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He Y, Wang S, Zeng S, Zhu J, Xu D, Han W, Wang J. NRIMD, a Web Server for Analyzing Protein Allosteric Interactions Based on Molecular Dynamics Simulation. J Chem Inf Model 2024. [PMID: 38991149 DOI: 10.1021/acs.jcim.4c00783] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/13/2024]
Abstract
Long-range allosteric communication between distant sites and active sites in proteins is central to biological regulation but still poorly characterized, limiting the development of protein engineering and drug design. Addressing this gap, NRIMD is an open-access web server for analyzing long-range interactions in proteins from molecular dynamics (MD) simulations, such as the effect of mutations at distal sites or allosteric ligand binding at allosteric sites on the active center. Based on our recent works on neural relational inference using graph neural networks, this cloud-based web server accepts MD simulation data on any length of residues in the alpha-carbon skeleton format from mainstream MD software. The input trajectory data are validated at the frontend deployed on the cloud and then processed on the backend deployed on a high-performance computer system with a collection of complementary tools. The web server provides a one-stop-shop MD analysis platform to predict long-range interactions and their paths between distant sites and active sites. It provides a user-friendly interface for detailed analysis and visualization. To the best of our knowledge, NRIMD is the first-of-its-kind online service to provide comprehensive long-range interaction analysis on MD simulations, which significantly lowers the barrier of predictions on protein long-range interactions using deep learning. The NRIMD web server is publicly available at https://nrimd.luddy.indianapolis.iu.edu/.
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Affiliation(s)
- Yi He
- Key Laboratory for Molecular Enzymology and Engineering of Ministry of Education, School of Life Sciences, Jilin University, Changchun 130012, China
| | - Shuang Wang
- Department of Computer Science, Luddy School of Informatics, Computing, and Engineering, Indiana University Bloomington, Bloomington, Indiana 47405, United States
| | - Shuai Zeng
- Department of Electrical Engineering and Computer Science, Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, Missouri 65211, United States
| | - Jingxuan Zhu
- Key Laboratory for Molecular Enzymology and Engineering of Ministry of Education, School of Life Sciences, Jilin University, Changchun 130012, China
| | - Dong Xu
- Department of Electrical Engineering and Computer Science, Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, Missouri 65211, United States
| | - Weiwei Han
- Key Laboratory for Molecular Enzymology and Engineering of Ministry of Education, School of Life Sciences, Jilin University, Changchun 130012, China
| | - Juexin Wang
- Department of BioHealth Informatics, Luddy School of Informatics, Computing, and Engineering, Indiana University Indianapolis, Indianapolis, Indiana 46202, United States
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2
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Chakraborty A, Samant D, Sarkar R, Sangeet S, Prusty S, Roy S. RNA's Dynamic Conformational Selection and Entropic Allosteric Mechanism in Controlling Cascade Protein Binding Events. J Phys Chem Lett 2024; 15:6115-6125. [PMID: 38830201 DOI: 10.1021/acs.jpclett.4c00740] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/05/2024]
Abstract
In the TAR RNA of immunodeficiency viruses, an allosteric communication exists between a distant loop and a bulge. The bulge interacts with the TAT protein vital for transactivating viral RNA, while the loop interacts with cyclin-T1, contingent on TAT binding. Through extensive atomistic and free energy simulations, we investigate TAR-TAT binding in nonpathogenic bovine immunodeficiency virus (BIV) and pathogenic human immunodeficiency virus (HIV). Thermodynamic analysis reveals enthalpically driven binding in BIV and entropically favored binding in HIV. The broader global basin in HIV is attributed to binding-induced loop fluctuation, corroborated by nuclear magnetic resonance (NMR), indicating classical entropic allostery onset. While this loop fluctuation affects the TAT binding affinity, it generates a binding-competent conformation that aids subsequent effector (cyclin-T1) binding. This study underscores how two structurally similar apo-RNA scaffolds adopt distinct conformational selection mechanisms to drive enthalpic and entropic allostery, influencing protein affinity in the signaling cascade.
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Affiliation(s)
- Amrita Chakraborty
- Department of Chemical Sciences, Indian Institute of Science Education and Research Kolkata, Kolkata, West Bengal 741246, India
| | - Dibyamanjaree Samant
- Department of Chemical Sciences, Indian Institute of Science Education and Research Kolkata, Kolkata, West Bengal 741246, India
| | - Raju Sarkar
- Department of Chemical Sciences, Indian Institute of Science Education and Research Kolkata, Kolkata, West Bengal 741246, India
| | - Satyam Sangeet
- Department of Chemical Sciences, Indian Institute of Science Education and Research Kolkata, Kolkata, West Bengal 741246, India
| | - Sangram Prusty
- Department of Chemical Sciences, Indian Institute of Science Education and Research Kolkata, Kolkata, West Bengal 741246, India
| | - Susmita Roy
- Department of Chemical Sciences, Indian Institute of Science Education and Research Kolkata, Kolkata, West Bengal 741246, India
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3
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Gonzales J, Kim I, Hwang W, Cho JH. Evolutionary rewiring of the dynamic network underpinning allosteric epistasis in NS1 of influenza A virus. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.24.595776. [PMID: 38826371 PMCID: PMC11142230 DOI: 10.1101/2024.05.24.595776] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2024]
Abstract
Viral proteins frequently mutate to evade or antagonize host innate immune responses, yet the impact of these mutations on the molecular energy landscape remains unclear. Epistasis, the intramolecular communications between mutations, often renders the combined mutational effects unpredictable. Nonstructural protein 1 (NS1) is a major virulence factor of the influenza A virus (IAV) that activates host PI3K by binding to its p85β subunit. Here, we present the deep analysis for the impact of evolutionary mutations in NS1 that emerged between the 1918 pandemic IAV strain and its descendant PR8 strain. Our analysis reveal how the mutations rewired inter-residue communications which underlies long-range allosteric and epistatic networks in NS1. Our findings show that PR8 NS1 binds to p85β with approximately 10-fold greater affinity than 1918 NS1 due to allosteric mutational effects. Notably, these mutations also exhibited long-range epistatic effects. NMR chemical shift perturbation and methyl-axis order parameter analyses revealed that the mutations induced long-range structural and dynamic changes in PR8 NS1, enhancing its affinity to p85β. Complementary MD simulations and graph-based network analysis uncover how these mutations rewire dynamic residue interaction networks, which underlies the long-range epistasis and allosteric effects on p85β-binding affinity. Significantly, we find that conformational dynamics of residues with high betweenness centrality play a crucial role in communications between network communities and are highly conserved across influenza A virus evolution. These findings advance our mechanistic understanding of the allosteric and epistatic communications between distant residues and provides insight into their role in the molecular evolution of NS1.
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4
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Nerín-Fonz F, Cournia Z. Machine learning approaches in predicting allosteric sites. Curr Opin Struct Biol 2024; 85:102774. [PMID: 38354652 DOI: 10.1016/j.sbi.2024.102774] [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: 10/08/2023] [Revised: 12/29/2023] [Accepted: 01/02/2024] [Indexed: 02/16/2024]
Abstract
Allosteric regulation is a fundamental biological mechanism that can control critical cellular processes via allosteric modulator binding to protein distal functional sites. The advantages of allosteric modulators over orthosteric ones have sparked the development of numerous computational approaches, such as the identification of allosteric binding sites, to facilitate allosteric drug discovery. Building on the success of machine learning (ML) models for solving complex problems in biology and chemistry, several ML models for predicting allosteric sites have been developed. In this review, we provide an overview of these models and discuss future perspectives powered by the field of artificial intelligence such as protein language models.
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Affiliation(s)
- Francho Nerín-Fonz
- Biomedical Research Foundation, Academy of Athens, 4 Soranou Ephesiou, Athens 11527, Greece; Vienna Doctoral School of Pharmaceutical, Nutritional and Sport Sciences, University of Vienna, Josef-Holaubek-Platz 2, 1090 Vienna, Austria.
| | - Zoe Cournia
- Biomedical Research Foundation, Academy of Athens, 4 Soranou Ephesiou, Athens 11527, Greece; Vienna Doctoral School of Pharmaceutical, Nutritional and Sport Sciences, University of Vienna, Josef-Holaubek-Platz 2, 1090 Vienna, Austria.
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5
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Babbitt GA, Rajendran M, Lynch ML, Asare-Bediako R, Mouli LT, Ryan CJ, Srivastava H, Rynkiewicz P, Phadke K, Reed ML, Moore N, Ferran MC, Fokoue EP. ATOMDANCE: Kernel-based denoising and choreographic analysis for protein dynamic comparison. Biophys J 2024:S0006-3495(24)00204-2. [PMID: 38515299 DOI: 10.1016/j.bpj.2024.03.024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Revised: 12/16/2023] [Accepted: 03/19/2024] [Indexed: 03/23/2024] Open
Abstract
Comparative methods in molecular evolution and structural biology rely heavily upon the site-wise analysis of DNA sequence and protein structure, both static forms of information. However, it is widely accepted that protein function results from nanoscale nonrandom machine-like motions induced by evolutionarily conserved molecular interactions. Comparisons of molecular dynamics (MD) simulations conducted between homologous sites representative of different functional or mutational states can potentially identify local effects on binding interaction and protein evolution. In addition, comparisons of different (i.e., nonhomologous) sites within MD simulations could be employed to identify functional shifts in local time-coordinated dynamics indicative of logic gating within proteins. However, comparative MD analysis is challenged by the large fraction of protein motion caused by random thermal noise in the surrounding solvent. Therefore, properly denoised MD comparisons could reveal functional sites involving these machine-like dynamics with good accuracy. Here, we introduce ATOMDANCE, a user-interfaced suite of comparative machine learning-based denoising tools designed for identifying functional sites and the patterns of coordinated motion they can create within MD simulations. ATOMDANCE-maxDemon4.0 employs Gaussian kernel functions to compute site-wise maximum mean discrepancy between learned features of motion, thereby assessing denoised differences in the nonrandom motions between functional or evolutionary states (e.g., ligand bound versus unbound, wild-type versus mutant). ATOMDANCE-maxDemon4.0 also employs maximum mean discrepancy to analyze potential random amino acid replacements allowing for a site-wise test of neutral versus nonneutral evolution on the divergence of dynamic function in protein homologs. Finally, ATOMDANCE-Choreograph2.0 employs mixed-model analysis of variance and graph network to detect regions where time-synchronized shifts in dynamics occur. Here, we demonstrate ATOMDANCE's utility for identifying key sites involved in dynamic responses during functional binding interactions involving DNA, small-molecule drugs, and virus-host recognition, as well as understanding shifts in global and local site coordination occurring during allosteric activation of a pathogenic protease.
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Affiliation(s)
- Gregory A Babbitt
- Thomas H. Gosnell School of Life Sciences, Rochester Institute of Technology, Rochester, New York.
| | - Madhusudan Rajendran
- Thomas H. Gosnell School of Life Sciences, Rochester Institute of Technology, Rochester, New York
| | - Miranda L Lynch
- Hauptmann Woodward Medical Research Institute, Buffalo, New York
| | - Richmond Asare-Bediako
- Thomas H. Gosnell School of Life Sciences, Rochester Institute of Technology, Rochester, New York
| | - Leora T Mouli
- Thomas H. Gosnell School of Life Sciences, Rochester Institute of Technology, Rochester, New York
| | - Cameron J Ryan
- McQuaid Jesuit High School Computer Club, Rochester, New York
| | | | - Patrick Rynkiewicz
- Thomas H. Gosnell School of Life Sciences, Rochester Institute of Technology, Rochester, New York
| | - Kavya Phadke
- Thomas H. Gosnell School of Life Sciences, Rochester Institute of Technology, Rochester, New York
| | - Makayla L Reed
- Thomas H. Gosnell School of Life Sciences, Rochester Institute of Technology, Rochester, New York
| | - Nadia Moore
- Thomas H. Gosnell School of Life Sciences, Rochester Institute of Technology, Rochester, New York
| | - Maureen C Ferran
- Thomas H. Gosnell School of Life Sciences, Rochester Institute of Technology, Rochester, New York
| | - Ernest P Fokoue
- School of Mathematical Sciences, Rochester Institute of Technology, Rochester, New York.
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6
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Skeens E, Sinha S, Ahsan M, D’Ordine AM, Jogl G, Palermo G, Lisi GP. High-fidelity, hyper-accurate, and evolved mutants rewire atomic-level communication in CRISPR-Cas9. SCIENCE ADVANCES 2024; 10:eadl1045. [PMID: 38446895 PMCID: PMC10917355 DOI: 10.1126/sciadv.adl1045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Accepted: 01/29/2024] [Indexed: 03/08/2024]
Abstract
The high-fidelity (HF1), hyper-accurate (Hypa), and evolved (Evo) variants of the CRISPR-associated protein 9 (Cas9) endonuclease are critical tools to mitigate off-target effects in the application of CRISPR-Cas9 technology. The mechanisms by which mutations in recognition subdomain 3 (Rec3) mediate specificity in these variants are poorly understood. Here, solution nuclear magnetic resonance and molecular dynamics simulations establish the structural and dynamic effects of high-specificity mutations in Rec3, and how they propagate the allosteric signal of Cas9. We reveal conserved structural changes and dynamic differences at regions of Rec3 that interface with the RNA:DNA hybrid, transducing chemical signals from Rec3 to the catalytic His-Asn-His (HNH) domain. The variants remodel the communication sourcing from the Rec3 α helix 37, previously shown to sense target DNA complementarity, either directly or allosterically. This mechanism increases communication between the DNA mismatch recognition helix and the HNH active site, shedding light on the structure and dynamics underlying Cas9 specificity and providing insight for future engineering principles.
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Affiliation(s)
- Erin Skeens
- Department of Molecular Biology, Cell Biology and Biochemistry, Brown University, Providence, RI, USA
| | - Souvik Sinha
- Department of Bioengineering, University of California, Riverside, Riverside, CA, USA
| | - Mohd Ahsan
- Department of Bioengineering, University of California, Riverside, Riverside, CA, USA
| | - Alexandra M. D’Ordine
- Department of Molecular Biology, Cell Biology and Biochemistry, Brown University, Providence, RI, USA
| | - Gerwald Jogl
- Department of Molecular Biology, Cell Biology and Biochemistry, Brown University, Providence, RI, USA
| | - Giulia Palermo
- Department of Bioengineering, University of California, Riverside, Riverside, CA, USA
- Department of Chemistry, University of California, Riverside, Riverside, CA, USA
| | - George P. Lisi
- Department of Molecular Biology, Cell Biology and Biochemistry, Brown University, Providence, RI, USA
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7
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Tee WV, Berezovsky IN. Allosteric drugs: New principles and design approaches. Curr Opin Struct Biol 2024; 84:102758. [PMID: 38171188 DOI: 10.1016/j.sbi.2023.102758] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Accepted: 11/30/2023] [Indexed: 01/05/2024]
Abstract
Focusing on an important biomedical implication of allostery - design of allosteric drugs, we describe characteristics of allosteric sites, effectors, and their modes of actions distinguishing them from the orthosteric counterparts and calling for new principles and protocols in the quests for allosteric drugs. We show the importance of considering both binding affinity and allosteric signaling in establishing the structure-activity relationships (SARs) toward design of allosteric effectors, arguing that pairs of allosteric sites and their effector ligands - the site-effector pairs - should be generated and adjusted simultaneously in the framework of what we call directed design protocol. Key ideas and approaches for designing allosteric effectors including reverse perturbation, targeted and agnostic analysis are also discussed here. Several promising computational approaches are highlighted, along with the need for and potential advantages of utilizing generative models to facilitate discovery/design of new allosteric drugs.
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Affiliation(s)
- Wei-Ven Tee
- Bioinformatics Institute (BII), Agency for Science, Technology and Research (A∗STAR), 30 Biopolis Street, #07-01, Matrix, Singapore 138671.
| | - Igor N Berezovsky
- Bioinformatics Institute (BII), Agency for Science, Technology and Research (A∗STAR), 30 Biopolis Street, #07-01, Matrix, Singapore 138671; Department of Biological Sciences (DBS), National University of Singapore (NUS), 8 Medical Drive, 117579, Singapore.
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8
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Molina Vargas A, Sinha S, Osborn R, Arantes P, Patel A, Dewhurst S, Hardy D, Cameron A, Palermo G, O’Connell M. New design strategies for ultra-specific CRISPR-Cas13a-based RNA detection with single-nucleotide mismatch sensitivity. Nucleic Acids Res 2024; 52:921-939. [PMID: 38033324 PMCID: PMC10810210 DOI: 10.1093/nar/gkad1132] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Revised: 10/27/2023] [Accepted: 11/09/2023] [Indexed: 12/02/2023] Open
Abstract
An increasingly pressing need for clinical diagnostics has required the development of novel nucleic acid-based detection technologies that are sensitive, fast, and inexpensive, and that can be deployed at point-of-care. Recently, the RNA-guided ribonuclease CRISPR-Cas13 has been successfully harnessed for such purposes. However, developing assays for detection of genetic variability, for example single-nucleotide polymorphisms, is still challenging and previously described design strategies are not always generalizable. Here, we expanded our characterization of LbuCas13a RNA-detection specificity by performing a combination of experimental RNA mismatch tolerance profiling, molecular dynamics simulations, protein, and crRNA engineering. We found certain positions in the crRNA-target-RNA duplex that are particularly sensitive to mismatches and establish the effect of RNA concentration in mismatch tolerance. Additionally, we determined that shortening the crRNA spacer or modifying the direct repeat of the crRNA leads to stricter specificities. Furthermore, we harnessed our understanding of LbuCas13a allosteric activation pathways through molecular dynamics and structure-guided engineering to develop novel Cas13a variants that display increased sensitivities to single-nucleotide mismatches. We deployed these Cas13a variants and crRNA design strategies to achieve superior discrimination of SARS-CoV-2 strains compared to wild-type LbuCas13a. Together, our work provides new design criteria and Cas13a variants to use in future easier-to-implement Cas13-based RNA detection applications.
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Affiliation(s)
- Adrian M Molina Vargas
- Department of Biochemistry and Biophysics, School of Medicine and Dentistry, University of Rochester, Rochester, NY, USA
- Center for RNA Biology, School of Medicine and Dentistry, University of Rochester, Rochester, NY, USA
- Department of Biomedical Genetics, School of Medicine and Dentistry, University of Rochester, Rochester, NY, USA
| | - Souvik Sinha
- Department of Bioengineering, University of California Riverside, Riverside, CA, USA
| | - Raven Osborn
- Clinical and Translational Sciences Institute, School of Medicine and Dentistry, University of Rochester, Rochester, NY, USA
- Department of Microbiology and Immunology, School of Medicine and Dentistry, University of Rochester, Rochester, NY, USA
| | - Pablo R Arantes
- Department of Bioengineering, University of California Riverside, Riverside, CA, USA
| | - Amun Patel
- Department of Bioengineering, University of California Riverside, Riverside, CA, USA
| | - Stephen Dewhurst
- Department of Microbiology and Immunology, School of Medicine and Dentistry, University of Rochester, Rochester, NY, USA
| | - Dwight J Hardy
- Department of Microbiology and Immunology, School of Medicine and Dentistry, University of Rochester, Rochester, NY, USA
- Department of Pathology and Laboratory Medicine, School of Medicine and Dentistry, University of Rochester, Rochester, NY, USA
| | - Andrew Cameron
- Department of Pathology and Laboratory Medicine, School of Medicine and Dentistry, University of Rochester, Rochester, NY, USA
| | - Giulia Palermo
- Department of Bioengineering, University of California Riverside, Riverside, CA, USA
- Department of Chemistry, University of California Riverside, Riverside, CA, USA
| | - Mitchell R O’Connell
- Department of Biochemistry and Biophysics, School of Medicine and Dentistry, University of Rochester, Rochester, NY, USA
- Center for RNA Biology, School of Medicine and Dentistry, University of Rochester, Rochester, NY, USA
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9
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Sinha S, Molina Vargas A, Arantes P, Patel A, O’Connell M, Palermo G. Unveiling the RNA-mediated allosteric activation discloses functional hotspots in CRISPR-Cas13a. Nucleic Acids Res 2024; 52:906-920. [PMID: 38033317 PMCID: PMC10810222 DOI: 10.1093/nar/gkad1127] [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: 07/27/2023] [Revised: 10/25/2023] [Accepted: 11/09/2023] [Indexed: 12/02/2023] Open
Abstract
Cas13a is a recent addition to the CRISPR-Cas toolkit that exclusively targets RNA, which makes it a promising tool for RNA detection. It utilizes a CRISPR RNA (crRNA) to target RNA sequences and trigger a composite active site formed by two 'Higher Eukaryotes and Prokaryotes Nucleotide' (HEPN) domains, cleaving any solvent-exposed RNA. In this system, an intriguing form of allosteric communication controls the RNA cleavage activity, yet its molecular details are unknown. Here, multiple-microsecond molecular dynamics simulations are combined with graph theory to decipher this intricate activation mechanism. We show that the binding of a target RNA acts as an allosteric effector, by amplifying the communication signals over the dynamical noise through interactions of the crRNA at the buried HEPN1-2 interface. By introducing a novel Signal-to-Noise Ratio (SNR) of communication efficiency, we reveal critical allosteric residues-R377, N378, and R973-that rearrange their interactions upon target RNA binding. Alanine mutation of these residues is shown to select target RNA over an extended complementary sequence beyond guide-target duplex for RNA cleavage, establishing the functional significance of these hotspots. Collectively our findings offer a fundamental understanding of the Cas13a mechanism of action and pave new avenues for the development of highly selective RNA-based cleavage and detection tools.
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Affiliation(s)
- Souvik Sinha
- Department of Bioengineering, University of California Riverside, Riverside, CA , 92521, USA
| | - Adrian M Molina Vargas
- Department of Biochemistry and Biophysics, University of Rochester School of Medicine and Dentistry, Rochester, NY, USA
- Center for RNA Biology, University of Rochester School of Medicine and Dentistry, Rochester, NY, USA
- Department of Biomedical Genetics, University of Rochester School of Medicine and Dentistry, Rochester, NY, USA
| | - Pablo R Arantes
- Department of Bioengineering, University of California Riverside, Riverside, CA , 92521, USA
| | - Amun Patel
- Department of Bioengineering, University of California Riverside, Riverside, CA , 92521, USA
| | - Mitchell R O’Connell
- Department of Biochemistry and Biophysics, University of Rochester School of Medicine and Dentistry, Rochester, NY, USA
- Center for RNA Biology, University of Rochester School of Medicine and Dentistry, Rochester, NY, USA
| | - Giulia Palermo
- Department of Bioengineering, University of California Riverside, Riverside, CA , 92521, USA
- Department of Chemistry, University of California Riverside, Riverside, CA, 92521, USA
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10
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Lu X, Lan X, Lu S, Zhang J. Progressive computational approaches to facilitate decryption of allosteric mechanism and drug discovery. Curr Opin Struct Biol 2023; 83:102701. [PMID: 37716092 DOI: 10.1016/j.sbi.2023.102701] [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: 07/12/2023] [Revised: 08/17/2023] [Accepted: 08/21/2023] [Indexed: 09/18/2023]
Abstract
Allostery is a ubiquitous biological phenomenon where perturbation at topologically distal areas of a protein serves as a trigger to fine-tune the orthosteric site and thus regulate protein function. The investigation of allosteric regulation greatly enhances our understanding of human diseases and broadens avenue for drug discovery. For decades, owing to the difficulty in allostery characterization through serendipitous experimental screening, researchers have developed several innovative computational approaches, which proves to accelerate the elucidation of allostery. Herein, we review the state-of-the-art advance of computational methodologies for allostery study, with particular emphasis on promising trends emerging over the past two years. We expect this review will outline the latest landscape of allostery study and inspire researchers to further facilitate this field.
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Affiliation(s)
- Xun Lu
- School of Pharmacy, Ningxia Medical University, Yinchuan 750004, China; State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China; Medicinal Chemistry and Bioinformatics Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Xiaobing Lan
- School of Pharmacy, Ningxia Medical University, Yinchuan 750004, China
| | - Shaoyong Lu
- School of Pharmacy, Ningxia Medical University, Yinchuan 750004, China; State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China; Medicinal Chemistry and Bioinformatics Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Jian Zhang
- School of Pharmacy, Ningxia Medical University, Yinchuan 750004, China; State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China; Medicinal Chemistry and Bioinformatics Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China; School of Pharmaceutical Sciences, Zhengzhou University, Zhengzhou, China.
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11
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Skeens E, Sinha S, Ahsan M, D'Ordine AM, Jogl G, Palermo G, Lisi GP. High-Fidelity, Hyper-Accurate, and Evolved Mutants Rewire Atomic Level Communication in CRISPR-Cas9. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.25.554853. [PMID: 37662375 PMCID: PMC10473742 DOI: 10.1101/2023.08.25.554853] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/05/2023]
Abstract
The Cas9-HF1, HypaCas9, and evoCas9 variants of the Cas9 endonuclease are critical tools to mitigate off-target effects in the application of CRISPR-Cas9 technology. The mechanisms by which mutations in the Rec3 domain mediate specificity in these variants are poorly understood. Here, solution NMR and molecular dynamics simulations establish the structural and dynamic effects of high-specificity mutations in Rec3, and how they propagate the allosteric signal of Cas9. We reveal conserved structural changes and peculiar dynamic differences at regions of Rec3 that interface with the RNA:DNA hybrid, transducing chemical signals from Rec3 to the catalytic HNH domain. The variants remodel the communication sourcing from the Rec3 α-helix 37, previously shown to sense target DNA complementarity, either directly or allosterically. This mechanism increases communication between the DNA mismatch recognition helix and the HNH active site, shedding light on the structure and dynamics underlying Cas9 specificity and providing insight for future engineering principles.
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12
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Vargas AMM, Osborn R, Sinha S, Arantes PR, Patel A, Dewhurst S, Palermo G, O'Connell MR. New design strategies for ultra-specific CRISPR-Cas13a-based RNA-diagnostic tools with single-nucleotide mismatch sensitivity. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.26.550755. [PMID: 37547020 PMCID: PMC10402140 DOI: 10.1101/2023.07.26.550755] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/08/2023]
Abstract
The pressing need for clinical diagnostics has required the development of novel nucleic acid-based detection technologies that are sensitive, fast, and inexpensive, and that can be deployed at point-of-care. Recently, the RNA-guided ribonuclease CRISPR-Cas13 has been successfully harnessed for such purposes. However, developing assays for detection of genetic variability, for example single-nucleotide polymorphisms, is still challenging and previously described design strategies are not always generalizable. Here, we expanded our characterization of LbuCas13a RNA-detection specificity by performing a combination of experimental RNA mismatch tolerance profiling, molecular dynamics simulations, protein, and crRNA engineering. We found certain positions in the crRNA-target-RNA duplex that are particularly sensitive to mismatches and establish the effect of RNA concentration in mismatch tolerance. Additionally, we determined that shortening the crRNA spacer or modifying the direct repeat of the crRNA leads to stricter specificities. Furthermore, we harnessed our understanding of LbuCas13a allosteric activation pathways through molecular dynamics and structure-guided engineering to develop novel Cas13a variants that display increased sensitivities to single-nucleotide mismatches. We deployed these Cas13a variants and crRNA design strategies to achieve superior discrimination of SARS-CoV-2 strains compared to wild-type LbuCas13a. Together, our work provides new design criteria and new Cas13a variants for easier-to-implement Cas13-based diagnostics. KEY POINTS Certain positions in the Cas13a crRNA-target-RNA duplex are particularly sensitive to mismatches.Understanding Cas13a's allosteric activation pathway allowed us to develop novel high-fidelity Cas13a variants.These Cas13a variants and crRNA design strategies achieve superior discrimination of SARS-CoV-2 strains. GRAPHICAL ABSTRACT
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Sinha S, Molina Vargas AM, Arantes PR, Patel A, O'Connell MR, Palermo G. RNA-mediated Allosteric Activation in CRISPR-Cas13a. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.27.550797. [PMID: 37546822 PMCID: PMC10402131 DOI: 10.1101/2023.07.27.550797] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/08/2023]
Abstract
Cas13a is a recent addition to the CRISPR-Cas toolkit that exclusively targets RNA, which makes it a promising tool for RNA detection. The protein uses a CRISPR RNA (crRNA) to target RNA sequences, which are cleaved by a composite active site formed by two 'Higher Eukaryotes and Prokaryotes Nucleotide' (HEPN) catalytic domains. In this system, an intriguing form of allosteric communication controls RNA cleavage activity, yet its molecular details are unknown. Here, multiple-microsecond molecular dynamics simulations are combined with graph theory and RNA cleavage assays to decipher this activation mechanism. We show that the binding of a target RNA acts as an allosteric effector of the spatially distant HEPN catalytic cleft, by amplifying the allosteric signals over the dynamical noise, that passes through the buried HEPN interface. Critical residues in this region - N378, R973, and R377 - rearrange their interactions upon target RNA binding, and alter allosteric signalling. Alanine mutation of these residues is experimentally shown to select target RNA over an extended complementary sequence beyond guide-target duplex, for RNA cleavage. Altogether, our findings offer a fundamental understanding of the Cas13a mechanism of action and pave new avenues for the development of more selective RNA-based cleavage and detection tools.
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14
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Xiao S, Verkhivker GM, Tao P. Machine learning and protein allostery. Trends Biochem Sci 2023; 48:375-390. [PMID: 36564251 PMCID: PMC10023316 DOI: 10.1016/j.tibs.2022.12.001] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Revised: 11/16/2022] [Accepted: 12/02/2022] [Indexed: 12/23/2022]
Abstract
The fundamental biological importance and complexity of allosterically regulated proteins stem from their central role in signal transduction and cellular processes. Recently, machine-learning approaches have been developed and actively deployed to facilitate theoretical and experimental studies of protein dynamics and allosteric mechanisms. In this review, we survey recent developments in applications of machine-learning methods for studies of allosteric mechanisms, prediction of allosteric effects and allostery-related physicochemical properties, and allosteric protein engineering. We also review the applications of machine-learning strategies for characterization of allosteric mechanisms and drug design targeting SARS-CoV-2. Continuous development and task-specific adaptation of machine-learning methods for protein allosteric mechanisms will have an increasingly important role in bridging a wide spectrum of data-intensive experimental and theoretical technologies.
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Affiliation(s)
- Sian Xiao
- Department of Chemistry, Center for Research Computing, Center for Drug Discovery, Design, and Delivery (CD4), Southern Methodist University, Dallas, TX 75205, USA.
| | - Gennady M Verkhivker
- Graduate Program in Computational and Data Sciences, Schmid College of Science and Technology, Chapman University, Orange, CA 92866, USA; Department of Biomedical and Pharmaceutical Sciences, Chapman University School of Pharmacy, Irvine, CA 92618, USA
| | - Peng Tao
- Department of Chemistry, Center for Research Computing, Center for Drug Discovery, Design, and Delivery (CD4), Southern Methodist University, Dallas, TX 75205, USA.
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Berezovsky IN, Nussinov R. Multiscale Allostery: Basic Mechanisms and Versatility in Diagnostics and Drug Design. J Mol Biol 2022; 434:167751. [PMID: 35863488 DOI: 10.1016/j.jmb.2022.167751] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Affiliation(s)
- Igor N Berezovsky
- Bioinformatics Institute (BII), Agency for Science, Technology and Research (A*STAR), 30 Biopolis Street, #07-01, Matrix, Singapore 138671, Singapore; Department of Biological Sciences (DBS), National University of Singapore (NUS), 8 Medical Drive, 117579, Singapore.
| | - Ruth Nussinov
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research in the Cancer Innovation Laboraory, National Cancer Institute, Frederick, MD 21702, USA; Department of Human Molecular Genetics and Biochemistry, Sackler School of Medicine, Tel Aviv University, Tel Aviv 69978, Israel.
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16
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Saha A, Arantes PR, Palermo G. Dynamics and mechanisms of CRISPR-Cas9 through the lens of computational methods. Curr Opin Struct Biol 2022; 75:102400. [PMID: 35689914 DOI: 10.1016/j.sbi.2022.102400] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Revised: 04/25/2022] [Accepted: 05/07/2022] [Indexed: 12/24/2022]
Abstract
The clustered regularly interspaced short palindromic repeat (CRISPR) genome-editing revolution established the beginning of a new era in life sciences. Here, we review the role of state-of-the-art computations in the CRISPR-Cas9 revolution, from the early refinement of cryo-EM data to enhanced simulations of large-scale conformational transitions. Molecular simulations reported a mechanism for RNA binding and the formation of a catalytically competent Cas9 enzyme, in agreement with subsequent structural studies. Inspired by single-molecule experiments, molecular dynamics offered a rationale for the onset of off-target effects, while graph theory unveiled the allosteric regulation. Finally, the use of a mixed quantum-classical approach established the catalytic mechanism of DNA cleavage. Overall, molecular simulations have been instrumental in understanding the dynamics and mechanism of CRISPR-Cas9, contributing to understanding function, catalysis, allostery, and specificity.
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Affiliation(s)
- Aakash Saha
- Department of Bioengineering, University of California Riverside, 900 University Avenue, Riverside, CA, 52512, United States. https://twitter.com/@aakashsahha
| | - Pablo R Arantes
- Department of Bioengineering, University of California Riverside, 900 University Avenue, Riverside, CA, 52512, United States. https://twitter.com/@pablitoarantes
| | - Giulia Palermo
- Department of Bioengineering, University of California Riverside, 900 University Avenue, Riverside, CA, 52512, United States; Department of Chemistry, University of California Riverside, 900 University Avenue, Riverside, CA, 52512, United States.
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17
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Wang J, Skeens E, Arantes PR, Maschietto F, Allen B, Kyro GW, Lisi GP, Palermo G, Batista VS. Structural Basis for Reduced Dynamics of Three Engineered HNH Endonuclease Lys-to-Ala Mutants for the Clustered Regularly Interspaced Short Palindromic Repeat (CRISPR)-Associated 9 (CRISPR/Cas9) Enzyme. Biochemistry 2022; 61:785-794. [PMID: 35420793 DOI: 10.1021/acs.biochem.2c00127] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
Many bacteria possess type-II immunity against invading phages or plasmids known as the clustered regularly interspaced short palindromic repeat (CRISPR)/CRISPR-associated 9 (Cas9) system to detect and degrade the foreign DNA sequences. The Cas9 protein has two endonucleases responsible for double-strand breaks (the HNH domain for cleaving the target strand of DNA duplexes and RuvC domain for the nontarget strand, respectively) and a single-guide RNA-binding domain where the RNA and target DNA strands are base-paired. Three engineered single Lys-to-Ala HNH mutants (K810A, K848A, and K855A) exhibit an enhanced substrate specificity for cleavage of the target DNA strand. We report in this study that in the wild-type (wt) enzyme, D835, Y836, and D837 within the Y836-containing loop (comprising E827-D837) adjacent to the catalytic site have uncharacterizable broadened 1H15N nuclear magnetic resonance (NMR) features, whereas remaining residues in the loop have different extents of broadened NMR spectra. We find that this loop in the wt enzyme exhibits three distinct conformations over the duration of the molecular dynamics simulations, whereas the three Lys-to-Ala mutants retain only one conformation. The versatility of multiple alternate conformations of this loop in the wt enzyme could help to recruit noncognate DNA substrates into the HNH active site for cleavage, thereby reducing its substrate specificity relative to the three mutants. Our study provides further experimental and computational evidence that Lys-to-Ala substitutions reduce dynamics of proteins and thus increase their stability.
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Affiliation(s)
- Jimin Wang
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, Connecticut 06520-8114, United States
| | - Erin Skeens
- Department of Molecular and Cell Biology and Biochemistry, Brown University, Providence, Rhode Island 02912, United States
| | - Pablo R Arantes
- Department of Bioengineering and Department of Chemistry, University of California Riverside, Riverside, California 92521-9800, United States
| | - Federica Maschietto
- Department of Chemistry, Yale University, New Haven, Connecticut 06511-8499, United States
| | - Brandon Allen
- Department of Chemistry, Yale University, New Haven, Connecticut 06511-8499, United States
| | - Gregory W Kyro
- Department of Chemistry, Yale University, New Haven, Connecticut 06511-8499, United States
| | - George P Lisi
- Department of Molecular and Cell Biology and Biochemistry, Brown University, Providence, Rhode Island 02912, United States
| | - Giulia Palermo
- Department of Bioengineering and Department of Chemistry, University of California Riverside, Riverside, California 92521-9800, United States
| | - Victor S Batista
- Department of Chemistry, Yale University, New Haven, Connecticut 06511-8499, United States
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18
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Wang J, Arantes PR, Ahsan M, Sinha S, Kyro GW, Maschietto F, Allen B, Skeens E, Lisi GP, Batista VS, Palermo G. Twisting and swiveling domain motions in Cas9 to recognize target DNA duplexes, make double-strand breaks, and release cleaved duplexes. Front Mol Biosci 2022; 9:1072733. [PMID: 36699705 PMCID: PMC9868570 DOI: 10.3389/fmolb.2022.1072733] [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: 10/17/2022] [Accepted: 12/12/2022] [Indexed: 01/11/2023] Open
Abstract
The CRISPR-associated protein 9 (Cas9) has been engineered as a precise gene editing tool to make double-strand breaks. CRISPR-associated protein 9 binds the folded guide RNA (gRNA) that serves as a binding scaffold to guide it to the target DNA duplex via a RecA-like strand-displacement mechanism but without ATP binding or hydrolysis. The target search begins with the protospacer adjacent motif or PAM-interacting domain, recognizing it at the major groove of the duplex and melting its downstream duplex where an RNA-DNA heteroduplex is formed at nanomolar affinity. The rate-limiting step is the formation of an R-loop structure where the HNH domain inserts between the target heteroduplex and the displaced non-target DNA strand. Once the R-loop structure is formed, the non-target strand is rapidly cleaved by RuvC and ejected from the active site. This event is immediately followed by cleavage of the target DNA strand by the HNH domain and product release. Within CRISPR-associated protein 9, the HNH domain is inserted into the RuvC domain near the RuvC active site via two linker loops that provide allosteric communication between the two active sites. Due to the high flexibility of these loops and active sites, biophysical techniques have been instrumental in characterizing the dynamics and mechanism of the CRISPR-associated protein 9 nucleases, aiding structural studies in the visualization of the complete active sites and relevant linker structures. Here, we review biochemical, structural, and biophysical studies on the underlying mechanism with emphasis on how CRISPR-associated protein 9 selects the target DNA duplex and rejects non-target sequences.
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Affiliation(s)
- Jimin Wang
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, United States
| | - Pablo R Arantes
- Department of Bioengineering and Department of Chemistry, University of California, Riverside, Riverside, CA, United States
| | - Mohd Ahsan
- Department of Bioengineering and Department of Chemistry, University of California, Riverside, Riverside, CA, United States
| | - Souvik Sinha
- Department of Bioengineering and Department of Chemistry, University of California, Riverside, Riverside, CA, United States
| | - Gregory W Kyro
- Department of Chemistry, Yale University, New Haven, CT, United States
| | | | - Brandon Allen
- Department of Chemistry, Yale University, New Haven, CT, United States
| | - Erin Skeens
- Department of Molecular and Cell Biology and Biochemistry, Brown University, Providence, RI, United States
| | - George P Lisi
- Department of Molecular and Cell Biology and Biochemistry, Brown University, Providence, RI, United States
| | - Victor S Batista
- Department of Chemistry, Yale University, New Haven, CT, United States
| | - Giulia Palermo
- Department of Bioengineering and Department of Chemistry, University of California, Riverside, Riverside, CA, United States
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