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Demirtaş K, Erman B, Haliloğlu T. Dynamic correlations: exact and approximate methods for mutual information. Bioinformatics 2024; 40:btae076. [PMID: 38341647 PMCID: PMC10898342 DOI: 10.1093/bioinformatics/btae076] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Revised: 01/17/2024] [Accepted: 02/08/2024] [Indexed: 02/12/2024] Open
Abstract
MOTIVATION Proteins are dynamic entities that undergo conformational changes critical for their functions. Understanding the communication pathways and information transfer within proteins is crucial for elucidating allosteric interactions in their mechanisms. This study utilizes mutual information (MI) analysis to probe dynamic allostery. Using two cases, Ubiquitin and PLpro, we have evaluated the accuracy and limitations of different approximations including the exact anisotropic and isotropic models, multivariate Gaussian model, isotropic Gaussian model, and the Gaussian Network Model (GNM) in revealing allosteric interactions. RESULTS Our findings emphasize the required trajectory length for capturing accurate mutual information profiles. Long molecular dynamics trajectories, 1 ms for Ubiquitin and 100 µs for PLpro are used as benchmarks, assuming they represent the ground truth. Trajectory lengths of approximately 5 µs for Ubiquitin and 1 µs for PLpro marked the onset of convergence, while the multivariate Gaussian model accurately captured mutual information with trajectories of 5 ns for Ubiquitin and 350 ns for PLpro. However, the isotropic Gaussian model is less successful in representing the anisotropic nature of protein dynamics, particularly in the case of PLpro, highlighting its limitations. The GNM, however, provides reasonable approximations of long-range information exchange as a minimalist network model based on a single crystal structure. Overall, the optimum trajectory lengths for effective Gaussian approximations of long-time dynamic behavior depend on the inherent dynamics within the protein's topology. The GNM, by showcasing dynamics across relatively diverse time scales, can be used either as a standalone method or to gauge the adequacy of MD simulation lengths. AVAILABILITY AND IMPLEMENTATION Mutual information codes are available at https://github.com/kemaldemirtas/prc-MI.git.
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Affiliation(s)
- Kemal Demirtaş
- Department of Chemical Engineering, Bogazici University, 34342 Istanbul, Turkey
- Polymer Research Center, Bogazici University, 34342 Istanbul, Turkey
| | - Burak Erman
- Department of Chemical and Biological Engineering, Koc University, 34450 Istanbul, Turkey
| | - Türkan Haliloğlu
- Department of Chemical Engineering, Bogazici University, 34342 Istanbul, Turkey
- Polymer Research Center, Bogazici University, 34342 Istanbul, Turkey
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2
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Yildirim A, Tekpinar M. Building Quantitative Bridges between Dynamics and Sequences of SARS-CoV-2 Main Protease and a Diverse Set of Thirty-Two Proteins. J Chem Inf Model 2023; 63:9-19. [PMID: 36513349 DOI: 10.1021/acs.jcim.2c01206] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Proteases are major drug targets for many viral diseases. However, mutations can render several antiprotease drugs inefficient rapidly even though these mutations may not alter protein structures significantly. Understanding relations between quickly mutating residues, protease structures, and the dynamics of the proteases is crucial for designing potent drugs. Due to this reason, we studied relations between the evolutionary information on residues in the amino acid sequences and protein dynamics for SARS-CoV-2 main protease. More precisely, we analyzed three dynamical quantities (Schlitter entropy, root-mean-square fluctuations, and dynamical flexibility index) and their relation to the amino acid conservation extracted from multiple sequence alignments of the main protease. We showed that a quantifiable similarity can be built between a sequence-based quantity called Jensen-Shannon conservation and those three dynamical quantities. We validated this similarity for a diverse set of 32 different proteins, other than the SARS-CoV-2 main protease. We believe that establishing these kinds of quantitative bridges will have larger implications for all viral proteases as well as all proteins.
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Affiliation(s)
- Ahmet Yildirim
- Department of Biology, Siirt University, 56100Siirt, Turkey
| | - Mustafa Tekpinar
- CNRS, IBPS, Laboratoire de Biologie Computationnelle et Quantitative - UMR 7238, Sorbonne University, 75005Paris, France
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3
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Yang T, Han L, Huo S. Dynamics and Allosteric Information Pathways of Unphosphorylated c-Cbl. J Chem Inf Model 2022; 62:6148-6159. [PMID: 36442893 DOI: 10.1021/acs.jcim.2c01022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Human c-Cbl is a RING-type ligase and plays a central role in the protein degradation cascade. To elucidate its conformational changes related to substrate binding, we performed molecular dynamics simulations of different variants/states of c-Cbl for a cumulative time of 68 μs. Our simulations demonstrate that before the substrate binds, the RING domain samples a broad set of conformational states at a biologically relevant salt concentration, including the closed, partially open, and fully open states, whereas substrate binding leads to a restricted conformational sampling. Phe378 and the C-terminal region play an essential role in stabilizing the partially open state. To visualize the allosteric signal transmission pathways from the substrate-binding site to the 40 Å apart RING domain and identify the critical residues for allostery, we have created a subgraph from the optimal and suboptimal paths. Redundant paths are seen in the SH2 domain where the substrate binds, while the major bottlenecks are found at the junction between the SH2 domain and the linker helix region as well as that between the SH2 domain and the 4H bundle. These bottlenecks separate the paths into two overall routes. The nodes/residues at the bottlenecks on the subgraph are considered allosteric hot spots. This subgraph approach provides a general tool for network visualization and determination of critical residues for allostery. The structurally and allosterically critical residues identified in our work are testable and would provide valuable insights into the emerging strategies for drug discovery, such as targeted protein degradation.
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Affiliation(s)
- Tianyi Yang
- Gustaf H. Carlson School of Chemistry and Biochemistry, Clark University, 950 Main Street, Worcester, Massachusetts 01610, United States
| | - Li Han
- Department of Computer Science, Clark University, 950 Main Street, Worcester, Massachusetts 01610, United States
| | - Shuanghong Huo
- Gustaf H. Carlson School of Chemistry and Biochemistry, Clark University, 950 Main Street, Worcester, Massachusetts 01610, United States
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Parmar M, Thumar R, Patel B, Athar M, Jha PC, Patel D. Structural differences in 3C-like protease (Mpro) from SARS-CoV and SARS-CoV-2: molecular insights revealed by Molecular Dynamics Simulations. Struct Chem 2022; 34:1-18. [PMID: 36467259 PMCID: PMC9686461 DOI: 10.1007/s11224-022-02089-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Accepted: 10/29/2022] [Indexed: 11/25/2022]
Abstract
Novel coronavirus SARS-CoV-2 has infected millions of people with thousands of mortalities globally. The main protease (Mpro) is vital in processing replicase polyproteins. Both the CoV's Mpro shares 97% identity, with 12 mutations, but none are present in the active site. Although many therapeutics and vaccines are available to combat SARS-CoV-2, these treatments may not be practical due to their high mutational rate. On the other hand, Mpro has a high degree of conservation throughout variants, making Mpro a stout drug target. Here, we report a detailed comparison of both the monomeric Mpro and the biologically active dimeric Mpro using MD simulation to understand the impact of the 12 divergent residues (T35V, A46S, S65N, L86V, R88K, S94A, H134F, K180N, L202V, A267S, T285A and I286L) on the molecular microenvironment and the interaction between crucial residues. The present study concluded that the change in the microenvironment of residues at the entrance (T25, T26, M49 and Q189), near the catalytic site (F140, H163, H164, M165 and H172) and in the substrate-binding site (V35, N65, K88 and N180) is due to 12 mutations in the SARS-CoV-2 Mpro. Furthermore, the involvement of F140, E166 and H172 residues in dimerization stabilizes the Mpro dimer, which should be considered. We anticipate that networks and microenvironment changes identified here might guide repurposing attempts and optimization of new Mpro inhibitors. Supplementary Information The online version contains supplementary material available at 10.1007/s11224-022-02089-6.
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Affiliation(s)
- Meet Parmar
- Department of Biotechnology and Bioengineering, Institute of Advanced Research, Gandhinagar, Gujarat 382426 India
| | - Ritik Thumar
- Department of Biotechnology and Bioengineering, Institute of Advanced Research, Gandhinagar, Gujarat 382426 India
| | - Bhumi Patel
- Department of Biotechnology and Bioengineering, Institute of Advanced Research, Gandhinagar, Gujarat 382426 India
| | - Mohd Athar
- Center for Chemical Biology and Therapeutics, InStem, Bangalore, 560065 Karnataka India
- Physics Department, University of Cagliari, Monserrato (CA), Italy
| | - Prakash C. Jha
- School of Applied Material Sciences, Central University of Gujarat, Gandhinagar, 382030 Gujarat India
| | - Dhaval Patel
- Department of Biotechnology and Bioengineering, Institute of Advanced Research, Gandhinagar, Gujarat 382426 India
- Gujarat Biotechnology University, Gujarat International Finance Tec-City, Gandhinagar, 382355 Gujarat India
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5
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Alzyoud L, Ghattas MA, Atatreh N. Allosteric Binding Sites of the SARS-CoV-2 Main Protease: Potential Targets for Broad-Spectrum Anti-Coronavirus Agents. Drug Des Devel Ther 2022; 16:2463-2478. [PMID: 35941927 PMCID: PMC9356625 DOI: 10.2147/dddt.s370574] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Accepted: 07/23/2022] [Indexed: 12/23/2022] Open
Abstract
The current pandemic caused by the COVID-19 disease has reached everywhere in the world and has affected every aspect of our lives. As of the current data, the World Health Organization (WHO) has reported more than 300 million confirmed COVID-19 cases worldwide and more than 5 million deaths. Mpro is an enzyme that plays a key role in the life cycle of the SARS-CoV-2 virus, and it is vital for the disease progression. The Mpro enzyme seems to have several allosteric sites that can hinder the enzyme catalytic activity. Furthermore, some of these allosteric sites are located at or nearby the dimerization interface which is essential for the overall Mpro activity. In this review paper, we investigate the potential of the Mpro allosteric site to act as a drug target, especially since they interestingly appear to be resistant to mutation. The work is illustrated through three subsequent sections: First, the two main categories of Mpro allosteric sites have been explained and discussed. Second, a total of six pockets have been studied and evaluated for their druggability and cavity characteristics. Third, the experimental and computational attempts for the discovery of new allosteric inhibitors have been illustrated and discussed. To sum up, this review paper gives a detailed insight into the feasibility of developing new Mpro inhibitors to act as a potential treatment for the COVID-19 disease.
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Affiliation(s)
- Lara Alzyoud
- College of Pharmacy, Al Ain University, Abu Dhabi, United Arab Emirates
| | - Mohammad A Ghattas
- College of Pharmacy, Al Ain University, Abu Dhabi, United Arab Emirates
- AAU Health and Biomedical Research Center, Al Ain University, Abu Dhabi, United Arab Emirates
- Correspondence: Mohammad A Ghattas; Noor Atatreh, Email ;
| | - Noor Atatreh
- College of Pharmacy, Al Ain University, Abu Dhabi, United Arab Emirates
- AAU Health and Biomedical Research Center, Al Ain University, Abu Dhabi, United Arab Emirates
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Alvarado YJ, Olivarez Y, Lossada C, Vera-Villalobos J, Paz JL, Vera E, Loroño M, Vivas A, Torres FJ, Jeffreys LN, Hurtado-León ML, González-Paz L. Interaction of the new inhibitor paxlovid (PF-07321332) and ivermectin with the monomer of the main protease SARS-CoV-2: A volumetric study based on molecular dynamics, elastic networks, classical thermodynamics and SPT. Comput Biol Chem 2022; 99:107692. [PMID: 35640480 PMCID: PMC9107165 DOI: 10.1016/j.compbiolchem.2022.107692] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Revised: 04/28/2022] [Accepted: 05/02/2022] [Indexed: 02/04/2023]
Abstract
The COVID-19 pandemic has accelerated the study of drugs, most notably ivermectin and more recently Paxlovid (PF-07321332) which is in phase III clinical trials with experimental data showing covalent binding to the viral protease Mpro. Theoretical developments of catalytic site-directed docking support thermodynamically feasible non-covalent binding to Mpro. Here we show that Paxlovid binds non-covalently at regions other than the catalytic sites with energies stronger than reported and at the same binding site as the ivermectin B1a homologue, all through theoretical methodologies, including blind docking. We volumetrically characterize the non-covalent interaction of the ivermectin homologues (avermectins B1a and B1b) and Paxlovid with the mMpro monomer, through molecular dynamics and scaled particle theory (SPT). Using the fluctuation-dissipation theorem (FDT), we estimated the electric dipole moment fluctuations at the surface of each of complex involved in this study, with similar trends to that observed in the interaction volume. Using fluctuations of the intrinsic volume and the number of flexible fragments of proteins using anisotropic and Gaussian elastic networks (ANM+GNM) suggests the complexes with ivermectin are more dynamic and flexible than the unbound monomer. In contrast, the binding of Paxlovid to mMpro shows that the mMpro-PF complex is the least structurally dynamic of all the species measured in this investigation. The results support a differential molecular mechanism of the ivermectin and PF homologues in the mMpro monomer. Finally, the results showed that Paxlovid despite beingbound in different sites through covalent or non-covalent forms behaves similarly in terms of its structural flexibility and volumetric behaviour.
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Affiliation(s)
- Ysaias José Alvarado
- Instituto Venezolano de Investigaciones Científicas (IVIC), Centro de Investigación y Tecnología de Materiales (CITeMA), Laboratorio de Caracterización Molecular y Biomolecular, 4001 Maracaibo, Bolivarian Republic of Venezuela,Corresponding author
| | - Yosmari Olivarez
- Universidad del Zulia (LUZ). Facultad Experimental de Ciencias (FEC), Departamento de Quimica, Laboratorio de Electronica Molecular, 4001 Maracaibo, Bolivarian Republic of Venezuela
| | - Carla Lossada
- Instituto Venezolano de Investigaciones Científicas (IVIC), Centro de Investigación y Tecnología de Materiales (CITeMA), Laboratorio de Caracterización Molecular y Biomolecular, 4001 Maracaibo, Bolivarian Republic of Venezuela
| | - Joan Vera-Villalobos
- Facultad de Ciencias Naturales y Matemáticas, Departamento de Química y Ciencias Ambientales, Laboratorio de Análisis Químico Instrumental (LAQUINS), Escuela Superior Politécnica del Litoral, Guayaquil, Ecuador
| | - José Luis Paz
- Departamento Académico de Química Inorgánica, Facultad de Química e Ingeniería Química, Universidad Nacional Mayor de San Marcos, Lima, Peru
| | - Eddy Vera
- Universidad del Zulia (LUZ). Facultad Experimental de Ciencias (FEC), Departamento de Quimica, Laboratorio de Electronica Molecular, 4001 Maracaibo, Bolivarian Republic of Venezuela
| | - Marcos Loroño
- Departamento Académico de Química Analítica e Instrumental, Facultad de Química e Ingeniería Química, Universidad Nacional Mayor de San Marcos, Lima, Peru
| | - Alejandro Vivas
- Universidad del Zulia (LUZ). Facultad Experimental de Ciencias (FEC), Departamento de Quimica, Laboratorio de Electronica Molecular, 4001 Maracaibo, Bolivarian Republic of Venezuela
| | - Fernando Javier Torres
- Grupo de Química Computacional y Teórica (QCT-UR), Facultad de Ciencias Naturales, Universidad del Rosario, Bogotá, Colombia,Grupo de Química Computacional y Teórica (QCT-USFQ), Instituto de Simulación Computacional (ISC-USFQ), Departamento de Ingeniería Química, Universidad San Francisco de Quito (USFQ), Quito, Ecuador
| | - Laura N. Jeffreys
- Centre for Drugs and Diagnostics, Department of Tropical Disease Biology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool L3 5QA, UK
| | - María Laura Hurtado-León
- Universidad del Zulia (LUZ), Facultad Experimental de Ciencias (FEC), Departamento de Biología, Laboratorio de Genética y Biología Molecular (LGBM), Maracaibo 4001, Zulia, Bolivarian Republic of Venezuela
| | - Lenin González-Paz
- Universidad del Zulia (LUZ), Facultad Experimental de Ciencias (FEC), Departamento de Biología, Laboratorio de Genética y Biología Molecular (LGBM), Maracaibo 4001, Zulia, Bolivarian Republic of Venezuela,Instituto Venezolano de Investigaciones Científicas (IVIC), Centro de Estudios Botanicos y Agroforestales, (CEBA), Laboratorio de Proteccion Vegetal, 4001 Maracaibo, Bolivarian Republic of Venezuela,Corresponding author at: Instituto Venezolano de Investigaciones Científicas (IVIC), Centro de Estudios Botanicos y Agroforestales, (CEBA), Laboratorio de Proteccion Vegetal, 4001 Maracaibo, Bolivarian Republic of Venezuela
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Campitelli P, Lu J, Ozkan SB. Dynamic Allostery Highlights the Evolutionary Differences between the CoV-1 and CoV-2 Main Proteases. Biophys J 2022; 121:1483-1492. [PMID: 35300968 PMCID: PMC8920573 DOI: 10.1016/j.bpj.2022.03.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Revised: 12/28/2021] [Accepted: 03/08/2022] [Indexed: 11/02/2022] Open
Abstract
The SARS-CoV-2 coronavirus has become one of the most immediate and widely-studied systems since its identification and subsequent global outbreak from 2019-2021. In an effort to understand the biophysical changes as a result of mutations, the mechanistic details of multiple different proteins within the SARS-CoV-2 virus have been studied and compared with SARS-CoV-1. Focusing on the main protease (mPro), we first explored the long-range dynamics using the Dynamic Coupling Index (DCI) to investigate the dynamic coupling between the catalytic site residues and the rest of the protein, both inter and intra chain, for the CoV-1 and CoV-2 mPro. We found that there is significant cross-chain coupling between these active sites and specific distal residues in the CoV-2 mPro not present in CoV-1. The enhanced long distance interactions, particularly between the two chains, suggest subsequently enhanced cooperativity for CoV-2. A further comparative analysis of the dynamic flexibility using the Dynamic Flexibility Index (DFI) between the CoV-1 and CoV-2 mPros shows that the inhibitor binding near active sites induces change in flexibility to a distal region of the protein, opposite in behavior between the two systems; this region becomes more flexible upon inhibitor binding in CoV-1 while it becomes less flexible in the CoV-2 mPro. Upon inspection, we show that, on average, the dynamic flexibility of the sites substituted from CoV-1 to CoV-2 changes significantly less than the average calculated across all residues within the structure, indicating that the differences in behaviors between the two systems is likely the result of allosteric influence, where the new substitutions in CoV-2 induce flexibility and dynamical changes elsewhere in the structure.
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Aida H, Shigeta Y, Harada R. Ligand Binding Path Sampling Based on Parallel Cascade Selection Molecular Dynamics: LB-PaCS-MD. MATERIALS 2022; 15:ma15041490. [PMID: 35208030 PMCID: PMC8878848 DOI: 10.3390/ma15041490] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Revised: 02/08/2022] [Accepted: 02/10/2022] [Indexed: 01/09/2023]
Abstract
Parallel cascade selection molecular dynamics (PaCS-MD) is a rare-event sampling method that generates transition pathways between a reactant and product. To sample the transition pathways, PaCS-MD repeats short-time MD simulations from important configurations as conformational resampling cycles. In this study, PaCS-MD was extended to sample ligand binding pathways toward a target protein, which is referred to as LB-PaCS-MD. In a ligand-concentrated environment, where multiple ligand copies are randomly arranged around the target protein, LB-PaCS-MD allows for the frequent sampling of ligand binding pathways. To select the important configurations, we specified the center of mass (COM) distance between each ligand and the relevant binding site of the target protein, where snapshots generated by the short-time MD simulations were ranked by their COM distance values. From each cycle, snapshots with smaller COM distance values were selected as the important configurations to be resampled using the short-time MD simulations. By repeating conformational resampling cycles, the COM distance values gradually decreased and converged to constants, meaning that a set of ligand binding pathways toward the target protein was sampled by LB-PaCS-MD. To demonstrate relative efficiency, LB-PaCS-MD was applied to several proteins, and their ligand binding pathways were sampled more frequently than conventional MD simulations.
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Affiliation(s)
- Hayato Aida
- Graduate School of Science and Technology, University of Tsukuba, 1-1-1 Tennodai, Tsukuba 305-8577, Japan;
| | - Yasuteru Shigeta
- Center for Computational Sciences, University of Tsukuba, 1-1-1 Tennodai, Tsukuba 305-8577, Japan;
| | - Ryuhei Harada
- Center for Computational Sciences, University of Tsukuba, 1-1-1 Tennodai, Tsukuba 305-8577, Japan;
- Correspondence:
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Jamiu AT, Pohl CH, Bello S, Adedoja T, Sabiu S. A review on molecular docking analysis of phytocompounds against SARS-CoV-2 druggable targets. ALL LIFE 2021. [DOI: 10.1080/26895293.2021.2013327] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022] Open
Affiliation(s)
- Abdullahi Temitope Jamiu
- Department of Microbiology and Biochemistry, University of the Free State, Bloemfontein, South Africa
- Department of Biological Sciences, Al-Hikmah University, Ilorin, Nigeria
| | - Carolina H. Pohl
- Department of Microbiology and Biochemistry, University of the Free State, Bloemfontein, South Africa
| | - Sharafa Bello
- Department of Biological Sciences, Al-Hikmah University, Ilorin, Nigeria
| | - Toluwase Adedoja
- Department of Microbiology and Biochemistry, University of the Free State, Bloemfontein, South Africa
| | - Saheed Sabiu
- Department of Biotechnology and Food Science, Durban University of Technology, Durban, South Africa
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Tekpinar M, Neron B, Delarue M. Extracting Dynamical Correlations and Identifying Key Residues for Allosteric Communication in Proteins by correlationplus. J Chem Inf Model 2021; 61:4832-4838. [PMID: 34652149 DOI: 10.1021/acs.jcim.1c00742] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Extracting dynamical pairwise correlations and identifying key residues from large molecular dynamics trajectories or normal-mode analysis of coarse-grained models are important for explaining various processes like ligand binding, mutational effects, and long-distance interactions. Efficient and flexible tools to perform this task can provide new insights about residues involved in allosteric regulation and protein function. In addition, combining and comparing dynamical coupling information with sequence coevolution data can help to understand better protein function. To this aim, we developed a Python package called correlationplus to calculate, visualize, and analyze pairwise correlations. In this way, the package aids to identify key residues and interactions in proteins. The source code of correlationplus is available under LGPL version 3 at https://github.com/tekpinar/correlationplus. The current version of the package (0.2.0) can be installed with common installation methods like conda or pip in addition to source code installation. Moreover, docker images are also available for usage of the code without installation.
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Affiliation(s)
- Mustafa Tekpinar
- Unit of Architecture and Dynamics of Biological Macromolecules, Pasteur Institute, UMR 3528 CNRS, 25 Rue du Dr. Roux, 75015 Paris, France
| | - Bertrand Neron
- Computational Biology Department, Bioinformatics and Biostatistics Hub, 28 Rue du Dr. Roux, 75015 Paris, France
| | - Marc Delarue
- Unit of Architecture and Dynamics of Biological Macromolecules, Pasteur Institute, UMR 3528 CNRS, 25 Rue du Dr. Roux, 75015 Paris, France
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11
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Yan F, Gao F. An overview of potential inhibitors targeting non-structural proteins 3 (PL pro and Mac1) and 5 (3CL pro/M pro) of SARS-CoV-2. Comput Struct Biotechnol J 2021; 19:4868-4883. [PMID: 34457214 PMCID: PMC8382591 DOI: 10.1016/j.csbj.2021.08.036] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2021] [Revised: 08/02/2021] [Accepted: 08/21/2021] [Indexed: 12/11/2022] Open
Abstract
There is an urgent need to develop effective treatments for coronavirus disease 2019 (COVID-19), which is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The rapid spread of SARS-CoV-2 has resulted in a global pandemic that has not only affected the daily lives of individuals but also had a significant impact on the global economy and public health. Although extensive research has been conducted to identify inhibitors targeting SARS-CoV-2, there are still no effective treatment strategies to combat COVID-19. SARS-CoV-2 comprises two important proteolytic enzymes, namely, the papain-like proteinase, located within non-structural protein 3 (nsp3), and nsp5, both of which cleave large replicase polypeptides into multiple fragments that are required for viral replication. Moreover, a domain within nsp3, known as the macrodomain (Mac1), also plays an important role in viral replication. Inhibition of their functions should be able to significantly interfere with the replication cycle of the virus, and therefore these key proteins may serve as potential therapeutic targets. The functions of the above viral targets and their corresponding inhibitors have been summarized in the current review. This review provides comprehensive updates of nsp3 and nsp5 inhibitor development and would help advance the discovery of novel anti-viral therapeutics against SARS-CoV-2.
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Affiliation(s)
- Fangfang Yan
- Department of Physics, School of Science, Tianjin University, Tianjin 300072, China
| | - Feng Gao
- Department of Physics, School of Science, Tianjin University, Tianjin 300072, China
- Frontiers Science Center for Synthetic Biology and Key Laboratory of Systems Bioengineering (Ministry of Education), Tianjin University, Tianjin 300072, China
- SynBio Research Platform, Collaborative Innovation Center of Chemical Science and Engineering (Tianjin), Tianjin 300072, China
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