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Yadav AJ, Kumar S, Maurya S, Bhagat K, Padhi AK. Interface design of SARS-CoV-2 symmetrical nsp7 dimer and machine learning-guided nsp7 sequence prediction reveals physicochemical properties and hotspots for nsp7 stability, adaptation, and therapeutic design. Phys Chem Chem Phys 2024; 26:14046-14061. [PMID: 38686454 DOI: 10.1039/d4cp01014k] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/02/2024]
Abstract
The COVID-19 pandemic, driven by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), necessitates a profound understanding of the virus and its lifecycle. As an RNA virus with high mutation rates, SARS-CoV-2 exhibits genetic variability leading to the emergence of variants with potential implications. Among its key proteins, the RNA-dependent RNA polymerase (RdRp) is pivotal for viral replication. Notably, RdRp forms dimers via non-structural protein (nsp) subunits, particularly nsp7, crucial for efficient viral RNA copying. Similar to the main protease (Mpro) of SARS-CoV-2, there is a possibility that the nsp7 might also undergo mutational selection events to generate more stable and adaptable versions of nsp7 dimer during virus evolution. However, efforts to obtain such cohesive and comprehensive information are lacking. To address this, we performed this study focused on deciphering the molecular intricacies of nsp7 dimerization using a multifaceted approach. Leveraging computational protein design (CPD), machine learning (ML), AlphaFold v2.0-based structural analysis, and several related computational approaches, we aimed to identify critical residues and mutations influencing nsp7 dimer stability and adaptation. Our methodology involved identifying potential hotspot residues within the dimeric nsp7 interface using an interface-based CPD approach. Through Rosetta-based symmetrical protein design, we designed and modulated nsp7 dimerization, considering selected interface residues. Analysis of physicochemical features revealed acceptable structural changes and several structural and residue-specific insights emphasizing the intricate nature of such protein-protein complexes. Our ML models, particularly the random forest regressor (RFR), accurately predicted binding affinities and ML-guided sequence predictions corroborated CPD findings, elucidating potential nsp7 mutations and their impact on binding affinity. Validation against clinical sequencing data demonstrated the predictive accuracy of our approach. Moreover, AlphaFold v2.0 structural analyses validated optimal dimeric configurations of affinity-enhancing designs, affirming methodological precision. Affinity-enhancing designs exhibited favourable energetics and higher binding affinity as compared to their counterparts. The obtained physicochemical properties, molecular interactions, and sequence predictions advance our understanding of SARS-CoV-2 evolution and inform potential avenues for therapeutic intervention against COVID-19.
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Affiliation(s)
- Amar Jeet Yadav
- Laboratory for Computational Biology & Biomolecular Design, School of Biochemical Engineering, Indian Institute of Technology (BHU), Varanasi 221005, Uttar Pradesh, India.
| | - Shivank Kumar
- Laboratory for Computational Biology & Biomolecular Design, School of Biochemical Engineering, Indian Institute of Technology (BHU), Varanasi 221005, Uttar Pradesh, India.
| | - Shweata Maurya
- Laboratory for Computational Biology & Biomolecular Design, School of Biochemical Engineering, Indian Institute of Technology (BHU), Varanasi 221005, Uttar Pradesh, India.
| | - Khushboo Bhagat
- Laboratory for Computational Biology & Biomolecular Design, School of Biochemical Engineering, Indian Institute of Technology (BHU), Varanasi 221005, Uttar Pradesh, India.
| | - Aditya K Padhi
- Laboratory for Computational Biology & Biomolecular Design, School of Biochemical Engineering, Indian Institute of Technology (BHU), Varanasi 221005, Uttar Pradesh, India.
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Padhi AK, Kalita P, Maurya S, Poluri KM, Tripathi T. From De Novo Design to Redesign: Harnessing Computational Protein Design for Understanding SARS-CoV-2 Molecular Mechanisms and Developing Therapeutics. J Phys Chem B 2023; 127:8717-8735. [PMID: 37815479 DOI: 10.1021/acs.jpcb.3c04542] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/11/2023]
Abstract
The continuous emergence of novel SARS-CoV-2 variants and subvariants serves as compelling evidence that COVID-19 is an ongoing concern. The swift, well-coordinated response to the pandemic highlights how technological advancements can accelerate the detection, monitoring, and treatment of the disease. Robust surveillance systems have been established to understand the clinical characteristics of new variants, although the unpredictable nature of these variants presents significant challenges. Some variants have shown resistance to current treatments, but innovative technologies like computational protein design (CPD) offer promising solutions and versatile therapeutics against SARS-CoV-2. Advances in computing power, coupled with open-source platforms like AlphaFold and RFdiffusion (employing deep neural network and diffusion generative models), among many others, have accelerated the design of protein therapeutics with precise structures and intended functions. CPD has played a pivotal role in developing peptide inhibitors, mini proteins, protein mimics, decoy receptors, nanobodies, monoclonal antibodies, identifying drug-resistance mutations, and even redesigning native SARS-CoV-2 proteins. Pending regulatory approval, these designed therapies hold the potential for a lasting impact on human health and sustainability. As SARS-CoV-2 continues to evolve, use of such technologies enables the ongoing development of alternative strategies, thus equipping us for the "New Normal".
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Affiliation(s)
- Aditya K Padhi
- Laboratory for Computational Biology & Biomolecular Design, School of Biochemical Engineering, Indian Institute of Technology (BHU), Varanasi 221005, Uttar Pradesh, India
| | - Parismita Kalita
- Molecular and Structural Biophysics Laboratory, Department of Biochemistry, North-Eastern Hill University, Shillong 793022, India
| | - Shweata Maurya
- Laboratory for Computational Biology & Biomolecular Design, School of Biochemical Engineering, Indian Institute of Technology (BHU), Varanasi 221005, Uttar Pradesh, India
| | - Krishna Mohan Poluri
- Department of Biosciences and Bioengineering, Indian Institute of Technology Roorkee, Roorkee 247667, Uttarakhand, India
- Centre for Nanotechnology, Indian Institute of Technology Roorkee, Roorkee 247667, Uttarakhand, India
| | - Timir Tripathi
- Molecular and Structural Biophysics Laboratory, Department of Biochemistry, North-Eastern Hill University, Shillong 793022, India
- Department of Zoology, School of Life Sciences, North-Eastern Hill University, Shillong 793022, India
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Padhi AK, Tripathi T. Hotspot residues and resistance mutations in the nirmatrelvir-binding site of SARS-CoV-2 main protease: Design, identification, and correlation with globally circulating viral genomes. Biochem Biophys Res Commun 2022; 629:54-60. [PMID: 36113178 PMCID: PMC9450486 DOI: 10.1016/j.bbrc.2022.09.010] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Accepted: 09/02/2022] [Indexed: 12/15/2022]
Abstract
Shortly after the onset of the COVID-19 pandemic, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has acquired numerous variations in its intracellular proteins to adapt quickly, become more infectious, and ultimately develop drug resistance by mutating certain hotspot residues. To keep the emerging variants at bay, including Omicron and subvariants, FDA has approved the antiviral nirmatrelvir for mild-to-moderate and high-risk COVID-19 cases. Like other viruses, SARS-CoV-2 could acquire mutations in its main protease (Mpro) to adapt and develop resistance against nirmatrelvir. Employing a unique high-throughput protein design technique, the hotspot residues, and signatures of adaptation of Mpro having the highest probability of mutating and rendering nirmatrelvir ineffective were identified. Our results show that ∼40% of the designed mutations in Mpro already exist in the globally circulating SARS-CoV-2 lineages and several predicted mutations. Moreover, several high-frequency, designed mutations were found to be in corroboration with the experimentally reported nirmatrelvir-resistant mutants and are naturally occurring. Our work on the targeted design of the nirmatrelvir-binding site offers a comprehensive picture of potential hotspot sites and resistance mutations in Mpro and is thus crucial in comprehending viral adaptation, robust antiviral design, and surveillance of evolving Mpro variations.
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Affiliation(s)
- Aditya K Padhi
- Laboratory for Computational Biology & Biomolecular Design, School of Biochemical Engineering, Indian Institute of Technology (BHU), Varanasi, 221005, Uttar Pradesh, India.
| | - Timir Tripathi
- Molecular and Structural Biophysics Laboratory, Department of Biochemistry, North-Eastern Hill University, Shillong, 793022, India; Regional Director's Office, Indira Gandhi National Open University, Regional Centre Kohima, Kenuozou, Kohima, 797001, India.
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Padhi AK, Tripathi T. A comprehensive protein design protocol to identify resistance mutations and signatures of adaptation in pathogens. Brief Funct Genomics 2022; 22:195-203. [PMID: 35851634 DOI: 10.1093/bfgp/elac020] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Revised: 06/01/2022] [Accepted: 06/20/2022] [Indexed: 12/17/2022] Open
Abstract
Abstract
Most pathogens mutate and evolve over time to escape immune and drug pressure. To achieve this, they alter specific hotspot residues in their intracellular proteins to render the targeted drug(s) ineffective and develop resistance. Such hotspot residues may be located as a cluster or uniformly as a signature of adaptation in a protein. Identifying the hotspots and signatures is extremely important to comprehensively understand the disease pathogenesis and rapidly develop next-generation therapeutics. As experimental methods are time-consuming and often cumbersome, there is a need to develop efficient computational protocols and adequately utilize them. To address this issue, we present a unique computational protein design protocol that identifies hotspot residues, resistance mutations and signatures of adaptation in a pathogen’s protein against a bound drug. Using the protocol, the binding affinity between the designed mutants and drug is computed quickly, which offers predictions for comparison with biophysical experiments. The applicability and accuracy of the protocol are shown using case studies of a few protein–drug complexes. As a validation, resistance mutations in severe acute respiratory syndrome coronavirus 2 main protease (Mpro) against narlaprevir (an inhibitor of hepatitis C NS3/4A serine protease) are identified. Notably, a detailed methodology and description of the working principles of the protocol are presented. In conclusion, our protocol will assist in providing a first-hand explanation of adaptation, hotspot-residue variations and surveillance of evolving resistance mutations in a pathogenic protein.
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Singh E, Jha RK, Khan RJ, Kumar A, Jain M, Muthukumaran J, Singh AK. A computational essential dynamics approach to investigate structural influences of ligand binding on Papain like protease from SARS-CoV-2. Comput Biol Chem 2022; 99:107721. [PMID: 35835027 PMCID: PMC9238113 DOI: 10.1016/j.compbiolchem.2022.107721] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Revised: 06/21/2022] [Accepted: 06/25/2022] [Indexed: 11/28/2022]
Abstract
Papain like protease (PLpro) is a cysteine protease from the coronaviridae family of viruses. Coronaviruses possess a positive sense, single-strand RNA, leading to the translation of two viral polypeptides containing viral structural, non-structural and accessory proteins. PLpro is responsible for the cleavage of nsp1–3 from the viral polypeptide. PLpro also possesses deubiquitinating and deISGlyating activity, which sequesters the virus from the host's immune system. This indispensable attribute of PLpro makes it a protein of interest as a drug target. The present study aims to analyze the structural influences of ligand binding on PLpro. First, PLpro was screened against the ZINC-in-trials library, from which four lead compounds were identified based on estimated binding affinity and interaction patterns. Next, based on molecular docking results, ZINC000000596945, ZINC000064033452 and VIR251 (control molecule) were subjected to molecular dynamics simulation. The study evaluated global and essential dynamics analyses utilising principal component analyses, dynamic cross-correlation matrix, free energy landscape and time-dependant essential dynamics to predict the structural changes observed in PLpro upon ligand binding in a simulated environment. The MM/PBSA-based binding free energy calculations of the two selected molecules, ZINC000000596945 (−41.23 ± 3.70 kcal/mol) and ZINC000064033452 (−25.10 ± 2.65 kcal/mol), displayed significant values which delineate them as potential inhibitors of PLpro from SARS-CoV-2.
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Affiliation(s)
- Ekampreet Singh
- Department of Biotechnology, School of Engineering and Technology, Sharda University, 201310 Greater Noida, Uttar Pradesh, India
| | - Rajat Kumar Jha
- Department of Biotechnology, School of Engineering and Technology, Sharda University, 201310 Greater Noida, Uttar Pradesh, India
| | - Rameez Jabeer Khan
- Department of Biotechnology, School of Engineering and Technology, Sharda University, 201310 Greater Noida, Uttar Pradesh, India
| | - Ankit Kumar
- Department of Biotechnology, School of Engineering and Technology, Sharda University, 201310 Greater Noida, Uttar Pradesh, India
| | - Monika Jain
- Department of Biotechnology, School of Engineering and Technology, Sharda University, 201310 Greater Noida, Uttar Pradesh, India
| | - Jayaraman Muthukumaran
- Department of Biotechnology, School of Engineering and Technology, Sharda University, 201310 Greater Noida, Uttar Pradesh, India.
| | - Amit Kumar Singh
- Department of Biotechnology, School of Engineering and Technology, Sharda University, 201310 Greater Noida, Uttar Pradesh, India.
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