1
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Faran M, Ray D, Nag S, Raucci U, Parrinello M, Bisker G. A Stochastic Landscape Approach for Protein Folding State Classification. J Chem Theory Comput 2024; 20:5428-5438. [PMID: 38924770 PMCID: PMC11238538 DOI: 10.1021/acs.jctc.4c00464] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2024] [Revised: 06/12/2024] [Accepted: 06/12/2024] [Indexed: 06/28/2024]
Abstract
Protein folding is a critical process that determines the functional state of proteins. Proper folding is essential for proteins to acquire their functional three-dimensional structures and execute their biological role, whereas misfolded proteins can lead to various diseases, including neurodegenerative disorders like Alzheimer's and Parkinson's. Therefore, a deeper understanding of protein folding is vital for understanding disease mechanisms and developing therapeutic strategies. This study introduces the Stochastic Landscape Classification (SLC), an innovative, automated, nonlearning algorithm that quantitatively analyzes protein folding dynamics. Focusing on collective variables (CVs) - low-dimensional representations of complex dynamical systems like molecular dynamics (MD) of macromolecules - the SLC approach segments the CVs into distinct macrostates, revealing the protein folding pathway explored by MD simulations. The segmentation is achieved by analyzing changes in CV trends and clustering these segments using a standard density-based spatial clustering of applications with noise (DBSCAN) scheme. Applied to the MD-based CV trajectories of Chignolin and Trp-Cage proteins, the SLC demonstrates apposite accuracy, validated by comparing standard classification metrics against ground-truth data. These metrics affirm the efficacy of the SLC in capturing intricate protein dynamics and offer a method to evaluate and select the most informative CVs. The practical application of this technique lies in its ability to provide a detailed, quantitative description of protein folding processes, with significant implications for understanding and manipulating protein behavior in industrial and pharmaceutical contexts.
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Affiliation(s)
- Michael Faran
- Department
of Biomedical Engineering, Faculty of Engineering, Tel Aviv University, Tel Aviv 69978, Israel
| | - Dhiman Ray
- Atomistic
Simulations, Italian Institute of Technology, Via Enrico Melen 83, 16152 Genova, Italy
| | - Shubhadeep Nag
- Department
of Biomedical Engineering, Faculty of Engineering, Tel Aviv University, Tel Aviv 69978, Israel
| | - Umberto Raucci
- Atomistic
Simulations, Italian Institute of Technology, Via Enrico Melen 83, 16152 Genova, Italy
| | - Michele Parrinello
- Atomistic
Simulations, Italian Institute of Technology, Via Enrico Melen 83, 16152 Genova, Italy
| | - Gili Bisker
- Department
of Biomedical Engineering, Faculty of Engineering, Tel Aviv University, Tel Aviv 69978, Israel
- The
Center for Physics and Chemistry of Living Systems, Tel Aviv University, Tel Aviv 6997801, Israel
- The
Center for Nanoscience and Nanotechnology, Tel Aviv University, Tel Aviv 6997801, Israel
- The
Center for Light-Matter Interaction, Tel
Aviv University, Tel Aviv 6997801, Israel
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2
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Zhu Y, Gu J, Zhao Z, Chan AWE, Mojica MF, Hujer AM, Bonomo RA, Haider S. Deciphering the Coevolutionary Dynamics of L2 β-Lactamases via Deep Learning. J Chem Inf Model 2024; 64:3706-3717. [PMID: 38687957 PMCID: PMC11094718 DOI: 10.1021/acs.jcim.4c00189] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2024] [Revised: 03/10/2024] [Accepted: 04/09/2024] [Indexed: 05/02/2024]
Abstract
L2 β-lactamases, serine-based class A β-lactamases expressed by Stenotrophomonas maltophilia, play a pivotal role in antimicrobial resistance (AMR). However, limited studies have been conducted on these important enzymes. To understand the coevolutionary dynamics of L2 β-lactamase, innovative computational methodologies, including adaptive sampling molecular dynamics simulations, and deep learning methods (convolutional variational autoencoders and BindSiteS-CNN) explored conformational changes and correlations within the L2 β-lactamase family together with other representative class A enzymes including SME-1 and KPC-2. This work also investigated the potential role of hydrophobic nodes and binding site residues in facilitating the functional mechanisms. The convergence of analytical approaches utilized in this effort yielded comprehensive insights into the dynamic behavior of the β-lactamases, specifically from an evolutionary standpoint. In addition, this analysis presents a promising approach for understanding how the class A β-lactamases evolve in response to environmental pressure and establishes a theoretical foundation for forthcoming endeavors in drug development aimed at combating AMR.
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Affiliation(s)
- Yu Zhu
- Pharmaceutical
and Biological Chemistry, UCL School of
Pharmacy, London WC1N 1AX, U.K.
| | - Jing Gu
- Pharmaceutical
and Biological Chemistry, UCL School of
Pharmacy, London WC1N 1AX, U.K.
| | - Zhuoran Zhao
- Pharmaceutical
and Biological Chemistry, UCL School of
Pharmacy, London WC1N 1AX, U.K.
| | - A. W. Edith Chan
- Division
of Medicine, UCL School of Pharmacy, London WC1E 6BT, U.K.
| | - Maria F. Mojica
- Department
of Molecular Biology and Microbiology, Case
Western Reserve University School of Medicine, Cleveland, Ohio 44106-5029, United
States
- Research
Service, Department of Veterans Affairs Medical Center, Louis Stokes Cleveland, Cleveland, Ohio 44106-1702, United States
- CWRU-Cleveland
VAMC Center for Antimicrobial Resistance and Epidemiology (Case VA
CARES), Cleveland, Ohio 44106-5029, United States
| | - Andrea M. Hujer
- Research
Service, Department of Veterans Affairs Medical Center, Louis Stokes Cleveland, Cleveland, Ohio 44106-1702, United States
- Department
of Medicine, Case Western Reserve University
School of Medicine, Cleveland, Ohio 44106-5029, United States
| | - Robert A. Bonomo
- Research
Service, Department of Veterans Affairs Medical Center, Louis Stokes Cleveland, Cleveland, Ohio 44106-1702, United States
- CWRU-Cleveland
VAMC Center for Antimicrobial Resistance and Epidemiology (Case VA
CARES), Cleveland, Ohio 44106-5029, United States
- Clinician
Scientist Investigator, Department of Veterans Affairs Medical Center, Louis Stokes Cleveland, Cleveland, Ohio 44106-1702, United States
- Departments
of Pharmacology, Biochemistry, and Proteomics and Bioinformatics, Case Western Reserve University School of Medicine, Cleveland, Ohio 44106-5029, United
States
- Departments
of Molecular Biology and Microbiology, Medicine, Case Western Reserve University School of Medicine, Cleveland, Ohio 44106-5029, United
States
| | - Shozeb Haider
- Pharmaceutical
and Biological Chemistry, UCL School of
Pharmacy, London WC1N 1AX, U.K.
- UCL
Centre for Advanced Research in Computing, University College London, London WC1H 9RL, U.K.
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3
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Wei Y, Chen AX, Lin Y, Wei T, Qiao B. Allosteric regulation in SARS-CoV-2 spike protein. Phys Chem Chem Phys 2024; 26:6582-6589. [PMID: 38329233 DOI: 10.1039/d4cp00106k] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/09/2024]
Abstract
Allosteric regulation is common in protein-protein interactions and is thus promising in drug design. Previous experimental and simulation work supported the presence of allosteric regulation in the SARS-CoV-2 spike protein. Here the route of allosteric regulation in SARS-CoV-2 spike protein is examined by all-atom explicit solvent molecular dynamics simulations, contrastive machine learning, and the Ohm approach. It was found that peptide binding to the polybasic cleavage sites, especially the one at the first subunit of the trimeric spike protein, activates the fluctuation of the spike protein's backbone, which eventually propagates to the receptor-binding domain on the third subunit that binds to ACE2. Remarkably, the allosteric regulation routes starting from the polybasic cleavage sites share a high fraction (39-67%) of the critical amino acids with the routes starting from the nitrogen-terminal domains, suggesting the presence of an allosteric regulation network in the spike protein. Our study paves the way for the rational design of allosteric antibody inhibitors.
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Affiliation(s)
- Yong Wei
- Department of Computer Science, High Point University, High Point, NC 27268, USA
| | - Amy X Chen
- Thomas Jefferson High School for Science and Technology, Alexandria, VA 22312, USA
| | - Yuewei Lin
- Computational Science Initiative, Brookhaven National Laboratory, Upton, NY 11973, USA
| | - Tao Wei
- Department of Chemical Engineering and Department of Biomedical Engineering, University of South Carolina, Columbia, SC 29208, USA.
| | - Baofu Qiao
- Department of Natural Sciences, Baruch College, City University of New York, New York, NY 10010, USA.
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4
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Yoo P, Bhowmik D, Mehta K, Zhang P, Liu F, Lupo Pasini M, Irle S. Deep learning workflow for the inverse design of molecules with specific optoelectronic properties. Sci Rep 2023; 13:20031. [PMID: 37973879 PMCID: PMC10654498 DOI: 10.1038/s41598-023-45385-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Accepted: 10/19/2023] [Indexed: 11/19/2023] Open
Abstract
The inverse design of novel molecules with a desirable optoelectronic property requires consideration of the vast chemical spaces associated with varying chemical composition and molecular size. First principles-based property predictions have become increasingly helpful for assisting the selection of promising candidate chemical species for subsequent experimental validation. However, a brute-force computational screening of the entire chemical space is decidedly impossible. To alleviate the computational burden and accelerate rational molecular design, we here present an iterative deep learning workflow that combines (i) the density-functional tight-binding method for dynamic generation of property training data, (ii) a graph convolutional neural network surrogate model for rapid and reliable predictions of chemical and physical properties, and (iii) a masked language model. As proof of principle, we employ our workflow in the iterative generation of novel molecules with a target energy gap between the highest occupied molecular orbital (HOMO) and the lowest unoccupied molecular orbital (LUMO).
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Affiliation(s)
- Pilsun Yoo
- Computational Sciences and Engineering Division, Oak Ridge National Laboratory, 1 Bethel Valley Road, Oak Ridge, TN, 37831, USA.
| | - Debsindhu Bhowmik
- Computational Sciences and Engineering Division, Oak Ridge National Laboratory, 1 Bethel Valley Road, Oak Ridge, TN, 37831, USA
| | - Kshitij Mehta
- Computer Science and Mathematics Division, Oak Ridge National Laboratory, 1 Bethel Valley Road, Oak Ridge, TN, 37831, USA
| | - Pei Zhang
- Computational Sciences and Engineering Division, Oak Ridge National Laboratory, 1 Bethel Valley Road, Oak Ridge, TN, 37831, USA
| | - Frank Liu
- Computer Science and Mathematics Division, Oak Ridge National Laboratory, 1 Bethel Valley Road, Oak Ridge, TN, 37831, USA
| | - Massimiliano Lupo Pasini
- Computational Sciences and Engineering Division, Oak Ridge National Laboratory, 1 Bethel Valley Road, Oak Ridge, TN, 37831, USA
| | - Stephan Irle
- Computational Sciences and Engineering Division, Oak Ridge National Laboratory, 1 Bethel Valley Road, Oak Ridge, TN, 37831, USA.
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5
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Chen SH, Weiss KL, Stanley C, Bhowmik D. Structural characterization of an intrinsically disordered protein complex using integrated small-angle neutron scattering and computing. Protein Sci 2023; 32:e4772. [PMID: 37646172 PMCID: PMC10503416 DOI: 10.1002/pro.4772] [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: 04/28/2023] [Revised: 08/22/2023] [Accepted: 08/27/2023] [Indexed: 09/01/2023]
Abstract
Characterizing structural ensembles of intrinsically disordered proteins (IDPs) and intrinsically disordered regions (IDRs) of proteins is essential for studying structure-function relationships. Due to the different neutron scattering lengths of hydrogen and deuterium, selective labeling and contrast matching in small-angle neutron scattering (SANS) becomes an effective tool to study dynamic structures of disordered systems. However, experimental timescales typically capture measurements averaged over multiple conformations, leaving complex SANS data for disentanglement. We hereby demonstrate an integrated method to elucidate the structural ensemble of a complex formed by two IDRs. We use data from both full contrast and contrast matching with residue-specific deuterium labeling SANS experiments, microsecond all-atom molecular dynamics (MD) simulations with four molecular mechanics force fields, and an autoencoder-based deep learning (DL) algorithm. From our combined approach, we show that selective deuteration provides additional information that helps characterize structural ensembles. We find that among the four force fields, a99SB-disp and CHARMM36m show the strongest agreement with SANS and NMR experiments. In addition, our DL algorithm not only complements conventional structural analysis methods but also successfully differentiates NMR and MD structures which are indistinguishable on the free energy surface. Lastly, we present an ensemble that describes experimental SANS and NMR data better than MD ensembles generated by one single force field and reveal three clusters of distinct conformations. Our results demonstrate a new integrated approach for characterizing structural ensembles of IDPs.
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Affiliation(s)
- Serena H. Chen
- Computational Sciences and Engineering DivisionOak Ridge National LaboratoryOak RidgeTennesseeUSA
| | - Kevin L. Weiss
- Neutron Scattering DivisionOak Ridge National LaboratoryOak RidgeTennesseeUSA
| | - Christopher Stanley
- Computational Sciences and Engineering DivisionOak Ridge National LaboratoryOak RidgeTennesseeUSA
| | - Debsindhu Bhowmik
- Computational Sciences and Engineering DivisionOak Ridge National LaboratoryOak RidgeTennesseeUSA
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6
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Sahakyan H, Nazaryan K, Mushegian A, Sorokina I. A Study of a Protein-Folding Machine: Transient Rotation of the Polypeptide Backbone Facilitates Rapid Folding of Protein Domains in All-Atom Molecular Dynamics Simulations. Int J Mol Sci 2023; 24:10049. [PMID: 37373197 DOI: 10.3390/ijms241210049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2023] [Revised: 06/07/2023] [Accepted: 06/09/2023] [Indexed: 06/29/2023] Open
Abstract
Molecular dynamics simulations of protein folding typically consider the polypeptide chain at equilibrium and in isolation from the cellular components. We argue that in order to understand protein folding as it occurs in vivo, it should be modeled as an active, energy-dependent process, in which the cellular protein-folding machine directly manipulates the polypeptide. We conducted all-atom molecular dynamics simulations of four protein domains, whose folding from the extended state was augmented by the application of rotational force to the C-terminal amino acid, while the movement of the N-terminal amino acid was restrained. We have shown earlier that such a simple manipulation of peptide backbone facilitated the formation of native structures in diverse α-helical peptides. In this study, the simulation protocol was modified, to apply the backbone rotation and movement restriction only for a short time at the start of simulation. This transient application of a mechanical force to the peptide is sufficient to accelerate, by at least an order of magnitude, the folding of four protein domains from different structural classes to their native or native-like conformations. Our in silico experiments show that a compact stable fold may be attained more readily when the motions of the polypeptide are biased by external forces and constraints.
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Affiliation(s)
- Harutyun Sahakyan
- Institute of Molecular Biology, Academy of Sciences of Republic of Armenia, Yerevan 0014, Armenia
| | - Karen Nazaryan
- Institute of Molecular Biology, Academy of Sciences of Republic of Armenia, Yerevan 0014, Armenia
| | - Arcady Mushegian
- Division of Molecular and Cellular Biosciences, National Science Foundation, Alexandria, VA 22314, USA
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7
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Blanchard AE, Bhowmik D, Fox Z, Gounley J, Glaser J, Akpa BS, Irle S. Adaptive language model training for molecular design. J Cheminform 2023; 15:59. [PMID: 37291633 DOI: 10.1186/s13321-023-00719-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Accepted: 04/03/2023] [Indexed: 06/10/2023] Open
Abstract
The vast size of chemical space necessitates computational approaches to automate and accelerate the design of molecular sequences to guide experimental efforts for drug discovery. Genetic algorithms provide a useful framework to incrementally generate molecules by applying mutations to known chemical structures. Recently, masked language models have been applied to automate the mutation process by leveraging large compound libraries to learn commonly occurring chemical sequences (i.e., using tokenization) and predict rearrangements (i.e., using mask prediction). Here, we consider how language models can be adapted to improve molecule generation for different optimization tasks. We use two different generation strategies for comparison, fixed and adaptive. The fixed strategy uses a pre-trained model to generate mutations; the adaptive strategy trains the language model on each new generation of molecules selected for target properties during optimization. Our results show that the adaptive strategy allows the language model to more closely fit the distribution of molecules in the population. Therefore, for enhanced fitness optimization, we suggest the use of the fixed strategy during an initial phase followed by the use of the adaptive strategy. We demonstrate the impact of adaptive training by searching for molecules that optimize both heuristic metrics, drug-likeness and synthesizability, as well as predicted protein binding affinity from a surrogate model. Our results show that the adaptive strategy provides a significant improvement in fitness optimization compared to the fixed pre-trained model, empowering the application of language models to molecular design tasks.
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Affiliation(s)
- Andrew E Blanchard
- Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA
| | - Debsindhu Bhowmik
- Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA.
| | - Zachary Fox
- Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA
| | - John Gounley
- Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA
| | - Jens Glaser
- National Center for Computational Sciences, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA
| | - Belinda S Akpa
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA
- Chemical & Biomolecular Engineering, University of Tennessee, Knoxville, TN, 37996, USA
| | - Stephan Irle
- Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA
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8
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Wu M, Liao J, Shu Z, Chen C. Enhanced sampling in explicit solvent by deep learning module in FSATOOL. J Comput Chem 2023. [PMID: 37191088 DOI: 10.1002/jcc.27132] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Revised: 04/21/2023] [Accepted: 04/27/2023] [Indexed: 05/17/2023]
Abstract
FSATOOL is an integrated molecular simulation and data analysis program. Its old molecular dynamics engine only supports simulations in vacuum or implicit solvent. In this work, we implement the well-known smooth particle mesh Ewald method for simulations in explicit solvent. The new developed engine is runnable on both CPU and GPU. All the existed analysis modules in the program are compatible with the new engine. Moreover, we also build a complete deep learning module in FSATOOL. Based on the module, we further implement two useful trajectory analysis methods: state-free reversible VAMPnets and time-lagged autoencoder. They are good at searching the collective variables related to the conformational transitions of biomolecules. In FSATOOL, these collective variables can be further used to construct a bias potential for the enhanced sampling purpose. We introduce the implementation details of the methods and present their actual performances in FSATOOL by a few enhanced sampling simulations.
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Affiliation(s)
- Mincong Wu
- Biomolecular Physics and Modeling Group, School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Jun Liao
- Biomolecular Physics and Modeling Group, School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Zirui Shu
- Biomolecular Physics and Modeling Group, School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Changjun Chen
- Biomolecular Physics and Modeling Group, School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, China
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9
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Zhao Z, Shen X, Chen S, Gu J, Wang H, Mojica MF, Samanta M, Bhowmik D, Vila AJ, Bonomo RA, Haider S. Gating interactions steer loop conformational changes in the active site of the L1 metallo-β-lactamase. eLife 2023; 12:e83928. [PMID: 36826989 PMCID: PMC9977270 DOI: 10.7554/elife.83928] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Accepted: 02/03/2023] [Indexed: 02/25/2023] Open
Abstract
β-Lactam antibiotics are the most important and widely used antibacterial agents across the world. However, the widespread dissemination of β-lactamases among pathogenic bacteria limits the efficacy of β-lactam antibiotics. This has created a major public health crisis. The use of β-lactamase inhibitors has proven useful in restoring the activity of β-lactam antibiotics, yet, effective clinically approved inhibitors against class B metallo-β-lactamases are not available. L1, a class B3 enzyme expressed by Stenotrophomonas maltophilia, is a significant contributor to the β-lactam resistance displayed by this opportunistic pathogen. Structurally, L1 is a tetramer with two elongated loops, α3-β7 and β12-α5, present around the active site of each monomer. Residues in these two loops influence substrate/inhibitor binding. To study how the conformational changes of the elongated loops affect the active site in each monomer, enhanced sampling molecular dynamics simulations were performed, Markov State Models were built, and convolutional variational autoencoder-based deep learning was applied. The key identified residues (D150a, H151, P225, Y227, and R236) were mutated and the activity of the generated L1 variants was evaluated in cell-based experiments. The results demonstrate that there are extremely significant gating interactions between α3-β7 and β12-α5 loops. Taken together, the gating interactions with the conformational changes of the key residues play an important role in the structural remodeling of the active site. These observations offer insights into the potential for novel drug development exploiting these gating interactions.
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Affiliation(s)
- Zhuoran Zhao
- Department of Pharmaceutical and Biological Chemistry, School of Pharmacy, University College LondonLondonUnited Kingdom
| | - Xiayu Shen
- Department of Pharmaceutical and Biological Chemistry, School of Pharmacy, University College LondonLondonUnited Kingdom
| | - Shuang Chen
- Department of Pharmaceutical and Biological Chemistry, School of Pharmacy, University College LondonLondonUnited Kingdom
| | - Jing Gu
- Department of Pharmaceutical and Biological Chemistry, School of Pharmacy, University College LondonLondonUnited Kingdom
| | - Haun Wang
- Department of Pharmaceutical and Biological Chemistry, School of Pharmacy, University College LondonLondonUnited Kingdom
| | - Maria F Mojica
- Department of Molecular Biology and Microbiology, Case Western Reserve University School of MedicineClevelandUnited States
- Louis Stokes Cleveland Department of Veterans Affairs Medical CenterClevelandUnited States
- CWRU-Cleveland VAMC Center for Antimicrobial Resistance and Epidemiology (Case VA CARES)ClevelandUnited States
| | - Moumita Samanta
- College of Computing, Georgia Institute of TechnologyAtlantaUnited States
| | - Debsindhu Bhowmik
- Computer Science and Engineering Division, Oak Ridge National LaboratoriesOak RidgeUnited States
| | - Alejandro J Vila
- CWRU-Cleveland VAMC Center for Antimicrobial Resistance and Epidemiology (Case VA CARES)ClevelandUnited States
- Laboratorio de Metaloproteínas, Instituto de Biología Molecular y Celular de Rosario (IBR, CONICET-UNR)RosarioArgentina
- Area Biofísica, Facultad de Ciencias Bioquímicas y Farmacéuticas, Universidad Nacional de RosarioRosarioArgentina
| | - Robert A Bonomo
- Department of Molecular Biology and Microbiology, Case Western Reserve University School of MedicineClevelandUnited States
- Louis Stokes Cleveland Department of Veterans Affairs Medical CenterClevelandUnited States
- CWRU-Cleveland VAMC Center for Antimicrobial Resistance and Epidemiology (Case VA CARES)ClevelandUnited States
- Departments of Medicine, Biochemistry, Pharmacology, and Proteomics and Bioinformatics, Case Western Reserve University School of MedicineClevelandUnited States
| | - Shozeb Haider
- Department of Pharmaceutical and Biological Chemistry, School of Pharmacy, University College LondonLondonUnited Kingdom
- UCL Centre for Advanced Research Computing, University College LondonLondonUnited Kingdom
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10
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Dommer A, Casalino L, Kearns F, Rosenfeld M, Wauer N, Ahn SH, Russo J, Oliveira S, Morris C, Bogetti A, Trifan A, Brace A, Sztain T, Clyde A, Ma H, Chennubhotla C, Lee H, Turilli M, Khalid S, Tamayo-Mendoza T, Welborn M, Christensen A, Smith DG, Qiao Z, Sirumalla SK, O'Connor M, Manby F, Anandkumar A, Hardy D, Phillips J, Stern A, Romero J, Clark D, Dorrell M, Maiden T, Huang L, McCalpin J, Woods C, Gray A, Williams M, Barker B, Rajapaksha H, Pitts R, Gibbs T, Stone J, Zuckerman DM, Mulholland AJ, Miller T, Jha S, Ramanathan A, Chong L, Amaro RE. #COVIDisAirborne: AI-enabled multiscale computational microscopy of delta SARS-CoV-2 in a respiratory aerosol. THE INTERNATIONAL JOURNAL OF HIGH PERFORMANCE COMPUTING APPLICATIONS 2023; 37:28-44. [PMID: 36647365 PMCID: PMC9527558 DOI: 10.1177/10943420221128233] [Citation(s) in RCA: 22] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2023]
Abstract
We seek to completely revise current models of airborne transmission of respiratory viruses by providing never-before-seen atomic-level views of the SARS-CoV-2 virus within a respiratory aerosol. Our work dramatically extends the capabilities of multiscale computational microscopy to address the significant gaps that exist in current experimental methods, which are limited in their ability to interrogate aerosols at the atomic/molecular level and thus obscure our understanding of airborne transmission. We demonstrate how our integrated data-driven platform provides a new way of exploring the composition, structure, and dynamics of aerosols and aerosolized viruses, while driving simulation method development along several important axes. We present a series of initial scientific discoveries for the SARS-CoV-2 Delta variant, noting that the full scientific impact of this work has yet to be realized.
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Affiliation(s)
| | | | | | | | | | | | - John Russo
- Oregon Health & Science University, Portland, OR, USA
| | | | | | | | - Anda Trifan
- Argonne National Laboratory, Lemont, IL, USA
- University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Alexander Brace
- Argonne National Laboratory, Lemont, IL, USA
- University of Chicago, Chicago, IL, USA
| | - Terra Sztain
- UC San Diego, La Jolla, CA, USA
- Freie Universitat Berlin
| | - Austin Clyde
- Argonne National Laboratory, Lemont, IL, USA
- University of Chicago, Chicago, IL, USA
| | - Heng Ma
- Argonne National Laboratory, Lemont, IL, USA
| | | | - Hyungro Lee
- Brookhaven National Lab and Rutgers University
| | | | | | | | | | | | | | - Zhuoran Qiao
- California Institute of Technology, Pasadena, CA, USA
| | | | | | | | - Anima Anandkumar
- California Institute of Technology, Pasadena, CA, USA
- NVIDIA Corp, Santa Clara, CA, USA
| | - David Hardy
- University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - James Phillips
- University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | | | | | | | | | - Tom Maiden
- Pittsburgh Supercomputing Center, Pittsburgh, PA, USA
| | - Lei Huang
- Texas Advanced Computing Center, Austin, TX, USA
| | | | | | | | | | | | | | | | | | - John Stone
- University of Illinois at Urbana-Champaign, Urbana, IL, USA
- NVIDIA Corp, Santa Clara, CA, USA
| | | | | | - Thomas Miller
- Entos, Inc., San Diego, CA, USA
- California Institute of Technology, Pasadena, CA, USA
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11
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Trifan A, Gorgun D, Salim M, Li Z, Brace A, Zvyagin M, Ma H, Clyde A, Clark D, Hardy DJ, Burnley T, Huang L, McCalpin J, Emani M, Yoo H, Yin J, Tsaris A, Subbiah V, Raza T, Liu J, Trebesch N, Wells G, Mysore V, Gibbs T, Phillips J, Chennubhotla SC, Foster I, Stevens R, Anandkumar A, Vishwanath V, Stone JE, Tajkhorshid E, A. Harris S, Ramanathan A. Intelligent resolution: Integrating Cryo-EM with AI-driven multi-resolution simulations to observe the severe acute respiratory syndrome coronavirus-2 replication-transcription machinery in action. THE INTERNATIONAL JOURNAL OF HIGH PERFORMANCE COMPUTING APPLICATIONS 2022; 36:603-623. [PMID: 38464362 PMCID: PMC10923581 DOI: 10.1177/10943420221113513] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/12/2024]
Abstract
The severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) replication transcription complex (RTC) is a multi-domain protein responsible for replicating and transcribing the viral mRNA inside a human cell. Attacking RTC function with pharmaceutical compounds is a pathway to treating COVID-19. Conventional tools, e.g., cryo-electron microscopy and all-atom molecular dynamics (AAMD), do not provide sufficiently high resolution or timescale to capture important dynamics of this molecular machine. Consequently, we develop an innovative workflow that bridges the gap between these resolutions, using mesoscale fluctuating finite element analysis (FFEA) continuum simulations and a hierarchy of AI-methods that continually learn and infer features for maintaining consistency between AAMD and FFEA simulations. We leverage a multi-site distributed workflow manager to orchestrate AI, FFEA, and AAMD jobs, providing optimal resource utilization across HPC centers. Our study provides unprecedented access to study the SARS-CoV-2 RTC machinery, while providing general capability for AI-enabled multi-resolution simulations at scale.
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Affiliation(s)
- Anda Trifan
- Argonne National Laboratory
- University of Illinois Urbana-Champaign
| | - Defne Gorgun
- Argonne National Laboratory
- University of Illinois Urbana-Champaign
| | | | | | | | | | | | - Austin Clyde
- Argonne National Laboratory
- University of Chicago
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Ian Foster
- Argonne National Laboratory
- University of Chicago
| | - Rick Stevens
- Argonne National Laboratory
- University of Chicago
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12
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Chen J, Xu E, Wei Y, Chen M, Wei T, Zheng S. Graph Clustering Analyses of Discontinuous Molecular Dynamics Simulations: Study of Lysozyme Adsorption on a Graphene Surface. LANGMUIR : THE ACS JOURNAL OF SURFACES AND COLLOIDS 2022; 38:10817-10825. [PMID: 36001808 DOI: 10.1021/acs.langmuir.2c01331] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Understanding the interfacial behaviors of biomolecules is crucial to applications in biomaterials and nanoparticle-based biosensing technologies. In this work, we utilized autoencoder-based graph clustering to analyze discontinuous molecular dynamics (DMD) simulations of lysozyme adsorption on a graphene surface. Our high-throughput DMD simulations integrated with a Go̅-like protein-surface interaction model makes it possible to explore protein adsorption at a large temporal scale with sufficient accuracy. The graph autoencoder extracts a low-dimensional feature vector from a contact map. The sequence of the extracted feature vectors is then clustered, and thus the evolution of the protein molecule structure in the absorption process is segmented into stages. Our study demonstrated that the residue-surface hydrophobic interactions and the π-π stacking interactions play key roles in the five-stage adsorption. Upon adsorption, the tertiary structure of lysozyme collapsed, and the secondary structure was also affected. The folding stages obtained by autoencoder-based graph clustering were consistent with detailed analyses of the protein structure. The combination of machine learning analysis and efficient DMD simulations developed in this work could be an important tool to study biomolecules' interfacial behaviors.
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Affiliation(s)
- Jing Chen
- College of Materials and Chemistry & Chemical Engineering, Chengdu University of Technology, Chengdu, Sichuan 610059, P. R. China
| | | | - Yong Wei
- Department of Computer Science, High Point University, High Point, North Carolina 27268, United States
| | | | - Tao Wei
- Department of Chemical Engineering, Howard University, Washington, D.C. 20059, United States
| | - Size Zheng
- College of Materials and Chemistry & Chemical Engineering, Chengdu University of Technology, Chengdu, Sichuan 610059, P. R. China
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13
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Abstract
![]()
AlphaFold has burst into our lives. A powerful algorithm
that underscores
the strength of biological sequence data and artificial intelligence
(AI). AlphaFold has appended projects and research directions. The
database it has been creating promises an untold number of applications
with vast potential impacts that are still difficult to surmise. AI
approaches can revolutionize personalized treatments and usher in
better-informed clinical trials. They promise to make giant leaps
toward reshaping and revamping drug discovery strategies, selecting
and prioritizing combinations of drug targets. Here, we briefly overview
AI in structural biology, including in molecular dynamics simulations
and prediction of microbiota–human protein–protein interactions.
We highlight the advancements accomplished by the deep-learning-powered
AlphaFold in protein structure prediction and their powerful impact
on the life sciences. At the same time, AlphaFold does not resolve
the decades-long protein folding challenge, nor does it identify the
folding pathways. The models that AlphaFold provides do not capture
conformational mechanisms like frustration and allostery, which are
rooted in ensembles, and controlled by their dynamic distributions.
Allostery and signaling are properties of populations. AlphaFold also
does not generate ensembles of intrinsically disordered proteins and
regions, instead describing them by their low structural probabilities.
Since AlphaFold generates single ranked structures, rather than conformational
ensembles, it cannot elucidate the mechanisms of allosteric activating
driver hotspot mutations nor of allosteric drug resistance. However,
by capturing key features, deep learning techniques can use the single
predicted conformation as the basis for generating a diverse ensemble.
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Affiliation(s)
- Ruth Nussinov
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research, Frederick, Maryland 21702, United States.,Department of Human Molecular Genetics and Biochemistry, Sackler School of Medicine, Tel Aviv University, Tel Aviv 69978, Israel
| | - Mingzhen Zhang
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research, Frederick, Maryland 21702, United States
| | - Yonglan Liu
- Cancer Innovation Laboratory, National Cancer Institute, Frederick, Maryland 21702, United States
| | - Hyunbum Jang
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research, Frederick, Maryland 21702, United States
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14
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Bhatia H, Thiagarajan JJ, Anirudh R, TS J, Oppelstrup T, Ingólfsson HI, Lightstone F, Bremer PT. A Biology-Informed Similarity Metric for Simulated Patches of Human Cell Membrane. MACHINE LEARNING: SCIENCE AND TECHNOLOGY 2022. [DOI: 10.1088/2632-2153/ac8523] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022] Open
Abstract
Abstract
Complex scientific inquiries rely increasingly upon large and autonomous multiscale simulation campaigns, which fundamentally require similarity metrics to quantify “sufficient” changes among data and/or configurations. However, subject matter experts are often unable to articulate similarity precisely or in terms of well-formulated definitions, especially when new hypotheses are to be explored, making it challenging to design a meaningful metric. Furthermore, the key to practical usefulness of such metrics to enable autonomous simulations lies in in situ inference, which requires generalization to possibly substantial distributional shifts in unseen, future data. Here, we address these challenges in a cancer biology application and develop a meaningful similarity metric for “patches”— regions of simulated human cell membrane that express interactions between certain proteins of interest and relevant lipids. In the absence of well-defined conditions for similarity, we leverage several biology-informed notions about data and the underlying simulations to impose inductive biases on our metric learning framework, resulting in a suitable similarity metric that also generalizes well to significant distributional shifts encountered during the deployment. We combine these intuitions to organize the learned embedding space in a multiscale manner, which makes the metric robust to incomplete and even contradictory intuitions. Our approach delivers a metric that not only performs well on the conditions used for its development and other relevant criteria, but also learns key spatiotemporal relationships from statistical mechanics without ever being exposed to any such information during training.
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15
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Gupta A, Dey S, Hicks A, Zhou HX. Artificial intelligence guided conformational mining of intrinsically disordered proteins. Commun Biol 2022; 5:610. [PMID: 35725761 PMCID: PMC9209487 DOI: 10.1038/s42003-022-03562-y] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Accepted: 06/07/2022] [Indexed: 12/29/2022] Open
Abstract
Artificial intelligence recently achieved the breakthrough of predicting the three-dimensional structures of proteins. The next frontier is presented by intrinsically disordered proteins (IDPs), which, representing 30% to 50% of proteomes, readily access vast conformational space. Molecular dynamics (MD) simulations are promising in sampling IDP conformations, but only at extremely high computational cost. Here, we developed generative autoencoders that learn from short MD simulations and generate full conformational ensembles. An encoder represents IDP conformations as vectors in a reduced-dimensional latent space. The mean vector and covariance matrix of the training dataset are calculated to define a multivariate Gaussian distribution, from which vectors are sampled and fed to a decoder to generate new conformations. The ensembles of generated conformations cover those sampled by long MD simulations and are validated by small-angle X-ray scattering profile and NMR chemical shifts. This work illustrates the vast potential of artificial intelligence in conformational mining of IDPs.
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Affiliation(s)
- Aayush Gupta
- grid.185648.60000 0001 2175 0319Department of Chemistry, University of Illinois at Chicago, Chicago, IL 60607 USA
| | - Souvik Dey
- grid.185648.60000 0001 2175 0319Department of Chemistry, University of Illinois at Chicago, Chicago, IL 60607 USA
| | - Alan Hicks
- grid.185648.60000 0001 2175 0319Department of Chemistry, University of Illinois at Chicago, Chicago, IL 60607 USA
| | - Huan-Xiang Zhou
- grid.185648.60000 0001 2175 0319Department of Chemistry, University of Illinois at Chicago, Chicago, IL 60607 USA ,grid.185648.60000 0001 2175 0319Department of Physics, University of Illinois at Chicago, Chicago, IL 60607 USA
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16
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Russo JD, Zhang S, Leung JMG, Bogetti AT, Thompson JP, DeGrave AJ, Torrillo PA, Pratt AJ, Wong KF, Xia J, Copperman J, Adelman JL, Zwier MC, LeBard DN, Zuckerman DM, Chong LT. WESTPA 2.0: High-Performance Upgrades for Weighted Ensemble Simulations and Analysis of Longer-Timescale Applications. J Chem Theory Comput 2022; 18:638-649. [PMID: 35043623 DOI: 10.1021/acs.jctc.1c01154] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
The weighted ensemble (WE) family of methods is one of several statistical mechanics-based path sampling strategies that can provide estimates of key observables (rate constants and pathways) using a fraction of the time required by direct simulation methods such as molecular dynamics or discrete-state stochastic algorithms. WE methods oversee numerous parallel trajectories using intermittent overhead operations at fixed time intervals, enabling facile interoperability with any dynamics engine. Here, we report on the major upgrades to the WESTPA software package, an open-source, high-performance framework that implements both basic and recently developed WE methods. These upgrades offer substantial improvements over traditional WE methods. The key features of the new WESTPA 2.0 software enhance the efficiency and ease of use: an adaptive binning scheme for more efficient surmounting of large free energy barriers, streamlined handling of large simulation data sets, exponentially improved analysis of kinetics, and developer-friendly tools for creating new WE methods, including a Python API and resampler module for implementing both binned and "binless" WE strategies.
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Affiliation(s)
- John D Russo
- Department of Biomedical Engineering, Oregon Health and Science University, Portland, Oregon 97239-3098, United States
| | - She Zhang
- OpenEye Scientific, Santa Fe, New Mexico 87508, United States
| | - Jeremy M G Leung
- Department of Chemistry, University of Pittsburgh, Pittsburgh, Pennsylvania 15260, United States
| | - Anthony T Bogetti
- Department of Chemistry, University of Pittsburgh, Pittsburgh, Pennsylvania 15260, United States
| | - Jeff P Thompson
- OpenEye Scientific, Santa Fe, New Mexico 87508, United States
| | - Alex J DeGrave
- Department of Chemistry, University of Pittsburgh, Pittsburgh, Pennsylvania 15260, United States
| | - Paul A Torrillo
- Department of Chemistry, University of Pittsburgh, Pittsburgh, Pennsylvania 15260, United States
| | - A J Pratt
- Department of Chemistry, University of Pittsburgh, Pittsburgh, Pennsylvania 15260, United States
| | - Kim F Wong
- Department of Chemistry, University of Pittsburgh, Pittsburgh, Pennsylvania 15260, United States
| | - Junchao Xia
- OpenEye Scientific, Santa Fe, New Mexico 87508, United States
| | - Jeremy Copperman
- Department of Biomedical Engineering, Oregon Health and Science University, Portland, Oregon 97239-3098, United States
| | - Joshua L Adelman
- Department of Biological Sciences, University of Pittsburgh, Pittsburgh, Pennsylvania 15260, United States
| | - Matthew C Zwier
- Department of Chemistry, Drake University, Des Moines, Iowa 50311-4505, United States
| | - David N LeBard
- OpenEye Scientific, Santa Fe, New Mexico 87508, United States
| | - Daniel M Zuckerman
- Department of Biomedical Engineering, Oregon Health and Science University, Portland, Oregon 97239-3098, United States
| | - Lillian T Chong
- Department of Chemistry, University of Pittsburgh, Pittsburgh, Pennsylvania 15260, United States
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17
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Bhati AP, Wan S, Alfè D, Clyde AR, Bode M, Tan L, Titov M, Merzky A, Turilli M, Jha S, Highfield RR, Rocchia W, Scafuri N, Succi S, Kranzlmüller D, Mathias G, Wifling D, Donon Y, Di Meglio A, Vallecorsa S, Ma H, Trifan A, Ramanathan A, Brettin T, Partin A, Xia F, Duan X, Stevens R, Coveney PV. Pandemic drugs at pandemic speed: infrastructure for accelerating COVID-19 drug discovery with hybrid machine learning- and physics-based simulations on high-performance computers. Interface Focus 2021; 11:20210018. [PMID: 34956592 PMCID: PMC8504892 DOI: 10.1098/rsfs.2021.0018] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/07/2021] [Indexed: 12/13/2022] Open
Abstract
The race to meet the challenges of the global pandemic has served as a reminder that the existing drug discovery process is expensive, inefficient and slow. There is a major bottleneck screening the vast number of potential small molecules to shortlist lead compounds for antiviral drug development. New opportunities to accelerate drug discovery lie at the interface between machine learning methods, in this case, developed for linear accelerators, and physics-based methods. The two in silico methods, each have their own advantages and limitations which, interestingly, complement each other. Here, we present an innovative infrastructural development that combines both approaches to accelerate drug discovery. The scale of the potential resulting workflow is such that it is dependent on supercomputing to achieve extremely high throughput. We have demonstrated the viability of this workflow for the study of inhibitors for four COVID-19 target proteins and our ability to perform the required large-scale calculations to identify lead antiviral compounds through repurposing on a variety of supercomputers.
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Affiliation(s)
- Agastya P. Bhati
- Centre for Computational Science, University College London, Gordon Street, London WC1H 0AJ, UK
| | - Shunzhou Wan
- Centre for Computational Science, University College London, Gordon Street, London WC1H 0AJ, UK
| | - Dario Alfè
- Department of Earth Sciences, London Centre for Nanotechnology and Thomas Young Centre at University College London, University College London, Gower Street, London WC1E 6BT, UK
- Dipartimento di Fisica Ettore Pancini, Università di Napoli Federico II, Monte Sant'Angelo, Napoli 80126, Italy
| | - Austin R. Clyde
- Department of Computer Science, University of Chicago, Chicago, IL, USA
| | - Mathis Bode
- Institute for Combustion Technology, RWTH Aachen University, Aachen 52056, Germany
| | - Li Tan
- Brookhaven National Laboratory, Upton, NY 11973, USA
| | - Mikhail Titov
- Department of Electrical and Computer Engineering, Rutgers, the State University of New Jersey, Piscataway, NJ 08854, USA
| | - Andre Merzky
- Department of Electrical and Computer Engineering, Rutgers, the State University of New Jersey, Piscataway, NJ 08854, USA
| | - Matteo Turilli
- Department of Electrical and Computer Engineering, Rutgers, the State University of New Jersey, Piscataway, NJ 08854, USA
| | - Shantenu Jha
- Brookhaven National Laboratory, Upton, NY 11973, USA
- Department of Electrical and Computer Engineering, Rutgers, the State University of New Jersey, Piscataway, NJ 08854, USA
| | | | - Walter Rocchia
- Concept Lab, Italian Institute of Technology, Via Melen, Genova, Italy
| | - Nicola Scafuri
- Concept Lab, Italian Institute of Technology, Via Melen, Genova, Italy
| | - Sauro Succi
- Center for Life Nanosciences at La Sapienza, Italian Institute of Technology, viale Regina Elena, Roma, Italy
| | - Dieter Kranzlmüller
- Leibniz Supercomputing Centre (LRZ) of the Bavarian Academy of Sciences and Humanities, Boltzmannstrasse 1, Garching bei München 85748, Germany
| | - Gerald Mathias
- Leibniz Supercomputing Centre (LRZ) of the Bavarian Academy of Sciences and Humanities, Boltzmannstrasse 1, Garching bei München 85748, Germany
| | - David Wifling
- Leibniz Supercomputing Centre (LRZ) of the Bavarian Academy of Sciences and Humanities, Boltzmannstrasse 1, Garching bei München 85748, Germany
| | | | | | | | - Heng Ma
- Data Science and Learning Division, Argonne National Laboratory, Lemont, IL 60439, USA
| | - Anda Trifan
- Data Science and Learning Division, Argonne National Laboratory, Lemont, IL 60439, USA
| | - Arvind Ramanathan
- Data Science and Learning Division, Argonne National Laboratory, Lemont, IL 60439, USA
| | - Tom Brettin
- Computing, Environment and Life Sciences Directorate, Argonne National Laboratory, Lemont, IL 60439, USA
| | - Alexander Partin
- Data Science and Learning Division, Argonne National Laboratory, Lemont, IL 60439, USA
| | - Fangfang Xia
- Data Science and Learning Division, Argonne National Laboratory, Lemont, IL 60439, USA
| | - Xiaotan Duan
- Department of Computer Science, University of Chicago, Chicago, IL, USA
| | - Rick Stevens
- Computing, Environment and Life Sciences Directorate, Argonne National Laboratory, Lemont, IL 60439, USA
| | - Peter V. Coveney
- Centre for Computational Science, University College London, Gordon Street, London WC1H 0AJ, UK
- Institute for Informatics, University of Amsterdam, Science Park 904, Amsterdam 1098 XH, The Netherlands
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18
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Ghorbani M, Prasad S, Klauda JB, Brooks BR. Variational embedding of protein folding simulations using Gaussian mixture variational autoencoders. J Chem Phys 2021; 155:194108. [PMID: 34800961 DOI: 10.1063/5.0069708] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Conformational sampling of biomolecules using molecular dynamics simulations often produces a large amount of high dimensional data that makes it difficult to interpret using conventional analysis techniques. Dimensionality reduction methods are thus required to extract useful and relevant information. Here, we devise a machine learning method, Gaussian mixture variational autoencoder (GMVAE), that can simultaneously perform dimensionality reduction and clustering of biomolecular conformations in an unsupervised way. We show that GMVAE can learn a reduced representation of the free energy landscape of protein folding with highly separated clusters that correspond to the metastable states during folding. Since GMVAE uses a mixture of Gaussians as its prior, it can directly acknowledge the multi-basin nature of the protein folding free energy landscape. To make the model end-to-end differentiable, we use a Gumbel-softmax distribution. We test the model on three long-timescale protein folding trajectories and show that GMVAE embedding resembles the folding funnel with folded states down the funnel and unfolded states outside the funnel path. Additionally, we show that the latent space of GMVAE can be used for kinetic analysis and Markov state models built on this embedding produce folding and unfolding timescales that are in close agreement with other rigorous dynamical embeddings such as time independent component analysis.
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Affiliation(s)
- Mahdi Ghorbani
- Laboratory of Computational Biology, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland 20824, USA
| | - Samarjeet Prasad
- Laboratory of Computational Biology, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland 20824, USA
| | - Jeffery B Klauda
- Department of Chemical and Biomolecular Engineering, University of Maryland, College Park, Maryland 20742, USA
| | - Bernard R Brooks
- Laboratory of Computational Biology, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland 20824, USA
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19
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Bell DR, Domeniconi G, Yang CC, Zhou R, Zhang L, Cong G. Dynamics-Based Peptide-MHC Binding Optimization by a Convolutional Variational Autoencoder: A Use-Case Model for CASTELO. J Chem Theory Comput 2021; 17:7962-7971. [PMID: 34793168 DOI: 10.1021/acs.jctc.1c00870] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
An unsolved challenge in the development of antigen-specific immunotherapies is determining the optimal antigens to target. Comprehension of antigen-major histocompatibility complex (MHC) binding is paramount toward achieving this goal. Here, we apply CASTELO, a combined machine learning-molecular dynamics (ML-MD) approach, to identify per-residue antigen binding contributions and then design novel antigens of increased MHC-II binding affinity for a type 1 diabetes-implicated system. We build upon a small-molecule lead optimization algorithm by training a convolutional variational autoencoder (CVAE) on MD trajectories of 48 different systems across four antigens and four HLA serotypes. We develop several new machine learning metrics including a structure-based anchor residue classification model as well as cluster comparison scores. ML-MD predictions agree well with experimental binding results and free energy perturbation-predicted binding affinities. Moreover, ML-MD metrics are independent of traditional MD stability metrics such as contact area and root-mean-square fluctuations (RMSF), which do not reflect binding affinity data. Our work supports the role of structure-based deep learning techniques in antigen-specific immunotherapy design.
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Affiliation(s)
- David R Bell
- IBM Thomas J. Watson Research Center, Yorktown Heights, New York 10598, United States.,Advanced Biomedical Computational Science, Frederick National Laboratory for Cancer Research, Frederick, Maryland 21701, United States
| | - Giacomo Domeniconi
- IBM Thomas J. Watson Research Center, Yorktown Heights, New York 10598, United States
| | - Chih-Chieh Yang
- IBM Thomas J. Watson Research Center, Yorktown Heights, New York 10598, United States
| | - Ruhong Zhou
- IBM Thomas J. Watson Research Center, Yorktown Heights, New York 10598, United States.,Zhejiang University, 688 Yuhangtang Road, Hangzhou 310027, China
| | - Leili Zhang
- IBM Thomas J. Watson Research Center, Yorktown Heights, New York 10598, United States
| | - Guojing Cong
- IBM Thomas J. Watson Research Center, Yorktown Heights, New York 10598, United States.,Oak Ridge National Laboratory, 1 Bethel Valley Road, Oak Ridge, Tennessee 37830, United States
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20
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Dommer A, Casalino L, Kearns F, Rosenfeld M, Wauer N, Ahn SH, Russo J, Oliveira S, Morris C, Bogetti A, Trifan A, Brace A, Sztain T, Clyde A, Ma H, Chennubhotla C, Lee H, Turilli M, Khalid S, Tamayo-Mendoza T, Welborn M, Christensen A, Smith DGA, Qiao Z, Sirumalla SK, O'Connor M, Manby F, Anandkumar A, Hardy D, Phillips J, Stern A, Romero J, Clark D, Dorrell M, Maiden T, Huang L, McCalpin J, Woods C, Gray A, Williams M, Barker B, Rajapaksha H, Pitts R, Gibbs T, Stone J, Zuckerman D, Mulholland A, Miller T, Jha S, Ramanathan A, Chong L, Amaro R. #COVIDisAirborne: AI-Enabled Multiscale Computational Microscopy of Delta SARS-CoV-2 in a Respiratory Aerosol. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2021:2021.11.12.468428. [PMID: 34816263 PMCID: PMC8609898 DOI: 10.1101/2021.11.12.468428] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/12/2023]
Abstract
We seek to completely revise current models of airborne transmission of respiratory viruses by providing never-before-seen atomic-level views of the SARS-CoV-2 virus within a respiratory aerosol. Our work dramatically extends the capabilities of multiscale computational microscopy to address the significant gaps that exist in current experimental methods, which are limited in their ability to interrogate aerosols at the atomic/molecular level and thus ob-scure our understanding of airborne transmission. We demonstrate how our integrated data-driven platform provides a new way of exploring the composition, structure, and dynamics of aerosols and aerosolized viruses, while driving simulation method development along several important axes. We present a series of initial scientific discoveries for the SARS-CoV-2 Delta variant, noting that the full scientific impact of this work has yet to be realized. ACM REFERENCE FORMAT Abigail Dommer 1† , Lorenzo Casalino 1† , Fiona Kearns 1† , Mia Rosenfeld 1 , Nicholas Wauer 1 , Surl-Hee Ahn 1 , John Russo, 2 Sofia Oliveira 3 , Clare Morris 1 , AnthonyBogetti 4 , AndaTrifan 5,6 , Alexander Brace 5,7 , TerraSztain 1,8 , Austin Clyde 5,7 , Heng Ma 5 , Chakra Chennubhotla 4 , Hyungro Lee 9 , Matteo Turilli 9 , Syma Khalid 10 , Teresa Tamayo-Mendoza 11 , Matthew Welborn 11 , Anders Christensen 11 , Daniel G. A. Smith 11 , Zhuoran Qiao 12 , Sai Krishna Sirumalla 11 , Michael O'Connor 11 , Frederick Manby 11 , Anima Anandkumar 12,13 , David Hardy 6 , James Phillips 6 , Abraham Stern 13 , Josh Romero 13 , David Clark 13 , Mitchell Dorrell 14 , Tom Maiden 14 , Lei Huang 15 , John McCalpin 15 , Christo- pherWoods 3 , Alan Gray 13 , MattWilliams 3 , Bryan Barker 16 , HarindaRajapaksha 16 , Richard Pitts 16 , Tom Gibbs 13 , John Stone 6 , Daniel Zuckerman 2 *, Adrian Mulholland 3 *, Thomas MillerIII 11,12 *, ShantenuJha 9 *, Arvind Ramanathan 5 *, Lillian Chong 4 *, Rommie Amaro 1 *. 2021. #COVIDisAirborne: AI-Enabled Multiscale Computational Microscopy ofDeltaSARS-CoV-2 in a Respiratory Aerosol. In Supercomputing '21: International Conference for High Perfor-mance Computing, Networking, Storage, and Analysis . ACM, New York, NY, USA, 14 pages. https://doi.org/finalDOI.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | - Anda Trifan
- Argonne National Laboratory
- University of Illinois at Urbana-Champaign
| | | | | | - Austin Clyde
- Argonne National Laboratory
- University of Chicago
| | | | | | - Hyungro Lee
- Brookhaven National Lab & Rutgers University
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - John Stone
- University of Illinois at Urbana-Champaign
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21
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Bux K, Shen X, Tariq M, Yin J, Moin ST, Bhowmik D, Haider S. Inter-Subunit Dynamics Controls Tunnel Formation During the Oxygenation Process in Hemocyanin Hexamers. Front Mol Biosci 2021; 8:710623. [PMID: 34604302 PMCID: PMC8479113 DOI: 10.3389/fmolb.2021.710623] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2021] [Accepted: 08/23/2021] [Indexed: 11/13/2022] Open
Abstract
Hemocyanin from horseshoe crab in its active form is a homo-hexameric protein. It exists in open and closed conformations when transitioning between deoxygenated and oxygenated states. Here, we present a detailed dynamic atomistic investigation of the oxygenated and deoxygenated states of the hexameric hemocyanin using explicit solvent molecular dynamics simulations. We focus on the variation in solvent cavities and the formation of tunnels in the two conformational states. By employing principal component analysis and CVAE-based deep learning, we are able to differentiate between the dynamics of the deoxy- and oxygenated states of hemocyanin. Finally, our results identify the deoxygenated open conformation, which adopts a stable, closed conformation after the oxygenation process.
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Affiliation(s)
- Khair Bux
- Third World Center for Science and Technology, H.E.J. Research Institute of Chemistry, International Center for Chemical and Biological Sciences, University of Karachi, Karachi, Pakistan
| | - Xiayu Shen
- UCL School of Pharmacy, London, United Kingdom
| | - Muhammad Tariq
- Third World Center for Science and Technology, H.E.J. Research Institute of Chemistry, International Center for Chemical and Biological Sciences, University of Karachi, Karachi, Pakistan
| | - Junqi Yin
- Oak Ridge National Laboratory, Center for Computational Sciences, Oak Ridge, TN, United States
| | - Syed Tarique Moin
- Third World Center for Science and Technology, H.E.J. Research Institute of Chemistry, International Center for Chemical and Biological Sciences, University of Karachi, Karachi, Pakistan
| | - Debsindhu Bhowmik
- Oak Ridge National Laboratory, Computer Sciences and Engineering Division, Oak Ridge, TN, United States
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22
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Olehnovics E, Yin J, Pérez A, De Fabritiis G, Bonomo RA, Bhowmik D, Haider S. The Role of Hydrophobic Nodes in the Dynamics of Class A β-Lactamases. Front Microbiol 2021; 12:720991. [PMID: 34621251 PMCID: PMC8490755 DOI: 10.3389/fmicb.2021.720991] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2021] [Accepted: 08/09/2021] [Indexed: 11/16/2022] Open
Abstract
Class A β-lactamases are known for being able to rapidly gain broad spectrum catalytic efficiency against most β-lactamase inhibitor combinations as a result of elusively minor point mutations. The evolution in class A β-lactamases occurs through optimisation of their dynamic phenotypes at different timescales. At long-timescales, certain conformations are more catalytically permissive than others while at the short timescales, fine-grained optimisation of free energy barriers can improve efficiency in ligand processing by the active site. Free energy barriers, which define all coordinated movements, depend on the flexibility of the secondary structural elements. The most highly conserved residues in class A β-lactamases are hydrophobic nodes that stabilize the core. To assess how the stable hydrophobic core is linked to the structural dynamics of the active site, we carried out adaptively sampled molecular dynamics (MD) simulations in four representative class A β-lactamases (KPC-2, SME-1, TEM-1, and SHV-1). Using Markov State Models (MSM) and unsupervised deep learning, we show that the dynamics of the hydrophobic nodes is used as a metastable relay of kinetic information within the core and is coupled with the catalytically permissive conformation of the active site environment. Our results collectively demonstrate that the class A enzymes described here, share several important dynamic similarities and the hydrophobic nodes comprise of an informative set of dynamic variables in representative class A β-lactamases.
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Affiliation(s)
- Edgar Olehnovics
- Pharmaceutical and Biological Chemistry, University College London School of Pharmacy, London, United Kingdom
| | - Junqi Yin
- Oak Ridge National Laboratory, National Center for Computational Sciences, Oak Ridge, TN, United States
| | - Adrià Pérez
- Computational Science Laboratory, Barcelona Biomedical Research Park, Universitat Pompeu Fabra, Barcelona, Spain
| | - Gianni De Fabritiis
- Computational Science Laboratory, Barcelona Biomedical Research Park, Universitat Pompeu Fabra, Barcelona, Spain
- Institució Catalana de Recerca i Estudis Avançats, Barcelona, Spain
| | - Robert A. Bonomo
- Department of Molecular Biology and Microbiology, Case Western Reserve University, Cleveland, OH, United States
- Department of Medicine, School of Medicine, Case Western Reserve University, Cleveland, OH, United States
- Department of Biochemistry, Case Western Reserve University, Cleveland, OH, United States
- Department of Pharmacology, Case Western Reserve University, Cleveland, OH, United States
- Department of Proteomics and Bioinformatics, Case Western Reserve University, Cleveland, OH, United States
- CWRU-Cleveland VAMC Center for Antimicrobial Resistance and Epidemiology (Case VA CARES), Cleveland, OH, United States
- Veterans Affairs Northeast Ohio Healthcare System, Research Service, Cleveland, OH, United States
| | - Debsindhu Bhowmik
- Computer Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, TN, United States
| | - Shozeb Haider
- Pharmaceutical and Biological Chemistry, University College London School of Pharmacy, London, United Kingdom
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23
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Casalino L, Dommer AC, Gaieb Z, Barros EP, Sztain T, Ahn SH, Trifan A, Brace A, Bogetti AT, Clyde A, Ma H, Lee H, Turilli M, Khalid S, Chong LT, Simmerling C, Hardy DJ, Maia JD, Phillips JC, Kurth T, Stern AC, Huang L, McCalpin JD, Tatineni M, Gibbs T, Stone JE, Jha S, Ramanathan A, Amaro RE. AI-driven multiscale simulations illuminate mechanisms of SARS-CoV-2 spike dynamics. THE INTERNATIONAL JOURNAL OF HIGH PERFORMANCE COMPUTING APPLICATIONS 2021; 35:432-451. [PMID: 38603008 PMCID: PMC8064023 DOI: 10.1177/10943420211006452] [Citation(s) in RCA: 60] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/02/2023]
Abstract
We develop a generalizable AI-driven workflow that leverages heterogeneous HPC resources to explore the time-dependent dynamics of molecular systems. We use this workflow to investigate the mechanisms of infectivity of the SARS-CoV-2 spike protein, the main viral infection machinery. Our workflow enables more efficient investigation of spike dynamics in a variety of complex environments, including within a complete SARS-CoV-2 viral envelope simulation, which contains 305 million atoms and shows strong scaling on ORNL Summit using NAMD. We present several novel scientific discoveries, including the elucidation of the spike's full glycan shield, the role of spike glycans in modulating the infectivity of the virus, and the characterization of the flexible interactions between the spike and the human ACE2 receptor. We also demonstrate how AI can accelerate conformational sampling across different systems and pave the way for the future application of such methods to additional studies in SARS-CoV-2 and other molecular systems.
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Affiliation(s)
- Lorenzo Casalino
- University of California San Diego, La Jolla, CA, USA
- Authors with symbol indicate equal contribution
| | - Abigail C Dommer
- University of California San Diego, La Jolla, CA, USA
- Authors with symbol indicate equal contribution
| | - Zied Gaieb
- University of California San Diego, La Jolla, CA, USA
- Authors with symbol indicate equal contribution
| | | | - Terra Sztain
- University of California San Diego, La Jolla, CA, USA
| | - Surl-Hee Ahn
- University of California San Diego, La Jolla, CA, USA
| | - Anda Trifan
- Argonne National Lab, Lemont, IL, USA
- University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | | | | | - Austin Clyde
- Argonne National Lab, Lemont, IL, USA
- University of Chicago, Chicago, IL, USA
| | - Heng Ma
- Argonne National Lab, Lemont, IL, USA
| | | | | | | | | | | | - David J Hardy
- University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Julio Dc Maia
- University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | | | | | | | - Lei Huang
- Texas Advanced Computing Center, Austin, TX, USA
| | | | | | - Tom Gibbs
- NVIDIA Corporation, Santa Clara, CA, USA
| | - John E Stone
- University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Shantenu Jha
- Rutgers University, Piscataway, NJ, USA
- Brookhaven National Lab, Upton, NY, USA
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24
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Bogetti AT, Presti MF, Loh SN, Chong LT. The Next Frontier for Designing Switchable Proteins: Rational Enhancement of Kinetics. J Phys Chem B 2021; 125:9069-9077. [PMID: 34324338 DOI: 10.1021/acs.jpcb.1c04082] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Designing proteins that can switch between active (ON) and inactive (OFF) conformations in response to signals such as ligand binding and incident light has been a tantalizing endeavor in protein engineering for over a decade. While such designs have yielded novel biosensors, therapeutic agents, and smart biomaterials, the response times (times for switching ON and OFF) of many switches have been too slow to be of practical use. Among the defining properties of such switches, the kinetics of switching has been the most challenging to optimize. This is largely due to the difficulty of characterizing the structures of transient states, which are required for manipulating the height of the effective free energy barrier between the ON and OFF states. We share our perspective of the most promising new experimental and computational strategies over the past several years for tackling this next frontier for designing switchable proteins.
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Affiliation(s)
- Anthony T Bogetti
- Department of Chemistry, University of Pittsburgh, Pittsburgh, Pennsylvania 15260, United States
| | - Maria F Presti
- Department of Biochemistry and Molecular Biology, State University of New York Upstate Medical University, Syracuse, New York 13210, United States
| | - Stewart N Loh
- Department of Biochemistry and Molecular Biology, State University of New York Upstate Medical University, Syracuse, New York 13210, United States
| | - Lillian T Chong
- Department of Chemistry, University of Pittsburgh, Pittsburgh, Pennsylvania 15260, United States
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25
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Cho E, Rosa M, Anjum R, Mehmood S, Soban M, Mujtaba M, Bux K, Moin ST, Tanweer M, Dantu S, Pandini A, Yin J, Ma H, Ramanathan A, Islam B, Mey ASJ, Bhowmik D, Haider S. Dynamic Profiling of β-Coronavirus 3CL M pro Protease Ligand-Binding Sites. J Chem Inf Model 2021; 61:3058-3073. [PMID: 34124899 PMCID: PMC8230960 DOI: 10.1021/acs.jcim.1c00449] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Indexed: 01/11/2023]
Abstract
β-coronavirus (CoVs) alone has been responsible for three major global outbreaks in the 21st century. The current crisis has led to an urgent requirement to develop therapeutics. Even though a number of vaccines are available, alternative strategies targeting essential viral components are required as a backup against the emergence of lethal viral variants. One such target is the main protease (Mpro) that plays an indispensable role in viral replication. The availability of over 270 Mpro X-ray structures in complex with inhibitors provides unique insights into ligand-protein interactions. Herein, we provide a comprehensive comparison of all nonredundant ligand-binding sites available for SARS-CoV2, SARS-CoV, and MERS-CoV Mpro. Extensive adaptive sampling has been used to investigate structural conservation of ligand-binding sites using Markov state models (MSMs) and compare conformational dynamics employing convolutional variational auto-encoder-based deep learning. Our results indicate that not all ligand-binding sites are dynamically conserved despite high sequence and structural conservation across β-CoV homologs. This highlights the complexity in targeting all three Mpro enzymes with a single pan inhibitor.
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Affiliation(s)
- Eunice Cho
- UCL
School of Pharmacy, London WC1N 1AX, U.K.
| | | | - Ruhi Anjum
- Department
of Biochemistry, Aligarh Muslim University, Aligarh, Uttar Pradesh 202002, India
| | - Saman Mehmood
- Department
of Zoology, Aligarh Muslim University, Aligarh, Uttar Pradesh 202002, India
| | - Mariya Soban
- Department
of Biochemistry, Aligarh Muslim University, Aligarh, Uttar Pradesh 202002, India
| | - Moniza Mujtaba
- Herricks
High School, New Hyde
Park, New York 11040 United States
| | - Khair Bux
- Third
World Center for Science and Technology, H.E.J. Research Institute
of Chemistry, International Centre of Chemical and Biological Sciences, University of Karachi, Karachi 75270 Pakistan
| | - Syed T. Moin
- Third
World Center for Science and Technology, H.E.J. Research Institute
of Chemistry, International Centre of Chemical and Biological Sciences, University of Karachi, Karachi 75270 Pakistan
| | | | - Sarath Dantu
- Department
of Computer Science, Brunel University, Uxbridge UB8 3PH, U.K.
| | - Alessandro Pandini
- Department
of Computer Science, Brunel University, Uxbridge UB8 3PH, U.K.
| | - Junqi Yin
- Center
for Computational Sciences, Oak Ridge National
Laboratory, Oak Ridge, Tennessee 37830, United States
| | - Heng Ma
- Data
Science and Learning Division, Argonne National
Laboratory, Lemont, Illinois 60439, United States
| | - Arvind Ramanathan
- Data
Science and Learning Division, Argonne National
Laboratory, Lemont, Illinois 60439, United States
- Consortium
for Advanced Science and Engineering, University
of Chicago, Chicago, Illinois 60637, United
States
| | - Barira Islam
- Department
of Bioscience, University of Huddersfield, Huddersfield HD1 3DH, U.K.
| | - Antonia S. J.
S. Mey
- EaStCHEM
School of Chemistry, University of Edinburgh, David Brewster Road, Edinburgh EH9 3FJ, U.K.
| | - Debsindhu Bhowmik
- Computer
Sciences and Engineering Division, Oak Ridge
National Laboratory, Oak Ridge, Tennessee 37830, United States
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26
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Zhang L, Domeniconi G, Yang CC, Kang SG, Zhou R, Cong G. CASTELO: clustered atom subtypes aided lead optimization-a combined machine learning and molecular modeling method. BMC Bioinformatics 2021; 22:338. [PMID: 34157976 PMCID: PMC8218488 DOI: 10.1186/s12859-021-04214-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Accepted: 05/18/2021] [Indexed: 01/18/2023] Open
Abstract
Background Drug discovery is a multi-stage process that comprises two costly major steps: pre-clinical research and clinical trials. Among its stages, lead optimization easily consumes more than half of the pre-clinical budget. We propose a combined machine learning and molecular modeling approach that partially automates lead optimization workflow in silico, providing suggestions for modification hot spots. Results The initial data collection is achieved with physics-based molecular dynamics simulation. Contact matrices are calculated as the preliminary features extracted from the simulations. To take advantage of the temporal information from the simulations, we enhanced contact matrices data with temporal dynamism representation, which are then modeled with unsupervised convolutional variational autoencoder (CVAE). Finally, conventional and CVAE-based clustering methods are compared with metrics to rank the submolecular structures and propose potential candidates for lead optimization. Conclusion With no need for extensive structure-activity data, our method provides new hints for drug modification hotspots which can be used to improve drug potency and reduce the lead optimization time. It can potentially become a valuable tool for medicinal chemists. Supplementary Information The online version contains supplementary material available at 10.1186/s12859-021-04214-4.
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Affiliation(s)
- Leili Zhang
- IBM Thomas J. Watson Research Center, 1101 Kitchawan Rd, 10598, Yorktown Heights, NY, USA.
| | - Giacomo Domeniconi
- IBM Thomas J. Watson Research Center, 1101 Kitchawan Rd, 10598, Yorktown Heights, NY, USA.
| | - Chih-Chieh Yang
- IBM Thomas J. Watson Research Center, 1101 Kitchawan Rd, 10598, Yorktown Heights, NY, USA
| | - Seung-Gu Kang
- IBM Thomas J. Watson Research Center, 1101 Kitchawan Rd, 10598, Yorktown Heights, NY, USA
| | - Ruhong Zhou
- ZheJiang University, 688 Yuhangtang Road, Hangzhou, 310027, China
| | - Guojing Cong
- Oak Ridge national laboratory, 1 Bethel Valley Rd, 37830, Oak Ridge, TN, USA
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27
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Artificial intelligence in drug design: algorithms, applications, challenges and ethics. FUTURE DRUG DISCOVERY 2021. [DOI: 10.4155/fdd-2020-0028] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
The discovery paradigm of drugs is rapidly growing due to advances in machine learning (ML) and artificial intelligence (AI). This review covers myriad faces of AI and ML in drug design. There is a plethora of AI algorithms, the most common of which are summarized in this review. In addition, AI is fraught with challenges that are highlighted along with plausible solutions to them. Examples are provided to illustrate the use of AI and ML in drug discovery and in predicting drug properties such as binding affinities and interactions, solubility, toxicology, blood–brain barrier permeability and chemical properties. The review also includes examples depicting the implementation of AI and ML in tackling intractable diseases such as COVID-19, cancer and Alzheimer’s disease. Ethical considerations and future perspectives of AI are also covered in this review.
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28
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Plante A, Weinstein H. Ligand-Dependent Conformational Transitions in Molecular Dynamics Trajectories of GPCRs Revealed by a New Machine Learning Rare Event Detection Protocol. Molecules 2021; 26:molecules26103059. [PMID: 34065494 PMCID: PMC8161244 DOI: 10.3390/molecules26103059] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2021] [Revised: 05/11/2021] [Accepted: 05/11/2021] [Indexed: 01/14/2023] Open
Abstract
Central among the tools and approaches used for ligand discovery and design are Molecular Dynamics (MD) simulations, which follow the dynamic changes in molecular structure in response to the environmental condition, interactions with other proteins, and the effects of ligand binding. The need for, and successes of, MD simulations in providing this type of essential information are well documented, but so are the challenges presented by the size of the resulting datasets encoding the desired information. The difficulty of extracting information on mechanistically important state-to-state transitions in response to ligand binding and other interactions is compounded by these being rare events in the MD trajectories of complex molecular machines, such as G-protein-coupled receptors (GPCRs). To address this problem, we have developed a protocol for the efficient detection of such events. We show that the novel Rare Event Detection (RED) protocol reveals functionally relevant and pharmacologically discriminating responses to the binding of different ligands to the 5-HT2AR orthosteric site in terms of clearly defined, structurally coherent, and temporally ordered conformational transitions. This information from the RED protocol offers new insights into specific ligand-determined functional mechanisms encoded in the MD trajectories, which opens a new and rigorously reproducible path to understanding drug activity with application in drug discovery.
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Affiliation(s)
- Ambrose Plante
- Department of Physiology and Biophysics, Weill Cornell Medical College of Cornell University, New York, NY 10065, USA;
| | - Harel Weinstein
- Department of Physiology and Biophysics, Weill Cornell Medical College of Cornell University, New York, NY 10065, USA;
- Institute for Computational Biomedicine, Weill Cornell Medical College of Cornell University, New York, NY 10065, USA
- Correspondence: ; Tel.: +1-212-746-6358
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29
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Liu R, Zhai H, Meng Y, Zhu M, Wen T, Jin N. Adsorption Behaviors of Typical Proteins on BP, GR, and C 2N Surfaces. J Chem Inf Model 2021; 61:1300-1306. [PMID: 33666087 DOI: 10.1021/acs.jcim.0c01036] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
The biotoxicity of nanomaterials is very important for the application of nanomaterials in biomedical systems. In this study, proteins with varying secondary structures (α-helices, β-sheets, and mixed α/β structures) were employed to investigate the biological properties of three representative two-dimensional (2D) nanomaterials; these nanomaterials consisted of black phosphorus (BP), graphene (GR), and nitrogenized graphene (C2N) and were studied using molecular dynamics simulations. The results showed that the α-helix motif underwent a slight structural change on the BP surface and little structural change on the C2N surface. In contrast, the structure of the β-sheet motif remained fairly intact on both the BP and C2N surfaces. The α-helix and β-sheet motifs were able to freely migrate on the BP surface, but they were anchored to the C2N surface. In contrast to BP and C2N, GR severely disrupted the structures of the α-helix and β-sheet motifs. BBA protein with mixed α/β structures adsorbed on the BP and C2N surfaces and exhibited biological behaviors that were consistent with those of the α-helix and β-sheet motifs. In summary, C2N may possess better biocompatibility than BP and GR and is expected to have applications in the biomedical field. This study not only comprehensively evaluated the biological characteristics of nanomaterials but also provided a theoretical strategy to explore and distinguish the surface characteristics of nanomaterials.
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Affiliation(s)
- Ruirui Liu
- College of Chemistry & Chemical Engineering, Lanzhou University, Lanzhou 730000, PR China
| | - Honglin Zhai
- College of Chemistry & Chemical Engineering, Lanzhou University, Lanzhou 730000, PR China
| | - Yajie Meng
- College of Chemistry & Chemical Engineering, Lanzhou University, Lanzhou 730000, PR China
| | - Min Zhu
- College of Chemistry & Chemical Engineering, Lanzhou University, Lanzhou 730000, PR China
| | - Tao Wen
- Gansu Computing Center, Lanzhou 730030, China
| | - Nengzhi Jin
- Gansu Computing Center, Lanzhou 730030, China
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30
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Ramanathan A, Ma H, Parvatikar A, Chennubhotla SC. Artificial intelligence techniques for integrative structural biology of intrinsically disordered proteins. Curr Opin Struct Biol 2021; 66:216-224. [PMID: 33421906 DOI: 10.1016/j.sbi.2020.12.001] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Revised: 12/01/2020] [Accepted: 12/03/2020] [Indexed: 12/16/2022]
Abstract
We outline recent developments in artificial intelligence (AI) and machine learning (ML) techniques for integrative structural biology of intrinsically disordered proteins (IDP) ensembles. IDPs challenge the traditional protein structure-function paradigm by adapting their conformations in response to specific binding partners leading them to mediate diverse, and often complex cellular functions such as biological signaling, self-organization and compartmentalization. Obtaining mechanistic insights into their function can therefore be challenging for traditional structural determination techniques. Often, scientists have to rely on piecemeal evidence drawn from diverse experimental techniques to characterize their functional mechanisms. Multiscale simulations can help bridge critical knowledge gaps about IDP structure-function relationships-however, these techniques also face challenges in resolving emergent phenomena within IDP conformational ensembles. We posit that scalable statistical inference techniques can effectively integrate information gleaned from multiple experimental techniques as well as from simulations, thus providing access to atomistic details of these emergent phenomena.
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Affiliation(s)
- Arvind Ramanathan
- Data Science & Learning Division, Argonne National Laboratory, Lemont, IL 60439, United States; Consortium for Advanced Science and Engineering (CASE), University of Chicago, Hyde Park, IL, United States.
| | - Heng Ma
- Data Science & Learning Division, Argonne National Laboratory, Lemont, IL 60439, United States
| | - Akash Parvatikar
- Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA 15260, United States
| | - S Chakra Chennubhotla
- Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA 15260, United States
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31
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Acharya A, Agarwal R, Baker M, Baudry J, Bhowmik D, Boehm S, Byler KG, Chen S, Coates L, Cooper C, Demerdash O, Daidone I, Eblen J, Ellingson S, Forli S, Glaser J, Gumbart JC, Gunnels J, Hernandez O, Irle S, Kneller D, Kovalevsky A, Larkin J, Lawrence T, LeGrand S, Liu SH, Mitchell J, Park G, Parks J, Pavlova A, Petridis L, Poole D, Pouchard L, Ramanathan A, Rogers D, Santos-Martins D, Scheinberg A, Sedova A, Shen Y, Smith J, Smith M, Soto C, Tsaris A, Thavappiragasam M, Tillack A, Vermaas J, Vuong V, Yin J, Yoo S, Zahran M, Zanetti-Polzi L. Supercomputer-Based Ensemble Docking Drug Discovery Pipeline with Application to Covid-19. J Chem Inf Model 2020; 60:5832-5852. [PMID: 33326239 PMCID: PMC7754786 DOI: 10.1021/acs.jcim.0c01010] [Citation(s) in RCA: 109] [Impact Index Per Article: 27.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2020] [Indexed: 01/18/2023]
Abstract
We present a supercomputer-driven pipeline for in silico drug discovery using enhanced sampling molecular dynamics (MD) and ensemble docking. Ensemble docking makes use of MD results by docking compound databases into representative protein binding-site conformations, thus taking into account the dynamic properties of the binding sites. We also describe preliminary results obtained for 24 systems involving eight proteins of the proteome of SARS-CoV-2. The MD involves temperature replica exchange enhanced sampling, making use of massively parallel supercomputing to quickly sample the configurational space of protein drug targets. Using the Summit supercomputer at the Oak Ridge National Laboratory, more than 1 ms of enhanced sampling MD can be generated per day. We have ensemble docked repurposing databases to 10 configurations of each of the 24 SARS-CoV-2 systems using AutoDock Vina. Comparison to experiment demonstrates remarkably high hit rates for the top scoring tranches of compounds identified by our ensemble approach. We also demonstrate that, using Autodock-GPU on Summit, it is possible to perform exhaustive docking of one billion compounds in under 24 h. Finally, we discuss preliminary results and planned improvements to the pipeline, including the use of quantum mechanical (QM), machine learning, and artificial intelligence (AI) methods to cluster MD trajectories and rescore docking poses.
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Affiliation(s)
- A. Acharya
- School of Physics, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - R. Agarwal
- UT/ORNL Center for Molecular Biophysics, Oak Ridge National Laboratory, TN, 37830, USA
- The University of Tennessee, Knoxville. Department of Biochemistry & Cellular and Molecular Biology, 309 Ken and Blaire Mossman Bldg. 1311 Cumberland Avenue Knoxville, TN, 37996, USA
- Graduate School of Genome Science and Technology, University of Tennessee, Knoxville, TN, 37996, USA
| | - M. Baker
- Computer Science and Mathematics Division, Oak Ridge National Lab, Oak Ridge, TN 37830, USA
| | - J. Baudry
- The University of Alabama in Huntsville, Department of Biological Sciences. 301 Sparkman Drive, Huntsville, AL 35899, USA
| | - D. Bhowmik
- Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
| | - S. Boehm
- Computer Science and Mathematics Division, Oak Ridge National Lab, Oak Ridge, TN 37830, USA
| | - K. G. Byler
- The University of Alabama in Huntsville, Department of Biological Sciences. 301 Sparkman Drive, Huntsville, AL 35899, USA
| | - S.Y. Chen
- Computational Science Initiative, Brookhaven National Laboratory, Upton, NY 11973, USA
| | - L. Coates
- Neutron Scattering Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
| | - C.J. Cooper
- UT/ORNL Center for Molecular Biophysics, Oak Ridge National Laboratory, TN, 37830, USA
- Graduate School of Genome Science and Technology, University of Tennessee, Knoxville, TN, 37996, USA
| | - O. Demerdash
- Biosciences Division, Oak Ridge National Lab, Oak Ridge, TN 37830, USA
| | - I. Daidone
- Department of Physical and Chemical Sciences, University of L’Aquila, I-67010 L’Aquila, Italy
| | - J.D. Eblen
- UT/ORNL Center for Molecular Biophysics, Oak Ridge National Laboratory, TN, 37830, USA
- The University of Tennessee, Knoxville. Department of Biochemistry & Cellular and Molecular Biology, 309 Ken and Blaire Mossman Bldg. 1311 Cumberland Avenue Knoxville, TN, 37996, USA
| | - S. Ellingson
- University of Kentucky, Division of Biomedical Informatics, College of Medicine, UK Medical Center MN 150, Lexington KY, 40536, USA
| | - S. Forli
- Scripps Research, La Jolla, CA, 92037, USA
| | - J. Glaser
- National Center for Computational Sciences, Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA
| | - J. C. Gumbart
- School of Physics, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - J. Gunnels
- HPC Engineering, Amazon Web Services, Seattle, WA 98121, USA
| | - O. Hernandez
- Computer Science and Mathematics Division, Oak Ridge National Lab, Oak Ridge, TN 37830, USA
| | - S. Irle
- Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
- Chemical Sciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
- Bredesen Center for Interdisciplinary Research and Graduate Education, University of Tennessee, Knoxville, TN 37996, USA
| | - D.W. Kneller
- Neutron Scattering Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
| | - A. Kovalevsky
- Neutron Scattering Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
| | - J. Larkin
- NVIDIA Corporation, Santa Clara, CA 95051, USA
| | - T.J. Lawrence
- Biosciences Division, Oak Ridge National Lab, Oak Ridge, TN 37830, USA
| | - S. LeGrand
- NVIDIA Corporation, Santa Clara, CA 95051, USA
| | - S.-H. Liu
- UT/ORNL Center for Molecular Biophysics, Oak Ridge National Laboratory, TN, 37830, USA
- The University of Tennessee, Knoxville. Department of Biochemistry & Cellular and Molecular Biology, 309 Ken and Blaire Mossman Bldg. 1311 Cumberland Avenue Knoxville, TN, 37996, USA
| | - J.C. Mitchell
- Biosciences Division, Oak Ridge National Lab, Oak Ridge, TN 37830, USA
| | - G. Park
- Computational Science Initiative, Brookhaven National Laboratory, Upton, NY 11973, USA
| | - J.M. Parks
- UT/ORNL Center for Molecular Biophysics, Oak Ridge National Laboratory, TN, 37830, USA
- The University of Tennessee, Knoxville. Department of Biochemistry & Cellular and Molecular Biology, 309 Ken and Blaire Mossman Bldg. 1311 Cumberland Avenue Knoxville, TN, 37996, USA
- Graduate School of Genome Science and Technology, University of Tennessee, Knoxville, TN, 37996, USA
| | - A. Pavlova
- School of Physics, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - L. Petridis
- UT/ORNL Center for Molecular Biophysics, Oak Ridge National Laboratory, TN, 37830, USA
- The University of Tennessee, Knoxville. Department of Biochemistry & Cellular and Molecular Biology, 309 Ken and Blaire Mossman Bldg. 1311 Cumberland Avenue Knoxville, TN, 37996, USA
| | - D. Poole
- NVIDIA Corporation, Santa Clara, CA 95051, USA
| | - L. Pouchard
- Computational Science Initiative, Brookhaven National Laboratory, Upton, NY 11973, USA
| | - A. Ramanathan
- Data Science and Learning Division, Argonne National Lab, Lemont, IL 60439, USA
| | - D. Rogers
- National Center for Computational Sciences, Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA
| | | | | | - A. Sedova
- Biosciences Division, Oak Ridge National Lab, Oak Ridge, TN 37830, USA
| | - Y. Shen
- UT/ORNL Center for Molecular Biophysics, Oak Ridge National Laboratory, TN, 37830, USA
- The University of Tennessee, Knoxville. Department of Biochemistry & Cellular and Molecular Biology, 309 Ken and Blaire Mossman Bldg. 1311 Cumberland Avenue Knoxville, TN, 37996, USA
- Graduate School of Genome Science and Technology, University of Tennessee, Knoxville, TN, 37996, USA
| | - J.C. Smith
- UT/ORNL Center for Molecular Biophysics, Oak Ridge National Laboratory, TN, 37830, USA
- The University of Tennessee, Knoxville. Department of Biochemistry & Cellular and Molecular Biology, 309 Ken and Blaire Mossman Bldg. 1311 Cumberland Avenue Knoxville, TN, 37996, USA
| | - M.D. Smith
- UT/ORNL Center for Molecular Biophysics, Oak Ridge National Laboratory, TN, 37830, USA
- The University of Tennessee, Knoxville. Department of Biochemistry & Cellular and Molecular Biology, 309 Ken and Blaire Mossman Bldg. 1311 Cumberland Avenue Knoxville, TN, 37996, USA
| | - C. Soto
- Computational Science Initiative, Brookhaven National Laboratory, Upton, NY 11973, USA
| | - A. Tsaris
- National Center for Computational Sciences, Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA
| | | | | | - J.V. Vermaas
- National Center for Computational Sciences, Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA
| | - V.Q. Vuong
- Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
- Chemical Sciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
- Bredesen Center for Interdisciplinary Research and Graduate Education, University of Tennessee, Knoxville, TN 37996, USA
| | - J. Yin
- National Center for Computational Sciences, Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA
| | - S. Yoo
- Computational Science Initiative, Brookhaven National Laboratory, Upton, NY 11973, USA
| | - M. Zahran
- Department of Biological Sciences, New York City College of Technology, The City University of New York (CUNY), Brooklyn, NY 11201, USA
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Hoseini P, Zhao L, Shehu A. Generative deep learning for macromolecular structure and dynamics. Curr Opin Struct Biol 2020; 67:170-177. [PMID: 33338762 DOI: 10.1016/j.sbi.2020.11.012] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Revised: 11/16/2020] [Accepted: 11/23/2020] [Indexed: 01/06/2023]
Abstract
Much scientific enquiry across disciplines is founded upon a mechanistic treatment of dynamic systems that ties form to function. A highly visible instance of this is in molecular biology, where characterizing macromolecular structure and dynamics is central to a detailed, molecular-level understanding of biological processes in the living cell. The current computational paradigm utilizes optimization as the generative process for modeling both structure and structural dynamics. Computational biology researchers are now attempting to wield generative models employing deep neural networks as an alternative computational paradigm. In this review, we summarize such efforts. We highlight progress and shortcomings. More importantly, we expose challenges that macromolecular structure poses to deep generative models and take this opportunity to introduce the structural biology community to several recent advances in the deep learning community that promise a way forward.
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Affiliation(s)
- Pourya Hoseini
- Department of Computer Science, George Mason University, 4400 University Drive, Fairfax, VA 22030, USA; Center for Advancing Human-Machine Partnerships, George Mason University, 4400 University Drive, Fairfax, VA 22030, USA
| | - Liang Zhao
- Department of Computer Science, Emory University, 201 Dowman Dr, Atlanta, GA 30322, USA; Center for Advancing Human-Machine Partnerships, George Mason University, 4400 University Drive, Fairfax, VA 22030, USA
| | - Amarda Shehu
- Department of Computer Science, George Mason University, 4400 University Drive, Fairfax, VA 22030, USA; Center for Advancing Human-Machine Partnerships, George Mason University, 4400 University Drive, Fairfax, VA 22030, USA.
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Grazioso R, García-Viñuales S, D'Abrosca G, Baglivo I, Pedone PV, Milardi D, Fattorusso R, Isernia C, Russo L, Malgieri G. The change of conditions does not affect Ros87 downhill folding mechanism. Sci Rep 2020; 10:21067. [PMID: 33273582 PMCID: PMC7713307 DOI: 10.1038/s41598-020-78008-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2020] [Accepted: 11/19/2020] [Indexed: 11/20/2022] Open
Abstract
Downhill folding has been defined as a unique thermodynamic process involving a conformations ensemble that progressively loses structure with the decrease of protein stability. Downhill folders are estimated to be rather rare in nature as they miss an energetically substantial folding barrier that can protect against aggregation and proteolysis. We have previously demonstrated that the prokaryotic zinc finger protein Ros87 shows a bipartite folding/unfolding process in which a metal binding intermediate converts to the native structure through a delicate barrier-less downhill transition. Significant variation in folding scenarios can be detected within protein families with high sequence identity and very similar folds and for the same sequence by varying conditions. For this reason, we here show, by means of DSC, CD and NMR, that also in different pH and ionic strength conditions Ros87 retains its partly downhill folding scenario demonstrating that, at least in metallo-proteins, the downhill mechanism can be found under a much wider range of conditions and coupled to other different transitions. We also show that mutations of Ros87 zinc coordination sphere produces a different folding scenario demonstrating that the organization of the metal ion core is determinant in the folding process of this family of proteins.
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Affiliation(s)
- Rinaldo Grazioso
- Department of Environmental, Biological and Pharmaceutical Sciences and Technologies, Via Vivaldi 43, 81100, Caserta, Italy
| | | | - Gianluca D'Abrosca
- Department of Environmental, Biological and Pharmaceutical Sciences and Technologies, Via Vivaldi 43, 81100, Caserta, Italy
| | - Ilaria Baglivo
- Department of Environmental, Biological and Pharmaceutical Sciences and Technologies, Via Vivaldi 43, 81100, Caserta, Italy
| | - Paolo Vincenzo Pedone
- Department of Environmental, Biological and Pharmaceutical Sciences and Technologies, Via Vivaldi 43, 81100, Caserta, Italy
| | - Danilo Milardi
- Institute of Crystallography-CNR, Via Paolo Gaifami 18, 95126, Catania, Italy
| | - Roberto Fattorusso
- Department of Environmental, Biological and Pharmaceutical Sciences and Technologies, Via Vivaldi 43, 81100, Caserta, Italy
| | - Carla Isernia
- Department of Environmental, Biological and Pharmaceutical Sciences and Technologies, Via Vivaldi 43, 81100, Caserta, Italy
| | - Luigi Russo
- Department of Environmental, Biological and Pharmaceutical Sciences and Technologies, Via Vivaldi 43, 81100, Caserta, Italy.
| | - Gaetano Malgieri
- Department of Environmental, Biological and Pharmaceutical Sciences and Technologies, Via Vivaldi 43, 81100, Caserta, Italy.
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Casalino L, Dommer A, Gaieb Z, Barros EP, Sztain T, Ahn SH, Trifan A, Brace A, Bogetti A, Ma H, Lee H, Turilli M, Khalid S, Chong L, Simmerling C, Hardy DJ, Maia JDC, Phillips JC, Kurth T, Stern A, Huang L, McCalpin J, Tatineni M, Gibbs T, Stone JE, Jha S, Ramanathan A, Amaro RE. AI-Driven Multiscale Simulations Illuminate Mechanisms of SARS-CoV-2 Spike Dynamics. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2020:2020.11.19.390187. [PMID: 33236007 PMCID: PMC7685317 DOI: 10.1101/2020.11.19.390187] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/17/2023]
Abstract
We develop a generalizable AI-driven workflow that leverages heterogeneous HPC resources to explore the time-dependent dynamics of molecular systems. We use this workflow to investigate the mechanisms of infectivity of the SARS-CoV-2 spike protein, the main viral infection machinery. Our workflow enables more efficient investigation of spike dynamics in a variety of complex environments, including within a complete SARS-CoV-2 viral envelope simulation, which contains 305 million atoms and shows strong scaling on ORNL Summit using NAMD. We present several novel scientific discoveries, including the elucidation of the spike's full glycan shield, the role of spike glycans in modulating the infectivity of the virus, and the characterization of the flexible interactions between the spike and the human ACE2 receptor. We also demonstrate how AI can accelerate conformational sampling across different systems and pave the way for the future application of such methods to additional studies in SARS-CoV-2 and other molecular systems.
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Affiliation(s)
| | | | | | | | | | | | - Anda Trifan
- Argonne National Lab
- University of Illinois at Urbana-Champaign
| | | | | | | | - Hyungro Lee
- Rutgers University & Brookhaven National Lab
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Hameduh T, Haddad Y, Adam V, Heger Z. Homology modeling in the time of collective and artificial intelligence. Comput Struct Biotechnol J 2020; 18:3494-3506. [PMID: 33304450 PMCID: PMC7695898 DOI: 10.1016/j.csbj.2020.11.007] [Citation(s) in RCA: 48] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Revised: 11/04/2020] [Accepted: 11/04/2020] [Indexed: 12/12/2022] Open
Abstract
Homology modeling is a method for building protein 3D structures using protein primary sequence and utilizing prior knowledge gained from structural similarities with other proteins. The homology modeling process is done in sequential steps where sequence/structure alignment is optimized, then a backbone is built and later, side-chains are added. Once the low-homology loops are modeled, the whole 3D structure is optimized and validated. In the past three decades, a few collective and collaborative initiatives allowed for continuous progress in both homology and ab initio modeling. Critical Assessment of protein Structure Prediction (CASP) is a worldwide community experiment that has historically recorded the progress in this field. Folding@Home and Rosetta@Home are examples of crowd-sourcing initiatives where the community is sharing computational resources, whereas RosettaCommons is an example of an initiative where a community is sharing a codebase for the development of computational algorithms. Foldit is another initiative where participants compete with each other in a protein folding video game to predict 3D structure. In the past few years, contact maps deep machine learning was introduced to the 3D structure prediction process, adding more information and increasing the accuracy of models significantly. In this review, we will take the reader in a journey of exploration from the beginnings to the most recent turnabouts, which have revolutionized the field of homology modeling. Moreover, we discuss the new trends emerging in this rapidly growing field.
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Affiliation(s)
- Tareq Hameduh
- Department of Chemistry and Biochemistry, Mendel University in Brno, Zemedelska 1, CZ-613 00 Brno, Czech Republic
| | - Yazan Haddad
- Department of Chemistry and Biochemistry, Mendel University in Brno, Zemedelska 1, CZ-613 00 Brno, Czech Republic
- Central European Institute of Technology, Brno University of Technology, Purkynova 656/123, 612 00 Brno, Czech Republic
| | - Vojtech Adam
- Department of Chemistry and Biochemistry, Mendel University in Brno, Zemedelska 1, CZ-613 00 Brno, Czech Republic
- Central European Institute of Technology, Brno University of Technology, Purkynova 656/123, 612 00 Brno, Czech Republic
| | - Zbynek Heger
- Department of Chemistry and Biochemistry, Mendel University in Brno, Zemedelska 1, CZ-613 00 Brno, Czech Republic
- Central European Institute of Technology, Brno University of Technology, Purkynova 656/123, 612 00 Brno, Czech Republic
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36
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Sabbih GO, Korsah MA, Jeevanandam J, Danquah MK. Biophysical analysis of SARS-CoV-2 transmission and theranostic development via N protein computational characterization. Biotechnol Prog 2020; 37:e3096. [PMID: 33118327 PMCID: PMC7645878 DOI: 10.1002/btpr.3096] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2020] [Revised: 10/22/2020] [Accepted: 10/26/2020] [Indexed: 01/01/2023]
Abstract
Recently, SARS-CoV-2 has been identified as the causative factor of viral infection called COVID-19 that belongs to the zoonotic beta coronavirus family known to cause respiratory disorders or viral pneumonia, followed by an extensive attack on organs that express angiotensin-converting enzyme II (ACE2). Human transmission of this virus occurs via respiratory droplets from symptomatic and asymptomatic patients, which are released into the environment after sneezing or coughing. These droplets are capable of staying in the air as aerosols or surfaces and can be transmitted to persons through inhalation or contact with contaminated surfaces. Thus, there is an urgent need for advanced theranostic solutions to control the spread of COVID-19 infection. The development of such fit-for-purpose technologies hinges on a proper understanding of the transmission, incubation, and structural characteristics of the virus in the external environment and within the host. Hence, this article describes the development of an intrinsic model to describe the incubation characteristics of the virus under varying environmental factors. It also discusses on the evaluation of SARS-CoV-2 structural nucleocapsid protein properties via computational approaches to generate high-affinity binding probes for effective diagnosis and targeted treatment applications by specific targeting of viruses. In addition, this article provides useful insights on the transmission behavior of the virus and creates new opportunities for theranostics development.
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Affiliation(s)
- Godfred O Sabbih
- Department of Chemical Engineering, University of Tennessee, Chattanooga, Tennessee, USA
| | - Maame A Korsah
- Department of Mathematics, University of Tennessee, Chattanooga, Tennessee, USA
| | - Jaison Jeevanandam
- CQM - Centro de Química da Madeira, MMRG, Universidade da Madeira, Campus da Penteada, Funchal, Portugal
| | - Michael K Danquah
- Department of Chemical Engineering, University of Tennessee, Chattanooga, Tennessee, USA
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37
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Akere A, Chen SH, Liu X, Chen Y, Dantu SC, Pandini A, Bhowmik D, Haider S. Structure-based enzyme engineering improves donor-substrate recognition of Arabidopsis thaliana glycosyltransferases. Biochem J 2020; 477:2791-2805. [PMID: 32657326 PMCID: PMC7419078 DOI: 10.1042/bcj20200477] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2020] [Revised: 07/08/2020] [Accepted: 07/10/2020] [Indexed: 12/25/2022]
Abstract
Glycosylation of secondary metabolites involves plant UDP-dependent glycosyltransferases (UGTs). UGTs have shown promise as catalysts in the synthesis of glycosides for medical treatment. However, limited understanding at the molecular level due to insufficient biochemical and structural information has hindered potential applications of most of these UGTs. In the absence of experimental crystal structures, we employed advanced molecular modeling and simulations in conjunction with biochemical characterization to design a workflow to study five Group H Arabidopsis thaliana (76E1, 76E2, 76E4, 76E5, 76D1) UGTs. Based on our rational structural manipulation and analysis, we identified key amino acids (P129 in 76D1; D374 in 76E2; K275 in 76E4), which when mutated improved donor substrate recognition than wildtype UGTs. Molecular dynamics simulations and deep learning analysis identified structural differences, which drive substrate preferences. The design of these UGTs with broader substrate specificity may play important role in biotechnological and industrial applications. These findings can also serve as basis to study other plant UGTs and thereby advancing UGT enzyme engineering.
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Affiliation(s)
- Aishat Akere
- Pharmaceutical and Biological Chemistry, UCL School of Pharmacy, London WC1N 1AX, U.K
| | - Serena H. Chen
- Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, TN 37830, U.S.A
| | - Xiaohan Liu
- Pharmaceutical and Biological Chemistry, UCL School of Pharmacy, London WC1N 1AX, U.K
| | - Yanger Chen
- College of Life Science, Sichuan Agricultural University, Ya'an, China
| | | | - Alessandro Pandini
- Department of Computer Science, Brunel University London, Uxbridge UB8 3PH, U.K
| | - Debsindhu Bhowmik
- Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, TN 37830, U.S.A
| | - Shozeb Haider
- Pharmaceutical and Biological Chemistry, UCL School of Pharmacy, London WC1N 1AX, U.K
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38
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Acharya A, Agarwal R, Baker M, Baudry J, Bhowmik D, Boehm S, Byler KG, Coates L, Chen SY, Cooper CJ, Demerdash O, Daidone I, Eblen JD, Ellingson S, Forli S, Glaser J, Gumbart JC, Gunnels J, Hernandez O, Irle S, Larkin J, Lawrence TJ, LeGrand S, Liu SH, Mitchell JC, Park G, Parks JM, Pavlova A, Petridis L, Poole D, Pouchard L, Ramanathan A, Rogers D, Santos-Martins D, Scheinberg A, Sedova A, Shen S, Smith JC, Smith MD, Soto C, Tsaris A, Thavappiragasam M, Tillack AF, Vermaas JV, Vuong VQ, Yin J, Yoo S, Zahran M, Zanetti-Polzi L. Supercomputer-Based Ensemble Docking Drug Discovery Pipeline with Application to Covid-19. CHEMRXIV : THE PREPRINT SERVER FOR CHEMISTRY 2020:12725465. [PMID: 33200117 PMCID: PMC7668744 DOI: 10.26434/chemrxiv.12725465] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Revised: 07/29/2020] [Indexed: 01/18/2023]
Abstract
We present a supercomputer-driven pipeline for in-silico drug discovery using enhanced sampling molecular dynamics (MD) and ensemble docking. We also describe preliminary results obtained for 23 systems involving eight protein targets of the proteome of SARS CoV-2. THe MD performed is temperature replica-exchange enhanced sampling, making use of the massively parallel supercomputing on the SUMMIT supercomputer at Oak Ridge National Laboratory, with which more than 1ms of enhanced sampling MD can be generated per day. We have ensemble docked repurposing databases to ten configurations of each of the 23 SARS CoV-2 systems using AutoDock Vina. We also demonstrate that using Autodock-GPU on SUMMIT, it is possible to perform exhaustive docking of one billion compounds in under 24 hours. Finally, we discuss preliminary results and planned improvements to the pipeline, including the use of quantum mechanical (QM), machine learning, and AI methods to cluster MD trajectories and rescore docking poses.
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Affiliation(s)
- A Acharya
- School of Physics, Georgia Institute of Technology, Atlanta, GA 30332
| | - R Agarwal
- UT/ORNL Center for Molecular Biophysics, Oak Ridge National Laboratory, TN, 37830
- The University of Tennessee, Knoxville. Department of Biochemistry & Cellular and Molecular Biology, 309 Ken and Blaire Mossman Bldg. 1311 Cumberland Avenue Knoxville, TN, 37996
- Graduate School of Genome Science and Technology, University of Tennessee, Knoxville, TN, 37996
| | - M Baker
- Computer Science and Mathematics Division, Oak Ridge National Lab, Oak Ridge, TN 37830
| | - J Baudry
- The University of Alabama in Huntsville, Department of Biological Sciences. 301 Sparkman Drive, Huntsville, AL 35899
| | - D Bhowmik
- Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831
| | - S Boehm
- Computer Science and Mathematics Division, Oak Ridge National Lab, Oak Ridge, TN 37830
| | - K G Byler
- The University of Alabama in Huntsville, Department of Biological Sciences. 301 Sparkman Drive, Huntsville, AL 35899
| | - L Coates
- Neutron Scattering Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831
| | - S Y Chen
- Computational Science Initiative, Brookhaven National Laboratory, Upton, NY 11973
| | - C J Cooper
- UT/ORNL Center for Molecular Biophysics, Oak Ridge National Laboratory, TN, 37830
- Graduate School of Genome Science and Technology, University of Tennessee, Knoxville, TN, 37996
| | - O Demerdash
- Biosciences Division, Oak Ridge National Lab, Oak Ridge, TN 37830
| | - I Daidone
- Department of Physical and Chemical Sciences, University of L'Aquila, I-67010 L'Aquila, Italy
| | - J D Eblen
- UT/ORNL Center for Molecular Biophysics, Oak Ridge National Laboratory, TN, 37830
- The University of Tennessee, Knoxville. Department of Biochemistry & Cellular and Molecular Biology, 309 Ken and Blaire Mossman Bldg. 1311 Cumberland Avenue Knoxville, TN, 37996
| | - S Ellingson
- University of Kentucky, Division of Biomedical Informatics, College of Medicine, UK Medical Center MN 150, Lexington KY, 40536
| | - S Forli
- Scripps Research, La Jolla, CA, 92037
| | - J Glaser
- National Center for Computational Sciences, Oak Ridge National Laboratory, Oak Ridge, TN 37830
| | - J C Gumbart
- School of Physics, Georgia Institute of Technology, Atlanta, GA 30332
| | - J Gunnels
- HPC Engineering, Amazon Web Services, Seattle, WA 98121
| | - O Hernandez
- Computer Science and Mathematics Division, Oak Ridge National Lab, Oak Ridge, TN 37830
| | - S Irle
- Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831
- Chemical Sciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831
- Bredesen Center for Interdisciplinary Research and Graduate Education, University of Tennessee, Knoxville, TN 37996
| | - J Larkin
- NVIDIA Corporation, Santa Clara, CA 95051
| | - T J Lawrence
- Biosciences Division, Oak Ridge National Lab, Oak Ridge, TN 37830
| | - S LeGrand
- NVIDIA Corporation, Santa Clara, CA 95051
| | - S-H Liu
- UT/ORNL Center for Molecular Biophysics, Oak Ridge National Laboratory, TN, 37830
- The University of Tennessee, Knoxville. Department of Biochemistry & Cellular and Molecular Biology, 309 Ken and Blaire Mossman Bldg. 1311 Cumberland Avenue Knoxville, TN, 37996
| | - J C Mitchell
- Biosciences Division, Oak Ridge National Lab, Oak Ridge, TN 37830
| | - G Park
- Computational Science Initiative, Brookhaven National Laboratory, Upton, NY 11973
| | - J M Parks
- UT/ORNL Center for Molecular Biophysics, Oak Ridge National Laboratory, TN, 37830
- The University of Tennessee, Knoxville. Department of Biochemistry & Cellular and Molecular Biology, 309 Ken and Blaire Mossman Bldg. 1311 Cumberland Avenue Knoxville, TN, 37996
- Graduate School of Genome Science and Technology, University of Tennessee, Knoxville, TN, 37996
| | - A Pavlova
- School of Physics, Georgia Institute of Technology, Atlanta, GA 30332
| | - L Petridis
- UT/ORNL Center for Molecular Biophysics, Oak Ridge National Laboratory, TN, 37830
- The University of Tennessee, Knoxville. Department of Biochemistry & Cellular and Molecular Biology, 309 Ken and Blaire Mossman Bldg. 1311 Cumberland Avenue Knoxville, TN, 37996
| | - D Poole
- NVIDIA Corporation, Santa Clara, CA 95051
| | - L Pouchard
- Computational Science Initiative, Brookhaven National Laboratory, Upton, NY 11973
| | - A Ramanathan
- Data Science and Learning Division, Argonne National Lab, Lemont, IL 60439
| | - D Rogers
- National Center for Computational Sciences, Oak Ridge National Laboratory, Oak Ridge, TN 37830
| | | | | | - A Sedova
- Biosciences Division, Oak Ridge National Lab, Oak Ridge, TN 37830
| | - S Shen
- UT/ORNL Center for Molecular Biophysics, Oak Ridge National Laboratory, TN, 37830
- The University of Tennessee, Knoxville. Department of Biochemistry & Cellular and Molecular Biology, 309 Ken and Blaire Mossman Bldg. 1311 Cumberland Avenue Knoxville, TN, 37996
- Graduate School of Genome Science and Technology, University of Tennessee, Knoxville, TN, 37996
| | - J C Smith
- UT/ORNL Center for Molecular Biophysics, Oak Ridge National Laboratory, TN, 37830
- The University of Tennessee, Knoxville. Department of Biochemistry & Cellular and Molecular Biology, 309 Ken and Blaire Mossman Bldg. 1311 Cumberland Avenue Knoxville, TN, 37996
| | - M D Smith
- UT/ORNL Center for Molecular Biophysics, Oak Ridge National Laboratory, TN, 37830
- The University of Tennessee, Knoxville. Department of Biochemistry & Cellular and Molecular Biology, 309 Ken and Blaire Mossman Bldg. 1311 Cumberland Avenue Knoxville, TN, 37996
| | - C Soto
- Computational Science Initiative, Brookhaven National Laboratory, Upton, NY 11973
| | - A Tsaris
- National Center for Computational Sciences, Oak Ridge National Laboratory, Oak Ridge, TN 37830
| | | | | | - J V Vermaas
- National Center for Computational Sciences, Oak Ridge National Laboratory, Oak Ridge, TN 37830
| | - V Q Vuong
- Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831
- Chemical Sciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831
- Bredesen Center for Interdisciplinary Research and Graduate Education, University of Tennessee, Knoxville, TN 37996
| | - J Yin
- National Center for Computational Sciences, Oak Ridge National Laboratory, Oak Ridge, TN 37830
| | - S Yoo
- Computational Science Initiative, Brookhaven National Laboratory, Upton, NY 11973
| | - M Zahran
- Department of Biological Sciences, New York City College of Technology, The City University of New York (CUNY), Brooklyn, NY 11201
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39
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Bozkurt Varolgüneş Y, Bereau T, Rudzinski JF. Interpretable embeddings from molecular simulations using Gaussian mixture variational autoencoders. MACHINE LEARNING-SCIENCE AND TECHNOLOGY 2020. [DOI: 10.1088/2632-2153/ab80b7] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
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40
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Sidky H, Chen W, Ferguson AL. Machine learning for collective variable discovery and enhanced sampling in biomolecular simulation. Mol Phys 2020. [DOI: 10.1080/00268976.2020.1737742] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Affiliation(s)
- Hythem Sidky
- Pritzker School of Molecular Engineering, University of Chicago, Chicago, IL, USA
| | - Wei Chen
- Department of Physics, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Andrew L. Ferguson
- Pritzker School of Molecular Engineering, University of Chicago, Chicago, IL, USA
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41
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Mechanism of glucocerebrosidase activation and dysfunction in Gaucher disease unraveled by molecular dynamics and deep learning. Proc Natl Acad Sci U S A 2019; 116:5086-5095. [PMID: 30808805 DOI: 10.1073/pnas.1818411116] [Citation(s) in RCA: 45] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] Open
Abstract
The lysosomal enzyme glucocerebrosidase-1 (GCase) catalyzes the cleavage of a major glycolipid glucosylceramide into glucose and ceramide. The absence of fully functional GCase leads to the accumulation of its lipid substrates in lysosomes, causing Gaucher disease, an autosomal recessive disorder that displays profound genotype-phenotype nonconcordance. More than 250 disease-causing mutations in GBA1, the gene encoding GCase, have been discovered, although only one of these, N370S, causes 70% of disease. Here, we have used a knowledge-based docking protocol that considers experimental data of protein-protein binding to generate a complex between GCase and its known facilitator protein saposin C (SAPC). Multiscale molecular-dynamics simulations were used to study lipid self-assembly, membrane insertion, and the dynamics of the interactions between different components of the complex. Deep learning was applied to propose a model that explains the mechanism of GCase activation, which requires SAPC. Notably, we find that conformational changes in the loops at the entrance of the substrate-binding site are stabilized by direct interactions with SAPC and that the loss of such interactions induced by N370S and another common mutation, L444P, result in destabilization of the complex and reduced GCase activation. Our findings provide an atomistic-level explanation for GCase activation and the precise mechanism through which N370S and L444P cause Gaucher disease.
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