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Chi LA, Barnes JE, Suresh Patel J, Ytreberg FM. Exploring the ability of the MD+FoldX method to predict SARS-CoV-2 antibody escape mutations using large-scale data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.22.595230. [PMID: 38826284 PMCID: PMC11142147 DOI: 10.1101/2024.05.22.595230] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2024]
Abstract
Antibody escape mutations pose a significant challenge to the effectiveness of vaccines and antibody-based therapies. The ability to predict these escape mutations with computer simulations would allow us to detect threats early and develop effective countermeasures, but a lack of large-scale experimental data has hampered the validation of these calculations. In this study, we evaluate the ability of the MD+FoldX molecular modeling method to predict escape mutations by leveraging a large deep mutational scanning dataset, focusing on the SARS-CoV-2 receptor binding domain. Our results show a positive correlation between predicted and experimental data, indicating that mutations with reduced predicted binding affinity correlate moderately with higher experimental escape fractions. We also demonstrate that better performance can be achieved using affinity cutoffs tailored to distinct antibody-antigen interactions rather than a one-size-fits-all approach. We find that 70% of the systems surpass the 50% precision mark, and demonstrate success in identifying mutations present in significant variants of concern and variants of interest. Despite promising results for some systems, our study highlights the challenges in comparing predicted and experimental values. It also emphasizes the need for new binding affinity methods with improved accuracy that are fast enough to estimate hundreds to thousands of antibody-antigen binding affinities.
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Affiliation(s)
- L. América Chi
- Institute for Modeling Collaboration and Innovation, University of Idaho, Moscow, ID 83843, USA
| | - Jonathan E. Barnes
- Institute for Modeling Collaboration and Innovation, University of Idaho, Moscow, ID 83843, USA
| | - Jagdish Suresh Patel
- Institute for Modeling Collaboration and Innovation, University of Idaho, Moscow, ID 83843, USA
- Department of Chemical and Biological Engineering, University of Idaho, Moscow, ID 83843, USA
| | - F. Marty Ytreberg
- Institute for Modeling Collaboration and Innovation, University of Idaho, Moscow, ID 83843, USA
- Department of Physics, University of Idaho, Moscow, ID 83843, USA
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2
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Sapozhnikov Y, Patel JS, Ytreberg FM, Miller CR. Statistical modeling to quantify the uncertainty of FoldX-predicted protein folding and binding stability. BMC Bioinformatics 2023; 24:426. [PMID: 37953256 PMCID: PMC10642056 DOI: 10.1186/s12859-023-05537-0] [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: 05/08/2023] [Accepted: 10/17/2023] [Indexed: 11/14/2023] Open
Abstract
BACKGROUND Computational methods of predicting protein stability changes upon missense mutations are invaluable tools in high-throughput studies involving a large number of protein variants. However, they are limited by a wide variation in accuracy and difficulty of assessing prediction uncertainty. Using a popular computational tool, FoldX, we develop a statistical framework that quantifies the uncertainty of predicted changes in protein stability. RESULTS We show that multiple linear regression models can be used to quantify the uncertainty associated with FoldX prediction for individual mutations. Comparing the performance among models with varying degrees of complexity, we find that the model precision improves significantly when we utilize molecular dynamics simulation as part of the FoldX workflow. Based on the model that incorporates information from molecular dynamics, biochemical properties, as well as FoldX energy terms, we can generally expect upper bounds on the uncertainty of folding stability predictions of ± 2.9 kcal/mol and ± 3.5 kcal/mol for binding stability predictions. The uncertainty for individual mutations varies; our model estimates it using FoldX energy terms, biochemical properties of the mutated residue, as well as the variability among snapshots from molecular dynamics simulation. CONCLUSIONS Using a linear regression framework, we construct models to predict the uncertainty associated with FoldX prediction of stability changes upon mutation. This technique is straightforward and can be extended to other computational methods as well.
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Affiliation(s)
- Yesol Sapozhnikov
- Program in Bioinformatics and Computational Biology, University of Idaho, Moscow, ID, 83844, USA
| | - Jagdish Suresh Patel
- Department of Chemical and Biological Engineering, University of Idaho, Moscow, ID, 83844, USA
- Institute for Modeling Collaboration and Innovation, University of Idaho, Moscow, ID, 83844, USA
| | - F Marty Ytreberg
- Department of Physics, University of Idaho, Moscow, ID, 83844, USA
- Institute for Modeling Collaboration and Innovation, University of Idaho, Moscow, ID, 83844, USA
| | - Craig R Miller
- Department of Biological Sciences, University of Idaho, Moscow, ID, 83844, USA.
- Institute for Modeling Collaboration and Innovation, University of Idaho, Moscow, ID, 83844, USA.
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3
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Wells NGM, Smith CA. Predicting binding affinity changes from long-distance mutations using molecular dynamics simulations and Rosetta. Proteins 2023. [PMID: 36757060 DOI: 10.1002/prot.26477] [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: 10/13/2022] [Revised: 01/20/2023] [Accepted: 02/07/2023] [Indexed: 02/10/2023]
Abstract
Computationally modeling how mutations affect protein-protein binding not only helps uncover the biophysics of protein interfaces, but also enables the redesign and optimization of protein interactions. Traditional high-throughput methods for estimating binding free energy changes are currently limited to mutations directly at the interface due to difficulties in accurately modeling how long-distance mutations propagate their effects through the protein structure. However, the modeling and design of such mutations is of substantial interest as it allows for greater control and flexibility in protein design applications. We have developed a method that combines high-throughput Rosetta-based side-chain optimization with conformational sampling using classical molecular dynamics simulations, finding significant improvements in our ability to accurately predict long-distance mutational perturbations to protein binding. Our approach uses an analytical framework grounded in alchemical free energy calculations while enabling exploration of a vastly larger sequence space. When comparing to experimental data, we find that our method can predict internal long-distance mutational perturbations with a level of accuracy similar to that of traditional methods in predicting the effects of mutations at the protein-protein interface. This work represents a new and generalizable approach to optimize protein free energy landscapes for desired biological functions.
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Affiliation(s)
- Nicholas G M Wells
- Department of Chemistry, Wesleyan University, Middletown, Connecticut, USA
| | - Colin A Smith
- Department of Chemistry, Wesleyan University, Middletown, Connecticut, USA
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4
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Rayaprolu V, Fulton BO, Rafique A, Arturo E, Williams D, Hariharan C, Callaway H, Parvate A, Schendel SL, Parekh D, Hui S, Shaffer K, Pascal KE, Wloga E, Giordano S, Negron N, Ni M, Copin R, Atwal GS, Franklin M, Boytz RM, Donahue C, Davey R, Baum A, Kyratsous CA, Saphire EO. Structure of the Inmazeb cocktail and resistance to Ebola virus escape. Cell Host Microbe 2023; 31:260-272.e7. [PMID: 36708708 PMCID: PMC10375381 DOI: 10.1016/j.chom.2023.01.002] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Revised: 12/15/2022] [Accepted: 01/03/2023] [Indexed: 01/28/2023]
Abstract
Monoclonal antibodies can provide important pre- or post-exposure protection against infectious disease for those not yet vaccinated or in individuals that fail to mount a protective immune response after vaccination. Inmazeb (REGN-EB3), a three-antibody cocktail against Ebola virus, lessened disease and improved survival in a controlled trial. Here, we present the cryo-EM structure at 3.1 Å of the Ebola virus glycoprotein, determined without symmetry averaging, in a simultaneous complex with the antibodies in the Inmazeb cocktail. This structure allows the modeling of previously disordered portions of the glycoprotein glycan cap, maps the non-overlapping epitopes of Inmazeb, and illuminates the basis for complementary activities and residues critical for resistance to escape by these and other clinically relevant antibodies. We further provide direct evidence that Inmazeb protects against the rapid emergence of escape mutants, whereas monotherapies even against conserved epitopes do not, supporting the benefit of a cocktail versus a monotherapy approach.
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Affiliation(s)
| | | | | | - Emilia Arturo
- La Jolla Institute for Immunology, La Jolla, CA 92037, USA
| | - Dewight Williams
- Eyring Materials Center, Arizona State University, Tempe, AZ 85281, USA
| | | | | | - Amar Parvate
- La Jolla Institute for Immunology, La Jolla, CA 92037, USA
| | | | | | - Sean Hui
- La Jolla Institute for Immunology, La Jolla, CA 92037, USA
| | - Kelly Shaffer
- La Jolla Institute for Immunology, La Jolla, CA 92037, USA
| | | | | | | | | | - Min Ni
- Regeneron Pharmaceuticals, Tarrytown, NY 10591, USA
| | | | | | | | - Ruth Mabel Boytz
- Department of Microbiology, Boston University of Medicine and NEIDL, Boston University, Boston, MA 02118, USA
| | - Callie Donahue
- Department of Microbiology, Boston University of Medicine and NEIDL, Boston University, Boston, MA 02118, USA
| | - Robert Davey
- Department of Microbiology, Boston University of Medicine and NEIDL, Boston University, Boston, MA 02118, USA
| | - Alina Baum
- Regeneron Pharmaceuticals, Tarrytown, NY 10591, USA
| | | | - Erica Ollmann Saphire
- La Jolla Institute for Immunology, La Jolla, CA 92037, USA; Department of Medicine, University of California, San Diego, San Diego, CA 92037, USA.
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5
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Barnes JE, Lund-Andersen PK, Patel JS, Ytreberg FM. The effect of mutations on binding interactions between the SARS-CoV-2 receptor binding domain and neutralizing antibodies B38 and CB6. Sci Rep 2022; 12:18819. [PMID: 36335244 PMCID: PMC9637166 DOI: 10.1038/s41598-022-23482-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Accepted: 11/01/2022] [Indexed: 11/08/2022] Open
Abstract
SARS-CoV-2 is the pathogen responsible for COVID-19 that has claimed over six million lives as of July 2022. The severity of COVID-19 motivates a need to understand how it could evolve to escape potential treatments and to find ways to strengthen existing treatments. Here, we used the molecular modeling methods MD + FoldX and PyRosetta to study the SARS-CoV-2 spike receptor binding domain (S-RBD) bound to two neutralizing antibodies, B38 and CB6 and generated lists of antibody escape and antibody strengthening mutations. Our resulting watchlist contains potential antibody escape mutations against B38/CB6 and consists of 211/186 mutations across 35/22 S-RBD sites. Some of these mutations have been identified in previous studies as being significant in human populations (e.g., N501Y). The list of potential antibody strengthening mutations that are predicted to improve binding of B38/CB6 to S-RBD consists of 116/45 mutations across 29/13 sites. These mutations could be used to improve the therapeutic value of these antibodies.
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Affiliation(s)
- Jonathan E Barnes
- Institute for Modeling Collaboration and Innovation, University of Idaho, Moscow, ID, 83843, USA
| | - Peik K Lund-Andersen
- Institute for Modeling Collaboration and Innovation, University of Idaho, Moscow, ID, 83843, USA
- Department of Biological Sciences, University of Idaho, Moscow, ID, 83843, USA
| | - Jagdish Suresh Patel
- Institute for Modeling Collaboration and Innovation, University of Idaho, Moscow, ID, 83843, USA.
- Department of Biological Sciences, University of Idaho, Moscow, ID, 83843, USA.
| | - F Marty Ytreberg
- Institute for Modeling Collaboration and Innovation, University of Idaho, Moscow, ID, 83843, USA.
- Department of Physics, University of Idaho, Moscow, ID, 83843, USA.
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6
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Defining the HIV Capsid Binding Site of Nucleoporin 153. mSphere 2022; 7:e0031022. [PMID: 36040047 PMCID: PMC9599535 DOI: 10.1128/msphere.00310-22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
The interaction between the HIV-1 capsid and human nucleoporin 153 (NUP153) is vital for delivering the HIV-1 preintegration complex into the nucleus via the nuclear pore complex. The interaction with the capsid requires a phenylalanine/glycine-containing motif in the C-terminus of NUP153 (NUP153C). This study used molecular modeling and biochemical assays to comprehensively determine the amino acids in NUP153 that are important for capsid interaction. Molecular dynamics, FoldX, and PyRosetta simulations delineated the minimal capsid binding motif of NUP153 based on the known structure of NUP153 bound to the HIV-1 capsid hexamer. Computational predictions were experimentally validated by testing the interaction of NUP153 with capsid using an in vitro binding assay and a cell-based TRIM-NUP153C restriction assay. This work identified eight amino acids from P1411 to G1418 that stably engage with capsid, with significant correlations between the interactions predicted by molecular models and empirical experiments. This validated the usefulness of this multidisciplinary approach to rapidly characterize the interaction between human proteins and the HIV-1 capsid. IMPORTANCE The human immunodeficiency virus (HIV) can infect nondividing cells by interacting with the host nuclear pore complex. The host nuclear pore protein NUP153 directly interacts with the HIV capsid to promote viral nuclear entry. This study used a multidisciplinary approach combining computational and experimental techniques to comprehensively map the effect of mutating the amino acids of NUP153 on HIV capsid interaction. This work showed a significant correlation between computational and empirical data sets, revealing that the HIV capsid interacted specifically with only six amino acids of NUP153. The simplicity of the interaction motif suggested other FG-containing motifs could also interact with the HIV-1 capsid. Furthermore, it was predicted that naturally occurring polymorphisms in human and nonhuman primates would disrupt NUP153 interaction with capsid, potentially protecting certain populations from HIV-1 infection.
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7
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Beach SS, Hull MA, Ytreberg FM, Patel JS, Miura TA. Molecular Modeling Predicts Novel Antibody Escape Mutations in the Respiratory Syncytial Virus Fusion Glycoprotein. J Virol 2022; 96:e0035322. [PMID: 35678603 PMCID: PMC9278155 DOI: 10.1128/jvi.00353-22] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
Monoclonal antibodies are increasingly used for the prevention and/or treatment of viral infections. One caveat of their use is the ability of viruses to evolve resistance to antibody binding and neutralization. Computational strategies to identify viral mutations that may disrupt antibody binding would leverage the wealth of viral genomic sequence data to monitor for potential antibody-resistant mutations. The respiratory syncytial virus is an important pathogen for which monoclonal antibodies against the fusion (F) protein are used to prevent severe disease in high-risk infants. In this study, we used an approach that combines molecular dynamics simulations with FoldX to estimate changes in free energy in F protein folding and binding to the motavizumab antibody upon each possible amino acid change. We systematically selected 8 predicted escape mutations and tested them in an infectious clone. Consistent with our F protein stability predictions, replication-effective viruses were observed for each selected mutation. Six of the eight variants showed increased resistance to neutralization by motavizumab. Flow cytometry was used to validate the estimated (model-predicted) effects on antibody binding to F. Using surface plasmon resonance, we determined that changes in the on-rate of motavizumab binding were associated with the reduced affinity for two novel escape mutations. Our study empirically validated the accuracy of our molecular modeling approach and emphasized the role of biophysical protein modeling in predicting viral resistance to antibody-based therapeutics that can be used to monitor the emergence of resistant viruses and to design improved therapeutic antibodies. IMPORTANCE Respiratory syncytial virus (RSV) causes severe disease in young infants, particularly those with heart or lung diseases or born prematurely. Because no vaccine is currently available, monoclonal antibodies are used to prevent severe RSV disease in high-risk infants. While it is known that RSV evolves to avoid recognition by antibodies, screening tools that can predict which changes to the virus may lead to antibody resistance are greatly needed.
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Affiliation(s)
- Sierra S. Beach
- Department of Biological Sciences, University of Idahogrid.266456.5, Moscow, Idaho, USA
| | - McKenna A. Hull
- Department of Biological Sciences, University of Idahogrid.266456.5, Moscow, Idaho, USA
| | - F. Marty Ytreberg
- Department of Physics, University of Idahogrid.266456.5, Moscow, Idaho, USA
- Institute for Modeling Collaboration and Innovation, University of Idahogrid.266456.5, Moscow, Idaho, USA
| | - Jagdish Suresh Patel
- Department of Biological Sciences, University of Idahogrid.266456.5, Moscow, Idaho, USA
- Institute for Modeling Collaboration and Innovation, University of Idahogrid.266456.5, Moscow, Idaho, USA
| | - Tanya A. Miura
- Department of Biological Sciences, University of Idahogrid.266456.5, Moscow, Idaho, USA
- Institute for Modeling Collaboration and Innovation, University of Idahogrid.266456.5, Moscow, Idaho, USA
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8
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Tiper I, Kourout M, Lanning B, Fisher C, Konduru K, Purkayastha A, Kaplan G, Duncan R. Tracking ebolavirus genomic drift with a resequencing microarray. PLoS One 2022; 17:e0263732. [PMID: 35143574 PMCID: PMC8830711 DOI: 10.1371/journal.pone.0263732] [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: 07/02/2021] [Accepted: 01/25/2022] [Indexed: 11/17/2022] Open
Abstract
Filoviruses are emerging pathogens that cause acute fever with high fatality rate and present a global public health threat. During the 2013–2016 Ebola virus outbreak, genome sequencing allowed the study of virus evolution, mutations affecting pathogenicity and infectivity, and tracing the viral spread. In 2018, early sequence identification of the Ebolavirus as EBOV in the Democratic Republic of the Congo supported the use of an Ebola virus vaccine. However, field-deployable sequencing methods are needed to enable a rapid public health response. Resequencing microarrays (RMA) are a targeted method to obtain genomic sequence on clinical specimens rapidly, and sensitively, overcoming the need for extensive bioinformatic analysis. This study presents the design and initial evaluation of an ebolavirus resequencing microarray (Ebolavirus-RMA) system for sequencing the major genomic regions of four Ebolaviruses that cause disease in humans. The design of the Ebolavirus-RMA system is described and evaluated by sequencing repository samples of three Ebolaviruses and two EBOV variants. The ability of the system to identify genetic drift in a replicating virus was achieved by sequencing the ebolavirus glycoprotein gene in a recombinant virus cultured under pressure from a neutralizing antibody. Comparison of the Ebolavirus-RMA results to the Genbank database sequence file with the accession number given for the source RNA and Ebolavirus-RMA results compared to Next Generation Sequence results of the same RNA samples showed up to 99% agreement.
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Affiliation(s)
- Irina Tiper
- Division of Emerging and Transfusion-Transmitted Diseases, Office of Blood Research and Review, Center for Biologics Evaluation and Research, US Food and Drug Administration, Silver Spring, MD, United States of America
| | - Moussa Kourout
- Division of Emerging and Transfusion-Transmitted Diseases, Office of Blood Research and Review, Center for Biologics Evaluation and Research, US Food and Drug Administration, Silver Spring, MD, United States of America
| | - Bryan Lanning
- Division of Emerging and Transfusion-Transmitted Diseases, Office of Blood Research and Review, Center for Biologics Evaluation and Research, US Food and Drug Administration, Silver Spring, MD, United States of America
| | - Carolyn Fisher
- Division of Emerging and Transfusion-Transmitted Diseases, Office of Blood Research and Review, Center for Biologics Evaluation and Research, US Food and Drug Administration, Silver Spring, MD, United States of America
| | - Krishnamurthy Konduru
- Division of Emerging and Transfusion-Transmitted Diseases, Office of Blood Research and Review, Center for Biologics Evaluation and Research, US Food and Drug Administration, Silver Spring, MD, United States of America
| | | | - Gerardo Kaplan
- Division of Emerging and Transfusion-Transmitted Diseases, Office of Blood Research and Review, Center for Biologics Evaluation and Research, US Food and Drug Administration, Silver Spring, MD, United States of America
| | - Robert Duncan
- Division of Emerging and Transfusion-Transmitted Diseases, Office of Blood Research and Review, Center for Biologics Evaluation and Research, US Food and Drug Administration, Silver Spring, MD, United States of America
- * E-mail:
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9
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Ogbunugafor CB. The mutation effect reaction norm (mu-rn) highlights environmentally dependent mutation effects and epistatic interactions. Evolution 2022; 76:37-48. [PMID: 34989399 DOI: 10.1111/evo.14428] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2021] [Accepted: 12/23/2021] [Indexed: 11/27/2022]
Abstract
Since the modern synthesis, the fitness effects of mutations and epistasis have been central yet provocative concepts in evolutionary and population genetics. Studies of how the interactions between parcels of genetic information can change as a function of environmental context have added a layer of complexity to these discussions. Here I introduce the "mutation effect reaction norm" (Mu-RN), a new instrument through which one can analyze the phenotypic consequences of mutations and interactions across environmental contexts. It embodies the fusion of measurements of genetic interactions with the reaction norm, a classic depiction of the performance of genotypes across environments. I demonstrate the utility of the Mu-RN through the signature of a "compensatory ratchet" mutation that undermines reverse evolution of antimicrobial resistance. More broadly, I argue that the mutation effect reaction norm may help us resolve the dynamism and unpredictability of evolution, with implications for theoretical biology, genetic modification technology, and public health. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- C Brandon Ogbunugafor
- Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT, 06520, USA
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10
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Johnson KA, Bhattarai N, Budicini MR, LaBonia CM, Baker SCB, Gerstman BS, Chapagain PP, Stahelin RV. Cysteine Mutations in the Ebolavirus Matrix Protein VP40 Promote Phosphatidylserine Binding by Increasing the Flexibility of a Lipid-Binding Loop. Viruses 2021; 13:1375. [PMID: 34372582 PMCID: PMC8310056 DOI: 10.3390/v13071375] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2021] [Revised: 07/08/2021] [Accepted: 07/13/2021] [Indexed: 11/25/2022] Open
Abstract
Ebolavirus (EBOV) is a negative-sense RNA virus that causes severe hemorrhagic fever in humans. The matrix protein VP40 facilitates viral budding by binding to lipids in the host cell plasma membrane and driving the formation of filamentous, pleomorphic virus particles. The C-terminal domain of VP40 contains two highly-conserved cysteine residues at positions 311 and 314, but their role in the viral life cycle is unknown. We therefore investigated the properties of VP40 mutants in which the conserved cysteine residues were replaced with alanine. The C311A mutation significantly increased the affinity of VP40 for membranes containing phosphatidylserine (PS), resulting in the assembly of longer virus-like particles (VLPs) compared to wild-type VP40. The C314A mutation also increased the affinity of VP40 for membranes containing PS, albeit to a lesser degree than C311A. The double mutant behaved in a similar manner to the individual mutants. Computer modeling revealed that both cysteine residues restrain a loop segment containing lysine residues that interact with the plasma membrane, but Cys311 has the dominant role. Accordingly, the C311A mutation increases the flexibility of this membrane-binding loop, changes the profile of hydrogen bonding within VP40 and therefore binds to PS with greater affinity. This is the first evidence that mutations in VP40 can increase its affinity for biological membranes and modify the length of Ebola VLPs. The Cys311 and Cys314 residues therefore play an important role in dynamic interactions at the plasma membrane by modulating the ability of VP40 to bind PS.
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Affiliation(s)
- Kristen A. Johnson
- Department of Chemistry and Biochemistry, University of Notre Dame, Notre Dame, IN 46556, USA; (K.A.J.); (M.R.B.); (C.M.L.); (S.C.B.B.)
| | - Nisha Bhattarai
- Department of Physics, Florida International University, Miami, FL 33199, USA; (N.B.); (B.S.G.); (P.P.C.)
| | - Melissa R. Budicini
- Department of Chemistry and Biochemistry, University of Notre Dame, Notre Dame, IN 46556, USA; (K.A.J.); (M.R.B.); (C.M.L.); (S.C.B.B.)
| | - Carolyn M. LaBonia
- Department of Chemistry and Biochemistry, University of Notre Dame, Notre Dame, IN 46556, USA; (K.A.J.); (M.R.B.); (C.M.L.); (S.C.B.B.)
| | - Sarah Catherine B. Baker
- Department of Chemistry and Biochemistry, University of Notre Dame, Notre Dame, IN 46556, USA; (K.A.J.); (M.R.B.); (C.M.L.); (S.C.B.B.)
| | - Bernard S. Gerstman
- Department of Physics, Florida International University, Miami, FL 33199, USA; (N.B.); (B.S.G.); (P.P.C.)
- The Biomolecular Sciences Institute, Florida International University, Miami, FL 33199, USA
| | - Prem P. Chapagain
- Department of Physics, Florida International University, Miami, FL 33199, USA; (N.B.); (B.S.G.); (P.P.C.)
- The Biomolecular Sciences Institute, Florida International University, Miami, FL 33199, USA
| | - Robert V. Stahelin
- Department of Medicinal Chemistry and Molecular Pharmacology and the Purdue Institute of Inflammation, Immunology and Infectious Disease, Purdue University, West Lafayette, IN 47907, USA
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11
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Bazurto JV, Nayak DD, Ticak T, Davlieva M, Lee JA, Hellenbrand CN, Lambert LB, Benski OJ, Quates CJ, Johnson JL, Patel JS, Ytreberg FM, Shamoo Y, Marx CJ. EfgA is a conserved formaldehyde sensor that leads to bacterial growth arrest in response to elevated formaldehyde. PLoS Biol 2021; 19:e3001208. [PMID: 34038406 PMCID: PMC8153426 DOI: 10.1371/journal.pbio.3001208] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2020] [Accepted: 03/25/2021] [Indexed: 01/07/2023] Open
Abstract
Normal cellular processes give rise to toxic metabolites that cells must mitigate. Formaldehyde is a universal stressor and potent metabolic toxin that is generated in organisms from bacteria to humans. Methylotrophic bacteria such as Methylorubrum extorquens face an acute challenge due to their production of formaldehyde as an obligate central intermediate of single-carbon metabolism. Mechanisms to sense and respond to formaldehyde were speculated to exist in methylotrophs for decades but had never been discovered. Here, we identify a member of the DUF336 domain family, named efgA for enhanced formaldehyde growth, that plays an important role in endogenous formaldehyde stress response in M. extorquens PA1 and is found almost exclusively in methylotrophic taxa. Our experimental analyses reveal that EfgA is a formaldehyde sensor that rapidly arrests growth in response to elevated levels of formaldehyde. Heterologous expression of EfgA in Escherichia coli increases formaldehyde resistance, indicating that its interaction partners are widespread and conserved. EfgA represents the first example of a formaldehyde stress response system that does not involve enzymatic detoxification. Thus, EfgA comprises a unique stress response mechanism in bacteria, whereby a single protein directly senses elevated levels of a toxic intracellular metabolite and safeguards cells from potential damage.
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Affiliation(s)
- Jannell V. Bazurto
- Department of Biological Sciences, University of Idaho, Moscow, Idaho, United States of America
- Institute for Modeling Collaboration and Innovation, University of Idaho, Moscow, Idaho, United States of America
- Institute for Bioinformatics and Evolutionary Studies, University of Idaho, Moscow, Idaho, United States of America
- Department of Plant and Microbial Biology, University of Minnesota, Twin Cities, Minnesota, United States of America
- Microbial and Plant Genomics Institute, University of Minnesota, Twin Cities, Minnesota, United States of America
- Biotechnology Institute, University of Minnesota, Twin Cities, Minnesota, United States of America
- * E-mail: (JVB); (CJM)
| | - Dipti D. Nayak
- Department of Biological Sciences, University of Idaho, Moscow, Idaho, United States of America
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, Massachusetts, United States of America
- Department of Microbiology, University of Illinois, Urbana, Illinois, United States of America
| | - Tomislav Ticak
- Department of Biological Sciences, University of Idaho, Moscow, Idaho, United States of America
- Institute for Modeling Collaboration and Innovation, University of Idaho, Moscow, Idaho, United States of America
- Institute for Bioinformatics and Evolutionary Studies, University of Idaho, Moscow, Idaho, United States of America
| | - Milya Davlieva
- Department of Biosciences, Rice University, Houston, Texas, United States of America
| | - Jessica A. Lee
- Department of Biological Sciences, University of Idaho, Moscow, Idaho, United States of America
- Institute for Modeling Collaboration and Innovation, University of Idaho, Moscow, Idaho, United States of America
- Institute for Bioinformatics and Evolutionary Studies, University of Idaho, Moscow, Idaho, United States of America
- Space Biosciences Research Branch, NASA Ames Research Center, Moffett Field, California, United States of America
| | - Chandler N. Hellenbrand
- Department of Plant and Microbial Biology, University of Minnesota, Twin Cities, Minnesota, United States of America
| | - Leah B. Lambert
- Department of Biological Sciences, University of Idaho, Moscow, Idaho, United States of America
| | - Olivia J. Benski
- Department of Biological Sciences, University of Idaho, Moscow, Idaho, United States of America
| | - Caleb J. Quates
- Institute for Modeling Collaboration and Innovation, University of Idaho, Moscow, Idaho, United States of America
| | - Jill L. Johnson
- Department of Biological Sciences, University of Idaho, Moscow, Idaho, United States of America
- Institute for Modeling Collaboration and Innovation, University of Idaho, Moscow, Idaho, United States of America
- Institute for Bioinformatics and Evolutionary Studies, University of Idaho, Moscow, Idaho, United States of America
| | - Jagdish Suresh Patel
- Department of Biological Sciences, University of Idaho, Moscow, Idaho, United States of America
- Institute for Modeling Collaboration and Innovation, University of Idaho, Moscow, Idaho, United States of America
| | - F. Marty Ytreberg
- Institute for Modeling Collaboration and Innovation, University of Idaho, Moscow, Idaho, United States of America
- Institute for Bioinformatics and Evolutionary Studies, University of Idaho, Moscow, Idaho, United States of America
- Department of Physics, University of Idaho, Moscow, Idaho, United States of America
| | - Yousif Shamoo
- Department of Biosciences, Rice University, Houston, Texas, United States of America
| | - Christopher J. Marx
- Department of Biological Sciences, University of Idaho, Moscow, Idaho, United States of America
- Institute for Modeling Collaboration and Innovation, University of Idaho, Moscow, Idaho, United States of America
- Institute for Bioinformatics and Evolutionary Studies, University of Idaho, Moscow, Idaho, United States of America
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, Massachusetts, United States of America
- * E-mail: (JVB); (CJM)
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12
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Patel D, Patel JS, Ytreberg FM. Implementing and Assessing an Alchemical Method for Calculating Protein-Protein Binding Free Energy. J Chem Theory Comput 2021; 17:2457-2464. [PMID: 33709712 PMCID: PMC8044032 DOI: 10.1021/acs.jctc.0c01045] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
Protein-protein binding is fundamental to most biological processes. It is important to be able to use computation to accurately estimate the change in protein-protein binding free energy due to mutations in order to answer biological questions that would be experimentally challenging, laborious, or time-consuming. Although nonrigorous free-energy methods are faster, rigorous alchemical molecular dynamics-based methods are considerably more accurate and are becoming more feasible with the advancement of computer hardware and molecular simulation software. Even with sufficient computational resources, there are still major challenges to using alchemical free-energy methods for protein-protein complexes, such as generating hybrid structures and topologies, maintaining a neutral net charge of the system when there is a charge-changing mutation, and setting up the simulation. In the current study, we have used the pmx package to generate hybrid structures and topologies, and a double-system/single-box approach to maintain the net charge of the system. To test the approach, we predicted relative binding affinities for two protein-protein complexes using a nonequilibrium alchemical method based on the Crooks fluctuation theorem and compared the results with experimental values. The method correctly identified stabilizing from destabilizing mutations for a small protein-protein complex, and a larger, more challenging antibody complex. Strong correlations were obtained between predicted and experimental relative binding affinities for both protein-protein systems.
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Affiliation(s)
- Dharmeshkumar Patel
- Institute for Modeling Collaboration and Innovation, University of Idaho, Moscow, Idaho 83844, United States
| | - Jagdish Suresh Patel
- Institute for Modeling Collaboration and Innovation, University of Idaho, Moscow, Idaho 83844, United States
- Department of Biological Sciences, University of Idaho, Moscow, Idaho 83844, United States
| | - F Marty Ytreberg
- Institute for Modeling Collaboration and Innovation, University of Idaho, Moscow, Idaho 83844, United States
- Department of Physics, University of Idaho, Moscow, Idaho 83844, United States
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13
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Magar R, Yadav P, Barati Farimani A. Potential neutralizing antibodies discovered for novel corona virus using machine learning. Sci Rep 2021; 11:5261. [PMID: 33664393 PMCID: PMC7970853 DOI: 10.1038/s41598-021-84637-4] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2020] [Accepted: 02/17/2021] [Indexed: 02/07/2023] Open
Abstract
The fast and untraceable virus mutations take lives of thousands of people before the immune system can produce the inhibitory antibody. The recent outbreak of COVID-19 infected and killed thousands of people in the world. Rapid methods in finding peptides or antibody sequences that can inhibit the viral epitopes of SARS-CoV-2 will save the life of thousands. To predict neutralizing antibodies for SARS-CoV-2 in a high-throughput manner, in this paper, we use different machine learning (ML) model to predict the possible inhibitory synthetic antibodies for SARS-CoV-2. We collected 1933 virus-antibody sequences and their clinical patient neutralization response and trained an ML model to predict the antibody response. Using graph featurization with variety of ML methods, like XGBoost, Random Forest, Multilayered Perceptron, Support Vector Machine and Logistic Regression, we screened thousands of hypothetical antibody sequences and found nine stable antibodies that potentially inhibit SARS-CoV-2. We combined bioinformatics, structural biology, and Molecular Dynamics (MD) simulations to verify the stability of the candidate antibodies that can inhibit SARS-CoV-2.
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Affiliation(s)
- Rishikesh Magar
- Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA, 15213, USA
| | - Prakarsh Yadav
- Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA, 15213, USA
| | - Amir Barati Farimani
- Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA, 15213, USA.
- Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA, 15213, USA.
- Machine Learning Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, 15213, USA.
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14
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Magar R, Yadav P, Barati Farimani A. Potential neutralizing antibodies discovered for novel corona virus using machine learning. Sci Rep 2021; 11:5261. [PMID: 33664393 DOI: 10.1101/2020.03.14.992156] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2020] [Accepted: 02/17/2021] [Indexed: 05/22/2023] Open
Abstract
The fast and untraceable virus mutations take lives of thousands of people before the immune system can produce the inhibitory antibody. The recent outbreak of COVID-19 infected and killed thousands of people in the world. Rapid methods in finding peptides or antibody sequences that can inhibit the viral epitopes of SARS-CoV-2 will save the life of thousands. To predict neutralizing antibodies for SARS-CoV-2 in a high-throughput manner, in this paper, we use different machine learning (ML) model to predict the possible inhibitory synthetic antibodies for SARS-CoV-2. We collected 1933 virus-antibody sequences and their clinical patient neutralization response and trained an ML model to predict the antibody response. Using graph featurization with variety of ML methods, like XGBoost, Random Forest, Multilayered Perceptron, Support Vector Machine and Logistic Regression, we screened thousands of hypothetical antibody sequences and found nine stable antibodies that potentially inhibit SARS-CoV-2. We combined bioinformatics, structural biology, and Molecular Dynamics (MD) simulations to verify the stability of the candidate antibodies that can inhibit SARS-CoV-2.
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Affiliation(s)
- Rishikesh Magar
- Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA, 15213, USA
| | - Prakarsh Yadav
- Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA, 15213, USA
| | - Amir Barati Farimani
- Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA, 15213, USA.
- Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA, 15213, USA.
- Machine Learning Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, 15213, USA.
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15
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Gonzalez TR, Martin KP, Barnes JE, Patel JS, Ytreberg FM. Assessment of software methods for estimating protein-protein relative binding affinities. PLoS One 2020; 15:e0240573. [PMID: 33347442 PMCID: PMC7751979 DOI: 10.1371/journal.pone.0240573] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2020] [Accepted: 12/07/2020] [Indexed: 11/19/2022] Open
Abstract
A growing number of computational tools have been developed to accurately and rapidly predict the impact of amino acid mutations on protein-protein relative binding affinities. Such tools have many applications, for example, designing new drugs and studying evolutionary mechanisms. In the search for accuracy, many of these methods employ expensive yet rigorous molecular dynamics simulations. By contrast, non-rigorous methods use less exhaustive statistical mechanics, allowing for more efficient calculations. However, it is unclear if such methods retain enough accuracy to replace rigorous methods in binding affinity calculations. This trade-off between accuracy and computational expense makes it difficult to determine the best method for a particular system or study. Here, eight non-rigorous computational methods were assessed using eight antibody-antigen and eight non-antibody-antigen complexes for their ability to accurately predict relative binding affinities (ΔΔG) for 654 single mutations. In addition to assessing accuracy, we analyzed the CPU cost and performance for each method using a variety of physico-chemical structural features. This allowed us to posit scenarios in which each method may be best utilized. Most methods performed worse when applied to antibody-antigen complexes compared to non-antibody-antigen complexes. Rosetta-based JayZ and EasyE methods classified mutations as destabilizing (ΔΔG < -0.5 kcal/mol) with high (83-98%) accuracy and a relatively low computational cost for non-antibody-antigen complexes. Some of the most accurate results for antibody-antigen systems came from combining molecular dynamics with FoldX with a correlation coefficient (r) of 0.46, but this was also the most computationally expensive method. Overall, our results suggest these methods can be used to quickly and accurately predict stabilizing versus destabilizing mutations but are less accurate at predicting actual binding affinities. This study highlights the need for continued development of reliable, accessible, and reproducible methods for predicting binding affinities in antibody-antigen proteins and provides a recipe for using current methods.
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Affiliation(s)
- Tawny R. Gonzalez
- Institute for Modeling Collaboration and Innovation, University of Idaho, Moscow, Idaho, United States of America
| | - Kyle P. Martin
- Institute for Modeling Collaboration and Innovation, University of Idaho, Moscow, Idaho, United States of America
- Department of Physics, University of Idaho, Moscow, Idaho, United States of America
| | - Jonathan E. Barnes
- Institute for Modeling Collaboration and Innovation, University of Idaho, Moscow, Idaho, United States of America
- Department of Physics, University of Idaho, Moscow, Idaho, United States of America
| | - Jagdish Suresh Patel
- Institute for Modeling Collaboration and Innovation, University of Idaho, Moscow, Idaho, United States of America
- Department of Biological Sciences, University of Idaho, Moscow, Idaho, United States of America
| | - F. Marty Ytreberg
- Institute for Modeling Collaboration and Innovation, University of Idaho, Moscow, Idaho, United States of America
- Department of Physics, University of Idaho, Moscow, Idaho, United States of America
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16
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Remdesivir targets a structurally analogous region of the Ebola virus and SARS-CoV-2 polymerases. Proc Natl Acad Sci U S A 2020; 117:26946-26954. [PMID: 33028676 PMCID: PMC7604432 DOI: 10.1073/pnas.2012294117] [Citation(s) in RCA: 41] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Abstract
Remdesivir is a nucleotide analog prodrug that has been evaluated in humans against acute Ebola virus disease; it also recently received emergency use authorization for treating COVID-19. For antiviral product development, the Food and Drug Administration recommends the characterization of in vitro selected resistant viruses to define the specific antiviral mechanism of action. This study identified a single amino acid residue in the Ebola virus polymerase that conferred low-level resistance to remdesivir. The significance of our study lies not only in characterizing this particular mutation, but also in relating it to a resistance mutation observed in a similar structural motif of coronaviruses. Our findings thereby indicate a consistent mechanism of action by remdesivir across genetically divergent RNA viruses causing diseases of high consequence in humans. Remdesivir is a broad-spectrum antiviral nucleotide prodrug that has been clinically evaluated in Ebola virus patients and recently received emergency use authorization (EUA) for treatment of COVID-19. With approvals from the Federal Select Agent Program and the Centers for Disease Control and Prevention’s Institutional Biosecurity Board, we characterized the resistance profile of remdesivir by serially passaging Ebola virus under remdesivir selection; we generated lineages with low-level reduced susceptibility to remdesivir after 35 passages. We found that a single amino acid substitution, F548S, in the Ebola virus polymerase conferred low-level reduced susceptibility to remdesivir. The F548 residue is highly conserved in filoviruses but should be subject to specific surveillance among novel filoviruses, in newly emerging variants in ongoing outbreaks, and also in Ebola virus patients undergoing remdesivir therapy. Homology modeling suggests that the Ebola virus polymerase F548 residue lies in the F-motif of the polymerase active site, a region that was previously identified as susceptible to resistance mutations in coronaviruses. Our data suggest that molecular surveillance of this region of the polymerase in remdesivir-treated COVID-19 patients is also warranted.
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17
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Di Paola N, Sanchez-Lockhart M, Zeng X, Kuhn JH, Palacios G. Viral genomics in Ebola virus research. Nat Rev Microbiol 2020; 18:365-378. [PMID: 32367066 PMCID: PMC7223634 DOI: 10.1038/s41579-020-0354-7] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/09/2020] [Indexed: 12/20/2022]
Abstract
Filoviruses such as Ebola virus continue to pose a substantial health risk to humans. Advances in the sequencing and functional characterization of both pathogen and host genomes have provided a wealth of knowledge to clinicians, epidemiologists and public health responders during outbreaks of high-consequence viral disease. Here, we describe how genomics has been historically used to investigate Ebola virus disease outbreaks and how new technologies allow for rapid, large-scale data generation at the point of care. We highlight how genomics extends beyond consensus-level sequencing of the virus to include intra-host viral transcriptomics and the characterization of host responses in acute and persistently infected patients. Similar genomics techniques can also be applied to the characterization of non-human primate animal models and to known natural reservoirs of filoviruses, and metagenomic sequencing can be the key to the discovery of novel filoviruses. Finally, we outline the importance of reverse genetics systems that can swiftly characterize filoviruses as soon as their genome sequences are available.
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Affiliation(s)
- Nicholas Di Paola
- United States Army Medical Research Institute of Infectious Diseases, Fort Detrick, Frederick, MD, USA
| | - Mariano Sanchez-Lockhart
- United States Army Medical Research Institute of Infectious Diseases, Fort Detrick, Frederick, MD, USA
| | - Xiankun Zeng
- United States Army Medical Research Institute of Infectious Diseases, Fort Detrick, Frederick, MD, USA
| | - Jens H Kuhn
- Integrated Research Facility at Fort Detrick, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Frederick, MD, USA
| | - Gustavo Palacios
- United States Army Medical Research Institute of Infectious Diseases, Fort Detrick, Frederick, MD, USA.
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18
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Predicting the viability of beta-lactamase: How folding and binding free energies correlate with beta-lactamase fitness. PLoS One 2020; 15:e0233509. [PMID: 32470971 PMCID: PMC7259980 DOI: 10.1371/journal.pone.0233509] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2020] [Accepted: 05/06/2020] [Indexed: 12/25/2022] Open
Abstract
One of the long-standing holy grails of molecular evolution has been the ability to predict an organism’s fitness directly from its genotype. With such predictive abilities in hand, researchers would be able to more accurately forecast how organisms will evolve and how proteins with novel functions could be engineered, leading to revolutionary advances in medicine and biotechnology. In this work, we assemble the largest reported set of experimental TEM-1 β-lactamase folding free energies and use this data in conjunction with previously acquired fitness data and computational free energy predictions to determine how much of the fitness of β-lactamase can be directly predicted by thermodynamic folding and binding free energies. We focus upon β-lactamase because of its long history as a model enzyme and its central role in antibiotic resistance. Based upon a set of 21 β-lactamase single and double mutants expressly designed to influence protein folding, we first demonstrate that modeling software designed to compute folding free energies such as FoldX and PyRosetta can meaningfully, although not perfectly, predict the experimental folding free energies of single mutants. Interestingly, while these techniques also yield sensible double mutant free energies, we show that they do so for the wrong physical reasons. We then go on to assess how well both experimental and computational folding free energies explain single mutant fitness. We find that folding free energies account for, at most, 24% of the variance in β-lactamase fitness values according to linear models and, somewhat surprisingly, complementing folding free energies with computationally-predicted binding free energies of residues near the active site only increases the folding-only figure by a few percent. This strongly suggests that the majority of β-lactamase’s fitness is controlled by factors other than free energies. Overall, our results shed a bright light on to what extent the community is justified in using thermodynamic measures to infer protein fitness as well as how applicable modern computational techniques for predicting free energies will be to the large data sets of multiply-mutated proteins forthcoming.
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19
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Mirabzadeh CA, Ytreberg FM. Implementation of adaptive integration method for free energy calculations in molecular systems. PeerJ Comput Sci 2020; 6:e264. [PMID: 33457645 PMCID: PMC7808261 DOI: 10.7717/peerj-cs.264] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2019] [Accepted: 02/10/2020] [Indexed: 11/20/2022]
Abstract
Estimating free energy differences by computer simulation is useful for a wide variety of applications such as virtual screening for drug design and for understanding how amino acid mutations modify protein interactions. However, calculating free energy differences remains challenging and often requires extensive trial and error and very long simulation times in order to achieve converged results. Here, we present an implementation of the adaptive integration method (AIM). We tested our implementation on two molecular systems and compared results from AIM to those from a suite of other methods. The model systems tested here include calculating the solvation free energy of methane, and the free energy of mutating the peptide GAG to GVG. We show that AIM is more efficient than other tested methods for these systems, that is, AIM results converge to a higher level of accuracy and precision for a given simulation time.
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Affiliation(s)
| | - F. Marty Ytreberg
- Department of Physics, University of Idaho, Moscow, ID, United States of America
- Institute for Modeling Collaboration and Innovation, University of Idaho, Moscow, ID, United States of America
- Institute for Bioinformatics and Evolutionary Studies, University of Idaho, Moscow, ID, United States of America
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20
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Expanding the watch list for potential Ebola virus antibody escape mutations. PLoS One 2019; 14:e0211093. [PMID: 30897171 PMCID: PMC6428255 DOI: 10.1371/journal.pone.0211093] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2019] [Accepted: 03/06/2019] [Indexed: 12/28/2022] Open
Abstract
The 2014 outbreak of Ebola virus disease (EVD) in Western Africa is the largest recorded filovirus disease outbreak and led to the death of over 11,000 people. The recent EVD outbreaks (since May 2018) in the Democratic Republic of the Congo has already claimed the lives of over 250 people. Tackling Ebola virus (EBOV) outbreaks remains a challenge. Over the years, significant efforts have been put into developing vaccines or antibody therapies which rely on an envelope glycoprotein (GP) of Zaire ebolavirus (strain Mayinga-76). Therefore, one key approach for combating EVD epidemics is to predict mutations that may diminish the effectiveness of the treatment. In a previous study we generated a watch list of potential antibody escape mutations of EBOV GP against the monoclonal antibody KZ52. Molecular modeling methods were applied to the three-dimensional experimental structure of EBOV GP bound to KZ52 to predict the effect of every possible single mutation in EBOV GP. The final watch list contained 34 mutations that were predicted to destabilize binding of KZ52 to EBOV GP but did not affect EBOV GP folding and its ability to form trimers. In this study, we expand our watch list by including three more monoclonal antibodies with distinct epitopes on GP, namely Antibody 100 (Ab100), Antibody 114 (Ab114) and 13F6-1-2. Our updated watch list contains 127 mutations, three of which have been seen in humans or are experimentally associated with reduced efficacy of antibody treatment. We believe mutations on this watch list require attention since they provide information about circumstances in which interventions could lose the effectiveness.
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21
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Patel A, Park DH, Davis CW, Smith TRF, Leung A, Tierney K, Bryan A, Davidson E, Yu X, Racine T, Reed C, Gorman ME, Wise MC, Elliott STC, Esquivel R, Yan J, Chen J, Muthumani K, Doranz BJ, Saphire EO, Crowe JE, Broderick KE, Kobinger GP, He S, Qiu X, Kobasa D, Humeau L, Sardesai NY, Ahmed R, Weiner DB. In Vivo Delivery of Synthetic Human DNA-Encoded Monoclonal Antibodies Protect against Ebolavirus Infection in a Mouse Model. Cell Rep 2018; 25:1982-1993.e4. [PMID: 30428362 PMCID: PMC6319964 DOI: 10.1016/j.celrep.2018.10.062] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2018] [Revised: 08/27/2018] [Accepted: 10/16/2018] [Indexed: 12/14/2022] Open
Abstract
Synthetically engineered DNA-encoded monoclonal antibodies (DMAbs) are an in vivo platform for evaluation and delivery of human mAb to control against infectious disease. Here, we engineer DMAbs encoding potent anti-Zaire ebolavirus (EBOV) glycoprotein (GP) mAbs isolated from Ebola virus disease survivors. We demonstrate the development of a human IgG1 DMAb platform for in vivo EBOV-GP mAb delivery and evaluation in a mouse model. Using this approach, we show that DMAb-11 and DMAb-34 exhibit functional and molecular profiles comparable to recombinant mAb, have a wide window of expression, and provide rapid protection against lethal mouse-adapted EBOV challenge. The DMAb platform represents a simple, rapid, and reproducible approach for evaluating the activity of mAb during clinical development. DMAbs have the potential to be a mAb delivery system, which may be advantageous for protection against highly pathogenic infectious diseases, like EBOV, in resource-limited and other challenging settings.
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Affiliation(s)
- Ami Patel
- The Wistar Institute of Anatomy and Biology, Philadelphia, PA 19104, USA
| | - Daniel H Park
- The Wistar Institute of Anatomy and Biology, Philadelphia, PA 19104, USA
| | - Carl W Davis
- Emory Vaccine Center, Emory University, Atlanta, GA 30317, USA
| | | | - Anders Leung
- Public Health Agency of Canada, Winnipeg, MB R3E 3R2, Canada
| | - Kevin Tierney
- Public Health Agency of Canada, Winnipeg, MB R3E 3R2, Canada
| | | | | | - Xiaoying Yu
- The Scripps Research Institute, La Jolla, CA 92037, USA
| | - Trina Racine
- Public Health Agency of Canada, Winnipeg, MB R3E 3R2, Canada; University of Manitoba, Winnipeg, MB R3T 2N2, Canada
| | - Charles Reed
- Inovio Pharmaceuticals, Plymouth Meeting, PA 19462, USA
| | - Marguerite E Gorman
- The Wistar Institute of Anatomy and Biology, Philadelphia, PA 19104, USA; Boston College, Newton, MA 02467, USA
| | - Megan C Wise
- Inovio Pharmaceuticals, Plymouth Meeting, PA 19462, USA
| | - Sarah T C Elliott
- The Wistar Institute of Anatomy and Biology, Philadelphia, PA 19104, USA
| | - Rianne Esquivel
- The Wistar Institute of Anatomy and Biology, Philadelphia, PA 19104, USA
| | - Jian Yan
- Inovio Pharmaceuticals, Plymouth Meeting, PA 19462, USA
| | - Jing Chen
- Inovio Pharmaceuticals, Plymouth Meeting, PA 19462, USA
| | - Kar Muthumani
- The Wistar Institute of Anatomy and Biology, Philadelphia, PA 19104, USA
| | | | | | | | | | | | - Shihua He
- Public Health Agency of Canada, Winnipeg, MB R3E 3R2, Canada
| | - Xiangguo Qiu
- Public Health Agency of Canada, Winnipeg, MB R3E 3R2, Canada; University of Manitoba, Winnipeg, MB R3T 2N2, Canada
| | - Darwyn Kobasa
- Public Health Agency of Canada, Winnipeg, MB R3E 3R2, Canada; University of Manitoba, Winnipeg, MB R3T 2N2, Canada
| | | | | | - Rafi Ahmed
- Emory Vaccine Center, Emory University, Atlanta, GA 30317, USA
| | - David B Weiner
- The Wistar Institute of Anatomy and Biology, Philadelphia, PA 19104, USA.
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22
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Patel JS, Ytreberg FM. Fast Calculation of Protein-Protein Binding Free Energies Using Umbrella Sampling with a Coarse-Grained Model. J Chem Theory Comput 2018; 14:991-997. [PMID: 29286646 PMCID: PMC5813277 DOI: 10.1021/acs.jctc.7b00660] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
![]()
Determination
of protein–protein binding affinity values
is key to understanding various underlying biological phenomena, such
as how missense variations change protein–protein binding.
Most existing non-rigorous (fast) and rigorous (slow) methods that
rely on all-atom representation of the proteins force the user to
choose between speed and accuracy. In an attempt to achieve balance
between speed and accuracy, we have combined rigorous umbrella sampling
molecular dynamics simulation with a coarse-grained protein model.
We predicted the effect of missense variations on binding affinity
by selecting three protein–protein systems and comparing results
to empirical relative binding affinity values and to non-rigorous
modeling approaches. We obtained significant improvement both in our
ability to discern stabilizing from destabilizing missense variations
and in the correlation between predicted and experimental values compared
to non-rigorous approaches. Overall our results suggest that using
a rigorous affinity calculation method with coarse-grained protein
models could offer fast and reliable predictions of protein–protein
binding free energies.
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Affiliation(s)
- Jagdish Suresh Patel
- Center for Modeling Complex Interactions, University of Idaho , Moscow, Idaho 83844, United States
| | - F Marty Ytreberg
- Department of Physics, University of Idaho , Moscow, Idaho 83844, United States
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23
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González-González A, Hug SM, Rodríguez-Verdugo A, Patel JS, Gaut BS. Adaptive Mutations in RNA Polymerase and the Transcriptional Terminator Rho Have Similar Effects on Escherichia coli Gene Expression. Mol Biol Evol 2017; 34:2839-2855. [PMID: 28961910 PMCID: PMC5815632 DOI: 10.1093/molbev/msx216] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
Modifications to transcriptional regulators play a major role in adaptation. Here, we compared the effects of multiple beneficial mutations within and between Escherichia coli rpoB, the gene encoding the RNA polymerase β subunit, and rho, which encodes a transcriptional terminator. These two genes have harbored adaptive mutations in numerous E. coli evolution experiments but particularly in our previous large-scale thermal stress experiment, where the two genes characterized alternative adaptive pathways. To compare the effects of beneficial mutations, we engineered four advantageous mutations into each of the two genes and measured their effects on fitness, growth, gene expression and transcriptional termination at 42.2 °C. Among the eight mutations, two rho mutations had no detectable effect on relative fitness, suggesting they were beneficial only in the context of epistatic interactions. The remaining six mutations had an average relative fitness benefit of ∼20%. The rpoB mutations affected the expression of ∼1,700 genes; rho mutations affected the expression of fewer genes but most (83%) were a subset of those altered by rpoB mutants. Across the eight mutants, relative fitness correlated with the degree to which a mutation restored gene expression back to the unstressed, 37.0 °C state. The beneficial mutations in the two genes did not have identical effects on fitness, growth or gene expression, but they caused parallel phenotypic effects on gene expression and genome-wide transcriptional termination.
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Affiliation(s)
- Andrea González-González
- Department of Ecology and Evolutionary Biology, University of California,
Irvine, CA
- Department of Biological Sciences, University of Idaho, Moscow, ID
| | - Shaun M. Hug
- Department of Ecology and Evolutionary Biology, University of California,
Irvine, CA
| | - Alejandra Rodríguez-Verdugo
- Department of Environmental Systems Sciences, ETH Zürich, Zürich,
Switzerland
- Department of Environmental Microbiology, Eawag, Dübendorf,
Switzerland
| | | | - Brandon S. Gaut
- Department of Ecology and Evolutionary Biology, University of California,
Irvine, CA
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