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Velloso JPL, de Sá AGC, Pires DEV, Ascher DB. Engineering G protein-coupled receptors for stabilization. Protein Sci 2024; 33:e5000. [PMID: 38747401 PMCID: PMC11094779 DOI: 10.1002/pro.5000] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2023] [Revised: 03/21/2024] [Accepted: 04/10/2024] [Indexed: 05/19/2024]
Abstract
G protein-coupled receptors (GPCRs) are one of the most important families of targets for drug discovery. One of the limiting steps in the study of GPCRs has been their stability, with significant and time-consuming protein engineering often used to stabilize GPCRs for structural characterization and drug screening. Unfortunately, computational methods developed using globular soluble proteins have translated poorly to the rational engineering of GPCRs. To fill this gap, we propose GPCR-tm, a novel and personalized structurally driven web-based machine learning tool to study the impacts of mutations on GPCR stability. We show that GPCR-tm performs as well as or better than alternative methods, and that it can accurately rank the stability changes of a wide range of mutations occurring in various types of class A GPCRs. GPCR-tm achieved Pearson's correlation coefficients of 0.74 and 0.46 on 10-fold cross-validation and blind test sets, respectively. We observed that the (structural) graph-based signatures were the most important set of features for predicting destabilizing mutations, which points out that these signatures properly describe the changes in the environment where the mutations occur. More specifically, GPCR-tm was able to accurately rank mutations based on their effect on protein stability, guiding their rational stabilization. GPCR-tm is available through a user-friendly web server at https://biosig.lab.uq.edu.au/gpcr_tm/.
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Affiliation(s)
- João Paulo L. Velloso
- School of Chemistry and Molecular Biosciences, The Australian Centre for EcogenomicsThe University of QueenslandBrisbaneQueenslandAustralia
- Computational Biology and Clinical InformaticsBaker Heart and Diabetes InstituteMelbourneVictoriaAustralia
- Baker Department of Cardiometabolic HealthThe University of MelbourneParkvilleVictoriaAustralia
| | - Alex G. C. de Sá
- School of Chemistry and Molecular Biosciences, The Australian Centre for EcogenomicsThe University of QueenslandBrisbaneQueenslandAustralia
- Computational Biology and Clinical InformaticsBaker Heart and Diabetes InstituteMelbourneVictoriaAustralia
- Baker Department of Cardiometabolic HealthThe University of MelbourneParkvilleVictoriaAustralia
| | - Douglas E. V. Pires
- School of Computing and Information SystemsThe University of MelbourneParkvilleVictoriaAustralia
| | - David B. Ascher
- School of Chemistry and Molecular Biosciences, The Australian Centre for EcogenomicsThe University of QueenslandBrisbaneQueenslandAustralia
- Computational Biology and Clinical InformaticsBaker Heart and Diabetes InstituteMelbourneVictoriaAustralia
- Baker Department of Cardiometabolic HealthThe University of MelbourneParkvilleVictoriaAustralia
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2
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Abd-Elshafy DN, Nadeem R, Nasraa MH, Bahgat MM. Analysis of the SARS-CoV-2 nsp12 P323L/A529V mutations: coeffect in the transiently peaking lineage C.36.3 on protein structure and response to treatment in Egyptian records. Z NATURFORSCH C 2024; 79:13-24. [PMID: 38265042 DOI: 10.1515/znc-2023-0132] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Accepted: 01/09/2024] [Indexed: 01/25/2024]
Abstract
SARS-CoV-2 nsp12, the RNA-dependent RNA-polymerase plays a crucial role in virus replication. Monitoring the effect of its emerging mutants on viral replication and response to antiviral drugs is important. Nsp12 of two Egyptian isolates circulating in 2020 and 2021 were sequenced. Both isolates included P323L, one included the A529V. Tracking A529V mutant frequency, it relates to the transience peaked C.36.3 variant and its parent C.36, both peaked worldwide on February-August 2021, enlisted as high transmissible variants under investigation (VUI) on May 2021. Both Mutants were reported to originate from Egypt and showed an abrupt low frequency upon screening, we analyzed all 1104 nsp12 Egyptian sequences. A529V mutation was in 36 records with an abrupt low frequency on June 2021. As its possible reappearance might obligate actions for a candidate VUI, we analyzed the predicted co-effect of P323L and A529V mutations on protein stability and dynamics through protein structure simulations. Three available structures for drug-nsp12 interaction were used representing remdesivir, suramin and favipiravir drugs. Remdesivir and suramin showed an increase in structure stability and considerable change in flexibility while favipiravir showed an extreme interaction. Results predict a favored efficiency of the drugs except for favipiravir in case of the reported mutations.
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Affiliation(s)
- Dina N Abd-Elshafy
- Department of Water Pollution Research, Environmental and Climate Change Research Institute, The National Research Centre, Dokki, Cairo, Egypt
- Immune- and Bio-markers for Infection Research Group, The Center of Excellence for Advanced Sciences, The National Research Centre, Dokki, Cairo, Egypt
| | - Rola Nadeem
- Department of Therapeutic Chemistry, Pharmaceutical and Drug Industries Research Institute, The National Research Centre, Dokki, Cairo, Egypt
- Immune- and Bio-markers for Infection Research Group, The Center of Excellence for Advanced Sciences, The National Research Centre, Dokki, Cairo, Egypt
| | - Mohamed H Nasraa
- Department of Therapeutic Chemistry, Pharmaceutical and Drug Industries Research Institute, The National Research Centre, Dokki, Cairo, Egypt
- Immune- and Bio-markers for Infection Research Group, The Center of Excellence for Advanced Sciences, The National Research Centre, Dokki, Cairo, Egypt
| | - Mahmoud M Bahgat
- Department of Therapeutic Chemistry, Pharmaceutical and Drug Industries Research Institute, The National Research Centre, Dokki, Cairo, Egypt
- Immune- and Bio-markers for Infection Research Group, The Center of Excellence for Advanced Sciences, The National Research Centre, Dokki, Cairo, Egypt
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3
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Litso I, Plaitakis A, Fadouloglou VE, Providaki M, Kokkinidis M, Zaganas I. Structural Evolution of Primate Glutamate Dehydrogenase 2 as Revealed by In Silico Predictions and Experimentally Determined Structures. Biomolecules 2023; 14:22. [PMID: 38254622 PMCID: PMC10812971 DOI: 10.3390/biom14010022] [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: 10/24/2023] [Revised: 11/29/2023] [Accepted: 12/04/2023] [Indexed: 01/24/2024] Open
Abstract
Glutamate dehydrogenase (GDH) interconverts glutamate to a-ketoglutarate and ammonia, interconnecting amino acid and carbohydrate metabolism. In humans, two functional GDH genes, GLUD1 and GLUD2, encode for hGDH1 and hGDH2, respectively. GLUD2 evolved from retrotransposition of the GLUD1 gene in the common ancestor of modern apes. These two isoenzymes are involved in the pathophysiology of human metabolic, neoplastic, and neurodegenerative disorders. The 3D structures of hGDH1 and hGDH2 have been experimentally determined; however, no information is available about the path of GDH2 structure changes during primate evolution. Here, we compare the structures predicted by the AlphaFold Colab method for the GDH2 enzyme of modern apes and their extinct primate ancestors. Also, we analyze the individual effect of amino acid substitutions emerging during primate evolution. Our most important finding is that the predicted structure of GDH2 in the common ancestor of apes was the steppingstone for the structural evolution of primate GDH2s. Two changes with a strong functional impact occurring at the first evolutionary step, Arg443Ser and Gly456Ala, had a destabilizing and stabilizing effect, respectively, making this step the most important one. Subsequently, GDH2 underwent additional modifications that fine-tuned its enzymatic properties to adapt to the functional needs of modern-day primate tissues.
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Affiliation(s)
- Ionela Litso
- Neurology/Neurogenetics Laboratory, School of Medicine, University of Crete, Voutes, 71003 Heraklion, Greece; (I.L.); (A.P.)
| | - Andreas Plaitakis
- Neurology/Neurogenetics Laboratory, School of Medicine, University of Crete, Voutes, 71003 Heraklion, Greece; (I.L.); (A.P.)
| | - Vasiliki E. Fadouloglou
- Department of Molecular Biology and Genetics, Faculty of Health Sciences, Democritus University of Thrace, 68100 Alexandroupolis, Greece;
| | - Mary Providaki
- Institute of Molecular Biology and Biotechnology, Foundation of Research and Technology-Hellas, 70013 Heraklion, Greece; (M.P.); (M.K.)
| | - Michael Kokkinidis
- Institute of Molecular Biology and Biotechnology, Foundation of Research and Technology-Hellas, 70013 Heraklion, Greece; (M.P.); (M.K.)
- Department of Biology, University of Crete, Vasilika Vouton, 71409 Heraklion, Greece
| | - Ioannis Zaganas
- Neurology/Neurogenetics Laboratory, School of Medicine, University of Crete, Voutes, 71003 Heraklion, Greece; (I.L.); (A.P.)
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4
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Arya R, Tripathi P, Nayak K, Ganesh J, Bihani SC, Ghosh B, Prashar V, Kumar M. Insights into the evolution of mutations in SARS-CoV-2 non-spike proteins. Microb Pathog 2023; 185:106460. [PMID: 37995880 DOI: 10.1016/j.micpath.2023.106460] [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: 06/12/2023] [Revised: 10/16/2023] [Accepted: 11/17/2023] [Indexed: 11/25/2023]
Abstract
The COVID-19 pandemic has been driven by the emergence of SARS-CoV-2 variants with mutations across all the viral proteins. Although mutations in the spike protein have received significant attention, understanding the prevalence and potential impact of mutations in other viral proteins is essential for comprehending the evolution of SARS-CoV-2. Here, we conducted a comprehensive analysis of approximately 14 million sequences of SARS-CoV-2 deposited in the GISAID database until December 2022 to identify prevalent mutations in the non-spike proteins at the global and country levels. Additionally, we evaluated the energetics of each mutation to better understand their impact on protein stability. While the consequences of many mutations remain unclear, we discuss potential structural and functional significance of some mutations. Our study highlights the ongoing evolutionary process of SARS-CoV-2 and underscores the importance of understanding changes in non-spike proteins.
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Affiliation(s)
- Rimanshee Arya
- Protein Crystallography Section, Bhabha Atomic Research Centre, Mumbai, 400085, India; Homi Bhabha National Institute, Anushakti Nagar, Mumbai, 400094, India
| | - Preeti Tripathi
- Protein Crystallography Section, Bhabha Atomic Research Centre, Mumbai, 400085, India
| | - Karthik Nayak
- Protein Crystallography Section, Bhabha Atomic Research Centre, Mumbai, 400085, India; School of Chemical Sciences, UM-DAE Centre for Excellence in Basic Sciences, University of Mumbai, Vidyanagari, Mumbai, 400098, India
| | - Janani Ganesh
- Protein Crystallography Section, Bhabha Atomic Research Centre, Mumbai, 400085, India; Homi Bhabha National Institute, Anushakti Nagar, Mumbai, 400094, India
| | - Subhash C Bihani
- Protein Crystallography Section, Bhabha Atomic Research Centre, Mumbai, 400085, India; Homi Bhabha National Institute, Anushakti Nagar, Mumbai, 400094, India
| | - Biplab Ghosh
- Homi Bhabha National Institute, Anushakti Nagar, Mumbai, 400094, India; Beamline Development & Application Section, Bhabha Atomic Research Centre, Mumbai, 400085, India
| | - Vishal Prashar
- Protein Crystallography Section, Bhabha Atomic Research Centre, Mumbai, 400085, India; Homi Bhabha National Institute, Anushakti Nagar, Mumbai, 400094, India.
| | - Mukesh Kumar
- Protein Crystallography Section, Bhabha Atomic Research Centre, Mumbai, 400085, India; Homi Bhabha National Institute, Anushakti Nagar, Mumbai, 400094, India.
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5
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Xu SY, Zhou L, Xu Y, Hong HY, Dai C, Wang YJ, Zheng YG. Recent advances in structure-based enzyme engineering for functional reconstruction. Biotechnol Bioeng 2023; 120:3427-3445. [PMID: 37638646 DOI: 10.1002/bit.28540] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Revised: 07/27/2023] [Accepted: 08/15/2023] [Indexed: 08/29/2023]
Abstract
Structural information can help engineer enzymes. Usually, specific amino acids in particular regions are targeted for functional reconstruction to enhance the catalytic performance, including activity, stereoselectivity, and thermostability. Appropriate selection of target sites is the key to structure-based design, which requires elucidation of the structure-function relationships. Here, we summarize the mutations of residues in different specific regions, including active center, access tunnels, and flexible loops, on fine-tuning the catalytic performance of enzymes, and discuss the effects of altering the local structural environment on the functions. In addition, we keep up with the recent progress of structure-based approaches for enzyme engineering, aiming to provide some guidance on how to take advantage of the structural information.
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Affiliation(s)
- Shen-Yuan Xu
- Key Laboratory of Bioorganic Synthesis of Zhejiang Province, College of Biotechnology and Bioengineering, Zhejiang University of Technology, Hangzhou, Zhejiang, People's Republic of China
- Engineering Research Center of Bioconversion and Biopurification of the Ministry of Education, Zhejiang University of Technology, Hangzhou, Zhejiang, People's Republic of China
- The National and Local Joint Engineering Research Center for Biomanufacturing of Chiral Chemicals, Zhejiang University of Technology, Hangzhou, Zhejiang, People's Republic of China
| | - Lei Zhou
- Key Laboratory of Bioorganic Synthesis of Zhejiang Province, College of Biotechnology and Bioengineering, Zhejiang University of Technology, Hangzhou, Zhejiang, People's Republic of China
- Engineering Research Center of Bioconversion and Biopurification of the Ministry of Education, Zhejiang University of Technology, Hangzhou, Zhejiang, People's Republic of China
- The National and Local Joint Engineering Research Center for Biomanufacturing of Chiral Chemicals, Zhejiang University of Technology, Hangzhou, Zhejiang, People's Republic of China
| | - Ying Xu
- Key Laboratory of Bioorganic Synthesis of Zhejiang Province, College of Biotechnology and Bioengineering, Zhejiang University of Technology, Hangzhou, Zhejiang, People's Republic of China
- Engineering Research Center of Bioconversion and Biopurification of the Ministry of Education, Zhejiang University of Technology, Hangzhou, Zhejiang, People's Republic of China
- The National and Local Joint Engineering Research Center for Biomanufacturing of Chiral Chemicals, Zhejiang University of Technology, Hangzhou, Zhejiang, People's Republic of China
| | - Han-Yue Hong
- Key Laboratory of Bioorganic Synthesis of Zhejiang Province, College of Biotechnology and Bioengineering, Zhejiang University of Technology, Hangzhou, Zhejiang, People's Republic of China
- Engineering Research Center of Bioconversion and Biopurification of the Ministry of Education, Zhejiang University of Technology, Hangzhou, Zhejiang, People's Republic of China
- The National and Local Joint Engineering Research Center for Biomanufacturing of Chiral Chemicals, Zhejiang University of Technology, Hangzhou, Zhejiang, People's Republic of China
| | - Chen Dai
- Key Laboratory of Bioorganic Synthesis of Zhejiang Province, College of Biotechnology and Bioengineering, Zhejiang University of Technology, Hangzhou, Zhejiang, People's Republic of China
- Engineering Research Center of Bioconversion and Biopurification of the Ministry of Education, Zhejiang University of Technology, Hangzhou, Zhejiang, People's Republic of China
- The National and Local Joint Engineering Research Center for Biomanufacturing of Chiral Chemicals, Zhejiang University of Technology, Hangzhou, Zhejiang, People's Republic of China
| | - Ya-Jun Wang
- Key Laboratory of Bioorganic Synthesis of Zhejiang Province, College of Biotechnology and Bioengineering, Zhejiang University of Technology, Hangzhou, Zhejiang, People's Republic of China
- Engineering Research Center of Bioconversion and Biopurification of the Ministry of Education, Zhejiang University of Technology, Hangzhou, Zhejiang, People's Republic of China
- The National and Local Joint Engineering Research Center for Biomanufacturing of Chiral Chemicals, Zhejiang University of Technology, Hangzhou, Zhejiang, People's Republic of China
| | - Yu-Guo Zheng
- Key Laboratory of Bioorganic Synthesis of Zhejiang Province, College of Biotechnology and Bioengineering, Zhejiang University of Technology, Hangzhou, Zhejiang, People's Republic of China
- Engineering Research Center of Bioconversion and Biopurification of the Ministry of Education, Zhejiang University of Technology, Hangzhou, Zhejiang, People's Republic of China
- The National and Local Joint Engineering Research Center for Biomanufacturing of Chiral Chemicals, Zhejiang University of Technology, Hangzhou, Zhejiang, People's Republic of China
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6
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Shome S, Jia K, Sivasankar S, Jernigan RL. Characterizing interactions in E-cadherin assemblages. Biophys J 2023; 122:3069-3077. [PMID: 37345249 PMCID: PMC10432173 DOI: 10.1016/j.bpj.2023.06.009] [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: 04/21/2022] [Revised: 09/26/2022] [Accepted: 06/14/2023] [Indexed: 06/23/2023] Open
Abstract
Cadherin intermolecular interactions are critical for cell-cell adhesion and play essential roles in tissue formation and the maintenance of tissue structures. In this study, we focus on E-cadherin, a classical cadherin that connects epithelial cells, to understand how they interact in cis and trans conformations when attached to the same cell or opposing cells. We employ coevolutionary sequence analysis and molecular dynamics simulations to confirm previously known interaction sites as well as to identify new interaction sites. The sequence coevolutionary results yield a surprising result indicating that there are no strongly favored intermolecular interaction sites, which is unusual and suggests that many interaction sites may be possible, with none being strongly preferred over others. By using molecular dynamics, we test the persistence of these interactions and how they facilitate adhesion. We build several types of cadherin assemblages, with different numbers and combinations of cis and trans interfaces to understand how these conformations act to facilitate adhesion. Our results suggest that, in addition to the established interaction sites on the EC1 and EC2 domains, an additional plausible cis interface at the EC3-EC5 domain exists. Furthermore, we identify specific mutations at cis/trans binding sites that impair adhesion within E-cadherin assemblages.
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Affiliation(s)
- Sayane Shome
- Roy J. Carver Department of Biochemistry, Biophysics and Molecular Biology, Iowa State University, Ames, Iowa
| | - Kejue Jia
- Roy J. Carver Department of Biochemistry, Biophysics and Molecular Biology, Iowa State University, Ames, Iowa
| | - Sanjeevi Sivasankar
- Department of Biomedical Engineering, University of California, Davis, Davis, California
| | - Robert L Jernigan
- Roy J. Carver Department of Biochemistry, Biophysics and Molecular Biology, Iowa State University, Ames, Iowa.
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7
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Khoza N, Twesigomwe D, Othman H. Characterizing the combined effects of cytochrome P450 missense variation within star allele definitions. Pharmacogenomics 2023; 24:561-578. [PMID: 37503750 DOI: 10.2217/pgs-2023-0068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/29/2023] Open
Abstract
Background: Cytochrome P450 (CYP) genetic variation largely impacts drug response. However, many CYP star alleles (haplotypes) lack functional annotation, impeding our understanding of drug metabolism mechanisms. We aimed to investigate the impact of missense variant combinations on CYP protein structures. Methods: Normal mode analysis was conducted on 261 missense variants within 91 CYP haplotypes. CYP2D6*2 and CYP2D6*17 were prioritized for molecular dynamics simulation. Results: Normal mode analysis and molecular dynamics highlight the effects of known CYP missense variants on protein stability and conformational dynamics. Missense variants within haplotypes may have intermodulating effects on protein structure and function. Conclusion: This study highlights the utility of multiscale modeling in interpreting CYP missense variants and particularly their combinations within various star alleles.
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Affiliation(s)
- Nhlamulo Khoza
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, 9 Jubilee Road, Parktown, Johannesburg, 2193, South Africa
- Division of Human Genetics, National Health Laboratory Service and School of Pathology, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, 2001, South Africa
| | - David Twesigomwe
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, 9 Jubilee Road, Parktown, Johannesburg, 2193, South Africa
| | - Houcemeddine Othman
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, 9 Jubilee Road, Parktown, Johannesburg, 2193, South Africa
- Laboratory of Human Cytogenetics, Molecular Genetics and Reproductive Biology, Farhat Hached University Hospital, Sousse, 4000, Tunisia
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Hoşnut FÖ, Janecke AR, Şahin G, Vogel GF, Lafcı NG, Bichler P, Müller T, Huber LA, Valovka T, Aksu AÜ. SLC5A1 Variants in Turkish Patients with Congenital Glucose-Galactose Malabsorption. Genes (Basel) 2023; 14:1359. [PMID: 37510265 PMCID: PMC10379334 DOI: 10.3390/genes14071359] [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: 01/08/2023] [Revised: 06/02/2023] [Accepted: 06/20/2023] [Indexed: 07/30/2023] Open
Abstract
Congenital glucose-galactose malabsorption is a rare autosomal recessive disorder caused by mutations in SLC5A1 encoding the apical sodium/glucose cotransporter SGLT1. We present clinical and molecular data from eleven affected individuals with congenital glucose-galactose malabsorption from four unrelated, consanguineous Turkish families. Early recognition and timely management by eliminating glucose and galactose from the diet are fundamental for affected individuals to survive and develop normally. We identified novel SLC5A1 missense variants, p.Gly43Arg and p.Ala92Val, which were linked to disease in two families. Stable expression in CaCo-2 cells showed that the p.Ala92Val variant did not reach the plasma membrane, but was retained in the endoplasmic reticulum. The p.Gly43Arg variant, however, displayed processing and plasma membrane localization comparable to wild-type SGLT1. Glycine-43 displays nearly invariant conservation in the relevant structural family of cotransporters and exchangers, and localizes to SGLT1 transmembrane domain TM0. p.Gly43Arg represents the first disease-associated variant in TM0; however, the role of TM0 in the SGLT1 function has not been established. In summary, we are expanding the mutational spectrum of this rare disorder.
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Affiliation(s)
- Ferda Ö. Hoşnut
- Department of Pediatric Gastroenterology, Hepatology and Nutrition, Dr. Sami Ulus Maternity and Child Health and Diseases Training and Research Hospital, University of Health Sciences, 06080 Ankara, Turkey
| | - Andreas R. Janecke
- Department of Pediatrics I, Medical University of Innsbruck, 6020 Innsbruck, Austria (T.V.)
- Institute of Human Genetics, Medical University of Innsbruck, 6020 Innsbruck, Austria
| | - Gülseren Şahin
- Department of Pediatric Gastroenterology, Hepatology and Nutrition, Dr. Sami Ulus Maternity and Child Health and Diseases Training and Research Hospital, University of Health Sciences, 06080 Ankara, Turkey
| | - Georg F. Vogel
- Department of Pediatrics I, Medical University of Innsbruck, 6020 Innsbruck, Austria (T.V.)
- Institute of Cell Biology, Biocenter, Medical University of Innsbruck, 6020 Innsbruck, Austria
| | - Naz G. Lafcı
- Department of Medical Genetics, Faculty of Medicine, Hacettepe University, 06230 Ankara, Turkey
- Department of Medical Genetics, Dr. Sami Ulus Maternity and Child Health and Diseases Training and Research Hospital, University of Health Sciences, 06080 Ankara, Turkey
| | - Paul Bichler
- Department of Pediatrics I, Medical University of Innsbruck, 6020 Innsbruck, Austria (T.V.)
- Institute of Cell Biology, Biocenter, Medical University of Innsbruck, 6020 Innsbruck, Austria
| | - Thomas Müller
- Department of Pediatrics I, Medical University of Innsbruck, 6020 Innsbruck, Austria (T.V.)
| | - Lukas A. Huber
- Institute of Cell Biology, Biocenter, Medical University of Innsbruck, 6020 Innsbruck, Austria
| | - Taras Valovka
- Department of Pediatrics I, Medical University of Innsbruck, 6020 Innsbruck, Austria (T.V.)
- Institute of Cell Biology, Biocenter, Medical University of Innsbruck, 6020 Innsbruck, Austria
| | - Aysel Ü. Aksu
- Department of Pediatric Gastroenterology, Hepatology and Nutrition, Ankara Bilkent Hospital, University of Health Sciences, 06800 Ankara, Turkey;
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Senthilazhagan K, Sakthimani S, Kallanja D, Venkataraman S. SARS-CoV-2: analysis of the effects of mutations in non-structural proteins. Arch Virol 2023; 168:186. [PMID: 37344726 DOI: 10.1007/s00705-023-05818-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Accepted: 05/10/2023] [Indexed: 06/23/2023]
Abstract
A worldwide pandemic that started in China in late 2019 was caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a single-stranded RNA virus belonging to the family Coronaviridae. Due to its structural variability and mutability, this virus continues to evolve and pose a major health threat around the world. Its characteristics, such as transmissibility, antigenicity, and resistance to drugs and vaccines, are continually altered through mutations. Examining mutational hotspots and their structural repercussions can thus aid in the development of more-effective vaccinations and treatment plans. In this study, we used full genome sequences of SARS-CoV-2 variants to predict structural changes in viral proteins. These sequences were obtained from the Global Initiative on Sharing Avian Influenza Data (GISAID), and a set of significant mutations were identified in each of the non-structural proteins (NSP1-16) and structural proteins, including the envelope, nucleocapsid, membrane, and spike proteins. The mutations were characterized as stabilizing or destabilizing based on their effect on protein dynamics and stability, and their impact on structure and function was evaluated. Among all of the proteins, NSP6 stands out as especially variable. The results of this study augment our understanding of how mutational events influence virus pathogenicity and evolution.
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Affiliation(s)
- Kavya Senthilazhagan
- Department of Biotechnology, Anna University, 600025, Guindy, Chennai, Tamil Nādu, India
| | - Seshagiri Sakthimani
- Department of Biotechnology, Anna University, 600025, Guindy, Chennai, Tamil Nādu, India
| | - Deepthi Kallanja
- Department of Biotechnology, Anna University, 600025, Guindy, Chennai, Tamil Nādu, India
| | - Sangita Venkataraman
- Department of Biotechnology, Anna University, 600025, Guindy, Chennai, Tamil Nādu, India.
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10
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Banerjee A, Saha S, Tvedt NC, Yang LW, Bahar I. Mutually beneficial confluence of structure-based modeling of protein dynamics and machine learning methods. Curr Opin Struct Biol 2023; 78:102517. [PMID: 36587424 PMCID: PMC10038760 DOI: 10.1016/j.sbi.2022.102517] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Revised: 11/19/2022] [Accepted: 11/22/2022] [Indexed: 12/31/2022]
Abstract
Proteins sample an ensemble of conformers under physiological conditions, having access to a spectrum of modes of motions, also called intrinsic dynamics. These motions ensure the adaptation to various interactions in the cell, and largely assist in, if not determine, viable mechanisms of biological function. In recent years, machine learning frameworks have proven uniquely useful in structural biology, and recent studies further provide evidence to the utility and/or necessity of considering intrinsic dynamics for increasing their predictive ability. Efficient quantification of dynamics-based attributes by recently developed physics-based theories and models such as elastic network models provides a unique opportunity to generate data on dynamics for training ML models towards inferring mechanisms of protein function, assessing pathogenicity, or estimating binding affinities.
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Affiliation(s)
- Anupam Banerjee
- Computational and Systems Biology, University of Pittsburgh School of Medicine, Pittsburgh PA 15261, USA
| | - Satyaki Saha
- Computational and Systems Biology, University of Pittsburgh School of Medicine, Pittsburgh PA 15261, USA
| | - Nathan C Tvedt
- Computational and Systems Biology, University of Pittsburgh School of Medicine, Pittsburgh PA 15261, USA; Computational and Applied Mathematics and Statistics, The College of William and Mary, Williamsburg, VA 23185, USA
| | - Lee-Wei Yang
- Institute of Bioinformatics and Structural Biology, and PhD Program in Biomedical Artificial Intelligence, National Tsing Hua University, Hsinchu 300044, Taiwan; Physics Division, National Center for Theoretical Sciences, Taipei 106319, Taiwan
| | - Ivet Bahar
- Computational and Systems Biology, University of Pittsburgh School of Medicine, Pittsburgh PA 15261, USA.
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11
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Durairaj J, de Ridder D, van Dijk AD. Beyond sequence: Structure-based machine learning. Comput Struct Biotechnol J 2022; 21:630-643. [PMID: 36659927 PMCID: PMC9826903 DOI: 10.1016/j.csbj.2022.12.039] [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: 09/26/2022] [Revised: 12/21/2022] [Accepted: 12/21/2022] [Indexed: 12/31/2022] Open
Abstract
Recent breakthroughs in protein structure prediction demarcate the start of a new era in structural bioinformatics. Combined with various advances in experimental structure determination and the uninterrupted pace at which new structures are published, this promises an age in which protein structure information is as prevalent and ubiquitous as sequence. Machine learning in protein bioinformatics has been dominated by sequence-based methods, but this is now changing to make use of the deluge of rich structural information as input. Machine learning methods making use of structures are scattered across literature and cover a number of different applications and scopes; while some try to address questions and tasks within a single protein family, others aim to capture characteristics across all available proteins. In this review, we look at the variety of structure-based machine learning approaches, how structures can be used as input, and typical applications of these approaches in protein biology. We also discuss current challenges and opportunities in this all-important and increasingly popular field.
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Affiliation(s)
- Janani Durairaj
- Biozentrum, University of Basel, Basel, Switzerland
- Bioinformatics Group, Department of Plant Sciences, Wageningen University and Research, Wageningen, the Netherlands
| | - Dick de Ridder
- Bioinformatics Group, Department of Plant Sciences, Wageningen University and Research, Wageningen, the Netherlands
| | - Aalt D.J. van Dijk
- Bioinformatics Group, Department of Plant Sciences, Wageningen University and Research, Wageningen, the Netherlands
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12
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Chandrasekhar G, Chandra Sekar P, Srinivasan E, Amarnath A, Pengyong H, Rajasekaran R. Molecular simulation unravels the amyloidogenic misfolding of nascent ApoA1 protein, driven by deleterious point mutations occurring in between 170-178 hotspot region. J Biomol Struct Dyn 2022; 40:13278-13290. [PMID: 34613891 DOI: 10.1080/07391102.2021.1986134] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
Protein ApoA1 is extensively studied for its role in lipid metabolism. Its seedy dark side of amyloid formulation remains relatively understudied yet. Due to genetic mutations, the protein pathologically misshapes into its amyloid form that gets accumulated in various organs, including the heart. To contrive effective therapeutics against this debilitating congenital disorder, it is imperative to comprehend the structural ramifications induced by mutations in APoA1's dynamic conformation. Till now, several point mutations have been implicated in ApoA1's amyloidosis, although only a handful has been examined considerably. Especially, the single nucleotide polymorphisms (SNPs) that occur in-between 170-178 mutation hotspot site of APoA1 needs to be investigated, since most of them are culpable of amyloid deposition in the heart. To that effect, in the present study, we have computationally quantified and studied the ApoA1's biomolecular modifications fostered by SNPs in the 170-178 mutation hotspot. Findings from discrete molecular dynamics simulation studies indicate that the SNPs have noticeably steered the ApoA1's behaviour from its native structural dynamics. Analysis of protein's secondary structural changes exhibits a considerable change upon mutations. Further, subjecting the protein structures to simulated thermal denaturation shows increased resistance to denaturation among mutants when compared to native. Further, normal mode analysis of protein's dynamic motion also shows discrepancy in its dynamic structural change upon SNP. These structural digressions induced by SNPs can very well be the biomolecular incendiary that drives ApoA1 into its amyloidogenesis. And, understanding these structural modifications initiates a better understanding of SNP's amyloidogenic pathology on APoA1.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- G Chandrasekhar
- Bioinformatics Lab, Department of Biotechnology, School of Bio Sciences and Technology, Vellore Institute of Technology (Deemed to be University), Vellore, Tamil Nadu, India
| | - P Chandra Sekar
- Bioinformatics Lab, Department of Biotechnology, School of Bio Sciences and Technology, Vellore Institute of Technology (Deemed to be University), Vellore, Tamil Nadu, India
| | - E Srinivasan
- Bioinformatics Lab, Department of Biotechnology, School of Bio Sciences and Technology, Vellore Institute of Technology (Deemed to be University), Vellore, Tamil Nadu, India
| | - A Amarnath
- Bioinformatics Lab, Department of Biotechnology, School of Bio Sciences and Technology, Vellore Institute of Technology (Deemed to be University), Vellore, Tamil Nadu, India
| | - H Pengyong
- Central Lab, Changzhi Medical College, Changzhi, China
| | - R Rajasekaran
- Bioinformatics Lab, Department of Biotechnology, School of Bio Sciences and Technology, Vellore Institute of Technology (Deemed to be University), Vellore, Tamil Nadu, India
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13
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Mailhot O, Frappier V, Major F, Najmanovich RJ. Sequence-sensitive elastic network captures dynamical features necessary for miR-125a maturation. PLoS Comput Biol 2022; 18:e1010777. [PMID: 36516216 PMCID: PMC9797095 DOI: 10.1371/journal.pcbi.1010777] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Revised: 12/28/2022] [Accepted: 11/29/2022] [Indexed: 12/15/2022] Open
Abstract
The Elastic Network Contact Model (ENCoM) is a coarse-grained normal mode analysis (NMA) model unique in its all-atom sensitivity to the sequence of the studied macromolecule and thus to the effect of mutations. We adapted ENCoM to simulate the dynamics of ribonucleic acid (RNA) molecules, benchmarked its performance against other popular NMA models and used it to study the 3D structural dynamics of human microRNA miR-125a, leveraging high-throughput experimental maturation efficiency data of over 26 000 sequence variants. We also introduce a novel way of using dynamical information from NMA to train multivariate linear regression models, with the purpose of highlighting the most salient contributions of dynamics to function. ENCoM has a similar performance profile on RNA than on proteins when compared to the Anisotropic Network Model (ANM), the most widely used coarse-grained NMA model; it has the advantage on predicting large-scale motions while ANM performs better on B-factors prediction. A stringent benchmark from the miR-125a maturation dataset, in which the training set contains no sequence information in common with the testing set, reveals that ENCoM is the only tested model able to capture signal beyond the sequence. This ability translates to better predictive power on a second benchmark in which sequence features are shared between the train and test sets. When training the linear regression model using all available data, the dynamical features identified as necessary for miR-125a maturation point to known patterns but also offer new insights into the biogenesis of microRNAs. Our novel approach combining NMA with multivariate linear regression is generalizable to any macromolecule for which relatively high-throughput mutational data is available.
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Affiliation(s)
- Olivier Mailhot
- Department of Biochemistry and Molecular Medicine, Université de Montréal, Montreal, QC, Canada
- Department of Computer Science and Operations Research, Université de Montréal, Montreal, QC, Canada
- Institute for Research in Immunology and Cancer, Université de Montréal, Montreal, QC, Canada
- Department of Pharmacology and Physiology, Université de Montréal, Montreal, QC, Canada
| | - Vincent Frappier
- Generate Biomedicines, Cambridge, Massachusetts, United States of America
| | - François Major
- Department of Computer Science and Operations Research, Université de Montréal, Montreal, QC, Canada
- Institute for Research in Immunology and Cancer, Université de Montréal, Montreal, QC, Canada
- * E-mail: (FM); (RJN)
| | - Rafael J. Najmanovich
- Department of Pharmacology and Physiology, Université de Montréal, Montreal, QC, Canada
- * E-mail: (FM); (RJN)
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14
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Periwal N, Rathod SB, Sarma S, Johar GS, Jain A, Barnwal RP, Srivastava KR, Kaur B, Arora P, Sood V. Time Series Analysis of SARS-CoV-2 Genomes and Correlations among Highly Prevalent Mutations. Microbiol Spectr 2022; 10:e0121922. [PMID: 36069583 PMCID: PMC9603882 DOI: 10.1128/spectrum.01219-22] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Accepted: 08/03/2022] [Indexed: 12/30/2022] Open
Abstract
The efforts of the scientific community to tame the recent pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) seem to have been diluted by the emergence of new viral strains. Therefore, it is imperative to understand the effect of mutations on viral evolution. We performed a time series analysis on 59,541 SARS-CoV-2 genomic sequences from around the world to gain insights into the kinetics of the mutations arising in the viral genomes. These 59,541 genomes were grouped according to month (January 2020 to March 2021) based on the collection date. Meta-analysis of these data led us to identify significant mutations in viral genomes. Pearson correlation of these mutations led us to the identification of 16 comutations. Among these comutations, some of the individual mutations have been shown to contribute to viral replication and fitness, suggesting a possible role of other unexplored mutations in viral evolution. We observed that the mutations 241C>T in the 5' untranslated region (UTR), 3037C>T in nsp3, 14408C>T in the RNA-dependent RNA polymerase (RdRp), and 23403A>G in spike are correlated with each other and were grouped in a single cluster by hierarchical clustering. These mutations have replaced the wild-type nucleotides in SARS-CoV-2 sequences. Additionally, we employed a suite of computational tools to investigate the effects of T85I (1059C>T), P323L (14408C>T), and Q57H (25563G>T) mutations in nsp2, RdRp, and the ORF3a protein of SARS-CoV-2, respectively. We observed that the mutations T85I and Q57H tend to be deleterious and destabilize the respective wild-type protein, whereas P323L in RdRp tends to be neutral and has a stabilizing effect. IMPORTANCE We performed a meta-analysis on SARS-CoV-2 genomes categorized by collection month and identified several significant mutations. Pearson correlation analysis of these significant mutations identified 16 comutations having absolute correlation coefficients of >0.4 and a frequency of >30% in the genomes used in this study. The correlation results were further validated by another statistical tool called hierarchical clustering, where mutations were grouped in clusters on the basis of their similarity. We identified several positive and negative correlations among comutations in SARS-CoV-2 isolates from around the world which might contribute to viral pathogenesis. The negative correlations among some of the mutations in SARS-CoV-2 identified in this study warrant further investigations. Further analysis of mutations such as T85I in nsp2 and Q57H in ORF3a protein revealed that these mutations tend to destabilize the protein relative to the wild type, whereas P323L in RdRp is neutral and has a stabilizing effect. Thus, we have identified several comutations which can be further characterized to gain insights into SARS-CoV-2 evolution.
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Affiliation(s)
- Neha Periwal
- Department of Biochemistry, SCLS, Jamia Hamdard, New Delhi, India
| | - Shravan B. Rathod
- Department of Chemistry, Smt. S. M. Panchal Science College, Talod, Gujarat, India
| | - Sankritya Sarma
- Department of Zoology, Hansraj College, University of Delhi, New Delhi, India
| | | | - Avantika Jain
- Department of Biochemistry, SCLS, Jamia Hamdard, New Delhi, India
- Delhi Institute of Pharmaceutical Sciences and Research, New Delhi, Delhi, India
| | - Ravi P. Barnwal
- Department of Biophysics, Panjab University, Chandigarh, India
| | | | - Baljeet Kaur
- Department of Computer Science, Hansraj College, University of Delhi, New Delhi, India
| | - Pooja Arora
- Department of Zoology, Hansraj College, University of Delhi, New Delhi, India
| | - Vikas Sood
- Department of Biochemistry, SCLS, Jamia Hamdard, New Delhi, India
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15
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Oliveira MLG, Castelli EC, Veiga‐Castelli LC, Pereira ALE, Marcorin L, Carratto TMT, Souza AS, Andrade HS, Simões AL, Donadi EA, Courtin D, Sabbagh A, Giuliatti S, Mendes‐Junior CT. Genetic diversity of the
LILRB1
and
LILRB2
coding regions in an admixed Brazilian population sample. HLA 2022; 100:325-348. [DOI: 10.1111/tan.14725] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Revised: 06/02/2022] [Accepted: 06/24/2022] [Indexed: 11/27/2022]
Affiliation(s)
| | - Erick C. Castelli
- Pathology Department, School of Medicine São Paulo State University (UNESP) Botucatu State of São Paulo Brazil
- Molecular Genetics and Bioinformatics Laboratory, School of Medicine São Paulo State University (UNESP) Botucatu State of São Paulo Brazil
| | - Luciana C. Veiga‐Castelli
- Departamento de Genética, Faculdade de Medicina de Ribeirão Preto Universidade de São Paulo Ribeirão Preto SP Brazil
| | - Alison Luis E. Pereira
- Departamento de Genética, Faculdade de Medicina de Ribeirão Preto Universidade de São Paulo Ribeirão Preto SP Brazil
| | - Letícia Marcorin
- Departamento de Genética, Faculdade de Medicina de Ribeirão Preto Universidade de São Paulo Ribeirão Preto SP Brazil
| | - Thássia M. T. Carratto
- Departamento de Química, Laboratório de Pesquisas Forenses e Genômicas, Faculdade de Filosofia, Ciências e Letras de Ribeirão Preto Universidade de São Paulo Ribeirão Preto SP Brazil
| | - Andreia S. Souza
- Molecular Genetics and Bioinformatics Laboratory, School of Medicine São Paulo State University (UNESP) Botucatu State of São Paulo Brazil
| | - Heloisa S. Andrade
- Molecular Genetics and Bioinformatics Laboratory, School of Medicine São Paulo State University (UNESP) Botucatu State of São Paulo Brazil
| | - Aguinaldo L. Simões
- Departamento de Genética, Faculdade de Medicina de Ribeirão Preto Universidade de São Paulo Ribeirão Preto SP Brazil
| | - Eduardo A. Donadi
- Departamento de Clínica Médica, Faculdade de Medicina de Ribeirão Preto Universidade de São Paulo Ribeirão Preto SP Brazil
| | | | | | - Silvana Giuliatti
- Departamento de Genética, Faculdade de Medicina de Ribeirão Preto Universidade de São Paulo Ribeirão Preto SP Brazil
| | - Celso Teixeira Mendes‐Junior
- Departamento de Química, Laboratório de Pesquisas Forenses e Genômicas, Faculdade de Filosofia, Ciências e Letras de Ribeirão Preto Universidade de São Paulo Ribeirão Preto SP Brazil
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16
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PSP-GNM: Predicting Protein Stability Changes upon Point Mutations with a Gaussian Network Model. Int J Mol Sci 2022; 23:ijms231810711. [PMID: 36142614 PMCID: PMC9505940 DOI: 10.3390/ijms231810711] [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: 08/14/2022] [Revised: 09/05/2022] [Accepted: 09/09/2022] [Indexed: 11/26/2022] Open
Abstract
Understanding the effects of missense mutations on protein stability is a widely acknowledged significant biological problem. Genomic missense mutations may alter one or more amino acids, leading to increased or decreased stability of the encoded proteins. In this study, we describe a novel approach—Protein Stability Prediction with a Gaussian Network Model (PSP-GNM)—to measure the unfolding Gibbs free energy change (ΔΔG) and evaluate the effects of single amino acid substitutions on protein stability. Specifically, PSP-GNM employs a coarse-grained Gaussian Network Model (GNM) that has interactions between amino acids weighted by the Miyazawa–Jernigan statistical potential. We used PSP-GNM to simulate partial unfolding of the wildtype and mutant protein structures, and then used the difference in the energies and entropies of the unfolded wildtype and mutant proteins to calculate ΔΔG. The extent of the agreement between the ΔΔG calculated by PSP-GNM and the experimental ΔΔG was evaluated on three benchmark datasets: 350 forward mutations (S350 dataset), 669 forward and reverse mutations (S669 dataset) and 611 forward and reverse mutations (S611 dataset). We observed a Pearson correlation coefficient as high as 0.61, which is comparable to many of the existing state-of-the-art methods. The agreement with experimental ΔΔG further increased when we considered only those measurements made close to 25 °C and neutral pH, suggesting dependence on experimental conditions. We also assessed for the antisymmetry (ΔΔGreverse = −ΔΔGforward) between the forward and reverse mutations on the Ssym+ dataset, which has 352 forward and reverse mutations. While most available methods do not display significant antisymmetry, PSP-GNM demonstrated near-perfect antisymmetry, with a Pearson correlation of −0.97. PSP-GNM is written in Python and can be downloaded as a stand-alone code.
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17
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Chakraborty C, Bhattacharya M, Sharma AR, Mallik B. Omicron (B.1.1.529) - A new heavily mutated variant: Mapped location and probable properties of its mutations with an emphasis on S-glycoprotein. Int J Biol Macromol 2022; 219:980-997. [PMID: 35952818 PMCID: PMC9359758 DOI: 10.1016/j.ijbiomac.2022.07.254] [Citation(s) in RCA: 33] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Revised: 05/23/2022] [Accepted: 07/31/2022] [Indexed: 12/17/2022]
Abstract
Omicron, another SARS-CoV-2 variant, has been recorded and reported as a VoC. It has already spread across >30 countries and is a highly mutated variant. We tried to understand the role of mutations in the investigated variants by comparison with previous characterized VoC. We have mapped the mutations in Omicron S-glycoprotein's secondary and tertiary structure landscape using bioinformatics tools and statistical software and developed different models. In addition, we analyzed the effect of diverse mutations in antibody binding regions of the S-glycoprotein on the binding affinity of the investigated antibodies. This study has chosen eight significant mutations in Omicron (D614G, E484A, N501Y, Q493K, K417N, S477N, Y505H G496S), and seven of them are located in the RBD region. We also performed a comparative analysis of the ΔΔG score of these mutations to understand the stabilizing or destabilizing properties of the investigated mutations. The analysis outcome shows that D614G, Q493K, and S477N mutations are stable mutations with ΔΔG scores of 0.351 kcal/mol, 0.470 kcal/mol, and 0.628 kcal/mol, respectively, according to DynaMut estimations. While other mutations (E484A, N501Y, K417N, Y505H, G496S) showed destabilizing results. The D614G, E484A, N501Y, K417N, Y505H, and G496S mutations increased the molecular flexibility of S-glycoprotein to interact with the ACE2 receptor, increasing the variant's infectivity. Our study will contribute to research on the SARS-CoV-2 variant, Omicron, by providing information on the mutational pattern and exciting properties of these eight significant mutations, such as antibody escape and infectivity quotient (stabilizing or destabilizing; increased or decreased molecular flexibility of S-glycoprotein to interact with the human ACE2 receptor).
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Affiliation(s)
- Chiranjib Chakraborty
- Department of Biotechnology, School of Life Science and Biotechnology, Adamas University, Kolkata, West Bengal 700126, India.
| | - Manojit Bhattacharya
- Department of Zoology, Fakir Mohan University, Vyasa Vihar, Balasore 756020, Odisha, India
| | - Ashish Ranjan Sharma
- Institute for Skeletal Aging & Orthopaedic Surgery, Hallym University-Chuncheon Sacred Heart Hospital, Chuncheon-si 24252, Gangwon-do, South Korea
| | - Bidyut Mallik
- Department of Applied Science, Galgotias College of Engineering and Technology, Knowledge Park-II, Greater Noida, Uttar Pradesh 201306, India
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18
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Gröhs Ferrareze PA, Zimerman RA, Franceschi VB, Caldana GD, Netz PA, Thompson CE. Molecular evolution and structural analyses of the spike glycoprotein from Brazilian SARS-CoV-2 genomes: the impact of selected mutations. J Biomol Struct Dyn 2022; 41:3110-3128. [PMID: 35594172 DOI: 10.1080/07391102.2022.2076154] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
The COVID-19 pandemic caused by SARS-CoV-2 has reached by February 2022 more than 380 million cases and 5.5 million deaths worldwide since its beginning in late 2019, leading to enhanced concern in the scientific community and the general population. One of the most important pieces of this host-pathogen interaction is the spike protein, which binds to the hACE2 cell receptor, mediates the membrane fusion and is the major target of neutralizing antibodies against SARS-CoV-2. The multiple amino acid substitutions observed in this region, specially in RBD have enhanced the hACE2 binding affinity and led to several modifications in the mechanisms of SARS-CoV-2 pathogenesis, improving the viral fitness and/or promoting immune evasion, with potential impact in the vaccine development. In this work, we identified 48 sites under selective pressures, 17 of them with the strongest evidence by the HyPhy tests, including VOC related mutation sites 138, 142, 222, 262, 484, 681, and 845, among others. The coevolutionary analysis identified 28 sites found not to be conditionally independent, such as E484K-N501Y. The molecular dynamics and free energy estimates showed the structural stabilizing effect and the higher impact of E484K for enhanced binding affinity between the spike RBD and hACE2 in P.1 and P.2 lineages (specially with L452V). Structural changes were also identified in the hACE molecule when interacting with B.1.1.7 RDB. Despite some destabilizing substitutions, a stabilizing effect was identified for the majority of the positively selected mutations.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Patrícia Aline Gröhs Ferrareze
- Graduate Program in Health Sciences, Universidade Federal de Ciências da Saúde de Porto Alegre (UFCSPA), Porto Alegre, RS, Brazil
| | | | - Vinícius Bonetti Franceschi
- Center of Biotechnology, Graduate Program in Cell and Molecular Biology (PPGBCM), Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, RS, Brazil
| | - Gabriel Dickin Caldana
- Graduate Program in Health Sciences, Universidade Federal de Ciências da Saúde de Porto Alegre (UFCSPA), Porto Alegre, RS, Brazil
| | - Paulo Augusto Netz
- Graduate Program in Chemistry, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, RS, Brazil
| | - Claudia Elizabeth Thompson
- Graduate Program in Health Sciences, Universidade Federal de Ciências da Saúde de Porto Alegre (UFCSPA), Porto Alegre, RS, Brazil.,Center of Biotechnology, Graduate Program in Cell and Molecular Biology (PPGBCM), Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, RS, Brazil.,Department of Pharmacosciences, Universidade Federal de Ciências da Saúde de Porto Alegre (UFCSPA), Porto Alegre, RS, Brazil
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19
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Lauffer P, Boudin E, van der Kaay DCM, Koene S, van Haeringen A, van Tellingen V, Van Hul W, Prickett TCR, Mortier G, Espiner EA, van Duyvenvoorde HA. Broadening the Spectrum of Loss-of-Function Variants in NPR-C-Related Extreme Tall Stature. J Endocr Soc 2022; 6:bvac019. [PMID: 35233476 PMCID: PMC8879884 DOI: 10.1210/jendso/bvac019] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Indexed: 11/28/2022] Open
Abstract
CONTEXT Natriuretic peptide receptor-C (NPR-C, encoded by NPR3) belongs to a family of cell membrane-integral proteins implicated in various physiological processes, including longitudinal bone growth. NPR-C acts as a clearance receptor of natriuretic peptides, including C-type natriuretic peptide (CNP), that stimulate the cGMP-forming guanylyl cyclase-coupled receptors NPR-A and NPR-B. Pathogenic variants in CNP, NPR2, and NPR3 may cause a tall stature phenotype associated with macrodactyly of the halluces and epiphyseal dysplasia. OBJECTIVE Here we report on a boy with 2 novel biallelic inactivating variants of NPR3. METHODS History and clinical characteristics were collected. Biochemical indices of natriuretic peptide clearance and in vitro cellular localization of NPR-C were studied to investigate causality of the identified variants. RESULTS We identified 2 novel compound heterozygous NPR3 variants c.943G>A p.(Ala315Thr) and c.1294A>T p.(Ile432Phe) in a boy with tall stature and macrodactyly of the halluces. In silico analysis indicated decreased stability of NPR-C, presumably resulting in increased degradation or trafficking defects. Compared to other patients with NPR-C loss-of-function, the phenotype seemed to be milder: pseudo-epiphyses in hands and feet were absent, biochemical features were less severe, and there was some co-localization of p.(Ile432Phe) NPR-C with the cell membrane, as opposed to complete cytoplasmic retention. CONCLUSION With this report on a boy with tall stature and macrodactyly of the halluces we further broaden the genotypic and phenotypic spectrum of NPR-C-related tall stature.
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Affiliation(s)
- Peter Lauffer
- Department of Pediatric Endocrinology, Emma Children’s Hospital, Amsterdam University Medical Center, 1105 AZ Amsterdam, the Netherlands
| | - Eveline Boudin
- Department of Medical Genetics, Antwerp University Hospital and University of Antwerp, 2650 Edegem, Belgium
| | - Daniëlle C M van der Kaay
- Department of Pediatric Endocrinology, Sophia Children’s Hospital, Erasmus Medical Center, 3015 GD Rotterdam, the Netherlands
| | - Saskia Koene
- Department of Clinical Genetics, Leiden University Medical Center, 2333 ZA Leiden, the Netherlands
| | - Arie van Haeringen
- Department of Clinical Genetics, Leiden University Medical Center, 2333 ZA Leiden, the Netherlands
| | - Vera van Tellingen
- Department of Pediatrics, Catharina Hospital, 5623 EJ Eindhoven, the Netherlands
| | - Wim Van Hul
- Department of Medical Genetics, Antwerp University Hospital and University of Antwerp, 2650 Edegem, Belgium
| | | | - Geert Mortier
- Department of Medical Genetics, Antwerp University Hospital and University of Antwerp, 2650 Edegem, Belgium
| | - Eric A Espiner
- Department of Medicine, University of Otago, 8140 Christchurch, New Zealand
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20
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Xiao F, Zhou Z, Song X, Gan M, Long J, Verkhivker G, Hu G. Dissecting mutational allosteric effects in alkaline phosphatases associated with different Hypophosphatasia phenotypes: An integrative computational investigation. PLoS Comput Biol 2022; 18:e1010009. [PMID: 35320273 PMCID: PMC8979438 DOI: 10.1371/journal.pcbi.1010009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Revised: 04/04/2022] [Accepted: 03/10/2022] [Indexed: 11/18/2022] Open
Abstract
Hypophosphatasia (HPP) is a rare inherited disorder characterized by defective bone mineralization and is highly variable in its clinical phenotype. The disease occurs due to various loss-of-function mutations in ALPL, the gene encoding tissue-nonspecific alkaline phosphatase (TNSALP). In this work, a data-driven and biophysics-based approach is proposed for the large-scale analysis of ALPL mutations-from nonpathogenic to severe HPPs. By using a pipeline of synergistic approaches including sequence-structure analysis, network modeling, elastic network models and atomistic simulations, we characterized allosteric signatures and effects of the ALPL mutations on protein dynamics and function. Statistical analysis of molecular features computed for the ALPL mutations showed a significant difference between the control, mild and severe HPP phenotypes. Molecular dynamics simulations coupled with protein structure network analysis were employed to analyze the effect of single-residue variation on conformational dynamics of TNSALP dimers, and the developed machine learning model suggested that the topological network parameters could serve as a robust indicator of severe mutations. The results indicated that the severity of disease-associated mutations is often linked with mutation-induced modulation of allosteric communications in the protein. This study suggested that ALPL mutations associated with mild and more severe HPPs can exert markedly distinct effects on the protein stability and long-range network communications. By linking the disease phenotypes with dynamic and allosteric molecular signatures, the proposed integrative computational approach enabled to characterize and quantify the allosteric effects of ALPL mutations and role of allostery in the pathogenesis of HPPs.
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Affiliation(s)
- Fei Xiao
- Center for Systems Biology, Department of Bioinformatics, School of Biology and Basic Medical Sciences, Soochow University, Suzhou, China
| | - Ziyun Zhou
- Center for Systems Biology, Department of Bioinformatics, School of Biology and Basic Medical Sciences, Soochow University, Suzhou, China
| | - Xingyu Song
- Department of Chemistry, Multiscale Research Institute of Complex Systems and Institute of Biomedical Sciences, Fudan University, Shanghai, China
| | - Mi Gan
- Center for Systems Biology, Department of Bioinformatics, School of Biology and Basic Medical Sciences, Soochow University, Suzhou, China
| | - Jie Long
- Center for Systems Biology, Department of Bioinformatics, School of Biology and Basic Medical Sciences, Soochow University, Suzhou, China
| | - Gennady Verkhivker
- Department of Computational and Data Sciences, Chapman University, One University Drive, Orange, California, United States of America
- Department of Biomedical and Pharmaceutical Sciences, Chapman University Pharmacy School 9401 Jeronimo Rd, Irvine, California, United States of America
| | - Guang Hu
- Center for Systems Biology, Department of Bioinformatics, School of Biology and Basic Medical Sciences, Soochow University, Suzhou, China
- * E-mail:
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21
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SWAAT Bioinformatics Workflow for Protein Structure-Based Annotation of ADME Gene Variants. J Pers Med 2022; 12:jpm12020263. [PMID: 35207751 PMCID: PMC8875676 DOI: 10.3390/jpm12020263] [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: 11/27/2021] [Revised: 01/26/2022] [Accepted: 02/01/2022] [Indexed: 02/01/2023] Open
Abstract
Recent genomic studies have revealed the critical impact of genetic diversity within small population groups in determining the way individuals respond to drugs. One of the biggest challenges is to accurately predict the effect of single nucleotide variants and to get the relevant information that allows for a better functional interpretation of genetic data. Different conformational scenarios upon the changing in amino acid sequences of pharmacologically important proteins might impact their stability and plasticity, which in turn might alter the interaction with the drug. Current sequence-based annotation methods have limited power to access this type of information. Motivated by these calls, we have developed the Structural Workflow for Annotating ADME Targets (SWAAT) that allows for the prediction of the variant effect based on structural properties. SWAAT annotates a panel of 36 ADME genes including 22 out of the 23 clinically important members identified by the PharmVar consortium. The workflow consists of a set of Python codes of which the execution is managed within Nextflow to annotate coding variants based on 37 criteria. SWAAT also includes an auxiliary workflow allowing a versatile use for genes other than ADME members. Our tool also includes a machine learning random forest binary classifier that showed an accuracy of 73%. Moreover, SWAAT outperformed six commonly used sequence-based variant prediction tools (PROVEAN, SIFT, PolyPhen-2, CADD, MetaSVM, and FATHMM) in terms of sensitivity and has comparable specificity. SWAAT is available as an open-source tool.
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22
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Erman B. Gaussian network model revisited: Effects of mutation and ligand binding on protein behavior. Phys Biol 2022; 19. [PMID: 35105836 DOI: 10.1088/1478-3975/ac50ba] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2021] [Accepted: 02/01/2022] [Indexed: 11/12/2022]
Abstract
The coarse-grained Gaussian Network model, GNM, considers only the alpha carbons of the folded protein. Therefore it is not directly applicable to the study of mutation or ligand binding problems where atomic detail is required. This shortcoming is improved by including all atom pairs within the coordination shell of each other into the Kirchoff Adjacency Matrix. Counting all contacts rather than only alpha carbon contacts diminishes the magnitude of fluctuations in the system. But more importantly, it changes the graph-like connectivity structure, i.e., the Kirchoff Adjacency Matrix of the protein. This change depends on amino acid type which introduces amino acid specific and position specific information into the classical coarse-grained GNM which was originally modelled in analogy with the phantom network model of rubber elasticity. With this modification, it is now possible to explain the consequences of mutation and ligand binding on residue fluctuations, their pair-correlations and mutual information (MI) shared by each pair. We refer to the new model as 'all-atom GNM'. Using examples from published data we show that the all-atom GNM gives B-factors that are in better agreement with experiment, can explain effects of mutation on long range communication in PDZ domains and can predict effects of GDP and GTP binding on the dimerization of KRAS.
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Affiliation(s)
- Burak Erman
- Department of Chemical and Biological Engineering, Koc University, Rumeifeneri Yolu, Istanbul, Istanbul, 34450, TURKEY
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23
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Alabbas AB, Alamri MA. Analyzing the Effect of Mutations in SARS-CoV2 Papain-Like Protease from Saudi Isolates on Protein Structure and Drug-Protein Binding: Molecular Modelling and Dynamics Studies. Saudi J Biol Sci 2021; 29:526-533. [PMID: 34548835 PMCID: PMC8447498 DOI: 10.1016/j.sjbs.2021.09.028] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2021] [Revised: 08/31/2021] [Accepted: 09/09/2021] [Indexed: 02/09/2023] Open
Abstract
The continuous and rapid development of the severe acute
respiratory syndrome coronavirus-2 (SARS-CoV-2) virus remains a health concern
especially with the emergence of numerous variants and mutations worldwide. As
with other RNA viruses, SARS-CoV-2 has a genetically high mutation rate. These
mutations have an impact on the virus characteristics, including
transmissibility, antigenicity and development of drug and vaccine resistance.
This work was pursued to identify the differences that exist in the papain-like
protease (PLPro) from 58 Saudi isolates in comparison to the
first reported sequence from Wuhan, China and determine their implications on
protein structure and the inhibitor binding. PLpro is a key
protease enzyme for the host cells invasion and viral proteolytic cleavage,
hence, it emerges as a valuable antiviral therapeutic target. Two mutations were
identified including D108G and A249V and shown to increase the molecular
flexibility of PLPro protein and alter the protein stability,
particularly with D108G mutation. The effect of these mutations on the stability
and dynamic behavior of PLPro structures as well as their
effect on the binding of a known inhibitor; GRL0617 were further investigated by
molecular docking and dynamic simulation.
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Affiliation(s)
- Alhumaidi B Alabbas
- Department of Pharmaceutical Chemistry, College of Pharmacy, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia
| | - Mubarak A Alamri
- Department of Pharmaceutical Chemistry, College of Pharmacy, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia
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24
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NGS in Hereditary Ataxia: When Rare Becomes Frequent. Int J Mol Sci 2021; 22:ijms22168490. [PMID: 34445196 PMCID: PMC8395181 DOI: 10.3390/ijms22168490] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Revised: 08/03/2021] [Accepted: 08/04/2021] [Indexed: 12/17/2022] Open
Abstract
The term hereditary ataxia (HA) refers to a heterogeneous group of neurological disorders with multiple genetic etiologies and a wide spectrum of ataxia-dominated phenotypes. Massive gene analysis in next-generation sequencing has entered the HA scenario, broadening our genetic and clinical knowledge of these conditions. In this study, we employed a targeted resequencing panel (TRP) in a large and highly heterogeneous cohort of 377 patients with a clinical diagnosis of HA, but no molecular diagnosis on routine genetic tests. We obtained a positive result (genetic diagnosis) in 33.2% of the patients, a rate significantly higher than those reported in similar studies employing TRP (average 19.4%), and in line with those performed using exome sequencing (ES, average 34.6%). Moreover, 15.6% of the patients had an uncertain molecular diagnosis. STUB1, PRKCG, and SPG7 were the most common causative genes. A comparison with published literature data showed that our panel would have identified 97% of the positive cases reported in previous TRP-based studies and 92% of those diagnosed by ES. Proper use of multigene panels, when combined with detailed phenotypic data, seems to be even more efficient than ES in clinical practice.
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25
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Teruel N, Mailhot O, Najmanovich RJ. Modelling conformational state dynamics and its role on infection for SARS-CoV-2 Spike protein variants. PLoS Comput Biol 2021; 17:e1009286. [PMID: 34351895 PMCID: PMC8384204 DOI: 10.1371/journal.pcbi.1009286] [Citation(s) in RCA: 54] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Revised: 08/24/2021] [Accepted: 07/17/2021] [Indexed: 01/21/2023] Open
Abstract
The SARS-CoV-2 Spike protein needs to be in an open-state conformation to interact with ACE2 to initiate viral entry. We utilise coarse-grained normal mode analysis to model the dynamics of Spike and calculate transition probabilities between states for 17081 variants including experimentally observed variants. Our results correctly model an increase in open-state occupancy for the more infectious D614G via an increase in flexibility of the closed-state and decrease of flexibility of the open-state. We predict the same effect for several mutations on glycine residues (404, 416, 504, 252) as well as residues K417, D467 and N501, including the N501Y mutation recently observed within the B.1.1.7, 501.V2 and P1 strains. This is, to our knowledge, the first use of normal mode analysis to model conformational state transitions and the effect of mutations on such transitions. The specific mutations of Spike identified here may guide future studies to increase our understanding of SARS-CoV-2 infection mechanisms and guide public health in their surveillance efforts. The present work explores the molecular mechanisms underlying and potentially helping new strains of SARS-CoV-2 to gain an evolutionary advantage during the ongoing COVID-19 pandemics. We show how a computational method called normal mode analysis that treats protein dynamics in a simplified manner is capable to predict the higher propensity of the Spike protein to be in the open state in which it is capable to interact with the human ACE2 receptor and thus facilitate cell entry. Because the simulation of the simplified computational model is relatively less demanding on resources than alternative methods, we were able to simulate over 17000 mutations in the SARS-CoV-2 Spike protein to identify multiple mutations that if they were to appear as the virus continues to evolve, could confer an evolutionary advantage. As a matter of fact, our predictions foresaw the emergence of particular mutations such as N501Y that appeared in several variants of concern. Our results can inform public health regarding new variants and serves as a proof of concept for the application of normal mode analysis to study the effect of mutations on both, protein dynamics and conformational transitions in a high-throughput manner.
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Affiliation(s)
- Natália Teruel
- Department of Pharmacology and Physiology, Faculty of Medicine, Université de Montréal, Montreal, Canada
| | - Olivier Mailhot
- Department of Pharmacology and Physiology, Faculty of Medicine, Université de Montréal, Montreal, Canada
- Institute for Research in Immunology and Cancer (IRIC), Faculty of Medicine, Université de Montréal, Montreal, Canada
| | - Rafael J. Najmanovich
- Department of Pharmacology and Physiology, Faculty of Medicine, Université de Montréal, Montreal, Canada
- * E-mail:
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26
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Rios JJ, Denton K, Russell J, Kozlitina J, Ferreira CR, Lewanda AF, Mayfield JE, Moresco E, Ludwig S, Tang M, Li X, Lyon S, Khanshour A, Paria N, Khalid A, Li Y, Xie X, Feng JQ, Xu Q, Lu Y, Hammer RE, Wise CA, Beutler B. Germline Saturation Mutagenesis Induces Skeletal Phenotypes in Mice. J Bone Miner Res 2021; 36:1548-1565. [PMID: 33905568 PMCID: PMC8862308 DOI: 10.1002/jbmr.4323] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/06/2020] [Revised: 04/07/2021] [Accepted: 04/21/2021] [Indexed: 12/28/2022]
Abstract
Proper embryonic and postnatal skeletal development require coordination of myriad complex molecular mechanisms. Disruption of these processes, through genetic mutation, contributes to variation in skeletal development. We developed a high-throughput N-ethyl-N-nitrosourea (ENU)-induced saturation mutagenesis skeletal screening approach in mice to identify genes required for proper skeletal development. Here, we report initial results from live-animal X-ray and dual-energy X-ray absorptiometry (DXA) imaging of 27,607 G3 mice from 806 pedigrees, testing the effects of 32,198 coding/splicing mutations in 13,020 genes. A total of 39.7% of all autosomal genes were severely damaged or destroyed by mutations tested twice or more in the homozygous state. Results from our study demonstrate the feasibility of in vivo mutagenesis to identify mouse models of skeletal disease. Furthermore, our study demonstrates how ENU mutagenesis provides opportunities to create and characterize putative hypomorphic mutations in developmentally essential genes. Finally, we present a viable mouse model and case report of recessive skeletal disease caused by mutations in FAM20B. Results from this study, including engineered mouse models, are made publicly available via the online Mutagenetix database. © 2021 American Society for Bone and Mineral Research (ASBMR).
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Affiliation(s)
- Jonathan J Rios
- Center for Pediatric Bone Biology and Translational Research, Scottish Rite for Children, Dallas, TX, USA.,Department of Pediatrics, UT Southwestern Medical Center, Dallas, TX, USA.,McDermott Center for Human Growth and Development, UT Southwestern Medical Center, Dallas, TX, USA.,Department of Orthopaedic Surgery, UT Southwestern Medical Center, Dallas, TX, USA.,Simmons Comprehensive Cancer Center, UT Southwestern Medical Center, Dallas, TX, USA
| | - Kristin Denton
- Center for Pediatric Bone Biology and Translational Research, Scottish Rite for Children, Dallas, TX, USA
| | - Jamie Russell
- Center for Genetics of Host Defense, UT Southwestern Medical Center, Dallas, TX, USA
| | - Julia Kozlitina
- McDermott Center for Human Growth and Development, UT Southwestern Medical Center, Dallas, TX, USA
| | - Carlos R Ferreira
- Skeletal Genomics Unit, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Amy F Lewanda
- Rare Disease Institute, Children's National Hospital, Washington, DC, USA
| | - Joshua E Mayfield
- Department of Pharmacology, University of California, San Diego, CA, USA
| | - Eva Moresco
- Center for Genetics of Host Defense, UT Southwestern Medical Center, Dallas, TX, USA
| | - Sara Ludwig
- Center for Genetics of Host Defense, UT Southwestern Medical Center, Dallas, TX, USA
| | - Miao Tang
- Center for Genetics of Host Defense, UT Southwestern Medical Center, Dallas, TX, USA
| | - Xiaohong Li
- Center for Genetics of Host Defense, UT Southwestern Medical Center, Dallas, TX, USA
| | - Stephen Lyon
- Center for Genetics of Host Defense, UT Southwestern Medical Center, Dallas, TX, USA
| | - Anas Khanshour
- Center for Pediatric Bone Biology and Translational Research, Scottish Rite for Children, Dallas, TX, USA
| | - Nandina Paria
- Center for Pediatric Bone Biology and Translational Research, Scottish Rite for Children, Dallas, TX, USA
| | - Aysha Khalid
- Center for Pediatric Bone Biology and Translational Research, Scottish Rite for Children, Dallas, TX, USA
| | - Yang Li
- Center for Pediatric Bone Biology and Translational Research, Scottish Rite for Children, Dallas, TX, USA
| | - Xudong Xie
- Department of Restorative Sciences, School of Dentistry, Texas A&M University, Dallas, TX, USA
| | - Jian Q Feng
- Department of Restorative Sciences, School of Dentistry, Texas A&M University, Dallas, TX, USA
| | - Qian Xu
- Department of Restorative Sciences, School of Dentistry, Texas A&M University, Dallas, TX, USA
| | - Yongbo Lu
- Department of Restorative Sciences, School of Dentistry, Texas A&M University, Dallas, TX, USA
| | - Robert E Hammer
- Department of Biochemistry, UT Southwestern Medical Center, Dallas, TX, USA
| | - Carol A Wise
- Center for Pediatric Bone Biology and Translational Research, Scottish Rite for Children, Dallas, TX, USA.,Department of Pediatrics, UT Southwestern Medical Center, Dallas, TX, USA.,McDermott Center for Human Growth and Development, UT Southwestern Medical Center, Dallas, TX, USA.,Department of Orthopaedic Surgery, UT Southwestern Medical Center, Dallas, TX, USA
| | - Bruce Beutler
- Center for Genetics of Host Defense, UT Southwestern Medical Center, Dallas, TX, USA
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27
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Sequeiros-Borja CE, Surpeta B, Brezovsky J. Recent advances in user-friendly computational tools to engineer protein function. Brief Bioinform 2021; 22:bbaa150. [PMID: 32743637 PMCID: PMC8138880 DOI: 10.1093/bib/bbaa150] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2020] [Revised: 06/03/2020] [Accepted: 06/16/2020] [Indexed: 12/14/2022] Open
Abstract
Progress in technology and algorithms throughout the past decade has transformed the field of protein design and engineering. Computational approaches have become well-engrained in the processes of tailoring proteins for various biotechnological applications. Many tools and methods are developed and upgraded each year to satisfy the increasing demands and challenges of protein engineering. To help protein engineers and bioinformaticians navigate this emerging wave of dedicated software, we have critically evaluated recent additions to the toolbox regarding their application for semi-rational and rational protein engineering. These newly developed tools identify and prioritize hotspots and analyze the effects of mutations for a variety of properties, comprising ligand binding, protein-protein and protein-nucleic acid interactions, and electrostatic potential. We also discuss notable progress to target elusive protein dynamics and associated properties like ligand-transport processes and allosteric communication. Finally, we discuss several challenges these tools face and provide our perspectives on the further development of readily applicable methods to guide protein engineering efforts.
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Affiliation(s)
- Carlos Eduardo Sequeiros-Borja
- Laboratory of Biomolecular Interactions and Transport, Department of Gene Expression, Institute of Molecular Biology and Biotechnology, Faculty of Biology, Adam Mickiewicz University and the International Institute of Molecular and Cell Biology in Warsaw, Warsaw, Poland
| | - Bartłomiej Surpeta
- Laboratory of Biomolecular Interactions and Transport, Department of Gene Expression, Institute of Molecular Biology and Biotechnology, Faculty of Biology, Adam Mickiewicz University and the International Institute of Molecular and Cell Biology in Warsaw, Warsaw, Poland
| | - Jan Brezovsky
- Laboratory of Biomolecular Interactions and Transport, Department of Gene Expression, Institute of Molecular Biology and Biotechnology, Faculty of Biology, Adam Mickiewicz University and the International Institute of Molecular and Cell Biology in Warsaw
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28
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Singh P, Jamal S, Ahmed F, Saqib N, Mehra S, Ali W, Roy D, Ehtesham NZ, Hasnain SE. Computational modeling and bioinformatic analyses of functional mutations in drug target genes in Mycobacterium tuberculosis. Comput Struct Biotechnol J 2021; 19:2423-2446. [PMID: 34025934 PMCID: PMC8113780 DOI: 10.1016/j.csbj.2021.04.034] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Revised: 04/09/2021] [Accepted: 04/15/2021] [Indexed: 11/29/2022] Open
Abstract
MycoTRAP-DB, a database of mutations and their impact on normal functionality of protein in M.tb genes. Several secondary mutations were identified with significant impact on protein structure and function. Comprehensive information gives insight for screening of suspected hotspots in advance to combat drug resistant TB.
Tuberculosis (TB) continues to be the leading cause of deaths due to its persistent drug resistance and the consequent ineffectiveness of anti-TB treatment. Recent years witnessed huge amount of sequencing data, revealing mutations responsible for drug resistance. However, the lack of an up-to-date repository remains a barrier towards utilization of these data and identifying major mutations-associated with resistance. Amongst all mutations, non-synonymous mutations alter the amino acid sequence of a protein and have a much greater effect on pathogenicity. Hence, this type of gene mutation is of prime interest of the present study. The purpose of this study is to develop an updated database comprising almost all reported substitutions within the Mycobacterium tuberculosis (M.tb) drug target genes rpoB, inhA, katG, pncA, gyrA and gyrB. Various bioinformatics prediction tools were used to assess the structural and biophysical impacts of the resistance causing non-synonymous single nucleotide polymorphisms (nsSNPs) at the molecular level. This was followed by evaluating the impact of these mutations on binding affinity of the drugs to target proteins. We have developed a comprehensive online resource named MycoTRAP-DB (Mycobacterium tuberculosis Resistance Associated Polymorphisms Database) that connects mutations in genes with their structural, functional and pathogenic implications on protein. This database is accessible at http://139.59.12.92. This integrated platform would enable comprehensive analysis and prioritization of SNPs for the development of improved diagnostics and antimycobacterial medications. Moreover, our study puts forward secondary mutations that can be important for prognostic assessments of drug-resistance mechanism and actionable anti-TB drugs.
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Affiliation(s)
- Pooja Singh
- Jamia Hamdard Institute of Molecular Medicine, Jamia Hamdard, New Delhi 110062, India
| | - Salma Jamal
- Jamia Hamdard Institute of Molecular Medicine, Jamia Hamdard, New Delhi 110062, India
| | - Faraz Ahmed
- Jamia Hamdard Institute of Molecular Medicine, Jamia Hamdard, New Delhi 110062, India
| | - Najumu Saqib
- Jamia Hamdard Institute of Molecular Medicine, Jamia Hamdard, New Delhi 110062, India
| | - Seema Mehra
- Jamia Hamdard Institute of Molecular Medicine, Jamia Hamdard, New Delhi 110062, India
| | - Waseem Ali
- Jamia Hamdard Institute of Molecular Medicine, Jamia Hamdard, New Delhi 110062, India
| | - Deodutta Roy
- Department of Environmental and Occupational Health, Florida International University, Miami 33029, USA
| | - Nasreen Z Ehtesham
- ICMR-National Institute of Pathology, Safdarjung Hospital Campus, New Delhi, India
| | - Seyed E Hasnain
- Department of Life Sciences, School of Basic Sciences and Research, Sharda University, Greater Noida 201301, India.,Department of Biochemical Engineering and Biotechnology, Indian Institute of Technology, Delhi (IIT-D), Hauz Khas, New Delhi 110016, India
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29
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Periwal N, Rathod SB, Pal R, Sharma P, Nebhnani L, Barnwal RP, Arora P, Srivastava KR, Sood V. In silico characterization of mutations circulating in SARS-CoV-2 structural proteins. J Biomol Struct Dyn 2021; 40:8216-8231. [PMID: 33797336 PMCID: PMC8043164 DOI: 10.1080/07391102.2021.1908170] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
SARS-CoV-2 has recently emerged as a pandemic that has caused more than 2.4 million deaths worldwide. Since the onset of infections, several full-length sequences of viral genome have been made available which have been used to gain insights into viral dynamics. We utilised a meta-data driven comparative analysis tool for sequences (Meta-CATS) algorithm to identify mutations in 829 SARS-CoV-2 genomes from around the world. The algorithm predicted sixty-one mutations among SARS-CoV-2 genomes. We observed that most of the mutations were concentrated around three protein coding genes viz nsp3 (non-structural protein 3), RdRp (RNA-directed RNA polymerase) and Nucleocapsid (N) proteins of SARS-CoV-2. We used various computational tools including normal mode analysis (NMA), C-α discrete molecular dynamics (DMD) and all-atom molecular dynamic simulations (MD) to study the effect of mutations on functionality, stability and flexibility of SARS-CoV-2 structural proteins including envelope (E), N and spike (S) proteins. PredictSNP predictor suggested that four mutations (L37H in E, R203K and P344S in N and D614G in S) out of seven were predicted to be neutral whilst the remaining ones (P13L, S197L and G204R in N) were predicted to be deleterious in nature thereby impacting protein functionality. NMA, C-α DMD and all-atom MD suggested some mutations to have stabilizing roles (P13L, S197L and R203K in N protein) where remaining ones were predicted to destabilize mutant protein. In summary, we identified significant mutations in SARS-CoV-2 genomes as well as used computational approaches to further characterize the possible effect of highly significant mutations on SARS-CoV-2 structural proteins. Communicated by Ramaswamy H. Sarma
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Affiliation(s)
- Neha Periwal
- Department of Biochemistry, School of Chemical & Life Sciences, Jamia Hamdard, New Delhi, India
| | - Shravan B Rathod
- Department of Chemistry, Smt. S. M. Panchal Science College, Talod, India
| | - Ranjan Pal
- Biocatalysis and Enzyme Engineering Lab, Regional Centre for Biotechnology, Faridabad, India
| | - Priya Sharma
- Department of Biochemistry, School of Chemical & Life Sciences, Jamia Hamdard, New Delhi, India
| | - Lata Nebhnani
- Department of Chemistry, Gujarat University, Ahmedabad, India
| | - Ravi P Barnwal
- Department of Biophysics, Panjab University, Chandigarh, India
| | - Pooja Arora
- Department of Zoology, Hansraj College, University of Delhi, New Delhi, India
| | - Kinshuk Raj Srivastava
- Biocatalysis and Enzyme Engineering Lab, Regional Centre for Biotechnology, Faridabad, India
| | - Vikas Sood
- Department of Biochemistry, School of Chemical & Life Sciences, Jamia Hamdard, New Delhi, India
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30
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Figueiredo FB, Silva WA, Giuliatti S, Tomaselli PJ, Lourenço CM, Gouvêa SDP, Covaleski APPM, Hallak JE, Marques W. GDAP1 mutations are frequent among Brazilian patients with autosomal recessive axonal Charcot-Marie-Tooth disease. Neuromuscul Disord 2021; 31:505-511. [PMID: 33903021 DOI: 10.1016/j.nmd.2021.03.005] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2020] [Revised: 02/26/2021] [Accepted: 03/15/2021] [Indexed: 12/21/2022]
Abstract
Mutations in ganglioside-induced differentiation-associated-protein 1 (GDAP1) are associated with several subtypes of Charcot-Marie-Tooth (CMT) disease, including autosomal recessive and demyelinating (CMT4A); autosomal recessive and axonal (AR-CMT2K); autosomal dominant and axonal (CMT2K); and an intermediate and recessive form (CMTRIA). To date, at least 103 mutations in this gene have been described, but the relative frequency of GDAP1 mutations in the Brazilian CMT population is unknown. In this study, we investigated the frequency of GDAP1 mutations in a cohort of 100 unrelated Brazilian CMT patients. We identified five variants in unrelated axonal CMT patients, among which two were novel and probably pathogenic (N64S, P119T) one was novel and was classified as VUS (K207L) and two were known pathogenic variants (R125* and Q163*). The prevalence rate of GDAP1 among the axonal CMT cases was 7,14% (5/70), all of them of recessive inheritance, thus suggesting that the prevalence was higher than what is observed in most countries. All patients exhibited severe early-onset CMT that was rapidly progressive. Additionally, this study widens the mutational spectrum of GDAP1-related CMT through identification of two novel likely pathogenic variants.
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Affiliation(s)
- Fernanda Barbosa Figueiredo
- Department of Neurosciences and Behavior Sciences, Ribeirão Preto Medical School, University of São Paulo, Brazil
| | - Wilson Araújo Silva
- Department of Genetics, Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, São Paulo, Brazil
| | - Silvana Giuliatti
- Department of Genetics, Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, São Paulo, Brazil
| | - Pedro José Tomaselli
- Department of Neurosciences and Behavior Sciences, Ribeirão Preto Medical School, University of São Paulo, Brazil
| | - Charles Marques Lourenço
- Department of Neurosciences and Behavior Sciences, Ribeirão Preto Medical School, University of São Paulo, Brazil
| | - Silmara de Paula Gouvêa
- Department of Neurosciences and Behavior Sciences, Ribeirão Preto Medical School, University of São Paulo, Brazil
| | | | - Jaime E Hallak
- Department of Neurosciences and Behavior Sciences, Ribeirão Preto Medical School, University of São Paulo, Brazil; National Institute of Sciences and Technology - INCT-Translational Medicine - CNPq/FAPESP, São Paulo, Brazil
| | - Wilson Marques
- Department of Neurosciences and Behavior Sciences, Ribeirão Preto Medical School, University of São Paulo, Brazil; National Institute of Sciences and Technology - INCT-Translational Medicine - CNPq/FAPESP, São Paulo, Brazil.
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31
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Mailhot O, Najmanovich R. The NRGTEN Python package: an extensible toolkit for coarse-grained normal mode analysis of proteins, nucleic acids, small molecules and their complexes. Bioinformatics 2021; 37:3369-3371. [PMID: 33742655 DOI: 10.1093/bioinformatics/btab189] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2020] [Revised: 02/24/2021] [Accepted: 03/17/2021] [Indexed: 01/11/2023] Open
Abstract
SUMMARY The Najmanovich Research Group Toolkit for Elastic Networks (NRGTEN) is a Python toolkit that implements four different NMA models in addition to popular and novel metrics to benchmark and measure properties from these models. Furthermore, the toolkit is available as a public Python package and is easily extensible for the development or implementation of additional NMA models. The inclusion of the ENCoM model (Elastic Network Contact Model) developed in our group within NRGTEN is noteworthy, owing to its account for the specific chemical nature of atomic interactions. AVAILABILITY AND IMPLEMENTATION https://github.com/gregorpatof/nrgten_package/.
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Affiliation(s)
- Olivier Mailhot
- Department of Pharmacology and Physiology, Faculty of Medicine, Université de Montréal.,Institute for Research in Immunology and Cancer (IRIC), Faculty of Medicine, Université de Montréal
| | - Rafael Najmanovich
- Department of Pharmacology and Physiology, Faculty of Medicine, Université de Montréal
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32
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Fang Y, Chen H, Chen Q, Wang C, Liang L. Compound hemizygous variants in SERPINA7 gene cause thyroxine-binding globulin deficiency. Mol Genet Genomic Med 2021; 9:e1571. [PMID: 33554479 PMCID: PMC8077092 DOI: 10.1002/mgg3.1571] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2020] [Revised: 09/17/2020] [Accepted: 10/29/2020] [Indexed: 11/30/2022] Open
Abstract
Sub‐heading Compound hemizygous variants in SERPINA7 gene. Background Thyroxine‐binding globulin (TBG) is encoded by SERPINA7 (OMIM. 314200) which is located on Xq22.3. SERPINA7 variants caused TBG deficiency which does not require treatment, but the decreased thyroxine may be misdiagnosed as hypothyroidism. We discovered some variants of TBG caused by alterations that differ from previously reported. Materials and Methods In this study, we enrolled 32 subjects from 10 families and sequenced the SERPINA7 genes of TBG‐deficient subjects. Then, variants were analyzed to assess their effect on TBG expression and secretion. Bioinformatics database, protein structure, and dynamics simulation were used to evaluate the deleterious effects. Finally, we identified 2 novel and 4 known variants, and found 26 of 30 subjects carried the p.L303F. The DynaMut predictions indicated the variants (p.E91K, p.I92T, p.R294C, and p.L303F) exhibited decreased stability. Conclusion Analyses revealed the p.L303F change the protein stability and flexibility, and it had an impact on the function of TBG, but when coexisted with other variants it might change the conformational structure of the protein and aggravate the damage to the protein. We speculated that the existence of a higher number of variants resulted in lower TBG secretion.
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Affiliation(s)
- Yanlan Fang
- Pediatric Department, The 1st Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Hong Chen
- Pediatric Department, The 1st Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Qingqing Chen
- Pediatric Department, The 1st Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Chunlin Wang
- Pediatric Department, The 1st Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Li Liang
- Pediatric Department, The 1st Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
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HARP: a database of structural impacts of systematic missense mutations in drug targets of Mycobacterium leprae. Comput Struct Biotechnol J 2020; 18:3692-3704. [PMID: 33304465 PMCID: PMC7711215 DOI: 10.1016/j.csbj.2020.11.013] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2020] [Accepted: 11/08/2020] [Indexed: 12/20/2022] Open
Abstract
Computational Saturation Mutagenesis is an in-silico approach that employs systematic mutagenesis of each amino acid residue in the protein to all other amino acid types, and predicts changes in thermodynamic stability and affinity to the other subunits/protein counterparts, ligands and nucleic acid molecules. The data thus generated are useful in understanding the functional consequences of mutations in antimicrobial resistance phenotypes. In this study, we applied computational saturation mutagenesis to three important drug-targets in Mycobacterium leprae (M. leprae) for the drugs dapsone, rifampin and ofloxacin namely Dihydropteroate Synthase (DHPS), RNA Polymerase (RNAP) and DNA Gyrase (GYR), respectively. M. leprae causes leprosy and is an obligate intracellular bacillus with limited protein structural information associating mutations with phenotypic resistance outcomes in leprosy. Experimentally solved structures of DHPS, RNAP and GYR of M. leprae are not available in the Protein Data Bank, therefore, we modelled the structures of these proteins using template-based comparative modelling and introduced systematic mutations in each model generating 80,902 mutations and mutant structures for all the three proteins. Impacts of mutations on stability and protein-subunit, protein-ligand and protein-nucleic acid affinities were computed using various in-house developed and other published protein stability and affinity prediction software. A consensus impact was estimated for each mutation using qualitative scoring metrics for physicochemical properties and by a categorical grouping of stability and affinity predictions. We developed a web database named HARP (a database of Hansen's Disease Antimicrobial Resistance Profiles), which is accessible at the URL - https://harp-leprosy.org and provides the details to each of these predictions.
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Gerasimavicius L, Liu X, Marsh JA. Identification of pathogenic missense mutations using protein stability predictors. Sci Rep 2020; 10:15387. [PMID: 32958805 PMCID: PMC7506547 DOI: 10.1038/s41598-020-72404-w] [Citation(s) in RCA: 57] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2020] [Accepted: 08/31/2020] [Indexed: 12/17/2022] Open
Abstract
Attempts at using protein structures to identify disease-causing mutations have been dominated by the idea that most pathogenic mutations are disruptive at a structural level. Therefore, computational stability predictors, which assess whether a mutation is likely to be stabilising or destabilising to protein structure, have been commonly used when evaluating new candidate disease variants, despite not having been developed specifically for this purpose. We therefore tested 13 different stability predictors for their ability to discriminate between pathogenic and putatively benign missense variants. We find that one method, FoldX, significantly outperforms all other predictors in the identification of disease variants. Moreover, we demonstrate that employing predicted absolute energy change scores improves performance of nearly all predictors in distinguishing pathogenic from benign variants. Importantly, however, we observe that the utility of computational stability predictors is highly heterogeneous across different proteins, and that they are all inferior to the best performing variant effect predictors for identifying pathogenic mutations. We suggest that this is largely due to alternate molecular mechanisms other than protein destabilisation underlying many pathogenic mutations. Thus, better ways of incorporating protein structural information and molecular mechanisms into computational variant effect predictors will be required for improved disease variant prioritisation.
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Affiliation(s)
- Lukas Gerasimavicius
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, EH4 2XU, UK
| | - Xin Liu
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, EH4 2XU, UK
| | - Joseph A Marsh
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, EH4 2XU, UK.
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35
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Khan FI, Hassan F, Anwer R, Juan F, Lai D. Comparative Analysis of Bacteriophytochrome Agp2 and Its Engineered Photoactivatable NIR Fluorescent Proteins PAiRFP1 and PAiRFP2. Biomolecules 2020; 10:biom10091286. [PMID: 32906690 PMCID: PMC7564321 DOI: 10.3390/biom10091286] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2020] [Revised: 08/27/2020] [Accepted: 09/04/2020] [Indexed: 12/11/2022] Open
Abstract
Two photoactivatable near infrared fluorescent proteins (NIR FPs) named “PAiRFP1” and “PAiRFP2” are formed by directed molecular evolution from Agp2, a bathy bacteriophytochrome of Agrobacterium tumefaciens C58. There are 15 and 24 amino acid substitutions in the structure of PAiRFP1 and PAiRFP2, respectively. A comprehensive molecular exploration of these bacteriophytochrome photoreceptors (BphPs) are required to understand the structure dynamics. In this study, the NIR fluorescence emission spectra for PAiRFP1 were recorded upon repeated excitation and the fluorescence intensity of PAiRFP1 tends to increase as the irradiation time was prolonged. We also predicted that mutations Q168L, V244F, and A480V in Agp2 will enhance the molecular stability and flexibility. During molecular dynamics (MD) simulations, the average root mean square deviations of Agp2, PAiRFP1, and PAiRFP2 were found to be 0.40, 0.49, and 0.48 nm, respectively. The structure of PAiRFP1 and PAiRFP2 were more deviated than Agp2 from its native conformation and the hydrophobic regions that were buried in PAiRFP1 and PAiRFP2 core exposed to solvent molecules. The eigenvalues and the trace of covariance matrix were found to be high for PAiRFP1 (597.90 nm2) and PAiRFP2 (726.74 nm2) when compared with Agp2 (535.79 nm2). It was also found that PAiRFP1 has more sharp Gibbs free energy global minima than Agp2 and PAiRFP2. This comparative analysis will help to gain deeper understanding on the structural changes during the evolution of photoactivatable NIR FPs. Further work can be carried out by combining PCR-based directed mutagenesis and spectroscopic methods to provide strategies for the rational designing of these PAiRFPs.
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Affiliation(s)
- Faez Iqbal Khan
- School of Electronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China;
| | - Fakhrul Hassan
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China; (F.H.); (F.J.)
| | - Razique Anwer
- Department of Pathology, College of Medicine, Imam Mohammad ibn Saud Islamic University (IMSIU), Riyadh 13317, Saudi Arabia;
| | - Feng Juan
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China; (F.H.); (F.J.)
| | - Dakun Lai
- School of Electronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China;
- Correspondence: ; Tel.: +86-182-0052-9516
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Krieger JM, Doruker P, Scott AL, Perahia D, Bahar I. Towards gaining sight of multiscale events: utilizing network models and normal modes in hybrid methods. Curr Opin Struct Biol 2020; 64:34-41. [PMID: 32622329 DOI: 10.1016/j.sbi.2020.05.013] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2020] [Revised: 05/13/2020] [Accepted: 05/20/2020] [Indexed: 11/28/2022]
Abstract
With the explosion of normal mode analyses (NMAs) based on elastic network models (ENMs) in the last decade, and the proven precision of MD simulations for visualizing interactions at atomic scale, many hybrid methods have been proposed in recent years. These aim at exploiting the best of both worlds: the atomic precision of MD that often fall short of exploring time and length scales of biological interest, and the capability of ENM-NMA to predict the cooperative and often functional rearrangements of large structures and assemblies, albeit at low resolution. We present an overview of recent progress in the field with examples of successful applications highlighting the utility of such hybrid methods and pointing to emerging future directions guided by advances in experimental characterization of biomolecular systems structure and dynamics.
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Affiliation(s)
- James M Krieger
- Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, 3501 Fifth Ave, Suite 3064 BST3, Pittsburgh, PA 15260, USA
| | - Pemra Doruker
- Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, 3501 Fifth Ave, Suite 3064 BST3, Pittsburgh, PA 15260, USA
| | - Ana Ligia Scott
- Laboratory of Bioinformatics and Computational Biology, Federal University of ABC, Santo André, SP, Brazil
| | - David Perahia
- Laboratoire de Biologie et de Pharmacologie Appliquée, Ecole Normale Superieure Paris-Saclay, UMR 8113, CNRS, 4 Avenue des Sciences, 91190 Gif-sur-Yvette, France
| | - Ivet Bahar
- Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, 3501 Fifth Ave, Suite 3064 BST3, Pittsburgh, PA 15260, USA.
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37
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Marabotti A, Scafuri B, Facchiano A. Predicting the stability of mutant proteins by computational approaches: an overview. Brief Bioinform 2020; 22:5850907. [PMID: 32496523 DOI: 10.1093/bib/bbaa074] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2020] [Revised: 04/07/2020] [Accepted: 04/10/2020] [Indexed: 01/06/2023] Open
Abstract
A very large number of computational methods to predict the change in thermodynamic stability of proteins due to mutations have been developed during the last 30 years, and many different web servers are currently available. Nevertheless, most of them suffer from severe drawbacks that decrease their general reliability and, consequently, their applicability to different goals such as protein engineering or the predictions of the effects of mutations in genetic diseases. In this review, we have summarized all the main approaches used to develop these tools, with a survey of the web servers currently available. Moreover, we have also reviewed the different assessments made during the years, in order to allow the reader to check directly the different performances of these tools, to select the one that best fits his/her needs, and to help naïve users in finding the best option for their needs.
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38
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Broom A, Trainor K, Jacobi Z, Meiering EM. Computational Modeling of Protein Stability: Quantitative Analysis Reveals Solutions to Pervasive Problems. Structure 2020; 28:717-726.e3. [DOI: 10.1016/j.str.2020.04.003] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2019] [Revised: 03/26/2020] [Accepted: 04/06/2020] [Indexed: 12/20/2022]
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39
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Alemasov NA, Ivanisenko NV, Ivanisenko VA. Learning the changes of barnase mutants thermostability from structural fluctuations obtained using anisotropic network modeling. J Mol Graph Model 2020; 97:107572. [PMID: 32114079 DOI: 10.1016/j.jmgm.2020.107572] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2019] [Revised: 01/29/2020] [Accepted: 02/19/2020] [Indexed: 11/17/2022]
Abstract
In biotechnology applications, rational design of new proteins with improved physico-chemical properties includes a number of important tasks. One of the greatest practical and fundamental challenges is the design of highly thermostable protein enzymes that maintain catalytic activity at high temperatures. This problem may be solved by introducing mutations into the wild-type enzyme protein. In this work, to predict the impact of such mutations in barnase protein we applied the anisotropic network modeling approach, revealing atomic fluctuations in structural regions that are changed in mutants compared to the wild-type protein. A regression model was constructed based on these structural features that can allow one to predict the thermal stability of new barnase mutants. Moreover, the analysis of regression model provides a mechanistic explanation of how the structural features can contribute to the thermal stability of barnase mutants.
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Affiliation(s)
- Nikolay A Alemasov
- The Federal Research Center Institute of Cytology and Genetics, The Siberian Branch of the Russian Academy of Sciences, 630090, Prospekt Lavrentyeva 10, Novosibirsk, Russia; The Kurchatov's Genomics Center of the Institute of Cytology and Genetics, The Siberian Branch of the Russian Academy of Sciences, 630090, Prospekt Lavrentyeva 10, Novosibirsk, Russia.
| | - Nikita V Ivanisenko
- The Federal Research Center Institute of Cytology and Genetics, The Siberian Branch of the Russian Academy of Sciences, 630090, Prospekt Lavrentyeva 10, Novosibirsk, Russia; The Kurchatov's Genomics Center of the Institute of Cytology and Genetics, The Siberian Branch of the Russian Academy of Sciences, 630090, Prospekt Lavrentyeva 10, Novosibirsk, Russia
| | - Vladimir A Ivanisenko
- The Federal Research Center Institute of Cytology and Genetics, The Siberian Branch of the Russian Academy of Sciences, 630090, Prospekt Lavrentyeva 10, Novosibirsk, Russia; The Kurchatov's Genomics Center of the Institute of Cytology and Genetics, The Siberian Branch of the Russian Academy of Sciences, 630090, Prospekt Lavrentyeva 10, Novosibirsk, Russia
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40
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Benabdessalem C, Othman H, Ouni R, Ghouibi N, Dahman A, Riahi R, Larguach B, Srairi-Abid N, Barbouche MR, Fathallah MD. N-glycosylation and homodimeric folding significantly enhance the immunoreactivity of Mycobacterium tuberculosis virulence factor CFP32 when produced in the yeast Pichia pastoris. Biochem Biophys Res Commun 2019; 516:845-850. [PMID: 31262446 DOI: 10.1016/j.bbrc.2019.06.140] [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: 06/17/2019] [Accepted: 06/25/2019] [Indexed: 11/18/2022]
Abstract
We previously reported that immunoreactivity of recombinant CFP32 (Rv0577), a virulence factor of Mycobacterium tuberculosis, was higher when produced in transformed Pichia pastoris as compared to transformed E. coli. In this study, we show that this difference is partly due to the N-glycosylation of the recombinant CFP32 (rCFP32) by the yeast Pichia pastoris. In addition, SDS-PAGE and western blotting analysis of Mycobacterium bovis BCG and yeast-produced rCFP32 showed the presence of a band corresponding to a homodimeric state of the protein, unlike that of rCFP32 produced in E. coli. Computational modeling indicates that a single cysteine residue at position 193 of each monomer might bond to stabilize the homodimeric state of CFP32. Computational study showed that this residue is buried inside the protein core of E. coli-produced rCFP32 suggesting that rCFP32 may adopt a different folding in P. pastoris and BCG, in which C193 is solvent exposed. Surprisingly, an enzyme-linked immunosorbent assay using a generated monoclonal antibody (14D4) reveals the presence of a differential epitope that appears to be the consequence of the protein dimerization of the yeast- and BCG-, but not E.coli- produced, CFP32 recombinant form. We conclude that, in addition to N-glycosylation, homodimeric folding significantly enhances the immunoreactivity of rCFP32 and may these post-translational modifications may factor into the structure and function of native M. tuberculosis CFP32.
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Affiliation(s)
- Chaouki Benabdessalem
- Laboratory of Transmission, Control and Immunobiology of Infection - LR11IPT-02, Institut Pasteur de Tunis, Tunisia; University Tunis El Manar, Tunisia.
| | - Houcemeddine Othman
- Université de Tunis El Manar, Institut Pasteur de Tunis, LR16IPT08 Venins et Biomolécules Thérapeutiques, 1002, Tunis, Tunisia
| | - Rym Ouni
- Laboratory of Transmission, Control and Immunobiology of Infection - LR11IPT-02, Institut Pasteur de Tunis, Tunisia; University of Carthage, Tunisia
| | - Nabila Ghouibi
- Laboratory of Transmission, Control and Immunobiology of Infection - LR11IPT-02, Institut Pasteur de Tunis, Tunisia; University Tunis El Manar, Tunisia
| | - Amira Dahman
- Laboratory of Transmission, Control and Immunobiology of Infection - LR11IPT-02, Institut Pasteur de Tunis, Tunisia
| | - Rachid Riahi
- Laboratory of Transmission, Control and Immunobiology of Infection - LR11IPT-02, Institut Pasteur de Tunis, Tunisia
| | - Beya Larguach
- Laboratory of Transmission, Control and Immunobiology of Infection - LR11IPT-02, Institut Pasteur de Tunis, Tunisia
| | - Najet Srairi-Abid
- Université de Tunis El Manar, Institut Pasteur de Tunis, LR16IPT08 Venins et Biomolécules Thérapeutiques, 1002, Tunis, Tunisia; University Tunis El Manar, Tunisia
| | - Mohamed-Ridha Barbouche
- Laboratory of Transmission, Control and Immunobiology of Infection - LR11IPT-02, Institut Pasteur de Tunis, Tunisia; University Tunis El Manar, Tunisia
| | - M Dahmani Fathallah
- Laboratory of Transmission, Control and Immunobiology of Infection - LR11IPT-02, Institut Pasteur de Tunis, Tunisia
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Kumar V, Pandey P, Idrees D, Prakash A, Lynn A. Delineating the effect of mutations on the conformational dynamics of N-terminal domain of TDP-43. Biophys Chem 2019; 250:106174. [DOI: 10.1016/j.bpc.2019.106174] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2018] [Revised: 03/06/2019] [Accepted: 04/21/2019] [Indexed: 12/12/2022]
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42
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Frezza E, Lavery R. Internal Coordinate Normal Mode Analysis: A Strategy To Predict Protein Conformational Transitions. J Phys Chem B 2019; 123:1294-1301. [DOI: 10.1021/acs.jpcb.8b11913] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Affiliation(s)
- Elisa Frezza
- MMSB, UMR 5086 CNRS/Univ. Lyon I, Institut de Biologie et Chimie des Protéines, 7 passage du Vercors, Lyon 69367, France
| | - Richard Lavery
- MMSB, UMR 5086 CNRS/Univ. Lyon I, Institut de Biologie et Chimie des Protéines, 7 passage du Vercors, Lyon 69367, France
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43
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Advances in coarse-grained modeling of macromolecular complexes. Curr Opin Struct Biol 2018; 52:119-126. [PMID: 30508766 DOI: 10.1016/j.sbi.2018.11.005] [Citation(s) in RCA: 83] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2018] [Revised: 11/05/2018] [Accepted: 11/17/2018] [Indexed: 01/12/2023]
Abstract
Recent progress in coarse-grained (CG) molecular modeling and simulation has facilitated an influx of computational studies on biological macromolecules and their complexes. Given the large separation of length-scales and time-scales that dictate macromolecular biophysics, CG modeling and simulation are well-suited to bridge the microscopic and mesoscopic or macroscopic details observed from all-atom molecular simulations and experiments, respectively. In this review, we first summarize recent innovations in the development of CG models, which broadly include structure-based, knowledge-based, and dynamics-based approaches. We then discuss recent applications of different classes of CG models to explore various macromolecular complexes. Finally, we conclude with an outlook for the future in this ever-growing field of biomolecular modeling.
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Modeling of Protein Structural Flexibility and Large-Scale Dynamics: Coarse-Grained Simulations and Elastic Network Models. Int J Mol Sci 2018; 19:ijms19113496. [PMID: 30404229 PMCID: PMC6274762 DOI: 10.3390/ijms19113496] [Citation(s) in RCA: 42] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2018] [Revised: 10/29/2018] [Accepted: 10/31/2018] [Indexed: 12/13/2022] Open
Abstract
Fluctuations of protein three-dimensional structures and large-scale conformational transitions are crucial for the biological function of proteins and their complexes. Experimental studies of such phenomena remain very challenging and therefore molecular modeling can be a good alternative or a valuable supporting tool for the investigation of large molecular systems and long-time events. In this minireview, we present two alternative approaches to the coarse-grained (CG) modeling of dynamic properties of protein systems. We discuss two CG representations of polypeptide chains used for Monte Carlo dynamics simulations of protein local dynamics and conformational transitions, and highly simplified structure-based elastic network models of protein flexibility. In contrast to classical all-atom molecular dynamics, the modeling strategies discussed here allow the quite accurate modeling of much larger systems and longer-time dynamic phenomena. We briefly describe the main features of these models and outline some of their applications, including modeling of near-native structure fluctuations, sampling of large regions of the protein conformational space, or possible support for the structure prediction of large proteins and their complexes.
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45
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Karami Y, Bitard-Feildel T, Laine E, Carbone A. "Infostery" analysis of short molecular dynamics simulations identifies highly sensitive residues and predicts deleterious mutations. Sci Rep 2018; 8:16126. [PMID: 30382169 PMCID: PMC6208415 DOI: 10.1038/s41598-018-34508-2] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2018] [Accepted: 10/17/2018] [Indexed: 11/09/2022] Open
Abstract
Characterizing a protein mutational landscape is a very challenging problem in Biology. Many disease-associated mutations do not seem to produce any effect on the global shape nor motions of the protein. Here, we use relatively short all-atom biomolecular simulations to predict mutational outcomes and we quantitatively assess the predictions on several hundreds of mutants. We perform simulations of the wild type and 175 mutants of PSD95’s third PDZ domain in complex with its cognate ligand. By recording residue displacements correlations and interactions, we identify “communication pathways” and quantify them to predict the severity of the mutations. Moreover, we show that by exploiting simulations of the wild type, one can detect 80% of the positions highly sensitive to mutations with a precision of 89%. Importantly, our analysis describes the role of these positions in the inter-residue communication and dynamical architecture of the complex. We assess our approach on three different systems using data from deep mutational scanning experiments and high-throughput exome sequencing. We refer to our analysis as “infostery”, from “info” - information - and “steric” - arrangement of residues in space. We provide a fully automated tool, COMMA2 (www.lcqb.upmc.fr/COMMA2), that can be used to guide medicinal research by selecting important positions/mutations.
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Affiliation(s)
- Yasaman Karami
- Sorbonne Université, CNRS, IBPS, Laboratoire de Biologie Computationnelle et Quantitative (LCQB), 75005, Paris, France
| | - Tristan Bitard-Feildel
- Sorbonne Université, CNRS, IBPS, Laboratoire de Biologie Computationnelle et Quantitative (LCQB), 75005, Paris, France.,Sorbonne Université, Institut des Sciences du Calcul et de des Données (ISCD), Paris, France
| | - Elodie Laine
- Sorbonne Université, CNRS, IBPS, Laboratoire de Biologie Computationnelle et Quantitative (LCQB), 75005, Paris, France.
| | - Alessandra Carbone
- Sorbonne Université, CNRS, IBPS, Laboratoire de Biologie Computationnelle et Quantitative (LCQB), 75005, Paris, France. .,Institut Universitaire de France (IUF), Paris, France.
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Rodrigues CHM, Pires DEV, Ascher DB. DynaMut: predicting the impact of mutations on protein conformation, flexibility and stability. Nucleic Acids Res 2018; 46:W350-W355. [PMID: 29718330 PMCID: PMC6031064 DOI: 10.1093/nar/gky300] [Citation(s) in RCA: 635] [Impact Index Per Article: 105.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2018] [Revised: 04/03/2018] [Accepted: 04/16/2018] [Indexed: 12/31/2022] Open
Abstract
Proteins are highly dynamic molecules, whose function is intrinsically linked to their molecular motions. Despite the pivotal role of protein dynamics, their computational simulation cost has led to most structure-based approaches for assessing the impact of mutations on protein structure and function relying upon static structures. Here we present DynaMut, a web server implementing two distinct, well established normal mode approaches, which can be used to analyze and visualize protein dynamics by sampling conformations and assess the impact of mutations on protein dynamics and stability resulting from vibrational entropy changes. DynaMut integrates our graph-based signatures along with normal mode dynamics to generate a consensus prediction of the impact of a mutation on protein stability. We demonstrate our approach outperforms alternative approaches to predict the effects of mutations on protein stability and flexibility (P-value < 0.001), achieving a correlation of up to 0.70 on blind tests. DynaMut also provides a comprehensive suite for protein motion and flexibility analysis and visualization via a freely available, user friendly web server at http://biosig.unimelb.edu.au/dynamut/.
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Affiliation(s)
- Carlos HM Rodrigues
- Department of Biochemistry and Molecular Biology, Bio21 Molecular Science and Biotechnology Institute, University of Melbourne, Australia
| | | | - David B Ascher
- Department of Biochemistry and Molecular Biology, Bio21 Molecular Science and Biotechnology Institute, University of Melbourne, Australia
- Instituto René Rachou, Fundação Oswaldo Cruz, Brazil
- Department of Biochemistry, University of Cambridge, UK
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47
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Smith IN, Thacker S, Jaini R, Eng C. Dynamics and structural stability effects of germline PTEN mutations associated with cancer versus autism phenotypes. J Biomol Struct Dyn 2018; 37:1766-1782. [PMID: 29663862 DOI: 10.1080/07391102.2018.1465854] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
Abstract
Individuals with germline mutations in the tumor suppressor gene phosphatase and tensin homolog (PTEN), irrespective of clinical presentation, are diagnosed with PTEN hamartoma tumor syndrome (PHTS). PHTS confers a high risk of breast, thyroid, and other cancers or autism spectrum disorder (ASD) with macrocephaly. It remains unclear why mutations in one gene can lead to seemingly disparate phenotypes. Thus, we sought to identify differences in ASD vs. cancer-associated germline PTEN missense mutations by investigating putative structural effects induced by each mutation. We utilized a theoretical computational approach combining in silico structural analysis and molecular dynamics (MD) to interrogate 17 selected mutations from our patient population: six mutations were observed in patients with ASD (only), six mutations in patients with PHTS-associated cancer (only), four mutations shared across both phenotypes, and one mutation with both ASD and cancer. We demonstrate structural stability changes where all six cancer-associated mutations showed a global decrease in structural stability and increased dynamics across the domain interface with a proclivity to unfold, mediating a closed (inactive) active site. In contrast, five of the six ASD-associated mutations showed localized destabilization that contribute to the partial opening of the active site. Our results lend insight into distinctive structural effects of germline PTEN mutations associated with PTEN-ASD vs. those associated with PTEN-cancer, potentially aiding in identification of the shared and separate molecular features that contribute to autism or cancer, thus, providing a deeper understanding of genotype-phenotype relationships for germline PTEN mutations.
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Affiliation(s)
- Iris Nira Smith
- a Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic , Cleveland , OH , USA
| | - Stetson Thacker
- a Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic , Cleveland , OH , USA.,e Cleveland Clinic Lerner College of Medicine , Cleveland , OH , USA
| | - Ritika Jaini
- a Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic , Cleveland , OH , USA.,d Germline High Risk Cancer Focus Group , Comprehensive Cancer Center, Case Western Reserve University School of Medicine , Cleveland , OH , USA.,e Cleveland Clinic Lerner College of Medicine , Cleveland , OH , USA
| | - Charis Eng
- a Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic , Cleveland , OH , USA.,b Taussig Cancer Institute, Cleveland Clinic , Cleveland , OH , USA.,c Department of Genetics and Genome Sciences, Case Westren Reserve University School of Medicine , Cleveland , OH , USA.,d Germline High Risk Cancer Focus Group , Comprehensive Cancer Center, Case Western Reserve University School of Medicine , Cleveland , OH , USA.,e Cleveland Clinic Lerner College of Medicine , Cleveland , OH , USA
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48
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Chiang RZH, Gan SKE, Su CTT. A computational study for rational HIV-1 non-nucleoside reverse transcriptase inhibitor selection and the discovery of novel allosteric pockets for inhibitor design. Biosci Rep 2018; 38:BSR20171113. [PMID: 29437904 PMCID: PMC5835713 DOI: 10.1042/bsr20171113] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2017] [Revised: 01/29/2018] [Accepted: 01/29/2018] [Indexed: 12/15/2022] Open
Abstract
HIV drug resistant mutations that render the current Highly Active Anti-Retroviral Therapy (HAART) cocktail drugs ineffective are increasingly reported. To study the mechanisms of these mutations in conferring drug resistance, we computationally analyzed 14 reverse transcriptase (RT) structures of HIV-1 on the following parameters: drug-binding pocket volume, allosteric effects caused by the mutations, and structural thermal stability. We constructed structural correlation-based networks of the mutant RT-drug complexes and the analyses support the use of efavirenz (EFZ) as the first-line drug, given that cross-resistance is least likely to develop from EFZ-resistant mutations. On the other hand, rilpivirine (RPV)-resistant mutations showed the highest cross-resistance to the other non-nucleoside RT inhibitors. With significant drug cross-resistance associated with the known allosteric drug-binding site, there is a need to identify new allosteric druggable sites in the structure of RT. Through computational analyses, we found such a novel druggable pocket on the HIV-1 RT structure that is comparable with the original allosteric drug site, opening the possibility to the design of new inhibitors.
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Affiliation(s)
- Ron Zhi-Hui Chiang
- Bioinformatics Institute, Agency for Science, Technology, and Research (A*STAR), Singapore 138671
| | - Samuel Ken-En Gan
- Bioinformatics Institute, Agency for Science, Technology, and Research (A*STAR), Singapore 138671
- p53 Laboratory, Agency for Science, Technology, and Research (A*STAR), Singapore 138648
| | - Chinh Tran-To Su
- Bioinformatics Institute, Agency for Science, Technology, and Research (A*STAR), Singapore 138671
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Alemasov NA, Ivanisenko NV, Ramachandran S, Ivanisenko VA. Molecular mechanisms underlying the impact of mutations in SOD1 on its conformational properties associated with amyotrophic lateral sclerosis as revealed with molecular modelling. BMC STRUCTURAL BIOLOGY 2018; 18:1. [PMID: 29431095 PMCID: PMC5808480 DOI: 10.1186/s12900-018-0080-9] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Background So far, little is known about the molecular mechanisms of amyotrophic lateral sclerosis onset and progression caused by SOD1 mutations. One of the hypotheses is based on SOD1 misfolding resulting from mutations and subsequent deposition of its cytotoxic aggregates. This hypothesis is complicated by the fact that known SOD1 mutations of similar clinical effect could be distributed over the whole protein structure. Results In this work, a measure of hydrogen bond stability in conformational states was studied with elastic network analysis of 35 SOD1 mutants. Twenty-eight hydrogen bonds were detected in nine of 35 mutants with their stability being significantly different from that with the wild-type. These hydrogen bonds were formed by the amino acid residues known from the literature to be located in contact between SOD1 aggregates. Additionally, residues disposed between copper binding sites of both protein subunits were found from the models to form a stiff core, which can be involved in mechanical impulse transduction between these active centres. Conclusions The modelling highlights that both stability of the copper binding site and stability of the dimer can play an important role in ALS progression. Electronic supplementary material The online version of this article (10.1186/s12900-018-0080-9) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Nikolay A Alemasov
- The Federal Research Center Institute of Cytology and Genetics, The Siberian Branch of the Russian Academy of Sciences, 630090, Novosibirsk, Russia.
| | - Nikita V Ivanisenko
- The Federal Research Center Institute of Cytology and Genetics, The Siberian Branch of the Russian Academy of Sciences, 630090, Novosibirsk, Russia.,Novosibirsk State University, 630090, Novosibirsk, Russia
| | - Srinivasan Ramachandran
- Functional Genomics Unit, Council of Scientific and Industrial Research-Institute of Genomics and Integrative Biology (CSIR-IGIB), South Campus, New Delhi, 110025, India.,Academy of Scientific and Innovative Research, CSIR-IGIB, South Campus, New Delhi, 110025, India
| | - Vladimir A Ivanisenko
- The Federal Research Center Institute of Cytology and Genetics, The Siberian Branch of the Russian Academy of Sciences, 630090, Novosibirsk, Russia
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50
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Sankar K, Mishra SK, Jernigan RL. Comparisons of Protein Dynamics from Experimental Structure Ensembles, Molecular Dynamics Ensembles, and Coarse-Grained Elastic Network Models. J Phys Chem B 2018; 122:5409-5417. [DOI: 10.1021/acs.jpcb.7b11668] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Affiliation(s)
- Kannan Sankar
- Bioinformatics and Computational Biology Interdepartmental Graduate Program, Iowa State University, Ames, Iowa 50011-1178, United States
- Roy J. Carver Department of Biochemistry, Biophysics, and Molecular Biology, Iowa State University, Ames, Iowa 50011-1178, United States
| | - Sambit K. Mishra
- Bioinformatics and Computational Biology Interdepartmental Graduate Program, Iowa State University, Ames, Iowa 50011-1178, United States
- Roy J. Carver Department of Biochemistry, Biophysics, and Molecular Biology, Iowa State University, Ames, Iowa 50011-1178, United States
| | - Robert L. Jernigan
- Bioinformatics and Computational Biology Interdepartmental Graduate Program, Iowa State University, Ames, Iowa 50011-1178, United States
- Roy J. Carver Department of Biochemistry, Biophysics, and Molecular Biology, Iowa State University, Ames, Iowa 50011-1178, United States
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