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Chen L, Roe DR, Kochert M, Simmerling C, Miranda-Quintana RA. k-Means NANI: An Improved Clustering Algorithm for Molecular Dynamics Simulations. J Chem Theory Comput 2024; 20:5583-5597. [PMID: 38905589 DOI: 10.1021/acs.jctc.4c00308] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/23/2024]
Abstract
One of the key challenges of k-means clustering is the seed selection or the initial centroid estimation since the clustering result depends heavily on this choice. Alternatives such as k-means++ have mitigated this limitation by estimating the centroids using an empirical probability distribution. However, with high-dimensional and complex data sets such as those obtained from molecular simulation, k-means++ fails to partition the data in an optimal manner. Furthermore, stochastic elements in all flavors of k-means++ will lead to a lack of reproducibility. K-means N-Ary Natural Initiation (NANI) is presented as an alternative to tackle this challenge by using efficient n-ary comparisons to both identify high-density regions in the data and select a diverse set of initial conformations. Centroids generated from NANI are not only representative of the data and different from one another, helping k-means to partition the data accurately, but also deterministic, providing consistent cluster populations across replicates. From peptide and protein folding molecular simulations, NANI was able to create compact and well-separated clusters as well as accurately find the metastable states that agree with the literature. NANI can cluster diverse data sets and be used as a standalone tool or as part of our MDANCE clustering package.
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Affiliation(s)
- Lexin Chen
- Department of Chemistry, University of Florida, Gainesville, Florida 32611, United States
- Quantum Theory Project, University of Florida, Gainesville, Florida 32611, United States
| | - Daniel R Roe
- Laboratory of Computational Biology, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland 20892, United States
| | - Matthew Kochert
- Laufer Center for Physical & Quantitative Biology, Stony Brook University, Stony Brook, New York 11794, United States
- Department of Chemistry, Stony Brook University, Stony Brook, New York 11794, United States
| | - Carlos Simmerling
- Department of Chemistry, Stony Brook University, Stony Brook, New York 11794, United States
- Laufer Center for Physical & Quantitative Biology, Stony Brook University, Stony Brook, New York 11794, United States
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York 11794, United States
| | - Ramón Alain Miranda-Quintana
- Department of Chemistry, University of Florida, Gainesville, Florida 32611, United States
- Quantum Theory Project, University of Florida, Gainesville, Florida 32611, United States
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Han ISM, Thayer KM. Reconnaissance of Allostery via the Restoration of Native p53 DNA-Binding Domain Dynamics in Y220C Mutant p53 Tumor Suppressor Protein. ACS OMEGA 2024; 9:19837-19847. [PMID: 38737036 PMCID: PMC11079909 DOI: 10.1021/acsomega.3c08509] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Revised: 03/30/2024] [Accepted: 04/03/2024] [Indexed: 05/14/2024]
Abstract
Allosteric regulation of protein dynamics infers a long-range deliberate propagation of information via micro- and macroscale interactions. The Y220C structural mutant is one of the most frequent cancerous p53 mutants. The mutation is distally located from the DNA-binding site of the p53 DNA-binding domain yet causes changes in DNA recognition. This system presents a unique opportunity to examine the allosteric control of mutated proteins under a drug design paradigm. We focus on the key case study of p53 Y220C mutation restoration by a series of new compounds suggested to have Y220C reactivation properties in comparison to our previous findings on the restorative potential of PK11000, a compound studied extensively for reactivation in vitro and in vivo. Previously, we implemented all-atom molecular dynamics (MD) simulations and our lab's techniques of MD-Sectors and MD-Markov state models on the wild type, the Y220C mutant, and Y220C with PK11000 to characterize the effector's restorative properties in terms of conformational dynamics and hydrogen bonding. In this study, we turn to probing the effects made by docking the battery of a new but less well-tested set of aminobenzothiazole derivative compounds reported by Baud et al., which show promise of Y220C rescue. We find that while complete and precise reconstitution of p53 WT molecular dynamics may not be observed as was the case with PK11000, dispersed local reconstitution of loop dynamics provides evidence of rescuing effects by aminobenzothiazole derivative N,2-dihydroxy-3,5-diiodo-4-(1H-pyrrol-1-yl)benzamide, Effector 22, like what we observed for PK11000. Generalizable insights into the mutation and allosteric reactivation of p53 by various effectors by reconstitution of WT dynamics observed in statistical conformational ensemble analysis and network inference are discussed, considering the development of allosteric drug design rooted in first principles.
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Affiliation(s)
- In Sub M. Han
- College of Integrated Sciences, Wesleyan University, Hall-Atwater Laboratories, Middletown, Connecticut 06459-0180, United States
| | - Kelly M. Thayer
- College of Integrated Sciences, Wesleyan University, Hall-Atwater Laboratories, Middletown, Connecticut 06459-0180, United States
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Chen L, Roe DR, Kochert M, Simmerling C, Miranda-Quintana RA. k-Means NANI: an improved clustering algorithm for Molecular Dynamics simulations. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.07.583975. [PMID: 38496504 PMCID: PMC10942464 DOI: 10.1101/2024.03.07.583975] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
One of the key challenges of k-means clustering is the seed selection or the initial centroid estimation since the clustering result depends heavily on this choice. Alternatives such as k-means++ have mitigated this limitation by estimating the centroids using an empirical probability distribution. However, with high-dimensional and complex datasets such as those obtained from molecular simulation, k-means++ fails to partition the data in an optimal manner. Furthermore, stochastic elements in all flavors of k-means++ will lead to a lack of reproducibility. K-means N-Ary Natural Initiation (NANI) is presented as an alternative to tackle this challenge by using efficient n-ary comparisons to both identify high-density regions in the data and select a diverse set of initial conformations. Centroids generated from NANI are not only representative of the data and different from one another, helping k-means to partition the data accurately, but also deterministic, providing consistent cluster populations across replicates. From peptide and protein folding molecular simulations, NANI was able to create compact and well-separated clusters as well as accurately find the metastable states that agree with the literature. NANI can cluster diverse datasets and be used as a standalone tool or as part of our MDANCE clustering package.
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Affiliation(s)
- Lexin Chen
- Department of Chemistry, University of Florida, FL, USA
- Quantum Theory Project, University of Florida, FL, USA
| | - Daniel R Roe
- Laboratory of Computational Biology, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Matthew Kochert
- Laufer Center for Physical & Quantitative Biology, Stony Brook University, Stony Brook, 11794, USA
- Department of Chemistry, Stony Brook University, Stony Brook 11794, USA
| | - Carlos Simmerling
- Laufer Center for Physical & Quantitative Biology, Stony Brook University, Stony Brook, 11794, USA
- Department of Chemistry, Stony Brook University, Stony Brook 11794, USA
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook 11794, USA
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Lau B, Emani PS, Chapman J, Yao L, Lam T, Merrill P, Warrell J, Gerstein MB, Lam HYK. Insights from incorporating quantum computing into drug design workflows. Bioinformatics 2023; 39:6881079. [PMID: 36477833 PMCID: PMC9825754 DOI: 10.1093/bioinformatics/btac789] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Revised: 10/14/2022] [Accepted: 12/06/2022] [Indexed: 12/12/2022] Open
Abstract
MOTIVATION While many quantum computing (QC) methods promise theoretical advantages over classical counterparts, quantum hardware remains limited. Exploiting near-term QC in computer-aided drug design (CADD) thus requires judicious partitioning between classical and quantum calculations. RESULTS We present HypaCADD, a hybrid classical-quantum workflow for finding ligands binding to proteins, while accounting for genetic mutations. We explicitly identify modules of our drug-design workflow currently amenable to replacement by QC: non-intuitively, we identify the mutation-impact predictor as the best candidate. HypaCADD thus combines classical docking and molecular dynamics with quantum machine learning (QML) to infer the impact of mutations. We present a case study with the coronavirus (SARS-CoV-2) protease and associated mutants. We map a classical machine-learning module onto QC, using a neural network constructed from qubit-rotation gates. We have implemented this in simulation and on two commercial quantum computers. We find that the QML models can perform on par with, if not better than, classical baselines. In summary, HypaCADD offers a successful strategy for leveraging QC for CADD. AVAILABILITY AND IMPLEMENTATION Jupyter Notebooks with Python code are freely available for academic use on GitHub: https://www.github.com/hypahub/hypacadd_notebook. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
| | | | | | - Lijing Yao
- HypaHealth, HypaHub Inc., San Jose, CA 95128, USA
| | - Tarsus Lam
- HypaHealth, HypaHub Inc., San Jose, CA 95128, USA
| | - Paul Merrill
- HypaHealth, HypaHub Inc., San Jose, CA 95128, USA
| | - Jonathan Warrell
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
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Chaves OA, Lima CR, Fintelman-Rodrigues N, Sacramento CQ, de Freitas CS, Vazquez L, Temerozo JR, Rocha ME, Dias SS, Carels N, Bozza PT, Castro-Faria-Neto HC, Souza TML. Agathisflavone, a natural biflavonoid that inhibits SARS-CoV-2 replication by targeting its proteases. Int J Biol Macromol 2022; 222:1015-1026. [PMID: 36183752 PMCID: PMC9525951 DOI: 10.1016/j.ijbiomac.2022.09.204] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Revised: 09/20/2022] [Accepted: 09/22/2022] [Indexed: 11/16/2022]
Abstract
Despite the fast development of vaccines, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) still circulates through variants of concern (VoC) and escape the humoral immune response. SARS-CoV-2 has provoked over 200,000 deaths/months since its emergence and only a few antiviral drugs showed clinical benefit up to this moment. Thus, chemical structures endowed with anti-SARS-CoV-2 activity are important for continuous antiviral development and natural products represent a fruitful source of substances with biological activity. In the present study, agathisflavone (AGT), a biflavonoid from Anacardium occidentale was investigated as a candidate anti-SARS-CoV-2 compound. In silico and enzymatic analysis indicated that AGT may target mainly the viral main protease (Mpro) and not the papain-like protease (PLpro) in a non-competitive way. Cell-based assays in type II pneumocytes cell lineage (Calu-3) showed that SARS-CoV-2 is more susceptible to AGT than to apigenin (APG, monomer of AGT), in a dose-dependent manner, with an EC50 of 4.23 ± 0.21 μM and CC50 of 61.3 ± 0.1 μM and with a capacity to inhibit the level of pro-inflammatory mediator tumor necrosis factor-alpha (TNF-α). These results configure AGT as an interesting chemical scaffold for the development of novel semisynthetic antivirals against SARS-CoV-2.
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Han ISM, Abramson D, Thayer KM. Insights into Rational Design of a New Class of Allosteric Effectors with Molecular Dynamics Markov State Models and Network Theory. ACS OMEGA 2022; 7:2831-2841. [PMID: 35097279 PMCID: PMC8792916 DOI: 10.1021/acsomega.1c05624] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Accepted: 12/16/2021] [Indexed: 05/12/2023]
Abstract
The development of drugs to restore protein function has been a major advance facilitated by molecular medicine. Allosteric regulation, a phenomenon widely observed in nature, in which a molecule binds to control a distance active site, holds great promise for regulating proteins, yet how to rationally design such a molecule remains a mystery. Over the past few years, we and others have developed several techniques based on molecular dynamics (MD) simulations: MD-Markov state models to capture global conformational substates, and network theory approach utilizing the interaction energy within the protein to confer local allosteric control. We focus on the key case study of the p53 Y220C mutation restoration by PK11000, a compound experimentally shown to reactivate p53 native function in Y220C mutant present tumors. We gain insights into the mutation and allosteric reactivation of the protein, which we anticipate will be applicable to de novo design to engineer new compounds not only for this mutation, but in other macromolecular systems as well.
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De Boer D, Nguyen N, Mao J, Moore J, Sorin EJ. A Comprehensive Review of Cholinesterase Modeling and Simulation. Biomolecules 2021; 11:580. [PMID: 33920972 PMCID: PMC8071298 DOI: 10.3390/biom11040580] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2021] [Revised: 04/08/2021] [Accepted: 04/11/2021] [Indexed: 01/18/2023] Open
Abstract
The present article reviews published efforts to study acetylcholinesterase and butyrylcholinesterase structure and function using computer-based modeling and simulation techniques. Structures and models of both enzymes from various organisms, including rays, mice, and humans, are discussed to highlight key structural similarities in the active site gorges of the two enzymes, such as flexibility, binding site location, and function, as well as differences, such as gorge volume and binding site residue composition. Catalytic studies are also described, with an emphasis on the mechanism of acetylcholine hydrolysis by each enzyme and novel mutants that increase catalytic efficiency. The inhibitory activities of myriad compounds have been computationally assessed, primarily through Monte Carlo-based docking calculations and molecular dynamics simulations. Pharmaceutical compounds examined herein include FDA-approved therapeutics and their derivatives, as well as several other prescription drug derivatives. Cholinesterase interactions with both narcotics and organophosphate compounds are discussed, with the latter focusing primarily on molecular recognition studies of potential therapeutic value and on improving our understanding of the reactivation of cholinesterases that are bound to toxins. This review also explores the inhibitory properties of several other organic and biological moieties, as well as advancements in virtual screening methodologies with respect to these enzymes.
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Affiliation(s)
- Danna De Boer
- Department of Chemistry & Biochemistry, California State University, Long Beach, CA 90840, USA;
| | - Nguyet Nguyen
- Department of Chemical Engineering, California State University, Long Beach, CA 90840, USA; (N.N.); (J.M.)
| | - Jia Mao
- Department of Chemical Engineering, California State University, Long Beach, CA 90840, USA; (N.N.); (J.M.)
| | - Jessica Moore
- Department of Biomedical Engineering, California State University, Long Beach, CA 90840, USA;
| | - Eric J. Sorin
- Department of Chemistry & Biochemistry, California State University, Long Beach, CA 90840, USA;
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