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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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2
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Abstract
This Perspective reviews the use of Transition Path Sampling methods to study enzymatically catalyzed chemical reactions. First applied by our group to an enzymatic reaction over 15 years ago, the method has uncovered basic principles in enzymatic catalysis such as the protein promoting vibration, and it has also helped harmonize such ideas as electrostatic preorganization with dynamic views of enzyme function. It is now being used to help uncover principles of protein design necessary to artificial enzyme creation.
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Affiliation(s)
- Steven D Schwartz
- Department of Chemistry and Biochemistry University of Arizona Tucson, Arizona 85721, United States
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Abstract
The treatment of slow and rare transitions in the simulation of complex systems poses a great computational challenge. A powerful approach to tackle this challenge is the string method, which represents the transition path as a one-dimensional curve in a multidimensional space of collective variables. Commonly used strategies for pathway optimization include aligning the tangent of the string to the local mean force or to the mean drift determined from swarms of short trajectories. Here, a novel strategy is proposed, allowing the string to be optimized based on a variational principle involving the unidirectional reactive flux expressed in terms of the time-correlation function of the committor. The method is illustrated with model systems and then probed with the alanine dipeptide and a coarse-grained model of the barstar-barnase protein complex. Successive iterations variationally refine the string toward an optimal transition pathway following the gradient of the committor between two metastable states.
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Affiliation(s)
- Ziwei He
- Department of Chemistry, The University of Chicago, 5735 S. Ellis Avenue, Chicago60637, Illinois, United States
| | - Christophe Chipot
- Laboratoire International Associé Centre National de la Recherche Scientifique et University of Illinois at Urbana-Champaign, Unité Mixte de Recherche No. 7019, Université de Lorraine, B.P. 70239, Vandœuvre-lès-Nancy cedex54506, France
| | - Benoît Roux
- Department of Chemistry, The University of Chicago, 5735 S. Ellis Avenue, Chicago60637, Illinois, United States
- Department of Biochemistry and Molecular Biology, The University of Chicago, Chicago60637, IllinoisUnited States
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Rahman MU, Song K, Da LT, Chen HF. Early aggregation mechanism of Aβ 16-22 revealed by Markov state models. Int J Biol Macromol 2022; 204:606-616. [PMID: 35134456 DOI: 10.1016/j.ijbiomac.2022.02.001] [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: 11/30/2021] [Revised: 01/24/2022] [Accepted: 02/01/2022] [Indexed: 12/19/2022]
Abstract
Aβ16-22 is believed to have critical role in early aggregation of full length amyloids that are associated with the Alzheimer's disease and can aggregate to form amyloid fibrils. However, the early aggregation mechanism is still unsolved. Here, multiple long-term molecular dynamics simulations combining with Markov state model were used to probe the early oligomerization mechanism of Aβ16-22 peptides. The identified dimeric form adopted either globular random-coil or extended β-strand like conformations. The observed dimers of these variants shared many overall conformational characteristics but differed in several aspects at detailed level. In all cases, the most common type of secondary structure was intermolecular antiparallel β-sheets. The inter-state transitions were very frequent ranges from few to hundred nanoseconds. More strikingly, those states which contain fraction of β secondary structure and significant amount of extended coiled structures, therefore exposed to the solvent, were majorly participated in aggregation. The assembly of low-energy dimers, in which the peptides form antiparallel β sheets, occurred by multiple pathways with the formation of an obligatory intermediates. We proposed that these states might facilitate the Aβ16-22 aggregation through a significant component of the conformational selection mechanism, because they might increase the aggregates population by promoting the inter-chain hydrophobic and the hydrogen bond contacts. The formation of early stage antiparallel β sheet structures is critical for oligomerization, and at the same time provided a flat geometry to seed the ordered β-strand packing of the fibrils. Our findings hint at reorganization of this part of the molecule as a potentially critical step in Aβ aggregation and will insight into early oligomerization for large β amyloids.
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Affiliation(s)
- Mueed Ur Rahman
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, Department of Bioinformatics and Biostatistics, National Experimental Teaching Center for Life Sciences and Biotechnology, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Kaiyuan Song
- Key Laboratory of System Biomedicine (Ministry of Education), Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Lin-Tai Da
- Key Laboratory of System Biomedicine (Ministry of Education), Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Hai-Feng Chen
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, Department of Bioinformatics and Biostatistics, National Experimental Teaching Center for Life Sciences and Biotechnology, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China; Shanghai Center for Bioinformation Technology, Shanghai, 200235, China.
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Wang Y, Li M, Liang W, Shi X, Fan J, Kong R, Liu Y, Zhang J, Chen T, Lu S. Delineating the activation mechanism and conformational landscape of a class B G protein-coupled receptor glucagon receptor. Comput Struct Biotechnol J 2022; 20:628-639. [PMID: 35140883 PMCID: PMC8801358 DOI: 10.1016/j.csbj.2022.01.015] [Citation(s) in RCA: 33] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Revised: 01/17/2022] [Accepted: 01/17/2022] [Indexed: 02/09/2023] Open
Abstract
Class B G protein-coupled receptors (GPCRs) are important targets in the treatment of metabolic syndrome and diabetes. Although multiple structures of class B GPCRs-G protein complexes have been elucidated, the detailed activation mechanism of the receptors remains unclear. Here, we combine Gaussian accelerated molecular dynamics simulations and Markov state models (MSM) to investigate the activation mechanism of a canonical class B GPCR, human glucagon receptor-GCGR, including the negative allosteric modulator-bound inactive state, the agonist glucagon-bound active state, and both glucagon- and Gs-bound fully active state. The free-energy landscapes of GCGR show the conformational ensemble consisting of three activation-associated states: inactive, active, and fully active. The structural analysis indicates the high dynamics of GCGR upon glucagon binding with both active and inactive conformations in the ensemble. Significantly, the H8 and TM6 exhibits distinct features from the inactive to the active states. The additional simulations demonstrate the role of H8 in the recruitment of Gs. Gs binding presents a crucial function of stabilizing the glucagon binding site and MSM highlights the absolute requirement of Gs to help the GCGR reach the fully active state. Together, our results reveal the detailed activation mechanism of GCGR from the view of conformational dynamics.
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Affiliation(s)
- Ying Wang
- Department of Pathophysiology, Key Laboratory of Cell Differentiation and Apoptosis of Chinese Ministry of Education, Shanghai Jiao Tong University, School of Medicine, Shanghai 200025, China
- Medicinal Chemistry and Bioinformatics Center, Shanghai Jiao Tong University, School of Medicine, Shanghai 200025, China
| | - Mingyu Li
- Department of Pathophysiology, Key Laboratory of Cell Differentiation and Apoptosis of Chinese Ministry of Education, Shanghai Jiao Tong University, School of Medicine, Shanghai 200025, China
| | - Wenqi Liang
- Department of Emergency, Changhai Hospital, Naval Medical University, Shanghai 200433, China
| | - Xinchao Shi
- Department of Pathophysiology, Key Laboratory of Cell Differentiation and Apoptosis of Chinese Ministry of Education, Shanghai Jiao Tong University, School of Medicine, Shanghai 200025, China
| | - Jigang Fan
- Department of Pathophysiology, Key Laboratory of Cell Differentiation and Apoptosis of Chinese Ministry of Education, Shanghai Jiao Tong University, School of Medicine, Shanghai 200025, China
| | - Ren Kong
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou 213001, China
| | - Yaqin Liu
- Medicinal Chemistry and Bioinformatics Center, Shanghai Jiao Tong University, School of Medicine, Shanghai 200025, China
| | - Jian Zhang
- Department of Pathophysiology, Key Laboratory of Cell Differentiation and Apoptosis of Chinese Ministry of Education, Shanghai Jiao Tong University, School of Medicine, Shanghai 200025, China
- Medicinal Chemistry and Bioinformatics Center, Shanghai Jiao Tong University, School of Medicine, Shanghai 200025, China
| | - Ting Chen
- Department of Cardiology, Changzheng Hospital, Naval Medical University, Shanghai 200023, China
| | - Shaoyong Lu
- Department of Pathophysiology, Key Laboratory of Cell Differentiation and Apoptosis of Chinese Ministry of Education, Shanghai Jiao Tong University, School of Medicine, Shanghai 200025, China
- Medicinal Chemistry and Bioinformatics Center, Shanghai Jiao Tong University, School of Medicine, Shanghai 200025, China
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Dingfelder F, Macocco I, Benke S, Nettels D, Faccioli P, Schuler B. Slow Escape from a Helical Misfolded State of the Pore-Forming Toxin Cytolysin A. JACS AU 2021; 1:1217-1230. [PMID: 34467360 PMCID: PMC8397351 DOI: 10.1021/jacsau.1c00175] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Indexed: 05/12/2023]
Abstract
The pore-forming toxin cytolysin A (ClyA) is expressed as a large α-helical monomer that, upon interaction with membranes, undergoes a major conformational rearrangement into the protomer conformation, which then assembles into a cytolytic pore. Here, we investigate the folding kinetics of the ClyA monomer with single-molecule Förster resonance energy transfer spectroscopy in combination with microfluidic mixing, stopped-flow circular dichroism experiments, and molecular simulations. The complex folding process occurs over a broad range of time scales, from hundreds of nanoseconds to minutes. The very slow formation of the native state occurs from a rapidly formed and highly collapsed intermediate with large helical content and nonnative topology. Molecular dynamics simulations suggest pronounced non-native interactions as the origin of the slow escape from this deep trap in the free-energy surface, and a variational enhanced path-sampling approach enables a glimpse of the folding process that is supported by the experimental data.
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Affiliation(s)
- Fabian Dingfelder
- Department
of Biochemistry, University of Zurich, Winterthurerstrasse 190, 8057 Zurich, Switzerland
| | - Iuri Macocco
- Department
of Physics, Trento University, Via Sommarive 14, 38123 Povo (Trento), Italy
- SISSA, Via Bonomea 265, 34136 Trieste, Italy
| | - Stephan Benke
- Department
of Biochemistry, University of Zurich, Winterthurerstrasse 190, 8057 Zurich, Switzerland
| | - Daniel Nettels
- Department
of Biochemistry, University of Zurich, Winterthurerstrasse 190, 8057 Zurich, Switzerland
| | - Pietro Faccioli
- Department
of Physics, Trento University, Via Sommarive 14, 38123 Povo (Trento), Italy
- INFN-TIFPA, Via Sommarive 14, 38123 Povo (Trento), Italy
| | - Benjamin Schuler
- Department
of Biochemistry, University of Zurich, Winterthurerstrasse 190, 8057 Zurich, Switzerland
- Department
of Physics, University of Zurich, Winterthurerstrasse 190, 8057 Zurich, Switzerland
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Narayan B, Buchete NV, Elber R. Computer Simulations of the Dissociation Mechanism of Gleevec from Abl Kinase with Milestoning. J Phys Chem B 2021; 125:5706-5715. [PMID: 33930271 DOI: 10.1021/acs.jpcb.1c00264] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Gleevec (a.k.a., imatinib) is an important anticancer (e.g., chronic myeloid leukemia) chemotherapeutic drug due to its inhibitory interaction with the Abl kinase. Here, we use atomically detailed simulations within the Milestoning framework to study the molecular dissociation mechanism of Gleevec from Abl kinase. We compute the dissociation free energy profile, the mean first passage time for unbinding, and explore the transition state ensemble of conformations. The milestones form a multidimensional network with average connectivity of about 2.93, which is significantly higher than the connectivity for a one-dimensional reaction coordinate. The free energy barrier for Gleevec dissociation is estimated to be ∼10 kcal/mol, and the exit time is ∼55 ms. We examined the transition state conformations using both, the committor and transition function. We show that near the transition state the highly conserved salt bridge K217 and E286 is transiently broken. Together with the calculated free energy profile, these calculations can advance the understanding of the molecular interaction mechanisms between Gleevec and Abl kinase and play a role in future drug design and optimization studies.
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Affiliation(s)
- Brajesh Narayan
- School of Physics, University College Dublin, Belfield, Dublin 4, Ireland.,Institute for Discovery, University College Dublin, Belfield, Dublin 4, Ireland
| | - Nicolae-Viorel Buchete
- School of Physics, University College Dublin, Belfield, Dublin 4, Ireland.,Institute for Discovery, University College Dublin, Belfield, Dublin 4, Ireland
| | - Ron Elber
- Oden Institute for Computational Engineering and Science, Department of Chemistry, University of Texas at Austin, Austin Texas 78712, United States
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