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Aslan A, Ari Yuka S. Therapeutic peptides for coronary artery diseases: in silico methods and current perspectives. Amino Acids 2024; 56:37. [PMID: 38822212 PMCID: PMC11143054 DOI: 10.1007/s00726-024-03397-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2024] [Accepted: 05/06/2024] [Indexed: 06/02/2024]
Abstract
Many drug formulations containing small active molecules are used for the treatment of coronary artery disease, which affects a significant part of the world's population. However, the inadequate profile of these molecules in terms of therapeutic efficacy has led to the therapeutic use of protein and peptide-based biomolecules with superior properties, such as target-specific affinity and low immunogenicity, in critical diseases. Protein‒protein interactions, as a consequence of advances in molecular techniques with strategies involving the combined use of in silico methods, have enabled the design of therapeutic peptides to reach an advanced dimension. In particular, with the advantages provided by protein/peptide structural modeling, molecular docking for the study of their interactions, molecular dynamics simulations for their interactions under physiological conditions and machine learning techniques that can work in combination with all these, significant progress has been made in approaches to developing therapeutic peptides that can modulate the development and progression of coronary artery diseases. In this scope, this review discusses in silico methods for the development of peptide therapeutics for the treatment of coronary artery disease and strategies for identifying the molecular mechanisms that can be modulated by these designs and provides a comprehensive perspective for future studies.
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Affiliation(s)
- Ayca Aslan
- Department of Bioengineering, Faculty of Chemical and Metallurgical Engineering, Yildiz Technical University, Esenler, Istanbul, Turkey
- Health Biotechnology Joint Research and Application Center of Excellence, Esenler, Istanbul, Turkey
| | - Selcen Ari Yuka
- Department of Bioengineering, Faculty of Chemical and Metallurgical Engineering, Yildiz Technical University, Esenler, Istanbul, Turkey.
- Health Biotechnology Joint Research and Application Center of Excellence, Esenler, Istanbul, Turkey.
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2
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Bayarsaikhan B, Zsidó BZ, Börzsei R, Hetényi C. Efficient Refinement of Complex Structures of Flexible Histone Peptides Using Post-Docking Molecular Dynamics Protocols. Int J Mol Sci 2024; 25:5945. [PMID: 38892133 PMCID: PMC11172440 DOI: 10.3390/ijms25115945] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2024] [Revised: 05/26/2024] [Accepted: 05/27/2024] [Indexed: 06/21/2024] Open
Abstract
Histones are keys to many epigenetic events and their complexes have therapeutic and diagnostic importance. The determination of the structures of histone complexes is fundamental in the design of new drugs. Computational molecular docking is widely used for the prediction of target-ligand complexes. Large, linear peptides like the tail regions of histones are challenging ligands for docking due to their large conformational flexibility, extensive hydration, and weak interactions with the shallow binding pockets of their reader proteins. Thus, fast docking methods often fail to produce complex structures of such peptide ligands at a level appropriate for drug design. To address this challenge, and improve the structural quality of the docked complexes, post-docking refinement has been applied using various molecular dynamics (MD) approaches. However, a final consensus has not been reached on the desired MD refinement protocol. In this present study, MD refinement strategies were systematically explored on a set of problematic complexes of histone peptide ligands with relatively large errors in their docked geometries. Six protocols were compared that differ in their MD simulation parameters. In all cases, pre-MD hydration of the complex interface regions was applied to avoid the unwanted presence of empty cavities. The best-performing protocol achieved a median of 32% improvement over the docked structures in terms of the change in root mean squared deviations from the experimental references. The influence of structural factors and explicit hydration on the performance of post-docking MD refinements are also discussed to help with their implementation in future methods and applications.
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Affiliation(s)
- Bayartsetseg Bayarsaikhan
- Pharmacoinformatics Unit, Department of Pharmacology and Pharmacotherapy, Medical School, University of Pécs, Szigeti út 12, H-7624 Pécs, Hungary; (B.B.); (B.Z.Z.); (R.B.)
| | - Balázs Zoltán Zsidó
- Pharmacoinformatics Unit, Department of Pharmacology and Pharmacotherapy, Medical School, University of Pécs, Szigeti út 12, H-7624 Pécs, Hungary; (B.B.); (B.Z.Z.); (R.B.)
| | - Rita Börzsei
- Pharmacoinformatics Unit, Department of Pharmacology and Pharmacotherapy, Medical School, University of Pécs, Szigeti út 12, H-7624 Pécs, Hungary; (B.B.); (B.Z.Z.); (R.B.)
| | - Csaba Hetényi
- Pharmacoinformatics Unit, Department of Pharmacology and Pharmacotherapy, Medical School, University of Pécs, Szigeti út 12, H-7624 Pécs, Hungary; (B.B.); (B.Z.Z.); (R.B.)
- National Laboratory for Drug Research and Development, Magyar tudósok krt. 2, H-1117 Budapest, Hungary
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3
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Iwaniak A, Minkiewicz P, Darewicz M. Bioinformatics and bioactive peptides from foods: Do they work together? ADVANCES IN FOOD AND NUTRITION RESEARCH 2024; 108:35-111. [PMID: 38461003 DOI: 10.1016/bs.afnr.2023.09.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/11/2024]
Abstract
We live in the Big Data Era which affects many aspects of science, including research on bioactive peptides derived from foods, which during the last few decades have been a focus of interest for scientists. These two issues, i.e., the development of computer technologies and progress in the discovery of novel peptides with health-beneficial properties, are closely interrelated. This Chapter presents the example applications of bioinformatics for studying biopeptides, focusing on main aspects of peptide analysis as the starting point, including: (i) the role of peptide databases; (ii) aspects of bioactivity prediction; (iii) simulation of peptide release from proteins. Bioinformatics can also be used for predicting other features of peptides, including ADMET, QSAR, structure, and taste. To answer the question asked "bioinformatics and bioactive peptides from foods: do they work together?", currently it is almost impossible to find examples of peptide research with no bioinformatics involved. However, theoretical predictions are not equivalent to experimental work and always require critical scrutiny. The aspects of compatibility of in silico and in vitro results are also summarized herein.
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Affiliation(s)
- Anna Iwaniak
- Chair of Food Biochemistry, Faculty of Food Science, University of Warmia and Mazury in Olsztyn, Olsztyn-Kortowo, Poland.
| | - Piotr Minkiewicz
- Chair of Food Biochemistry, Faculty of Food Science, University of Warmia and Mazury in Olsztyn, Olsztyn-Kortowo, Poland
| | - Małgorzata Darewicz
- Chair of Food Biochemistry, Faculty of Food Science, University of Warmia and Mazury in Olsztyn, Olsztyn-Kortowo, Poland
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4
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Vincenzi M, Mercurio FA, Leone M. Virtual Screening of Peptide Libraries: The Search for Peptide-Based Therapeutics Using Computational Tools. Int J Mol Sci 2024; 25:1798. [PMID: 38339078 PMCID: PMC10855943 DOI: 10.3390/ijms25031798] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Revised: 01/26/2024] [Accepted: 01/30/2024] [Indexed: 02/12/2024] Open
Abstract
Over the last few decades, we have witnessed growing interest from both academic and industrial laboratories in peptides as possible therapeutics. Bioactive peptides have a high potential to treat various diseases with specificity and biological safety. Compared to small molecules, peptides represent better candidates as inhibitors (or general modulators) of key protein-protein interactions. In fact, undruggable proteins containing large and smooth surfaces can be more easily targeted with the conformational plasticity of peptides. The discovery of bioactive peptides, working against disease-relevant protein targets, generally requires the high-throughput screening of large libraries, and in silico approaches are highly exploited for their low-cost incidence and efficiency. The present review reports on the potential challenges linked to the employment of peptides as therapeutics and describes computational approaches, mainly structure-based virtual screening (SBVS), to support the identification of novel peptides for therapeutic implementations. Cutting-edge SBVS strategies are reviewed along with examples of applications focused on diverse classes of bioactive peptides (i.e., anticancer, antimicrobial/antiviral peptides, peptides blocking amyloid fiber formation).
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Affiliation(s)
| | | | - Marilisa Leone
- Institute of Biostructures and Bioimaging, Via Pietro Castellino 111, 80131 Naples, Italy; (M.V.); (F.A.M.)
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5
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Zhang L, Liu H. Exploring binding positions and backbone conformations of peptide ligands of proteins with a backbone-centred statistical energy function. J Comput Aided Mol Des 2023; 37:463-478. [PMID: 37498491 DOI: 10.1007/s10822-023-00518-0] [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: 04/18/2023] [Accepted: 07/05/2023] [Indexed: 07/28/2023]
Abstract
When designing peptide ligands based on the structure of a protein receptor, it can be very useful to narrow down the possible binding positions and bound conformations of the ligand without the need to choose its amino acid sequence in advance. Here, we construct and benchmark a tool for this purpose based on a recently reported statistical energy model named SCUBA (Sidechain-Unknown Backbone Arrangement) for designing protein backbones without considering specific amino acid sequences. With this tool, backbone fragments of different local conformation types are generated and optimized with SCUBA-driven stochastic simulations and simulated annealing, and then ranked and clustered to obtain representative backbone fragment poses of strong SCUBA interaction energies with the receptor. We computationally benchmarked the tool on 111 known protein-peptide complex structures. When the bound ligands are in the strand conformation, the method is able to generate backbone fragments of both low SCUBA energies and low root mean square deviations from experimental structures of peptide ligands. When the bound ligands are helices or coils, low-energy backbone fragments with binding poses similar to experimental structures have been generated for approximately 50% of benchmark cases. We have examined a number of predicted ligand-receptor complexes by atomistic molecular dynamics simulations, in which the peptide ligands have been found to stay at the predicted binding sites and to maintain their local conformations. These results suggest that promising backbone structures of peptides bound to protein receptors can be designed by identifying outstanding minima on the SCUBA-modeled backbone energy landscape.
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Affiliation(s)
- Lu Zhang
- MOE Key Laboratory for Membraneless Organelles and Cellular Dynamics, Hefei National Laboratory for Physical Sciences at the Microscale, School of Life Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230027, Anhui, China
| | - Haiyan Liu
- MOE Key Laboratory for Membraneless Organelles and Cellular Dynamics, Hefei National Laboratory for Physical Sciences at the Microscale, School of Life Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230027, Anhui, China.
- Biomedical Sciences and Health Laboratory of Anhui Province, University of Science and Technology of China, Hefei, 230027, Anhui, China.
- School of Data Science, University of Science and Technology of China, Hefei, 230027, Anhui, China.
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Jukič M, Kralj S, Kolarič A, Bren U. Design of Tetra-Peptide Ligands of Antibody Fc Regions Using In Silico Combinatorial Library Screening. Pharmaceuticals (Basel) 2023; 16:1170. [PMID: 37631085 PMCID: PMC10459493 DOI: 10.3390/ph16081170] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Revised: 08/09/2023] [Accepted: 08/10/2023] [Indexed: 08/27/2023] Open
Abstract
Peptides, or short chains of amino-acid residues, are becoming increasingly important as active ingredients of drugs and as crucial probes and/or tools in medical, biotechnological, and pharmaceutical research. Situated at the interface between small molecules and larger macromolecular systems, they pose a difficult challenge for computational methods. We report an in silico peptide library generation and prioritization workflow using CmDock for identifying tetrapeptide ligands that bind to Fc regions of antibodies that is analogous to known in vitro recombinant peptide libraries' display and expression systems. The results of our in silico study are in accordance with existing scientific literature on in vitro peptides that bind to antibody Fc regions. In addition, we postulate an evolving in silico library design workflow that will help circumvent the combinatorial problem of in vitro comprehensive peptide libraries by focusing on peptide subunits that exhibit favorable interaction profiles in initial in silico peptide generation and testing.
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Affiliation(s)
- Marko Jukič
- Laboratory of Physical Chemistry and Chemical Thermodynamics, Faculty of Chemistry and Chemical Engineering, University of Maribor, Smetanova ulica 17, SI-2000 Maribor, Slovenia
- Faculty of Mathematics, Natural Sciences and Information Technologies, University of Primorska, Glagoljaška ulica 8, SI-6000 Koper, Slovenia
- Institute of Environmental Protection and Sensors, Beloruska ulica 7, SI-2000 Maribor, Slovenia
| | - Sebastjan Kralj
- Laboratory of Physical Chemistry and Chemical Thermodynamics, Faculty of Chemistry and Chemical Engineering, University of Maribor, Smetanova ulica 17, SI-2000 Maribor, Slovenia
| | - Anja Kolarič
- Laboratory of Physical Chemistry and Chemical Thermodynamics, Faculty of Chemistry and Chemical Engineering, University of Maribor, Smetanova ulica 17, SI-2000 Maribor, Slovenia
- Institute of Environmental Protection and Sensors, Beloruska ulica 7, SI-2000 Maribor, Slovenia
| | - Urban Bren
- Laboratory of Physical Chemistry and Chemical Thermodynamics, Faculty of Chemistry and Chemical Engineering, University of Maribor, Smetanova ulica 17, SI-2000 Maribor, Slovenia
- Faculty of Mathematics, Natural Sciences and Information Technologies, University of Primorska, Glagoljaška ulica 8, SI-6000 Koper, Slovenia
- Institute of Environmental Protection and Sensors, Beloruska ulica 7, SI-2000 Maribor, Slovenia
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Shukla N, Srivastava N, Gupta R, Srivastava P, Narayan J. COVID Variants, Villain and Victory: A Bioinformatics Perspective. Microorganisms 2023; 11:2039. [PMID: 37630599 PMCID: PMC10459809 DOI: 10.3390/microorganisms11082039] [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: 06/21/2023] [Revised: 07/11/2023] [Accepted: 07/11/2023] [Indexed: 08/27/2023] Open
Abstract
The SARS-CoV-2 virus, a novel member of the Coronaviridae family, is responsible for the viral infection known as Coronavirus Disease 2019 (COVID-19). In response to the urgent and critical need for rapid detection, diagnosis, analysis, interpretation, and treatment of COVID-19, a wide variety of bioinformatics tools have been developed. Given the virulence of SARS-CoV-2, it is crucial to explore the pathophysiology of the virus. We intend to examine how bioinformatics, in conjunction with next-generation sequencing techniques, can be leveraged to improve current diagnostic tools and streamline vaccine development for emerging SARS-CoV-2 variants. We also emphasize how bioinformatics, in general, can contribute to critical areas of biomedicine, including clinical diagnostics, SARS-CoV-2 genomic surveillance and its evolution, identification of potential drug targets, and development of therapeutic strategies. Currently, state-of-the-art bioinformatics tools have helped overcome technical obstacles with respect to genomic surveillance and have assisted in rapid detection, diagnosis, and delivering precise treatment to individuals on time.
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Affiliation(s)
- Nityendra Shukla
- CSIR Institute of Genomics and Integrative Biology, Mall Road, Delhi 110007, India; (N.S.); (R.G.)
| | - Neha Srivastava
- Amity Institute of Biotechnology, Amity University, Uttar Pradesh, Lucknow Campus, Lucknow 226010, India; (N.S.); (P.S.)
| | - Rohit Gupta
- CSIR Institute of Genomics and Integrative Biology, Mall Road, Delhi 110007, India; (N.S.); (R.G.)
| | - Prachi Srivastava
- Amity Institute of Biotechnology, Amity University, Uttar Pradesh, Lucknow Campus, Lucknow 226010, India; (N.S.); (P.S.)
| | - Jitendra Narayan
- CSIR Institute of Genomics and Integrative Biology, Mall Road, Delhi 110007, India; (N.S.); (R.G.)
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Madlala T, Adeleke VT, Okpeku M, Tshilwane SI, Adeniyi AA, Adeleke MA. Screening of apical membrane antigen-1 (AMA1), dense granule protein-7 (GRA7) and rhoptry protein-16 (ROP16) antigens for a potential vaccine candidate against Toxoplasma gondii for chickens. Vaccine X 2023; 14:100347. [PMID: 37519774 PMCID: PMC10384181 DOI: 10.1016/j.jvacx.2023.100347] [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: 07/23/2022] [Revised: 06/23/2023] [Accepted: 07/06/2023] [Indexed: 08/01/2023] Open
Abstract
Toxoplasmosis is a zoonotic disease caused by the protozoan parasite, Toxoplasma gondii known to infect almost all animals, including birds and humans globally. This disease has impacted the livestock industry and public health, where infection of domestic animals increases the zoonotic risk of transmission of infection to humans, threatening public health. Hence the need to discover novel and safe vaccines to fight against toxoplasmosis. In the current study, a novel multiepitope vaccine was designed using immunoinformatics techniques targeting T. gondii AMA1, GRA7 and ROP16 antigens, consisting of antigenic, immunogenic, non-allergenic and cytokine inducing T-cell (9 CD8+ and 15 CD4+) epitopes and four (4) B-cell epitopes fused together using AAY, KK and GPGPG linkers. The tertiary model of the proposed vaccine was predicted and validated to confirm the structural quality of the vaccine. The designed vaccine was highly antigenic (antigenicity = 0.6645), immunogenic (score = 2.89998), with molecular weight of 73.35 kDa, instability and aliphatic index of 28.70 and 64.10, respectively; and GRAVY of -0.363. The binding interaction, stability and flexibility were assessed with molecular docking and dynamics simulation, which revealed the proposed vaccine to have good structural interaction (binding affinity = -106.882 kcal/mol) and stability when docked with Toll like receptor-4 (TLR4). The results revealed that the Profilin-adjuvanted vaccine is promising, as it predicted induction of enhanced immune responses through the production of cytokines and antibodies critical in blocking host invasion.
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Affiliation(s)
- Thabile Madlala
- Discipline of Genetics, School of Life Sciences, University of KwaZulu-Natal, Westville, P/Bag X54001, Durban 4000, South Africa
| | - Victoria T. Adeleke
- Department of Chemical Engineering, Mangosuthu University of Technology, Durban 4031, South Africa
| | - Moses Okpeku
- Discipline of Genetics, School of Life Sciences, University of KwaZulu-Natal, Westville, P/Bag X54001, Durban 4000, South Africa
| | - Selaelo I. Tshilwane
- Department of Veterinary Tropical Diseases, Faculty of Veterinary Science, University of Pretoria, Onderstepoort 0110, South Africa
| | - Adebayo A. Adeniyi
- Department of Industrial Chemistry, Federal University, Oye-Ekiti, P.O Box 370111, Nigeria
- Department of Chemistry, Faculty of Natural and Agricultural Sciences, University of the Free State, Bloemfontein, South Africa
| | - Matthew A. Adeleke
- Discipline of Genetics, School of Life Sciences, University of KwaZulu-Natal, Westville, P/Bag X54001, Durban 4000, South Africa
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Tonolo F, Grinzato A, Bindoli A, Rigobello MP. From In Silico to a Cellular Model: Molecular Docking Approach to Evaluate Antioxidant Bioactive Peptides. Antioxidants (Basel) 2023; 12:antiox12030665. [PMID: 36978913 PMCID: PMC10045749 DOI: 10.3390/antiox12030665] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Revised: 03/02/2023] [Accepted: 03/03/2023] [Indexed: 03/10/2023] Open
Abstract
The increasing need to counteract the redox imbalance in chronic diseases leads to focusing research on compounds with antioxidant activity. Among natural molecules with health-promoting effects on many body functions, bioactive peptides are gaining interest. They are protein fragments of 2–20 amino acids that can be released by various mechanisms, such as gastrointestinal digestion, food processing and microbial fermentation. Recent studies report the effects of bioactive peptides in the cellular environment, and there is evidence that these compounds can exert their action by modulating specific pathways. This review focuses on the newest approaches to the structure–function correlation of the antioxidant bioactive peptides, considering their molecular mechanism, by evaluating the activation of specific signaling pathways that are linked to antioxidant systems. The correlation between the results of in silico molecular docking analysis and the effects in a cellular model was highlighted. This knowledge is fundamental in order to propose the use of bioactive peptides as ingredients in functional foods or nutraceuticals.
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Affiliation(s)
- Federica Tonolo
- Department of Biomedical Sciences, University of Padova, Via U. Bassi 58/b, 35131 Padova, Italy
- Department of Comparative Biomedicine and Food Science, University of Padova, Viale dell’Università, 35020 Padova, Italy
| | - Alessandro Grinzato
- European Synchrotron Radiation Facility, 71 Avenue des Martyrs, 38000 Grenoble, France
| | - Alberto Bindoli
- Institute of Neuroscience (CNR), Viale G. Colombo 3, 35131 Padova, Italy
| | - Maria Pia Rigobello
- Department of Biomedical Sciences, University of Padova, Via U. Bassi 58/b, 35131 Padova, Italy
- Correspondence:
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Rehman AU, Khurshid B, Ali Y, Rasheed S, Wadood A, Ng HL, Chen HF, Wei Z, Luo R, Zhang J. Computational approaches for the design of modulators targeting protein-protein interactions. Expert Opin Drug Discov 2023; 18:315-333. [PMID: 36715303 PMCID: PMC10149343 DOI: 10.1080/17460441.2023.2171396] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Accepted: 01/18/2023] [Indexed: 01/31/2023]
Abstract
BACKGROUND Protein-protein interactions (PPIs) are intriguing targets for designing novel small-molecule inhibitors. The role of PPIs in various infectious and neurodegenerative disorders makes them potential therapeutic targets . Despite being portrayed as undruggable targets, due to their flat surfaces, disorderedness, and lack of grooves. Recent progresses in computational biology have led researchers to reconsider PPIs in drug discovery. AREAS COVERED In this review, we introduce in-silico methods used to identify PPI interfaces and present an in-depth overview of various computational methodologies that are successfully applied to annotate the PPIs. We also discuss several successful case studies that use computational tools to understand PPIs modulation and their key roles in various physiological processes. EXPERT OPINION Computational methods face challenges due to the inherent flexibility of proteins, which makes them expensive, and result in the use of rigid models. This problem becomes more significant in PPIs due to their flexible and flat interfaces. Computational methods like molecular dynamics (MD) simulation and machine learning can integrate the chemical structure data into biochemical and can be used for target identification and modulation. These computational methodologies have been crucial in understanding the structure of PPIs, designing PPI modulators, discovering new drug targets, and predicting treatment outcomes.
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Affiliation(s)
- Ashfaq Ur Rehman
- Departments of Molecular Biology and Biochemistry, Chemical and Biomolecular Engineering, Materials Science and Engineering, and Biomedical Engineering, Graduate Program in Chemical and Materials Physics, University of California Irvine, Irvine, California, USA
- Key Laboratory of Cell Differentiation and Apoptosis of Chinese Ministry of Education, Medicinal Bioinformatics Center, Shanghai Jiao-Tong University School of Medicine, Shanghai, Zhejiang, China
| | - Beenish Khurshid
- Department of Biochemistry, Abdul Wali Khan University Mardan, Pakistan
| | - Yasir Ali
- National Center for Bioinformatics, Quaid-e-Azam University, Islamabad, Pakistan
| | - Salman Rasheed
- National Center for Bioinformatics, Quaid-e-Azam University, Islamabad, Pakistan
| | - Abdul Wadood
- Department of Biochemistry, Abdul Wali Khan University Mardan, Pakistan
| | - Ho-Leung Ng
- Department of Biochemistry and Molecular Biophysics, Kansas State University, Manhattan, Kansas, USA
| | - Hai-Feng Chen
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, Department of Bioinformatics and Biostatistics, National Experimental Teaching Center for Life Sciences and Biotechnology, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, Zhejiang, China
| | - Zhiqiang Wei
- Medicinal Chemistry and Bioinformatics Center, Ocean University of China, Qingdao, Shandong, China
| | - Ray Luo
- Departments of Molecular Biology and Biochemistry, Chemical and Biomolecular Engineering, Materials Science and Engineering, and Biomedical Engineering, Graduate Program in Chemical and Materials Physics, University of California Irvine, Irvine, California, USA
| | - Jian Zhang
- Key Laboratory of Cell Differentiation and Apoptosis of Chinese Ministry of Education, Medicinal Bioinformatics Center, Shanghai Jiao-Tong University School of Medicine, Shanghai, Zhejiang, China
- School of Pharmaceutical Sciences, Zhengzhou University, Zhengzhou, Henan, China
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Immunoinformatics Approach for Epitope-Based Vaccine Design: Key Steps for Breast Cancer Vaccine. Diagnostics (Basel) 2022; 12:diagnostics12122981. [PMID: 36552988 PMCID: PMC9777080 DOI: 10.3390/diagnostics12122981] [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: 09/16/2022] [Revised: 11/23/2022] [Accepted: 11/24/2022] [Indexed: 11/29/2022] Open
Abstract
Vaccines are an upcoming medical intervention for breast cancer. By targeting the tumor antigen, cancer vaccines can be designed to train the immune system to recognize tumor cells. Therefore, along with technological advances, the vaccine design process is now starting to be carried out with more rational methods such as designing epitope-based peptide vaccines using immunoinformatics methods. Immunoinformatics methods can assist vaccine design in terms of antigenicity and safety. Common protocols used to design epitope-based peptide vaccines include tumor antigen identification, protein structure analysis, T cell epitope prediction, epitope characterization, and evaluation of protein-epitope interactions. Tumor antigen can be divided into two types: tumor associated antigen and tumor specific antigen. We will discuss the identification of tumor antigens using high-throughput technologies. Protein structure analysis comprises the physiochemical, hydrochemical, and antigenicity of the protein. T cell epitope prediction models are widely available with various prediction parameters as well as filtering tools for the prediction results. Epitope characterization such as allergenicity and toxicity can be done in silico as well using allergenicity and toxicity predictors. Evaluation of protein-epitope interactions can also be carried out in silico with molecular simulation. We will also discuss current and future developments of breast cancer vaccines using an immunoinformatics approach. Finally, although prediction models have high accuracy, the opposite can happen after being tested in vitro and in vivo. Therefore, further studies are needed to ensure the effectiveness of the vaccine to be developed. Although epitope-based peptide vaccines have the disadvantage of low immunogenicity, the addition of adjuvants can be a solution.
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12
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Tao H, Zhao X, Zhang K, Lin P, Huang SY. Docking cyclic peptides formed by a disulfide bond through a hierarchical strategy. Bioinformatics 2022; 38:4109-4116. [PMID: 35801933 DOI: 10.1093/bioinformatics/btac486] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Revised: 05/06/2022] [Accepted: 07/07/2022] [Indexed: 12/24/2022] Open
Abstract
MOTIVATION Cyclization is a common strategy to enhance the therapeutic potential of peptides. Many cyclic peptide drugs have been approved for clinical use, in which the disulfide-driven cyclic peptide is one of the most prevalent categories. Molecular docking is a powerful computational method to predict the binding modes of molecules. For protein-cyclic peptide docking, a big challenge is considering the flexibility of peptides with conformers constrained by cyclization. RESULTS Integrating our efficient peptide 3D conformation sampling algorithm MODPEP2.0 and knowledge-based scoring function ITScorePP, we have proposed an extended version of our hierarchical peptide docking algorithm, named HPEPDOCK2.0, to predict the binding modes of the peptide cyclized through a disulfide against a protein. Our HPEPDOCK2.0 approach was extensively evaluated on diverse test sets and compared with the state-of-the-art cyclic peptide docking program AutoDock CrankPep (ADCP). On a benchmark dataset of 18 cyclic peptide-protein complexes, HPEPDOCK2.0 obtained a native contact fraction of above 0.5 for 61% of the cases when the top prediction was considered, compared with 39% for ADCP. On a larger test set of 25 cyclic peptide-protein complexes, HPEPDOCK2.0 yielded a success rate of 44% for the top prediction, compared with 20% for ADCP. In addition, HPEPDOCK2.0 was also validated on two other test sets of 10 and 11 complexes with apo and predicted receptor structures, respectively. HPEPDOCK2.0 is computationally efficient and the average running time for docking a cyclic peptide is about 34 min on a single CPU core, compared with 496 min for ADCP. HPEPDOCK2.0 will facilitate the study of the interaction between cyclic peptides and proteins and the development of therapeutic cyclic peptide drugs. AVAILABILITY AND IMPLEMENTATION http://huanglab.phys.hust.edu.cn/hpepdock/. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Huanyu Tao
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Xuejun Zhao
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Keqiong Zhang
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Peicong Lin
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Sheng-You Huang
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
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13
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Bhat RAH, Thakuria D, Tandel RS, Khangembam VC, Dash P, Tripathi G, Sarma D. Tools and techniques for rational designing of antimicrobial peptides for aquaculture. FISH & SHELLFISH IMMUNOLOGY 2022; 127:1033-1050. [PMID: 35872334 DOI: 10.1016/j.fsi.2022.07.055] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Revised: 07/14/2022] [Accepted: 07/18/2022] [Indexed: 06/15/2023]
Abstract
Fisheries and aquaculture industries remain essential sources of food and nutrition for millions of people worldwide. Indiscriminate use of antibiotics has led to the emergence of antimicrobial-resistant bacteria and posed a severe threat to public health. Researchers have opined that antimicrobial peptides (AMPs) can be the best possible alternative to curb the rising tide of antimicrobial resistance in aquaculture. AMPs may also help to achieve the objectives of one health approach. The natural AMPs are associated with several shortcomings, like less in vivo stability, toxicity to host cell, high cost of production and low potency in a biological system. In this review, we have provided a comprehensive outline about the strategies for designing synthetic mimics of natural AMPs with high potency. Moreover, the freely available AMP databases and the information about the molecular docking tools are enlisted. We also provided in silico template for rationally designing the AMPs from fish piscidins or other peptides. The rationally designed piscidin (rP1 and rp2) may be used to tackle microbial infections in aquaculture. Further, the protocol can be used to develop the truncated mimics of natural AMPs having more potency and protease stability.
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Affiliation(s)
| | - Dimpal Thakuria
- ICAR-Directorate of Coldwater Fisheries Research, Bhimtal, 263136, Uttarakhand, India
| | | | - Victoria C Khangembam
- ICAR-Directorate of Coldwater Fisheries Research, Bhimtal, 263136, Uttarakhand, India
| | - Pragyan Dash
- ICAR-Directorate of Coldwater Fisheries Research, Bhimtal, 263136, Uttarakhand, India
| | - Gayatri Tripathi
- ICAR-Central Institute of Fisheries Education, Mumbai, 400061, Maharashtra, India
| | - Debajit Sarma
- ICAR-Directorate of Coldwater Fisheries Research, Bhimtal, 263136, Uttarakhand, India
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Paiva VDA, Gomes IDS, Monteiro CR, Mendonça MV, Martins PM, Santana CA, Gonçalves-Almeida V, Izidoro SC, Melo-Minardi RCD, Silveira SDA. Protein structural bioinformatics: An overview. Comput Biol Med 2022; 147:105695. [DOI: 10.1016/j.compbiomed.2022.105695] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Revised: 06/01/2022] [Accepted: 06/02/2022] [Indexed: 11/27/2022]
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15
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Matching protein surface structural patches for high-resolution blind peptide docking. Proc Natl Acad Sci U S A 2022; 119:e2121153119. [PMID: 35482919 PMCID: PMC9170164 DOI: 10.1073/pnas.2121153119] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
Modeling interactions between short peptides and their receptors is a challenging docking problem due to the peptide flexibility, resulting in a formidable sampling problem of peptide conformation in addition to its orientation. Alternatively, the peptide can be viewed as a piece that complements the receptor monomer structure. Here, we show that the peptide conformation can be determined based on the receptor backbone only and sampled using local structural motifs found in solved protein monomers and interfaces, independent of sequence similarity. This approach outperforms current peptide docking protocols and promotes new directions for peptide interface design. Peptide docking can be perceived as a subproblem of protein–protein docking. However, due to the short length and flexible nature of peptides, many do not adopt one defined conformation prior to binding. Therefore, to tackle a peptide docking problem, not only the relative orientation, but also the bound conformation of the peptide needs to be modeled. Traditional peptide-centered approaches use information about peptide sequences to generate representative conformer ensembles, which can then be rigid-body docked to the receptor. Alternatively, one may look at this problem from the viewpoint of the receptor, namely, that the protein surface defines the peptide-bound conformation. Here, we present PatchMAN (Patch-Motif AligNments), a global peptide-docking approach that uses structural motifs to map the receptor surface with backbone scaffolds extracted from protein structures. On a nonredundant set of protein–peptide complexes, starting from free receptor structures, PatchMAN successfully models and identifies near-native peptide–protein complexes in 58%/84% within 2.5 Å/5 Å interface backbone RMSD, with corresponding sampling in 81%/100% of the cases, outperforming other approaches. PatchMAN leverages the observation that structural units of peptides with their binding pocket can be found not only within interfaces, but also within monomers. We show that the bound peptide conformation is sampled based on the structural context of the receptor only, without taking into account any sequence information. Beyond peptide docking, this approach opens exciting new avenues to study principles of peptide–protein association, and to the design of new peptide binders. PatchMAN is available as a server at https://furmanlab.cs.huji.ac.il/patchman/.
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16
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Machine learning assessment of the binding region as a tool for more efficient computational receptor-ligand docking. J Mol Liq 2022; 353. [PMID: 35273421 PMCID: PMC8903148 DOI: 10.1016/j.molliq.2022.118759] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
We present a combined computational approach to protein-ligand binding, which consists of two steps: (1) a deep neural network is used to locate a binding region on a target protein, and (2) molecular docking of a ligand is performed within the specified region to obtain the best pose using Autodock Vina. Our in-house designed neural network was trained using the PepBDB dataset. Although the training dataset consisted of protein-peptide complexes, we show that the approach is not limited to peptides, but also works remarkably well for a large class of non-peptide ligands. The results are compared with those in which the binding region (first step) was provided by Accluster. In cases where no prior experimental data on the binding region are available, our deep neural network provides a fast and effective alternative to classical software for its localization. Our code is available at https://github.com/mksmd/NNforDocking.
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Two Different Therapeutic Approaches for SARS-CoV-2 in hiPSCs-Derived Lung Organoids. Cells 2022; 11:cells11071235. [PMID: 35406799 PMCID: PMC8997767 DOI: 10.3390/cells11071235] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Revised: 03/21/2022] [Accepted: 04/02/2022] [Indexed: 12/14/2022] Open
Abstract
The global health emergency for SARS-CoV-2 (COVID-19) created an urgent need to develop new treatments and therapeutic drugs. In this study, we tested, for the first time on human cells, a new tetravalent neutralizing antibody (15033-7) targeting Spike protein and a synthetic peptide homologous to dipeptidyl peptidase-4 (DPP4) receptor on host cells. Both could represent powerful immunotherapeutic candidates for COVID-19 treatment. The infection begins in the proximal airways, namely the alveolar type 2 (AT2) cells of the distal lung, which express both ACE2 and DPP4 receptors. Thus, to evaluate the efficacy of both approaches, we developed three-dimensional (3D) complex lung organoid structures (hLORGs) derived from human-induced pluripotent stem cells (iPSCs) and resembling the in vivo organ. Afterward, hLORGs were infected by different SARS-CoV-2 S pseudovirus variants and treated by the Ab15033-7 or DPP4 peptide. Using both approaches, we observed a significant reduction of viral entry and a modulation of the expression of genes implicated in innate immunity and inflammatory response. These data demonstrate the efficacy of such approaches in strongly reducing the infection efficiency in vitro and, importantly, provide proof-of-principle evidence that hiPSC-derived hLORGs represent an ideal in vitro system for testing both therapeutic and preventive modalities against COVID-19.
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18
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Unravelling the α-glucosidase inhibitory properties of chickpea protein by enzymatic hydrolysis and in silico analysis. FOOD BIOSCI 2021. [DOI: 10.1016/j.fbio.2021.101328] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
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19
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Lingasamy P, Põšnograjeva K, Kopanchuk S, Tobi A, Rinken A, General IJ, Asciutto EK, Teesalu T. PL1 Peptide Engages Acidic Surfaces on Tumor-Associated Fibronectin and Tenascin Isoforms to Trigger Cellular Uptake. Pharmaceutics 2021; 13:pharmaceutics13121998. [PMID: 34959279 PMCID: PMC8707168 DOI: 10.3390/pharmaceutics13121998] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2021] [Revised: 11/09/2021] [Accepted: 11/17/2021] [Indexed: 12/14/2022] Open
Abstract
Tumor extracellular matrix (ECM) is a high-capacity target for the precision delivery of affinity ligand-guided drugs and imaging agents. Recently, we developed a PL1 peptide (sequence: PPRRGLIKLKTS) for systemic targeting of malignant ECM. Here, we map the dynamics of PL1 binding to its receptors Fibronectin Extra Domain B (FN-EDB) and Tenascin C C-isoform (TNC-C) by computational modeling and cell-free binding studies on mutated receptor proteins, and study cellular binding and internalization of PL1 nanoparticles in cultured cells. Molecular dynamics simulation and docking analysis suggested that the engagement of PL1 peptide with both receptors is primarily driven by electrostatic interactions. Substituting acidic amino acid residues with neutral amino acids at predicted PL1 binding sites in FN-EDB (D52N-D49N-D12N) and TNC-C (D39N-D45N) resulted in the loss of binding of PL1 nanoparticles. Remarkably, PL1-functionalized nanoparticles (NPs) were not only deposited on the target ECM but bound the cells and initiated a robust cellular uptake via a pathway resembling macropinocytosis. Our studies establish the mode of engagement of the PL1 peptide with its receptors and suggest applications for intracellular delivery of nanoscale payloads. The outcomes of this work can be used for the development of PL1-derived peptides with improved stability, affinity, and specificity for precision targeting of the tumor ECM and malignant cells.
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Affiliation(s)
- Prakash Lingasamy
- Laboratory of Precision and Nanomedicine, Institute of Biomedicine and Translational Medicine, University of Tartu, 50411 Tartu, Estonia; (P.L.); (K.P.); (A.T.)
| | - Kristina Põšnograjeva
- Laboratory of Precision and Nanomedicine, Institute of Biomedicine and Translational Medicine, University of Tartu, 50411 Tartu, Estonia; (P.L.); (K.P.); (A.T.)
| | - Sergei Kopanchuk
- Institute of Chemistry, University of Tartu, Ravila 14, 50411 Tartu, Estonia; (S.K.); (A.R.)
| | - Allan Tobi
- Laboratory of Precision and Nanomedicine, Institute of Biomedicine and Translational Medicine, University of Tartu, 50411 Tartu, Estonia; (P.L.); (K.P.); (A.T.)
| | - Ago Rinken
- Institute of Chemistry, University of Tartu, Ravila 14, 50411 Tartu, Estonia; (S.K.); (A.R.)
| | - Ignacio J. General
- Center for Nanomedicine and Department of Cell, Molecular and Developmental Biology, University of California, Santa Barbara, Santa Barbara, CA 93106, USA;
| | - Eliana K. Asciutto
- School of Science and Technology, National University of San Martin (UNSAM), ICIFI and CONICET, 25 de Mayo y Francia, San Martín 1650, Argentina;
| | - Tambet Teesalu
- Laboratory of Precision and Nanomedicine, Institute of Biomedicine and Translational Medicine, University of Tartu, 50411 Tartu, Estonia; (P.L.); (K.P.); (A.T.)
- Center for Nanomedicine and Department of Cell, Molecular and Developmental Biology, University of California, Santa Barbara, Santa Barbara, CA 93106, USA;
- Correspondence: Estonia
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Masomian M, Lalani S, Poh CL. Molecular Docking of SP40 Peptide towards Cellular Receptors for Enterovirus 71 (EV-A71). Molecules 2021; 26:molecules26216576. [PMID: 34770987 PMCID: PMC8587434 DOI: 10.3390/molecules26216576] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Revised: 10/13/2021] [Accepted: 10/26/2021] [Indexed: 11/16/2022] Open
Abstract
Enterovirus 71 (EV-A71) is one of the predominant etiological agents of hand, foot and mouth disease (HMFD), which can cause severe central nervous system infections in young children. There is no clinically approved vaccine or antiviral agent against HFMD. The SP40 peptide, derived from the VP1 capsid of EV-A71, was reported to be a promising antiviral peptide that targeted the host receptor(s) involved in viral attachment or entry. So far, the mechanism of action of SP40 peptide is unknown. In this study, interactions between ten reported cell receptors of EV-A71 and the antiviral SP40 peptide were evaluated through molecular docking simulations, followed by in vitro receptor blocking with specific antibodies. The preferable binding region of each receptor to SP40 was predicted by global docking using HPEPDOCK and the cell receptor-SP40 peptide complexes were refined using FlexPepDock. Local molecular docking using GOLD (Genetic Optimization for Ligand Docking) showed that the SP40 peptide had the highest binding score to nucleolin followed by annexin A2, SCARB2 and human tryptophanyl-tRNA synthetase. The average GoldScore for 5 top-scoring models of human cyclophilin, fibronectin, human galectin, DC-SIGN and vimentin were almost similar. Analysis of the nucleolin-SP40 peptide complex showed that SP40 peptide binds to the RNA binding domains (RBDs) of nucleolin. Furthermore, receptor blocking by specific monoclonal antibody was performed for seven cell receptors of EV-A71 and the results showed that the blocking of nucleolin by anti-nucleolin alone conferred a 93% reduction in viral infectivity. Maximum viral inhibition (99.5%) occurred when SCARB2 was concurrently blocked with anti-SCARB2 and the SP40 peptide. This is the first report to reveal the mechanism of action of SP40 peptide in silico through molecular docking analysis. This study provides information on the possible binding site of SP40 peptide to EV-A71 cellular receptors. Such information could be useful to further validate the interaction of the SP40 peptide with nucleolin by site-directed mutagenesis of the nucleolin binding site.
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Affiliation(s)
- Malihe Masomian
- Correspondence: (M.M.); (C.L.P.); Tel.: +603-74918622 (ext. 7603) (M.M.); +603-74918622 (ext. 7338) (C.L.P.)
| | | | - Chit Laa Poh
- Correspondence: (M.M.); (C.L.P.); Tel.: +603-74918622 (ext. 7603) (M.M.); +603-74918622 (ext. 7338) (C.L.P.)
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21
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Madlala T, Adeleke VT, Fatoba AJ, Okpeku M, Adeniyi AA, Adeleke MA. Designing multiepitope-based vaccine against Eimeria from immune mapped protein 1 (IMP-1) antigen using immunoinformatic approach. Sci Rep 2021; 11:18295. [PMID: 34521964 PMCID: PMC8440781 DOI: 10.1038/s41598-021-97880-6] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Accepted: 08/31/2021] [Indexed: 02/08/2023] Open
Abstract
Drug resistance against coccidiosis has posed a significant threat to chicken welfare and productivity worldwide, putting daunting pressure on the poultry industry to reduce the use of chemoprophylactic drugs and live vaccines in poultry to treat intestinal diseases. Chicken coccidiosis, caused by an apicomplexan parasite of Eimeria spp., is a significant challenge worldwide. Due to the experience of economic loss in production and prevention of the disease, development of cost-effective vaccines or drugs that can stimulate defence against multiple Eimeria species is imperative to control coccidiosis. This study explored Eimeria immune mapped protein-1 (IMP-1) to develop a multiepitope-based vaccine against coccidiosis by identifying antigenic T-cell and B-cell epitope candidates through immunoinformatic techniques. This resulted in the design of 7 CD8+, 21 CD4+ T-cell epitopes and 6 B-cell epitopes, connected using AAY, GPGPG and KK linkers to form a vaccine construct. A Cholera Toxin B (CTB) adjuvant was attached to the N-terminal of the multiepitope construct to improve the immunogenicity of the vaccine. The designed vaccine was assessed for immunogenicity (8.59968), allergenicity and physiochemical parameters, which revealed the construct molecular weight of 73.25 kDa, theoretical pI of 8.23 and instability index of 33.40. Molecular docking simulation of vaccine with TLR-5 with binding affinity of - 151.893 kcal/mol revealed good structural interaction and stability of protein structure of vaccine construct. The designed vaccine predicts the induction of immunity and boosted host's immune system through production of antibodies and cytokines, vital in hindering surface entry of parasites into host. This is a very important step in vaccine development though further experimental study is still required to validate these results.
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Affiliation(s)
- Thabile Madlala
- grid.16463.360000 0001 0723 4123Discipline of Genetics, School of Life Sciences, University of KwaZulu-Natal, P/Bag X54001, Westville, Durban, 4000 South Africa
| | - Victoria T. Adeleke
- grid.16463.360000 0001 0723 4123Discipline of Chemical Engineering, University of KwaZulu-Natal, Howard Campus, Durban, 4041 South Africa
| | - Abiodun J. Fatoba
- grid.16463.360000 0001 0723 4123Discipline of Genetics, School of Life Sciences, University of KwaZulu-Natal, P/Bag X54001, Westville, Durban, 4000 South Africa
| | - Moses Okpeku
- grid.16463.360000 0001 0723 4123Discipline of Genetics, School of Life Sciences, University of KwaZulu-Natal, P/Bag X54001, Westville, Durban, 4000 South Africa
| | - Adebayo A. Adeniyi
- grid.412219.d0000 0001 2284 638XDepartment of Chemistry, Faculty of Natural and Agricultural Sciences, University of the Free State, Bloemfontein, South Africa ,grid.448729.40000 0004 6023 8256Department of Industrial Chemistry, Federal University Oye-Ekiti, Oye-Ekiti, Nigeria
| | - Matthew A. Adeleke
- grid.16463.360000 0001 0723 4123Discipline of Genetics, School of Life Sciences, University of KwaZulu-Natal, P/Bag X54001, Westville, Durban, 4000 South Africa
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22
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Sarma VR, Olotu FA, Soliman MES. Integrative immunoinformatics paradigm for predicting potential B-cell and T-cell epitopes as viable candidates for subunit vaccine design against COVID-19 virulence. Biomed J 2021; 44:447-460. [PMID: 34489196 PMCID: PMC8130595 DOI: 10.1016/j.bj.2021.05.001] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2020] [Revised: 08/16/2020] [Accepted: 05/03/2021] [Indexed: 01/02/2023] Open
Abstract
Background The increase in global mortality rates from SARS-COV2 (COVID-19) infection has been alarming thereby necessitating the continual search for viable therapeutic interventions. Due to minimal microbial components, subunit (peptide-based) vaccines have demonstrated improved efficacies in stimulating immunogenic responses by host B- and T-cells. Methods Integrative immunoinformatics algorithms were used to determine linear and discontinuous B-cell epitopes from the S-glycoprotein sequence. End-point selection of the most potential B-cell epitope was based on highly essential physicochemical attributes. NetCTL-I and NetMHC-II algorithms were used to predict probable MHC-I and II T-cell epitopes for globally frequent HLA-A∗O2:01, HLA-B∗35:01, HLA-B∗51:01 and HLA-DRB1∗15:02 molecules. Highly probable T-cell epitopes were selected based on their high propensities for C-terminal cleavage, transport protein (TAP) processing and MHC-I/II binding. Results Preferential epitope binding sites were further identified on the HLA molecules using a blind peptide-docking method. Phylogenetic analysis revealed close relativity between SARS-CoV-2 and SARS-CoV S-protein. LALHRSYLTPGDSSSGWTAGAA242→263 was the most probable B-cell epitope with optimal physicochemical attributes. MHC-I antigenic presentation pathway was highly favourable for YLQPRTFLL269-277 (HLA-A∗02:01), LPPAYTNSF24-32 (HLA-B∗35:01) and IPTNFTISV714-721 (HLA-B∗51:01). Also, LTDEMIAQYTSALLA865-881 exhibited the highest binding affinity to HLA-DR B1∗15:01 with core interactions mediated by IAQYTSALL870-878. COVID-19 YLQPRTFLL269-277 was preferentially bound to a previously undefined site on HLA-A∗02:01 suggestive of a novel site for MHC-I-mediated T-cell stimulation. Conclusion This study implemented combinatorial immunoinformatics methods to model B- and T-cell epitopes with high potentials to trigger immunogenic responses to the S protein of SARS-CoV-2.
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Affiliation(s)
- Vyshnavie R Sarma
- Molecular Bio-computation and Drug Design Laboratory, School of Health Sciences, University of KwaZulu-Natal, Westville Campus, Durban, South Africa
| | - Fisayo A Olotu
- Molecular Bio-computation and Drug Design Laboratory, School of Health Sciences, University of KwaZulu-Natal, Westville Campus, Durban, South Africa
| | - Mahmoud E S Soliman
- Molecular Bio-computation and Drug Design Laboratory, School of Health Sciences, University of KwaZulu-Natal, Westville Campus, Durban, South Africa.
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Peptide Platform as a Powerful Tool in the Fight against COVID-19. Viruses 2021; 13:v13081667. [PMID: 34452531 PMCID: PMC8402770 DOI: 10.3390/v13081667] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Revised: 08/17/2021] [Accepted: 08/17/2021] [Indexed: 01/02/2023] Open
Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has resulted in a global pandemic causing over 195 million infections and more than 4 million fatalities as of July 2021.To date, it has been demonstrated that a number of mutations in the spike glycoprotein (S protein) of SARS-CoV-2 variants of concern abrogate or reduce the neutralization potency of several therapeutic antibodies and vaccine-elicited antibodies. Therefore, the development of additional vaccine platforms with improved supply and logistic profile remains a pressing need. In this work, we have validated the applicability of a peptide-based strategy focused on a preventive as well as a therapeutic purpose. On the basis of the involvement of the dipeptidyl peptidase 4 (DPP4), in addition to the angiotensin converting enzyme 2 (ACE2) receptor in the mechanism of virus entry, we analyzed peptides bearing DPP4 sequences by protein-protein docking and assessed their ability to block pseudovirus infection in vitro. In parallel, we have selected and synthetized peptide sequences located within the highly conserved receptor-binding domain (RBD) of the S protein, and we found that RBD-based vaccines could better promote elicitation of high titers of neutralizing antibodies specific against the regions of interest, as confirmed by immunoinformatic methodologies and in vivo studies. These findings unveil a key antigenic site targeted by broadly neutralizing antibodies and pave the way to the design of pan-coronavirus vaccines.
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Fatoba AJ, Adeleke VT, Maharaj L, Okpeku M, Adeniyi AA, Adeleke MA. Immunoinformatics Design of Multiepitope Vaccine Against Enterococcus faecium Infection. Int J Pept Res Ther 2021. [DOI: 10.1007/s10989-021-10245-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
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25
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Bhatt P, Sharma M, Sharma S. Prediction and identification of T cell epitopes of COVID-19 with balanced cytokine response for the development of peptide based vaccines. In Silico Pharmacol 2021; 9:40. [PMID: 34221846 PMCID: PMC8237047 DOI: 10.1007/s40203-021-00098-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2021] [Accepted: 06/05/2021] [Indexed: 12/27/2022] Open
Abstract
Recent outbreak of 2019 novel Corona virus poses serious challenge for the global health system. In lieu of paucity of experimental data, tools and the very basic understanding of host immune responses against SARS-CoV-2, well thought effective measures are needed to control COVID-19 pandemic. We have identified specific overlapping antigenic peptide epitopes (OAPE) within the 4 structural proteins of SARS-CoV-2 predictive of triggering robust CD4 and CD8 T cell responses in host using bio-informatics tools (NetMHC4.0, IEDB, and Vaxijen2.0). We speculate an early release of pro-inflammatory cytokines for protection and later release of anti-inflammatory cytokines for prevention of immunopathology in designing a vaccine for Covid-19. Therefore, the selected immunogenic OAPE were subjected to in silico tools (IL-6-Pred, IFNepitope and PIP-EL) for analyzing their pro-inflammatory response. The OAPEs found to be pro-inflammatory in nature were further subjected to prediction servers (IL-4-Pred, IL-10-Pred, Pre-AIP) to characterize them as inducers of anti-inflammatory response as well. We finally filtered out 12 OAPE which had affinity for both CD4 and CD8 T cells as well as were inducers of pro-inflammatory and anti-inflammatory cytokines. On confirmation of OAPE binding affinity for respective T cell specific MHC allele using docking studies (pepATTRACT, Hex8.0 and Discovery studio) they were found to be have more immunogenic potential than the 3 negative control peptides (NCPs) included in the study. Additionally, we constructed CTxB-adjuvanated multi-epitopic vaccine inclusive of the 12 OAPEs which was non-toxic, non-allergenic and capable of inducing both pro-inflammatory and anti-inflammatory cytokines. A successful in silico cloning and docking of modeled subunit vaccine construct with toll like receptor-2 (TLR-2) confirmed the high efficacy of our multi-epitopic vaccine which can through a balanced interplay of cytokines help in creating a steady-state immune equilibrium. In silico immune simulation studies with the vaccine using C-ImmSim server also showed higher percentage of T cells along with production of pro-inflammatory as well as some anti-inflammatory cytokines. Experimental validation of this prediction based study on Peripheral Blood Mononuclear Cells (PBMCs) of un-infected individuals, patients and recovered individuals will facilitate production of high priority effective SARS -CoV-2 vaccine candidate. Supplementary Information The online version contains supplementary material available at 10.1007/s40203-021-00098-7.
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Affiliation(s)
- Parul Bhatt
- DS Kothari Central Facility for Interdisciplinary Research, Miranda House, University of Delhi, Delhi, 110007 India
- Department of Zoology, Miranda House, University of Delhi, Delhi, 110007 India
| | - Monika Sharma
- DS Kothari Central Facility for Interdisciplinary Research, Miranda House, University of Delhi, Delhi, 110007 India
- Department of Zoology, Miranda House, University of Delhi, Delhi, 110007 India
| | - Sadhna Sharma
- DS Kothari Central Facility for Interdisciplinary Research, Miranda House, University of Delhi, Delhi, 110007 India
- Department of Zoology, Miranda House, University of Delhi, Delhi, 110007 India
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26
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Olotu FA, Soliman MES. Immunoinformatics prediction of potential B-cell and T-cell epitopes as effective vaccine candidates for eliciting immunogenic responses against Epstein-Barr virus. Biomed J 2021; 44:317-337. [PMID: 34154948 PMCID: PMC8358216 DOI: 10.1016/j.bj.2020.01.002] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2019] [Revised: 11/15/2019] [Accepted: 01/21/2020] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND The ongoing search for viable treatment options to curtail Epstein Barr Virus (EBV) pathogenicity has necessitated a paradigmatic shift towards the design of peptide-based vaccines. Potential B-cell and T-cell epitopes were predicted for nine antigenic EBV proteins that mediate epithelial cell-attachment and spread, capsid self-assembly, DNA replication and processivity. METHODS Predictive algorithms incorporated in the Immune Epitope Database (IEDB) resources were used to determine potential B-cell epitopes based on their physicochemical attributes. These were combined with a string-kernel method and an antigenicity predictive AlgPred tool to enhance accuracy in the end-point selection of highly potential antigenic EBV B-cell epitopes. NetCTL 1.2 algorithms enabled the prediction of probable T-cell epitopes which were structurally modeled and subjected to blind peptide-protein docking with HLA-A*02:01. All-atom molecular dynamics (MD) simulation and Molecular Mechanics Generalized-Born Surface Area methods were used to investigate interaction dynamics and affinities of predicted T-cell peptide-protein complexes. RESULTS Computational predictions and sequence overlapping analysis yielded 18 linear (continuous) and discontinuous (conformational) subunit epitopes from the antigenic proteins with characteristic surface accessibility, flexibility and antigenicity, and predictive scores above the threshold value (1) set. A novel site was identified on HLA-A*02:01 with preferential affinity binding for modeled BMRF2, BXLF1 and BGLF4 T-cell epitopes. Interaction dynamics and energies were also computed in addition to crucial residues that mediated complex formation and stability. CONCLUSION This study implemented an integrative meta-analytical approach to model highly probable B-cell and T-cell epitopes as potential peptide-vaccine candidates for the treatment of EBV-related diseases.
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Affiliation(s)
- Fisayo A Olotu
- Molecular Bio-computation and Drug Design Laboratory, School of Health Sciences, University of KwaZulu-Natal, Westville Campus, Durban, South Africa
| | - Mahmoud E S Soliman
- Molecular Bio-computation and Drug Design Laboratory, School of Health Sciences, University of KwaZulu-Natal, Westville Campus, Durban, South Africa.
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Perez JJ, Perez RA, Perez A. Computational Modeling as a Tool to Investigate PPI: From Drug Design to Tissue Engineering. Front Mol Biosci 2021; 8:681617. [PMID: 34095231 PMCID: PMC8173110 DOI: 10.3389/fmolb.2021.681617] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Accepted: 05/05/2021] [Indexed: 12/13/2022] Open
Abstract
Protein-protein interactions (PPIs) mediate a large number of important regulatory pathways. Their modulation represents an important strategy for discovering novel therapeutic agents. However, the features of PPI binding surfaces make the use of structure-based drug discovery methods very challenging. Among the diverse approaches used in the literature to tackle the problem, linear peptides have demonstrated to be a suitable methodology to discover PPI disruptors. Unfortunately, the poor pharmacokinetic properties of linear peptides prevent their direct use as drugs. However, they can be used as models to design enzyme resistant analogs including, cyclic peptides, peptide surrogates or peptidomimetics. Small molecules have a narrower set of targets they can bind to, but the screening technology based on virtual docking is robust and well tested, adding to the computational tools used to disrupt PPI. We review computational approaches used to understand and modulate PPI and highlight applications in a few case studies involved in physiological processes such as cell growth, apoptosis and intercellular communication.
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Affiliation(s)
- Juan J Perez
- Department of Chemical Engineering, Universitat Politecnica de Catalunya, Barcelona, Spain
| | - Roman A Perez
- Bioengineering Institute of Technology, Universitat Internacional de Catalunya, Sant Cugat, Spain
| | - Alberto Perez
- The Quantum Theory Project, Department of Chemistry, University of Florida, Gainesville, FL, United States
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28
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Wang J, Miao Y. Peptide Gaussian accelerated molecular dynamics (Pep-GaMD): Enhanced sampling and free energy and kinetics calculations of peptide binding. J Chem Phys 2021; 153:154109. [PMID: 33092378 DOI: 10.1063/5.0021399] [Citation(s) in RCA: 66] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
Peptides mediate up to 40% of known protein-protein interactions in higher eukaryotes and play an important role in cellular signaling. However, it is challenging to simulate both binding and unbinding of peptides and calculate peptide binding free energies through conventional molecular dynamics, due to long biological timescales and extremely high flexibility of the peptides. Based on the Gaussian accelerated molecular dynamics (GaMD) enhanced sampling technique, we have developed a new computational method "Pep-GaMD," which selectively boosts essential potential energy of the peptide in order to effectively model its high flexibility. In addition, another boost potential is applied to the remaining potential energy of the entire system in a dual-boost algorithm. Pep-GaMD has been demonstrated on binding of three model peptides to the SH3 domains. Independent 1 µs dual-boost Pep-GaMD simulations have captured repetitive peptide dissociation and binding events, which enable us to calculate peptide binding thermodynamics and kinetics. The calculated binding free energies and kinetic rate constants agreed very well with available experimental data. Furthermore, the all-atom Pep-GaMD simulations have provided important insights into the mechanism of peptide binding to proteins that involves long-range electrostatic interactions and mainly conformational selection. In summary, Pep-GaMD provides a highly efficient, easy-to-use approach for unconstrained enhanced sampling and calculations of peptide binding free energies and kinetics.
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Affiliation(s)
- Jinan Wang
- Center for Computational Biology and Department of Molecular Biosciences, University of Kansas, Lawrence, Kansas 66047, USA
| | - Yinglong Miao
- Center for Computational Biology and Department of Molecular Biosciences, University of Kansas, Lawrence, Kansas 66047, USA
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29
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Zhu S, Wu M, Huang Z, An J. Trends in application of advancing computational approaches in GPCR ligand discovery. Exp Biol Med (Maywood) 2021; 246:1011-1024. [PMID: 33641446 PMCID: PMC8113737 DOI: 10.1177/1535370221993422] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
G protein-coupled receptors (GPCRs) comprise the most important superfamily of protein targets in current ligand discovery and drug development. GPCRs are integral membrane proteins that play key roles in various cellular signaling processes. Therefore, GPCR signaling pathways are closely associated with numerous diseases, including cancer and several neurological, immunological, and hematological disorders. Computer-aided drug design (CADD) can expedite the process of GPCR drug discovery and potentially reduce the actual cost of research and development. Increasing knowledge of biological structures, as well as improvements on computer power and algorithms, have led to unprecedented use of CADD for the discovery of novel GPCR modulators. Similarly, machine learning approaches are now widely applied in various fields of drug target research. This review briefly summarizes the application of rising CADD methodologies, as well as novel machine learning techniques, in GPCR structural studies and bioligand discovery in the past few years. Recent novel computational strategies and feasible workflows are updated, and representative cases addressing challenging issues on olfactory receptors, biased agonism, and drug-induced cardiotoxic effects are highlighted to provide insights into future GPCR drug discovery.
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Affiliation(s)
- Siyu Zhu
- Division of Infectious Diseases and Global Public Health, Department of Medicine, School of Medicine, University of California at San Diego, La Jolla, CA 92093, USA
- Ciechanover Institute of Precision and Regenerative Medicine, School of Life and Health Sciences, Chinese University of Hong Kong, Shenzhen 518172, China
| | - Meixian Wu
- Division of Infectious Diseases and Global Public Health, Department of Medicine, School of Medicine, University of California at San Diego, La Jolla, CA 92093, USA
| | - Ziwei Huang
- Division of Infectious Diseases and Global Public Health, Department of Medicine, School of Medicine, University of California at San Diego, La Jolla, CA 92093, USA
- Ciechanover Institute of Precision and Regenerative Medicine, School of Life and Health Sciences, Chinese University of Hong Kong, Shenzhen 518172, China
- School of Life Sciences, Tsinghua University, Beijing 100084, China
| | - Jing An
- Division of Infectious Diseases and Global Public Health, Department of Medicine, School of Medicine, University of California at San Diego, La Jolla, CA 92093, USA
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30
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Hashemi ZS, Zarei M, Fath MK, Ganji M, Farahani MS, Afsharnouri F, Pourzardosht N, Khalesi B, Jahangiri A, Rahbar MR, Khalili S. In silico Approaches for the Design and Optimization of Interfering Peptides Against Protein-Protein Interactions. Front Mol Biosci 2021; 8:669431. [PMID: 33996914 PMCID: PMC8113820 DOI: 10.3389/fmolb.2021.669431] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Accepted: 04/06/2021] [Indexed: 01/01/2023] Open
Abstract
Large contact surfaces of protein-protein interactions (PPIs) remain to be an ongoing issue in the discovery and design of small molecule modulators. Peptides are intrinsically capable of exploring larger surfaces, stable, and bioavailable, and therefore bear a high therapeutic value in the treatment of various diseases, including cancer, infectious diseases, and neurodegenerative diseases. Given these promising properties, a long way has been covered in the field of targeting PPIs via peptide design strategies. In silico tools have recently become an inevitable approach for the design and optimization of these interfering peptides. Various algorithms have been developed to scrutinize the PPI interfaces. Moreover, different databases and software tools have been created to predict the peptide structures and their interactions with target protein complexes. High-throughput screening of large peptide libraries against PPIs; "hotspot" identification; structure-based and off-structure approaches of peptide design; 3D peptide modeling; peptide optimization strategies like cyclization; and peptide binding energy evaluation are among the capabilities of in silico tools. In the present study, the most recent advances in the field of in silico approaches for the design of interfering peptides against PPIs will be reviewed. The future perspective of the field and its advantages and limitations will also be pinpointed.
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Affiliation(s)
- Zahra Sadat Hashemi
- ATMP Department, Breast Cancer Research Center, Motamed Cancer Institute, Academic Center for Education, Culture and Research, Tehran, Iran
| | - Mahboubeh Zarei
- Pharmaceutical Sciences Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Mohsen Karami Fath
- Department of Cellular and Molecular Biology, Faculty of Biological Sciences, Kharazmi University, Tehran, Iran
| | - Mahmoud Ganji
- Department of Medical Biotechnology, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran
| | - Mahboube Shahrabi Farahani
- Department of Medical Biotechnology, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran
| | - Fatemeh Afsharnouri
- Department of Medical Biotechnology, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran
| | - Navid Pourzardosht
- Cellular and Molecular Research Center, Faculty of Medicine, Guilan University of Medical Sciences, Rasht, Iran
- Department of Biochemistry, Guilan University of Medical Sciences, Rasht, Iran
| | - Bahman Khalesi
- Department of Research and Production of Poultry Viral Vaccine, Razi Vaccine and Serum Research Institute, Agricultural Research Education and Extension Organization, Karaj, Iran
| | - Abolfazl Jahangiri
- Applied Microbiology Research Center, Systems Biology and Poisonings Institute, Baqiyatallah University of Medical Sciences, Tehran, Iran
| | - Mohammad Reza Rahbar
- Pharmaceutical Sciences Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Saeed Khalili
- Department of Biology Sciences, Shahid Rajaee Teacher Training University, Tehran, Iran
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31
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Dehbashi M, Hojati Z, Motovali-Bashi M, Ganjalikhani-Hakemi M, Shimosaka A, Cho WC. Computational study for suppression of CD25/IL-2 interaction. Biol Chem 2021; 402:167-178. [PMID: 33544473 DOI: 10.1515/hsz-2020-0326] [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: 10/02/2020] [Accepted: 10/22/2020] [Indexed: 02/05/2023]
Abstract
Cancer recurrence presents a huge challenge in cancer patient management. Immune escape is a key mechanism of cancer progression and metastatic dissemination. CD25 is expressed in regulatory T (Treg) cells including tumor-infiltrating Treg cells (TI-Tregs). These cells specially activate and reinforce immune escape mechanism of cancers. The suppression of CD25/IL-2 interaction would be useful against Treg cells activation and ultimately immune escape of cancer. Here, software, web servers and databases were used, at which in silico designed small interfering RNAs (siRNAs), de novo designed peptides and virtual screened small molecules against CD25 were introduced for the prospect of eliminating cancer immune escape and obtaining successful treatment. We obtained siRNAs with low off-target effects. Further, small molecules based on the binding homology search in ligand and receptor similarity were introduced. Finally, the critical amino acids on CD25 were targeted by a de novo designed peptide with disulfide bond. Hence we introduced computational-based antagonists to lay a foundation for further in vitro and in vivo studies.
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Affiliation(s)
- Moein Dehbashi
- Division of Genetics, Department of Cell and Molecular Biology and Microbiology, Faculty of Biological Science and Technology, University of Isfahan, Isfahan, 81746-73441, Islamic Republic of Iran
| | - Zohreh Hojati
- Division of Genetics, Department of Cell and Molecular Biology and Microbiology, Faculty of Biological Science and Technology, University of Isfahan, Isfahan, 81746-73441, Islamic Republic of Iran
| | - Majid Motovali-Bashi
- Division of Genetics, Department of Cell and Molecular Biology and Microbiology, Faculty of Biological Science and Technology, University of Isfahan, Isfahan, 81746-73441, Islamic Republic of Iran
| | - Mazdak Ganjalikhani-Hakemi
- Department of Immunology, Faculty of Medicine, Isfahan University of Medical Sciences, 81746-73461, Isfahan, Islamic Republic of Iran.,Acquired Immunodeficiency Research Center, Isfahan University of Medical Sciences, Isfahan, Islamic Republic of Iran
| | | | - William C Cho
- Department of Clinical Oncology, Queen Elizabeth Hospital, HKSAR, China
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32
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Fatoba AJ, Maharaj L, Adeleke VT, Okpeku M, Adeniyi AA, Adeleke MA. Immunoinformatics prediction of overlapping CD8 + T-cell, IFN-γ and IL-4 inducer CD4 + T-cell and linear B-cell epitopes based vaccines against COVID-19 (SARS-CoV-2). Vaccine 2021; 39:1111-1121. [PMID: 33478794 PMCID: PMC7831457 DOI: 10.1016/j.vaccine.2021.01.003] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2020] [Revised: 12/11/2020] [Accepted: 01/02/2021] [Indexed: 02/08/2023]
Abstract
At the beginning of the year 2020, the world was struck with a global pandemic virus referred to as SARS-CoV-2 (COVID-19) which has left hundreds of thousands of people dead. To control this virus, vaccine design becomes imperative. In this study, potential epitopes-based vaccine candidates were explored. Six hundred (600) genomes of SARS-CoV-2 were retrieved from the viPR database to generate CD8+ T-cell, CD4+ T-cell and linear B-cell epitopes which were screened for antigenicity, immunogenicity and non-allergenicity. The results of this study provide 19 promising candidate CD8+ T-cell epitopes that strongly overlap with 8 promising B-cells epitopes. Another 19 CD4+ T-cell epitopes were also identified that can induce IFN-γ and IL-4 cytokines. The most conserved MHC-I and MHC-II for both CD8+ and CD4+ T-cell epitopes are HLA-A*02:06 and HLA-DRB1*01:01 respectively. These epitopes also bound to Toll-like receptor 3 (TLR3). The population coverage of the conserved Major Histocompatibility Complex Human Leukocyte Antigen (HLA) for both CD8+ T-cell and CD4+ T-cell ranged from 65.6% to 100%. The detailed analysis of the potential epitope-based vaccine and their mapping to the complete COVID-19 genome reveals that they are predominantly found in the location of the surface (S) and membrane (M) glycoproteins suggesting the potential involvement of these structural proteins in the immunogenic response and antigenicity of the virus. Since the majority of the potential epitopes are located on M protein, the design of multi-epitope vaccine with the structural protein is highly promising though the whole M protein could also serve as a viable epitope for the development of an attenuated vaccine. Our findings provide a baseline for the experimental design of a suitable vaccine against SARS-CoV-2.
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Affiliation(s)
- Abiodun J Fatoba
- Discipline of Genetics, School of Life Sciences, University of KwaZulu-Natal, Westville, P/Bag X54001, Durban 4000, South Africa
| | - Leah Maharaj
- Discipline of Genetics, School of Life Sciences, University of KwaZulu-Natal, Westville, P/Bag X54001, Durban 4000, South Africa
| | - Victoria T Adeleke
- Discipline of Chemical Engineering, University of KwaZulu-Natal, Howard Campus, Durban 4041, South Africa
| | - Moses Okpeku
- Discipline of Genetics, School of Life Sciences, University of KwaZulu-Natal, Westville, P/Bag X54001, Durban 4000, South Africa
| | - Adebayo A Adeniyi
- Department of Chemistry, Faculty of Natural and Agricultural Sciences, University of the Free State, Bloemfontein, South Africa; Department of Industrial Chemistry, Federal University, Oye-Ekiti, Nigeria.
| | - Matthew A Adeleke
- Discipline of Genetics, School of Life Sciences, University of KwaZulu-Natal, Westville, P/Bag X54001, Durban 4000, South Africa.
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33
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Sala V, Cnudde SJ, Murabito A, Massarotti A, Hirsch E, Ghigo A. Therapeutic peptides for the treatment of cystic fibrosis: Challenges and perspectives. Eur J Med Chem 2021; 213:113191. [PMID: 33493828 DOI: 10.1016/j.ejmech.2021.113191] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2020] [Revised: 12/21/2020] [Accepted: 01/08/2021] [Indexed: 02/07/2023]
Abstract
Cystic fibrosis (CF) is the most common amongst rare genetic diseases, affecting more than 70.000 people worldwide. CF is characterized by a dysfunctional chloride channel, termed cystic fibrosis conductance regulator (CFTR), which leads to the production of a thick and viscous mucus layer that clogs the lungs of CF patients and traps pathogens, leading to chronic infections and inflammation and, ultimately, lung damage. In recent years, the use of peptides for the treatment of respiratory diseases, including CF, has gained growing interest. Therapeutic peptides for CF include antimicrobial peptides, inhibitors of proteases, and modulators of ion channels, among others. Peptides display unique features that make them appealing candidates for clinical translation, like specificity of action, high efficacy, and low toxicity. Nevertheless, the intrinsic properties of peptides, together with the need of delivering these compounds locally, e.g. by inhalation, raise a number of concerns in the development of peptide therapeutics for CF lung disease. In this review, we discuss the challenges related to the use of peptides for the treatment of CF lung disease through inhalation, which include retention within mucus, proteolysis, immunogenicity and aggregation. Strategies for overcoming major shortcomings of peptide therapeutics will be presented, together with recent developments in peptide design and optimization, including computational analysis and high-throughput screening.
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Affiliation(s)
- Valentina Sala
- Department of Molecular Biotechnology and Health Sciences, Molecular Biotechnology Center, University of Torino, Via Nizza 52, 10126, Torino, Italy
| | - Sophie Julie Cnudde
- Department of Molecular Biotechnology and Health Sciences, Molecular Biotechnology Center, University of Torino, Via Nizza 52, 10126, Torino, Italy
| | - Alessandra Murabito
- Department of Molecular Biotechnology and Health Sciences, Molecular Biotechnology Center, University of Torino, Via Nizza 52, 10126, Torino, Italy
| | - Alberto Massarotti
- Department of Pharmaceutical Science, University of Piemonte Orientale "A. Avogadro", Largo Donegani 2, 28100, Novara, Italy
| | - Emilio Hirsch
- Department of Molecular Biotechnology and Health Sciences, Molecular Biotechnology Center, University of Torino, Via Nizza 52, 10126, Torino, Italy; Kither Biotech S.r.l., Via Nizza 52, 10126, Torino, Italy
| | - Alessandra Ghigo
- Department of Molecular Biotechnology and Health Sciences, Molecular Biotechnology Center, University of Torino, Via Nizza 52, 10126, Torino, Italy; Kither Biotech S.r.l., Via Nizza 52, 10126, Torino, Italy.
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Oo KK, Kamolhan T, Soni A, Thongchot S, Mitrpant C, O-Charoenrat P, Thuwajit C, Thuwajit P. Development of an engineered peptide antagonist against periostin to overcome doxorubicin resistance in breast cancer. BMC Cancer 2021; 21:65. [PMID: 33446140 PMCID: PMC7807878 DOI: 10.1186/s12885-020-07761-w] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2020] [Accepted: 12/22/2020] [Indexed: 12/18/2022] Open
Abstract
Background Chemoresistance is one of the main problems in treatment of cancer. Periostin (PN) is a stromal protein which is mostly secreted from cancer associated fibroblasts in the tumor microenvironment and can promote cancer progression including cell survival, metastasis, and chemoresistance. The main objective of this study was to develop an anti-PN peptide from the bacteriophage library to overcome PN effects in breast cancer (BCA) cells. Methods A twelve amino acids bacteriophage display library was used for biopanning against the PN active site. A selected clone was sequenced and analyzed for peptide primary structure. A peptide was synthesized and tested for the binding affinity to PN. PN effects including a proliferation, migration and a drug sensitivity test were performed using PN overexpression BCA cells or PN treatment and inhibited by an anti-PN peptide. An intracellular signaling mechanism of inhibition was studied by western blot analysis. Lastly, PN expressions in BCA patients were analyzed along with clinical data. Results The results showed that a candidate anti-PN peptide was synthesized and showed affinity binding to PN. PN could increase proliferation and migration of BCA cells and these effects could be inhibited by an anti-PN peptide. There was significant resistance to doxorubicin in PN-overexpressed BCA cells and this effect could be reversed by an anti-PN peptide in associations with phosphorylation of AKT and expression of survivin. In BCA patients, serum PN showed a correlation with tissue PN expression but there was no significant correlation with clinical data. Conclusions This finding supports that anti-PN peptide is expected to be used in the development of peptide therapy to reduce PN-induced chemoresistance in BCA. Supplementary Information The online version contains supplementary material available at 10.1186/s12885-020-07761-w.
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Affiliation(s)
- Khine Kyaw Oo
- Graduate Program in Immunology, Department of Immunology, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, 10700, Thailand
| | - Thanpawee Kamolhan
- Department of Immunology, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, 10700, Thailand
| | - Anish Soni
- Bachelor of Science Program in Biological Science (Biomedical Science), Mahidol University International College, Mahidol University, Nakhon Pathom, 73170, Thailand
| | - Suyanee Thongchot
- Department of Immunology, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, 10700, Thailand.,Siriraj Center of Research Excellence for Cancer Immunotherapy (SiCORE-CIT), Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, 10700, Thailand
| | - Chalermchai Mitrpant
- Department of Biochemistry, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, 10700, Thailand
| | - Pornchai O-Charoenrat
- Department of Surgery, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, 10700, Thailand.,Breast Center, Medpark Hospital, Bangkok, 10110, Thailand
| | - Chanitra Thuwajit
- Department of Immunology, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, 10700, Thailand
| | - Peti Thuwajit
- Department of Immunology, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, 10700, Thailand.
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Binding Ensembles of p53-MDM2 Peptide Inhibitors by Combining Bayesian Inference and Atomistic Simulations. Molecules 2021; 26:molecules26010198. [PMID: 33401765 PMCID: PMC7795311 DOI: 10.3390/molecules26010198] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2020] [Revised: 12/26/2020] [Accepted: 12/28/2020] [Indexed: 01/21/2023] Open
Abstract
Designing peptide inhibitors of the p53-MDM2 interaction against cancer is of wide interest. Computational modeling and virtual screening are a well established step in the rational design of small molecules. But they face challenges for binding flexible peptide molecules that fold upon binding. We look at the ability of five different peptides, three of which are intrinsically disordered, to bind to MDM2 with a new Bayesian inference approach (MELD × MD). The method is able to capture the folding upon binding mechanism and differentiate binding preferences between the five peptides. Processing the ensembles with statistical mechanics tools depicts the most likely bound conformations and hints at differences in the binding mechanism. Finally, the study shows the importance of capturing two driving forces to binding in this system: the ability of peptides to adopt bound conformations (ΔGconformation) and the interaction between interface residues (ΔGinteraction).
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36
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Rivero-Pino F, Espejo-Carpio FJ, Guadix EM. Antidiabetic Food-Derived Peptides for Functional Feeding: Production, Functionality and In Vivo Evidences. Foods 2020; 9:E983. [PMID: 32718070 PMCID: PMC7466190 DOI: 10.3390/foods9080983] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2020] [Revised: 07/15/2020] [Accepted: 07/21/2020] [Indexed: 12/11/2022] Open
Abstract
Bioactive peptides released from the enzymatic hydrolysis of food proteins are currently a trending topic in the scientific community. Their potential as antidiabetic agents, by regulating the glycemic index, and thus to be employed in food formulation, is one of the most important functions of these peptides. In this review, we aimed to summarize the whole process that must be considered when talking about including these molecules as a bioactive ingredient. In this regard, at first, the production, purification and identification of bioactive peptides is summed up. The detailed metabolic pathways described included carbohydrate hydrolases (glucosidase and amylase) and dipeptidyl-peptidase IV inhibition, due to their importance in the food-derived peptides research field. Then, their characterization, concerning bioavailability in vitro and in situ, stability and functionality in food matrices, and ultimately, the in vivo evidence (from invertebrate animals to humans), was described. The future applicability that these molecules have due to their biological potential as functional ingredients makes them an important field of research, which could help the world population avoid suffering from several diseases, such as diabetes.
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Affiliation(s)
- Fernando Rivero-Pino
- Department of Chemical Engineering, University of Granada, 18071 Granada, Spain; (F.J.E.-C.); (E.M.G.)
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37
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Lin L, Ting S, Yufei H, Wendong L, Yubo F, Jing Z. Epitope-based peptide vaccines predicted against novel coronavirus disease caused by SARS-CoV-2. Virus Res 2020; 288:198082. [PMID: 32621841 PMCID: PMC7328648 DOI: 10.1016/j.virusres.2020.198082] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2020] [Revised: 06/29/2020] [Accepted: 06/30/2020] [Indexed: 12/16/2022]
Abstract
Linear B-cell epitopes in RBD of S protein predicted against SARS-CoV-2. Discontinuous B-cell epitopes from S protein predicted against SARS-CoV-2. T-cell epitopes from S, M and N protein predicted against SARS-CoV-2.
The outbreak of the 2019 novel coronavirus (SARS-CoV-2) has infected millions of people with a large number of deaths across the globe. The existing therapies are limited in dealing with SARS-CoV-2 due to the sudden appearance of the virus. Therefore, vaccines and antiviral medicines are in desperate need. We took immune-informatics approaches to identify B- and T-cell epitopes for surface glycoprotein (S), membrane glycoprotein (M) and nucleocapsid protein (N) of SARS-CoV-2, followed by estimating their antigenicity and interactions with the human leukocyte antigen (HLA) alleles. Allergenicity, toxicity, physiochemical properties analysis and stability were examined to confirm the specificity and selectivity of the epitope candidates. We identified a total of five B cell epitopes in RBD of S protein, seven MHC class-I, and 18 MHC class-II binding T-cell epitopes from S, M and N protein which showed non-allergenic, non-toxic and highly antigenic features and non-mutated in 55,179 SARS-CoV-2 virus strains until June 25, 2020. The epitopes identified here can be a potentially good candidate repertoire for vaccine development.
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Affiliation(s)
- Li Lin
- Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, China; School of Biological Science and Medical Engineering, Beihang University, Beijing China
| | - Sun Ting
- Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, China; School of Biological Science and Medical Engineering, Beihang University, Beijing China
| | - He Yufei
- School of Biological Science and Medical Engineering, Beihang University, Beijing China
| | - Li Wendong
- Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, China; School of Biological Science and Medical Engineering, Beihang University, Beijing China
| | - Fan Yubo
- Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, China; School of Biological Science and Medical Engineering, Beihang University, Beijing China.
| | - Zhang Jing
- Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, China.
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38
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Zhao B, Su K, Mao X, Zhang X. Separation and identification of enzyme inhibition peptides from dark tea protein. Bioorg Chem 2020; 99:103772. [DOI: 10.1016/j.bioorg.2020.103772] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2020] [Revised: 03/15/2020] [Accepted: 03/16/2020] [Indexed: 12/26/2022]
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39
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Rivero-Pino F, Espejo-Carpio FJ, Guadix EM. Production and identification of dipeptidyl peptidase IV (DPP-IV) inhibitory peptides from discarded Sardine pilchardus protein. Food Chem 2020; 328:127096. [PMID: 32485583 DOI: 10.1016/j.foodchem.2020.127096] [Citation(s) in RCA: 54] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2020] [Revised: 05/15/2020] [Accepted: 05/17/2020] [Indexed: 12/12/2022]
Abstract
Production of bioactive peptides via enzymatic hydrolysis is a sustainable way to take advantage of proteinaceous by-products from food industry, such as fish discards. Sardine pilchardus protein was subjected to different enzymatic treatments using two endopeptidases of different selectivity and one exopeptidase in order to produce hydrolysates with antidiabetic activity. The highest dipeptidyl peptidase IV inhibitory activity was obtained by the combination of three enzymes (subtilisin, trypsin and flavourzyme) employed sequentially. This hydrolysate was subsequently purified by size exclusion chromatography to obtain fractions sorted by size (hydrodynamic volume). Peptides below 1400 Dalton had the highest activity, and these pools were analysed by mass spectrometry in order to identify the peptides responsible for that activity. Numerous peptides with adequate molecular features, it is, owning an alanine (A) as their penultimate N-terminal residue (e.g. NAPNPR, YACSVR) were identified and are proposed to be antidiabetic peptides from Sardine pilchardus muscle.
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Affiliation(s)
| | | | - Emilia M Guadix
- Department of Chemical Engineering, University of Granada, Granada, Spain
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40
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Roel-Touris J, Bonvin AM. Coarse-grained (hybrid) integrative modeling of biomolecular interactions. Comput Struct Biotechnol J 2020; 18:1182-1190. [PMID: 32514329 PMCID: PMC7264466 DOI: 10.1016/j.csbj.2020.05.002] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2020] [Revised: 04/23/2020] [Accepted: 05/06/2020] [Indexed: 12/23/2022] Open
Abstract
The computational modeling field has vastly evolved over the past decades. The early developments of simplified protein systems represented a stepping stone towards establishing more efficient approaches to sample intricated conformational landscapes. Downscaling the level of resolution of biomolecules to coarser representations allows for studying protein structure, dynamics and interactions that are not accessible by classical atomistic approaches. The combination of different resolutions, namely hybrid modeling, has also been proved as an alternative when mixed levels of details are required. In this review, we provide an overview of coarse-grained/hybrid models focusing on their applicability in the modeling of biomolecular interactions. We give a detailed list of ready-to-use modeling software for studying biomolecular interactions allowing various levels of coarse-graining and provide examples of complexes determined by integrative coarse-grained/hybrid approaches in combination with experimental information.
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41
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Andreani J, Quignot C, Guerois R. Structural prediction of protein interactions and docking using conservation and coevolution. WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL MOLECULAR SCIENCE 2020. [DOI: 10.1002/wcms.1470] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Affiliation(s)
- Jessica Andreani
- Université Paris‐Saclay CEA, CNRS, Institute for Integrative Biology of the Cell (I2BC) Gif‐sur‐Yvette France
| | - Chloé Quignot
- Université Paris‐Saclay CEA, CNRS, Institute for Integrative Biology of the Cell (I2BC) Gif‐sur‐Yvette France
| | - Raphael Guerois
- Université Paris‐Saclay CEA, CNRS, Institute for Integrative Biology of the Cell (I2BC) Gif‐sur‐Yvette France
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42
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Weng G, Gao J, Wang Z, Wang E, Hu X, Yao X, Cao D, Hou T. Comprehensive Evaluation of Fourteen Docking Programs on Protein–Peptide Complexes. J Chem Theory Comput 2020; 16:3959-3969. [DOI: 10.1021/acs.jctc.9b01208] [Citation(s) in RCA: 47] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Affiliation(s)
- Gaoqi Weng
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Junbo Gao
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Zhe Wang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Ercheng Wang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Xueping Hu
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Xiaojun Yao
- State Key Laboratory of Quality Research in Chinese Medicine, Macau Institute for Applied Research in Medicine and Health, Macau University of Science and Technology, Avenida Wai Long, Taipa, Macau (SAR), China
| | - Dongsheng Cao
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha 410013, Hunan, China
| | - Tingjun Hou
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
- State Key Lab of CAD&CG, Zhejiang University, Hangzhou 310058, Zhejiang, China
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43
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Tao H, Zhang Y, Huang SY. Improving Protein-Peptide Docking Results via Pose-Clustering and Rescoring with a Combined Knowledge-Based and MM-GBSA Scoring Function. J Chem Inf Model 2020; 60:2377-2387. [PMID: 32267149 DOI: 10.1021/acs.jcim.0c00058] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Protein-peptide docking, which predicts the complex structure between a protein and a peptide, is a valuable computational tool in peptide therapeutics development and the mechanistic investigation of peptides involved in cellular processes. Although current peptide docking approaches are often able to sample near-native peptide binding modes, correctly identifying those near-native modes from decoys is still challenging because of the extremely high complexity of the peptide binding energy landscape. In this study, we have developed an efficient postdocking rescoring protocol using a combined scoring function of knowledge-based ITScorePP potentials and physics-based MM-GBSA energies. Tested on five benchmark/docking test sets, our postdocking strategy showed an overall significantly better performance in binding mode prediction and score-rmsd correlation than original docking approaches. Specifically, our postdocking protocol outperformed original docking approaches with success rates of 15.8 versus 10.5% for pepATTRACT on the Global_57 benchmark, 5.3 versus 5.3% for CABS-dock on the Global_57 benchmark, 17.0 versus 11.3% for FlexPepDock on the LEADS-PEP data set, 40.3 versus 33.9% for HPEPDOCK on the Local_62 benchmark, and 64.2 versus 52.8% for HPEPDOCK on the LEADS-PEP data set when the top prediction was considered. These results demonstrated the efficacy and robustness of our postdocking protocol.
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Affiliation(s)
- Huanyu Tao
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, P. R. China
| | - Yanjun Zhang
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, P. R. China
| | - Sheng-You Huang
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, P. R. China
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44
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Santos KB, Guedes IA, Karl ALM, Dardenne LE. Highly Flexible Ligand Docking: Benchmarking of the DockThor Program on the LEADS-PEP Protein-Peptide Data Set. J Chem Inf Model 2020; 60:667-683. [PMID: 31922754 DOI: 10.1021/acs.jcim.9b00905] [Citation(s) in RCA: 101] [Impact Index Per Article: 25.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
Protein-peptide interactions play a crucial role in many cellular and biological functions, which justify the increasing interest in the development of peptide-based drugs. However, predicting experimental binding modes and affinities in protein-peptide docking remains a great challenge for most docking programs due to some particularities of this class of ligands, such as the high degree of flexibility. In this paper, we present the performance of the DockThor program on the LEADS-PEP data set, a benchmarking set composed of 53 diverse protein-peptide complexes with peptides ranging from 3 to 12 residues and with up to 51 rotatable bonds. The DockThor performance for pose prediction on redocking studies was compared with some state-of-the-art docking programs that were also evaluated on the LEADS-PEP data set, AutoDock, AutoDock Vina, Surflex, GOLD, Glide, rDock, and DINC, as well as with the task-specific docking protocol HPepDock. Our results indicate that DockThor could dock 40% of the cases with an overall backbone RMSD below 2.5 Å when the top-scored docking pose was considered, exhibiting similar results to Glide and outperforming other protein-ligand docking programs, whereas rDock and HPepDock achieved superior results. Assessing the docking poses closest to the crystal structure (i.e., best-RMSD pose), DockThor achieved a success rate of 60% in pose prediction. Due to the great overall performance of handling peptidic compounds, the DockThor program can be considered as suitable for docking highly flexible and challenging ligands, with up to 40 rotatable bonds. DockThor is freely available as a virtual screening Web server at https://www.dockthor.lncc.br/ .
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Affiliation(s)
- Karina B Santos
- National Laboratory for Scientific Computing - LNCC , Petrópolis , Rio de Janeiro 25651-075 , Brazil
| | - Isabella A Guedes
- National Laboratory for Scientific Computing - LNCC , Petrópolis , Rio de Janeiro 25651-075 , Brazil
| | - Ana L M Karl
- National Laboratory for Scientific Computing - LNCC , Petrópolis , Rio de Janeiro 25651-075 , Brazil
| | - Laurent E Dardenne
- National Laboratory for Scientific Computing - LNCC , Petrópolis , Rio de Janeiro 25651-075 , Brazil
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45
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Kurcinski M, Badaczewska‐Dawid A, Kolinski M, Kolinski A, Kmiecik S. Flexible docking of peptides to proteins using CABS-dock. Protein Sci 2020; 29:211-222. [PMID: 31682301 PMCID: PMC6933849 DOI: 10.1002/pro.3771] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2019] [Revised: 10/30/2019] [Accepted: 10/31/2019] [Indexed: 12/12/2022]
Abstract
Molecular docking of peptides to proteins can be a useful tool in the exploration of the possible peptide binding sites and poses. CABS-dock is a method for protein-peptide docking that features significant conformational flexibility of both the peptide and the protein molecules during the peptide search for a binding site. The CABS-dock has been made available as a web server and a standalone package. The web server is an easy to use tool with a simple web interface. The standalone package is a command-line program dedicated to professional users. It offers a number of advanced features, analysis tools and support for large-sized systems. In this article, we outline the current status of the CABS-dock method, its recent developments, applications, and challenges ahead.
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Affiliation(s)
- Mateusz Kurcinski
- Faculty of Chemistry, Biological and Chemical Research CenterUniversity of WarsawWarsawPoland
| | | | - Michal Kolinski
- Bioinformatics Laboratory, Mossakowski Medical Research CentrePolish Academy of SciencesWarsawPoland
| | - Andrzej Kolinski
- Faculty of Chemistry, Biological and Chemical Research CenterUniversity of WarsawWarsawPoland
| | - Sebastian Kmiecik
- Faculty of Chemistry, Biological and Chemical Research CenterUniversity of WarsawWarsawPoland
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46
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Glashagen G, de Vries S, Uciechowska-Kaczmarzyk U, Samsonov SA, Murail S, Tuffery P, Zacharias M. Coarse-grained and atomic resolution biomolecular docking with the ATTRACT approach. Proteins 2019; 88:1018-1028. [PMID: 31785163 DOI: 10.1002/prot.25860] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2019] [Revised: 11/20/2019] [Accepted: 11/27/2019] [Indexed: 01/17/2023]
Abstract
The ATTRACT protein-protein docking program has been employed to predict protein-protein complex structures in CAPRI rounds 38-45. For 11 out of 16 targets acceptable or better quality solutions have been submitted (~70%). It includes also several cases of peptide-protein docking and the successful prediction of the geometry of carbohydrate-protein interactions. The option of combining rigid body minimization and simultaneous optimization in collective degrees of freedom based on elastic network modes was employed and systematically evaluated. Application to a large benchmark set indicates a modest improvement in docking performance compared to rigid docking. Possible further improvements of the docking approach in particular at the scoring and the flexible refinement steps are discussed.
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Affiliation(s)
- Glenn Glashagen
- Physik-Department T38, Technische Universität München, Garching, Germany
| | - Sjoerd de Vries
- Université de Paris, CNRS UMR 8251, INSERM ERL U1133, Paris, France.,Ressource Parisienne en Bioinformatique Structurale (RPBS), Paris, France
| | | | | | - Samuel Murail
- Université de Paris, CNRS UMR 8251, INSERM ERL U1133, Paris, France
| | - Pierre Tuffery
- Université de Paris, CNRS UMR 8251, INSERM ERL U1133, Paris, France.,Ressource Parisienne en Bioinformatique Structurale (RPBS), Paris, France
| | - Martin Zacharias
- Physik-Department T38, Technische Universität München, Garching, Germany
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47
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Blaszczyk M, Ciemny MP, Kolinski A, Kurcinski M, Kmiecik S. Protein-peptide docking using CABS-dock and contact information. Brief Bioinform 2019; 20:2299-2305. [PMID: 30247502 PMCID: PMC6954405 DOI: 10.1093/bib/bby080] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2018] [Revised: 08/09/2018] [Accepted: 08/10/2018] [Indexed: 12/11/2022] Open
Abstract
CABS-dock is a computational method for protein-peptide molecular docking that does not require predefinition of the binding site. The peptide is treated as fully flexible, while the protein backbone undergoes small fluctuations and, optionally, large-scale rearrangements. Here, we present a specific CABS-dock protocol that enhances the docking procedure using fragmentary information about protein-peptide contacts. The contact information is used to narrow down the search for the binding peptide pose to the proximity of the binding site. We used information on a single-chosen and randomly chosen native protein-peptide contact to validate the protocol on the peptiDB benchmark. The contact information significantly improved CABS-dock performance. The protocol has been made available as a new feature of the CABS-dock web server (at http://biocomp.chem.uw.edu.pl/CABSdock/). SHORT ABSTRACT CABS-dock is a tool for flexible docking of peptides to proteins. In this article, we present a protocol for CABS-dock docking driven by information about protein-peptide contact(s). Using information on individual protein-peptide contacts allows to improve the accuracy of CABS-dock docking.
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48
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Wang J, Alekseenko A, Kozakov D, Miao Y. Improved Modeling of Peptide-Protein Binding Through Global Docking and Accelerated Molecular Dynamics Simulations. Front Mol Biosci 2019; 6:112. [PMID: 31737642 PMCID: PMC6835073 DOI: 10.3389/fmolb.2019.00112] [Citation(s) in RCA: 43] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2019] [Accepted: 10/09/2019] [Indexed: 01/31/2023] Open
Abstract
Peptides mediate up to 40% of known protein-protein interactions in higher eukaryotes and play a key role in cellular signaling, protein trafficking, immunology, and oncology. However, it is challenging to predict peptide-protein binding with conventional computational modeling approaches, due to slow dynamics and high peptide flexibility. Here, we present a prototype of the approach which combines global peptide docking using ClusPro PeptiDock and all-atom enhanced simulations using Gaussian accelerated molecular dynamics (GaMD). For three distinct model peptides, the lowest backbone root-mean-square deviations (RMSDs) of their bound conformations relative to X-ray structures obtained from PeptiDock were 3.3–4.8 Å, being medium quality predictions according to the Critical Assessment of PRediction of Interactions (CAPRI) criteria. GaMD simulations refined the peptide-protein complex structures with significantly reduced peptide backbone RMSDs of 0.6–2.7 Å, yielding two high quality (sub-angstrom) and one medium quality models. Furthermore, the GaMD simulations identified important low-energy conformational states and revealed the mechanism of peptide binding to the target proteins. Therefore, PeptiDock+GaMD is a promising approach for exploring peptide-protein interactions.
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Affiliation(s)
- Jinan Wang
- Center for Computational Biology and Department of Molecular Biosciences, University of Kansas, Lawrence, KS, United States
| | - Andrey Alekseenko
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, United States.,Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, United States
| | - Dima Kozakov
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, United States.,Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, United States
| | - Yinglong Miao
- Center for Computational Biology and Department of Molecular Biosciences, University of Kansas, Lawrence, KS, United States
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49
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Wen Z, He J, Tao H, Huang SY. PepBDB: a comprehensive structural database of biological peptide-protein interactions. Bioinformatics 2019; 35:175-177. [PMID: 29982280 DOI: 10.1093/bioinformatics/bty579] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2017] [Accepted: 07/04/2018] [Indexed: 02/01/2023] Open
Abstract
Summary A structural database of peptide-protein interactions is important for drug discovery targeting peptide-mediated interactions. Although some peptide databases, especially for special types of peptides, have been developed, a comprehensive database of cleaned peptide-protein complex structures is still not available. Such cleaned structures are valuable for docking and scoring studies in structure-based drug design. Here, we have developed PepBDB-a curated Peptide Binding DataBase of biological complex structures from the Protein Data Bank (PDB). PepBDB presents not only cleaned structures but also extensive information about biological peptide-protein interactions, and allows users to search the database with a variety of options and interactively visualize the search results. Availability and implementation PepBDB is available at http://huanglab.phys.hust.edu.cn/pepbdb/.
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Affiliation(s)
- Zeyu Wen
- Institute of Biophysics, School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Jiahua He
- Institute of Biophysics, School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Huanyu Tao
- Institute of Biophysics, School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Sheng-You Huang
- Institute of Biophysics, School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, China
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50
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Roel-Touris J, Don CG, V Honorato R, Rodrigues JPGLM, Bonvin AMJJ. Less Is More: Coarse-Grained Integrative Modeling of Large Biomolecular Assemblies with HADDOCK. J Chem Theory Comput 2019; 15:6358-6367. [PMID: 31539250 PMCID: PMC6854652 DOI: 10.1021/acs.jctc.9b00310] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
Predicting the 3D structure of protein interactions remains a challenge in the field of computational structural biology. This is in part due to difficulties in sampling the complex energy landscape of multiple interacting flexible polypeptide chains. Coarse-graining approaches, which reduce the number of degrees of freedom of the system, help address this limitation by smoothing the energy landscape, allowing an easier identification of the global energy minimum. They also accelerate the calculations, allowing for modeling larger assemblies. Here, we present the implementation of the MARTINI coarse-grained force field for proteins into HADDOCK, our integrative modeling platform. Docking and refinement are performed at the coarse-grained level, and the resulting models are then converted back to atomistic resolution through a distance restraints-guided morphing procedure. Our protocol, tested on the largest complexes of the protein docking benchmark 5, shows an overall ∼7-fold speed increase compared to standard all-atom calculations, while maintaining a similar accuracy and yielding substantially more near-native solutions. To showcase the potential of our method, we performed simultaneous 7 body docking to model the 1:6 KaiC-KaiB complex, integrating mutagenesis and hydrogen/deuterium exchange data from mass spectrometry with symmetry restraints, and validated the resulting models against a recently published cryo-EM structure.
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Affiliation(s)
- Jorge Roel-Touris
- Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry , Utrecht University , Utrecht 3584CH , The Netherlands
| | - Charleen G Don
- Department of Pharmaceutical Sciences , University of Basel , 4056 Basel , Switzerland
| | - Rodrigo V Honorato
- Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry , Utrecht University , Utrecht 3584CH , The Netherlands
| | - João P G L M Rodrigues
- Department of Structural Biology , Stanford University School of Medicine , Stanford , California 94305 , United States
| | - Alexandre M J J Bonvin
- Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry , Utrecht University , Utrecht 3584CH , The Netherlands
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