, India
- *Correspondence: Prashanth Suravajhala, ; Jayaraman Valadi,
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Li V, Lee C, Yoo D, Cho S, Kim H. In silico SARS-CoV-2 vaccine development for Omicron strain using reverse vaccinology. Genes Genomics 2022; 44:937-944. [PMID: 35665465 PMCID: PMC9166176 DOI: 10.1007/s13258-022-01255-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Accepted: 03/31/2022] [Indexed: 11/04/2022]
Abstract
BACKGROUND The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic began in 2019 but it remains as a serious threat today. To reduce and prevent spread of the virus, multiple vaccines have been developed. Despite the efforts in developing vaccines, Omicron strain of the virus has recently been designated as a variant of concern (VOC) by the World Health Organization (WHO). OBJECTIVE To develop a vaccine candidate against Omicron strain (B.1.1.529, BA.1) of the SARS-CoV-19. METHODS We applied reverse vaccinology methods for BA.1 and BA.2 as the vaccine target and a control, respectively. First, we predicted MHC I, MHC II and B cell epitopes based on their viral genome sequences. Second, after estimation of antigenicity, allergenicity and toxicity, a vaccine construct was assembled and tested for physicochemical properties and solubility. Third, AlphaFold2, RaptorX and RoseTTAfold servers were used to predict secondary structures and 3D structures of the vaccine construct. Fourth, molecular docking analysis was performed to test binding of our construct with angiotensin converting enzyme 2 (ACE2). Lastly, we compared mutation profiles on the epitopes between BA.1, BA.2, and wild type to estimate the efficacy of the vaccine. RESULTS We collected a total of 10 MHC I, 9 MHC II and 5 B cell epitopes for the final vaccine construct for Omicron strain. All epitopes were predicted to be antigenic, non-allergenic and non-toxic. The construct was estimated to have proper stability and solubility. The best modelled tertiary structures were selected for molecular docking analysis with ACE2 receptor. CONCLUSIONS These results suggest the potential efficacy of our newly developed vaccine construct as a novel vaccine candidate against Omicron strain of the coronavirus.
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Affiliation(s)
- Vladimir Li
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Republic of Korea
| | - Chul Lee
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Republic of Korea
| | - DongAhn Yoo
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Republic of Korea
| | | | - Heebal Kim
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Republic of Korea.
- Department of Agricultural Biotechnology, Research Institute for Agriculture and Life Sciences, Seoul National University, Seoul, Republic of Korea.
- eGnome, Seoul, Republic of Korea.
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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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