1
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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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2
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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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3
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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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4
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Ishizone T, Matsunaga Y, Fuchigami S, Nakamura K. Representation of Protein Dynamics Disentangled by Time-Structure-Based Prior. J Chem Theory Comput 2024; 20:436-450. [PMID: 38151233 DOI: 10.1021/acs.jctc.3c01025] [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: 12/29/2023]
Abstract
Representation learning (RL) is a universal technique for deriving low-dimensional disentangled representations from high-dimensional observations, aiding in a multitude of downstream tasks. RL has been extensively applied to various data types, including images and natural language. Here, we analyze molecular dynamics (MD) simulation data of biomolecules in terms of RL. Currently, state-of-the-art RL techniques, mainly motivated by the variational principle, try to capture slow motions in the representation (latent) space. Here, we propose two methods based on an alternative perspective on the disentanglement in the latent space. By disentanglement, we here mean the separation of underlying factors in the simulation data, aiding in detecting physically important coordinates for conformational transitions. The proposed methods introduce a simple prior that imposes temporal constraints in the latent space, serving as a regularization term to facilitate the capture of disentangled representations of dynamics. Comparison with other methods via the analysis of MD simulation trajectories for alanine dipeptide and chignolin validates that the proposed methods construct Markov state models (MSMs) whose implied time scales are comparable to those of the state-of-the-art methods. Using a measure based on total variation, we quantitatively evaluated that the proposed methods successfully disentangle physically important coordinates, aiding the interpretation of folding/unfolding transitions of chignolin. Overall, our methods provide good representations of complex biomolecular dynamics for downstream tasks, allowing for better interpretations of the conformational transitions.
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Affiliation(s)
- Tsuyoshi Ishizone
- Mathematical Sciences Program, Graduate School of Advanced Mathematical Sciences, Meiji University, Nakano 4-21-1, Nakano-ku, Tokyo 164-8525, Japan
| | - Yasuhiro Matsunaga
- Graduate School of Science and Engineering, Saitama University, Shimo-Okubo 255, Sakura-ku, Saitama-shi, Saitama 338-8570, Japan
| | - Sotaro Fuchigami
- Physical Biochemistry Laboratory, Division of Pharmaceutical Sciences, School of Pharmaceutical Sciences, University of Shizuoka, 52-1 Yada, Suruga-ku, Shizuoka 422-8526, Japan
| | - Kazuyuki Nakamura
- Department of Mathematical Sciences Based on Modeling and Analysis, School of Interdisciplinary Mathematical Sciences, Meiji University, Nakano 4-21-1, Nakano-ku, Tokyo 164-8525, Japan
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5
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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: 2] [Impact Index Per Article: 2.0] [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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6
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Jiang H, Li H, Wong WH, Fan X. Revealing Free Energy Landscape From MD Data via Conditional Angle Partition Tree. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:1384-1394. [PMID: 35503836 DOI: 10.1109/tcbb.2022.3172352] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Deciphering the free energy landscape of biomolecular structure space is crucial for understanding many complex molecular processes, such as protein-protein interaction, RNA folding, and protein folding. A major source of current dynamic structure data is Molecular Dynamics (MD) simulations. Several methods have been proposed to investigate the free energy landscape from MD data, but all of them rely on the assumption that kinetic similarity is associated with global geometric similarity, which may lead to unsatisfactory results. In this paper, we proposed a new method called Conditional Angle Partition Tree to reveal the hierarchical free energy landscape by correlating local geometric similarity with kinetic similarity. Its application on the benchmark alanine dipeptide MD data showed a much better performance than existing methods in exploring and understanding the free energy landscape. We also applied it to the MD data of Villin HP35. Our results are more reasonable on various aspects than those from other methods and very informative on the hierarchical structure of its energy landscape.
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7
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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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8
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Gu H, Wang W, Cao S, Unarta IC, Yao Y, Sheong FK, Huang X. RPnet: a reverse-projection-based neural network for coarse-graining metastable conformational states for protein dynamics. Phys Chem Chem Phys 2022; 24:1462-1474. [PMID: 34985469 DOI: 10.1039/d1cp03622j] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
The Markov State Model (MSM) is a powerful tool for modeling long timescale dynamics based on numerous short molecular dynamics (MD) simulation trajectories, which makes it a useful tool for elucidating the conformational changes of biological macromolecules. By partitioning the phase space into discretized states and estimating the probabilities of inter-state transitions based on short MD trajectories, one can construct a kinetic network model that could be used to extrapolate long-timescale kinetics if the Markovian condition is met. However, meeting the Markovian condition often requires hundreds or even thousands of states (microstates), which greatly hinders the comprehension of the conformational dynamics of complex biomolecules. Kinetic lumping algorithms can coarse grain numerous microstates into a handful of metastable states (macrostates), which would greatly facilitate the elucidation of biological mechanisms. In this work, we have developed a reverse-projection-based neural network (RPnet) to lump microstates into macrostates, by making use of a physics-based loss function that is based on the projection operator framework of conformational dynamics. By recognizing that microstate and macrostate transition modes can be related through a projection process, we have developed a reverse-projection scheme to directly compare the microstate and macrostate dynamics. Based on this reverse-projection scheme, we designed a loss function that allows the effective assessment of the quality of a given kinetic lumping. We then make use of a neural network to efficiently minimize this loss function to obtain an optimized set of macrostates. We have demonstrated the power of our RPnet in analyzing the dynamics of a numerical 2D potential, alanine dipeptide, and the clamp opening of an RNA polymerase. In all these systems, we have illustrated that our method could yield comparable or better results than competing methods in terms of state partitioning and reproduction of slow dynamics. We expect that our RPnet holds promise in analyzing the conformational dynamics of biological macromolecules.
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Affiliation(s)
- Hanlin Gu
- Department of Mathematics, Hong Kong University of Science and Technology, Kowloon, Hong Kong
| | - Wei Wang
- Department of Chemistry, Hong Kong University of Science and Technology, Kowloon, Hong Kong.
| | - Siqin Cao
- Department of Chemistry, Hong Kong University of Science and Technology, Kowloon, Hong Kong.
| | - Ilona Christy Unarta
- Department of Chemical and Biological Engineering, Hong Kong University of Science and Technology, Kowloon, Hong Kong
| | - Yuan Yao
- Department of Mathematics, Hong Kong University of Science and Technology, Kowloon, Hong Kong
| | - Fu Kit Sheong
- Department of Chemistry, Hong Kong University of Science and Technology, Kowloon, Hong Kong. .,Institute for Advanced Study, Hong Kong University of Science and Technology, Kowloon, Hong Kong
| | - Xuhui Huang
- Department of Chemistry, Hong Kong University of Science and Technology, Kowloon, Hong Kong. .,Department of Chemical and Biological Engineering, Hong Kong University of Science and Technology, Kowloon, Hong Kong
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9
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Damjanovic J, Murphy JM, Lin YS. CATBOSS: Cluster Analysis of Trajectories Based on Segment Splitting. J Chem Inf Model 2021; 61:5066-5081. [PMID: 34608796 PMCID: PMC8549068 DOI: 10.1021/acs.jcim.1c00598] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
Abstract
![]()
Molecular dynamics
(MD) simulations are an exceedingly and increasingly
potent tool for molecular behavior prediction and analysis. However,
the enormous wealth of data generated by these simulations can be
difficult to process and render in a human-readable fashion. Cluster
analysis is a commonly used way to partition data into structurally
distinct states. We present a method that improves on the state of
the art by taking advantage of the temporal information of MD trajectories
to enable more accurate clustering at a lower memory cost. To date,
cluster analysis of MD simulations has generally treated simulation
snapshots as a mere collection of independent data points and attempted
to separate them into different clusters based on structural similarity.
This new method, cluster analysis of trajectories based on segment
splitting (CATBOSS), applies density-peak-based clustering to classify trajectory segments learned by change detection. Applying
the method to a synthetic toy model as well as four real-life data
sets–trajectories of MD simulations of alanine dipeptide and
valine dipeptide as well as two fast-folding proteins–we find
CATBOSS to be robust and highly performant, yielding natural-looking
cluster boundaries and greatly improving clustering resolution. As
the classification of points into segments emphasizes density gaps
in the data by grouping them close to the state means, CATBOSS applied
to the valine dipeptide system is even able to account for a degree
of freedom deliberately omitted from the input data set. We also demonstrate
the potential utility of CATBOSS in distinguishing metastable states
from transition segments as well as promising application to cases
where there is little or no advance knowledge of intrinsic coordinates,
making for a highly versatile analysis tool.
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Affiliation(s)
- Jovan Damjanovic
- Department of Chemistry, Tufts University, Medford, Massachusetts 02155, United States
| | - James M Murphy
- Department of Mathematics, Tufts University, Medford, Massachusetts 02155, United States
| | - Yu-Shan Lin
- Department of Chemistry, Tufts University, Medford, Massachusetts 02155, United States
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10
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Jiang H, Fan X. The Two-Step Clustering Approach for Metastable States Learning. Int J Mol Sci 2021; 22:6576. [PMID: 34205252 PMCID: PMC8233889 DOI: 10.3390/ijms22126576] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Revised: 06/14/2021] [Accepted: 06/14/2021] [Indexed: 01/20/2023] Open
Abstract
Understanding the energy landscape and the conformational dynamics is crucial for studying many biological or chemical processes, such as protein-protein interaction and RNA folding. Molecular Dynamics (MD) simulations have been a major source of dynamic structure. Although many methods were proposed for learning metastable states from MD data, some key problems are still in need of further investigation. Here, we give a brief review on recent progresses in this field, with an emphasis on some popular methods belonging to a two-step clustering framework, and hope to draw more researchers to contribute to this area.
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Affiliation(s)
- Hangjin Jiang
- Center for Data Science, Zhejiang University, Hangzhou 310058, China;
| | - Xiaodan Fan
- Department of Statistics, The Chinese University of Hong Kong, Hong Kong, China
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11
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Quinn TR, Steussy CN, Haines BE, Lei J, Wang W, Sheong FK, Stauffacher CV, Huang X, Norrby PO, Helquist P, Wiest O. Microsecond timescale MD simulations at the transition state of PmHMGR predict remote allosteric residues. Chem Sci 2021; 12:6413-6418. [PMID: 34084441 PMCID: PMC8115266 DOI: 10.1039/d1sc00102g] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023] Open
Abstract
Understanding the mechanisms of enzymatic catalysis requires a detailed understanding of the complex interplay of structure and dynamics of large systems that is a challenge for both experimental and computational approaches. More importantly, the computational demands of QM/MM simulations mean that the dynamics of the reaction can only be considered on a timescale of nanoseconds even though the conformational changes needed to reach the catalytically active state happen on a much slower timescale. Here we demonstrate an alternative approach that uses transition state force fields (TSFFs) derived by the quantum-guided molecular mechanics (Q2MM) method that provides a consistent treatment of the entire system at the classical molecular mechanics level and allows simulations at the microsecond timescale. Application of this approach to the second hydride transfer transition state of HMG-CoA reductase from Pseudomonas mevalonii (PmHMGR) identified three remote residues, R396, E399 and L407, (15-27 Å away from the active site) that have a remote dynamic effect on enzyme activity. The predictions were subsequently validated experimentally via site-directed mutagenesis. These results show that microsecond timescale MD simulations of transition states are possible and can predict rather than just rationalize remote allosteric residues.
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Affiliation(s)
- Taylor R Quinn
- Department of Chemistry and Biochemistry, University of Notre Dame Notre Dame IN 46556 USA .,Early TDE Discovery, Early Oncology, Oncology R&D, AstraZeneca Boston USA
| | - Calvin N Steussy
- Department of Biological Sciences, Purdue Center for Cancer Research, Purdue University West Lafayette IN 47907 USA
| | - Brandon E Haines
- Department of Chemistry, Westmont College Santa Barbara CA 93108 USA
| | - Jinping Lei
- Department of Chemistry, The Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong China.,School of Pharmaceutical Sciences, Sun Yat-sen University Guangzhou 510006 China
| | - Wei Wang
- Department of Chemistry, The Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong China
| | - Fu Kit Sheong
- Department of Chemistry, The Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong China
| | - Cynthia V Stauffacher
- Department of Biological Sciences, Purdue Center for Cancer Research, Purdue University West Lafayette IN 47907 USA
| | - Xuhui Huang
- Department of Chemistry, The Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong China
| | - Per-Ola Norrby
- Department of Chemistry and Biochemistry, University of Notre Dame Notre Dame IN 46556 USA .,Data Science and Modelling, Pharmaceutical Sciences, R&D, AstraZeneca Gothenburg Pepparedsleden 1 SE-431 83 Mölndal Sweden
| | - Paul Helquist
- Department of Chemistry and Biochemistry, University of Notre Dame Notre Dame IN 46556 USA
| | - Olaf Wiest
- Department of Chemistry and Biochemistry, University of Notre Dame Notre Dame IN 46556 USA .,Lab of Computational Chemistry and Drug Design, School of Chemical Biology and Biotechnology, Peking University, Shenzhen Graduate School Shenzhen China
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12
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Ligand-bound glutamine binding protein assumes multiple metastable binding sites with different binding affinities. Commun Biol 2020; 3:419. [PMID: 32747735 PMCID: PMC7400645 DOI: 10.1038/s42003-020-01149-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2020] [Accepted: 07/14/2020] [Indexed: 11/08/2022] Open
Abstract
Protein dynamics plays key roles in ligand binding. However, the microscopic description of conformational dynamics-coupled ligand binding remains a challenge. In this study, we integrate molecular dynamics simulations, Markov state model (MSM) analysis and experimental methods to characterize the conformational dynamics of ligand-bound glutamine binding protein (GlnBP). We show that ligand-bound GlnBP has high conformational flexibility and additional metastable binding sites, presenting a more complex energy landscape than the scenario in the absence of ligand. The diverse conformations of GlnBP demonstrate different binding affinities and entail complex transition kinetics, implicating a concerted ligand binding mechanism. Single molecule fluorescence resonance energy transfer measurements and mutagenesis experiments are performed to validate our MSM-derived structure ensemble as well as the binding mechanism. Collectively, our study provides deeper insights into the protein dynamics-coupled ligand binding, revealing an intricate regulatory network underlying the apparent binding affinity. Zhang, Wu, Feng et al. show that ligand-bound glutamine binding protein assumes multiple metastable binding sites, presenting a more dynamic energy landscape than its ligand-free form. This study provides insights into the ligand-binding mechanisms coupled with protein dynamics that underly the apparent binding affinity.
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13
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Noé F. Machine Learning for Molecular Dynamics on Long Timescales. MACHINE LEARNING MEETS QUANTUM PHYSICS 2020. [DOI: 10.1007/978-3-030-40245-7_16] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
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14
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Affiliation(s)
- Frank Noé
- Department of Mathematics and Computer Science, Freie Universität Berlin, Berlin, Germany
- Department of Physics, Freie Universität Berlin, Berlin, Germany
| | - Edina Rosta
- Department of Chemistry, Kings College London, London, England
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15
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Thiede EH, Giannakis D, Dinner AR, Weare J. Galerkin approximation of dynamical quantities using trajectory data. J Chem Phys 2019; 150:244111. [PMID: 31255053 PMCID: PMC6824902 DOI: 10.1063/1.5063730] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2018] [Accepted: 05/13/2019] [Indexed: 11/14/2022] Open
Abstract
Understanding chemical mechanisms requires estimating dynamical statistics such as expected hitting times, reaction rates, and committors. Here, we present a general framework for calculating these dynamical quantities by approximating boundary value problems using dynamical operators with a Galerkin expansion. A specific choice of basis set in the expansion corresponds to the estimation of dynamical quantities using a Markov state model. More generally, the boundary conditions impose restrictions on the choice of basis sets. We demonstrate how an alternative basis can be constructed using ideas from diffusion maps. In our numerical experiments, this basis gives results of comparable or better accuracy to Markov state models. Additionally, we show that delay embedding can reduce the information lost when projecting the system's dynamics for model construction; this improves estimates of dynamical statistics considerably over the standard practice of increasing the lag time.
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Affiliation(s)
- Erik H Thiede
- Department of Chemistry and James Franck Institute, The University of Chicago, Chicago, Illinois 60637, USA
| | - Dimitrios Giannakis
- Courant Institute of Mathematical Sciences, New York University, New York, New York 10012, USA
| | - Aaron R Dinner
- Department of Chemistry and James Franck Institute, The University of Chicago, Chicago, Illinois 60637, USA
| | - Jonathan Weare
- Courant Institute of Mathematical Sciences, New York University, New York, New York 10012, USA
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16
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Nagel D, Weber A, Lickert B, Stock G. Dynamical coring of Markov state models. J Chem Phys 2019; 150:094111. [DOI: 10.1063/1.5081767] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023] Open
Affiliation(s)
- Daniel Nagel
- Biomolecular Dynamics, Institute of Physics, Albert Ludwigs University, 79104 Freiburg, Germany
| | - Anna Weber
- Biomolecular Dynamics, Institute of Physics, Albert Ludwigs University, 79104 Freiburg, Germany
| | - Benjamin Lickert
- Biomolecular Dynamics, Institute of Physics, Albert Ludwigs University, 79104 Freiburg, Germany
| | - Gerhard Stock
- Biomolecular Dynamics, Institute of Physics, Albert Ludwigs University, 79104 Freiburg, Germany
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17
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Porter JR, Zimmerman MI, Bowman GR. Enspara: Modeling molecular ensembles with scalable data structures and parallel computing. J Chem Phys 2019; 150:044108. [PMID: 30709308 DOI: 10.1063/1.5063794] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Markov state models (MSMs) are quantitative models of protein dynamics that are useful for uncovering the structural fluctuations that proteins undergo, as well as the mechanisms of these conformational changes. Given the enormity of conformational space, there has been ongoing interest in identifying a small number of states that capture the essential features of a protein. Generally, this is achieved by making assumptions about the properties of relevant features-for example, that the most important features are those that change slowly. An alternative strategy is to keep as many degrees of freedom as possible and subsequently learn from the model which of the features are most important. In these larger models, however, traditional approaches quickly become computationally intractable. In this paper, we present enspara, a library for working with MSMs that provides several novel algorithms and specialized data structures that dramatically improve the scalability of traditional MSM methods. This includes ragged arrays for minimizing memory requirements, message passing interface-parallelized implementations of compute-intensive operations, and a flexible framework for model construction and analysis.
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Affiliation(s)
- J R Porter
- Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, 660 South Euclid Avenue, St. Louis, Missouri 63110, USA
| | - M I Zimmerman
- Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, 660 South Euclid Avenue, St. Louis, Missouri 63110, USA
| | - G R Bowman
- Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, 660 South Euclid Avenue, St. Louis, Missouri 63110, USA
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18
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Sun X, Singh S, Blumer KJ, Bowman GR. Simulation of spontaneous G protein activation reveals a new intermediate driving GDP unbinding. eLife 2018; 7:e38465. [PMID: 30289386 PMCID: PMC6224197 DOI: 10.7554/elife.38465] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2018] [Accepted: 10/04/2018] [Indexed: 12/12/2022] Open
Abstract
Activation of heterotrimeric G proteins is a key step in many signaling cascades. However, a complete mechanism for this process, which requires allosteric communication between binding sites that are ~30 Å apart, remains elusive. We construct an atomically detailed model of G protein activation by combining three powerful computational methods: metadynamics, Markov state models (MSMs), and CARDS analysis of correlated motions. We uncover a mechanism that is consistent with a wide variety of structural and biochemical data. Surprisingly, the rate-limiting step for GDP release correlates with tilting rather than translation of the GPCR-binding helix 5. β-Strands 1 - 3 and helix 1 emerge as hubs in the allosteric network that links conformational changes in the GPCR-binding site to disordering of the distal nucleotide-binding site and consequent GDP release. Our approach and insights provide foundations for understanding disease-implicated G protein mutants, illuminating slow events in allosteric networks, and examining unbinding processes with slow off-rates.
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Affiliation(s)
- Xianqiang Sun
- Department of Biochemistry and Molecular BiophysicsWashington University School of MedicineMissouriUnited States
| | - Sukrit Singh
- Department of Biochemistry and Molecular BiophysicsWashington University School of MedicineMissouriUnited States
| | - Kendall J Blumer
- Department of Cell Biology and PhysiologyWashington University School of MedicineMissouriUnited States
| | - Gregory R Bowman
- Department of Biochemistry and Molecular BiophysicsWashington University School of MedicineMissouriUnited States
- Center for Biological Systems EngineeringWashington University School of MedicineMissouriUnited States
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19
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Wang W, Liang T, Sheong FK, Fan X, Huang X. An efficient Bayesian kinetic lumping algorithm to identify metastable conformational states via Gibbs sampling. J Chem Phys 2018; 149:072337. [PMID: 30134698 DOI: 10.1063/1.5027001] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023] Open
Abstract
Markov State Model (MSM) has become a popular approach to study the conformational dynamics of complex biological systems in recent years. Built upon a large number of short molecular dynamics simulation trajectories, MSM is able to predict the long time scale dynamics of complex systems. However, to achieve Markovianity, an MSM often contains hundreds or thousands of states (microstates), hindering human interpretation of the underlying system mechanism. One way to reduce the number of states is to lump kinetically similar states together and thus coarse-grain the microstates into macrostates. In this work, we introduce a probabilistic lumping algorithm, the Gibbs lumping algorithm, to assign a probability to any given kinetic lumping using the Bayesian inference. In our algorithm, the transitions among kinetically distinct macrostates are modeled by Poisson processes, which will well reflect the separation of time scales in the underlying free energy landscape of biomolecules. Furthermore, to facilitate the search for the optimal kinetic lumping (i.e., the lumped model with the highest probability), a Gibbs sampling algorithm is introduced. To demonstrate the power of our new method, we apply it to three systems: a 2D potential, alanine dipeptide, and a WW protein domain. In comparison with six other popular lumping algorithms, we show that our method can persistently produce the lumped macrostate model with the highest probability as well as the largest metastability. We anticipate that our Gibbs lumping algorithm holds great promise to be widely applied to investigate conformational changes in biological macromolecules.
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Affiliation(s)
- Wei Wang
- HKUST-Shenzhen Research Institute, Hi-Tech Park, Nanshan, Shenzhen 518057, China
| | - Tong Liang
- Department of Statistics, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong
| | - Fu Kit Sheong
- Department of Chemistry, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong
| | - Xiaodan Fan
- Department of Statistics, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong
| | - Xuhui Huang
- HKUST-Shenzhen Research Institute, Hi-Tech Park, Nanshan, Shenzhen 518057, China
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20
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Peng JH, Wang W, Yu YQ, Gu HL, Huang X. Clustering algorithms to analyze molecular dynamics simulation trajectories for complex chemical and biological systems. CHINESE J CHEM PHYS 2018. [DOI: 10.1063/1674-0068/31/cjcp1806147] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Affiliation(s)
- Jun-hui Peng
- HKUST-Shenzhen Research Institute, Hi-Tech Park, Nanshan, Shenzhen 518057, China
- Department of Chemistry, The Hong Kong University of Science and Technology, Kowloon, Hong Kong
| | - Wei Wang
- HKUST-Shenzhen Research Institute, Hi-Tech Park, Nanshan, Shenzhen 518057, China
- Department of Chemistry, The Hong Kong University of Science and Technology, Kowloon, Hong Kong
| | - Ye-qing Yu
- HKUST-Shenzhen Research Institute, Hi-Tech Park, Nanshan, Shenzhen 518057, China
- Department of Chemistry, The Hong Kong University of Science and Technology, Kowloon, Hong Kong
| | - Han-lin Gu
- Department of Mathematics, The Hong Kong University of Science and Technology, Kowloon, Hong Kong
| | - Xuhui Huang
- HKUST-Shenzhen Research Institute, Hi-Tech Park, Nanshan, Shenzhen 518057, China
- Department of Chemistry, The Hong Kong University of Science and Technology, Kowloon, Hong Kong
- Center of Systems Biology and Human Health, The Hong Kong University of Science and Technology, Kowloon, Hong Kong
- State Key Laboratory of Molecular Neuroscience, The Hong Kong University of Science and Technology, Kowloon, Hong Kong
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21
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Collins AP, Anderson PC. Complete Coupled Binding-Folding Pathway of the Intrinsically Disordered Transcription Factor Protein Brinker Revealed by Molecular Dynamics Simulations and Markov State Modeling. Biochemistry 2018; 57:4404-4420. [PMID: 29990433 DOI: 10.1021/acs.biochem.8b00441] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Intrinsically disordered proteins (IDPs) make up a large class of proteins that lack stable structures in solution, existing instead as dynamic conformational ensembles. To perform their biological functions, many IDPs bind to other proteins or nucleic acids. Although IDPs are unstructured in solution, when they interact with binding partners, they fold into defined three-dimensional structures via coupled binding-folding processes. Because they frequently underlie IDP function, the mechanisms of this coupled binding-folding process are of great interest. However, given the flexibility inherent to IDPs and the sparse populations of intermediate states, it is difficult to reveal binding-folding pathways at atomic resolution using experimental methods. Computer simulations are another tool for studying these pathways at high resolution. Accordingly, we have applied 40 μs of unbiased molecular dynamics simulations and Markov state modeling to map the complete binding-folding pathway of a model IDP, the 59-residue C-terminal portion of the DNA binding domain of Drosophila melanogaster transcription factor Brinker (BrkDBD). Our modeling indicates that BrkDBD binds to its cognate DNA and folds in ∼50 μs by an induced fit mechanism, acquiring most of its stable secondary and tertiary structure only after it reaches the final binding site on the DNA. The protein follows numerous pathways en route to its bound and folded conformation, occasionally becoming stuck in kinetic traps. Each binding-folding pathway involves weakly bound, increasingly folded intermediate states located at different sites on the DNA surface. These findings agree with experimental data and provide additional insight into the BrkDBD folding mechanism and kinetics.
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Affiliation(s)
- Andrew P Collins
- Physical Sciences Division , University of Washington Bothell , Bothell , Washington 98011-8246 , United States
| | - Peter C Anderson
- Physical Sciences Division , University of Washington Bothell , Bothell , Washington 98011-8246 , United States
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22
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Zhu L, Sheong FK, Zeng X, Huang X. Elucidation of the conformational dynamics of multi-body systems by construction of Markov state models. Phys Chem Chem Phys 2018; 18:30228-30235. [PMID: 27314275 DOI: 10.1039/c6cp02545e] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
Constructing Markov State Models (MSMs) based on short molecular dynamics simulations is a powerful computational technique to complement experiments in predicting long-time kinetics of biomolecular processes at atomic resolution. Even though the MSM approach has been widely applied to study one-body processes such as protein folding and enzyme conformational changes, the majority of biological processes, e.g. protein-ligand recognition, signal transduction, and protein aggregation, essentially involve multiple entities. Here we review the attempts at constructing MSMs for multi-body systems, point out the challenges therein and discuss recent algorithmic progresses that alleviate these challenges. In particular, we describe an automatic kinetics based partitioning method that achieves optimal definition of the conformational states in a multi-body system, and discuss a novel maximum-likelihood approach that efficiently estimates the slow uphill kinetics utilizing pre-computed equilibrium populations of all states. We expect that these new algorithms and their combinations may boost investigations of important multi-body biological processes via the efficient construction of MSMs.
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Affiliation(s)
- Lizhe Zhu
- Department of Chemistry, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China. and Centre of Systems Biology and Human Health, School of Science and Institute for Advance Study, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China
| | - Fu Kit Sheong
- Department of Chemistry, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China.
| | - Xiangze Zeng
- Department of Chemistry, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China. and Centre of Systems Biology and Human Health, School of Science and Institute for Advance Study, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China
| | - Xuhui Huang
- Department of Chemistry, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China. and Centre of Systems Biology and Human Health, School of Science and Institute for Advance Study, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China
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23
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Affiliation(s)
- Brooke E. Husic
- Department of Chemistry, Stanford University, Stanford, California 94305, United States
| | - Vijay S. Pande
- Department of Chemistry, Stanford University, Stanford, California 94305, United States
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24
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Mardt A, Pasquali L, Wu H, Noé F. VAMPnets for deep learning of molecular kinetics. Nat Commun 2018; 9:5. [PMID: 29295994 PMCID: PMC5750224 DOI: 10.1038/s41467-017-02388-1] [Citation(s) in RCA: 229] [Impact Index Per Article: 38.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2017] [Accepted: 11/22/2017] [Indexed: 12/15/2022] Open
Abstract
There is an increasing demand for computing the relevant structures, equilibria, and long-timescale kinetics of biomolecular processes, such as protein-drug binding, from high-throughput molecular dynamics simulations. Current methods employ transformation of simulated coordinates into structural features, dimension reduction, clustering the dimension-reduced data, and estimation of a Markov state model or related model of the interconversion rates between molecular structures. This handcrafted approach demands a substantial amount of modeling expertise, as poor decisions at any step will lead to large modeling errors. Here we employ the variational approach for Markov processes (VAMP) to develop a deep learning framework for molecular kinetics using neural networks, dubbed VAMPnets. A VAMPnet encodes the entire mapping from molecular coordinates to Markov states, thus combining the whole data processing pipeline in a single end-to-end framework. Our method performs equally or better than state-of-the-art Markov modeling methods and provides easily interpretable few-state kinetic models. Extracting kinetic models from high-throughput molecular dynamics (MD) simulations is laborious and prone to human error. Here the authors introduce a deep learning framework that automates construction of Markov state models from MD simulation data.
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Affiliation(s)
- Andreas Mardt
- Department of Mathematics and Computer Science, Freie Universität Berlin, Arnimallee 6, 14195, Berlin, Germany
| | - Luca Pasquali
- Department of Mathematics and Computer Science, Freie Universität Berlin, Arnimallee 6, 14195, Berlin, Germany
| | - Hao Wu
- Department of Mathematics and Computer Science, Freie Universität Berlin, Arnimallee 6, 14195, Berlin, Germany
| | - Frank Noé
- Department of Mathematics and Computer Science, Freie Universität Berlin, Arnimallee 6, 14195, Berlin, Germany.
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25
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Cao Y, Jiang X, Han W. Self-Assembly Pathways of β-Sheet-Rich Amyloid-β(1-40) Dimers: Markov State Model Analysis on Millisecond Hybrid-Resolution Simulations. J Chem Theory Comput 2017; 13:5731-5744. [PMID: 29019683 DOI: 10.1021/acs.jctc.7b00803] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
Early oligomerization during amyloid-β (Aβ) aggregation is essential for Aβ neurotoxicity. Understanding how unstructured Aβs assemble into oligomers, especially those rich in β-sheets, is essential but remains challenging as the assembly process is too transient for experimental characterization and too slow for molecular dynamics simulations. So far, atomic simulations are limited only to studies of either oligomer structures or assembly pathways for short Aβ segments. To overcome the computational challenge, we combine in this study a hybrid-resolution model and adaptive sampling techniques to perform over 2.7 ms of simulations of formation of full-length Aβ40 dimers that are the earliest toxic oligomeric species. The Markov state model is further employed to characterize the transition pathways and associated kinetics. Our results show that for two major forms of β-sheet-rich structures reported experimentally, the corresponding assembly mechanisms are markedly different. Hairpin-containing structures are formed by direct binding of soluble Aβ in β-hairpin-like conformations. Formation of parallel, in-register structures resembling fibrils occurs ∼100-fold more slowly and involves a rapid encounter of Aβ in arbitrary conformations followed by a slow structural conversion. The structural conversion proceeds via diverse pathways but always requires transient unfolding of encounter complexes. We find that the transition kinetics could be affected differently by intra-/intermolecular interactions involving individual residues in a conformation-dependent manner. In particular, the interactions involving Aβ's N-terminal part promote the assembly into hairpin-containing structures but delay the formation of fibril-like structures, thus explaining puzzling observations reported previously regarding the roles of this region in the early assembly process.
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Affiliation(s)
- Yang Cao
- Key Laboratory of Chemical Genomics, School of Chemical Biology and Biotechnology, Peking University Shenzhen Graduate School , Shenzhen, 518055, China
| | - Xuehan Jiang
- Key Laboratory of Chemical Genomics, School of Chemical Biology and Biotechnology, Peking University Shenzhen Graduate School , Shenzhen, 518055, China
| | - Wei Han
- Key Laboratory of Chemical Genomics, School of Chemical Biology and Biotechnology, Peking University Shenzhen Graduate School , Shenzhen, 518055, China
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26
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Wang W, Cao S, Zhu L, Huang X. Constructing Markov State Models to elucidate the functional conformational changes of complex biomolecules. WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL MOLECULAR SCIENCE 2017. [DOI: 10.1002/wcms.1343] [Citation(s) in RCA: 52] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Affiliation(s)
- Wei Wang
- Department of ChemistryThe Hong Kong University of Science and Technology Kowloon Hong Kong
- Center of Systems Biology and Human HealthThe Hong Kong University of Science and Technology Kowloon Hong Kong
| | - Siqin Cao
- Department of ChemistryThe Hong Kong University of Science and Technology Kowloon Hong Kong
| | - Lizhe Zhu
- Department of ChemistryThe Hong Kong University of Science and Technology Kowloon Hong Kong
- Center of Systems Biology and Human HealthThe Hong Kong University of Science and Technology Kowloon Hong Kong
| | - Xuhui Huang
- Department of ChemistryThe Hong Kong University of Science and Technology Kowloon Hong Kong
- Center of Systems Biology and Human HealthThe Hong Kong University of Science and Technology Kowloon Hong Kong
- Hong Kong Branch of Chinese National Engineering Research Center for Tissue Restoration & ReconstructionThe Hong Kong University of Science and Technology Kowloon Hong Kong
- HKUST‐Shenzhen Research Institute Shenzhen China
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27
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Shamsi Z, Moffett AS, Shukla D. Enhanced unbiased sampling of protein dynamics using evolutionary coupling information. Sci Rep 2017; 7:12700. [PMID: 28983093 PMCID: PMC5629199 DOI: 10.1038/s41598-017-12874-7] [Citation(s) in RCA: 31] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2016] [Accepted: 09/14/2017] [Indexed: 12/25/2022] Open
Abstract
One of the major challenges in atomistic simulations of proteins is efficient sampling of pathways associated with rare conformational transitions. Recent developments in statistical methods for computation of direct evolutionary couplings between amino acids within and across polypeptide chains have allowed for inference of native residue contacts, informing accurate prediction of protein folds and multimeric structures. In this study, we assess the use of distances between evolutionarily coupled residues as natural choices for reaction coordinates which can be incorporated into Markov state model-based adaptive sampling schemes and potentially used to predict not only functional conformations but also pathways of conformational change, protein folding, and protein-protein association. We demonstrate the utility of evolutionary couplings in sampling and predicting activation pathways of the β 2-adrenergic receptor (β 2-AR), folding of the FiP35 WW domain, and dimerization of the E. coli molybdopterin synthase subunits. We find that the time required for β 2-AR activation and folding of the WW domain are greatly diminished using evolutionary couplings-guided adaptive sampling. Additionally, we were able to identify putative molybdopterin synthase association pathways and near-crystal structure complexes from protein-protein association simulations.
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Affiliation(s)
- Zahra Shamsi
- Department of Chemical and Biomolecular Engineering, University of Illinois, Urbana, IL, 61801, USA
| | - Alexander S Moffett
- Center for Biophysics and Quantitative Biology, University of Illinois, Urbana, IL, 61801, USA
| | - Diwakar Shukla
- Department of Chemical and Biomolecular Engineering, University of Illinois, Urbana, IL, 61801, USA.
- Center for Biophysics and Quantitative Biology, University of Illinois, Urbana, IL, 61801, USA.
- Department of Plant Biology, University of Illinois, Urbana, IL, 61801, USA.
- National Center for Supercomputing Applications, University of Illinois, Urbana, IL, 61801, USA.
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28
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Meng L, Sheong FK, Zeng X, Zhu L, Huang X. Path lumping: An efficient algorithm to identify metastable path channels for conformational dynamics of multi-body systems. J Chem Phys 2017; 147:044112. [DOI: 10.1063/1.4995558] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Affiliation(s)
- Luming Meng
- Department of Chemistry, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong
| | - Fu Kit Sheong
- Department of Chemistry, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong
| | - Xiangze Zeng
- Department of Chemistry, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong
| | - Lizhe Zhu
- Department of Chemistry, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong
- Center of Systems Biology and Human Health, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong
| | - Xuhui Huang
- Department of Chemistry, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong
- Center of Systems Biology and Human Health, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong
- Hong Kong Branch of Chinese National Engineering Research Center for Tissue Restoration and Reconstruction, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong
- HKUST-Shenzhen Research Institute, Hi-Tech Park, Nanshan, Shenzhen 518057, China
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29
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Elucidating Mechanisms of Molecular Recognition Between Human Argonaute and miRNA Using Computational Approaches. Methods Mol Biol 2016. [PMID: 27924488 DOI: 10.1007/978-1-4939-6563-2_18] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/05/2024]
Abstract
MicroRNA (miRNA) and Argonaute (AGO) protein together form the RNA-induced silencing complex (RISC) that plays an essential role in the regulation of gene expression. Elucidating the underlying mechanism of AGO-miRNA recognition is thus of great importance not only for the in-depth understanding of miRNA function but also for inspiring new drugs targeting miRNAs. In this chapter we introduce a combined computational approach of molecular dynamics (MD) simulations, Markov state models (MSMs), and protein-RNA docking to investigate AGO-miRNA recognition. Constructed from MD simulations, MSMs can elucidate the conformational dynamics of AGO at biologically relevant timescales. Protein-RNA docking can then efficiently identify the AGO conformations that are geometrically accessible to miRNA. Using our recent work on human AGO2 as an example, we explain the rationale and the workflow of our method in details. This combined approach holds great promise to complement experiments in unraveling the mechanisms of molecular recognition between large, flexible, and complex biomolecules.
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30
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Liu S, Zhu L, Sheong FK, Wang W, Huang X. Adaptive partitioning by local density-peaks: An efficient density-based clustering algorithm for analyzing molecular dynamics trajectories. J Comput Chem 2016; 38:152-160. [PMID: 27868222 DOI: 10.1002/jcc.24664] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2016] [Revised: 10/09/2016] [Accepted: 10/26/2016] [Indexed: 12/11/2022]
Abstract
We present an efficient density-based adaptive-resolution clustering method APLoD for analyzing large-scale molecular dynamics (MD) trajectories. APLoD performs the k-nearest-neighbors search to estimate the density of MD conformations in a local fashion, which can group MD conformations in the same high-density region into a cluster. APLoD greatly improves the popular density peaks algorithm by reducing the running time and the memory usage by 2-3 orders of magnitude for systems ranging from alanine dipeptide to a 370-residue Maltose-binding protein. In addition, we demonstrate that APLoD can produce clusters with various sizes that are adaptive to the underlying density (i.e., larger clusters at low-density regions, while smaller clusters at high-density regions), which is a clear advantage over other popular clustering algorithms including k-centers and k-medoids. We anticipate that APLoD can be widely applied to split ultra-large MD datasets containing millions of conformations for subsequent construction of Markov State Models. © 2016 Wiley Periodicals, Inc.
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Affiliation(s)
- Song Liu
- Department of Chemistry, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong
| | - Lizhe Zhu
- Department of Chemistry, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong.,Center of Systems Biology and Human Health, State Key Laboratory of Molecular Neuroscience, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong
| | - Fu Kit Sheong
- Department of Chemistry, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong
| | - Wei Wang
- Department of Chemistry, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong.,Center of Systems Biology and Human Health, State Key Laboratory of Molecular Neuroscience, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong
| | - Xuhui Huang
- Department of Chemistry, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong.,Center of Systems Biology and Human Health, State Key Laboratory of Molecular Neuroscience, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong
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31
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Noé F, Banisch R, Clementi C. Commute Maps: Separating Slowly Mixing Molecular Configurations for Kinetic Modeling. J Chem Theory Comput 2016; 12:5620-5630. [DOI: 10.1021/acs.jctc.6b00762] [Citation(s) in RCA: 43] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Frank Noé
- Department
of Mathematics, Computer Science and Bioinformatics, FU Berlin, Arnimallee
6, 14195 Berlin, Germany
| | - Ralf Banisch
- Department
of Mathematics, Computer Science and Bioinformatics, FU Berlin, Arnimallee
6, 14195 Berlin, Germany
| | - Cecilia Clementi
- Center
for Theoretical Biological Physics, and Department of Chemistry, Rice University, 6100 Main Street, Houston, Texas 77005, United States
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Zhang L, Pardo-Avila F, Unarta IC, Cheung PPH, Wang G, Wang D, Huang X. Elucidation of the Dynamics of Transcription Elongation by RNA Polymerase II using Kinetic Network Models. Acc Chem Res 2016; 49:687-94. [PMID: 26991064 DOI: 10.1021/acs.accounts.5b00536] [Citation(s) in RCA: 39] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
RNA polymerase II (Pol II) is an essential enzyme that catalyzes transcription with high efficiency and fidelity in eukaryotic cells. During transcription elongation, Pol II catalyzes the nucleotide addition cycle (NAC) to synthesize mRNA using DNA as the template. The transitions between the states of the NAC require conformational changes of both the protein and nucleotides. Although X-ray structures are available for most of these states, the dynamics of the transitions between states are largely unknown. Molecular dynamics (MD) simulations can predict structure-based molecular details and shed light on the mechanisms of these dynamic transitions. However, the employment of MD simulations on a macromolecule (tens to hundreds of nanoseconds) such as Pol II is challenging due to the difficulty of reaching biologically relevant timescales (tens of microseconds or even longer). For this challenge to be overcome, kinetic network models (KNMs), such as Markov State Models (MSMs), have become a popular approach to access long-timescale conformational changes using many short MD simulations. We describe here our application of KNMs to characterize the molecular mechanisms of the NAC of Pol II. First, we introduce the general background of MSMs and further explain procedures for the construction and validation of MSMs by providing some technical details. Next, we review our previous studies in which we applied MSMs to investigate the individual steps of the NAC, including translocation and pyrophosphate ion release. In particular, we describe in detail how we prepared the initial conformations of Pol II elongation complex, performed MD simulations, extracted MD conformations to construct MSMs, and further validated them. We also summarize our major findings on molecular mechanisms of Pol II elongation based on these MSMs. In addition, we have included discussions regarding various key points and challenges for applications of MSMs to systems as large as the Pol II elongation complex. Finally, to study the overall NAC, we combine the individual steps of the NAC into a five-state KNM based on a nonbranched Brownian ratchet scheme to explain the single-molecule optical tweezers experimental data. The studies complement experimental observations and provide molecular mechanisms for the transcription elongation cycle. In the long term, incorporation of sequence-dependent kinetic parameters into KNMs has great potential for identifying error-prone sequences and predicting transcription dynamics in genome-wide transcriptomes.
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Affiliation(s)
- Lu Zhang
- Department
of Chemistry and State Key Laboratory of Molecular Neuroscience, Center
for System Biology and Human Health, School of Science, and IAS, The Hong Kong University of Science and Technology, Kowloon, Hong Kong
| | - Fátima Pardo-Avila
- Department
of Chemistry and State Key Laboratory of Molecular Neuroscience, Center
for System Biology and Human Health, School of Science, and IAS, The Hong Kong University of Science and Technology, Kowloon, Hong Kong
| | - Ilona Christy Unarta
- Department
of Chemistry and State Key Laboratory of Molecular Neuroscience, Center
for System Biology and Human Health, School of Science, and IAS, The Hong Kong University of Science and Technology, Kowloon, Hong Kong
| | - Peter Pak-Hang Cheung
- Department
of Chemistry and State Key Laboratory of Molecular Neuroscience, Center
for System Biology and Human Health, School of Science, and IAS, The Hong Kong University of Science and Technology, Kowloon, Hong Kong
| | - Guo Wang
- Department
of Chemistry and State Key Laboratory of Molecular Neuroscience, Center
for System Biology and Human Health, School of Science, and IAS, The Hong Kong University of Science and Technology, Kowloon, Hong Kong
| | - Dong Wang
- Department
of Cellular and Molecular Medicine, Skaggs School of Pharmacy and
Pharmaceutical Sciences, University of California, San Diego, La Jolla, California 92093, United States
| | - Xuhui Huang
- Department
of Chemistry and State Key Laboratory of Molecular Neuroscience, Center
for System Biology and Human Health, School of Science, and IAS, The Hong Kong University of Science and Technology, Kowloon, Hong Kong
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Zeng X, Li B, Qiao Q, Zhu L, Lu ZY, Huang X. Elucidating dominant pathways of the nano-particle self-assembly process. Phys Chem Chem Phys 2016; 18:23494-9. [DOI: 10.1039/c6cp01808d] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Self-assembly processes play a key role in the fabrication of functional nano-structures with wide application in drug delivery and micro-reactors.
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Affiliation(s)
- Xiangze Zeng
- Department of Chemistry
- The Hong Kong University of Science and Technology
- Kowloon
- Hong Kong
| | - Bin Li
- State Key Laboratory of Supramolecular Structure and Materials
- Institute of Theoretical Chemistry
- Jilin University
- Changchun
- China
| | - Qin Qiao
- Department of Chemistry
- The Hong Kong University of Science and Technology
- Kowloon
- Hong Kong
| | - Lizhe Zhu
- Department of Chemistry
- The Hong Kong University of Science and Technology
- Kowloon
- Hong Kong
| | - Zhong-Yuan Lu
- State Key Laboratory of Supramolecular Structure and Materials
- Institute of Theoretical Chemistry
- Jilin University
- Changchun
- China
| | - Xuhui Huang
- Department of Chemistry
- The Hong Kong University of Science and Technology
- Kowloon
- Hong Kong
- Division of Biomedical Engineering
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Zhang L, Jiang H, Sheong F, Pardo-Avila F, Cheung PH, Huang X. Constructing Kinetic Network Models to Elucidate Mechanisms of Functional Conformational Changes of Enzymes and Their Recognition with Ligands. Methods Enzymol 2016; 578:343-71. [DOI: 10.1016/bs.mie.2016.05.026] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
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Scherer MK, Trendelkamp-Schroer B, Paul F, Pérez-Hernández G, Hoffmann M, Plattner N, Wehmeyer C, Prinz JH, Noé F. PyEMMA 2: A Software Package for Estimation, Validation, and Analysis of Markov Models. J Chem Theory Comput 2015; 11:5525-42. [PMID: 26574340 DOI: 10.1021/acs.jctc.5b00743] [Citation(s) in RCA: 701] [Impact Index Per Article: 77.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Markov (state) models (MSMs) and related models of molecular kinetics have recently received a surge of interest as they can systematically reconcile simulation data from either a few long or many short simulations and allow us to analyze the essential metastable structures, thermodynamics, and kinetics of the molecular system under investigation. However, the estimation, validation, and analysis of such models is far from trivial and involves sophisticated and often numerically sensitive methods. In this work we present the open-source Python package PyEMMA ( http://pyemma.org ) that provides accurate and efficient algorithms for kinetic model construction. PyEMMA can read all common molecular dynamics data formats, helps in the selection of input features, provides easy access to dimension reduction algorithms such as principal component analysis (PCA) and time-lagged independent component analysis (TICA) and clustering algorithms such as k-means, and contains estimators for MSMs, hidden Markov models, and several other models. Systematic model validation and error calculation methods are provided. PyEMMA offers a wealth of analysis functions such that the user can conveniently compute molecular observables of interest. We have derived a systematic and accurate way to coarse-grain MSMs to few states and to illustrate the structures of the metastable states of the system. Plotting functions to produce a manuscript-ready presentation of the results are available. In this work, we demonstrate the features of the software and show new methodological concepts and results produced by PyEMMA.
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Affiliation(s)
- Martin K Scherer
- Department for Mathematics and Computer Science, Freie Universität , Arnimallee 6, Berlin 14195, Germany
| | | | - Fabian Paul
- Department for Mathematics and Computer Science, Freie Universität , Arnimallee 6, Berlin 14195, Germany
| | - Guillermo Pérez-Hernández
- Department for Mathematics and Computer Science, Freie Universität , Arnimallee 6, Berlin 14195, Germany
| | - Moritz Hoffmann
- Department for Mathematics and Computer Science, Freie Universität , Arnimallee 6, Berlin 14195, Germany
| | - Nuria Plattner
- Department for Mathematics and Computer Science, Freie Universität , Arnimallee 6, Berlin 14195, Germany
| | - Christoph Wehmeyer
- Department for Mathematics and Computer Science, Freie Universität , Arnimallee 6, Berlin 14195, Germany
| | - Jan-Hendrik Prinz
- Department for Mathematics and Computer Science, Freie Universität , Arnimallee 6, Berlin 14195, Germany
| | - Frank Noé
- Department for Mathematics and Computer Science, Freie Universität , Arnimallee 6, Berlin 14195, Germany
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Vitalini F, Noé F, Keller BG. A Basis Set for Peptides for the Variational Approach to Conformational Kinetics. J Chem Theory Comput 2015; 11:3992-4004. [DOI: 10.1021/acs.jctc.5b00498] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Affiliation(s)
- F. Vitalini
- Department
of Biology, Chemistry, Pharmacy, Freie Universität Berlin, Takustraße
3, D-14195 Berlin, Germany
| | - F. Noé
- Department
of Mathematics and Computer Science, Freie Universität Berlin, Arnimallee 6, D-14195 Berlin, Germany
| | - B. G. Keller
- Department
of Biology, Chemistry, Pharmacy, Freie Universität Berlin, Takustraße
3, D-14195 Berlin, Germany
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Jiang H, Sheong FK, Zhu L, Gao X, Bernauer J, Huang X. Markov State Models Reveal a Two-Step Mechanism of miRNA Loading into the Human Argonaute Protein: Selective Binding followed by Structural Re-arrangement. PLoS Comput Biol 2015; 11:e1004404. [PMID: 26181723 PMCID: PMC4504477 DOI: 10.1371/journal.pcbi.1004404] [Citation(s) in RCA: 48] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2015] [Accepted: 06/16/2015] [Indexed: 01/17/2023] Open
Abstract
Argonaute (Ago) proteins and microRNAs (miRNAs) are central components in RNA interference, which is a key cellular mechanism for sequence-specific gene silencing. Despite intensive studies, molecular mechanisms of how Ago recognizes miRNA remain largely elusive. In this study, we propose a two-step mechanism for this molecular recognition: selective binding followed by structural re-arrangement. Our model is based on the results of a combination of Markov State Models (MSMs), large-scale protein-RNA docking, and molecular dynamics (MD) simulations. Using MSMs, we identify an open state of apo human Ago-2 in fast equilibrium with partially open and closed states. Conformations in this open state are distinguished by their largely exposed binding grooves that can geometrically accommodate miRNA as indicated in our protein-RNA docking studies. miRNA may then selectively bind to these open conformations. Upon the initial binding, the complex may perform further structural re-arrangement as shown in our MD simulations and eventually reach the stable binary complex structure. Our results provide novel insights in Ago-miRNA recognition mechanisms and our methodology holds great potential to be widely applied in the studies of other important molecular recognition systems. In RNA interference, Argonaute proteins and microRNAs together form the functional core that regulates the gene expression with high sequence specificity. Elucidating the detailed mechanism of molecular recognition between Argonaute proteins and microRNAs is thus important not only for the fundamental understanding of RNA interference, but also for the further development of microRNA-based therapeutic application. In this work, we propose a two-step model to understand the mechanism of microRNA loading into human Argonaute-2: selective binding followed by structural re-arrangement. Our model is based on the results from a combined approach of molecular dynamics simulations, Markov State Models and protein-RNA docking. In particular, we identify a metastable open state of apo hAgo2 in rapid equilibrium with other states. Some of conformations in this open state have largely exposed RNA binding groove that can accommodate microRNA. We further show that the initial Argonaute-microRNA binding complex undergoes structural re-arrangement to reach stable binary crystal structure. These results provide novel insights into the underlying mechanism of Argonaute-microRNA recognition. In addition, our method is readily applicable to the investigation of other complex molecular recognition events such as protein-protein interactions and protein-ligand binding.
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Affiliation(s)
- Hanlun Jiang
- Bioengineering Graduate Program, Division of Biomedical Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong
- The HKUST Shenzhen Research Institute, Shenzhen, China
| | - Fu Kit Sheong
- Department of Chemistry, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong
| | - Lizhe Zhu
- The HKUST Shenzhen Research Institute, Shenzhen, China
- Department of Chemistry, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong
- Center of Systems Biology and Human Health, School of Science and Institute for Advance Study, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong
| | - Xin Gao
- Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
| | - Julie Bernauer
- Inria Saclay-Île de France, Bâtiment Alan Turing, Campus de l’École Polytechnique, Palaiseau, France
- Laboratoire d’Informatique de l’École Polytechnique (LIX), CNRS UMR 7161, École Polytechnique, Palaiseau, France
- * E-mail: (JB); (XH)
| | - Xuhui Huang
- Bioengineering Graduate Program, Division of Biomedical Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong
- The HKUST Shenzhen Research Institute, Shenzhen, China
- Department of Chemistry, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong
- Center of Systems Biology and Human Health, School of Science and Institute for Advance Study, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong
- * E-mail: (JB); (XH)
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