1
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Zaman AB, Inan TT, De Jong K, Shehu A. Adaptive Stochastic Optimization to Improve Protein Conformation Sampling. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:2759-2771. [PMID: 34882562 DOI: 10.1109/tcbb.2021.3134103] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
We have long known that characterizing protein structures structure is key to understanding protein function. Computational approaches have largely addressed a narrow formulation of the problem, seeking to compute one native structure from an amino-acid sequence. Now AlphaFold2 is shown to be able to reveal a high-quality native structure for many proteins. However, researchers over the years have argued for broadening our view to account for the multiplicity of native structures. We now know that many protein molecules switch between different structures to regulate interactions with molecular partners in the cell. Elucidating such structures de novo is exceptionally difficult, as it requires exploration of possibly a very large structure space in search of competing, near-optimal structures. Here we report on a novel stochastic optimization method capable of revealing very different structures for a given protein from knowledge of its amino-acid sequence. The method leverages evolutionary search techniques and adapts its exploration of the search space to balance between exploration and exploitation in the presence of a computational budget. In addition to demonstrating the utility of this method for identifying multiple native structures, we additionally provide a benchmark dataset for researchers to continue work on this problem.
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2
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Kabir KL, Ma B, Nussinov R, Shehu A. Fewer Dimensions, More Structures for Improved Discrete Models of Dynamics of Free versus Antigen-Bound Antibody. Biomolecules 2022; 12:biom12071011. [PMID: 35883567 PMCID: PMC9313177 DOI: 10.3390/biom12071011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Revised: 07/12/2022] [Accepted: 07/19/2022] [Indexed: 12/10/2022] Open
Abstract
Over the past decade, Markov State Models (MSM) have emerged as powerful methodologies to build discrete models of dynamics over structures obtained from Molecular Dynamics trajectories. The identification of macrostates for the MSM is a central decision that impacts the quality of the MSM but depends on both the selected representation of a structure and the clustering algorithm utilized over the featurized structures. Motivated by a large molecular system in its free and bound state, this paper investigates two directions of research, further reducing the representation dimensionality in a non-parametric, data-driven manner and including more structures in the computation. Rigorous evaluation of the quality of obtained MSMs via various statistical tests in a comparative setting firmly shows that fewer dimensions and more structures result in a better MSM. Many interesting findings emerge from the best MSM, advancing our understanding of the relationship between antibody dynamics and antibody–antigen recognition.
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Affiliation(s)
- Kazi Lutful Kabir
- Department of Computer Science, George Mason University, Fairfax, VA 22030, USA;
- Correspondence: ; Tel.: +1-571-201-5070
| | - Buyong Ma
- Engineering Research Center of Cell & Therapeutic Antibody School of Pharmacy, Shanghai Jiaotong University, Shanghai 200240, China;
| | - Ruth Nussinov
- Computational Structural Biology Section, Cancer Innovation Laboratory, Frederick National Laboratory for Cancer Research, National Cancer Institute, Frederick, MD 21702, USA;
| | - Amarda Shehu
- Department of Computer Science, George Mason University, Fairfax, VA 22030, USA;
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3
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Arasteh S, Zhang BW, Levy RM. Protein Loop Conformational Free Energy Changes via an Alchemical Path without Reaction Coordinates. J Phys Chem Lett 2021; 12:4368-4377. [PMID: 33938761 PMCID: PMC8170697 DOI: 10.1021/acs.jpclett.1c00778] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
We introduce a method called restrain-free energy perturbation-release 2.0 (R-FEP-R 2.0) to estimate conformational free energy changes of protein loops via an alchemical path. R-FEP-R 2.0 is a generalization of the method called restrain-free energy perturbation-release (R-FEP-R) that can only estimate conformational free energy changes of protein side chains but not loops. The reorganization of protein loops is a central feature of many biological processes. Unlike other advanced sampling algorithms such as umbrella sampling and metadynamics, R-FEP-R and R-FEP-R 2.0 do not require predetermined collective coordinates and transition pathways that connect the two endpoint conformational states. The R-FEP-R 2.0 method was applied to estimate the conformational free energy change of a β-turn flip in the protein ubiquitin. The result obtained by R-FEP-R 2.0 agrees with the benchmarks very well. We also comment on problems commonly encountered when applying umbrella sampling to calculate protein conformational free energy changes.
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Affiliation(s)
- Shima Arasteh
- Center for Biophysics and Computational Biology and Department of Chemistry, Temple University, Philadelphia, Pennsylvania 19122, United States
| | - Bin W Zhang
- Center for Biophysics and Computational Biology and Department of Chemistry, Temple University, Philadelphia, Pennsylvania 19122, United States
| | - Ronald M Levy
- Center for Biophysics and Computational Biology and Department of Chemistry, Temple University, Philadelphia, Pennsylvania 19122, United States
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4
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Afrasiabi F, Dehghanpoor R, Haspel N. Integrating Rigidity Analysis into the Exploration of Protein Conformational Pathways Using RRT* and MC. Molecules 2021; 26:molecules26082329. [PMID: 33923805 PMCID: PMC8073574 DOI: 10.3390/molecules26082329] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Revised: 04/12/2021] [Accepted: 04/13/2021] [Indexed: 11/16/2022] Open
Abstract
To understand how proteins function on a cellular level, it is of paramount importance to understand their structures and dynamics, including the conformational changes they undergo to carry out their function. For the aforementioned reasons, the study of large conformational changes in proteins has been an interest to researchers for years. However, since some proteins experience rapid and transient conformational changes, it is hard to experimentally capture the intermediate structures. Additionally, computational brute force methods are computationally intractable, which makes it impossible to find these pathways which require a search in a high-dimensional, complex space. In our previous work, we implemented a hybrid algorithm that combines Monte-Carlo (MC) sampling and RRT*, a version of the Rapidly Exploring Random Trees (RRT) robotics-based method, to make the conformational exploration more accurate and efficient, and produce smooth conformational pathways. In this work, we integrated the rigidity analysis of proteins into our algorithm to guide the search to explore flexible regions. We demonstrate that rigidity analysis dramatically reduces the run time and accelerates convergence.
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5
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Zaman AB, Shehu A. Building maps of protein structure spaces in template-free protein structure prediction. J Bioinform Comput Biol 2020; 17:1940013. [DOI: 10.1142/s0219720019400134] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
An important goal in template-free protein structure prediction is how to control the quality of computed tertiary structures of a target amino-acid sequence. Despite great advances in algorithmic research, given the size, dimensionality, and inherent characteristics of the protein structure space, this task remains exceptionally challenging. It is current practice to aim to generate as many structures as can be afforded so as to increase the likelihood that some of them will reside near the sought but unknown biologically-active/native structure. When operating within a given computational budget, this is impractical and uninformed by any metrics of interest. In this paper, we propose instead to equip algorithms that generate tertiary structures, also known as decoy generation algorithms, with memory of the protein structure space that they explore. Specifically, we propose an evolving, granularity-controllable map of the protein structure space that makes use of low-dimensional representations of protein structures. Evaluations on diverse target sequences that include recent hard CASP targets show that drastic reductions in storage can be made without sacrificing decoy quality. The presented results make the case that integrating a map of the protein structure space is a promising mechanism to enhance decoy generation algorithms in template-free protein structure prediction.
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Affiliation(s)
- Ahmed Bin Zaman
- Department of Computer Science, George Mason University, Fairfax, VA 22030, USA
| | - Amarda Shehu
- Department of Computer Science, George Mason University, Fairfax, VA 22030, USA
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6
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Maximova T, Plaku E, Shehu A. Structure-Guided Protein Transition Modeling with a Probabilistic Roadmap Algorithm. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2018; 15:1783-1796. [PMID: 27411226 DOI: 10.1109/tcbb.2016.2586044] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
Proteins are macromolecules in perpetual motion, switching between structural states to modulate their function. A detailed characterization of the precise yet complex relationship between protein structure, dynamics, and function requires elucidating transitions between functionally-relevant states. Doing so challenges both wet and dry laboratories, as protein dynamics involves disparate temporal scales. In this paper, we present a novel, sampling-based algorithm to compute transition paths. The algorithm exploits two main ideas. First, it leverages known structures to initialize its search and define a reduced conformation space for rapid sampling. This is key to address the insufficient sampling issue suffered by sampling-based algorithms. Second, the algorithm embeds samples in a nearest-neighbor graph where transition paths can be efficiently computed via queries. The algorithm adapts the probabilistic roadmap framework that is popular in robot motion planning. In addition to efficiently computing lowest-cost paths between any given structures, the algorithm allows investigating hypotheses regarding the order of experimentally-known structures in a transition event. This novel contribution is likely to open up new venues of research. Detailed analysis is presented on multiple-basin proteins of relevance to human disease. Multiscaling and the AMBER ff14SB force field are used to obtain energetically-credible paths at atomistic detail.
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7
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He P, Zhang BW, Arasteh S, Wang L, Abel R, Levy RM. Conformational Free Energy Changes via an Alchemical Path without Reaction Coordinates. J Phys Chem Lett 2018; 9:4428-4435. [PMID: 30024165 PMCID: PMC6092130 DOI: 10.1021/acs.jpclett.8b01851] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
We introduce a novel method called restrain-free energy perturbation-release (R-FEP-R) to estimate conformational free energy changes via an alchemical path, which for some conformational landscapes like those associated with cellular signaling proteins in the kinase family is more direct and readily converged than the corresponding free energy changes along the physical path. The R-FEP-R method was developed from the dual topology free energy perturbation method that is widely applied to estimate the binding free energy difference between two ligands. In R-FEP-R, the free energy change between two conformational basins is calculated by free energy perturbations that remove those atoms involved in the conformational change from their initial conformational basin while simultaneously growing them back according to the final conformational basin. Both the initial and final dual topology states are unphysical, but they are designed in a way such that the unphysical contributions to the initial and final partition functions cancel. Compared with other advanced sampling algorithms such as umbrella sampling and metadynamics, the R-FEP-R method does not require predetermined transition pathways or reaction coordinates that connect the two conformational states. As a first illustration, the R-FEP-R method was applied to calculate the free energy change between conformational basins for alanine dipeptide in solution and for a side chain in the binding pocket of T4 lysozyme. The results obtained by R-FEP-R agree with the benchmarks very well.
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Affiliation(s)
- Peng He
- Center for Biophysics and Computational Biology, Department of Chemistry and Institute for Computational Molecular Science, Temple University, Philadelphia, Pennsylvania 19122, United States
| | - Bin W. Zhang
- Center for Biophysics and Computational Biology, Department of Chemistry and Institute for Computational Molecular Science, Temple University, Philadelphia, Pennsylvania 19122, United States
| | - Shima Arasteh
- Center for Biophysics and Computational Biology, Department of Chemistry and Institute for Computational Molecular Science, Temple University, Philadelphia, Pennsylvania 19122, United States
| | - Lingle Wang
- Schrödinger, Inc., 120 West 45th Street, New York, New York 10036, United States
| | - Robert Abel
- Schrödinger, Inc., 120 West 45th Street, New York, New York 10036, United States
| | - Ronald M. Levy
- Center for Biophysics and Computational Biology, Department of Chemistry and Institute for Computational Molecular Science, Temple University, Philadelphia, Pennsylvania 19122, United States
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8
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Maximova T, Zhang Z, Carr DB, Plaku E, Shehu A. Sample-Based Models of Protein Energy Landscapes and Slow Structural Rearrangements. J Comput Biol 2018; 25:33-50. [DOI: 10.1089/cmb.2017.0158] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023] Open
Affiliation(s)
- Tatiana Maximova
- Department of Computer Science, George Mason University, Fairfax, Virginia
| | - Zijing Zhang
- Department of Statistics, George Mason University, Fairfax, Virginia
| | - Daniel B. Carr
- Department of Statistics, George Mason University, Fairfax, Virginia
| | - Erion Plaku
- Department of Electrical Engineering and Computer Science, The Catholic University of America, Washington, D.C
| | - Amarda Shehu
- Department of Computer Science, George Mason University, Fairfax, Virginia
- Department of Bioengineering, George Mason University, Fairfax, Virginia
- School of Systems Biology, George Mason University, Manassas, Virginia
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9
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Abstract
Background Understanding protein structure and dynamics is essential for understanding their function. This is a challenging task due to the high complexity of the conformational landscapes of proteins and their rugged energy levels. In particular, it is important to detect highly populated regions which could correspond to intermediate structures or local minima. Results We present a hierarchical clustering and algebraic topology based method that detects regions of interest in protein conformational space. The method is based on several techniques. We use coarse grained protein conformational search, efficient robust dimensionality reduction and topological analysis via persistent homology as the main tools. We use two dimensionality reduction methods as well, robust Principal Component Analysis (PCA) and Isomap, to generate a reduced representation of the data while preserving most of the variance in the data. Conclusions Our hierarchical clustering method was able to produce compact, well separated clusters for all the tested examples.
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Affiliation(s)
- Nurit Haspel
- Department of Computer Science, University of Massachusetts Boston, 100 Morrissey Blvd., Boston, 02125, MA, USA.
| | - Dong Luo
- Department of Computer Science, University of Massachusetts Boston, 100 Morrissey Blvd., Boston, 02125, MA, USA
| | - Eduardo González
- Department of Mathematics, University of Massachusetts Boston, 100 Morrissey Blvd., Boston, 02125, MA, USA
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10
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Budday D, Fonseca R, Leyendecker S, van den Bedem H. Frustration-guided motion planning reveals conformational transitions in proteins. Proteins 2017; 85:1795-1807. [DOI: 10.1002/prot.25333] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2017] [Revised: 05/19/2017] [Accepted: 06/07/2017] [Indexed: 01/27/2023]
Affiliation(s)
- Dominik Budday
- Chair of Applied Dynamics, University of Erlangen-Nuremberg; Erlangen Germany
| | - Rasmus Fonseca
- Department of Molecular and Cellular Physiology; Stanford University; California Menlo Park
- Biosciences Division; SLAC National Accelerator Laboratory, Stanford University; California Menlo Park
| | - Sigrid Leyendecker
- Chair of Applied Dynamics, University of Erlangen-Nuremberg; Erlangen Germany
| | - Henry van den Bedem
- Biosciences Division; SLAC National Accelerator Laboratory, Stanford University; California Menlo Park
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11
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Zhang BW, Dai W, Gallicchio E, He P, Xia J, Tan Z, Levy RM. Simulating Replica Exchange: Markov State Models, Proposal Schemes, and the Infinite Swapping Limit. J Phys Chem B 2016; 120:8289-301. [PMID: 27079355 DOI: 10.1021/acs.jpcb.6b02015] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
Replica exchange molecular dynamics is a multicanonical simulation technique commonly used to enhance the sampling of solvated biomolecules on rugged free energy landscapes. While replica exchange is relatively easy to implement, there are many unanswered questions about how to use this technique most efficiently, especially because it is frequently the case in practice that replica exchange simulations are not fully converged. A replica exchange cycle consists of a series of molecular dynamics steps of a set of replicas moving under different Hamiltonians or at different thermodynamic states followed by one or more replica exchange attempts to swap replicas among the different states. How the replica exchange cycle is constructed affects how rapidly the system equilibrates. We have constructed a Markov state model of replica exchange (MSMRE) using long molecular dynamics simulations of a host-guest binding system as an example, in order to study how different implementations of the replica exchange cycle can affect the sampling efficiency. We analyze how the number of replica exchange attempts per cycle, the number of MD steps per cycle, and the interaction between the two parameters affects the largest implied time scale of the MSMRE simulation. The infinite swapping limit is an important concept in replica exchange. We show how to estimate the infinite swapping limit from the diagonal elements of the exchange transition matrix constructed from MSMRE "simulations of simulations" as well as from relatively short runs of the actual replica exchange simulations.
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Affiliation(s)
- Bin W Zhang
- Center for Biophysics and Computational Biology, Department of Chemistry and Institute for Computational Molecular Science, Temple University , Philadelphia, Pennsylvania 19122, United States
| | - Wei Dai
- Department of Physics and Astronomy, Rutgers, the State University of New Jersey , Piscataway, New Jersey 08854, United States
| | - Emilio Gallicchio
- Department of Chemistry, Brooklyn College of the City University of New York , Brooklyn, New York 11210, United States
| | - Peng He
- Center for Biophysics and Computational Biology, Department of Chemistry and Institute for Computational Molecular Science, Temple University , Philadelphia, Pennsylvania 19122, United States
| | - Junchao Xia
- Center for Biophysics and Computational Biology, Department of Chemistry and Institute for Computational Molecular Science, Temple University , Philadelphia, Pennsylvania 19122, United States
| | - Zhiqiang Tan
- Department of Statistics, Rutgers, the State University of New Jersey , Piscataway, New Jersey 08854, United States
| | - Ronald M Levy
- Center for Biophysics and Computational Biology, Department of Chemistry and Institute for Computational Molecular Science, Temple University , Philadelphia, Pennsylvania 19122, United States
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12
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Maximova T, Moffatt R, Ma B, Nussinov R, Shehu A. Principles and Overview of Sampling Methods for Modeling Macromolecular Structure and Dynamics. PLoS Comput Biol 2016; 12:e1004619. [PMID: 27124275 PMCID: PMC4849799 DOI: 10.1371/journal.pcbi.1004619] [Citation(s) in RCA: 132] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Abstract
Investigation of macromolecular structure and dynamics is fundamental to understanding how macromolecules carry out their functions in the cell. Significant advances have been made toward this end in silico, with a growing number of computational methods proposed yearly to study and simulate various aspects of macromolecular structure and dynamics. This review aims to provide an overview of recent advances, focusing primarily on methods proposed for exploring the structure space of macromolecules in isolation and in assemblies for the purpose of characterizing equilibrium structure and dynamics. In addition to surveying recent applications that showcase current capabilities of computational methods, this review highlights state-of-the-art algorithmic techniques proposed to overcome challenges posed in silico by the disparate spatial and time scales accessed by dynamic macromolecules. This review is not meant to be exhaustive, as such an endeavor is impossible, but rather aims to balance breadth and depth of strategies for modeling macromolecular structure and dynamics for a broad audience of novices and experts.
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Affiliation(s)
- Tatiana Maximova
- Department of Computer Science, George Mason University, Fairfax, Virginia, United States of America
| | - Ryan Moffatt
- Department of Computer Science, George Mason University, Fairfax, Virginia, United States of America
| | - Buyong Ma
- Basic Science Program, Leidos Biomedical Research, Inc. Cancer and Inflammation Program, National Cancer Institute, Frederick, Maryland, United States of America
| | - Ruth Nussinov
- Basic Science Program, Leidos Biomedical Research, Inc. Cancer and Inflammation Program, National Cancer Institute, Frederick, Maryland, United States of America
- Sackler Institute of Molecular Medicine, Department of Human Genetics and Molecular Medicine, Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Amarda Shehu
- Department of Computer Science, George Mason University, Fairfax, Virginia, United States of America
- Department of Biongineering, George Mason University, Fairfax, Virginia, United States of America
- School of Systems Biology, George Mason University, Manassas, Virginia, United States of America
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13
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Molloy K, Shehu A. A General, Adaptive, Roadmap-Based Algorithm for Protein Motion Computation. IEEE Trans Nanobioscience 2016; 15:158-65. [PMID: 26863668 DOI: 10.1109/tnb.2016.2519246] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Precious information on protein function can be extracted from a detailed characterization of protein equilibrium dynamics. This remains elusive in wet and dry laboratories, as function-modulating transitions of a protein between functionally-relevant, thermodynamically-stable and meta-stable structural states often span disparate time scales. In this paper we propose a novel, robotics-inspired algorithm that circumvents time-scale challenges by drawing analogies between protein motion and robot motion. The algorithm adapts the popular roadmap-based framework in robot motion computation to handle the more complex protein conformation space and its underlying rugged energy surface. Given known structures representing stable and meta-stable states of a protein, the algorithm yields a time- and energy-prioritized list of transition paths between the structures, with each path represented as a series of conformations. The algorithm balances computational resources between a global search aimed at obtaining a global view of the network of protein conformations and their connectivity and a detailed local search focused on realizing such connections with physically-realistic models. Promising results are presented on a variety of proteins that demonstrate the general utility of the algorithm and its capability to improve the state of the art without employing system-specific insight.
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Abstract
SUMMARYEvidence is emerging that the role of protein structure in disease needs to be rethought. Sequence mutations in proteins are often found to affect the rate at which a protein switches between structures. Modeling structural transitions in wildtype and variant proteins is central to understanding the molecular basis of disease. This paper investigates an efficient algorithmic realization of the stochastic roadmap simulation framework to model structural transitions in wildtype and variants of proteins implicated in human disorders. Our results indicate that the algorithm is able to extract useful information on the impact of mutations on protein structure and function.
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15
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Plaku E, Karaman S. Motion planning with temporal-logic specifications: Progress and challenges. AI COMMUN 2015. [DOI: 10.3233/aic-150682] [Citation(s) in RCA: 39] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Affiliation(s)
- Erion Plaku
- Department of Electrical Engineering and Computer Science, Catholic University of America, Washington, DC, USA. E-mail:
| | - Sertac Karaman
- Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, Cambridge, MA, USA. E-mail:
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16
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17
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Shehu A. A Review of Evolutionary Algorithms for Computing Functional Conformations of Protein Molecules. METHODS IN PHARMACOLOGY AND TOXICOLOGY 2015. [DOI: 10.1007/7653_2015_47] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
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