1
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Tolokh IS, Folescu DE, Onufriev AV. Inclusion of Water Multipoles into the Implicit Solvation Framework Leads to Accuracy Gains. J Phys Chem B 2024; 128:5855-5873. [PMID: 38860842 PMCID: PMC11194828 DOI: 10.1021/acs.jpcb.4c00254] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2024] [Revised: 05/28/2024] [Accepted: 05/29/2024] [Indexed: 06/12/2024]
Abstract
The current practical "workhorses" of the atomistic implicit solvation─the Poisson-Boltzmann (PB) and generalized Born (GB) models─face fundamental accuracy limitations. Here, we propose a computationally efficient implicit solvation framework, the Implicit Water Multipole GB (IWM-GB) model, that systematically incorporates the effects of multipole moments of water molecules in the first hydration shell of a solute, beyond the dipole water polarization already present at the PB/GB level. The framework explicitly accounts for coupling between polar and nonpolar contributions to the total solvation energy, which is missing from many implicit solvation models. An implementation of the framework, utilizing the GAFF force field and AM1-BCC atomic partial charges model, is parametrized and tested against the experimental hydration free energies of small molecules from the FreeSolv database. The resulting accuracy on the test set (RMSE ∼ 0.9 kcal/mol) is 12% better than that of the explicit solvation (TIP3P) treatment, which is orders of magnitude slower. We also find that the coupling between polar and nonpolar parts of the solvation free energy is essential to ensuring that several features of the IWM-GB model are physically meaningful, including the sign of the nonpolar contributions.
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Affiliation(s)
- Igor S. Tolokh
- Department
of Computer Science, Virginia Tech, Blacksburg, Virginia 24061, United States
| | - Dan E. Folescu
- Department
of Computer Science, Virginia Tech, Blacksburg, Virginia 24061, United States
- Department
of Mathematics, Virginia Tech, Blacksburg, Virginia 24061, United States
| | - Alexey V. Onufriev
- Department
of Computer Science, Virginia Tech, Blacksburg, Virginia 24061, United States
- Department
of Physics, Virginia Tech, Blacksburg, Virginia 24061, United States
- Center
for Soft Matter and Biological Physics, Virginia Tech, Blacksburg, Virginia 24061, United States
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2
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Hameduh T, Mokry M, Miller AD, Heger Z, Haddad Y. Solvent Accessibility Promotes Rotamer Errors during Protein Modeling with Major Side-Chain Prediction Programs. J Chem Inf Model 2023. [PMID: 37410883 PMCID: PMC10369486 DOI: 10.1021/acs.jcim.3c00134] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/08/2023]
Abstract
Side-chain rotamer prediction is one of the most critical late stages in protein 3D structure building. Highly advanced and specialized algorithms (e.g., FASPR, RASP, SCWRL4, and SCWRL4v) optimize this process by use of rotamer libraries, combinatorial searches, and scoring functions. We seek to identify the sources of key rotamer errors as a basis for correcting and improving the accuracy of protein modeling going forward. In order to evaluate the aforementioned programs, we process 2496 high-quality single-chained all-atom filtered 30% homology protein 3D structures and use discretized rotamer analysis to compare original with calculated structures. Among 513,024 filtered residue records, increased amino acid residue-dependent rotamer errors─associated in particular with polar and charged amino acid residues (ARG, LYS, and GLN)─clearly correlate with increased amino acid residue solvent accessibility and an increased residue tendency toward the adoption of non-canonical off rotamers which modeling programs struggle to predict accurately. Understanding the impact of solvent accessibility now appears key to improved side-chain prediction accuracies.
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Affiliation(s)
- Tareq Hameduh
- Department of Chemistry and Biochemistry, Mendel University in Brno, Zemědělská 1665/1, CZ-613 00 Brno, Czech Republic
| | - Michal Mokry
- Department of Chemistry and Biochemistry, Mendel University in Brno, Zemědělská 1665/1, CZ-613 00 Brno, Czech Republic
| | - Andrew D Miller
- Department of Chemistry and Biochemistry, Mendel University in Brno, Zemědělská 1665/1, CZ-613 00 Brno, Czech Republic
- Veterinary Research Institute, Hudcova 296/70, CZ-621 00 Brno, Czech Republic
- KP Therapeutics (Europe) s.r.o., Purkyňova 649/127, CZ-612 00 Brno, Czech Republic
| | - Zbynek Heger
- Department of Chemistry and Biochemistry, Mendel University in Brno, Zemědělská 1665/1, CZ-613 00 Brno, Czech Republic
| | - Yazan Haddad
- Department of Chemistry and Biochemistry, Mendel University in Brno, Zemědělská 1665/1, CZ-613 00 Brno, Czech Republic
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3
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Padariya M, Baginski M, Babak M, Kalathiya U. Organic solvents aggregating and shaping structural folding of protein, a case study of the protease enzyme. Biophys Chem 2022; 291:106909. [DOI: 10.1016/j.bpc.2022.106909] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Revised: 09/27/2022] [Accepted: 10/14/2022] [Indexed: 11/16/2022]
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4
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Selvaraj C, Selvaraj G, Mohamed Ismail R, Vijayakumar R, Baazeem A, Wei DQ, Singh SK. Interrogation of Bacillus anthracis SrtA active site loop forming open/close lid conformations through extensive MD simulations for understanding binding selectivity of SrtA inhibitors. Saudi J Biol Sci 2021; 28:3650-3659. [PMID: 34220215 PMCID: PMC8241892 DOI: 10.1016/j.sjbs.2021.05.009] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Revised: 04/25/2021] [Accepted: 05/02/2021] [Indexed: 02/07/2023] Open
Abstract
Bacillus anthracis is a gram positive, deadly spore forming bacteria causing anthrax and these bacteria having the complex mechanism in the cell wall envelope, which can adopt the changes in environmental conditions. In this, the membrane bound cell wall proteins are said to progressive drug target for the inhibition of Bacillus anthracis. Among the cell wall proteins, the SrtA is one of the important mechanistic protein, which mediate the ligation with LPXTG motif by forming the amide bonds. The SrtA plays the vital role in cell signalling, cell wall formation, and biofilm formations. Inhibition of SrtA leads to rupture of the cell wall and biofilm formation, and that leads to inhibition of Bacillus anthracis and thus, SrtA is core important enzyme to study the inhibition mechanism. In this study, we have examined 28 compounds, which have the inhibitory activity against the Bacillus anthracis SrtA for developing the 3D-QSAR and also, compounds binding selectivity with both open and closed SrtA conformations, obtained from 100 ns of MD simulations. The binding site loop deviate in forming the open and closed gate mechanism is investigated to understand the inhibitory profile of reported compounds, and results show the closed state active site conformations are required for ligand binding specificity. Overall, the present study may offer an opportunity for better understanding of the mechanism of action and can be aided to further designing of a novel and highly potent SrtA inhibitors.
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Affiliation(s)
- Chandrabose Selvaraj
- Department of Bioinformatics, Computer Aided Drug Design and Molecular Modelling Lab, Science Block, Alagappa University, Karaikudi, Tamil Nadu, India
- Corresponding authors.
| | - Gurudeeban Selvaraj
- Centre for Research in Molecular Modelling, Concordia University, 5618 Montreal, Quebec, Canada
| | - Randa Mohamed Ismail
- Department of Medical Laboratory Sciences, College of Applied Medical Sciences, Majmaah University, Al Majmaah 11952, Saudi Arabia
- Department of Microbiology and Immunology, Veterinary Research Division, National Research Center (NRC), Giza, Egypt
| | - Rajendran Vijayakumar
- Department of Biology, College of Science in Zulfi, Majmaah University, Majmaah 11952, Saudi Arabia
| | - Alaa Baazeem
- Department of Biology, College of Science, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia
| | - Dong-Qing Wei
- Department of Bioinformatics and Biological Statistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Sanjeev Kumar Singh
- Department of Bioinformatics, Computer Aided Drug Design and Molecular Modelling Lab, Science Block, Alagappa University, Karaikudi, Tamil Nadu, India
- Corresponding authors.
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5
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Fine J, Chopra G. Lemon: a framework for rapidly mining structural information from the Protein Data Bank. Bioinformatics 2020; 35:4165-4167. [PMID: 30873531 PMCID: PMC6792122 DOI: 10.1093/bioinformatics/btz178] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2018] [Revised: 02/16/2019] [Accepted: 03/13/2019] [Indexed: 11/17/2022] Open
Abstract
Motivation The Protein Data Bank (PDB) currently holds over 140 000 biomolecular structures and continues to release new structures on a weekly basis. The PDB is an essential resource to the structural bioinformatics community to develop software that mine, use, categorize and analyze such data. New computational biology methods are evaluated using custom benchmarking sets derived as subsets of 3D experimentally determined structures and structural features from the PDB. Currently, such benchmarking features are manually curated with custom scripts in a non-standardized manner that results in slow distribution and updates with new experimental structures. Finally, there is a scarcity of standardized tools to rapidly query 3D descriptors of the entire PDB. Results Our solution is the Lemon framework, a C++11 library with Python bindings, which provides a consistent workflow methodology for selecting biomolecular interactions based on user criterion and computing desired 3D structural features. This framework can parse and characterize the entire PDB in <10 min on modern, multithreaded hardware. The speed in parsing is obtained by using the recently developed MacroMolecule Transmission Format to reduce the computational cost of reading text-based PDB files. The use of C++ lambda functions and Python bindings provide extensive flexibility for analysis and categorization of the PDB by allowing the user to write custom functions to suite their objective. We think Lemon will become a one-stop-shop to quickly mine the entire PDB to generate desired structural biology features. Availability and implementation The Lemon software is available as a C++ header library along with a PyPI package and example functions at https://github.com/chopralab/lemon. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Jonathan Fine
- Department of Chemistry, Purdue University, West Lafayette, IN, USA
| | - Gaurav Chopra
- Department of Chemistry, Purdue University, West Lafayette, IN, USA
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6
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Chakravorty A, Pandey S, Pahari S, Zhao S, Alexov E. Capturing the Effects of Explicit Waters in Implicit Electrostatics Modeling: Qualitative Justification of Gaussian-Based Dielectric Models in DelPhi. J Chem Inf Model 2020; 60:2229-2246. [PMID: 32155062 PMCID: PMC9883665 DOI: 10.1021/acs.jcim.0c00151] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
Abstract
Our group has implemented a smooth Gaussian-based dielectric function in DelPhi (J. Chem. Theory Comput. 2013, 9 (4), 2126-2136) which models the solute as an object with inhomogeneous dielectric permittivity and provides a smooth transition of dielectric permittivity from surface-bound water to bulk solvent. Although it is well-understood that the protein hydrophobic core is less polarizable than the hydrophilic protein surface, less attention is paid to the polarizability of water molecules inside the solute and on its surface. Here, we apply explicit water simulations to study the behavior of water molecules buried inside a protein and on the surface of that protein and contrast it with the behavior of the bulk water. We selected a protein that is experimentally shown to have five cavities, most of which are occupied by water molecules. We demonstrate through molecular dynamics (MD) simulations that the behavior of water in the cavity is drastically different from that in the bulk. These observations were made by comparing the mean residence times, dipole orientation relaxation times, and average dipole moment fluctuations. We also show that the bulk region has a nonuniform distribution of these tempo-spatial properties. From the perspective of continuum electrostatics, we argue that the dielectric "constant" in water-filled cavities of proteins and the space close to the molecular surface should differ from that assigned to the bulk water. This provides support for the Gaussian-based smooth dielectric model for solving electrostatics in the Poisson-Boltzmann equation framework. Furthermore, we demonstrate that using a well-parametrized Gaussian-based model with a single energy-minimized configuration of a protein can also reproduce its ensemble-averaged polar solvation energy. Thus, we argue that the Gaussian-based smooth dielectric model not only captures accurate physics but also provides an efficient way of computing ensemble-averaged quantities.
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Affiliation(s)
- Arghya Chakravorty
- Computational Biophysics and Bioinformatics, Department of Physics and Astronomy, Clemson University, Clemson, South Carolina 29634, United States,Corresponding Authors:,
| | - Shailesh Pandey
- Computational Biophysics and Bioinformatics, Department of Physics and Astronomy, Clemson University, Clemson, South Carolina 29634, United States
| | - Swagata Pahari
- Computational Biophysics and Bioinformatics, Department of Physics and Astronomy, Clemson University, Clemson, South Carolina 29634, United States
| | - Shan Zhao
- Department of Mathematics, University of Alabama, Tuscaloosa, Alabama 35487, Unites States
| | - Emil Alexov
- Computational Biophysics and Bioinformatics, Department of Physics and Astronomy, Clemson University, Clemson, South Carolina 29634, United States,Corresponding Authors:,
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7
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Fine J, Konc J, Samudrala R, Chopra G. CANDOCK: Chemical Atomic Network-Based Hierarchical Flexible Docking Algorithm Using Generalized Statistical Potentials. J Chem Inf Model 2020; 60:1509-1527. [PMID: 32069042 DOI: 10.1021/acs.jcim.9b00686] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Small-molecule docking has proven to be invaluable for drug design and discovery. However, existing docking methods have several limitations such as improper treatment of the interactions of essential components in the chemical environment of the binding pocket (e.g., cofactors, metal ions, etc.), incomplete sampling of chemically relevant ligand conformational space, and the inability to consistently correlate docking scores of the best binding pose with experimental binding affinities. We present CANDOCK, a novel docking algorithm, that utilizes a hierarchical approach to reconstruct ligands from an atomic grid using graph theory and generalized statistical potential functions to sample biologically relevant ligand conformations. Our algorithm accounts for protein flexibility, solvent, metal ions, and cofactor interactions in the binding pocket that are traditionally ignored by current methods. We evaluate the algorithm on the PDBbind, Astex, and PINC proteins to show its ability to reproduce the binding mode of the ligands that is independent of the initial ligand conformation in these benchmarks. Finally, we identify the best selector and ranker potential functions such that the statistical score of the best selected docked pose correlates with the experimental binding affinities of the ligands for any given protein target. Our results indicate that CANDOCK is a generalized flexible docking method that addresses several limitations of current docking methods by considering all interactions in the chemical environment of a binding pocket for correlating the best-docked pose with biological activity. CANDOCK along with all structures and scripts used for benchmarking is available at https://github.com/chopralab/candock_benchmark.
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Affiliation(s)
- Jonathan Fine
- Department of Chemistry, Purdue University, 720 Clinic Drive, West Lafayette, Indiana 47906, United States
| | - Janez Konc
- National Institute of Chemistry, Hajdrihova 19, SI-1000, Ljubljana, Slovenia
| | - Ram Samudrala
- Department of Biomedical Informatics, SUNY, Buffalo, New York 14260, United States
| | - Gaurav Chopra
- Department of Chemistry, Purdue University, 720 Clinic Drive, West Lafayette, Indiana 47906, United States.,Purdue Institute for Drug Discovery, West Lafayette, Indiana 47907, United States.,Purdue Center for Cancer Research, West Lafayette, Indiana 47907, United States.,Purdue Institute for Inflammation, Immunology and Infectious Disease, West Lafayette, Indiana 47907, United States.,Purdue Institute for Integrative Neuroscience, West Lafayette, Indiana 47907, United States.,Integrative Data Science Initiative, West Lafayette, Indiana 47907, United States
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8
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Geng H, Chen F, Ye J, Jiang F. Applications of Molecular Dynamics Simulation in Structure Prediction of Peptides and Proteins. Comput Struct Biotechnol J 2019; 17:1162-1170. [PMID: 31462972 PMCID: PMC6709365 DOI: 10.1016/j.csbj.2019.07.010] [Citation(s) in RCA: 54] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2019] [Revised: 07/07/2019] [Accepted: 07/23/2019] [Indexed: 12/21/2022] Open
Abstract
Compared with rapid accumulation of protein sequences from high-throughput DNA sequencing, obtaining experimental 3D structures of proteins is still much more difficult, making protein structure prediction (PSP) potentially very useful. Currently, a vast majority of PSP efforts are based on data mining of known sequences, structures and their relationships (informatics-based). However, if closely related template is not available, these methods are usually much less reliable than experiments. They may also be problematic in predicting the structures of naturally occurring or designed peptides. On the other hand, physics-based methods including molecular dynamics (MD) can utilize our understanding of detailed atomic interactions determining biomolecular structures. In this mini-review, we show that all-atom MD can predict structures of cyclic peptides and other peptide foldamers with accuracy similar to experiments. Then, some notable successes in reproducing experimental 3D structures of small proteins through MD simulations (some with replica-exchange) of the folding were summarized. We also describe advancements of MD-based refinement of structure models, and the integration of limited experimental or bioinformatics data into MD-based structure modeling.
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Affiliation(s)
- Hao Geng
- Lab of Computational Chemistry and Drug Design, State Key Laboratory of Chemical Oncogenomics, Peking University Shenzhen Graduate School, Shenzhen 518055, China
| | - Fangfang Chen
- Guangdong and Shenzhen Key Laboratory of Male Reproductive Medicine and Genetics, Peking University Shenzhen Hospital, Shenzhen PKU-HKUST Medical Center, Shenzhen 518036, China
| | - Jing Ye
- Guangdong and Shenzhen Key Laboratory of Male Reproductive Medicine and Genetics, Peking University Shenzhen Hospital, Shenzhen PKU-HKUST Medical Center, Shenzhen 518036, China
| | - Fan Jiang
- Lab of Computational Chemistry and Drug Design, State Key Laboratory of Chemical Oncogenomics, Peking University Shenzhen Graduate School, Shenzhen 518055, China
- NanoAI Biotech Co.,Ltd., Silicon Valley Compound, Longhua District, Shenzhen 518109, China
- Corresponding author at: Lab of Computational Chemistry and Drug Design, State Key Laboratory of Chemical Oncogenomics, Peking University Shenzhen Graduate School, Shenzhen 518055, China.
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9
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Urzhumtsev AG, Lunin VY. Introduction to crystallographic refinement of macromolecular atomic models. CRYSTALLOGR REV 2019. [DOI: 10.1080/0889311x.2019.1631817] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Affiliation(s)
- Alexandre G. Urzhumtsev
- Centre for Integrative Biology, IGBMC, CNRS–INSERM–UdS, Illkirch, France
- Département de Physique, Faculté des Sciences et des Technologies, Université de Lorraine, Vandoeuvre-lès-Nancy, France
| | - Vladimir Y. Lunin
- Institute of Mathematical Problems of Biology RAS, Keldysh Institute of Applied Mathematics of Russian Academy of Sciences, Pushchino, Russia
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10
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Zavadlav J, Marrink SJ, Praprotnik M. SWINGER: a clustering algorithm for concurrent coupling of atomistic and supramolecular liquids. Interface Focus 2019; 9:20180075. [PMID: 31065343 PMCID: PMC6501350 DOI: 10.1098/rsfs.2018.0075] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/01/2019] [Indexed: 12/11/2022] Open
Abstract
In this contribution, we review recent developments and applications of a dynamic clustering algorithm SWINGER tailored for the multiscale molecular simulations of biomolecular systems. The algorithm on-the-fly redistributes solvent molecules among supramolecular clusters. In particular, we focus on its applications in combination with the adaptive resolution scheme, which concurrently couples atomistic and coarse-grained molecular representations. We showcase the versatility of our multiscale approach on a few applications to biomolecular systems coupling atomistic and supramolecular water models such as the well-established MARTINI and dissipative particle dynamics models and provide an outlook for future work.
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Affiliation(s)
- Julija Zavadlav
- Computational Science and Engineering Laboratory, ETH-Zurich, Clausiusstrasse 33, 8092 Zurich, Switzerland
| | - Siewert J. Marrink
- Groningen Biomolecular Sciences and Biotechnology Institute and Zernike Institute for Advanced Materials, University of Groningen, Nijenborgh 7, 9747, AG Groningen, The Netherlands
| | - Matej Praprotnik
- Laboratory for Molecular Modeling, National Institute of Chemistry, Hajdrihova 19, 1001 Ljubljana, Slovenia
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11
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Abstract
Solving crystal structures of large biological macromolecules in the absence of experimental phase information, especially at low resolution, is a tedious problem prone to mistakes and overfitting. Due to errors stemming from poorly interpretable parts of the electron density map, human experience and intuition are imperative for building a correct atomistic model. For this reason, tools for automatic structure determination used nowadays require constant human supervision. Here we present a method that can overcome the difficulties; it greatly reduces or even eliminates human involvement in the solution process by working with ensembles of possible solutions. This approach can find solution of a higher quality than can humans and can solve difficult cases not amenable to other methods. We present a method for automatic solution of protein crystal structures. The method proceeds with a single initial model obtained, for instance, by molecular replacement (MR). If a good-quality search model is not available, as often is the case with MR of distant homologs, our method first can automatically screen a large pool of poorly placed models and single out promising candidates for further processing if there are any. We demonstrate its utility by solving a set of synthetic cases in the 2.9- to 3.45-Å resolution.
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12
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In silico structural elucidation of RNA-dependent RNA polymerase towards the identification of potential Crimean-Congo Hemorrhagic Fever Virus inhibitors. Sci Rep 2019; 9:6809. [PMID: 31048746 PMCID: PMC6497722 DOI: 10.1038/s41598-019-43129-2] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2018] [Accepted: 04/17/2019] [Indexed: 01/05/2023] Open
Abstract
The Crimean-Congo Hemorrhagic Fever virus (CCHFV) is a segmented negative single-stranded RNA virus (-ssRNA) which causes severe hemorrhagic fever in humans with a mortality rate of ~50%. To date, no vaccine has been approved. Treatment is limited to supportive care with few investigational drugs in practice. Previous studies have identified viral RNA dependent RNA Polymerase (RdRp) as a potential drug target due to its significant role in viral replication and transcription. Since no crystal structure is available yet, we report the structural elucidation of CCHFV-RdRp by in-depth homology modeling. Even with low sequence identity, the generated model suggests a similar overall structure as previously reported RdRps. More specifically, the model suggests the presence of structural/functional conserved RdRp motifs for polymerase function, the configuration of uniform spatial arrangement of core RdRp sub-domains, and predicted positively charged entry/exit tunnels, as seen in sNSV polymerases. Extensive pharmacophore modeling based on per-residue energy contribution with investigational drugs allowed the concise mapping of pharmacophoric features and identified potential hits. The combination of pharmacophoric features with interaction energy analysis revealed functionally important residues in the conserved motifs together with in silico predicted common inhibitory binding modes with highly potent reference compounds.
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13
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Effect of truncating electrostatic interactions on predicting thermodynamic properties of water–methanol systems. MOLECULAR SIMULATION 2018. [DOI: 10.1080/08927022.2018.1547824] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
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14
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Chen M, Lin X, Lu W, Schafer NP, Onuchic JN, Wolynes PG. Template-Guided Protein Structure Prediction and Refinement Using Optimized Folding Landscape Force Fields. J Chem Theory Comput 2018; 14:6102-6116. [PMID: 30240202 DOI: 10.1021/acs.jctc.8b00683] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
When good structural templates can be identified, template-based modeling is the most reliable way to predict the tertiary structure of proteins. In this study, we combine template-based modeling with a realistic coarse-grained force field, AWSEM, that has been optimized using the principles of energy landscape theory. The Associative memory, Water mediated, Structure and Energy Model (AWSEM) is a coarse-grained force field having both transferable tertiary interactions and knowledge-based local-in-sequence interaction terms. We incorporate template information into AWSEM by introducing soft collective biases to the template structures, resulting in a model that we call AWSEM-Template. Structure prediction tests on eight targets, four of which are in the low sequence identity "twilight zone" of homology modeling, show that AWSEM-Template can achieve high-resolution structure prediction. Our results also confirm that using a combination of AWSEM and a template-guided potential leads to more accurate prediction of protein structures than simply using a template-guided potential alone. Free energy profile analyses demonstrate that the soft collective biases to the template effectively increase funneling toward native-like structures while still allowing significant flexibility so as to allow for correction of discrepancies between the target structure and the template. A further stage of refinement using all-atom molecular dynamics augmented with soft collective biases to the structures predicted by AWSEM-Template leads to a further improvement of both backbone and side-chain accuracy by maintaining sufficient flexibility but at the same time discouraging unproductive unfolding events often seen in unrestrained all-atom refinement simulations. The all-atom refinement simulations also reduce patches of frustration of the initial predictions. Some of the backbones found among the structures produced during the initial coarse-grained prediction step already have CE-RMSD values of less than 3 Å with 90% or more of the residues aligned to the experimentally solved structure for all targets. All-atom structures generated during the following all-atom refinement simulations, which started from coarse-grained structures that were chosen without reference to any knowledge about the native structure, have CE-RMSD values of less than 2.5 Å with 90% or more of the residues aligned for 6 out of 8 targets. Clustering low energy structures generated during the initial coarse-grained annealing picks out reliably structures that are within 1 Å of the best sampled structures in 5 out of 8 cases. After the all-atom refinement, structures that are within 1 Å of the best sampled structures can be selected using a simple algorithm based on energetic features alone in 7 out of 8 cases.
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Affiliation(s)
- Mingchen Chen
- Center for Theoretical Biological Physics, Rice University , Houston , Texas 77030 , United States.,Department of Bioengineering , Rice University , Houston , Texas 77005 , United States
| | - Xingcheng Lin
- Center for Theoretical Biological Physics, Rice University , Houston , Texas 77030 , United States.,Department of Physics and Astronomy , Rice University , Houston , Texas 77005 , United States
| | - Wei Lu
- Center for Theoretical Biological Physics, Rice University , Houston , Texas 77030 , United States.,Department of Physics and Astronomy , Rice University , Houston , Texas 77005 , United States
| | - Nicholas P Schafer
- Center for Theoretical Biological Physics, Rice University , Houston , Texas 77030 , United States.,Department of Chemistry , Rice University , Houston , Texas 77005 , United States
| | - José N Onuchic
- Center for Theoretical Biological Physics, Rice University , Houston , Texas 77030 , United States.,Department of Physics and Astronomy , Rice University , Houston , Texas 77005 , United States.,Department of Chemistry , Rice University , Houston , Texas 77005 , United States.,Department of Biosciences , Rice University , Houston , Texas 77005 , United States
| | - Peter G Wolynes
- Center for Theoretical Biological Physics, Rice University , Houston , Texas 77030 , United States.,Department of Chemistry , Rice University , Houston , Texas 77005 , United States.,Department of Biosciences , Rice University , Houston , Texas 77005 , United States
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15
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Dutagaci B, Heo L, Feig M. Structure refinement of membrane proteins via molecular dynamics simulations. Proteins 2018; 86:738-750. [PMID: 29675899 PMCID: PMC6013386 DOI: 10.1002/prot.25508] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2018] [Revised: 04/09/2018] [Accepted: 04/14/2018] [Indexed: 12/12/2022]
Abstract
A refinement protocol based on physics-based techniques established for water soluble proteins is tested for membrane protein structures. Initial structures were generated by homology modeling and sampled via molecular dynamics simulations in explicit lipid bilayer and aqueous solvent systems. Snapshots from the simulations were selected based on scoring with either knowledge-based or implicit membrane-based scoring functions and averaged to obtain refined models. The protocol resulted in consistent and significant refinement of the membrane protein structures similar to the performance of refinement methods for soluble proteins. Refinement success was similar between sampling in the presence of lipid bilayers and aqueous solvent but the presence of lipid bilayers may benefit the improvement of lipid-facing residues. Scoring with knowledge-based functions (DFIRE and RWplus) was found to be as good as scoring using implicit membrane-based scoring functions suggesting that differences in internal packing is more important than orientations relative to the membrane during the refinement of membrane protein homology models.
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Affiliation(s)
- Bercem Dutagaci
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI, USA
| | - Lim Heo
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI, USA
| | - Michael Feig
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI, USA
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16
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Mukherjee S, Nithin C, Divakaruni Y, Bahadur RP. Dissecting water binding sites at protein–protein interfaces: a lesson from the atomic structures in the Protein Data Bank. J Biomol Struct Dyn 2018; 37:1204-1219. [DOI: 10.1080/07391102.2018.1453379] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Affiliation(s)
- Sunandan Mukherjee
- Computational Structural Biology Lab, Department of Biotechnology, Indian Institute of Technology Kharagpur, Kharagpur, India
| | - Chandran Nithin
- Computational Structural Biology Lab, Department of Biotechnology, Indian Institute of Technology Kharagpur, Kharagpur, India
| | - Yasaswi Divakaruni
- Computational Structural Biology Lab, Department of Biotechnology, Indian Institute of Technology Kharagpur, Kharagpur, India
| | - Ranjit Prasad Bahadur
- Computational Structural Biology Lab, Department of Biotechnology, Indian Institute of Technology Kharagpur, Kharagpur, India
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17
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Blinov N, Khorvash M, Wishart DS, Cashman NR, Kovalenko A. Initial Structural Models of the Aβ42 Dimer from Replica Exchange Molecular Dynamics Simulations. ACS OMEGA 2017; 2:7621-7636. [PMID: 31457321 PMCID: PMC6645216 DOI: 10.1021/acsomega.7b00805] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/16/2017] [Accepted: 10/26/2017] [Indexed: 05/27/2023]
Abstract
Experimental characterization of the molecular structure of small amyloid (A)β oligomers that are currently considered as toxic agents in Alzheimer's disease is a formidably difficult task due to their transient nature and tendency to aggregate. Such structural information is of importance because it can help in developing diagnostics and an effective therapy for the disease. In this study, molecular simulations and protein-protein docking are employed to explore a possible connection between the structure of Aβ monomers and the properties of the intermonomer interface in the Aβ42 dimer. A structurally diverse ensemble of conformations of the monomer was sampled in microsecond timescale implicit solvent replica exchange molecular dynamics simulations. Representative structures with different solvent exposure of hydrophobic residues and secondary structure content were selected to build structural models of the dimer. Analysis of these models reveals that formation of an intramonomer salt bridge (SB) between Asp23 and Lys28 residues can prevent the building of a hydrophobic interface between the central hydrophobic clusters (CHCs) of monomers upon dimerization. This structural feature of the Aβ42 dimer is related to the difference in packing of hydrophobic residues in monomers with the Asp23-Lys28 SB in on and off states, in particular, to a lower propensity to form hydrophobic contacts between the CHC domain and C-terminal residues in monomers with a formed SB. These findings could have important implications for understanding the difference between aggregation pathways of Aβ monomers leading to neurotoxic oligomers or inert fibrillar structures.
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Affiliation(s)
- Nikolay Blinov
- Department
of Mechanical Engineering, University of
Alberta, Edmonton, Alberta T6G 1H9, Canada
- National
Institute for Nanotechnology, National Research
Council of Canada, Edmonton, Alberta T6G 2M9, Canada
| | - Massih Khorvash
- Department
of Medicine, University of British Columbia, Vancouver, British Columbia V6T 2B5, Canada
| | - David S. Wishart
- Departments
of Computing Science and Biological Sciences, University of Alberta, Edmonton, Alberta T6G 2E8, Canada
| | - Neil R. Cashman
- Department
of Medicine, University of British Columbia, Vancouver, British Columbia V6T 2B5, Canada
| | - Andriy Kovalenko
- Department
of Mechanical Engineering, University of
Alberta, Edmonton, Alberta T6G 1H9, Canada
- National
Institute for Nanotechnology, National Research
Council of Canada, Edmonton, Alberta T6G 2M9, Canada
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18
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Chopra G, Samudrala R. Exploring Polypharmacology in Drug Discovery and Repurposing Using the CANDO Platform. Curr Pharm Des 2017; 22:3109-23. [PMID: 27013226 DOI: 10.2174/1381612822666160325121943] [Citation(s) in RCA: 42] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2016] [Accepted: 03/01/2015] [Indexed: 01/05/2023]
Abstract
BACKGROUND Traditional drug discovery approaches focus on a limited set of target molecules for treatment against specific indications/diseases. However, drug absorption, dispersion, metabolism, and excretion (ADME) involve interactions with multiple protein systems. Drugs approved for particular indication(s) may be repurposed as novel therapeutics for others. The severely declining rate of discovery and increasing costs of new drugs illustrate the limitations of the traditional reductionist paradigm in drug discovery. METHODS We developed the Computational Analysis of Novel Drug Opportunities (CANDO) platform based on a hypothesis that drugs function by interacting with multiple protein targets to create a molecular interaction signature that can be exploited for therapeutic repurposing and discovery. We compiled a library of compounds that are human ingestible with minimal side effects, followed by an 'all-compounds' vs 'all-proteins' fragment-based multitarget docking with dynamics screen to construct compound-proteome interaction matrices that were then analyzed to determine similarity of drug behavior. The proteomic signature similarity of drugs is then ranked to make putative drug predictions for all indications in a shotgun manner. RESULTS We have previously applied this platform with success in both retrospective benchmarking and prospective validation, and to understand the effect of druggable protein classes on repurposing accuracy. Here we use the CANDO platform to analyze and determine the contribution of multitargeting (polypharmacology) to drug repurposing benchmarking accuracy. Taken together with the previous work, our results indicate that a large number of protein structures with diverse fold space and a specific polypharmacological interactome is necessary for accurate drug predictions using our proteomic and evolutionary drug discovery and repurposing platform. CONCLUSION These results have implications for future drug development and repurposing in the context of polypharmacology.
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Affiliation(s)
- Gaurav Chopra
- Department of Chemistry, Purdue University, West Lafayette, IN, USA.
| | - Ram Samudrala
- Department of Biomedical Informatics, SUNY, Buffalo, NY, USA.
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19
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Onufriev AV, Izadi S. Water models for biomolecular simulations. WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL MOLECULAR SCIENCE 2017. [DOI: 10.1002/wcms.1347] [Citation(s) in RCA: 94] [Impact Index Per Article: 13.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Affiliation(s)
- Alexey V. Onufriev
- Department of Physics; Virginia Tech; Blacksburg VA USA
- Department of Computer Science; Virginia Tech; Blacksburg VA USA
- Center for Soft Matter and Biological Physics; Virginia Tech; Blacksburg VA USA
| | - Saeed Izadi
- Early Stage Pharmaceutical Development; Genentech Inc.; South San Francisco, CA USA
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20
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Cheng Q, Joung I, Lee J. A Simple and Efficient Protein Structure Refinement Method. J Chem Theory Comput 2017; 13:5146-5162. [PMID: 28800396 DOI: 10.1021/acs.jctc.7b00470] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Improving the quality of a given protein structure can serve as the ultimate solution for accurate protein structure prediction, and seeking such a method is currently a challenge in computational structural biology. In order to promote and encourage much needed such efforts, CASP (Critical Assessment of Structure Prediction) has been providing an ideal computational experimental platform, where it was reported only recently (since CASP10) that systematic protein structure refinement is possible by carrying out extensive (approximately millisecond) MD simulations with proper restraints generated from the given structure. Using an explicit solvent model and much reduced positional and distance restraints than previously exercised, we propose a refinement protocol that combines a series of short (5 ns) MD simulations with energy minimization procedures. Testing and benchmarking on 54 CASP8-10 refinement targets and 34 CASP11 refinement targets shows quite promising results. Using only a small fraction of MD simulation steps (nanosecond versus millisecond), systematic protein structure refinement was demonstrated in this work, indicating that refinement of a given model can be achieved using a few hours of desktop computing.
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Affiliation(s)
- Qianyi Cheng
- Center for In Silico Protein Science and School of Computational Sciences, Korea Institute for Advanced Study , Seoul 02455, Korea
| | - InSuk Joung
- Center for In Silico Protein Science and School of Computational Sciences, Korea Institute for Advanced Study , Seoul 02455, Korea
| | - Jooyoung Lee
- Center for In Silico Protein Science and School of Computational Sciences, Korea Institute for Advanced Study , Seoul 02455, Korea
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21
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Zavadlav J, Bevc S, Praprotnik M. Adaptive resolution simulations of biomolecular systems. EUROPEAN BIOPHYSICS JOURNAL: EBJ 2017; 46:821-835. [PMID: 28905203 DOI: 10.1007/s00249-017-1248-0] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/28/2017] [Revised: 06/12/2017] [Accepted: 08/15/2017] [Indexed: 10/18/2022]
Abstract
In this review article, we discuss and analyze some recently developed hybrid atomistic-mesoscopic solvent models for multiscale biomolecular simulations. We focus on the biomolecular applications of the adaptive resolution scheme (AdResS), which allows solvent molecules to change their resolution back and forth between atomistic and coarse-grained representations according to their positions in the system. First, we discuss coupling of atomistic and coarse-grained models of salt solution using a 1-to-1 molecular mapping-i.e., one coarse-grained bead represents one water molecule-for development of a multiscale salt solution model. In order to make use of coarse-grained molecular models that are compatible with the MARTINI force field, one has to resort to a supramolecular mapping, in particular to a 4-to-1 mapping, where four water molecules are represented with one coarse-grained bead. To this end, bundled atomistic water models are employed, i.e., the relative movement of water molecules that are mapped to the same coarse-grained bead is restricted by employing harmonic springs. Supramolecular coupling has recently also been extended to polarizable coarse-grained water models with explicit charges. Since these coarse-grained models consist of several interaction sites, orientational degrees of freedom of the atomistic and coarse-grained representations are coupled via a harmonic energy penalty term. The latter aligns the dipole moments of both representations. The reviewed multiscale solvent models are ready to be used in biomolecular simulations, as illustrated in a few examples.
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Affiliation(s)
- Julija Zavadlav
- Department of Molecular Modeling, National Institute of Chemistry, Hajdrihova 19, 1001, Ljubljana, Slovenia.,Department of Physics, Faculty of Mathematics and Physics, University of Ljubljana, Jadranska 19, 1000, Ljubljana, Slovenia.,Chair of Computational Science, ETH Zurich, Clausiusstrasse 33, 8092, Zurich, Switzerland
| | - Staš Bevc
- Department of Molecular Modeling, National Institute of Chemistry, Hajdrihova 19, 1001, Ljubljana, Slovenia
| | - Matej Praprotnik
- Department of Molecular Modeling, National Institute of Chemistry, Hajdrihova 19, 1001, Ljubljana, Slovenia. .,Department of Physics, Faculty of Mathematics and Physics, University of Ljubljana, Jadranska 19, 1000, Ljubljana, Slovenia.
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22
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Feig M. Computational protein structure refinement: Almost there, yet still so far to go. WILEY INTERDISCIPLINARY REVIEWS. COMPUTATIONAL MOLECULAR SCIENCE 2017; 7:e1307. [PMID: 30613211 PMCID: PMC6319934 DOI: 10.1002/wcms.1307] [Citation(s) in RCA: 46] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Protein structures are essential in modern biology yet experimental methods are far from being able to catch up with the rapid increase in available genomic data. Computational protein structure prediction methods aim to fill the gap while the role of protein structure refinement is to take approximate initial template-based models and bring them closer to the true native structure. Current methods for computational structure refinement rely on molecular dynamics simulations, related sampling methods, or iterative structure optimization protocols. The best methods are able to achieve moderate degrees of refinement but consistent refinement that can reach near-experimental accuracy remains elusive. Key issues revolve around the accuracy of the energy function, the inability to reliably rank multiple models, and the use of restraints that keep sampling close to the native state but also limit the degree of possible refinement. A different aspect is the question of what exactly the target of high-resolution refinement should be as experimental structures are affected by experimental conditions and different biological questions require varying levels of accuracy. While improvement of the global protein structure is a difficult problem, high-resolution refinement methods that improves local structural quality such as favorable stereochemistry and the avoidance of atomic clashes are much more successful.
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Affiliation(s)
- Michael Feig
- Department of Biochemistry and Molecular Biology, Michigan State University, 603 Wilson Rd., Room 218 BCH, East Lansing, MI, USA, ; 517-432-7439
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23
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Steinbrecher T, Abel R, Clark A, Friesner R. Free Energy Perturbation Calculations of the Thermodynamics of Protein Side-Chain Mutations. J Mol Biol 2017; 429:923-929. [DOI: 10.1016/j.jmb.2017.03.002] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2017] [Revised: 02/28/2017] [Accepted: 03/02/2017] [Indexed: 01/20/2023]
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24
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Khoury GA, Smadbeck J, Kieslich CA, Koskosidis AJ, Guzman YA, Tamamis P, Floudas CA. Princeton_TIGRESS 2.0: High refinement consistency and net gains through support vector machines and molecular dynamics in double-blind predictions during the CASP11 experiment. Proteins 2017; 85:1078-1098. [PMID: 28241391 DOI: 10.1002/prot.25274] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2016] [Revised: 02/01/2017] [Accepted: 02/14/2017] [Indexed: 12/28/2022]
Abstract
Protein structure refinement is the challenging problem of operating on any protein structure prediction to improve its accuracy with respect to the native structure in a blind fashion. Although many approaches have been developed and tested during the last four CASP experiments, a majority of the methods continue to degrade models rather than improve them. Princeton_TIGRESS (Khoury et al., Proteins 2014;82:794-814) was developed previously and utilizes separate sampling and selection stages involving Monte Carlo and molecular dynamics simulations and classification using an SVM predictor. The initial implementation was shown to consistently refine protein structures 76% of the time in our own internal benchmarking on CASP 7-10 targets. In this work, we improved the sampling and selection stages and tested the method in blind predictions during CASP11. We added a decomposition of physics-based and hybrid energy functions, as well as a coordinate-free representation of the protein structure through distance-binning Cα-Cα distances to capture fine-grained movements. We performed parameter estimation to optimize the adjustable SVM parameters to maximize precision while balancing sensitivity and specificity across all cross-validated data sets, finding enrichment in our ability to select models from the populations of similar decoys generated for targets in CASPs 7-10. The MD stage was enhanced such that larger structures could be further refined. Among refinement methods that are currently implemented as web-servers, Princeton_TIGRESS 2.0 demonstrated the most consistent and most substantial net refinement in blind predictions during CASP11. The enhanced refinement protocol Princeton_TIGRESS 2.0 is freely available as a web server at http://atlas.engr.tamu.edu/refinement/. Proteins 2017; 85:1078-1098. © 2017 Wiley Periodicals, Inc.
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Affiliation(s)
- George A Khoury
- Department of Chemical and Biological Engineering, Princeton University, Princeton, New Jersey
| | - James Smadbeck
- Department of Chemical and Biological Engineering, Princeton University, Princeton, New Jersey
| | - Chris A Kieslich
- Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, Texas.,Texas A&M Energy Institute, Texas A&M University, College Station, Texas
| | - Alexandra J Koskosidis
- Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, Texas.,Texas A&M Energy Institute, Texas A&M University, College Station, Texas
| | - Yannis A Guzman
- Department of Chemical and Biological Engineering, Princeton University, Princeton, New Jersey.,Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, Texas.,Texas A&M Energy Institute, Texas A&M University, College Station, Texas
| | - Phanourios Tamamis
- Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, Texas.,Texas A&M Energy Institute, Texas A&M University, College Station, Texas
| | - Christodoulos A Floudas
- Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, Texas.,Texas A&M Energy Institute, Texas A&M University, College Station, Texas
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25
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Tang Z, Roberts CC, Chang CEA. Understanding ligand-receptor non-covalent binding kinetics using molecular modeling. FRONT BIOSCI-LANDMRK 2017; 22:960-981. [PMID: 27814657 PMCID: PMC5470370 DOI: 10.2741/4527] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Kinetic properties may serve as critical differentiators and predictors of drug efficacy and safety, in addition to the traditionally focused binding affinity. However the quantitative structure-kinetics relationship (QSKR) for modeling and ligand design is still poorly understood. This review provides an introduction to the kinetics of drug binding from a fundamental chemistry perspective. We focus on recent developments of computational tools and their applications to non-covalent binding kinetics.
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Affiliation(s)
- Zhiye Tang
- Department of Chemistry, University of California, Riverside, CA 92521
| | | | - Chia-En A Chang
- Department of Chemistry, University of California, Riverside, CA 92521,
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26
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Combating Ebola with Repurposed Therapeutics Using the CANDO Platform. Molecules 2016; 21:molecules21121537. [PMID: 27898018 PMCID: PMC5958544 DOI: 10.3390/molecules21121537] [Citation(s) in RCA: 38] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2016] [Revised: 10/23/2016] [Accepted: 10/28/2016] [Indexed: 12/20/2022] Open
Abstract
Ebola virus disease (EVD) is extremely virulent with an estimated mortality rate of up to 90%. However, the state-of-the-art treatment for EVD is limited to quarantine and supportive care. The 2014 Ebola epidemic in West Africa, the largest in history, is believed to have caused more than 11,000 fatalities. The countries worst affected are also among the poorest in the world. Given the complexities, time, and resources required for a novel drug development, finding efficient drug discovery pathways is going to be crucial in the fight against future outbreaks. We have developed a Computational Analysis of Novel Drug Opportunities (CANDO) platform based on the hypothesis that drugs function by interacting with multiple protein targets to create a molecular interaction signature that can be exploited for rapid therapeutic repurposing and discovery. We used the CANDO platform to identify and rank FDA-approved drug candidates that bind and inhibit all proteins encoded by the genomes of five different Ebola virus strains. Top ranking drug candidates for EVD treatment generated by CANDO were compared to in vitro screening studies against Ebola virus-like particles (VLPs) by Kouznetsova et al. and genetically engineered Ebola virus and cell viability studies by Johansen et al. to identify drug overlaps between the in virtuale and in vitro studies as putative treatments for future EVD outbreaks. Our results indicate that integrating computational docking predictions on a proteomic scale with results from in vitro screening studies may be used to select and prioritize compounds for further in vivo and clinical testing. This approach will significantly reduce the lead time, risk, cost, and resources required to determine efficacious therapies against future EVD outbreaks.
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27
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Pang YP. FF12MC: A revised AMBER forcefield and new protein simulation protocol. Proteins 2016; 84:1490-516. [PMID: 27348292 PMCID: PMC5129589 DOI: 10.1002/prot.25094] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2016] [Revised: 06/16/2016] [Accepted: 06/18/2016] [Indexed: 12/25/2022]
Abstract
Specialized to simulate proteins in molecular dynamics (MD) simulations with explicit solvation, FF12MC is a combination of a new protein simulation protocol employing uniformly reduced atomic masses by tenfold and a revised AMBER forcefield FF99 with (i) shortened CH bonds, (ii) removal of torsions involving a nonperipheral sp(3) atom, and (iii) reduced 1-4 interaction scaling factors of torsions ϕ and ψ. This article reports that in multiple, distinct, independent, unrestricted, unbiased, isobaric-isothermal, and classical MD simulations FF12MC can (i) simulate the experimentally observed flipping between left- and right-handed configurations for C14-C38 of BPTI in solution, (ii) autonomously fold chignolin, CLN025, and Trp-cage with folding times that agree with the experimental values, (iii) simulate subsequent unfolding and refolding of these miniproteins, and (iv) achieve a robust Z score of 1.33 for refining protein models TMR01, TMR04, and TMR07. By comparison, the latest general-purpose AMBER forcefield FF14SB locks the C14-C38 bond to the right-handed configuration in solution under the same protein simulation conditions. Statistical survival analysis shows that FF12MC folds chignolin and CLN025 in isobaric-isothermal MD simulations 2-4 times faster than FF14SB under the same protein simulation conditions. These results suggest that FF12MC may be used for protein simulations to study kinetics and thermodynamics of miniprotein folding as well as protein structure and dynamics. Proteins 2016; 84:1490-1516. © 2016 The Authors Proteins: Structure, Function, and Bioinformatics Published by Wiley Periodicals, Inc.
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Affiliation(s)
- Yuan-Ping Pang
- Computer-Aided Molecular Design Laboratory, Mayo Clinic, Rochester, MN, 55905, USA.
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28
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Pang YP. Use of multiple picosecond high-mass molecular dynamics simulations to predict crystallographic B-factors of folded globular proteins. Heliyon 2016; 2:e00161. [PMID: 27699282 PMCID: PMC5035356 DOI: 10.1016/j.heliyon.2016.e00161] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2016] [Revised: 08/18/2016] [Accepted: 09/12/2016] [Indexed: 12/22/2022] Open
Abstract
Predicting crystallographic B-factors of a protein from a conventional molecular dynamics simulation is challenging, in part because the B-factors calculated through sampling the atomic positional fluctuations in a picosecond molecular dynamics simulation are unreliable, and the sampling of a longer simulation yields overly large root mean square deviations between calculated and experimental B-factors. This article reports improved B-factor prediction achieved by sampling the atomic positional fluctuations in multiple picosecond molecular dynamics simulations that use uniformly increased atomic masses by 100-fold to increase time resolution. Using the third immunoglobulin-binding domain of protein G, bovine pancreatic trypsin inhibitor, ubiquitin, and lysozyme as model systems, the B-factor root mean square deviations (mean ± standard error) of these proteins were 3.1 ± 0.2–9 ± 1 Å2 for Cα and 7.3 ± 0.9–9.6 ± 0.2 Å2 for Cγ, when the sampling was done for each of these proteins over 20 distinct, independent, and 50-picosecond high-mass molecular dynamics simulations with AMBER forcefield FF12MC or FF14SB. These results suggest that sampling the atomic positional fluctuations in multiple picosecond high-mass molecular dynamics simulations may be conducive to a priori prediction of crystallographic B-factors of a folded globular protein.
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Affiliation(s)
- Yuan-Ping Pang
- Computer-Aided Molecular Design Laboratory, Mayo Clinic, Rochester, MN 55905, USA
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29
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Examination of the quality of various force fields and solvation models for the equilibrium simulations of GA88 and GB88. J Mol Model 2016; 22:177. [DOI: 10.1007/s00894-016-3027-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2015] [Accepted: 05/31/2016] [Indexed: 10/21/2022]
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30
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Stanzione F, Jayaraman A. Hybrid Atomistic and Coarse-Grained Molecular Dynamics Simulations of Polyethylene Glycol (PEG) in Explicit Water. J Phys Chem B 2016; 120:4160-73. [DOI: 10.1021/acs.jpcb.6b02327] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Francesca Stanzione
- Department
of Chemical and Biomolecular Engineering, University of Delaware, Newark, Delaware 19716, United States
| | - Arthi Jayaraman
- Department
of Chemical and Biomolecular Engineering, University of Delaware, Newark, Delaware 19716, United States
- Department
of Materials Science and Engineering, University of Delaware, Newark, Delaware 19716, United States
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31
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Sethi G, Chopra G, Samudrala R. Multiscale modelling of relationships between protein classes and drug behavior across all diseases using the CANDO platform. Mini Rev Med Chem 2016; 15:705-17. [PMID: 25694071 DOI: 10.2174/1389557515666150219145148] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2014] [Revised: 10/30/2014] [Accepted: 11/25/2014] [Indexed: 01/27/2023]
Abstract
We have examined the effect of eight different protein classes (channels, GPCRs, kinases, ligases, nuclear receptors, proteases, phosphatases, transporters) on the benchmarking performance of the CANDO drug discovery and repurposing platform (http://protinfo.org/cando). The first version of the CANDO platform utilizes a matrix of predicted interactions between 48278 proteins and 3733 human ingestible compounds (including FDA approved drugs and supplements) that map to 2030 indications/diseases using a hierarchical chem and bio-informatic fragment based docking with dynamics protocol (> one billion predicted interactions considered). The platform uses similarity of compound-proteome interaction signatures as indicative of similar functional behavior and benchmarking accuracy is calculated across 1439 indications/diseases with more than one approved drug. The CANDO platform yields a significant correlation (0.99, p-value < 0.0001) between the number of proteins considered and benchmarking accuracy obtained indicating the importance of multitargeting for drug discovery. Average benchmarking accuracies range from 6.2 % to 7.6 % for the eight classes when the top 10 ranked compounds are considered, in contrast to a range of 5.5 % to 11.7 % obtained for the comparison/control sets consisting of 10, 100, 1000, and 10000 single best performing proteins. These results are generally two orders of magnitude better than the average accuracy of 0.2% obtained when randomly generated (fully scrambled) matrices are used. Different indications perform well when different classes are used but the best accuracies (up to 11.7% for the top 10 ranked compounds) are achieved when a combination of classes are used containing the broadest distribution of protein folds. Our results illustrate the utility of the CANDO approach and the consideration of different protein classes for devising indication specific protocols for drug repurposing as well as drug discovery.
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Affiliation(s)
| | | | - Ram Samudrala
- Department of Biomedical Informatics, School of Medicine and Biomedical Sciences, State University of New York (SUNY), 923 Main Street, Buffalo, NY 14203, USA.
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32
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Nasrul Islam M, Iqbal S, Katebi AR, Tamjidul Hoque M. A balanced secondary structure predictor. J Theor Biol 2016; 389:60-71. [DOI: 10.1016/j.jtbi.2015.10.015] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2015] [Revised: 10/14/2015] [Accepted: 10/22/2015] [Indexed: 11/30/2022]
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33
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Kumar A, Campitelli P, Thorpe MF, Ozkan SB. Partial unfolding and refolding for structure refinement: A unified approach of geometric simulations and molecular dynamics. Proteins 2015; 83:2279-92. [PMID: 26476100 DOI: 10.1002/prot.24947] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2015] [Revised: 09/11/2015] [Accepted: 09/29/2015] [Indexed: 12/26/2022]
Abstract
The most successful protein structure prediction methods to date have been template-based modeling (TBM) or homology modeling, which predicts protein structure based on experimental structures. These high accuracy predictions sometimes retain structural errors due to incorrect templates or a lack of accurate templates in the case of low sequence similarity, making these structures inadequate in drug-design studies or molecular dynamics simulations. We have developed a new physics based approach to the protein refinement problem by mimicking the mechanism of chaperons that rehabilitate misfolded proteins. The template structure is unfolded by selectively (targeted) pulling on different portions of the protein using the geometric based technique FRODA, and then refolded using hierarchically restrained replica exchange molecular dynamics simulations (hr-REMD). FRODA unfolding is used to create a diverse set of topologies for surveying near native-like structures from a template and to provide a set of persistent contacts to be employed during re-folding. We have tested our approach on 13 previous CASP targets and observed that this method of folding an ensemble of partially unfolded structures, through the hierarchical addition of contact restraints (that is, first local and then nonlocal interactions), leads to a refolding of the structure along with refinement in most cases (12/13). Although this approach yields refined models through advancement in sampling, the task of blind selection of the best refined models still needs to be solved. Overall, the method can be useful for improved sampling for low resolution models where certain of the portions of the structure are incorrectly modeled.
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Affiliation(s)
- Avishek Kumar
- Department of Physics and Center for Biological Physics, Arizona State University, Tempe, Arizona
| | - Paul Campitelli
- Department of Physics and Center for Biological Physics, Arizona State University, Tempe, Arizona
| | - M F Thorpe
- Department of Physics and Center for Biological Physics, Arizona State University, Tempe, Arizona.,Rudolf Peierls Center for Theoretical Physics, University of Oxford, Oxford, OX1 3NP, United Kingdom
| | - S Banu Ozkan
- Department of Physics and Center for Biological Physics, Arizona State University, Tempe, Arizona
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34
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Della Corte D, Wildberg A, Schröder GF. Protein structure refinement with adaptively restrained homologous replicas. Proteins 2015; 84 Suppl 1:302-13. [PMID: 26441154 DOI: 10.1002/prot.24939] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2015] [Revised: 09/02/2015] [Accepted: 09/29/2015] [Indexed: 12/27/2022]
Abstract
A novel protein refinement protocol is presented which utilizes molecular dynamics (MD) simulations of an ensemble of adaptively restrained homologous replicas. This approach adds evolutionary information to the force field and reduces random conformational fluctuations by coupling of several replicas. It is shown that this protocol refines the majority of models from the CASP11 refinement category and that larger conformational changes of the starting structure are possible than with current state of the art methods. The performance of this protocol in the CASP11 experiment is discussed. We found that the quality of the refined model is correlated with the structural variance of the coupled replicas, which therefore provides a good estimator of model quality. Furthermore, some remarkable refinement results are discussed in detail. Proteins 2016; 84(Suppl 1):302-313. © 2015 Wiley Periodicals, Inc.
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Affiliation(s)
- Dennis Della Corte
- Institute of Complex Systems (ICS-6), Forschungszentrum Jülich, Jülich, 52425, Germany
| | - André Wildberg
- Institute of Complex Systems (ICS-6), Forschungszentrum Jülich, Jülich, 52425, Germany
| | - Gunnar F Schröder
- Institute of Complex Systems (ICS-6), Forschungszentrum Jülich, Jülich, 52425, Germany. .,Physics Department, University of Düsseldorf, Düsseldorf, 40225, Germany.
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35
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Raval A, Piana S, Eastwood MP, Shaw DE. Assessment of the utility of contact-based restraints in accelerating the prediction of protein structure using molecular dynamics simulations. Protein Sci 2015; 25:19-29. [PMID: 26266489 PMCID: PMC4815320 DOI: 10.1002/pro.2770] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2015] [Revised: 08/07/2015] [Accepted: 08/11/2015] [Indexed: 12/15/2022]
Abstract
Molecular dynamics (MD) simulation is a well-established tool for the computational study of protein structure and dynamics, but its application to the important problem of protein structure prediction remains challenging, in part because extremely long timescales can be required to reach the native structure. Here, we examine the extent to which the use of low-resolution information in the form of residue-residue contacts, which can often be inferred from bioinformatics or experimental studies, can accelerate the determination of protein structure in simulation. We incorporated sets of 62, 31, or 15 contact-based restraints in MD simulations of ubiquitin, a benchmark system known to fold to the native state on the millisecond timescale in unrestrained simulations. One-third of the restrained simulations folded to the native state within a few tens of microseconds-a speedup of over an order of magnitude compared with unrestrained simulations and a demonstration of the potential for limited amounts of structural information to accelerate structure determination. Almost all of the remaining ubiquitin simulations reached near-native conformations within a few tens of microseconds, but remained trapped there, apparently due to the restraints. We discuss potential methodological improvements that would facilitate escape from these near-native traps and allow more simulations to quickly reach the native state. Finally, using a target from the Critical Assessment of protein Structure Prediction (CASP) experiment, we show that distance restraints can improve simulation accuracy: In our simulations, restraints stabilized the native state of the protein, enabling a reasonable structural model to be inferred.
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Affiliation(s)
- Alpan Raval
- D. E. Shaw Research, New York, New York, 10036
| | | | | | - David E Shaw
- D. E. Shaw Research, New York, New York, 10036.,Department of Biochemistry and Molecular Biophysics, Columbia University, New York, New York, 10032
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36
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Lee GR, Heo L, Seok C. Effective protein model structure refinement by loop modeling and overall relaxation. Proteins 2015; 84 Suppl 1:293-301. [PMID: 26172288 DOI: 10.1002/prot.24858] [Citation(s) in RCA: 60] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2015] [Revised: 06/29/2015] [Accepted: 07/06/2015] [Indexed: 12/25/2022]
Abstract
Protein structures predicted by state-of-the-art template-based methods may still have errors when the template proteins are not similar enough to the target protein. Overall target structure may deviate from the template structures owing to differences in sequences. Structural information for some local regions such as loops may not be available when there are sequence insertions or deletions. Those structural aspects that originate from deviations from templates can be dealt with by ab initio structure refinement methods to further improve model accuracy. In the CASP11 refinement experiment, we tested three different refinement methods that utilize overall structure relaxation, loop modeling, and quality assessment of multiple initial structures. From this experiment, we conclude that the overall relaxation method can consistently improve model quality. Loop modeling is the most useful when the initial model structure is high quality, with GDT-HA >60. The method that used multiple initial structures further refined the already refined models; the minor improvements with this method raise the issue of problem with the current energy function. Future research directions are also discussed. Proteins 2016; 84(Suppl 1):293-301. © 2015 Wiley Periodicals, Inc.
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Affiliation(s)
- Gyu Rie Lee
- Department of Chemistry, Seoul National University, Seoul, 151-747, Republic of Korea
| | - Lim Heo
- Department of Chemistry, Seoul National University, Seoul, 151-747, Republic of Korea
| | - Chaok Seok
- Department of Chemistry, Seoul National University, Seoul, 151-747, Republic of Korea.
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37
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Zachmann M, Mathias G, Antes I. Parameterization of the Hamiltonian Dielectric Solvent (HADES) Reaction-Field Method for the Solvation Free Energies of Amino Acid Side-Chain Analogs. Chemphyschem 2015; 16:1739-49. [PMID: 25820235 DOI: 10.1002/cphc.201402861] [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] [Received: 12/04/2014] [Revised: 02/02/2015] [Indexed: 11/10/2022]
Abstract
Optimization of the Hamiltonian dielectric solvent (HADES) method for biomolecular simulations in a dielectric continuum is presented with the goal of calculating accurate absolute solvation free energies while retaining the model's accuracy in predicting conformational free-energy differences. The solvation free energies of neutral and polar amino acid side-chain analogs calculated by using HADES, which may optionally include nonpolar contributions, were optimized against experimental data to reach a chemical accuracy of about 0.5 kcal mol(-1). The new parameters were evaluated for charged side-chain analogs. The HADES results were compared with explicit-solvent, generalized Born, Poisson-Boltzmann, and QM-based methods. The potentials of mean force (PMFs) between pairs of side-chain analogs obtained by using HADES and explicit-solvent simulations were used to evaluate the effects of the improved parameters optimized for solvation free energies on intermolecular potentials.
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Affiliation(s)
- Martin Zachmann
- Theoretical Chemical Biology and Protein Modelling Group, Technische Universiät München (Germany)
| | - Gerald Mathias
- Lehrstuhl für Biomolekulare Optik, Ludwig-Maximilians Universität München (Germany).
| | - Iris Antes
- Theoretical Chemical Biology and Protein Modelling Group, Technische Universiät München (Germany).
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38
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Xue Y, Skrynnikov NR. Ensemble MD simulations restrained via crystallographic data: accurate structure leads to accurate dynamics. Protein Sci 2015; 23:488-507. [PMID: 24452989 DOI: 10.1002/pro.2433] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2013] [Revised: 01/06/2014] [Accepted: 01/18/2014] [Indexed: 11/07/2022]
Abstract
Currently, the best existing molecular dynamics (MD) force fields cannot accurately reproduce the global free-energy minimum which realizes the experimental protein structure. As a result, long MD trajectories tend to drift away from the starting coordinates (e.g., crystallographic structures). To address this problem, we have devised a new simulation strategy aimed at protein crystals. An MD simulation of protein crystal is essentially an ensemble simulation involving multiple protein molecules in a crystal unit cell (or a block of unit cells). To ensure that average protein coordinates remain correct during the simulation, we introduced crystallography-based restraints into the MD protocol. Because these restraints are aimed at the ensemble-average structure, they have only minimal impact on conformational dynamics of the individual protein molecules. So long as the average structure remains reasonable, the proteins move in a native-like fashion as dictated by the original force field. To validate this approach, we have used the data from solid-state NMR spectroscopy, which is the orthogonal experimental technique uniquely sensitive to protein local dynamics. The new method has been tested on the well-established model protein, ubiquitin. The ensemble-restrained MD simulations produced lower crystallographic R factors than conventional simulations; they also led to more accurate predictions for crystallographic temperature factors, solid-state chemical shifts, and backbone order parameters. The predictions for (15) N R1 relaxation rates are at least as accurate as those obtained from conventional simulations. Taken together, these results suggest that the presented trajectories may be among the most realistic protein MD simulations ever reported. In this context, the ensemble restraints based on high-resolution crystallographic data can be viewed as protein-specific empirical corrections to the standard force fields.
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Affiliation(s)
- Yi Xue
- Department of Chemistry, Purdue University, 560 Oval Drive, West Lafayette, Indiana, 47907-2084, USA
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40
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Lapelosa M, Patapoff TW, Zarraga IE. Molecular Simulations of the Pairwise Interaction of Monoclonal Antibodies. J Phys Chem B 2014; 118:13132-41. [DOI: 10.1021/jp508729z] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Affiliation(s)
- Mauro Lapelosa
- Department of Late Stage Pharmaceutical Development and ‡Department of Early Stage Pharmaceutical
Development, Genentech Inc., member of Roche, South San Francisco, California 94080, United States
| | - Thomas W. Patapoff
- Department of Late Stage Pharmaceutical Development and ‡Department of Early Stage Pharmaceutical
Development, Genentech Inc., member of Roche, South San Francisco, California 94080, United States
| | - Isidro E. Zarraga
- Department of Late Stage Pharmaceutical Development and ‡Department of Early Stage Pharmaceutical
Development, Genentech Inc., member of Roche, South San Francisco, California 94080, United States
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41
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Ryu H, Kim TR, Ahn S, Ji S, Lee J. Protein NMR structures refined without NOE data. PLoS One 2014; 9:e108888. [PMID: 25279564 PMCID: PMC4184813 DOI: 10.1371/journal.pone.0108888] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2014] [Accepted: 09/04/2014] [Indexed: 12/31/2022] Open
Abstract
The refinement of low-quality structures is an important challenge in protein structure prediction. Many studies have been conducted on protein structure refinement; the refinement of structures derived from NMR spectroscopy has been especially intensively studied. In this study, we generated flat-bottom distance potential instead of NOE data because NOE data have ambiguity and uncertainty. The potential was derived from distance information from given structures and prevented structural dislocation during the refinement process. A simulated annealing protocol was used to minimize the potential energy of the structure. The protocol was tested on 134 NMR structures in the Protein Data Bank (PDB) that also have X-ray structures. Among them, 50 structures were used as a training set to find the optimal "width" parameter in the flat-bottom distance potential functions. In the validation set (the other 84 structures), most of the 12 quality assessment scores of the refined structures were significantly improved (total score increased from 1.215 to 2.044). Moreover, the secondary structure similarity of the refined structure was improved over that of the original structure. Finally, we demonstrate that the combination of two energy potentials, statistical torsion angle potential (STAP) and the flat-bottom distance potential, can drive the refinement of NMR structures.
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Affiliation(s)
- Hyojung Ryu
- Korean Bioinformation Center (KOBIC), Korea Research Institute of Bioscience and Biotechnology, Daejeon, The Republic of Korea
- Department of Bioinformatics, University of Science and Technology, Daejeon, The Republic of Korea
| | - Tae-Rae Kim
- Department of Chemistry, Seoul National University, Seoul, The Republic of Korea
| | - SeonJoo Ahn
- Korean Bioinformation Center (KOBIC), Korea Research Institute of Bioscience and Biotechnology, Daejeon, The Republic of Korea
| | - Sunyoung Ji
- Korean Bioinformation Center (KOBIC), Korea Research Institute of Bioscience and Biotechnology, Daejeon, The Republic of Korea
- Department of Bioinformatics, University of Science and Technology, Daejeon, The Republic of Korea
| | - Jinhyuk Lee
- Korean Bioinformation Center (KOBIC), Korea Research Institute of Bioscience and Biotechnology, Daejeon, The Republic of Korea
- Department of Bioinformatics, University of Science and Technology, Daejeon, The Republic of Korea
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42
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Khoury GA, Liwo A, Khatib F, Zhou H, Chopra G, Bacardit J, Bortot LO, Faccioli RA, Deng X, He Y, Krupa P, Li J, Mozolewska MA, Sieradzan AK, Smadbeck J, Wirecki T, Cooper S, Flatten J, Xu K, Baker D, Cheng J, Delbem ACB, Floudas CA, Keasar C, Levitt M, Popović Z, Scheraga HA, Skolnick J, Crivelli SN, Players F. WeFold: a coopetition for protein structure prediction. Proteins 2014; 82:1850-68. [PMID: 24677212 PMCID: PMC4249725 DOI: 10.1002/prot.24538] [Citation(s) in RCA: 40] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2013] [Revised: 01/25/2014] [Accepted: 02/08/2014] [Indexed: 12/19/2022]
Abstract
The protein structure prediction problem continues to elude scientists. Despite the introduction of many methods, only modest gains were made over the last decade for certain classes of prediction targets. To address this challenge, a social-media based worldwide collaborative effort, named WeFold, was undertaken by 13 labs. During the collaboration, the laboratories were simultaneously competing with each other. Here, we present the first attempt at "coopetition" in scientific research applied to the protein structure prediction and refinement problems. The coopetition was possible by allowing the participating labs to contribute different components of their protein structure prediction pipelines and create new hybrid pipelines that they tested during CASP10. This manuscript describes both successes and areas needing improvement as identified throughout the first WeFold experiment and discusses the efforts that are underway to advance this initiative. A footprint of all contributions and structures are publicly accessible at http://www.wefold.org.
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Affiliation(s)
- George A. Khoury
- Department of Chemical and Biological Engineering, Princeton University, USA
| | - Adam Liwo
- Faculty of Chemistry, University of Gdansk, Poland
| | - Firas Khatib
- Department of Biochemistry, University of Washington, USA
| | - Hongyi Zhou
- Center for the Study of Systems Biology, School of Biology, Georgia Institute of Technology, USA
| | - Gaurav Chopra
- Department of Structural Biology, School of Medicine, Stanford University, USA
- Diabetes Center, School of Medicine, University of California San Francisco (UCSF), USA
| | - Jaume Bacardit
- School of Computing Science, Newcastle University, United Kingdom
| | - Leandro O. Bortot
- Laboratory of Biological Physics, Faculty of Pharmaceutical Sciences at Ribeirão Preto, University of São Paulo, Brazil
| | - Rodrigo A. Faccioli
- Institute of Mathematical and Computer Sciences, University of São Paulo, Brazil
| | - Xin Deng
- Department of Computer Science, University of Missouri, USA
| | - Yi He
- Baker Laboratory of Chemistry and Chemical Biology, Cornell University, Ithaca, NY 14853-1301, USA
| | - Pawel Krupa
- Faculty of Chemistry, University of Gdansk, Poland
- Baker Laboratory of Chemistry and Chemical Biology, Cornell University, Ithaca, NY 14853-1301, USA
| | - Jilong Li
- Department of Computer Science, University of Missouri, USA
| | - Magdalena A. Mozolewska
- Faculty of Chemistry, University of Gdansk, Poland
- Baker Laboratory of Chemistry and Chemical Biology, Cornell University, Ithaca, NY 14853-1301, USA
| | | | - James Smadbeck
- Department of Chemical and Biological Engineering, Princeton University, USA
| | - Tomasz Wirecki
- Faculty of Chemistry, University of Gdansk, Poland
- Baker Laboratory of Chemistry and Chemical Biology, Cornell University, Ithaca, NY 14853-1301, USA
| | - Seth Cooper
- Center for Game Science, Department of Computer Science & Engineering, University of Washington, USA
| | - Jeff Flatten
- Center for Game Science, Department of Computer Science & Engineering, University of Washington, USA
| | - Kefan Xu
- Center for Game Science, Department of Computer Science & Engineering, University of Washington, USA
| | - David Baker
- Department of Biochemistry, University of Washington, USA
| | - Jianlin Cheng
- Department of Computer Science, University of Missouri, USA
| | | | | | - Chen Keasar
- Departments of Computer Science and Life Sciences, Ben Gurion University of the Negev, Israel
| | - Michael Levitt
- Department of Structural Biology, School of Medicine, Stanford University, USA
| | - Zoran Popović
- Center for Game Science, Department of Computer Science & Engineering, University of Washington, USA
| | - Harold A. Scheraga
- Baker Laboratory of Chemistry and Chemical Biology, Cornell University, Ithaca, NY 14853-1301, USA
| | - Jeffrey Skolnick
- Center for the Study of Systems Biology, School of Biology, Georgia Institute of Technology, USA
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43
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Kleinjung J, Fraternali F. Design and application of implicit solvent models in biomolecular simulations. Curr Opin Struct Biol 2014; 25:126-34. [PMID: 24841242 PMCID: PMC4045398 DOI: 10.1016/j.sbi.2014.04.003] [Citation(s) in RCA: 106] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2014] [Revised: 04/07/2014] [Accepted: 04/09/2014] [Indexed: 11/23/2022]
Abstract
Implicit solvent replaces explicit water by a potential of mean force. Popular models are SASA, VOL and Generalized Born. Implicit solvent is used in MD, protein modelling, folding, design, prediction and drug screening. Large-scale simulations allow for parametrisation via force matching. Application to nucleic acids and membranes is challenging.
We review implicit solvent models and their parametrisation by introducing the concepts and recent devlopments of the most popular models with a focus on parametrisation via force matching. An overview of recent applications of the solvation energy term in protein dynamics, modelling, design and prediction is given to illustrate the usability and versatility of implicit solvation in reproducing the physical behaviour of biomolecular systems. Limitations of implicit modes are discussed through the example of more challenging systems like nucleic acids and membranes.
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Affiliation(s)
- Jens Kleinjung
- Division of Mathematical Biology, MRC National Institute for Medical Research, The Ridgeway, London NW7 1AA, United Kingdom
| | - Franca Fraternali
- Randall Division of Cell and Molecular Biophysics, King's College London, New Hunt's House, London SE1 1UL, United Kingdom.
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44
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45
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Li M, Petukh M, Alexov E, Panchenko AR. Predicting the Impact of Missense Mutations on Protein-Protein Binding Affinity. J Chem Theory Comput 2014; 10:1770-1780. [PMID: 24803870 PMCID: PMC3985714 DOI: 10.1021/ct401022c] [Citation(s) in RCA: 84] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2013] [Indexed: 01/22/2023]
Abstract
The crucial prerequisite for proper biological function is the protein's ability to establish highly selective interactions with macromolecular partners. A missense mutation that alters the protein binding affinity may cause significant perturbations or complete abolishment of the function, potentially leading to diseases. The availability of computational methods to evaluate the impact of mutations on protein-protein binding is critical for a wide range of biomedical applications. Here, we report an efficient computational approach for predicting the effect of single and multiple missense mutations on protein-protein binding affinity. It is based on a well-tested simulation protocol for structure minimization, modified MM-PBSA and statistical scoring energy functions with parameters optimized on experimental sets of several thousands of mutations. Our simulation protocol yields very good agreement between predicted and experimental values with Pearson correlation coefficients of 0.69 and 0.63 and root-mean-square errors of 1.20 and 1.90 kcal mol-1 for single and multiple mutations, respectively. Compared with other available methods, our approach achieves high speed and prediction accuracy and can be applied to large datasets generated by modern genomics initiatives. In addition, we report a crucial role of water model and the polar solvation energy in estimating the changes in binding affinity. Our analysis also reveals that prediction accuracy and effect of mutations on binding strongly depends on the type of mutation and its location in a protein complex.
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Affiliation(s)
- Minghui Li
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health , Bethesda, Maryland 20894, United States
| | - Marharyta Petukh
- Computational Biophysics and Bioinformatics, Department of Physics, Clemson University , Clemson, South Carolina 29634, United States
| | - Emil Alexov
- Computational Biophysics and Bioinformatics, Department of Physics, Clemson University , Clemson, South Carolina 29634, United States
| | - Anna R Panchenko
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health , Bethesda, Maryland 20894, United States
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46
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Zavadlav J, Melo MN, Marrink SJ, Praprotnik M. Adaptive resolution simulation of an atomistic protein in MARTINI water. J Chem Phys 2014; 140:054114. [DOI: 10.1063/1.4863329] [Citation(s) in RCA: 69] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
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47
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Mirjalili V, Noyes K, Feig M. Physics-based protein structure refinement through multiple molecular dynamics trajectories and structure averaging. Proteins 2014; 82 Suppl 2:196-207. [PMID: 23737254 PMCID: PMC4212311 DOI: 10.1002/prot.24336] [Citation(s) in RCA: 87] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2013] [Revised: 04/30/2013] [Accepted: 05/09/2013] [Indexed: 12/26/2022]
Abstract
We used molecular dynamics (MD) simulations for structure refinement of Critical Assessment of Techniques for Protein Structure Prediction 10 (CASP10) targets. Refinement was achieved by selecting structures from the MD-based ensembles followed by structural averaging. The overall performance of this method in CASP10 is described, and specific aspects are analyzed in detail to provide insight into key components. In particular, the use of different restraint types, sampling from multiple short simulations versus a single long simulation, the success of a quality assessment criterion, the application of scoring versus averaging, and the impact of a final refinement step are discussed in detail.
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Affiliation(s)
- Vahid Mirjalili
- Department of Mechanical Engineering Michigan State University East Lansing, MI 48824; USA
- Department of Biochemistry and Molecular Biology Michigan State University East Lansing, MI 48824; USA
| | - Keenan Noyes
- Department of Chemistry Michigan State University East Lansing, MI 48824; USA
| | - Michael Feig
- Department of Biochemistry and Molecular Biology Michigan State University East Lansing, MI 48824; USA
- Department of Chemistry Michigan State University East Lansing, MI 48824; USA
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48
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Khoury GA, Tamamis P, Pinnaduwage N, Smadbeck J, Kieslich CA, Floudas CA. Princeton_TIGRESS: protein geometry refinement using simulations and support vector machines. Proteins 2013; 82:794-814. [PMID: 24174311 DOI: 10.1002/prot.24459] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2013] [Revised: 10/18/2013] [Accepted: 10/22/2013] [Indexed: 12/30/2022]
Abstract
Protein structure refinement aims to perform a set of operations given a predicted structure to improve model quality and accuracy with respect to the native in a blind fashion. Despite the numerous computational approaches to the protein refinement problem reported in the previous three CASPs, an overwhelming majority of methods degrade models rather than improve them. We initially developed a method tested using blind predictions during CASP10 which was officially ranked in 5th place among all methods in the refinement category. Here, we present Princeton_TIGRESS, which when benchmarked on all CASP 7,8,9, and 10 refinement targets, simultaneously increased GDT_TS 76% of the time with an average improvement of 0.83 GDT_TS points per structure. The method was additionally benchmarked on models produced by top performing three-dimensional structure prediction servers during CASP10. The robustness of the Princeton_TIGRESS protocol was also tested for different random seeds. We make the Princeton_TIGRESS refinement protocol freely available as a web server at http://atlas.princeton.edu/refinement. Using this protocol, one can consistently refine a prediction to help bridge the gap between a predicted structure and the actual native structure.
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Affiliation(s)
- George A Khoury
- Department of Chemical and Biological Engineering, Princeton University, Princeton, New Jersey, 08540
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49
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Ruvinsky AM, Kirys T, Tuzikov AV, Vakser IA. Ensemble-based characterization of unbound and bound states on protein energy landscape. Protein Sci 2013; 22:734-44. [PMID: 23526684 DOI: 10.1002/pro.2256] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2012] [Revised: 02/02/2013] [Accepted: 03/15/2013] [Indexed: 11/07/2022]
Abstract
Physicochemical description of numerous cell processes is fundamentally based on the energy landscapes of protein molecules involved. Although the whole energy landscape is difficult to reconstruct, increased attention to particular targets has provided enough structures for mapping functionally important subspaces associated with the unbound and bound protein structures. The subspace mapping produces a discrete representation of the landscape, further called energy spectrum. We compiled and characterized ensembles of bound and unbound conformations of six small proteins and explored their spectra in implicit solvent. First, the analysis of the unbound-to-bound changes points to conformational selection as the binding mechanism for four proteins. Second, results show that bound and unbound spectra often significantly overlap. Moreover, the larger the overlap the smaller the root mean square deviation (RMSD) between the bound and unbound conformational ensembles. Third, the center of the unbound spectrum has a higher energy than the center of the corresponding bound spectrum of the dimeric and multimeric states for most of the proteins. This suggests that the unbound states often have larger entropy than the bound states. Fourth, the exhaustively long minimization, making small intrarotamer adjustments (all-atom RMSD ≤ 0.7 Å), dramatically reduces the distance between the centers of the bound and unbound spectra as well as the spectra extent. It condenses unbound and bound energy levels into a thin layer at the bottom of the energy landscape with the energy spacing that varies between 0.8-4.6 and 3.5-10.5 kcal/mol for the unbound and bound states correspondingly. Finally, the analysis of protein energy fluctuations showed that protein vibrations itself can excite the interstate transitions, including the unbound-to-bound ones.
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Affiliation(s)
- Anatoly M Ruvinsky
- Center for Bioinformatics, The University of Kansas, Lawrence, Kansas 66047, USA.
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Goldtzvik Y, Goldstein M, Benny Gerber R. On the crystallographic accuracy of structure prediction by implicit water models: Tests for cyclic peptides. Chem Phys 2013. [DOI: 10.1016/j.chemphys.2013.01.039] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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