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  1. Article ; Online: Potts Hamiltonian Models and Molecular Dynamics Free Energy Simulations for Predicting the Impact of Mutations on Protein Kinase Stability.

    Thakur, Abhishek / Gizzio, Joan / Levy, Ronald M

    The journal of physical chemistry. B

    2024  Volume 128, Issue 7, Page(s) 1656–1667

    Abstract: Single-point mutations in kinase proteins can affect their stability and fitness, and computational analysis of these effects can provide insights into the relationships among protein sequence, structure, and function for this enzyme family. To assess ... ...

    Abstract Single-point mutations in kinase proteins can affect their stability and fitness, and computational analysis of these effects can provide insights into the relationships among protein sequence, structure, and function for this enzyme family. To assess the impact of mutations on protein stability, we used a sequence-based Potts Hamiltonian model trained on a kinase family multiple-sequence alignment (MSA) to calculate the statistical energy (fitness) effects of mutations and compared these against relative folding free energies (ΔΔ
    MeSH term(s) Humans ; Molecular Dynamics Simulation ; Protein Kinases/genetics ; Thermodynamics ; Mutation ; Protein Stability ; Neoplasms
    Chemical Substances Protein Kinases (EC 2.7.-)
    Language English
    Publishing date 2024-02-13
    Publishing country United States
    Document type Journal Article
    ISSN 1520-5207
    ISSN (online) 1520-5207
    DOI 10.1021/acs.jpcb.3c08097
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: Insights into the energy landscapes of chromosome organization proteins from coevolutionary sequence variation and structural modeling.

    Levy, Ronald M

    Proceedings of the National Academy of Sciences of the United States of America

    2020  Volume 117, Issue 5, Page(s) 2241–2242

    MeSH term(s) Chromosomes ; Physical Phenomena ; Protein Conformation ; Proteins/genetics
    Chemical Substances Proteins
    Language English
    Publishing date 2020-01-10
    Publishing country United States
    Document type Journal Article ; Comment
    ZDB-ID 209104-5
    ISSN 1091-6490 ; 0027-8424
    ISSN (online) 1091-6490
    ISSN 0027-8424
    DOI 10.1073/pnas.1921727117
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    In stock of ZB MED Cologne/Königswinter

    Zs.A 1148: Show issues Location:
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  3. Article ; Online: Elucidating the Molecular Determinants of the Binding Modes of a Third-Generation HIV-1 Integrase Strand Transfer Inhibitor: The Importance of Side Chain and Solvent Reorganization.

    Sun, Qinfang / Biswas, Avik / Lyumkis, Dmitry / Levy, Ronald / Deng, Nanjie

    Viruses

    2024  Volume 16, Issue 1

    Abstract: The first- and second-generation clinically used HIV-1 integrase (IN) strand transfer inhibitors (INSTIs) are key components of antiretroviral therapy (ART), which work by blocking the integration step in the HIV-1 replication cycle that is catalyzed by ... ...

    Abstract The first- and second-generation clinically used HIV-1 integrase (IN) strand transfer inhibitors (INSTIs) are key components of antiretroviral therapy (ART), which work by blocking the integration step in the HIV-1 replication cycle that is catalyzed by a nucleoprotein assembly called an intasome. However, resistance to even the latest clinically used INSTIs is beginning to emerge. Developmental third-generation INSTIs, based on naphthyridine scaffolds, are promising candidates to combat drug-resistant viral variants. Among these novel INSTIs, compound
    MeSH term(s) Humans ; Ligands ; Binding Sites ; Catalysis ; HIV Integrase/genetics ; HIV Seropositivity ; HIV-1/genetics
    Chemical Substances p31 integrase protein, Human immunodeficiency virus 1 (YY6481J2FF) ; Ligands ; HIV Integrase (EC 2.7.7.-)
    Language English
    Publishing date 2024-01-02
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2516098-9
    ISSN 1999-4915 ; 1999-4915
    ISSN (online) 1999-4915
    ISSN 1999-4915
    DOI 10.3390/v16010076
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  4. Article: Evolutionary sequence and structural basis for the distinct conformational landscapes of Tyr and Ser/Thr kinases.

    Gizzio, Joan / Thakur, Abhishek / Haldane, Allan / Levy, Ronald M

    bioRxiv : the preprint server for biology

    2024  

    Abstract: Protein kinases are molecular machines with rich sequence variation that distinguishes the two main evolutionary branches - tyrosine kinases (TKs) from serine/threonine kinases (STKs). Using a sequence co-variation Potts statistical energy model we ... ...

    Abstract Protein kinases are molecular machines with rich sequence variation that distinguishes the two main evolutionary branches - tyrosine kinases (TKs) from serine/threonine kinases (STKs). Using a sequence co-variation Potts statistical energy model we previously concluded that TK catalytic domains are more likely than STKs to adopt an inactive conformation with the activation loop in an autoinhibitory "folded" conformation, due to intrinsic sequence effects. Here we investigated the structural basis for this phenomenon by integrating the sequence-based model with structure-based molecular dynamics (MD) to determine the effects of mutations on the free energy difference between active and inactive conformations, using a novel thermodynamic cycle involving many (n=108) protein-mutation free energy perturbation (FEP) simulations in the active and inactive conformations. The sequence and structure-based results are consistent and support the hypothesis that the inactive conformation "DFG-out Activation Loop Folded", is a functional regulatory state that has been stabilized in TKs relative to STKs over the course of their evolution via the accumulation of residue substitutions in the activation loop and catalytic loop that facilitate distinct substrate binding modes
    Language English
    Publishing date 2024-03-12
    Publishing country United States
    Document type Preprint
    DOI 10.1101/2024.03.08.584161
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  5. Article ; Online: Kinetic coevolutionary models predict the temporal emergence of HIV-1 resistance mutations under drug selection pressure.

    Biswas, Avik / Choudhuri, Indrani / Arnold, Eddy / Lyumkis, Dmitry / Haldane, Allan / Levy, Ronald M

    Proceedings of the National Academy of Sciences of the United States of America

    2024  Volume 121, Issue 15, Page(s) e2316662121

    Abstract: Drug resistance in HIV type 1 (HIV-1) is a pervasive problem that affects the lives of millions of people worldwide. Although records of drug-resistant mutations (DRMs) have been extensively tabulated within public repositories, our understanding of the ... ...

    Abstract Drug resistance in HIV type 1 (HIV-1) is a pervasive problem that affects the lives of millions of people worldwide. Although records of drug-resistant mutations (DRMs) have been extensively tabulated within public repositories, our understanding of the evolutionary kinetics of DRMs and how they evolve together remains limited. Epistasis, the interaction between a DRM and other residues in HIV-1 protein sequences, is key to the temporal evolution of drug resistance. We use a Potts sequence-covariation statistical-energy model of HIV-1 protein fitness under drug selection pressure, which captures epistatic interactions between all positions, combined with kinetic Monte-Carlo simulations of sequence evolutionary trajectories, to explore the acquisition of DRMs as they arise in an ensemble of drug-naive patient protein sequences. We follow the time course of 52 DRMs in the enzymes protease, RT, and integrase, the primary targets of antiretroviral therapy. The rates at which DRMs emerge are highly correlated with their observed acquisition rates reported in the literature when drug pressure is applied. This result highlights the central role of epistasis in determining the kinetics governing DRM emergence. Whereas rapidly acquired DRMs begin to accumulate as soon as drug pressure is applied, slowly acquired DRMs are contingent on accessory mutations that appear only after prolonged drug pressure. We provide a foundation for using computational methods to determine the temporal evolution of drug resistance using Potts statistical potentials, which can be used to gain mechanistic insights into drug resistance pathways in HIV-1 and other infectious agents.
    MeSH term(s) Humans ; HIV-1/genetics ; Drug Resistance, Viral/genetics ; Genotype ; HIV Infections/drug therapy ; HIV Infections/genetics ; HIV Seropositivity ; Mutation ; Anti-HIV Agents/pharmacology ; Anti-HIV Agents/therapeutic use
    Chemical Substances Anti-HIV Agents
    Language English
    Publishing date 2024-04-01
    Publishing country United States
    Document type Journal Article
    ZDB-ID 209104-5
    ISSN 1091-6490 ; 0027-8424
    ISSN (online) 1091-6490
    ISSN 0027-8424
    DOI 10.1073/pnas.2316662121
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    In stock of ZB MED Cologne/Königswinter

    Zs.A 1148: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
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  6. Article: Elucidating the molecular determinants for binding modes of a third-generation HIV-1 integrase strand transfer inhibitor: Importance of side chain and solvent reorganization.

    Sun, Qinfang / Biswas, Avik / Lyumkis, Dmitry / Levy, Ronald / Deng, Nanjie

    bioRxiv : the preprint server for biology

    2023  

    Abstract: The first and second-generation clinically used HIV-1 integrase (IN) strand transfer inhibitors (INSTIs) are key components of antiretroviral therapy (ART), which work by blocking the integration step in the HIV-1 replication cycle that is catalyzed by a ...

    Abstract The first and second-generation clinically used HIV-1 integrase (IN) strand transfer inhibitors (INSTIs) are key components of antiretroviral therapy (ART), which work by blocking the integration step in the HIV-1 replication cycle that is catalyzed by a nucleoprotein assembly called an intasome. However, resistance to even the latest clinically used INSTIs is beginning to emerge. Developmental third-generation INSTIs, based on naphthyridine scaffold, are promising candidates to combat drug-resistant viral variants. Among these novel INSTIs, compound 4f exhibits two distinct conformations when binding to intasomes from HIV-1 and the closely related prototype foamy virus (PFV), despite the high structural similarity of their INSTI binding pockets. The molecular mechanism and the key active site residues responsible for these differing binding modes in closely related intasomes remain elusive. To unravel the molecular determinants governing the two distinct binding modes, we employ a novel molecular dynamics-based free energy approach that utilizes alchemical pathways to overcome the sampling challenges associated with transitioning between two ligand conformations within crowded environments along physical pathways. The calculated conformational free energies successfully recapitulate the experimentally observed binding mode preferences in the two viral intasomes. Analysis of the simulated structures suggests that the observed binding mode preferences are caused by amino acid residue differences in both the front and the central catalytic sub-pocket of the INSTI binding site in HIV-1 and PFV. Additional free energy calculations on mutants of HIV-1 and PFV revealed that while both sub-pockets contribute to the binding mode selection, the central sub-pocket plays a more important role. These results highlight the importance of both side chain and solvent reorganization, as well as the conformational entropy in determining the ligand binding mode and will help inform the development of more effective INSTIs for combatting drug-resistant viral variants.
    Language English
    Publishing date 2023-12-01
    Publishing country United States
    Document type Preprint
    DOI 10.1101/2023.11.29.569269
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  7. Article ; Online: Limits to detecting epistasis in the fitness landscape of HIV.

    Biswas, Avik / Haldane, Allan / Levy, Ronald M

    PloS one

    2022  Volume 17, Issue 1, Page(s) e0262314

    Abstract: The rapid evolution of HIV is constrained by interactions between mutations which affect viral fitness. In this work, we explore the role of epistasis in determining the mutational fitness landscape of HIV for multiple drug target proteins, including ... ...

    Abstract The rapid evolution of HIV is constrained by interactions between mutations which affect viral fitness. In this work, we explore the role of epistasis in determining the mutational fitness landscape of HIV for multiple drug target proteins, including Protease, Reverse Transcriptase, and Integrase. Epistatic interactions between residues modulate the mutation patterns involved in drug resistance, with unambiguous signatures of epistasis best seen in the comparison of the Potts model predicted and experimental HIV sequence "prevalences" expressed as higher-order marginals (beyond triplets) of the sequence probability distribution. In contrast, experimental measures of fitness such as viral replicative capacities generally probe fitness effects of point mutations in a single background, providing weak evidence for epistasis in viral systems. The detectable effects of epistasis are obscured by higher evolutionary conservation at sites. While double mutant cycles in principle, provide one of the best ways to probe epistatic interactions experimentally without reference to a particular background, we show that the analysis is complicated by the small dynamic range of measurements. Overall, we show that global pairwise interaction Potts models are necessary for predicting the mutational landscape of viral proteins.
    MeSH term(s) Epistasis, Genetic ; Evolution, Molecular ; Genetic Fitness ; HIV Infections/genetics ; HIV Infections/virology ; HIV Protease/genetics ; HIV-1/genetics ; Humans ; Mutation ; Viral Proteins/genetics ; Virus Replication
    Chemical Substances Viral Proteins ; HIV Protease (EC 3.4.23.-)
    Language English
    Publishing date 2022-01-18
    Publishing country United States
    Document type Journal Article ; Research Support, N.I.H., Extramural ; Research Support, U.S. Gov't, Non-P.H.S.
    ZDB-ID 2267670-3
    ISSN 1932-6203 ; 1932-6203
    ISSN (online) 1932-6203
    ISSN 1932-6203
    DOI 10.1371/journal.pone.0262314
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  8. Article: Mi3-GPU: MCMC-based Inverse Ising Inference on GPUs for protein covariation analysis.

    Haldane, Allan / Levy, Ronald M

    Computer physics communications

    2020  Volume 260

    Abstract: Inverse Ising inference is a method for inferring the coupling parameters of a Potts/Ising model based on observed site-covariation, which has found important applications in protein physics for detecting interactions between residues in protein families. ...

    Abstract Inverse Ising inference is a method for inferring the coupling parameters of a Potts/Ising model based on observed site-covariation, which has found important applications in protein physics for detecting interactions between residues in protein families. We introduce Mi3-GPU ("mee-three", for
    Language English
    Publishing date 2020-04-17
    Publishing country Netherlands
    Document type Journal Article
    ZDB-ID 1466511-6
    ISSN 0010-4655
    ISSN 0010-4655
    DOI 10.1016/j.cpc.2020.107312
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  9. Article ; Online: Recollection.

    Levy, Ronald M

    Protein science : a publication of the Protein Society

    2016  Volume 25, Issue 1, Page(s) 9–11

    MeSH term(s) Drug Design ; History, 20th Century ; History, 21st Century ; Ligands ; Molecular Dynamics Simulation/history ; Nuclear Magnetic Resonance, Biomolecular/history ; Protein Folding ; Proteins/chemistry ; United States
    Chemical Substances Ligands ; Proteins
    Language English
    Publishing date 2016-01
    Publishing country United States
    Document type Historical Article ; Journal Article ; Personal Narratives
    ZDB-ID 1106283-6
    ISSN 1469-896X ; 0961-8368
    ISSN (online) 1469-896X
    ISSN 0961-8368
    DOI 10.1002/pro.2844
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    Zs.A 3407: Show issues Location:
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    Zs.MO 1: Show issues

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  10. Article ; Online: Influence of multiple-sequence-alignment depth on Potts statistical models of protein covariation.

    Haldane, Allan / Levy, Ronald M

    Physical review. E

    2019  Volume 99, Issue 3-1, Page(s) 32405

    Abstract: Potts statistical models have become a popular and promising way to analyze mutational covariation in protein multiple sequence alignments (MSAs) in order to understand protein structure, function, and fitness. But the statistical limitations of these ... ...

    Abstract Potts statistical models have become a popular and promising way to analyze mutational covariation in protein multiple sequence alignments (MSAs) in order to understand protein structure, function, and fitness. But the statistical limitations of these models, which can have millions of parameters and are fit to MSAs of only thousands or hundreds of effective sequences using a procedure known as inverse Ising inference, are incompletely understood. In this work we predict how model quality degrades as a function of the number of sequences N, sequence length L, amino-acid alphabet size q, and the degree of conservation of the MSA, in different applications of the Potts models: in "fitness" predictions of individual protein sequences, in predictions of the effects of single-point mutations, in "double mutant cycle" predictions of epistasis, and in 3D contact prediction in protein structure. We show how as MSA depth N decreases an "overfitting" effect occurs such that sequences in the training MSA have overestimated fitness, and we predict the magnitude of this effect and discuss how regularization can help correct for it, using a regularization procedure motivated by statistical analysis of the effects of finite sampling. We find that as N decreases the quality of point-mutation effect predictions degrade least, fitness and epistasis predictions degrade more rapidly, and contact predictions are most affected. However, overfitting becomes negligible for MSA depths of more than a few thousand effective sequences, as often used in practice, and regularization becomes less necessary. We discuss the implications of these results for users of Potts covariation analysis.
    MeSH term(s) Algorithms ; Amino Acid Sequence ; Computer Simulation ; Models, Genetic ; Models, Molecular ; Models, Statistical ; Mutation ; Protein Conformation ; Proteins/chemistry ; Proteins/genetics ; Proteins/metabolism ; Sequence Alignment
    Chemical Substances Proteins
    Language English
    Publishing date 2019-04-12
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2844562-4
    ISSN 2470-0053 ; 2470-0045
    ISSN (online) 2470-0053
    ISSN 2470-0045
    DOI 10.1103/PhysRevE.99.032405
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