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Harding-Larsen D, Funk J, Madsen NG, Gharabli H, Acevedo-Rocha CG, Mazurenko S, Welner DH. Protein representations: Encoding biological information for machine learning in biocatalysis. Biotechnol Adv 2024; 77:108459. [PMID: 39366493 DOI: 10.1016/j.biotechadv.2024.108459] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2024] [Revised: 09/19/2024] [Accepted: 09/29/2024] [Indexed: 10/06/2024]
Abstract
Enzymes offer a more environmentally friendly and low-impact solution to conventional chemistry, but they often require additional engineering for their application in industrial settings, an endeavour that is challenging and laborious. To address this issue, the power of machine learning can be harnessed to produce predictive models that enable the in silico study and engineering of improved enzymatic properties. Such machine learning models, however, require the conversion of the complex biological information to a numerical input, also called protein representations. These inputs demand special attention to ensure the training of accurate and precise models, and, in this review, we therefore examine the critical step of encoding protein information to numeric representations for use in machine learning. We selected the most important approaches for encoding the three distinct biological protein representations - primary sequence, 3D structure, and dynamics - to explore their requirements for employment and inductive biases. Combined representations of proteins and substrates are also introduced as emergent tools in biocatalysis. We propose the division of fixed representations, a collection of rule-based encoding strategies, and learned representations extracted from the latent spaces of large neural networks. To select the most suitable protein representation, we propose two main factors to consider. The first one is the model setup, which is influenced by the size of the training dataset and the choice of architecture. The second factor is the model objectives such as consideration about the assayed property, the difference between wild-type models and mutant predictors, and requirements for explainability. This review is aimed at serving as a source of information and guidance for properly representing enzymes in future machine learning models for biocatalysis.
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Affiliation(s)
- David Harding-Larsen
- The Novo Nordisk Center for Biosustainability, Technical University of Denmark, Søltofts Plads, Bygning 220, 2800 Kgs. Lyngby, Denmark
| | - Jonathan Funk
- The Novo Nordisk Center for Biosustainability, Technical University of Denmark, Søltofts Plads, Bygning 220, 2800 Kgs. Lyngby, Denmark
| | - Niklas Gesmar Madsen
- The Novo Nordisk Center for Biosustainability, Technical University of Denmark, Søltofts Plads, Bygning 220, 2800 Kgs. Lyngby, Denmark
| | - Hani Gharabli
- The Novo Nordisk Center for Biosustainability, Technical University of Denmark, Søltofts Plads, Bygning 220, 2800 Kgs. Lyngby, Denmark
| | - Carlos G Acevedo-Rocha
- The Novo Nordisk Center for Biosustainability, Technical University of Denmark, Søltofts Plads, Bygning 220, 2800 Kgs. Lyngby, Denmark
| | - Stanislav Mazurenko
- Loschmidt Laboratories, Department of Experimental Biology and RECETOX, Faculty of Science, Masaryk University, Kamenice 5, 625 00 Brno, Czech Republic; International Clinical Research Center, St. Anne's University Hospital Brno, Pekarska 53, 656 91 Brno, Czech Republic
| | - Ditte Hededam Welner
- The Novo Nordisk Center for Biosustainability, Technical University of Denmark, Søltofts Plads, Bygning 220, 2800 Kgs. Lyngby, Denmark.
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Kacher J, Sokolova OS, Tarek M. A Deep Learning Approach to Uncover Voltage-Gated Ion Channels' Intermediate States. J Phys Chem B 2024; 128:8724-8736. [PMID: 39213618 DOI: 10.1021/acs.jpcb.4c03182] [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: 09/04/2024]
Abstract
Owing to recent advancements in cryo-electron microscopy, voltage-gated ion channels have gained a greater comprehension of their structural characteristics. However, a significant enigma remains unsolved for a large majority of these channels: their gating mechanism. This mechanism, which encompasses the conformational changes between open and closed states, is pivotal to their proper functioning. Beyond the binary states of open and closed, an ensemble of intermediate states defines the transition path in-between. Due to the lack of experimental data, one might resort to molecular dynamics simulations as an alternative to decipher these states and the transitions between them. However, the high-energy barriers and the colossal time scales involved hinder access to the latter. We present here an application of deep learning as a reliable pipeline for a comprehensive exploration of voltage-gated ion channel conformational rearrangements during gating. We showcase the pipeline performance specifically on the Kv1.2 voltage sensor domain and confront the results with existing data. We demonstrate how our physics-based deep learning approach contributes to the theoretical understanding of these channels and how it might provide further insights into the exploration of channelopathies.
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Affiliation(s)
- Julia Kacher
- Université de Lorraine, CNRS, LPCT, F-54000 Nancy, France
| | - Olga S Sokolova
- Faculty of Biology, Lomonosov Moscow State University, 1-12 Leninskie Gory, 119234 Moscow, Russia
- Shenzhen MSU-BIT University, 1 International University Park Road, Dayun New Town, Longgang District, Shenzhen 518172, China
| | - Mounir Tarek
- Université de Lorraine, CNRS, LPCT, F-54000 Nancy, France
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3
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Nabi F, Ahmad O, Khan A, Hassan MN, Hisamuddin M, Malik S, Chaari A, Khan RH. Natural compound plumbagin based inhibition of hIAPP revealed by Markov state models based on MD data along with experimental validations. Proteins 2024; 92:1070-1084. [PMID: 38497314 DOI: 10.1002/prot.26682] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Revised: 02/26/2024] [Accepted: 02/28/2024] [Indexed: 03/19/2024]
Abstract
Human islet amyloid polypeptide (amylin or hIAPP) is a 37 residue hormone co-secreted with insulin from β cells of the pancreas. In patients suffering from type-2 diabetes, amylin self-assembles into amyloid fibrils, ultimately leading to the death of the pancreatic cells. However, a research gap exists in preventing and treating such amyloidosis. Plumbagin, a natural compound, has previously been demonstrated to have inhibitory potential against insulin amyloidosis. Our investigation unveils collapsible regions within hIAPP that, upon collapse, facilitates hydrophobic and pi-pi interactions, ultimately leading to aggregation. Intriguingly plumbagin exhibits the ability to bind these specific collapsible regions, thereby impeding the aforementioned interactions that would otherwise drive hIAPP aggregation. We have used atomistic molecular dynamics approach to determine secondary structural changes. MSM shows metastable states forming native like hIAPP structure in presence of PGN. Our in silico results concur with in vitro results. The ThT assay revealed a striking 50% decrease in fluorescence intensity at a 1:1 ratio of hIAPP to Plumbagin. This finding suggests a significant inhibition of amyloid fibril formation by plumbagin, as ThT fluorescence directly correlates with the presence of these fibrils. Further TEM images revealed disappearance of hIAPP fibrils in plumbagin pre-treated hIAPP samples. Also, we have shown that plumbagin disrupts the intermolecular hydrogen bonding in hIAPP fibrils leading to an increase in the average beta strand spacing, thereby causing disaggregation of pre-formed fibrils demonstrating overall disruption of the aggregation machinery of hIAPP. Our work is the first to report a detailed atomistic simulation of 22 μs for hIAPP. Overall, our studies put plumbagin as a potential candidate for both preventive and therapeutic candidate for hIAPP amyloidosis.
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Affiliation(s)
- Faisal Nabi
- Interdisciplinary Biotechnology Unit, Aligarh Muslim University, Aligarh, India
| | - Owais Ahmad
- Interdisciplinary Biotechnology Unit, Aligarh Muslim University, Aligarh, India
| | - Adeeba Khan
- Zakir Hussain College of Engineering and Technology, Aligarh Muslim University, Aligarh, India
| | - Md Nadir Hassan
- Interdisciplinary Biotechnology Unit, Aligarh Muslim University, Aligarh, India
| | - Malik Hisamuddin
- Interdisciplinary Biotechnology Unit, Aligarh Muslim University, Aligarh, India
| | - Sadia Malik
- Interdisciplinary Biotechnology Unit, Aligarh Muslim University, Aligarh, India
| | - Ali Chaari
- Premedical Division, Weill Cornell Medicine Qatar, Qatar Foundation, Doha, Qatar
| | - Rizwan Hasan Khan
- Interdisciplinary Biotechnology Unit, Aligarh Muslim University, Aligarh, India
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4
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Huang Y, Zhang H, Lin Z, Wei Y, Xi W. RevGraphVAMP: A protein molecular simulation analysis model combining graph convolutional neural networks and physical constraints. Methods 2024; 229:163-174. [PMID: 38972499 DOI: 10.1016/j.ymeth.2024.06.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2023] [Revised: 06/19/2024] [Accepted: 06/24/2024] [Indexed: 07/09/2024] Open
Abstract
Molecular dynamics simulation is a crucial research domain within the life sciences, focusing on comprehending the mechanisms of biomolecular interactions at atomic scales. Protein simulation, as a critical subfield, often utilizes MD for implementation, with trajectory data play a pivotal role in drug discovery. The advancement of high-performance computing and deep learning technology becomes popular and critical to predict protein properties from vast trajectory data, posing challenges regarding data features extraction from the complicated simulation data and dimensionality reduction. Simultaneously, it is essential to provide a meaningful explanation of the biological mechanism behind dimensionality. To tackle this challenge, we propose a new unsupervised model named RevGraphVAMP to intelligently analyze the simulation trajectory. This model is based on the variational approach for Markov processes (VAMP) and integrates graph convolutional neural networks and physical constraint optimization to enhance the learning performance. Additionally, we introduce attention mechanism to assess the importance of key interaction region, facilitating the interpretation of molecular mechanism. In comparison to other VAMPNets models, our model showcases competitive performance, improved accuracy in state transition prediction, as demonstrated through its application to two public datasets and the Shank3-Rap1 complex, which is associated with autism spectrum disorder. Moreover, it enhanced dimensionality reduction discrimination across different substates and provides interpretable results for protein structural characterization.
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Affiliation(s)
- Ying Huang
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Huiling Zhang
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; College of Mathematics and Informatics, South China Agricultural University, Guangzhou, 510642, China
| | - Zhenli Lin
- Department of Ophthalmology, Shenzhen University General Hospital, Shenzhen 518055, China
| | - Yanjie Wei
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; Faculty of Computer Science and Control Engineering, Shenzhen University of Advanced Technology, Shenzhen 518107, China.
| | - Wenhui Xi
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; Faculty of Computer Science and Control Engineering, Shenzhen University of Advanced Technology, Shenzhen 518107, China.
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5
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Gou Y, Li T, Wang Y. Active-Site Oxygen Accessibility and Catalytic Loop Dynamics of Plant Aromatic Amino Acid Decarboxylases from Molecular Simulations. Biochemistry 2024; 63:1980-1990. [PMID: 39008055 PMCID: PMC11308512 DOI: 10.1021/acs.biochem.4c00144] [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: 03/17/2024] [Revised: 07/05/2024] [Accepted: 07/05/2024] [Indexed: 07/16/2024]
Abstract
Aromatic amino acid decarboxylases (AAADs) are pyridoxal-5'-phosphate (PLP)-dependent enzymes that catalyze the decarboxylation of aromatic amino acid l-amino acids. In plants, apart from canonical AAADs that catalyze the straightforward decarboxylation reaction, other members of the AAAD family function as aromatic acetaldehyde synthases (AASs) and catalyze more complex decarboxylation-dependent oxidative deamination. The interconversion between a canonical AAAD and an AAS can be achieved by a single tyrosine-phenylalanine mutation in the large catalytic loop of the enzymes. In this work, we report implicit ligand sampling (ILS) calculations of the canonical l-tyrosine decarboxylase from Papaver somniferum (PsTyDC) that catalyzes l-tyrosine decarboxylation and its Y350F mutant that instead catalyzes the decarboxylation-dependent oxidative deamination of the same substrate. Through comparative analysis of the resulting three-dimensional (3D) O2 free energy profiles, we evaluate the impact of the key tyrosine/phenylalanine mutation on oxygen accessibility to both the wild type and Y350F mutant of PsTyDC. Additionally, using molecular dynamics (MD) simulations of the l-tryptophan decarboxylase from Catharanthus roseus (CrTDC), we further investigate the dynamics of a large catalytic loop known to be indispensable to all AAADs. Results of our ILS and MD calculations shed new light on how key structural elements and loop conformational dynamics underlie the enzymatic functions of different members of the plant AAAD family.
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Affiliation(s)
- Yitao Gou
- Department of Physics, The
Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China
| | - Tianjie Li
- Department of Physics, The
Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China
| | - Yi Wang
- Department of Physics, The
Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China
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6
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Wang D, Qiu Y, Beyerle ER, Huang X, Tiwary P. Information Bottleneck Approach for Markov Model Construction. J Chem Theory Comput 2024; 20:5352-5367. [PMID: 38859575 PMCID: PMC11199095 DOI: 10.1021/acs.jctc.4c00449] [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] [Indexed: 06/12/2024]
Abstract
Markov state models (MSMs) have proven valuable in studying the dynamics of protein conformational changes via statistical analysis of molecular dynamics simulations. In MSMs, the complex configuration space is coarse-grained into conformational states, with dynamics modeled by a series of Markovian transitions among these states at discrete lag times. Constructing the Markovian model at a specific lag time necessitates defining states that circumvent significant internal energy barriers, enabling internal dynamics relaxation within the lag time. This process effectively coarse-grains time and space, integrating out rapid motions within metastable states. Thus, MSMs possess a multiresolution nature, where the granularity of states can be adjusted according to the time-resolution, offering flexibility in capturing system dynamics. This work introduces a continuous embedding approach for molecular conformations using the state predictive information bottleneck (SPIB), a framework that unifies dimensionality reduction and state space partitioning via a continuous, machine learned basis set. Without explicit optimization of the VAMP-based scores, SPIB demonstrates state-of-the-art performance in identifying slow dynamical processes and constructing predictive multiresolution Markovian models. Through applications to well-validated mini-proteins, SPIB showcases unique advantages compared to competing methods. It autonomously and self-consistently adjusts the number of metastable states based on a specified minimal time resolution, eliminating the need for manual tuning. While maintaining efficacy in dynamical properties, SPIB excels in accurately distinguishing metastable states and capturing numerous well-populated macrostates. This contrasts with existing VAMP-based methods, which often emphasize slow dynamics at the expense of incorporating numerous sparsely populated states. Furthermore, SPIB's ability to learn a low-dimensional continuous embedding of the underlying MSMs enhances the interpretation of dynamic pathways. With these benefits, we propose SPIB as an easy-to-implement methodology for end-to-end MSM construction.
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Affiliation(s)
- Dedi Wang
- Biophysics Program and Institute for Physical Science and Technology, University of Maryland, College Park, MD 20742, United States
| | - Yunrui Qiu
- Department of Chemistry, Theoretical Chemistry Institute, University of Wisconsin-Madison, Madison, WI 53706, United States
- Data Science Institute, University of Wisconsin-Madison, Madison, WI, 53706, United States
| | - Eric R. Beyerle
- Institute for Physical Science and Technology, University of Maryland, College Park, MD 20742, United States
| | - Xuhui Huang
- Department of Chemistry, Theoretical Chemistry Institute, University of Wisconsin-Madison, Madison, WI 53706, United States
- Data Science Institute, University of Wisconsin-Madison, Madison, WI, 53706, United States
| | - Pratyush Tiwary
- Department of Chemistry and Biochemistry and Institute for Physical Science and Technology, University of Maryland, College Park, MD 20742, United States
- University of Maryland Institute for Health Computing, Bethesda, MD 20852, United States
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7
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Selvam B, Paul A, Yu YC, Chen LQ, Shukla D. SWEET family transporters act as water conducting carrier proteins in plants. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.23.600272. [PMID: 38979333 PMCID: PMC11230166 DOI: 10.1101/2024.06.23.600272] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/10/2024]
Abstract
Dedicated water channels are involved in the facilitated diffusion of water molecules across the cell membrane in plants. Transporter proteins are also known to transport water molecules along with substrates, however the molecular mechanism of water permeation is not well understood in plant transporters. Here, we show plant sugar transporters from the SWEET (Sugar Will Eventually be Exported Transporter) family act as water-conducting carrier proteins via a variety of passive and active mechanisms that allow diffusion of water molecules from one side of the membrane to the other. This study provides a molecular perspective on how plant membrane transporters act as water carrier proteins, a topic that has not been extensively explored in literature. Water permeation in membrane transporters could occur via four distinct mechanisms which form our hypothesis for water transport in SWEETs. These hypothesis are tested using molecular dynamics simulations of the outward-facing, occluded, and inward-facing state of AtSWEET1 to identify the water permeation pathways and the flux associated with them. The hydrophobic gates at the center of the transport tunnel act as a barrier that restricts water permeation. We have performed in silico single and double mutations of the hydrophobic gate residues to examine the changes in the water conductivity. Surprisingly, the double mutant allows the water permeation to the intracellular half of the membrane and forms a continuous water channel. These computational results are validated by experimentally examining the transport of hydrogen peroxide molecules by the AtSWEET family of transporters. We have also shown that the transport of hydrogen peroxide follows the similar mechanism as water transport in AtSWEET1. Finally, we conclude that similar water-conduction states are also present in other SWEET transporters due to the high sequence and structure conservation exhibited by this transporter family.
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Affiliation(s)
- Balaji Selvam
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Champaign, Illinois 61801, United States
| | - Arnav Paul
- Department of Chemistry, University of Illinois at Urbana-Champaign, Champaign, Illinois 61801, United States
| | - Ya-Chi Yu
- Department of Plant Biology, University of Illinois at Urbana-Champaign, Champaign, Illinois 61801, United States
| | - Li-Qing Chen
- Department of Plant Biology, University of Illinois at Urbana-Champaign, Champaign, Illinois 61801, United States
| | - Diwakar Shukla
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Champaign, Illinois 61801, United States
- Department of Plant Biology, University of Illinois at Urbana-Champaign, Champaign, Illinois 61801, United States
- Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, IL 61801, United States
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8
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Weigle AT, Shukla D. The Arabidopsis AtSWEET13 transporter discriminates sugars by selective facial and positional substrate recognition. Commun Biol 2024; 7:764. [PMID: 38914639 PMCID: PMC11196581 DOI: 10.1038/s42003-024-06291-6] [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: 10/26/2022] [Accepted: 05/03/2024] [Indexed: 06/26/2024] Open
Abstract
Transporters are targeted by endogenous metabolites and exogenous molecules to reach cellular destinations, but it is generally not understood how different substrate classes exploit the same transporter's mechanism. Any disclosure of plasticity in transporter mechanism when treated with different substrates becomes critical for developing general selectivity principles in membrane transport catalysis. Using extensive molecular dynamics simulations with an enhanced sampling approach, we select the Arabidopsis sugar transporter AtSWEET13 as a model system to identify the basis for glucose versus sucrose molecular recognition and transport. Here we find that AtSWEET13 chemical selectivity originates from a conserved substrate facial selectivity demonstrated when committing alternate access, despite mono-/di-saccharides experiencing differing degrees of conformational and positional freedom throughout other stages of transport. However, substrate interactions with structural hallmarks associated with known functional annotations can help reinforce selective preferences in molecular transport.
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Affiliation(s)
- Austin T Weigle
- Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA
| | - Diwakar Shukla
- Department of Chemical & Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA.
- Department of Plant Biology, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA.
- Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA.
- Center for Biophysics and Computational Biology, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA.
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9
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Wang D, Qiu Y, Beyerle ER, Huang X, Tiwary P. An Information Bottleneck Approach for Markov Model Construction. ARXIV 2024:arXiv:2404.02856v2. [PMID: 38947932 PMCID: PMC11213129] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 07/02/2024]
Abstract
Markov state models (MSMs) have proven valuable in studying dynamics of protein conformational changes via statistical analysis of molecular dynamics (MD) simulations. In MSMs, the complex configuration space is coarse-grained into conformational states, with dynamics modeled by a series of Markovian transitions among these states at discrete lag times. Constructing the Markovian model at a specific lag time necessitates defining states that circumvent significant internal energy barriers, enabling internal dynamics relaxation within the lag time. This process effectively coarse-grains time and space, integrating out rapid motions within metastable states. Thus, MSMs possess a multi-resolution nature, where the granularity of states can be adjusted according to the time-resolution, offering flexibility in capturing system dynamics. This work introduces a continuous embedding approach for molecular conformations using the state predictive information bottleneck (SPIB), a framework that unifies dimensionality reduction and state space partitioning via a continuous, machine learned basis set. Without explicit optimization of the VAMP-based scores, SPIB demonstrates state-of-the-art performance in identifying slow dynamical processes and constructing predictive multi-resolution Markovian models. Through applications to well-validated mini-proteins, SPIB showcases unique advantages compared to competing methods. It autonomously and self-consistently adjusts the number of metastable states based on specified minimal time resolution, eliminating the need for manual tuning. While maintaining efficacy in dynamical properties, SPIB excels in accurately distinguishing metastable states and capturing numerous well-populated macrostates. This contrasts with existing VAMP-based methods, which often emphasize slow dynamics at the expense of incorporating numerous sparsely populated states. Furthermore, SPIB's ability to learn a low-dimensional continuous embedding of the underlying MSMs enhances the interpretation of dynamic pathways. With these benefits, we propose SPIB as an easy-to-implement methodology for end-to-end MSMs construction.
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Affiliation(s)
- Dedi Wang
- Biophysics Program and Institute for Physical Science and Technology, University of Maryland, College Park, MD 20742, United States
| | - Yunrui Qiu
- Department of Chemistry, Theoretical Chemistry Institute, University of Wisconsin-Madison, Madison, WI 53706, United States
- Data Science Institute, University of Wisconsin-Madison, Madison, WI, 53706, United States
| | - Eric R. Beyerle
- Institute for Physical Science and Technology, University of Maryland, College Park, MD 20742, United States
| | - Xuhui Huang
- Department of Chemistry, Theoretical Chemistry Institute, University of Wisconsin-Madison, Madison, WI 53706, United States
- Data Science Institute, University of Wisconsin-Madison, Madison, WI, 53706, United States
| | - Pratyush Tiwary
- Department of Chemistry and Biochemistry and Institute for Physical Science and Technology, University of Maryland, College Park, MD 20742, United States
- University of Maryland Institute for Health Computing, Bethesda, MD 20852, United States
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10
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Ray Chaudhuri N, Ghosh Dastidar S. Adaptive Workflows of Machine Learning Illuminate the Sequential Operation Mechanism of the TAK1's Allosteric Network. Biochemistry 2024; 63:1474-1492. [PMID: 38743619 DOI: 10.1021/acs.biochem.3c00643] [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: 05/16/2024]
Abstract
Allostery is a fundamental mechanism driving biomolecular processes that holds significant therapeutic concern. Our study rigorously investigates how two distinct machine-learning algorithms uniquely classify two already close-to-active DFG-in states of TAK1, differing just by the presence or absence of its allosteric activator TAB1, from an ensemble mixture of conformations (obtained from 2.4 μs molecular dynamics (MD) simulations). The novelty, however, lies in understanding the deeper algorithmic potentials to systematically derive a diverse set of differential residue connectivity features that reconstruct the essential mechanistic architecture for TAK1-TAB1 allostery in such a close-to-active biochemical scenario. While the recursive, random forest-based workflow displays the potential of conducting discretized, hierarchical derivation of allosteric features, a multilayer perceptron-based approach gains considerable efficacy in revealing fluid connected patterns of features when hybridized with mutual information scoring. Interestingly, both pipelines benchmark similar directions of functional conformational changes for TAK1's activation. The findings significantly advance the depth of mechanistic understanding by highlighting crucial activation signatures along a directed C-lobe → activation loop → ATP pocket channel of information flow, including (1) the αF-αE biterminal alignments and (2) the "catalytic" drift of the activation loop toward kinase active site. Besides, some novel allosteric hotspots (K253, Y206, N189, etc.) are further recognized as TAB1 sensors, transducers, and responders, including a benchmark E70 mutation site, precisely mapping the important structural segments for sequential allosteric execution. Hence, our work demonstrates how to navigate through greater structural depths and dimensions of dynamic allosteric machineries just by leveraging standard ML methods in suitable streamlined workflows adaptive to the specific system and objectives.
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Affiliation(s)
- Nibedita Ray Chaudhuri
- Biological Sciences, Bose Institute, EN 80, Sector V, Bidhan Nagar, Kolkata 700091, India
| | - Shubhra Ghosh Dastidar
- Biological Sciences, Bose Institute, EN 80, Sector V, Bidhan Nagar, Kolkata 700091, India
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11
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Miller JJ, Mallimadugula UL, Zimmerman MI, Stuchell-Brereton MD, Soranno A, Bowman GR. Accounting for fast vs slow exchange in single molecule FRET experiments reveals hidden conformational states. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.03.597137. [PMID: 38895430 PMCID: PMC11185552 DOI: 10.1101/2024.06.03.597137] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/21/2024]
Abstract
Proteins are dynamic systems whose structural preferences determine their function. Unfortunately, building atomically detailed models of protein structural ensembles remains challenging, limiting our understanding of the relationships between sequence, structure, and function. Combining single molecule Förster resonance energy transfer (smFRET) experiments with molecular dynamics simulations could provide experimentally grounded, all-atom models of a protein's structural ensemble. However, agreement between the two techniques is often insufficient to achieve this goal. Here, we explore whether accounting for important experimental details like averaging across structures sampled during a given smFRET measurement is responsible for this apparent discrepancy. We present an approach to account for this time-averaging by leveraging the kinetic information available from Markov state models of a protein's dynamics. This allows us to accurately assess which timescales are averaged during an experiment. We find this approach significantly improves agreement between simulations and experiments in proteins with varying degrees of dynamics, including the well-ordered protein T4 lysozyme, the partially disordered protein apolipoprotein E (ApoE), and a disordered amyloid protein (Aβ40). We find evidence for hidden states that are not apparent in smFRET experiments because of time averaging with other structures, akin to states in fast exchange in NMR, and evaluate different force fields. Finally, we show how remaining discrepancies between computations and experiments can be used to guide additional simulations and build structural models for states that were previously unaccounted for. We expect our approach will enable combining simulations and experiments to understand the link between sequence, structure, and function in many settings.
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Affiliation(s)
- Justin J. Miller
- Departments of Biochemistry & Biophysics and Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, United States
- Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, St. Louis, Missouri 63110, United States
| | - Upasana L. Mallimadugula
- Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, St. Louis, Missouri 63110, United States
| | - Maxwell I. Zimmerman
- Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, St. Louis, Missouri 63110, United States
| | - Melissa D. Stuchell-Brereton
- Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, St. Louis, Missouri 63110, United States
| | - Andrea Soranno
- Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, St. Louis, Missouri 63110, United States
| | - Gregory R. Bowman
- Departments of Biochemistry & Biophysics and Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, United States
- Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, St. Louis, Missouri 63110, United States
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12
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Hao Z, Guo Q, Peng W, Da LT. A kinetic model reveals the critical gating motifs for donor-substrate loading into Actinobacillus pleuropneumoniae N-glycosyltransferase. Phys Chem Chem Phys 2024; 26:13441-13451. [PMID: 38647259 DOI: 10.1039/d3cp06034a] [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: 04/25/2024]
Abstract
Soluble N-glycosyltransferase from Actinobacillus pleuropneumoniae (ApNGT) catalyzes the glycosylation of asparagine residues, and represents one of the most encouraging biocatalysts for N-glycoprotein production. Since the sugar tolerance of ApNGT is restricted to limited monosaccharides (e.g., Glc, GlcN, Gal, Xyl, and Man), tremendous efforts are devoted to expanding the substrate scope of ApNGT via enzyme engineering. However, rational design of novel NGT variants suffers from an elusive understanding of the substrate-binding process from a dynamic point of view. Here, by employing extensive all-atom molecular dynamics (MD) simulations integrated with a kinetic model, we reveal, at the atomic level, the complete donor-substrate binding process from the bulk solvent to the ApNGT active-site, and the key intermediate states of UDP-Glc during its loading dynamics. We are able to determine the critical transition event that limits the overall binding rate, which guides us to pinpoint the key ApNGT residues dictating the donor-substrate entry. The functional roles of several identified gating residues were evaluated through site-directed mutagenesis and enzymatic assays. Two single-point mutations, N471A and S496A, could profoundly enhance the catalytic activity of ApNGT. Our work provides deep mechanistic insights into the structural dynamics of the donor-substrate loading process for ApNGT, which sets a rational basis for design of novel NGT variants with desired substrate specificity.
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Affiliation(s)
- Zhiqiang Hao
- Key Laboratory of Systems Biomedicine (Ministry of Education), Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai 200240, China.
| | - Qiang Guo
- Key Laboratory of Systems Biomedicine (Ministry of Education), Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai 200240, China.
| | - Wenjie Peng
- Key Laboratory of Systems Biomedicine (Ministry of Education), Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai 200240, China.
| | - Lin-Tai Da
- Key Laboratory of Systems Biomedicine (Ministry of Education), Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai 200240, China.
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13
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Xu T, Li Y, Gao X, Zhang L. Understanding the Fast-Triggering Unfolding Dynamics of FK-11 upon Photoexcitation of Azobenzene. J Phys Chem Lett 2024; 15:3531-3540. [PMID: 38526058 DOI: 10.1021/acs.jpclett.4c00091] [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: 03/26/2024]
Abstract
Photoswitchable molecules can control the activity and functions of biomolecules by triggering conformational changes. However, it is still challenging to fully understand such fast-triggering conformational evolution from nonequilibrium to equilibrium distribution at the molecular level. Herein, we successfully simulated the unfolding of the FK-11 peptide upon the photoinduced trans-to-cis isomerization of azobenzene based on the Markov state model. We found that the ensemble of FK-11 contains five conformational states, constituting two unfolding pathways. More intriguingly, we observed the microsecond-scale conformational propagation of the FK-11 peptide from the fully folded state to the equilibrium populations of the five states. The computed CD spectra match well with the experimental data, validating our simulation method. Overall, our study not only offers a protocol to study the photoisomerization-induced conformational changes of enzymes but also could orientate the rational design of a photoswitchable molecule to manipulate biological functions.
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Affiliation(s)
- Tiantian Xu
- State Key Laboratory of Structural Chemistry, Fujian Institute of Research on the Structure of Matter, Chinese Academy of Sciences, Fuzhou, Fujian 350002, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yongfang Li
- State Key Laboratory of Structural Chemistry, Fujian Institute of Research on the Structure of Matter, Chinese Academy of Sciences, Fuzhou, Fujian 350002, China
| | - Xin Gao
- Computational Bioscience Research Center, King Abdullah University of Science and Technology (KAUST), Thuwal 23955, Saudi Arabia
- Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955, Saudi Arabia
| | - Lu Zhang
- State Key Laboratory of Structural Chemistry, Fujian Institute of Research on the Structure of Matter, Chinese Academy of Sciences, Fuzhou, Fujian 350002, China
- University of Chinese Academy of Sciences, Beijing 100049, China
- Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, Fuzhou, Fujian 361005, China
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14
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Wu Y, Cao S, Qiu Y, Huang X. Tutorial on how to build non-Markovian dynamic models from molecular dynamics simulations for studying protein conformational changes. J Chem Phys 2024; 160:121501. [PMID: 38516972 DOI: 10.1063/5.0189429] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Accepted: 02/20/2024] [Indexed: 03/23/2024] Open
Abstract
Protein conformational changes play crucial roles in their biological functions. In recent years, the Markov State Model (MSM) constructed from extensive Molecular Dynamics (MD) simulations has emerged as a powerful tool for modeling complex protein conformational changes. In MSMs, dynamics are modeled as a sequence of Markovian transitions among metastable conformational states at discrete time intervals (called lag time). A major challenge for MSMs is that the lag time must be long enough to allow transitions among states to become memoryless (or Markovian). However, this lag time is constrained by the length of individual MD simulations available to track these transitions. To address this challenge, we have recently developed Generalized Master Equation (GME)-based approaches, encoding non-Markovian dynamics using a time-dependent memory kernel. In this Tutorial, we introduce the theory behind two recently developed GME-based non-Markovian dynamic models: the quasi-Markov State Model (qMSM) and the Integrative Generalized Master Equation (IGME). We subsequently outline the procedures for constructing these models and provide a step-by-step tutorial on applying qMSM and IGME to study two peptide systems: alanine dipeptide and villin headpiece. This Tutorial is available at https://github.com/xuhuihuang/GME_tutorials. The protocols detailed in this Tutorial aim to be accessible for non-experts interested in studying the biomolecular dynamics using these non-Markovian dynamic models.
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Affiliation(s)
- Yue Wu
- Department of Chemistry, Theoretical Chemistry Institute, University of Wisconsin-Madison, Madison, Wisconsin 53706, USA
| | - Siqin Cao
- Department of Chemistry, Theoretical Chemistry Institute, University of Wisconsin-Madison, Madison, Wisconsin 53706, USA
| | - Yunrui Qiu
- Department of Chemistry, Theoretical Chemistry Institute, University of Wisconsin-Madison, Madison, Wisconsin 53706, USA
| | - Xuhui Huang
- Department of Chemistry, Theoretical Chemistry Institute, University of Wisconsin-Madison, Madison, Wisconsin 53706, USA
- Data Science Institute, University of Wisconsin-Madison, Madison, Wisconsin 53706, USA
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15
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Li M, Zhang X, Li S, Guo J. Unraveling the Interplay of Extracellular Domain Conformational Changes and Parathyroid Hormone Type 1 Receptor Activation in Class B1 G Protein-Coupled Receptors: Integrating Enhanced Sampling Molecular Dynamics Simulations and Markov State Models. ACS Chem Neurosci 2024; 15:844-853. [PMID: 38314550 DOI: 10.1021/acschemneuro.3c00747] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2024] Open
Abstract
Parathyroid hormone (PTH) type 1 receptor (PTH1R), as a typical class B1 G protein-coupled receptor (GPCR), is responsible for regulating bone turnover and maintaining calcium homeostasis, and its dysregulation has been implicated in the development of several diseases. The extracellular domain (ECD) of PTH1R is crucial for the recognition and binding of ligands, and the receptor may exhibit an autoinhibited state with the closure of the ECD in the absence of ligands. However, the correlation between ECD conformations and PTH1R activation remains unclear. Thus, this study combines enhanced sampling molecular dynamics (MD) simulations and Markov state models (MSMs) to reveal the possible relevance between the ECD conformations and the activation of PTH1R. First, 22 intermediate structures are generated from the autoinhibited state to the active state and conducted for 10 independent 200 ns simulations each. Then, the MSM is constructed based on the cumulative 44 μs simulations with six identified microstates. Finally, the potential interplay between ECD conformational changes and PTH1R activation as well as cryptic allosteric pockets in the intermediate states during receptor activation is revealed. Overall, our findings reveal that the activation of PTH1R has a specific correlation with ECD conformational changes and provide essential insights for GPCR biology and developing novel allosteric modulators targeting cryptic sites.
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Affiliation(s)
- Mengrong Li
- School of Physics and Astronomy & Institute of Natural Sciences, Shanghai Jiao Tong University, Shanghai 200240, China
- College of Life Sciences, Nanjing Agricultural University, Nanjing 210095, China
| | - Xiaoxiao Zhang
- College of Life Sciences, Nanjing Agricultural University, Nanjing 210095, China
| | - Shu Li
- Centre in Artificial Intelligence Driven Drug Discovery, Faculty of Applied Sciences, Macao Polytechnic University, Macao 999078, China
| | - Jingjing Guo
- Centre in Artificial Intelligence Driven Drug Discovery, Faculty of Applied Sciences, Macao Polytechnic University, Macao 999078, China
- Engineering Research Centre of Applied Technology on Machine Translation and Artificial Intelligence, Macao Polytechnic University, Macao 999078, China
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16
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Colberg M, Schofield J. Diffusive dynamics of a model protein chain in solution. J Chem Phys 2024; 160:075101. [PMID: 38375905 DOI: 10.1063/5.0182607] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Accepted: 01/24/2024] [Indexed: 02/21/2024] Open
Abstract
A Markov state model is a powerful tool that can be used to track the evolution of populations of configurations in an atomistic representation of a protein. For a coarse-grained linear chain model with discontinuous interactions, the transition rates among states that appear in the Markov model when the monomer dynamics is diffusive can be determined by computing the relative entropy of states and their mean first passage times, quantities that are unchanged by the specification of the energies of the relevant states. In this paper, we verify the folding dynamics described by a diffusive linear chain model of the crambin protein in three distinct solvent systems, each differing in complexity: a hard-sphere solvent, a solvent undergoing multi-particle collision dynamics, and an implicit solvent model. The predicted transition rates among configurations agree quantitatively with those observed in explicit molecular dynamics simulations for all three solvent models. These results suggest that the local monomer-monomer interactions provide sufficient friction for the monomer dynamics to be diffusive on timescales relevant to changes in conformation. Factors such as structural ordering and dynamic hydrodynamic effects appear to have minimal influence on transition rates within the studied solvent densities.
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Affiliation(s)
- Margarita Colberg
- Chemical Physics Theory Group, Department of Chemistry, University of Toronto, Toronto, Ontario M5S 3H6, Canada
| | - Jeremy Schofield
- Chemical Physics Theory Group, Department of Chemistry, University of Toronto, Toronto, Ontario M5S 3H6, Canada
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17
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Fazeel N, Chatterjee A, Bhattacharya S. Quantifying Disorder in a Protein by Mapping its Locally Correlated Structure and Kinetics. J Phys Chem B 2024; 128:1179-1187. [PMID: 38276946 DOI: 10.1021/acs.jpcb.3c06251] [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: 01/27/2024]
Abstract
Proteins under physiological conditions are inherently mobile and sample a vast array of structures. Consequently, the need arises, on the one hand, at a local level to determine the independent moving parts and their associated conformations and kinetics, and on the other hand, at a global level, to quantify the disorder in the full protein molecule. We present an approach that provides these quantities in the form of local kinetic network models, which are constructed by analyzing the molecular dynamics (MD) trajectories of the protein molecule. Entropies of independent parts of the molecule are quantified. The method outlined here, using the Trp-cage miniprotein prototype, offers a new tool to understand the dynamic structural changes that ultimately govern the functioning of a protein. The method is particularly suited to problems where there are subtle changes in the structure or dynamics at local levels, for example, due to ligand binding.
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Affiliation(s)
- Nadmaan Fazeel
- Department of Chemical Engineering, Indian Institute of Technology Bombay, Mumbai 400076, India
| | - Abhijit Chatterjee
- Department of Chemical Engineering, Indian Institute of Technology Bombay, Mumbai 400076, India
| | - Swati Bhattacharya
- Department of Chemical Engineering, Indian Institute of Technology Bombay, Mumbai 400076, India
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18
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Sisk TR, Robustelli P. Folding-upon-binding pathways of an intrinsically disordered protein from a deep Markov state model. Proc Natl Acad Sci U S A 2024; 121:e2313360121. [PMID: 38294935 PMCID: PMC10861926 DOI: 10.1073/pnas.2313360121] [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: 08/10/2023] [Accepted: 11/22/2023] [Indexed: 02/02/2024] Open
Abstract
A central challenge in the study of intrinsically disordered proteins is the characterization of the mechanisms by which they bind their physiological interaction partners. Here, we utilize a deep learning-based Markov state modeling approach to characterize the folding-upon-binding pathways observed in a long timescale molecular dynamics simulation of a disordered region of the measles virus nucleoprotein NTAIL reversibly binding the X domain of the measles virus phosphoprotein complex. We find that folding-upon-binding predominantly occurs via two distinct encounter complexes that are differentiated by the binding orientation, helical content, and conformational heterogeneity of NTAIL. We observe that folding-upon-binding predominantly proceeds through a multi-step induced fit mechanism with several intermediates and do not find evidence for the existence of canonical conformational selection pathways. We observe four kinetically separated native-like bound states that interconvert on timescales of eighty to five hundred nanoseconds. These bound states share a core set of native intermolecular contacts and stable NTAIL helices and are differentiated by a sequential formation of native and non-native contacts and additional helical turns. Our analyses provide an atomic resolution structural description of intermediate states in a folding-upon-binding pathway and elucidate the nature of the kinetic barriers between metastable states in a dynamic and heterogenous, or "fuzzy", protein complex.
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Affiliation(s)
- Thomas R. Sisk
- Department of Chemistry, Dartmouth College, Hanover, NH03755
| | - Paul Robustelli
- Department of Chemistry, Dartmouth College, Hanover, NH03755
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19
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Wu H, Noé F. Reaction coordinate flows for model reduction of molecular kinetics. J Chem Phys 2024; 160:044109. [PMID: 38270975 DOI: 10.1063/5.0176078] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Accepted: 12/26/2023] [Indexed: 01/26/2024] Open
Abstract
In this work, we introduce a flow based machine learning approach called reaction coordinate (RC) flow for the discovery of low-dimensional kinetic models of molecular systems. The RC flow utilizes a normalizing flow to design the coordinate transformation and a Brownian dynamics model to approximate the kinetics of RC, where all model parameters can be estimated in a data-driven manner. In contrast to existing model reduction methods for molecular kinetics, RC flow offers a trainable and tractable model of reduced kinetics in continuous time and space due to the invertibility of the normalizing flow. Furthermore, the Brownian dynamics-based reduced kinetic model investigated in this work yields a readily discernible representation of metastable states within the phase space of the molecular system. Numerical experiments demonstrate how effectively the proposed method discovers interpretable and accurate low-dimensional representations of given full-state kinetics from simulations.
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Affiliation(s)
- Hao Wu
- School of Mathematical Sciences, Institute of Natural Sciences and MOE-LSC, Shanghai Jiao Tong University, Shanghai, People's Republic of China
| | - Frank Noé
- Department of Mathematics and Computer Science and Department of Physics, Freie Universität Berlin, Berlin, Germany
- Department of Chemistry, Rice University, Houston, Texas 77005, USA
- Microsoft Research AI4Science, Berlin, Germany
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20
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Amaya-Rodriguez CA, Carvajal-Zamorano K, Bustos D, Alegría-Arcos M, Castillo K. A journey from molecule to physiology and in silico tools for drug discovery targeting the transient receptor potential vanilloid type 1 (TRPV1) channel. Front Pharmacol 2024; 14:1251061. [PMID: 38328578 PMCID: PMC10847257 DOI: 10.3389/fphar.2023.1251061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Accepted: 12/14/2023] [Indexed: 02/09/2024] Open
Abstract
The heat and capsaicin receptor TRPV1 channel is widely expressed in nerve terminals of dorsal root ganglia (DRGs) and trigeminal ganglia innervating the body and face, respectively, as well as in other tissues and organs including central nervous system. The TRPV1 channel is a versatile receptor that detects harmful heat, pain, and various internal and external ligands. Hence, it operates as a polymodal sensory channel. Many pathological conditions including neuroinflammation, cancer, psychiatric disorders, and pathological pain, are linked to the abnormal functioning of the TRPV1 in peripheral tissues. Intense biomedical research is underway to discover compounds that can modulate the channel and provide pain relief. The molecular mechanisms underlying temperature sensing remain largely unknown, although they are closely linked to pain transduction. Prolonged exposure to capsaicin generates analgesia, hence numerous capsaicin analogs have been developed to discover efficient analgesics for pain relief. The emergence of in silico tools offered significant techniques for molecular modeling and machine learning algorithms to indentify druggable sites in the channel and for repositioning of current drugs aimed at TRPV1. Here we recapitulate the physiological and pathophysiological functions of the TRPV1 channel, including structural models obtained through cryo-EM, pharmacological compounds tested on TRPV1, and the in silico tools for drug discovery and repositioning.
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Affiliation(s)
- Cesar A. Amaya-Rodriguez
- Centro Interdisciplinario de Neurociencia de Valparaíso, Facultad de Ciencias, Universidad de Valparaíso, Valparaíso, Chile
- Departamento de Fisiología y Comportamiento Animal, Facultad de Ciencias Naturales, Exactas y Tecnología, Universidad de Panamá, Ciudad de Panamá, Panamá
| | - Karina Carvajal-Zamorano
- Centro Interdisciplinario de Neurociencia de Valparaíso, Facultad de Ciencias, Universidad de Valparaíso, Valparaíso, Chile
| | - Daniel Bustos
- Centro de Investigación de Estudios Avanzados del Maule (CIEAM), Vicerrectoría de Investigación y Postgrado Universidad Católica del Maule, Talca, Chile
- Laboratorio de Bioinformática y Química Computacional, Departamento de Medicina Traslacional, Facultad de Medicina, Universidad Católica del Maule, Talca, Chile
| | - Melissa Alegría-Arcos
- Núcleo de Investigación en Data Science, Facultad de Ingeniería y Negocios, Universidad de las Américas, Santiago, Chile
| | - Karen Castillo
- Centro Interdisciplinario de Neurociencia de Valparaíso, Facultad de Ciencias, Universidad de Valparaíso, Valparaíso, Chile
- Centro de Investigación de Estudios Avanzados del Maule (CIEAM), Vicerrectoría de Investigación y Postgrado Universidad Católica del Maule, Talca, Chile
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21
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Kim MJ, Martin CA, Kim J, Jablonski MM. Computational methods in glaucoma research: Current status and future outlook. Mol Aspects Med 2023; 94:101222. [PMID: 37925783 PMCID: PMC10842846 DOI: 10.1016/j.mam.2023.101222] [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: 08/01/2023] [Revised: 10/06/2023] [Accepted: 10/19/2023] [Indexed: 11/07/2023]
Abstract
Advancements in computational techniques have transformed glaucoma research, providing a deeper understanding of genetics, disease mechanisms, and potential therapeutic targets. Systems genetics integrates genomic and clinical data, aiding in identifying drug targets, comprehending disease mechanisms, and personalizing treatment strategies for glaucoma. Molecular dynamics simulations offer valuable molecular-level insights into glaucoma-related biomolecule behavior and drug interactions, guiding experimental studies and drug discovery efforts. Artificial intelligence (AI) technologies hold promise in revolutionizing glaucoma research, enhancing disease diagnosis, target identification, and drug candidate selection. The generalized protocols for systems genetics, MD simulations, and AI model development are included as a guide for glaucoma researchers. These computational methods, however, are not separate and work harmoniously together to discover novel ways to combat glaucoma. Ongoing research and progresses in genomics technologies, MD simulations, and AI methodologies project computational methods to become an integral part of glaucoma research in the future.
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Affiliation(s)
- Minjae J Kim
- Department of Ophthalmology, The Hamilton Eye Institute, The University of Tennessee Health Science Center, Memphis, TN, 38163, USA.
| | - Cole A Martin
- Department of Ophthalmology, The Hamilton Eye Institute, The University of Tennessee Health Science Center, Memphis, TN, 38163, USA.
| | - Jinhwa Kim
- Graduate School of Artificial Intelligence, Graduate School of Metaverse, Department of Management Information Systems, Sogang University, 1 Shinsoo-Dong, Mapo-Gu, Seoul, South Korea.
| | - Monica M Jablonski
- Department of Ophthalmology, The Hamilton Eye Institute, The University of Tennessee Health Science Center, Memphis, TN, 38163, USA.
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22
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Weber JK, Morrone JA, Kang SG, Zhang L, Lang L, Chowell D, Krishna C, Huynh T, Parthasarathy P, Luan B, Alban TJ, Cornell WD, Chan TA. Unsupervised and supervised AI on molecular dynamics simulations reveals complex characteristics of HLA-A2-peptide immunogenicity. Brief Bioinform 2023; 25:bbad504. [PMID: 38233090 PMCID: PMC10793977 DOI: 10.1093/bib/bbad504] [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: 08/25/2023] [Revised: 11/03/2023] [Accepted: 12/03/2023] [Indexed: 01/19/2024] Open
Abstract
Immunologic recognition of peptide antigens bound to class I major histocompatibility complex (MHC) molecules is essential to both novel immunotherapeutic development and human health at large. Current methods for predicting antigen peptide immunogenicity rely primarily on simple sequence representations, which allow for some understanding of immunogenic features but provide inadequate consideration of the full scale of molecular mechanisms tied to peptide recognition. We here characterize contributions that unsupervised and supervised artificial intelligence (AI) methods can make toward understanding and predicting MHC(HLA-A2)-peptide complex immunogenicity when applied to large ensembles of molecular dynamics simulations. We first show that an unsupervised AI method allows us to identify subtle features that drive immunogenicity differences between a cancer neoantigen and its wild-type peptide counterpart. Next, we demonstrate that a supervised AI method for class I MHC(HLA-A2)-peptide complex classification significantly outperforms a sequence model on small datasets corrected for trivial sequence correlations. Furthermore, we show that both unsupervised and supervised approaches reveal determinants of immunogenicity based on time-dependent molecular fluctuations and anchor position dynamics outside the MHC binding groove. We discuss implications of these structural and dynamic immunogenicity correlates for the induction of T cell responses and therapeutic T cell receptor design.
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Affiliation(s)
- Jeffrey K Weber
- IBM Thomas J. Watson Research Center, Yorktown Heights, NY 10598USA
| | - Joseph A Morrone
- IBM Thomas J. Watson Research Center, Yorktown Heights, NY 10598USA
| | - Seung-gu Kang
- IBM Thomas J. Watson Research Center, Yorktown Heights, NY 10598USA
| | - Leili Zhang
- IBM Thomas J. Watson Research Center, Yorktown Heights, NY 10598USA
| | - Lijun Lang
- IBM Thomas J. Watson Research Center, Yorktown Heights, NY 10598USA
| | - Diego Chowell
- Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029
| | - Chirag Krishna
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Tien Huynh
- IBM Thomas J. Watson Research Center, Yorktown Heights, NY 10598USA
| | - Prerana Parthasarathy
- Center for Immunotherapy and Precision Immuno-Oncology, Cleveland Clinic, Cleveland, OH 44195USA
- Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44015USA
| | - Binquan Luan
- IBM Thomas J. Watson Research Center, Yorktown Heights, NY 10598USA
| | - Tyler J Alban
- Center for Immunotherapy and Precision Immuno-Oncology, Cleveland Clinic, Cleveland, OH 44195USA
- Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44015USA
| | - Wendy D Cornell
- IBM Thomas J. Watson Research Center, Yorktown Heights, NY 10598USA
| | - Timothy A Chan
- Center for Immunotherapy and Precision Immuno-Oncology, Cleveland Clinic, Cleveland, OH 44195USA
- Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44015USA
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY 10065USA
- Taussig Cancer Institute, Cleveland Clinic, Cleveland, OH 44015USA
- National Center for Regenerative Medicine, Cleveland Clinic, Cleveland, OH 44015USA
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23
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Juber S, Schäfer LV. Dynamics of organophosphate guest encapsulation in heteroleptic coordination cages. Phys Chem Chem Phys 2023; 25:29496-29505. [PMID: 37888835 DOI: 10.1039/d3cp04342h] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2023]
Abstract
Heteroleptic coordination cages allow the design of different host structures that can bind guest molecules within their cavities. In a previous work, the energetics of organophosphate encapsulation in palladium(II)-based heteroleptic coordination cages that differ in terms of their ability to form hydrogen bonds have been investigated [Platzek et al., Endohedrally Functionalized Heteroleptic Coordination Cages for Phosphate Ester Binding, Angew. Chem., Int. Ed. 2022, 61, e2022093]. The present work focuses on the dynamics of this system. Dynamic information is obtained through the application of a Markov state model (MSM) to unbiased multi-microsecond atomistic molecular dynamics simulations of guest binding and release. The MSM reveals that both the bound state and the binding/unbinding pathways are highly dynamic, with different types of interactions mediating the binding of the diphenylphosphate guest. Thus, the simulations highlight the dynamic nature of the nanoconfinement in the host-guest systems, with possible implications for the use of such coordination cages as catalysts.
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Affiliation(s)
- Selina Juber
- Theoretical Chemistry, Ruhr University Bochum, D-44780 Bochum, Germany.
| | - Lars V Schäfer
- Theoretical Chemistry, Ruhr University Bochum, D-44780 Bochum, Germany.
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24
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Lemcke S, Appeldorn JH, Wand M, Speck T. Toward a structural identification of metastable molecular conformations. J Chem Phys 2023; 159:114105. [PMID: 37712784 DOI: 10.1063/5.0164145] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Accepted: 08/21/2023] [Indexed: 09/16/2023] Open
Abstract
Interpreting high-dimensional data from molecular dynamics simulations is a persistent challenge. In this paper, we show that for a small peptide, deca-alanine, metastable states can be identified through a neural net based on structural information alone. While processing molecular dynamics data, dimensionality reduction is a necessary step that projects high-dimensional data onto a low-dimensional representation that, ideally, captures the conformational changes in the underlying data. Conventional methods make use of the temporal information contained in trajectories generated through integrating the equations of motion, which forgoes more efficient sampling schemes. We demonstrate that EncoderMap, an autoencoder architecture with an additional distance metric, can find a suitable low-dimensional representation to identify long-lived molecular conformations using exclusively structural information. For deca-alanine, which exhibits several helix-forming pathways, we show that this approach allows us to combine simulations with different biasing forces and yields representations comparable in quality to other established methods. Our results contribute to computational strategies for the rapid automatic exploration of the configuration space of peptides and proteins.
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Affiliation(s)
- Simon Lemcke
- Institut für Physik, Johannes Gutenberg-Universität Mainz, Staudingerweg 7-9, 55128 Mainz, Germany
| | - Jörn H Appeldorn
- Institut für Physik, Johannes Gutenberg-Universität Mainz, Staudingerweg 7-9, 55128 Mainz, Germany
| | - Michael Wand
- Institut für Informatik, Johannes Gutenberg-Universität Mainz, Staudingerweg 9, 55128 Mainz, Germany
| | - Thomas Speck
- Institut für Theoretische Physik IV, Universität Stuttgart, Heisenbergstr. 3, 70569 Stuttgart, Germany
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25
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Liu B, Xue M, Qiu Y, Konovalov KA, O’Connor MS, Huang X. GraphVAMPnets for uncovering slow collective variables of self-assembly dynamics. J Chem Phys 2023; 159:094901. [PMID: 37655771 PMCID: PMC11005469 DOI: 10.1063/5.0158903] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Accepted: 08/11/2023] [Indexed: 09/02/2023] Open
Abstract
Uncovering slow collective variables (CVs) of self-assembly dynamics is important to elucidate its numerous kinetic assembly pathways and drive the design of novel structures for advanced materials through the bottom-up approach. However, identifying the CVs for self-assembly presents several challenges. First, self-assembly systems often consist of identical monomers, and the feature representations should be invariant to permutations and rotational symmetries. Physical coordinates, such as aggregate size, lack high-resolution detail, while common geometric coordinates like pairwise distances are hindered by the permutation and rotational symmetry challenges. Second, self-assembly is usually a downhill process, and the trajectories often suffer from insufficient sampling of backward transitions that correspond to the dissociation of self-assembled structures. Popular dimensionality reduction methods, such as time-structure independent component analysis, impose detailed balance constraints, potentially obscuring the true dynamics of self-assembly. In this work, we employ GraphVAMPnets, which combines graph neural networks with a variational approach for Markovian process (VAMP) theory to identify the slow CVs of the self-assembly processes. First, GraphVAMPnets bears the advantages of graph neural networks, in which the graph embeddings can represent self-assembly structures in high-resolution while being invariant to permutations and rotational symmetries. Second, it is built upon VAMP theory, which studies Markov processes without forcing detailed balance constraints, which addresses the out-of-equilibrium challenge in the self-assembly process. We demonstrate GraphVAMPnets for identifying slow CVs of self-assembly kinetics in two systems: the aggregation of two hydrophobic molecules and the self-assembly of patchy particles. We expect that our GraphVAMPnets can be widely applied to molecular self-assembly.
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Affiliation(s)
- Bojun Liu
- Department of Chemistry, Theoretical Chemistry Institute, University of Wisconsin-Madison, Madison, Wisconsin 53706, USA
| | - Mingyi Xue
- Department of Chemistry, Theoretical Chemistry Institute, University of Wisconsin-Madison, Madison, Wisconsin 53706, USA
| | - Yunrui Qiu
- Department of Chemistry, Theoretical Chemistry Institute, University of Wisconsin-Madison, Madison, Wisconsin 53706, USA
| | - Kirill A. Konovalov
- Department of Chemistry, Theoretical Chemistry Institute, University of Wisconsin-Madison, Madison, Wisconsin 53706, USA
| | - Michael S. O’Connor
- Biophysics Graduate Program, University of Wisconsin-Madison, Madison, Wisconsin 53706, USA
| | - Xuhui Huang
- Author to whom correspondence should be addressed:
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Nagel D, Sartore S, Stock G. Toward a Benchmark for Markov State Models: The Folding of HP35. J Phys Chem Lett 2023; 14:6956-6967. [PMID: 37504674 DOI: 10.1021/acs.jpclett.3c01561] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
Adopting a 300 μs long MD trajectory of the folding of villin headpiece (HP35) by D. E. Shaw Research, we recently constructed a Markov state model (MSM) based on inter-residue contacts. The model reproduces the folding time and predicts that the native basin and unfolded region consist of metastable substates that are structurally well-characterized. Recognizing the need to establish well-defined benchmark problems, we study to what extent and in what sense this MSM can be employed as a reference model. Hence, we test the robustness of the MSM by comparing it to models that use alternative combinations of features, dimensionality reduction methods, and clustering schemes. The study suggests some main characteristics of the folding of HP35 that should be reproduced by other competitive models. Moreover, the discussion reveals which parts of the MSM workflow matter most for the considered problem and illustrates the promises and pitfalls of state-based models for the interpretation of biomolecular simulations.
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Affiliation(s)
- Daniel Nagel
- Biomolecular Dynamics, Institute of Physics, University of Freiburg, 79104 Freiburg, Germany
| | - Sofia Sartore
- Biomolecular Dynamics, Institute of Physics, University of Freiburg, 79104 Freiburg, Germany
| | - Gerhard Stock
- Biomolecular Dynamics, Institute of Physics, University of Freiburg, 79104 Freiburg, Germany
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27
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Sisk T, Robustelli P. Folding-upon-binding pathways of an intrinsically disordered protein from a deep Markov state model. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.21.550103. [PMID: 37546728 PMCID: PMC10401938 DOI: 10.1101/2023.07.21.550103] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/08/2023]
Abstract
A central challenge in the study of intrinsically disordered proteins is the characterization of the mechanisms by which they bind their physiological interaction partners. Here, we utilize a deep learning based Markov state modeling approach to characterize the folding-upon-binding pathways observed in a long-time scale molecular dynamics simulation of a disordered region of the measles virus nucleoprotein NTAIL reversibly binding the X domain of the measles virus phosphoprotein complex. We find that folding-upon-binding predominantly occurs via two distinct encounter complexes that are differentiated by the binding orientation, helical content, and conformational heterogeneity of NTAIL. We do not, however, find evidence for the existence of canonical conformational selection or induced fit binding pathways. We observe four kinetically separated native-like bound states that interconvert on time scales of eighty to five hundred nanoseconds. These bound states share a core set of native intermolecular contacts and stable NTAIL helices and are differentiated by a sequential formation of native and non-native contacts and additional helical turns. Our analyses provide an atomic resolution structural description of intermediate states in a folding-upon-binding pathway and elucidate the nature of the kinetic barriers between metastable states in a dynamic and heterogenous, or "fuzzy", protein complex.
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Affiliation(s)
- Thomas Sisk
- Dartmouth College, Department of Chemistry, Hanover, NH, 03755
| | - Paul Robustelli
- Dartmouth College, Department of Chemistry, Hanover, NH, 03755
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28
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Qiu Y, O’Connor MS, Xue M, Liu B, Huang X. An Efficient Path Classification Algorithm Based on Variational Autoencoder to Identify Metastable Path Channels for Complex Conformational Changes. J Chem Theory Comput 2023; 19:4728-4742. [PMID: 37382437 PMCID: PMC11042546 DOI: 10.1021/acs.jctc.3c00318] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/30/2023]
Abstract
Conformational changes (i.e., dynamic transitions between pairs of conformational states) play important roles in many chemical and biological processes. Constructing the Markov state model (MSM) from extensive molecular dynamics (MD) simulations is an effective approach to dissect the mechanism of conformational changes. When combined with transition path theory (TPT), MSM can be applied to elucidate the ensemble of kinetic pathways connecting pairs of conformational states. However, the application of TPT to analyze complex conformational changes often results in a vast number of kinetic pathways with comparable fluxes. This obstacle is particularly pronounced in heterogeneous self-assembly and aggregation processes. The large number of kinetic pathways makes it challenging to comprehend the molecular mechanisms underlying conformational changes of interest. To address this challenge, we have developed a path classification algorithm named latent-space path clustering (LPC) that efficiently lumps parallel kinetic pathways into distinct metastable path channels, making them easier to comprehend. In our algorithm, MD conformations are first projected onto a low-dimensional space containing a small set of collective variables (CVs) by time-structure-based independent component analysis (tICA) with kinetic mapping. Then, MSM and TPT are constructed to obtain the ensemble of pathways, and a deep learning architecture named the variational autoencoder (VAE) is used to learn the spatial distributions of kinetic pathways in the continuous CV space. Based on the trained VAE model, the TPT-generated ensemble of kinetic pathways can be embedded into a latent space, where the classification becomes clear. We show that LPC can efficiently and accurately identify the metastable path channels in three systems: a 2D potential, the aggregation of two hydrophobic particles in water, and the folding of the Fip35 WW domain. Using the 2D potential, we further demonstrate that our LPC algorithm outperforms the previous path-lumping algorithms by making substantially fewer incorrect assignments of individual pathways to four path channels. We expect that LPC can be widely applied to identify the dominant kinetic pathways underlying complex conformational changes.
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Affiliation(s)
- Yunrui Qiu
- Department of Chemistry, Theoretical Chemistry Institute, University of Wisconsin-Madison, Madison, WI, 53706, USA
| | - Michael S. O’Connor
- Biophysics Graduate Program, University of Wisconsin-Madison, Madison, WI, 53706, USA
| | - Mingyi Xue
- Department of Chemistry, Theoretical Chemistry Institute, University of Wisconsin-Madison, Madison, WI, 53706, USA
| | - Bojun Liu
- Department of Chemistry, Theoretical Chemistry Institute, University of Wisconsin-Madison, Madison, WI, 53706, USA
| | - Xuhui Huang
- Department of Chemistry, Theoretical Chemistry Institute, University of Wisconsin-Madison, Madison, WI, 53706, USA
- Biophysics Graduate Program, University of Wisconsin-Madison, Madison, WI, 53706, USA
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29
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Nagel D, Sartore S, Stock G. Selecting Features for Markov Modeling: A Case Study on HP35. J Chem Theory Comput 2023. [PMID: 37167425 DOI: 10.1021/acs.jctc.3c00240] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/13/2023]
Abstract
Markov state models represent a popular means to interpret molecular dynamics trajectories in terms of memoryless transitions between metastable conformational states. To provide a mechanistic understanding of the considered biomolecular process, these states should reflect structurally distinct conformations and ensure a time scale separation between fast intrastate and slow interstate dynamics. Adopting the folding of villin headpiece (HP35) as a well-established model problem, here we discuss the selection of suitable input coordinates or "features", such as backbone dihedral angles and interresidue distances. We show that dihedral angles account accurately for the structure of the native energy basin of HP35, while the unfolded region of the free energy landscape and the folding process are best described by tertiary contacts of the protein. To construct a contact-based model, we consider various ways to define and select contact distances and introduce a low-pass filtering of the feature trajectory as well as a correlation-based characterization of states. Relying on input data that faithfully account for the mechanistic origin of the studied process, the states of the resulting Markov model are clearly discriminated by the features, describe consistently the hierarchical structure of the free energy landscape, and─as a consequence─correctly reproduce the slow time scales of the process.
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Affiliation(s)
- Daniel Nagel
- Biomolecular Dynamics, Institute of Physics, University of Freiburg, 79104 Freiburg, Germany
| | - Sofia Sartore
- Biomolecular Dynamics, Institute of Physics, University of Freiburg, 79104 Freiburg, Germany
| | - Gerhard Stock
- Biomolecular Dynamics, Institute of Physics, University of Freiburg, 79104 Freiburg, Germany
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30
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Dutta S, Shukla D. Distinct activation mechanisms regulate subtype selectivity of Cannabinoid receptors. Commun Biol 2023; 6:485. [PMID: 37147497 PMCID: PMC10163236 DOI: 10.1038/s42003-023-04868-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Accepted: 04/24/2023] [Indexed: 05/07/2023] Open
Abstract
Design of cannabinergic subtype selective ligands is challenging because of high sequence and structural similarities of cannabinoid receptors (CB1 and CB2). We hypothesize that the subtype selectivity of designed selective ligands can be explained by the ligand binding to the conformationally distinct states between cannabinoid receptors. Analysis of ~ 700 μs of unbiased simulations using Markov state models and VAMPnets identifies the similarities and distinctions between the activation mechanism of both receptors. Structural and dynamic comparisons of metastable intermediate states allow us to observe the distinction in the binding pocket volume change during CB1 and CB2 activation. Docking analysis reveals that only a few of the intermediate metastable states of CB1 show high affinity towards CB2 selective agonists. In contrast, all the CB2 metastable states show a similar affinity for these agonists. These results mechanistically explain the subtype selectivity of these agonists by deciphering the activation mechanism of cannabinoid receptors.
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Affiliation(s)
- Soumajit Dutta
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Diwakar Shukla
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA.
- Center for Biophysics and Quantitative Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA.
- Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA.
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31
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Bhat V, Callaway CP, Risko C. Computational Approaches for Organic Semiconductors: From Chemical and Physical Understanding to Predicting New Materials. Chem Rev 2023. [PMID: 37141497 DOI: 10.1021/acs.chemrev.2c00704] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/06/2023]
Abstract
While a complete understanding of organic semiconductor (OSC) design principles remains elusive, computational methods─ranging from techniques based in classical and quantum mechanics to more recent data-enabled models─can complement experimental observations and provide deep physicochemical insights into OSC structure-processing-property relationships, offering new capabilities for in silico OSC discovery and design. In this Review, we trace the evolution of these computational methods and their application to OSCs, beginning with early quantum-chemical methods to investigate resonance in benzene and building to recent machine-learning (ML) techniques and their application to ever more sophisticated OSC scientific and engineering challenges. Along the way, we highlight the limitations of the methods and how sophisticated physical and mathematical frameworks have been created to overcome those limitations. We illustrate applications of these methods to a range of specific challenges in OSCs derived from π-conjugated polymers and molecules, including predicting charge-carrier transport, modeling chain conformations and bulk morphology, estimating thermomechanical properties, and describing phonons and thermal transport, to name a few. Through these examples, we demonstrate how advances in computational methods accelerate the deployment of OSCsin wide-ranging technologies, such as organic photovoltaics (OPVs), organic light-emitting diodes (OLEDs), organic thermoelectrics, organic batteries, and organic (bio)sensors. We conclude by providing an outlook for the future development of computational techniques to discover and assess the properties of high-performing OSCs with greater accuracy.
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Affiliation(s)
- Vinayak Bhat
- Department of Chemistry & Center for Applied Energy Research, University of Kentucky, Lexington, Kentucky 40506-0055, United States
| | - Connor P Callaway
- Department of Chemistry & Center for Applied Energy Research, University of Kentucky, Lexington, Kentucky 40506-0055, United States
| | - Chad Risko
- Department of Chemistry & Center for Applied Energy Research, University of Kentucky, Lexington, Kentucky 40506-0055, United States
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32
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Dominic AJ, Cao S, Montoya-Castillo A, Huang X. Memory Unlocks the Future of Biomolecular Dynamics: Transformative Tools to Uncover Physical Insights Accurately and Efficiently. J Am Chem Soc 2023; 145:9916-9927. [PMID: 37104720 DOI: 10.1021/jacs.3c01095] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/29/2023]
Abstract
Conformational changes underpin function and encode complex biomolecular mechanisms. Gaining atomic-level detail of how such changes occur has the potential to reveal these mechanisms and is of critical importance in identifying drug targets, facilitating rational drug design, and enabling bioengineering applications. While the past two decades have brought Markov state model techniques to the point where practitioners can regularly use them to glimpse the long-time dynamics of slow conformations in complex systems, many systems are still beyond their reach. In this Perspective, we discuss how including memory (i.e., non-Markovian effects) can reduce the computational cost to predict the long-time dynamics in these complex systems by orders of magnitude and with greater accuracy and resolution than state-of-the-art Markov state models. We illustrate how memory lies at the heart of successful and promising techniques, ranging from the Fokker-Planck and generalized Langevin equations to deep-learning recurrent neural networks and generalized master equations. We delineate how these techniques work, identify insights that they can offer in biomolecular systems, and discuss their advantages and disadvantages in practical settings. We show how generalized master equations can enable the investigation of, for example, the gate-opening process in RNA polymerase II and demonstrate how our recent advances tame the deleterious influence of statistical underconvergence of the molecular dynamics simulations used to parameterize these techniques. This represents a significant leap forward that will enable our memory-based techniques to interrogate systems that are currently beyond the reach of even the best Markov state models. We conclude by discussing some current challenges and future prospects for how exploiting memory will open the door to many exciting opportunities.
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Affiliation(s)
- Anthony J Dominic
- Department of Chemistry, University of Colorado Boulder, Boulder, Colorado 80309, USA
| | - Siqin Cao
- Department of Chemistry, Theoretical Chemistry Institute, University of Wisconsin-Madison, Madison, Wisconsin 53706, USA
| | | | - Xuhui Huang
- Department of Chemistry, Theoretical Chemistry Institute, University of Wisconsin-Madison, Madison, Wisconsin 53706, USA
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33
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Shen Z, Sun Y, Zhu G, Xu G, Yu Z, Lu H, Chen Y. Molecular Insights into the Improved Bioactivity of Interferon Conjugates Attached to a Helical Polyglutamate. LANGMUIR : THE ACS JOURNAL OF SURFACES AND COLLOIDS 2023; 39:6539-6547. [PMID: 37127842 DOI: 10.1021/acs.langmuir.3c00501] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
Attaching polymers, especially polyethylene glycol (PEG), to protein drugs has emerged as a successful strategy to prolong circulation time in the bloodstream. The hypothesis is that the flexible chain wobbles on the protein's surface, thus resisting potential nonspecific adsorption. Such a theoretical framework may be challenged when a helical polyglutamate is used to conjugate with target proteins. In this study, we investigated the structure-activity relationships of polyglutamate-interferon conjugates P(EG3Glu)-IFN using molecular simulations. Our results show that the local crowding effect induced by oligoethylene glycols (i.e., EG3) is the primary driving force for helix formation in P(EG3Glu), and its helicity can be effectively increased by reducing the free volume of the two termini. Furthermore, it was found that the steric hindrance induced by IFN is not conductive to the helicity of P(EG3Glu) but contributes to its dominant orientation relative to interferon. The orientation of IFN relative to the helical P(EG3Glu) can help to protect the protein drug from neutralizing antibodies while maintaining its bioactivity. These findings suggest that the helical structure and its orientation are critical factors to consider when updating the theoretical framework for protein-polymer conjugates.
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Affiliation(s)
- Zhuanglin Shen
- Shenzhen Key Laboratory of Environmental Chemistry and Ecological Remediation, College of Chemistry and Environmental Engineering, Shenzhen University, Shenzhen, Guangdong 518060, China
- Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Shenzhen University Health Science Center, Shenzhen, Guangdong 518060, China
| | - Yiming Sun
- Beijing National Laboratory for Molecular Sciences, Center for Soft Matter Science and Engineering, Key Laboratory of Polymer Chemistry and Physics of Ministry of Education, College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China
| | - Guoliang Zhu
- Shenzhen Key Laboratory of Environmental Chemistry and Ecological Remediation, College of Chemistry and Environmental Engineering, Shenzhen University, Shenzhen, Guangdong 518060, China
| | - Gaixia Xu
- Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Shenzhen University Health Science Center, Shenzhen, Guangdong 518060, China
| | - Zhenqiang Yu
- Shenzhen Key Laboratory of Environmental Chemistry and Ecological Remediation, College of Chemistry and Environmental Engineering, Shenzhen University, Shenzhen, Guangdong 518060, China
| | - Hua Lu
- Beijing National Laboratory for Molecular Sciences, Center for Soft Matter Science and Engineering, Key Laboratory of Polymer Chemistry and Physics of Ministry of Education, College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China
| | - Yantao Chen
- Shenzhen Key Laboratory of Environmental Chemistry and Ecological Remediation, College of Chemistry and Environmental Engineering, Shenzhen University, Shenzhen, Guangdong 518060, China
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34
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Ahalawat N, Sahil M, Mondal J. Resolving Protein Conformational Plasticity and Substrate Binding via Machine Learning. J Chem Theory Comput 2023; 19:2644-2657. [PMID: 37068044 DOI: 10.1021/acs.jctc.2c00932] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/18/2023]
Abstract
A long-standing target in elucidating the biomolecular recognition process is the identification of binding-competent conformations of the receptor protein. However, protein conformational plasticity and the stochastic nature of the recognition processes often preclude the assignment of a specific protein conformation to an individual ligand-bound pose. Here, we demonstrate that a computational framework coined as RF-TICA-MD, which integrates an ensemble decision-tree-based Random Forest (RF) machine learning (ML) technique with an unsupervised dimension reduction approach time-structured independent component analysis (TICA), provides an efficient and unambiguous solution toward resolving protein conformational plasticity and the substrate binding process. In particular, we consider multimicrosecond-long molecular dynamics (MD) simulation trajectories of a ligand recognition process in solvent-inaccessible cavities of archetypal proteins T4 lysozyme and cytochrome P450cam. We show that in a scenario in which clear correspondence between protein conformation and binding-competent macrostates could not be obtained via an unsupervised dimension reduction approach, an a priori decision-tree-based supervised classification of the simulated recognition trajectories via RF would help characterize key amino acid residue pairs of the protein that are deemed sensitive for ligand binding. A subsequent unsupervised dimensional reduction of the selected residue pairs via TICA would then delineate a conformational landscape of protein which is able to demarcate ligand-bound poses from unbound ones. The proposed RF-TICA-MD approach is shown to be data agnostic and found to be robust when using other ML-based classification methods such as XGBoost. As a promising spinoff of the protocol, the framework is found to be capable of identifying distal protein locations which would be allosterically important for ligand binding and would characterize their roles in recognition pathways. A Python implementation of a proposed ML workflow is available in GitHub https://github.com/navjeet0211/rf-tica-md.
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Affiliation(s)
- Navjeet Ahalawat
- Department of Bioinformatics and Computational Biology, College of Biotechnology, CCS Haryana Agricultural University, Hisar 125 004, Haryana, India
| | - Mohammad Sahil
- Center for Interdisciplinary Sciences, Tata Institute of Fundamental Research, Hyderabad 500046, India
| | - Jagannath Mondal
- Center for Interdisciplinary Sciences, Tata Institute of Fundamental Research, Hyderabad 500046, India
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35
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Xiao S, Verkhivker GM, Tao P. Machine learning and protein allostery. Trends Biochem Sci 2023; 48:375-390. [PMID: 36564251 PMCID: PMC10023316 DOI: 10.1016/j.tibs.2022.12.001] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Revised: 11/16/2022] [Accepted: 12/02/2022] [Indexed: 12/23/2022]
Abstract
The fundamental biological importance and complexity of allosterically regulated proteins stem from their central role in signal transduction and cellular processes. Recently, machine-learning approaches have been developed and actively deployed to facilitate theoretical and experimental studies of protein dynamics and allosteric mechanisms. In this review, we survey recent developments in applications of machine-learning methods for studies of allosteric mechanisms, prediction of allosteric effects and allostery-related physicochemical properties, and allosteric protein engineering. We also review the applications of machine-learning strategies for characterization of allosteric mechanisms and drug design targeting SARS-CoV-2. Continuous development and task-specific adaptation of machine-learning methods for protein allosteric mechanisms will have an increasingly important role in bridging a wide spectrum of data-intensive experimental and theoretical technologies.
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Affiliation(s)
- Sian Xiao
- Department of Chemistry, Center for Research Computing, Center for Drug Discovery, Design, and Delivery (CD4), Southern Methodist University, Dallas, TX 75205, USA.
| | - Gennady M Verkhivker
- Graduate Program in Computational and Data Sciences, Schmid College of Science and Technology, Chapman University, Orange, CA 92866, USA; Department of Biomedical and Pharmaceutical Sciences, Chapman University School of Pharmacy, Irvine, CA 92618, USA
| | - Peng Tao
- Department of Chemistry, Center for Research Computing, Center for Drug Discovery, Design, and Delivery (CD4), Southern Methodist University, Dallas, TX 75205, USA.
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36
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Meller A, Bhakat S, Solieva S, Bowman GR. Accelerating Cryptic Pocket Discovery Using AlphaFold. J Chem Theory Comput 2023. [PMID: 36948209 PMCID: PMC10373493 DOI: 10.1021/acs.jctc.2c01189] [Citation(s) in RCA: 23] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/24/2023]
Abstract
Cryptic pockets, or pockets absent in ligand-free, experimentally determined structures, hold great potential as drug targets. However, cryptic pocket openings are often beyond the reach of conventional biomolecular simulations because certain cryptic pocket openings involve slow motions. Here, we investigate whether AlphaFold can be used to accelerate cryptic pocket discovery either by generating structures with open pockets directly or generating structures with partially open pockets that can be used as starting points for simulations. We use AlphaFold to generate ensembles for 10 known cryptic pocket examples, including five that were deposited after AlphaFold's training data were extracted from the PDB. We find that in 6 out of 10 cases AlphaFold samples the open state. For plasmepsin II, an aspartic protease from the causative agent of malaria, AlphaFold only captures a partial pocket opening. As a result, we ran simulations from an ensemble of AlphaFold-generated structures and show that this strategy samples cryptic pocket opening, even though an equivalent amount of simulations launched from a ligand-free experimental structure fails to do so. Markov state models (MSMs) constructed from the AlphaFold-seeded simulations quickly yield a free energy landscape of cryptic pocket opening that is in good agreement with the same landscape generated with well-tempered metadynamics. Taken together, our results demonstrate that AlphaFold has a useful role to play in cryptic pocket discovery but that many cryptic pockets may remain difficult to sample using AlphaFold alone.
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Affiliation(s)
- Artur Meller
- Department of Biochemistry and Molecular Biophysics, Washington University in St. Louis, 660 S. Euclid Ave., St. Louis, Missouri 63110, United States
- Medical Scientist Training Program, Washington University in St. Louis, 660 S. Euclid Ave., St. Louis, Missouri 63110, United States
| | - Soumendranath Bhakat
- Department of Biochemistry and Molecular Biophysics, Washington University in St. Louis, 660 S. Euclid Ave., St. Louis, Missouri 63110, United States
- Department of Biochemistry and Biophysics, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
| | - Shahlo Solieva
- Department of Biochemistry and Biophysics, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
| | - Gregory R Bowman
- Department of Biochemistry and Molecular Biophysics, Washington University in St. Louis, 660 S. Euclid Ave., St. Louis, Missouri 63110, United States
- Department of Biochemistry and Biophysics, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
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37
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Dominic AJ, Sayer T, Cao S, Markland TE, Huang X, Montoya-Castillo A. Building insightful, memory-enriched models to capture long-time biochemical processes from short-time simulations. Proc Natl Acad Sci U S A 2023; 120:e2221048120. [PMID: 36920924 PMCID: PMC10041170 DOI: 10.1073/pnas.2221048120] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2022] [Accepted: 02/21/2023] [Indexed: 03/16/2023] Open
Abstract
The ability to predict and understand complex molecular motions occurring over diverse timescales ranging from picoseconds to seconds and even hours in biological systems remains one of the largest challenges to chemical theory. Markov state models (MSMs), which provide a memoryless description of the transitions between different states of a biochemical system, have provided numerous important physically transparent insights into biological function. However, constructing these models often necessitates performing extremely long molecular simulations to converge the rates. Here, we show that by incorporating memory via the time-convolutionless generalized master equation (TCL-GME) one can build a theoretically transparent and physically intuitive memory-enriched model of biochemical processes with up to a three order of magnitude reduction in the simulation data required while also providing a higher temporal resolution. We derive the conditions under which the TCL-GME provides a more efficient means to capture slow dynamics than MSMs and rigorously prove when the two provide equally valid and efficient descriptions of the slow configurational dynamics. We further introduce a simple averaging procedure that enables our TCL-GME approach to quickly converge and accurately predict long-time dynamics even when parameterized with noisy reference data arising from short trajectories. We illustrate the advantages of the TCL-GME using alanine dipeptide, the human argonaute complex, and FiP35 WW domain.
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Affiliation(s)
| | - Thomas Sayer
- Department of Chemistry, University of Colorado, Boulder, CO80309
| | - Siqin Cao
- Department of Chemistry, University of Wisconsin-Madison, Madison, WI53706
| | | | - Xuhui Huang
- Department of Chemistry, University of Wisconsin-Madison, Madison, WI53706
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Agajanian S, Alshahrani M, Bai F, Tao P, Verkhivker GM. Exploring and Learning the Universe of Protein Allostery Using Artificial Intelligence Augmented Biophysical and Computational Approaches. J Chem Inf Model 2023; 63:1413-1428. [PMID: 36827465 PMCID: PMC11162550 DOI: 10.1021/acs.jcim.2c01634] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/26/2023]
Abstract
Allosteric mechanisms are commonly employed regulatory tools used by proteins to orchestrate complex biochemical processes and control communications in cells. The quantitative understanding and characterization of allosteric molecular events are among major challenges in modern biology and require integration of innovative computational experimental approaches to obtain atomistic-level knowledge of the allosteric states, interactions, and dynamic conformational landscapes. The growing body of computational and experimental studies empowered by emerging artificial intelligence (AI) technologies has opened up new paradigms for exploring and learning the universe of protein allostery from first principles. In this review we analyze recent developments in high-throughput deep mutational scanning of allosteric protein functions; applications and latest adaptations of Alpha-fold structural prediction methods for studies of protein dynamics and allostery; new frontiers in integrating machine learning and enhanced sampling techniques for characterization of allostery; and recent advances in structural biology approaches for studies of allosteric systems. We also highlight recent computational and experimental studies of the SARS-CoV-2 spike (S) proteins revealing an important and often hidden role of allosteric regulation driving functional conformational changes, binding interactions with the host receptor, and mutational escape mechanisms of S proteins which are critical for viral infection. We conclude with a summary and outlook of future directions suggesting that AI-augmented biophysical and computer simulation approaches are beginning to transform studies of protein allostery toward systematic characterization of allosteric landscapes, hidden allosteric states, and mechanisms which may bring about a new revolution in molecular biology and drug discovery.
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Affiliation(s)
- Steve Agajanian
- Keck Center for Science and Engineering, Graduate Program in Computational and Data Sciences, Schmid College of Science and Technology, Chapman University, Orange, California 92866, United States
| | - Mohammed Alshahrani
- Keck Center for Science and Engineering, Graduate Program in Computational and Data Sciences, Schmid College of Science and Technology, Chapman University, Orange, California 92866, United States
| | - Fang Bai
- Shanghai Institute for Advanced Immunochemical Studies, School of Life Science and Technology and Information Science and Technology, Shanghai Tech University, 393 Middle Huaxia Road, Shanghai 201210, China
| | - Peng Tao
- Department of Chemistry, Center for Research Computing, Center for Drug Discovery, Design, and Delivery (CD4), Southern Methodist University, Dallas, Texas 75205, United States
| | - Gennady M Verkhivker
- Keck Center for Science and Engineering, Graduate Program in Computational and Data Sciences, Schmid College of Science and Technology, Chapman University, Orange, California 92866, United States
- Department of Biomedical and Pharmaceutical Sciences, Chapman University School of Pharmacy, Irvine, California 92618, United States
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39
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Banerjee A, Saha S, Tvedt NC, Yang LW, Bahar I. Mutually beneficial confluence of structure-based modeling of protein dynamics and machine learning methods. Curr Opin Struct Biol 2023; 78:102517. [PMID: 36587424 PMCID: PMC10038760 DOI: 10.1016/j.sbi.2022.102517] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Revised: 11/19/2022] [Accepted: 11/22/2022] [Indexed: 12/31/2022]
Abstract
Proteins sample an ensemble of conformers under physiological conditions, having access to a spectrum of modes of motions, also called intrinsic dynamics. These motions ensure the adaptation to various interactions in the cell, and largely assist in, if not determine, viable mechanisms of biological function. In recent years, machine learning frameworks have proven uniquely useful in structural biology, and recent studies further provide evidence to the utility and/or necessity of considering intrinsic dynamics for increasing their predictive ability. Efficient quantification of dynamics-based attributes by recently developed physics-based theories and models such as elastic network models provides a unique opportunity to generate data on dynamics for training ML models towards inferring mechanisms of protein function, assessing pathogenicity, or estimating binding affinities.
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Affiliation(s)
- Anupam Banerjee
- Computational and Systems Biology, University of Pittsburgh School of Medicine, Pittsburgh PA 15261, USA
| | - Satyaki Saha
- Computational and Systems Biology, University of Pittsburgh School of Medicine, Pittsburgh PA 15261, USA
| | - Nathan C Tvedt
- Computational and Systems Biology, University of Pittsburgh School of Medicine, Pittsburgh PA 15261, USA; Computational and Applied Mathematics and Statistics, The College of William and Mary, Williamsburg, VA 23185, USA
| | - Lee-Wei Yang
- Institute of Bioinformatics and Structural Biology, and PhD Program in Biomedical Artificial Intelligence, National Tsing Hua University, Hsinchu 300044, Taiwan; Physics Division, National Center for Theoretical Sciences, Taipei 106319, Taiwan
| | - Ivet Bahar
- Computational and Systems Biology, University of Pittsburgh School of Medicine, Pittsburgh PA 15261, USA.
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40
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Unarta IC, Goonetilleke EC, Wang D, Huang X. Nucleotide addition and cleavage by RNA polymerase II: Coordination of two catalytic reactions using a single active site. J Biol Chem 2022; 299:102844. [PMID: 36581202 PMCID: PMC9860460 DOI: 10.1016/j.jbc.2022.102844] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2022] [Revised: 12/19/2022] [Accepted: 12/22/2022] [Indexed: 12/28/2022] Open
Abstract
RNA polymerase II (Pol II) incorporates complementary ribonucleotides into the growing RNA chain one at a time via the nucleotide addition cycle. The nucleotide addition cycle, however, is prone to misincorporation of noncomplementary nucleotides. Thus, to ensure transcriptional fidelity, Pol II backtracks and then cleaves the misincorporated nucleotides. These two reverse reactions, nucleotide addition and cleavage, are catalyzed in the same active site of Pol II, which is different from DNA polymerases or other endonucleases. Recently, substantial progress has been made to understand how Pol II effectively performs its dual role in the same active site. Our review highlights these recent studies and provides an overall model of the catalytic mechanisms of Pol II. In particular, RNA extension follows the two-metal-ion mechanism, and several Pol II residues play important roles to facilitate the catalysis. In sharp contrast, the cleavage reaction is independent of any Pol II residues. Interestingly, Pol II relies on its residues to recognize the misincorporated nucleotides during the backtracking process, prior to cleavage. In this way, Pol II efficiently compartmentalizes its two distinct catalytic functions using the same active site. Lastly, we also discuss a new perspective on the potential third Mg2+ in the nucleotide addition and intrinsic cleavage reactions.
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Affiliation(s)
- Ilona Christy Unarta
- Department of Chemistry, Theoretical Chemistry Institute, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Eshani C Goonetilleke
- Department of Chemistry, Theoretical Chemistry Institute, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Dong Wang
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, California, USA; Department of Cellular and Molecular Medicine, School of Medicine, University of California, San Diego, La Jolla, California, USA; Department of Chemistry and Biochemistry, University of California, San Diego, La Jolla, California, USA.
| | - Xuhui Huang
- Department of Chemistry, Theoretical Chemistry Institute, University of Wisconsin-Madison, Madison, Wisconsin, USA.
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41
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Mardt A, Hempel T, Clementi C, Noé F. Deep learning to decompose macromolecules into independent Markovian domains. Nat Commun 2022; 13:7101. [PMID: 36402768 PMCID: PMC9675806 DOI: 10.1038/s41467-022-34603-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Accepted: 10/27/2022] [Indexed: 11/21/2022] Open
Abstract
The increasing interest in modeling the dynamics of ever larger proteins has revealed a fundamental problem with models that describe the molecular system as being in a global configuration state. This notion limits our ability to gather sufficient statistics of state probabilities or state-to-state transitions because for large molecular systems the number of metastable states grows exponentially with size. In this manuscript, we approach this challenge by introducing a method that combines our recent progress on independent Markov decomposition (IMD) with VAMPnets, a deep learning approach to Markov modeling. We establish a training objective that quantifies how well a given decomposition of the molecular system into independent subdomains with Markovian dynamics approximates the overall dynamics. By constructing an end-to-end learning framework, the decomposition into such subdomains and their individual Markov state models are simultaneously learned, providing a data-efficient and easily interpretable summary of the complex system dynamics. While learning the dynamical coupling between Markovian subdomains is still an open issue, the present results are a significant step towards learning Ising models of large molecular complexes from simulation data.
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Affiliation(s)
- Andreas Mardt
- grid.14095.390000 0000 9116 4836Freie Universität Berlin, Department of Mathematics and Computer Science, Berlin, Germany
| | - Tim Hempel
- grid.14095.390000 0000 9116 4836Freie Universität Berlin, Department of Mathematics and Computer Science, Berlin, Germany ,grid.14095.390000 0000 9116 4836Freie Universität Berlin, Department of Physics, Berlin, Germany
| | - Cecilia Clementi
- grid.14095.390000 0000 9116 4836Freie Universität Berlin, Department of Physics, Berlin, Germany ,grid.21940.3e0000 0004 1936 8278Rice University, Department of Chemistry, Houston, TX USA ,grid.509984.90000 0004 5907 3802Rice University, Center for Theoretical Biological Physics, Houston, TX USA
| | - Frank Noé
- grid.14095.390000 0000 9116 4836Freie Universität Berlin, Department of Mathematics and Computer Science, Berlin, Germany ,grid.14095.390000 0000 9116 4836Freie Universität Berlin, Department of Physics, Berlin, Germany ,grid.21940.3e0000 0004 1936 8278Rice University, Department of Chemistry, Houston, TX USA ,Microsoft Research AI4Science, Berlin, Germany
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42
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Weigle AT, Feng J, Shukla D. Thirty years of molecular dynamics simulations on posttranslational modifications of proteins. Phys Chem Chem Phys 2022; 24:26371-26397. [PMID: 36285789 PMCID: PMC9704509 DOI: 10.1039/d2cp02883b] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/06/2023]
Abstract
Posttranslational modifications (PTMs) are an integral component to how cells respond to perturbation. While experimental advances have enabled improved PTM identification capabilities, the same throughput for characterizing how structural changes caused by PTMs equate to altered physiological function has not been maintained. In this Perspective, we cover the history of computational modeling and molecular dynamics simulations which have characterized the structural implications of PTMs. We distinguish results from different molecular dynamics studies based upon the timescales simulated and analysis approaches used for PTM characterization. Lastly, we offer insights into how opportunities for modern research efforts on in silico PTM characterization may proceed given current state-of-the-art computing capabilities and methodological advancements.
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Affiliation(s)
- Austin T Weigle
- Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA
| | - Jiangyan Feng
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA
| | - Diwakar Shukla
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA
- Center for Biophysics and Quantitative Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA
- Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA
- Department of Plant Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA.
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43
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Mollica L, Cupaioli FA, Rossetti G, Chiappori F. An overview of structural approaches to study therapeutic RNAs. Front Mol Biosci 2022; 9:1044126. [PMID: 36387283 PMCID: PMC9649582 DOI: 10.3389/fmolb.2022.1044126] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Accepted: 10/18/2022] [Indexed: 11/07/2023] Open
Abstract
RNAs provide considerable opportunities as therapeutic agent to expand the plethora of classical therapeutic targets, from extracellular and surface proteins to intracellular nucleic acids and its regulators, in a wide range of diseases. RNA versatility can be exploited to recognize cell types, perform cell therapy, and develop new vaccine classes. Therapeutic RNAs (aptamers, antisense nucleotides, siRNA, miRNA, mRNA and CRISPR-Cas9) can modulate or induce protein expression, inhibit molecular interactions, achieve genome editing as well as exon-skipping. A common RNA thread, which makes it very promising for therapeutic applications, is its structure, flexibility, and binding specificity. Moreover, RNA displays peculiar structural plasticity compared to proteins as well as to DNA. Here we summarize the recent advances and applications of therapeutic RNAs, and the experimental and computational methods to analyze their structure, by biophysical techniques (liquid-state NMR, scattering, reactivity, and computational simulations), with a focus on dynamic and flexibility aspects and to binding analysis. This will provide insights on the currently available RNA therapeutic applications and on the best techniques to evaluate its dynamics and reactivity.
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Affiliation(s)
- Luca Mollica
- Department of Medical Biotechnologies and Translational Medicine, L.I.T.A/University of Milan, Milan, Italy
| | | | | | - Federica Chiappori
- National Research Council—Institute for Biomedical Technologies, Milan, Italy
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44
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Rodríguez Serrano AF, Hsing IM. Prediction of Aptamer-Small-Molecule Interactions Using Metastable States from Multiple Independent Molecular Dynamics Simulations. J Chem Inf Model 2022; 62:4799-4809. [PMID: 36134737 DOI: 10.1021/acs.jcim.2c00734] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Understanding aptamer-ligand interactions is necessary to rationally design aptamer-based systems. Commonly used in silico tools have proven to be accurate to predict RNA and DNA oligonucleotide tertiary structures. However, given the complexity of nucleic acids, the most thermodynamically stable conformation is not necessarily the one with the highest affinity for a specific ligand. Because many metastable states may coexist, it remains challenging to predict binding sites through molecular docking simulations using available computational pipelines. In this study, we used independent simulations to broaden the conformational diversity sampled from DNA initial models of distinct stability and assessed the binding affinity of selected metastable representative structures. In our results, utilizing multiple metastable conformations for molecular docking analysis helped identify structures favorable for ligand binding and accurately predict the binding sites. Our workflow was able to correctly identify the binding sites of the characterized adenosine monophosphate and l-argininamide aptamers. Additionally, we demonstrated that our pipeline can be used to aid the design of competition assays that are conducive to aptasensing strategies using an uncharacterized aflatoxin B1 aptamer. We foresee that this approach may help rationally design effective and truncated aptamer sequences interacting with protein biomarkers or small molecules of interest for drug design and sensor applications.
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Affiliation(s)
- Alan Fernando Rodríguez Serrano
- Department of Chemical and Biological Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong SAR 999077, China
| | - I-Ming Hsing
- Department of Chemical and Biological Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong SAR 999077, China
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45
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Avery C, Patterson J, Grear T, Frater T, Jacobs DJ. Protein Function Analysis through Machine Learning. Biomolecules 2022; 12:1246. [PMID: 36139085 PMCID: PMC9496392 DOI: 10.3390/biom12091246] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2022] [Revised: 08/22/2022] [Accepted: 08/31/2022] [Indexed: 11/16/2022] Open
Abstract
Machine learning (ML) has been an important arsenal in computational biology used to elucidate protein function for decades. With the recent burgeoning of novel ML methods and applications, new ML approaches have been incorporated into many areas of computational biology dealing with protein function. We examine how ML has been integrated into a wide range of computational models to improve prediction accuracy and gain a better understanding of protein function. The applications discussed are protein structure prediction, protein engineering using sequence modifications to achieve stability and druggability characteristics, molecular docking in terms of protein-ligand binding, including allosteric effects, protein-protein interactions and protein-centric drug discovery. To quantify the mechanisms underlying protein function, a holistic approach that takes structure, flexibility, stability, and dynamics into account is required, as these aspects become inseparable through their interdependence. Another key component of protein function is conformational dynamics, which often manifest as protein kinetics. Computational methods that use ML to generate representative conformational ensembles and quantify differences in conformational ensembles important for function are included in this review. Future opportunities are highlighted for each of these topics.
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Affiliation(s)
- Chris Avery
- Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, Charlotte, NC 28223, USA
| | - John Patterson
- Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, Charlotte, NC 28223, USA
| | - Tyler Grear
- Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, Charlotte, NC 28223, USA
- Department of Physics and Optical Science, University of North Carolina at Charlotte, Charlotte, NC 28223, USA
| | - Theodore Frater
- Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, Charlotte, NC 28223, USA
| | - Donald J. Jacobs
- Department of Physics and Optical Science, University of North Carolina at Charlotte, Charlotte, NC 28223, USA
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46
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Mecha MF, Hutchinson RB, Lee JH, Cavagnero S. Protein folding in vitro and in the cell: From a solitary journey to a team effort. Biophys Chem 2022; 287:106821. [PMID: 35667131 PMCID: PMC9636488 DOI: 10.1016/j.bpc.2022.106821] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Revised: 04/18/2022] [Accepted: 04/21/2022] [Indexed: 12/22/2022]
Abstract
Correct protein folding is essential for the health and function of living organisms. Yet, it is not well understood how unfolded proteins reach their native state and avoid aggregation, especially within the cellular milieu. Some proteins, especially small, single-domain and apparent two-state folders, successfully attain their native state upon dilution from denaturant. Yet, many more proteins undergo misfolding and aggregation during this process, in a concentration-dependent fashion. Once formed, native and aggregated states are often kinetically trapped relative to each other. Hence, the early stages of protein life are absolutely critical for proper kinetic channeling to the folded state and for long-term solubility and function. This review summarizes current knowledge on protein folding/aggregation mechanisms in buffered solution and within the bacterial cell, highlighting early stages. Remarkably, teamwork between nascent chain, ribosome, trigger factor and Hsp70 molecular chaperones enables all proteins to overcome aggregation propensities and reach a long-lived bioactive state.
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Affiliation(s)
- Miranda F Mecha
- Department of Chemistry, University of Wisconsin-Madison, Madison, WI 53706, United States of America
| | - Rachel B Hutchinson
- Department of Chemistry, University of Wisconsin-Madison, Madison, WI 53706, United States of America
| | - Jung Ho Lee
- Department of Chemistry, University of Wisconsin-Madison, Madison, WI 53706, United States of America
| | - Silvia Cavagnero
- Department of Chemistry, University of Wisconsin-Madison, Madison, WI 53706, United States of America.
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47
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Zhang Y, Wang Y, Xia F, Cao Z, Xu X. Accurate and Efficient Estimation of Lennard-Jones Interactions for Coarse-Grained Particles via a Potential Matching Method. J Chem Theory Comput 2022; 18:4879-4890. [PMID: 35838523 DOI: 10.1021/acs.jctc.2c00513] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
The Lennard-Jones (LJ) energy functions are commonly used to describe the nonbonded interactions in bulk coarse-grained (CG) models, which contribute significantly to the stabilization of a local binding configuration or a self-assembly system. In many cases, systematic development of the LJ interaction parameters in a CG model requires a comprehensive sampling of the objective molecules at the all-atom (AA) level, which is therefore extremely time-consuming for large systems. Inspired by the concept of electrostatic potential (ESP), we define the LJ static potential (LJSP), by which the embedding potential energy surface can be constructed analytically. A semianalytic approach, namely, the LJSP matching method, is developed here to derive the CG parameters by minimizing the LJSP difference between the AA and the CG models, which provides a universal way to derive the CG LJ parameters from the AA models without doing presampling. The LJSP matching method is successful not only in deriving the LJ interaction energy landscape in the CG models for proteins, lipids, and DNA but also in reproducing the critical properties such as intermediate structures and enthalpy contributions as exemplified in simulating the self-assembly process of the dipalmitoylphosphatidylcholine (DPPC) lipids.
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Affiliation(s)
- Yuwei Zhang
- Collaborative Innovation Center of Chemistry for Energy Materials, Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials, MOE Key Laboratory of Computational Physical Sciences, Departments of Chemistry, Fudan University, Shanghai 200433, China
| | - Yunchu Wang
- LSEC, Institute of Computational Mathematics and Scientific/Engineering Computing, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China
| | - Fei Xia
- School of Chemistry and Molecular Engineering, NYU-ECNU Center for Computational Chemistry at NYU Shanghai, East China Normal University, Shanghai 200062, China
| | - Zexing Cao
- State Key Laboratory of Physical Chemistry of Solid Surfaces and Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, College of Chemistry and Chemistry Engineering, Xiamen University, Xiamen 361005, China
| | - Xin Xu
- Collaborative Innovation Center of Chemistry for Energy Materials, Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials, MOE Key Laboratory of Computational Physical Sciences, Departments of Chemistry, Fudan University, Shanghai 200433, China
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48
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Liu H, Zhou Y, Song Y, Zhang Q, Kan Y, Tang X, Xiao Q, Xiang Q, Liu H, Luo Y, Bao R. Structural and Dynamics Studies of the Spcas9 Variant Provide Insights into the Regulatory Role of the REC1 Domain. ACS Catal 2022. [DOI: 10.1021/acscatal.2c01804] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Affiliation(s)
- Huayi Liu
- Frontiers Science Center of Synthetic Biology and Key Laboratory of Systems Bioengineering (Ministry of Education), School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China
| | - Yi Zhou
- Center of Infectious Diseases, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Yingjie Song
- Center of Infectious Diseases, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Qianqian Zhang
- School of Pharmacy, Lanzhou University, Lanzhou 730000, China
| | - Yeyi Kan
- Center of Infectious Diseases, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Xinyue Tang
- Center of Infectious Diseases, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Qingjie Xiao
- National Facility for Protein Science in Shanghai, Shanghai Advanced Research Institute (Zhangjiang Laboratory), Chinese Academy of Sciences, Shanghai 200135, China
| | - Qianyin Xiang
- Center of Infectious Diseases, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Huanxiang Liu
- Faculty of Applied Science, Macao Polytechnic University, Macao, SAR 999078, China
| | - Yunzi Luo
- Center of Infectious Diseases, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu 610041, China
- Frontiers Science Center of Synthetic Biology and Key Laboratory of Systems Bioengineering (Ministry of Education), School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China
| | - Rui Bao
- Center of Infectious Diseases, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu 610041, China
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49
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Integration of machine learning with computational structural biology of plants. Biochem J 2022; 479:921-928. [PMID: 35484946 DOI: 10.1042/bcj20200942] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Revised: 04/01/2022] [Accepted: 04/06/2022] [Indexed: 11/17/2022]
Abstract
Computational structural biology of proteins has developed rapidly in recent decades with the development of new computational tools and the advancement of computing hardware. However, while these techniques have widely been used to make advancements in human medicine, these methods have seen less utilization in the plant sciences. In the last several years, machine learning methods have gained popularity in computational structural biology. These methods have enabled the development of new tools which are able to address the major challenges that have hampered the wide adoption of the computational structural biology of plants. This perspective examines the remaining challenges in computational structural biology and how the development of machine learning techniques enables more in-depth computational structural biology of plants.
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50
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Zeng J, Chen J, Xia F, Cui Q, Deng X, Xu X. Identification of functional substates of KRas during GTP hydrolysis with enhanced sampling simulations. Phys Chem Chem Phys 2022; 24:7653-7665. [PMID: 35297922 PMCID: PMC8972078 DOI: 10.1039/d2cp00274d] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
As the hub of major signaling pathways, Ras proteins are implicated in 19% of tumor-caused cancers due to perturbations in their conformational and/or catalytic properties. Despite numerous studies, the functions of the conformational substates for the most important isoform, KRas, remain elusive. In this work, we perform an extensive simulation analysis on the conformational landscape of KRas in its various chemical states during the GTP hydrolysis cycle: the reactant state KRasGTP·Mg2+, the intermediate state KRasGDP·Pi·Mg2+ and the product state KRasGDP·Mg2+. The results from enhanced sampling simulations reveal that State 1 of KRasGTP·Mg2+ has multiple stable substates in solution, one of which might account for interacting with GEFs. State 2 of KRasGTP·Mg2+ features two substates "Tyr32in" and "Tyr32out", which are poised to interact with effectors and GAPs, respectively. For the intermediate state KRasGDP·Pi·Mg2+, Gln61 and Pi are found to assume a broad set of conformations, which might account for the weak oncogenic effect of Gln61 mutations in KRas in contrast to the situation in HRas and NRas. Finally, the product state KRasGDP·Mg2+ has more than two stable substates in solution, pointing to a conformation-selection mechanism for complexation with GEFs. Based on these results, some specific inhibition strategies for targeting the binding sites of the high-energy substates of KRas during GTP hydrolysis are discussed.
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Affiliation(s)
- Juan Zeng
- School of Biomedical Engineering, Guangdong Medical University, Dongguan 523808, China
| | - Jian Chen
- School of Chemistry and Molecular Engineering, NYU-ECNU Center for Computational Chemistry at NYU Shanghai, East China Normal University, Shanghai 200062, China.
| | - Fei Xia
- School of Chemistry and Molecular Engineering, NYU-ECNU Center for Computational Chemistry at NYU Shanghai, East China Normal University, Shanghai 200062, China.
| | - Qiang Cui
- Departments of Chemistry, Physics and Biomedical Engineering, Boston University, 590 Commonwealth Avenue, Boston, MA 02215, USA
| | - Xianming Deng
- State Key Laboratory of Cellular Stress Biology Innovation Center for Cell Signaling Network, School of Life Sciences, Xiamen University, Fujian 361101, China.
| | - Xin Xu
- Collaborative Innovation Center of Chemistry for Energy Materials, Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials, MOE Key Laboratory of Computational Physical Sciences, Department of Chemistry, Fudan University, Shanghai 200433, China.
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