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Tang J, Hu R, Liu Y, Liu J, Wang G, Lv J, Cheng L, He T, Liu Y, Shao PL, Zhang B. Deciphering ACE2-RBD binding affinity through peptide scanning: A molecular dynamics simulation approach. Comput Biol Med 2024; 173:108325. [PMID: 38513389 DOI: 10.1016/j.compbiomed.2024.108325] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Revised: 03/15/2024] [Accepted: 03/15/2024] [Indexed: 03/23/2024]
Abstract
Rapid discovery of target information for protein-protein interactions (PPIs) is significant in drug design, diagnostics, vaccine development, antibody therapy, etc. Peptide microarray is an ideal tool for revealing epitope information of PPIs. In this work, the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) spike receptor-binding domain (RBD) and the host cell receptor angiotensin-converting enzyme 2 (ACE2) were introduced as a model to study the epitope information of RBD-specific binding to ACE2 via a combination of theoretical calculations and experimental validation. Through dock and molecular dynamics simulations, it was found that among the 22 peptide fragments that consist of RBD, #14 (YNYLYRLFRKSNLKP) has the highest binding strength. Subsequently, the experiments of peptide microarray constructed based on plasmonic materials chip also confirmed the theoretical calculation data. Compared to other methods, such as phage display technology and surface plasmon resonance (SPR), this method is rapid and cost-effective, providing insights into the investigation of pathogen invasion processes and the timely development of peptide drugs and other fields.
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Affiliation(s)
- Jiahu Tang
- Guangdong Provincial Key Laboratory of Advanced Biomaterials, Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, 518055, China; Key Laboratory of Molecular Target & Clinical Pharmacology and the State Key Laboratory of Respiratory Disease, School of Pharmaceutical Sciences & the Fifth Affiliated Hospital, Guangzhou Medical University, Guangzhou, 511436, China
| | - Ruibin Hu
- Guangdong Provincial Key Laboratory of Advanced Biomaterials, Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, 518055, China; Xianghu Laboratory, Hangzhou, 311231, China
| | - Yiyi Liu
- Guangdong Provincial Key Laboratory of Advanced Biomaterials, Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, 518055, China
| | - Jingchao Liu
- Institute of Forestry and Pomology, Tianjin Academy of Agricultural Sciences, Tianjin, 300384, China
| | - Guanghui Wang
- Guangdong Provincial Key Laboratory of Advanced Biomaterials, Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, 518055, China
| | - Jiahui Lv
- Guangdong Provincial Key Laboratory of Advanced Biomaterials, Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, 518055, China
| | - Li Cheng
- Guangdong Provincial Key Laboratory of Advanced Biomaterials, Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, 518055, China
| | - Tingzhen He
- Guangdong Provincial Key Laboratory of Advanced Biomaterials, Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, 518055, China
| | - Ying Liu
- Guangdong Provincial Key Laboratory of Advanced Biomaterials, Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, 518055, China.
| | - Pan-Lin Shao
- Key Laboratory of Molecular Target & Clinical Pharmacology and the State Key Laboratory of Respiratory Disease, School of Pharmaceutical Sciences & the Fifth Affiliated Hospital, Guangzhou Medical University, Guangzhou, 511436, China.
| | - Bo Zhang
- Guangdong Provincial Key Laboratory of Advanced Biomaterials, Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, 518055, China.
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Burgos G, Ambuludí A, Morales-Jadán D, Garcia-Bereguiain MA, Muslin C, Armijos-Jaramillo V. A tool for the cheap and rapid screening of SARS-CoV-2 variants of concern (VoCs) by Sanger sequencing. Microbiol Spectr 2023; 11:e0506422. [PMID: 37676038 PMCID: PMC10586709 DOI: 10.1128/spectrum.05064-22] [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: 12/09/2022] [Accepted: 07/05/2023] [Indexed: 09/08/2023] Open
Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is an emerging virus that, since March 2020, has been responsible for a global and ongoing pandemic. Its rapid spread over the past nearly 3 years has caused novel variants to arise. To monitor the circulation and emergence of SARS-CoV-2 variants, surveillance systems based on nucleotide mutations are required. In this regard, we searched in the spike, ORF8, and nucleocapsid genes to detect variable sites among SARS-CoV-2 variants. We describe polymorphic genetic regions that enable us to differentiate between the Alpha, Beta, Gamma, Delta, and Omicron variants of concern (VoCs). We found 21 relevant mutations, 13 of which are unique for Omicron lineages BA.1/BA.1.1, BA.2, BA.3, BA.4, and BA.5. This genetic profile enables the discrimination between VoCs using only four reverse transcription PCR fragments and Sanger sequencing, offering a cheaper and faster alternative to whole-genome sequencing for SARS-CoV-2 surveillance. IMPORTANCE Our work describes a new (Sanger sequencing-based) screening methodology for SARS-CoV-2, performing PCR amplifications of a few target regions to detect diagnostic mutations between virus variants. Using the methodology developed in this work, we were able to discriminate between the following VoCs: Alpha, Beta, Gamma, Delta, and Omicron (BA.1/BA.1.1, BA.2, BA.3, BA.4, and BA.5). This becomes important, especially in low-income countries where current methodologies like next-generation sequencing have prohibitive costs. Furthermore, rapid detection would allow sanitary authorities to take rapid measures to limit the spread of the virus and therefore reduce the probability of new virus dispersion. With this methodological approach, 13 previously unreported diagnostic mutations among several Omicron lineages were found.
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Affiliation(s)
- Germán Burgos
- Facultad de Medicina, Universidad de Las Américas (UDLA), Quito, Ecuador
- One Health Research Group, Faculty of Health Sciences, Universidad de Las Américas (UDLA), Quito, Ecuador
| | - Andrés Ambuludí
- Carrera de Ingeniería en Biotecnología, Facultad de Ingenierías y Ciencias Aplicadas, Universidad de Las Américas (UDLA), Quito, Ecuador
| | - USFQ SARS-CoV-2 Consortium
- Instituto de Microbiología, Colegio de Ciencias Biológicas y Ambientales (COCIBA), Universidad San Francisco de Quito (USFQ), Cumbaya, Ecuador
| | - Diana Morales-Jadán
- One Health Research Group, Faculty of Health Sciences, Universidad de Las Américas (UDLA), Quito, Ecuador
| | | | - Claire Muslin
- One Health Research Group, Faculty of Health Sciences, Universidad de Las Américas (UDLA), Quito, Ecuador
| | - Vinicio Armijos-Jaramillo
- Carrera de Ingeniería en Biotecnología, Facultad de Ingenierías y Ciencias Aplicadas, Universidad de Las Américas (UDLA), Quito, Ecuador
- Grupo de Bio-Quimioinformática, Universidad de Las Américas (UDLA), Quito, Ecuador
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Mechanistic Origin of Different Binding Affinities of SARS-CoV and SARS-CoV-2 Spike RBDs to Human ACE2. Cells 2022; 11:cells11081274. [PMID: 35455955 PMCID: PMC9032924 DOI: 10.3390/cells11081274] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2022] [Revised: 04/03/2022] [Accepted: 04/06/2022] [Indexed: 12/04/2022] Open
Abstract
The receptor-binding domain (RBD) of the SARS-CoV-2 spike protein (RBDCoV2) has a higher binding affinity to the human receptor angiotensin-converting enzyme 2 (ACE2) than the SARS-CoV RBD (RBDCoV). Here, we performed molecular dynamics (MD) simulations, binding free energy (BFE) calculations, and interface residue contact network (IRCN) analysis to explore the mechanistic origin of different ACE2-binding affinities of the two RBDs. The results demonstrate that, when compared to the RBDCoV2-ACE2 complex, RBDCoV-ACE2 features enhanced dynamicsand inter-protein positional movements and increased conformational entropy and conformational diversity. Although the inter-protein electrostatic attractive interactions are the primary determinant for the high ACE2-binding affinities of both RBDs, the significantly enhanced electrostatic attractive interactions between ACE2 and RBDCoV2 determine the higher ACE2-binding affinity of RBDCoV2 than of RBDCoV. Comprehensive comparative analyses of the residue BFE components and IRCNs between the two complexes reveal that it is the residue changes at the RBD interface that lead to the overall stronger inter-protein electrostatic attractive force in RBDCoV2-ACE2, which not only tightens the interface packing and suppresses the dynamics of RBDCoV2-ACE2, but also enhances the ACE2-binding affinity of RBDCoV2. Since the RBD residue changes involving gain/loss of the positively/negatively charged residues can greatly enhance the binding affinity, special attention should be paid to the SARS-CoV-2 variants carrying such mutations, particularly those near or at the binding interfaces with the potential to form hydrogen bonds and/or salt bridges with ACE2.
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Rahnavard A, Dawson T, Clement R, Stearrett N, Pérez-Losada M, Crandall KA. Epidemiological associations with genomic variation in SARS-CoV-2. Sci Rep 2021; 11:23023. [PMID: 34837008 PMCID: PMC8626494 DOI: 10.1038/s41598-021-02548-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Accepted: 11/16/2021] [Indexed: 11/24/2022] Open
Abstract
SARS-CoV-2 (CoV) is the etiological agent of the COVID-19 pandemic and evolves to evade both host immune systems and intervention strategies. We divided the CoV genome into 29 constituent regions and applied novel analytical approaches to identify associations between CoV genomic features and epidemiological metadata. Our results show that nonstructural protein 3 (nsp3) and Spike protein (S) have the highest variation and greatest correlation with the viral whole-genome variation. S protein variation is correlated with nsp3, nsp6, and 3'-to-5' exonuclease variation. Country of origin and time since the start of the pandemic were the most influential metadata associated with genomic variation, while host sex and age were the least influential. We define a novel statistic-coherence-and show its utility in identifying geographic regions (populations) with unusually high (many new variants) or low (isolated) viral phylogenetic diversity. Interestingly, at both global and regional scales, we identify geographic locations with high coherence neighboring regions of low coherence; this emphasizes the utility of this metric to inform public health measures for disease spread. Our results provide a direction to prioritize genes associated with outcome predictors (e.g., health, therapeutic, and vaccine outcomes) and to improve DNA tests for predicting disease status.
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Affiliation(s)
- Ali Rahnavard
- Computational Biology Institute, Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, The George Washington University, Washington, DC, USA.
| | - Tyson Dawson
- Computational Biology Institute, Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, The George Washington University, Washington, DC, USA
| | - Rebecca Clement
- Computational Biology Institute, Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, The George Washington University, Washington, DC, USA
| | - Nathaniel Stearrett
- Computational Biology Institute, Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, The George Washington University, Washington, DC, USA
| | - Marcos Pérez-Losada
- Computational Biology Institute, Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, The George Washington University, Washington, DC, USA
- CIBIO-InBIO, Centro de Investigação em Biodiversidade e Recursos Genéticos, Universidade do Porto, Campus Agrário de Vairão, Vairão, Portugal
| | - Keith A Crandall
- Computational Biology Institute, Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, The George Washington University, Washington, DC, USA
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