1
|
Adiyaman R, McGuffin LJ. Using Local Protein Model Quality Estimates to Guide a Molecular Dynamics-Based Refinement Strategy. Methods Mol Biol 2023; 2627:119-140. [PMID: 36959445 DOI: 10.1007/978-1-0716-2974-1_7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/25/2023]
Abstract
The refinement of predicted 3D models aims to bring them closer to the native structure by fixing errors including unusual bonds and torsion angles and irregular hydrogen bonding patterns. Refinement approaches based on molecular dynamics (MD) simulations using different types of restraints have performed well since CASP10. ReFOLD, developed by the McGuffin group, was one of the many MD-based refinement approaches, which were tested in CASP 12. When the performance of the ReFOLD method in CASP12 was evaluated, it was observed that ReFOLD suffered from the absence of a reliable guidance mechanism to reach consistent improvement for the quality of predicted 3D models, particularly in the case of template-based modelling (TBM) targets. Therefore, here we propose to utilize the local quality assessment score produced by ModFOLD6 to guide the MD-based refinement approach to further increase the accuracy of the predicted 3D models. The relative performance of the new local quality assessment guided MD-based refinement protocol and the original MD-based protocol ReFOLD are compared utilizing many different official scoring methods. By using the per-residue accuracy (or local quality) score to guide the refinement process, we are able to prevent the refined models from undesired structural deviations, thereby leading to more consistent improvements. This chapter will include a detailed analysis of the performance of the local quality assessment guided MD-based protocol versus that deployed in the original ReFOLD method.
Collapse
Affiliation(s)
- Recep Adiyaman
- School of Biological Sciences, University of Reading, Reading, UK
| | - Liam J McGuffin
- School of Biological Sciences, University of Reading, Reading, UK.
| |
Collapse
|
2
|
Yadav NS, Kumar P, Singh I. Structural and functional analysis of protein. Bioinformatics 2022. [DOI: 10.1016/b978-0-323-89775-4.00026-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022] Open
|
3
|
Protein Structure Prediction: Conventional and Deep Learning Perspectives. Protein J 2021; 40:522-544. [PMID: 34050498 DOI: 10.1007/s10930-021-10003-y] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/21/2021] [Indexed: 10/21/2022]
Abstract
Protein structure prediction is a way to bridge the sequence-structure gap, one of the main challenges in computational biology and chemistry. Predicting any protein's accurate structure is of paramount importance for the scientific community, as these structures govern their function. Moreover, this is one of the complicated optimization problems that computational biologists have ever faced. Experimental protein structure determination methods include X-ray crystallography, Nuclear Magnetic Resonance Spectroscopy and Electron Microscopy. All of these are tedious and time-consuming procedures that require expertise. To make the process less cumbersome, scientists use predictive tools as part of computational methods, using data consolidated in the protein repositories. In recent years, machine learning approaches have raised the interest of the structure prediction community. Most of the machine learning approaches for protein structure prediction are centred on co-evolution based methods. The accuracy of these approaches depends on the number of homologous protein sequences available in the databases. The prediction problem becomes challenging for many proteins, especially those without enough sequence homologs. Deep learning methods allow for the extraction of intricate features from protein sequence data without making any intuitions. Accurately predicted protein structures are employed for drug discovery, antibody designs, understanding protein-protein interactions, and interactions with other molecules. This article provides a review of conventional and deep learning approaches in protein structure prediction. We conclude this review by outlining a few publicly available datasets and deep learning architectures currently employed for protein structure prediction tasks.
Collapse
|
4
|
Mangangcha IR, Brojen Singh RK, Lebeche D, Ali S. Xanthone glucoside 2-β-D-glucopyranosyl-1,3,6,7-tetrahydroxy-9H-xanthen-9-one binds to the ATP-binding pocket of glycogen synthase kinase 3β and inhibits its activity: implications in prostate cancer and associated cardiovascular disease risk. J Biomol Struct Dyn 2021; 40:7868-7884. [PMID: 33769184 DOI: 10.1080/07391102.2021.1902857] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Abstract
Glycogen synthase kinase 3 (GSK3) is a serine/threonine kinase which in the presence of ATP in its ATP-binding pocket transfers a phosphate to a primed substrate. GSK3β is an isoform of GSK3 which has been projected as a potent therapeutic target in human diseases including cancers and metabolic syndrome. Incidentally, cardiovascular disease is a common cause of non-cancer related deaths in prostate cancer (PCa) patients, mainly due to the effects of androgen-deprivation therapy (ADT), a mainstay for PCa treatment. Several small molecular inhibitors of GSK3 are either ATP-competitive (bind to the ATP-binding pocket), or non-ATP-competitive inhibitors (binding to the substrate-binding site of the enzyme). In this study, 2-β-D-glucopyranosyl-1,3,6,7-tetrahydroxy-9H-xanthen-9-one (βDGT), a natural xanthonoid present in many plant species, is reported to bind to the ATP-binding pocket of GSK3β and inhibit its activity, as demonstrated by the molecular docking and molecular dynamics simulation analysis and experimental validation in vitro. A comparison of the binding affinities with five known ATP-competitive inhibitors of GSK3β suggested similarity in binding site residues in the ATP-binding pocket of the enzyme. The optimum inhibitory concentration of the xanthonoid as determined by the luminescent kinase assay was 200 µM. The study envisages the use of βDGT as a natural ATP-competitive inhibitor of GSK3β and implicates its use in PCa patients on ADT, a cardiovascular disease risk, and other pathological conditions where GSK3 inhibition may be clinically important. HighlightsGSK3β is a multifaceted kinase known for its role in cancers, cardiovascular, and other diseases.In this study, βDGT, a xanthonoid, is reported to bind to the ATP-binding pocket of GSK3β.A comparison of βDGT binding with 5 known ATP-competitive inhibitors of GSK3β suggested the involvement of residues at the ATP binding site.The binding site analysis suggested an ATP-competitive mechanism of enzyme inhibition.Study envisages the use of βDGT as a natural ATP-competitive inhibitor of GSK3β and implicates its use in prostate cancer patients on androgen-deprivation therapy, a cardiovascular disease risk, and other pathological conditions.Communicated by Ramaswamy H. Sarma.
Collapse
Affiliation(s)
- Irengbam Rocky Mangangcha
- Department of Biochemistry, School of Chemical and Life Sciences, Jamia Hamdard (Deemed University), Delhi, India.,School of Interdisciplinary Sciences and Technology, Jamia Hamdard (Deemed University), Delhi, India.,Bioinformatics Center, BIF, Jamia Hamdard (Deemed University), Delhi, India.,Department of Zoology, Deshbandhu College, University of Delhi, Delhi, India
| | - Raj Kumar Brojen Singh
- School of Computational & Integrative Sciences, Jawaharlal Nehru University, Delhi, India
| | - Djamel Lebeche
- Cardiovascular Research Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.,Diabetes, Obesity and Metabolism Institute, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA.,Graduate School of Biomedical Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Shakir Ali
- Department of Biochemistry, School of Chemical and Life Sciences, Jamia Hamdard (Deemed University), Delhi, India.,School of Interdisciplinary Sciences and Technology, Jamia Hamdard (Deemed University), Delhi, India.,Bioinformatics Center, BIF, Jamia Hamdard (Deemed University), Delhi, India
| |
Collapse
|
5
|
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: 43] [Impact Index Per Article: 8.6] [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.
Collapse
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.
| |
Collapse
|
6
|
Methods for the Refinement of Protein Structure 3D Models. Int J Mol Sci 2019; 20:ijms20092301. [PMID: 31075942 PMCID: PMC6539982 DOI: 10.3390/ijms20092301] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2019] [Revised: 04/24/2019] [Accepted: 05/07/2019] [Indexed: 12/25/2022] Open
Abstract
The refinement of predicted 3D protein models is crucial in bringing them closer towards experimental accuracy for further computational studies. Refinement approaches can be divided into two main stages: The sampling and scoring stages. Sampling strategies, such as the popular Molecular Dynamics (MD)-based protocols, aim to generate improved 3D models. However, generating 3D models that are closer to the native structure than the initial model remains challenging, as structural deviations from the native basin can be encountered due to force-field inaccuracies. Therefore, different restraint strategies have been applied in order to avoid deviations away from the native structure. For example, the accurate prediction of local errors and/or contacts in the initial models can be used to guide restraints. MD-based protocols, using physics-based force fields and smart restraints, have made significant progress towards a more consistent refinement of 3D models. The scoring stage, including energy functions and Model Quality Assessment Programs (MQAPs) are also used to discriminate near-native conformations from non-native conformations. Nevertheless, there are often very small differences among generated 3D models in refinement pipelines, which makes model discrimination and selection problematic. For this reason, the identification of the most native-like conformations remains a major challenge.
Collapse
|
7
|
Lee GR, Heo L, Seok C. Simultaneous refinement of inaccurate local regions and overall structure in the CASP12 protein model refinement experiment. Proteins 2017; 86 Suppl 1:168-176. [PMID: 29044810 DOI: 10.1002/prot.25404] [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: 07/03/2017] [Revised: 10/09/2017] [Accepted: 10/11/2017] [Indexed: 12/15/2022]
Abstract
Advances in protein model refinement techniques are required as diverse sources of protein structure information are available from low-resolution experiments or informatics-based computations such as cryo-EM, NMR, homology models, or predicted residue contacts. Given semi-reliable or incomplete structural information, structure quality of a protein model has to be improved by ab initio methods such as energy-based simulation. In this study, we describe a new automatic refinement server method designed to improve locally inaccurate regions and overall structure simultaneously. Locally inaccurate regions may occur in protein structures due to non-convergent or missing information in template structures used in homology modeling or due to intrinsic structural flexibilities not resolved by experimental techniques. However, such variable or dynamic regions often play important functional roles by participating in interactions with other biomolecules or in transitions between different functional states. The new refinement method introduced here utilizes diverse types of geometric operators which drive both local and global changes, and the effect of structure changes and relaxations are accumulated. This resulted in consistent refinement of both local and global structural features. Performance of this method in CASP12 is discussed.
Collapse
Affiliation(s)
- Gyu Rie Lee
- Department of Chemistry, Seoul National University, Seoul, Republic of Korea
| | - Lim Heo
- Department of Chemistry, Seoul National University, Seoul, Republic of Korea
| | - Chaok Seok
- Department of Chemistry, Seoul National University, Seoul, Republic of Korea
| |
Collapse
|
8
|
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.
Collapse
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
| |
Collapse
|
9
|
Xianwei T, Diannan L, Boxiong W. Substrate transport pathway inside outward open conformation of EmrD: a molecular dynamics simulation study. MOLECULAR BIOSYSTEMS 2017; 12:2634-41. [PMID: 27327574 DOI: 10.1039/c6mb00348f] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
The EmrD transporter, which is a classical major facilitator superfamily (MFS) protein, can extrude a range of drug molecules out of E. coil. The drug molecules transport through the channel of MFS in an outward open state, an important issue in research about bacterial drug resistance, which however, is still unknown. In this paper, we construct a starting outward-open model of the EmrD transporter using a state transition method. The starting model is refined by a conventional molecular dynamics simulation. Locally enhanced sampling simulation (LES) is used to validate the outward-open model of EmrD. In the locally enhanced sampling simulation, ten substrates are placed along the channel of the outward-open EmrD, and these substrates are sampled in the outward-open center cavity. It is found that the translocation pathway of these substrates from the inside to the outside of the cell through the EmrD transporter is composed of two sub-pathways, one sub-pathway, including H2, H4, and H5, and another sub-pathway, including H8, H10, and H11. The results give us have a further insight to the ways of substrate translocation of an MFS protein. The model method is based on common features of an MFS protein, so this modeling method can be used to construct various MFS protein models which have a desired state with other conformations not known in the alternating-access mechanism.
Collapse
Affiliation(s)
- Tan Xianwei
- School of Life Sciences, Tsinghua University, Beijing, China.
| | - Lu Diannan
- Department of Chemical Engineering, Tsinghua University, Beijing, China
| | - Wang Boxiong
- Department of Precision Instrument, Tsinghua University, Beijing, China
| |
Collapse
|
10
|
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: 48] [Impact Index Per Article: 6.9] [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.
Collapse
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
| |
Collapse
|
11
|
Villanelo F, Escalona Y, Pareja-Barrueto C, Garate JA, Skerrett IM, Perez-Acle T. Accessing gap-junction channel structure-function relationships through molecular modeling and simulations. BMC Cell Biol 2017; 18:5. [PMID: 28124624 PMCID: PMC5267332 DOI: 10.1186/s12860-016-0121-9] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023] Open
Abstract
Background Gap junction channels (GJCs) are massive protein channels connecting the cytoplasm of adjacent cells. These channels allow intercellular transfer of molecules up to ~1 kDa, including water, ions and other metabolites. Unveiling structure-function relationships coded into the molecular architecture of these channels is necessary to gain insight on their vast biological function including electrical synapse, inflammation, development and tissular homeostasis. From early works, computational methods have been critical to analyze and interpret experimental observations. Upon the availability of crystallographic structures, molecular modeling and simulations have become a valuable tool to assess structure-function relationships in GJCs. Modeling different connexin isoforms, simulating the transport process, and exploring molecular variants, have provided new hypotheses and out-of-the-box approaches to the study of these important channels. Methods Here, we review foundational structural studies and recent developments on GJCs using molecular modeling and simulation techniques, highlighting the methods and the cross-talk with experimental evidence. Results and discussion By comparing results obtained by molecular modeling and simulations techniques with structural and functional information obtained from both recent literature and structural databases, we provide a critical assesment of structure-function relationships that can be obtained from the junction between theoretical and experimental evidence.
Collapse
Affiliation(s)
- F Villanelo
- Computational Biology Lab. Fundación Ciencia & Vida, Santiago, Chile
| | - Y Escalona
- Computational Biology Lab. Fundación Ciencia & Vida, Santiago, Chile
| | - C Pareja-Barrueto
- Computational Biology Lab. Fundación Ciencia & Vida, Santiago, Chile
| | - J A Garate
- Computational Biology Lab. Fundación Ciencia & Vida, Santiago, Chile.,Centro Interdisciplinario de Neurociencia de Valparaíso, Universidad de Valparaíso, Playa Ancha, Valparaíso, Chile
| | - I M Skerrett
- State University of New York (SUNY) Buffalo State, Buffalo, NY, 14222, USA
| | - T Perez-Acle
- Computational Biology Lab. Fundación Ciencia & Vida, Santiago, Chile. .,Centro Interdisciplinario de Neurociencia de Valparaíso, Universidad de Valparaíso, Playa Ancha, Valparaíso, Chile.
| |
Collapse
|
12
|
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.
Collapse
Affiliation(s)
- Yuan-Ping Pang
- Computer-Aided Molecular Design Laboratory, Mayo Clinic, Rochester, MN, 55905, USA.
| |
Collapse
|
13
|
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.
Collapse
Affiliation(s)
- Yuan-Ping Pang
- Computer-Aided Molecular Design Laboratory, Mayo Clinic, Rochester, MN 55905, USA
| |
Collapse
|
14
|
Design of Self-Assembling Protein-Polymer Conjugates. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2016; 940:179-214. [PMID: 27677514 DOI: 10.1007/978-3-319-39196-0_9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Protein-polymer conjugates are of particular interest for nanobiotechnology applications because of the various and complementary roles that each component may play in composite hybrid-materials. This chapter focuses on the design principles and applications of self-assembling protein-polymer conjugate materials. We address the general design methodology, from both synthetic and genetic perspective, conjugation strategies, protein vs. polymer driven self-assembly and finally, emerging applications for conjugate materials. By marrying proteins and polymers into conjugated bio-hybrid materials, materials scientists, chemists, and biologists alike, have at their fingertips a vast toolkit for material design. These inherently hierarchical structures give rise to useful patterning, mechanical and transport properties that may help realize new, more efficient materials for energy generation, catalysis, nanorobots, etc.
Collapse
|
15
|
Lee MS, Olson MA. Assessment of Detection and Refinement Strategies for de novo Protein Structures Using Force Field and Statistical Potentials. J Chem Theory Comput 2015; 3:312-24. [PMID: 26627174 DOI: 10.1021/ct600195f] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
De novo predictions of protein structures at high resolution are plagued by the problem of detecting the native conformation from false energy minima. In this work, we provide an assessment of various detection and refinement protocols on a small subset of the second-generation all-atom Rosetta decoy set (Tsai et al. Proteins 2003, 53, 76-87) using two potentials: the all-atom CHARMM PARAM22 force field combined with generalized Born/surface-area (GB-SA) implicit solvation and the DFIRE-AA statistical potential. Detection schemes included DFIRE-AA conformational scoring and energy minimization followed by scoring with both GB-SA and DFIRE-AA potentials. Refinement methods included short-time (1-ps) molecular dynamics simulations, temperature-based replica exchange molecular dynamics, and a new computational unfold/refold procedure. Refinement methods include temperature-based replica exchange molecular dynamics and a new computational unfold/refold procedure. Our results indicate that simple detection with only minimization is the best protocol for finding the most nativelike structures in the decoy set. The refinement techniques that we tested are generally unsuccessful in improving detection; however, they provide marginal improvements to some of the decoy structures. Future directions in the development of refinement techniques are discussed in the context of the limitations of the protocols evaluated in this study.
Collapse
Affiliation(s)
- Michael S Lee
- Computational and Information Sciences Directorate, U.S. Army Research Laboratory, Aberdeen Proving Ground, Maryland 21005, and Department of Cell Biology and Biochemistry, U.S. Army Medical Research Institute of Infectious Diseases, Frederick, Maryland 21702
| | - Mark A Olson
- Computational and Information Sciences Directorate, U.S. Army Research Laboratory, Aberdeen Proving Ground, Maryland 21005, and Department of Cell Biology and Biochemistry, U.S. Army Medical Research Institute of Infectious Diseases, Frederick, Maryland 21702
| |
Collapse
|
16
|
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.
Collapse
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
| |
Collapse
|
17
|
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.
Collapse
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
| |
Collapse
|
18
|
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.
Collapse
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.
| |
Collapse
|
19
|
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.
Collapse
Affiliation(s)
- Yi Xue
- Department of Chemistry, Purdue University, 560 Oval Drive, West Lafayette, Indiana, 47907-2084, USA
| | | |
Collapse
|
20
|
Vaidehi N, Jain A. Internal coordinate molecular dynamics: a foundation for multiscale dynamics. J Phys Chem B 2015; 119:1233-42. [PMID: 25517406 PMCID: PMC4315417 DOI: 10.1021/jp509136y] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
![]()
Internal coordinates such as bond
lengths, bond angles, and torsion
angles (BAT) are natural coordinates for describing a bonded molecular
system. However, the molecular dynamics (MD) simulation methods that
are widely used for proteins, DNA, and polymers are based on Cartesian
coordinates owing to the mathematical simplicity of the equations
of motion. However, constraints are often needed with Cartesian MD
simulations to enhance the conformational sampling. This makes the
equations of motion in the Cartesian coordinates differential-algebraic,
which adversely impacts the complexity and the robustness of the simulations.
On the other hand, constraints can be easily placed in BAT coordinates
by removing the degrees of freedom that need to be constrained. Thus,
the internal coordinate MD (ICMD) offers an attractive alternative
to Cartesian coordinate MD for developing multiscale MD method. The
torsional MD method is a special adaptation of the ICMD method, where
all the bond lengths and bond angles are kept rigid. The advantages
of ICMD simulation methods are the longer time step size afforded
by freezing high frequency degrees of freedom and performing a conformational
search in the more important low frequency torsional degrees of freedom.
However, the advancements in the ICMD simulations have been slow and
stifled by long-standing mathematical bottlenecks. In this review,
we summarize the recent mathematical advancements we have made based
on spatial operator algebra, in developing a robust long time scale
ICMD simulation toolkit useful for various applications. We also present
the applications of ICMD simulations to study conformational changes
in proteins and protein structure refinement. We review the advantages
of the ICMD simulations over the Cartesian simulations when used with
enhanced sampling methods and project the future use of ICMD simulations
in protein dynamics.
Collapse
Affiliation(s)
- Nagarajan Vaidehi
- Department of Immunology, Beckman Research Institute of the City of Hope , Duarte, California 91010, United States
| | | |
Collapse
|
21
|
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.
Collapse
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
| |
Collapse
|
22
|
Larsson P, Pouya I, Lindahl E. From Side Chains Rattling on Picoseconds to Ensemble Simulations of Protein Folding. Isr J Chem 2014. [DOI: 10.1002/ijch.201400020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
|
23
|
Figaj D, Gieldon A, Polit A, Sobiecka-Szkatula A, Koper T, Denkiewicz M, Banecki B, Lesner A, Ciarkowski J, Lipinska B, Skorko-Glonek J. The LA loop as an important regulatory element of the HtrA (DegP) protease from Escherichia coli: structural and functional studies. J Biol Chem 2014; 289:15880-93. [PMID: 24737328 DOI: 10.1074/jbc.m113.532895] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023] Open
Abstract
Bacterial HtrAs are serine proteases engaged in extracytoplasmic protein quality control and are required for the virulence of several pathogenic species. The proteolytic activity of HtrA (DegP) from Escherichia coli, a model prokaryotic HtrA, is stimulated by stressful conditions; the regulation of this process is mediated by the LA, LD, L1, L2, and L3 loops. The precise mechanism of action of the LA loop is not known due to a lack of data concerning its three-dimensional structure as well as its mode of interaction with other regulatory elements. To address these issues we generated a theoretical model of the three-dimensional structure of the LA loop as per the resting state of HtrA and subsequently verified its correctness experimentally. We identified intra- and intersubunit contacts that formed with the LA loops; these played an important role in maintaining HtrA in its inactive conformation. The most significant proved to be the hydrophobic interactions connecting the LA loops of the hexamer and polar contacts between the LA' (the LA loop on an opposite subunit) and L1 loops on opposite subunits. Disturbance of these interactions caused the stimulation of HtrA proteolytic activity. We also demonstrated that LA loops contribute to the preservation of the integrity of the HtrA oligomer and to the stability of the monomer. The model presented in this work explains the regulatory role of the LA loop well; it should also be applicable to numerous Enterobacteriaceae pathogenic species as the amino acid sequences of the members of this bacterial family are highly conserved.
Collapse
Affiliation(s)
- Donata Figaj
- From the Department of Biochemistry, Faculty of Biology, University of Gdansk, Wita Stwosza 59, 80-308 Gdansk, Poland
| | - Artur Gieldon
- Department of Theoretical Chemistry, Faculty of Chemistry, University of Gdansk, Wita Stwosza 63, 80-952 Gdansk
| | - Agnieszka Polit
- Department of Physical Biochemistry, Faculty of Biochemistry, Biophysics, and Biotechnology, Jagiellonian University, Gronostajowa 7, 30-387 Krakow, Poland
| | - Anna Sobiecka-Szkatula
- From the Department of Biochemistry, Faculty of Biology, University of Gdansk, Wita Stwosza 59, 80-308 Gdansk, Poland
| | - Tomasz Koper
- From the Department of Biochemistry, Faculty of Biology, University of Gdansk, Wita Stwosza 59, 80-308 Gdansk, Poland
| | - Milena Denkiewicz
- From the Department of Biochemistry, Faculty of Biology, University of Gdansk, Wita Stwosza 59, 80-308 Gdansk, Poland
| | - Bogdan Banecki
- Department of Molecular and Cellular Biology, Intercollegiate Faculty of Biotechnology, University of Gdansk and Medical University of Gdansk, Kladki 24, 80-822 Gdansk, Poland, and
| | - Adam Lesner
- Department of Biochemistry, Faculty of Chemistry, University of Gdansk, Wita Stwosza 63, 80-952 Gdansk, Poland
| | - Jerzy Ciarkowski
- Department of Theoretical Chemistry, Faculty of Chemistry, University of Gdansk, Wita Stwosza 63, 80-952 Gdansk
| | - Barbara Lipinska
- From the Department of Biochemistry, Faculty of Biology, University of Gdansk, Wita Stwosza 59, 80-308 Gdansk, Poland
| | - Joanna Skorko-Glonek
- From the Department of Biochemistry, Faculty of Biology, University of Gdansk, Wita Stwosza 59, 80-308 Gdansk, Poland,
| |
Collapse
|
24
|
Larsen A, Wagner JR, Jain A, Vaidehi N. Protein structure refinement of CASP target proteins using GNEIMO torsional dynamics method. J Chem Inf Model 2014; 54:508-17. [PMID: 24397429 PMCID: PMC3985798 DOI: 10.1021/ci400484c] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2013] [Indexed: 11/30/2022]
Abstract
A longstanding challenge in using computational methods for protein structure prediction is the refinement of low-resolution structural models derived from comparative modeling methods into highly accurate atomistic models useful for detailed structural studies. Previously, we have developed and demonstrated the utility of the internal coordinate molecular dynamics (MD) technique, generalized Newton-Euler inverse mass operator (GNEIMO), for refinement of small proteins. Using GNEIMO, the high-frequency degrees of freedom are frozen and the protein is modeled as a collection of rigid clusters connected by torsional hinges. This physical model allows larger integration time steps and focuses the conformational search in the low frequency torsional degrees of freedom. Here, we have applied GNEIMO with temperature replica exchange to refine low-resolution protein models of 30 proteins taken from the continuous assessment of structure prediction (CASP) competition. We have shown that GNEIMO torsional MD method leads to refinement of up to 1.3 Å in the root-mean-square deviation in coordinates for 30 CASP target proteins without using any experimental data as restraints in performing the GNEIMO simulations. This is in contrast with the unconstrained all-atom Cartesian MD method performed under the same conditions, where refinement requires the use of restraints during the simulations.
Collapse
Affiliation(s)
- Adrien
B. Larsen
- Division
of Immunology, Beckman Research Institute
of the City of Hope, 1500, E. Duarte Road, Duarte, California 91010, United States
| | - Jeffrey R. Wagner
- Division
of Immunology, Beckman Research Institute
of the City of Hope, 1500, E. Duarte Road, Duarte, California 91010, United States
| | - Abhinandan Jain
- Jet
Propulsion Laboratory, California Institute
of Technology, Pasadena, California 91109, United States
| | - Nagarajan Vaidehi
- Division
of Immunology, Beckman Research Institute
of the City of Hope, 1500, E. Duarte Road, Duarte, California 91010, United States
| |
Collapse
|
25
|
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: 92] [Impact Index Per Article: 9.2] [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.
Collapse
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
| |
Collapse
|
26
|
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.
Collapse
Affiliation(s)
- George A Khoury
- Department of Chemical and Biological Engineering, Princeton University, Princeton, New Jersey, 08540
| | | | | | | | | | | |
Collapse
|
27
|
Chakraborty S, Venkatramani R, Rao BJ, Asgeirsson B, Dandekar AM. The electrostatic profile of consecutive Cβ atoms applied to protein structure quality assessment. F1000Res 2013; 2:243. [PMID: 25506420 PMCID: PMC4257144 DOI: 10.12688/f1000research.2-243.v1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 09/16/2014] [Indexed: 02/10/2024] Open
Abstract
The structure of a protein provides insight into its physiological interactions with other components of the cellular soup. Methods that predict putative structures from sequences typically yield multiple, closely-ranked possibilities. A critical component in the process is the model quality assessing program (MQAP), which selects the best candidate from this pool of structures. Here, we present a novel MQAP based on the physical properties of sidechain atoms. We propose a method for assessing the quality of protein structures based on the electrostatic potential difference (EPD) of Cβ atoms in consecutive residues. We demonstrate that the EPDs of Cβ atoms on consecutive residues provide unique signatures of the amino acid types. The EPD of Cβ atoms are learnt from a set of 1000 non-homologous protein structures with a resolution cuto of 1.6 Å obtained from the PISCES database. Based on the Boltzmann hypothesis that lower energy conformations are proportionately sampled more, and on Annsen's thermodynamic hypothesis that the native structure of a protein is the minimum free energy state, we hypothesize that the deviation of observed EPD values from the mean values obtained in the learning phase is minimized in the native structure. We achieved an average specificity of 0.91, 0.94 and 0.93 on hg_structal, 4state_reduced and ig_structal decoy sets, respectively, taken from the Decoys `R' Us database. The source code and manual is made available at https://github.com/sanchak/mqap and permanently available on 10.5281/zenodo.7134.
Collapse
Affiliation(s)
- Sandeep Chakraborty
- Department of Biological Sciences, Tata Institute of Fundamental Research, Mumbai, 400 005, India
| | - Ravindra Venkatramani
- Department of Chemical Sciences, Tata Institute of Fundamental Research, Mumbai, 400 005, India
| | - Basuthkar J. Rao
- Department of Biological Sciences, Tata Institute of Fundamental Research, Mumbai, 400 005, India
| | - Bjarni Asgeirsson
- Science Institute, Department of Biochemistry, University of Iceland, IS-107 Reykjavik, Iceland
| | - Abhaya M. Dandekar
- Plant Sciences Department, University of California,, Davis, CA, 95616, USA
| |
Collapse
|
28
|
Chakraborty S, Venkatramani R, Rao BJ, Asgeirsson B, Dandekar AM. Protein structure quality assessment based on the distance profiles of consecutive backbone Cα atoms. F1000Res 2013; 2:211. [PMID: 24555103 DOI: 10.12688/f1000research.2-211.v1] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 10/10/2013] [Indexed: 01/22/2023] Open
Abstract
Predicting the three dimensional native state structure of a protein from its primary sequence is an unsolved grand challenge in molecular biology. Two main computational approaches have evolved to obtain the structure from the protein sequence - ab initio/de novo methods and template-based modeling - both of which typically generate multiple possible native state structures. Model quality assessment programs (MQAP) validate these predicted structures in order to identify the correct native state structure. Here, we propose a MQAP for assessing the quality of protein structures based on the distances of consecutive Cα atoms. We hypothesize that the root-mean-square deviation of the distance of consecutive Cα (RDCC) atoms from the ideal value of 3.8 Å, derived from a statistical analysis of high quality protein structures (top100H database), is minimized in native structures. Based on tests with the top100H set, we propose a RDCC cutoff value of 0.012 Å, above which a structure can be filtered out as a non-native structure. We applied the RDCC discriminator on decoy sets from the Decoys 'R' Us database to show that the native structures in all decoy sets tested have RDCC below the 0.012 Å cutoff. While most decoy sets were either indistinguishable using this discriminator or had very few violations, all the decoy structures in the fisa decoy set were discriminated by applying the RDCC criterion. This highlights the physical non-viability of the fisa decoy set, and possible issues in benchmarking other methods using this set. The source code and manual is made available at https://github.com/sanchak/mqap and permanently available on 10.5281/zenodo.7134.
Collapse
Affiliation(s)
- Sandeep Chakraborty
- Department of Biological Sciences, Tata Institute of Fundamental Research, Mumbai, 400 005, India
| | - Ravindra Venkatramani
- Department of Chemical Sciences, Tata Institute of Fundamental Research, Mumbai, 400 005, India
| | - Basuthkar J Rao
- Department of Biological Sciences, Tata Institute of Fundamental Research, Mumbai, 400 005, India
| | - Bjarni Asgeirsson
- Science Institute, Department of Biochemistry, University of Iceland, Reykjavik, IS-107, Iceland
| | - Abhaya M Dandekar
- Plant Sciences Department, University of California, Davis, CA 95616, USA
| |
Collapse
|
29
|
Bhattacharya D, Cheng J. i3Drefine software for protein 3D structure refinement and its assessment in CASP10. PLoS One 2013; 8:e69648. [PMID: 23894517 PMCID: PMC3716612 DOI: 10.1371/journal.pone.0069648] [Citation(s) in RCA: 41] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2013] [Accepted: 06/13/2013] [Indexed: 12/25/2022] Open
Abstract
Protein structure refinement refers to the process of improving the qualities of protein structures during structure modeling processes to bring them closer to their native states. Structure refinement has been drawing increasing attention in the community-wide Critical Assessment of techniques for Protein Structure prediction (CASP) experiments since its addition in 8th CASP experiment. During the 9th and recently concluded 10th CASP experiments, a consistent growth in number of refinement targets and participating groups has been witnessed. Yet, protein structure refinement still remains a largely unsolved problem with majority of participating groups in CASP refinement category failed to consistently improve the quality of structures issued for refinement. In order to alleviate this need, we developed a completely automated and computationally efficient protein 3D structure refinement method, i3Drefine, based on an iterative and highly convergent energy minimization algorithm with a powerful all-atom composite physics and knowledge-based force fields and hydrogen bonding (HB) network optimization technique. In the recent community-wide blind experiment, CASP10, i3Drefine (as ‘MULTICOM-CONSTRUCT’) was ranked as the best method in the server section as per the official assessment of CASP10 experiment. Here we provide the community with free access to i3Drefine software and systematically analyse the performance of i3Drefine in strict blind mode on the refinement targets issued in CASP10 refinement category and compare with other state-of-the-art refinement methods participating in CASP10. Our analysis demonstrates that i3Drefine is only fully-automated server participating in CASP10 exhibiting consistent improvement over the initial structures in both global and local structural quality metrics. Executable version of i3Drefine is freely available at http://protein.rnet.missouri.edu/i3drefine/.
Collapse
Affiliation(s)
- Debswapna Bhattacharya
- Department of Computer Science, University of Missouri, Columbia, Missouri, United States of America
| | - Jianlin Cheng
- Department of Computer Science, Informatics Institute, Bond Life Science Center, University of Missouri, Columbia, Missouri, United States of America
- * E-mail:
| |
Collapse
|
30
|
Olson MA, Lee MS. Application of replica exchange umbrella sampling to protein structure refinement of nontemplate models. J Comput Chem 2013; 34:1785-93. [PMID: 23703032 DOI: 10.1002/jcc.23325] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2012] [Revised: 03/12/2013] [Accepted: 04/21/2013] [Indexed: 12/30/2022]
Abstract
We provide an assessment of a computational strategy for protein structure refinement that combines self-guided Langevin dynamics with umbrella-potential biasing replica exchange using the radius of gyration as a coordinate (Rg -ReX). Eight structurally nonredundant proteins and their decoys were examined by sampling conformational space at room temperature using the CHARMM22/GBMV2 force field to generate the ensemble of structures. Two atomic statistical potentials (RWplus and DFIRE) were analyzed for structure identification and compared to the simulation force-field potential. The results show that, while the Rg -ReX simulations were able to sample conformational basins that were more structurally similar to the X-ray crystallographic structures than the starting first-order ranked decoys, the potentials failed to detect these basins from refinement. Of the three potential functions, RWplus yielded the highest accuracy for recognition of structures that refined to an average of nearly 20% increase in native contacts relative to the starting decoys. The overall performance of Rg -ReX is compared to an earlier study of applying temperature-based replica exchange to refine the same decoy sets and highlights the general challenge of achieving consistently the sampling and detection threshold of 70% fraction of native contacts.
Collapse
Affiliation(s)
- Mark A Olson
- Department of Cell Biology and Biochemistry, USAMRIID, Fredrick, Maryland 21702, USA.
| | | |
Collapse
|
31
|
Mirjalili V, Feig M. Protein Structure Refinement through Structure Selection and Averaging from Molecular Dynamics Ensembles. J Chem Theory Comput 2013; 9:1294-1303. [PMID: 23526422 PMCID: PMC3603382 DOI: 10.1021/ct300962x] [Citation(s) in RCA: 68] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
A molecular dynamics (MD) simulation based protocol for structure refinement of template-based model predictions is described. The protocol involves the application of restraints, ensemble averaging of selected subsets, interpolation between initial and refined structures, and assessment of refinement success. It is found that sub-microsecond MD-based sampling when combined with ensemble averaging can produce moderate but consistent refinement for most systems in the CASP targets considered here.
Collapse
Affiliation(s)
- Vahid Mirjalili
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI 48824; USA
- Department of Mechanical Engineering, 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
| |
Collapse
|
32
|
Chakraborty S, Venkatramani R, Rao BJ, Asgeirsson B, Dandekar AM. The electrostatic profile of consecutive Cβ atoms applied to protein structure quality assessment. F1000Res 2013; 2:243. [PMID: 25506420 PMCID: PMC4257144 DOI: 10.12688/f1000research.2-243.v3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 09/16/2014] [Indexed: 12/23/2022] Open
Abstract
The structure of a protein provides insight into its physiological interactions with other components of the cellular soup. Methods that predict putative structures from sequences typically yield multiple, closely-ranked possibilities. A critical component in the process is the model quality assessing program (MQAP), which selects the best candidate from this pool of structures. Here, we present a novel MQAP based on the physical properties of sidechain atoms. We propose a method for assessing the quality of protein structures based on the electrostatic potential difference (EPD) of Cβ atoms in consecutive residues. We demonstrate that the EPDs of Cβ atoms on consecutive residues provide unique signatures of the amino acid types. The EPD of Cβ atoms are learnt from a set of 1000 non-homologous protein structures with a resolution cuto of 1.6 Å obtained from the PISCES database. Based on the Boltzmann hypothesis that lower energy conformations are proportionately sampled more, and on Annsen's thermodynamic hypothesis that the native structure of a protein is the minimum free energy state, we hypothesize that the deviation of observed EPD values from the mean values obtained in the learning phase is minimized in the native structure. We achieved an average specificity of 0.91, 0.94 and 0.93 on hg_structal, 4state_reduced and ig_structal decoy sets, respectively, taken from the Decoys `R' Us database. The source code and manual is made available at
https://github.com/sanchak/mqap and permanently available on 10.5281/zenodo.7134.
Collapse
Affiliation(s)
- Sandeep Chakraborty
- Department of Biological Sciences, Tata Institute of Fundamental Research, Mumbai, 400 005, India
| | - Ravindra Venkatramani
- Department of Chemical Sciences, Tata Institute of Fundamental Research, Mumbai, 400 005, India
| | - Basuthkar J Rao
- Department of Biological Sciences, Tata Institute of Fundamental Research, Mumbai, 400 005, India
| | - Bjarni Asgeirsson
- Science Institute, Department of Biochemistry, University of Iceland, IS-107 Reykjavik, Iceland
| | - Abhaya M Dandekar
- Plant Sciences Department, University of California,, Davis, CA, 95616, USA
| |
Collapse
|
33
|
Wang J. Prediction of the Binding Affinities of PSD95 PDZ Domain in Complex with the CRIPT Peptide. ACTA ACUST UNITED AC 2013. [DOI: 10.12720/jomb.2.2.137-141] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
|
34
|
Godschalk F, Genheden S, Söderhjelm P, Ryde U. Comparison of MM/GBSA calculations based on explicit and implicit solvent simulations. Phys Chem Chem Phys 2013; 15:7731-9. [DOI: 10.1039/c3cp00116d] [Citation(s) in RCA: 56] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
|
35
|
Vyas VK, Ukawala RD, Ghate M, Chintha C. Homology modeling a fast tool for drug discovery: current perspectives. Indian J Pharm Sci 2012. [PMID: 23204616 PMCID: PMC3507339 DOI: 10.4103/0250-474x.102537] [Citation(s) in RCA: 139] [Impact Index Per Article: 11.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
Major goal of structural biology involve formation of protein-ligand complexes; in which the protein molecules act energetically in the course of binding. Therefore, perceptive of protein-ligand interaction will be very important for structure based drug design. Lack of knowledge of 3D structures has hindered efforts to understand the binding specificities of ligands with protein. With increasing in modeling software and the growing number of known protein structures, homology modeling is rapidly becoming the method of choice for obtaining 3D coordinates of proteins. Homology modeling is a representation of the similarity of environmental residues at topologically corresponding positions in the reference proteins. In the absence of experimental data, model building on the basis of a known 3D structure of a homologous protein is at present the only reliable method to obtain the structural information. Knowledge of the 3D structures of proteins provides invaluable insights into the molecular basis of their functions. The recent advances in homology modeling, particularly in detecting and aligning sequences with template structures, distant homologues, modeling of loops and side chains as well as detecting errors in a model contributed to consistent prediction of protein structure, which was not possible even several years ago. This review focused on the features and a role of homology modeling in predicting protein structure and described current developments in this field with victorious applications at the different stages of the drug design and discovery.
Collapse
Affiliation(s)
- V K Vyas
- Department of Pharmaceutical Chemistry, Institute of Pharmacy, Nirma University, Ahmedabad-382 481, India
| | | | | | | |
Collapse
|
36
|
eThread: a highly optimized machine learning-based approach to meta-threading and the modeling of protein tertiary structures. PLoS One 2012. [PMID: 23185577 PMCID: PMC3503980 DOI: 10.1371/journal.pone.0050200] [Citation(s) in RCA: 30] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023] Open
Abstract
Template-based modeling that employs various meta-threading techniques is currently the most accurate, and consequently the most commonly used, approach for protein structure prediction. Despite the evident progress in this field, accurate structure models cannot be constructed for a significant fraction of gene products, thus the development of new algorithms is required. Here, we describe the development, optimization and large-scale benchmarking of eThread, a highly accurate meta-threading procedure for the identification of structural templates and the construction of corresponding target-to-template alignments. eThread integrates ten state-of-the-art threading/fold recognition algorithms in a local environment and extensively uses various machine learning techniques to carry out fully automated template-based protein structure modeling. Tertiary structure prediction employs two protocols based on widely used modeling algorithms: Modeller and TASSER-Lite. As a part of eThread, we also developed eContact, which is a Bayesian classifier for the prediction of inter-residue contacts and eRank, which effectively ranks generated multiple protein models and provides reliable confidence estimates as structure quality assessment. Excluding closely related templates from the modeling process, eThread generates models, which are correct at the fold level, for >80% of the targets; 40–50% of the constructed models are of a very high quality, which would be considered accurate at the family level. Furthermore, in large-scale benchmarking, we compare the performance of eThread to several alternative methods commonly used in protein structure prediction. Finally, we estimate the upper bound for this type of approach and discuss the directions towards further improvements.
Collapse
|
37
|
Olson MA, Lee MS. Structure refinement of protein model decoys requires accurate side-chain placement. Proteins 2012; 81:469-78. [PMID: 23070940 DOI: 10.1002/prot.24204] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2012] [Revised: 09/18/2012] [Accepted: 10/02/2012] [Indexed: 11/10/2022]
Abstract
In this study, the application of temperature-based replica-exchange (T-ReX) simulations for structure refinement of decoys taken from the I-TASSER dataset was examined. A set of eight nonredundant proteins was investigated using self-guided Langevin dynamics (SGLD) with a generalized Born implicit solvent model to sample conformational space. For two of the protein test cases, a comparison of the SGLD/T-ReX method with that of a hybrid explicit/implicit solvent molecular dynamics T-ReX simulation model is provided. Additionally, the effect of side-chain placement among the starting decoy structures, using alternative rotamer conformations taken from the SCWRL4 modeling program, was investigated. The simulation results showed that, despite having near-native backbone conformations among the starting decoys, the determinant of their refinement is side-chain packing to a level that satisfies a minimum threshold of native contacts to allow efficient excursions toward the downhill refinement regime on the energy landscape. By repacking using SCWRL4 and by applying the RWplus statistical potential for structure identification, the SGLD/T-ReX simulations achieved refinement to an average of 38% increase in the number of native contacts relative to the original I-TASSER decoy sets and a 25% reduction in values of C(α) root-mean-square deviation. The hybrid model succeeded in obtaining a sharper funnel to low-energy states for a modeled target than the implicit solvent SGLD model; yet, structure identification remained roughly the same. Without meeting a threshold of near-native packing of side chains, the T-ReX simulations degrade the accuracy of the decoys, and subsequently, refinement becomes tantamount to the protein folding problem.
Collapse
Affiliation(s)
- Mark A Olson
- Department of Cell Biology and Biochemistry, USAMRIID, Frederick, Maryland 21702, USA.
| | | |
Collapse
|
38
|
Morya VK, Dewaker V, Kim EK. In Silico Study and Validation of Phosphotransacetylase (PTA) as a Putative Drug Target for Staphylococcus aureus by Homology-Based Modelling and Virtual Screening. Appl Biochem Biotechnol 2012; 168:1792-805. [DOI: 10.1007/s12010-012-9897-z] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2012] [Accepted: 09/04/2012] [Indexed: 02/05/2023]
|
39
|
Li DW, Brüschweiler R. Dynamic and Thermodynamic Signatures of Native and Non-Native Protein States with Application to the Improvement of Protein Structures. J Chem Theory Comput 2012; 8:2531-9. [PMID: 26588978 DOI: 10.1021/ct300358u] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Accurate knowledge of the 3D structural ensemble of proteins is important for understanding of their biological function. We report here the application of microsecond all-atom molecular dynamics (MD) simulations in explicit solvent for the improvement of the quality of low-resolution structures obtained by protein structure prediction (decoys). Seventy MD simulations of ∼1 μs average duration were performed on 13 different protein systems starting from X-ray crystal structures and decoys. Their behavior can be divided into three groups: 22 trajectories converged toward the native state, 27 trajectories displayed a quasi-equilibrium by populating mainly a single non-native free energy basin, and 21 trajectories drifted away from their initial decoy structure transiently visiting multiple free energy minima. To determine whether the native structure can be identified among non-native ensembles, the free energy was determined for each basin by the MM/GBSA method together with the von Mises entropy estimator in dihedral angle space. For the proteins studied here, it is found that the ensembles belonging to free energy basins with the lowest free energies and the longest residence times are most native-like. The results demonstrate that explicit solvent microsecond MD simulations using the latest generation of protein force fields and free energy metrics are sufficiently accurate to permit positive identification of native state ensembles against low-resolution structural models and decoys. The approach can be applied to the direct refinement of predicted or experimental low-resolution protein structures.
Collapse
Affiliation(s)
- Da-Wei Li
- Chemical Sciences Laboratory, Department of Chemistry and Biochemistry and National High Magnetic Field Laboratory, Florida State University, Tallahassee, Florida 32306, United States
| | - Rafael Brüschweiler
- Chemical Sciences Laboratory, Department of Chemistry and Biochemistry and National High Magnetic Field Laboratory, Florida State University, Tallahassee, Florida 32306, United States
| |
Collapse
|
40
|
Raval A, Piana S, Eastwood MP, Dror RO, Shaw DE. Refinement of protein structure homology models via long, all-atom molecular dynamics simulations. Proteins 2012; 80:2071-9. [PMID: 22513870 DOI: 10.1002/prot.24098] [Citation(s) in RCA: 183] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2012] [Revised: 04/03/2012] [Accepted: 04/11/2012] [Indexed: 11/07/2022]
Abstract
Accurate computational prediction of protein structure represents a longstanding challenge in molecular biology and structure-based drug design. Although homology modeling techniques are widely used to produce low-resolution models, refining these models to high resolution has proven difficult. With long enough simulations and sufficiently accurate force fields, molecular dynamics (MD) simulations should in principle allow such refinement, but efforts to refine homology models using MD have for the most part yielded disappointing results. It has thus far been unclear whether MD-based refinement is limited primarily by accessible simulation timescales, force field accuracy, or both. Here, we examine MD as a technique for homology model refinement using all-atom simulations, each at least 100 μs long-more than 100 times longer than previous refinement simulations-and a physics-based force field that was recently shown to successfully fold a structurally diverse set of fast-folding proteins. In MD simulations of 24 proteins chosen from the refinement category of recent Critical Assessment of Structure Prediction (CASP) experiments, we find that in most cases, simulations initiated from homology models drift away from the native structure. Comparison with simulations initiated from the native structure suggests that force field accuracy is the primary factor limiting MD-based refinement. This problem can be mitigated to some extent by restricting sampling to the neighborhood of the initial model, leading to structural improvement that, while limited, is roughly comparable to the leading alternative methods.
Collapse
Affiliation(s)
- Alpan Raval
- D E Shaw Research, New York, New York 10036, USA
| | | | | | | | | |
Collapse
|
41
|
Modeling of the major gas vesicle protein, GvpA: from protein sequence to vesicle wall structure. J Struct Biol 2012; 179:18-28. [PMID: 22580065 DOI: 10.1016/j.jsb.2012.04.015] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2011] [Revised: 03/15/2012] [Accepted: 04/24/2012] [Indexed: 11/23/2022]
Abstract
The structure and assembly process of gas vesicles have received significant attention in recent decades, although relatively little is still known. This work combines state-of-the-art computational methods to develop a model for the major gas vesicle protein, GvpA, and explore its structure within the assembled vesicle. Elucidating this protein's structure has been challenging due to its adherent and aggregative nature, which has so far precluded in-depth biochemical analyses. Moreover, GvpA has extremely low similarity with any known protein structure, which renders homology modeling methods ineffective. Thus, alternate approaches were used to model its tertiary structure. Starting with the sequence from haloarchaeon Halobacterium sp. NRC-1, we performed ab initio modeling and threading to acquire a multitude of structure decoys, which were equilibrated and ranked using molecular dynamics and mechanics, respectively. The highest ranked predictions exhibited an α-β-β-α secondary structure in agreement with earlier experimental findings, as well as with our own secondary structure predictions. Afterwards, GvpA subunits were docked in a quasi-periodic arrangement to investigate the assembly of the vesicle wall and to conduct simulations of contact-mode atomic force microscopy imaging, which allowed us to reconcile the structure predictions with the available experimental data. Finally, the GvpA structure for two representative organisms, Anabaena flos-aquae and Calothrix sp. PCC 7601, was also predicted, which reproduced the major features of our GvpA model, supporting the expectation that homologous GvpA sequences synthesized by different organisms should exhibit similar structures.
Collapse
|
42
|
Wang J, Hou T. Develop and test a solvent accessible surface area-based model in conformational entropy calculations. J Chem Inf Model 2012; 52:1199-212. [PMID: 22497310 DOI: 10.1021/ci300064d] [Citation(s) in RCA: 87] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
It is of great interest in modern drug design to accurately calculate the free energies of protein-ligand or nucleic acid-ligand binding. MM-PBSA (molecular mechanics Poisson-Boltzmann surface area) and MM-GBSA (molecular mechanics generalized Born surface area) have gained popularity in this field. For both methods, the conformational entropy, which is usually calculated through normal-mode analysis (NMA), is needed to calculate the absolute binding free energies. Unfortunately, NMA is computationally demanding and becomes a bottleneck of the MM-PB/GBSA-NMA methods. In this work, we have developed a fast approach to estimate the conformational entropy based upon solvent accessible surface area calculations. In our approach, the conformational entropy of a molecule, S, can be obtained by summing up the contributions of all atoms, no matter they are buried or exposed. Each atom has two types of surface areas, solvent accessible surface area (SAS) and buried SAS (BSAS). The two types of surface areas are weighted to estimate the contribution of an atom to S. Atoms having the same atom type share the same weight and a general parameter k is applied to balance the contributions of the two types of surface areas. This entropy model was parametrized using a large set of small molecules for which their conformational entropies were calculated at the B3LYP/6-31G* level taking the solvent effect into account. The weighted solvent accessible surface area (WSAS) model was extensively evaluated in three tests. For convenience, TS values, the product of temperature T and conformational entropy S, were calculated in those tests. T was always set to 298.15 K through the text. First of all, good correlations were achieved between WSAS TS and NMA TS for 44 protein or nucleic acid systems sampled with molecular dynamics simulations (10 snapshots were collected for postentropy calculations): the mean correlation coefficient squares (R²) was 0.56. As to the 20 complexes, the TS changes upon binding; TΔS values were also calculated, and the mean R² was 0.67 between NMA and WSAS. In the second test, TS values were calculated for 12 proteins decoy sets (each set has 31 conformations) generated by the Rosetta software package. Again, good correlations were achieved for all decoy sets: the mean, maximum, and minimum of R² were 0.73, 0.89, and 0.55, respectively. Finally, binding free energies were calculated for 6 protein systems (the numbers of inhibitors range from 4 to 18) using four scoring functions. Compared to the measured binding free energies, the mean R² of the six protein systems were 0.51, 0.47, 0.40, and 0.43 for MM-GBSA-WSAS, MM-GBSA-NMA, MM-PBSA-WSAS, and MM-PBSA-NMA, respectively. The mean rms errors of prediction were 1.19, 1.24, 1.41, 1.29 kcal/mol for the four scoring functions, correspondingly. Therefore, the two scoring functions employing WSAS achieved a comparable prediction performance to that of the scoring functions using NMA. It should be emphasized that no minimization was performed prior to the WSAS calculation in the last test. Although WSAS is not as rigorous as physical models such as quasi-harmonic analysis and thermodynamic integration (TI), it is computationally very efficient as only surface area calculation is involved and no structural minimization is required. Moreover, WSAS has achieved a comparable performance to normal-mode analysis. We expect that this model could find its applications in the fields like high throughput screening (HTS), molecular docking, and rational protein design. In those fields, efficiency is crucial since there are a large number of compounds, docking poses, or protein models to be evaluated. A list of acronyms and abbreviations used in this work is provided for quick reference.
Collapse
Affiliation(s)
- Junmei Wang
- Department of Biochemistry, The University of Texas Southwestern Medical Center , 5323 Harry Hines Blvd., Dallas, Texas 75390, USA.
| | | |
Collapse
|
43
|
Atomic-level protein structure refinement using fragment-guided molecular dynamics conformation sampling. Structure 2012; 19:1784-95. [PMID: 22153501 DOI: 10.1016/j.str.2011.09.022] [Citation(s) in RCA: 248] [Impact Index Per Article: 20.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2011] [Revised: 09/19/2011] [Accepted: 09/24/2011] [Indexed: 11/22/2022]
Abstract
One of critical difficulties of molecular dynamics (MD) simulations in protein structure refinement is that the physics-based energy landscape lacks a middle-range funnel to guide nonnative conformations toward near-native states. We propose to use the target model as a probe to identify fragmental analogs from PDB. The distance maps are then used to reshape the MD energy funnel. The protocol was tested on 181 benchmarking and 26 CASP targets. It was found that structure models of correct folds with TM-score >0.5 can be often pulled closer to native with higher GDT-HA score, but improvement for the models of incorrect folds (TM-score <0.5) are much less pronounced. These data indicate that template-based fragmental distance maps essentially reshaped the MD energy landscape from golf-course-like to funnel-like ones in the successfully refined targets with a radius of TM-score ∼0.5. These results demonstrate a new avenue to improve high-resolution structures by combining knowledge-based template information with physics-based MD simulations.
Collapse
|
44
|
Gront D, Kmiecik S, Blaszczyk M, Ekonomiuk D, Koliński A. Optimization of protein models. WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL MOLECULAR SCIENCE 2012. [DOI: 10.1002/wcms.1090] [Citation(s) in RCA: 23] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Affiliation(s)
- Dominik Gront
- Laboratory of Theory of Biopolymers, Faculty of Chemistry, University of Warsaw, Warsaw, Poland
| | - Sebastian Kmiecik
- Laboratory of Theory of Biopolymers, Faculty of Chemistry, University of Warsaw, Warsaw, Poland
| | - Maciej Blaszczyk
- Laboratory of Theory of Biopolymers, Faculty of Chemistry, University of Warsaw, Warsaw, Poland
| | - Dariusz Ekonomiuk
- Laboratory of Theory of Biopolymers, Faculty of Chemistry, University of Warsaw, Warsaw, Poland
| | - Andrzej Koliński
- Laboratory of Theory of Biopolymers, Faculty of Chemistry, University of Warsaw, Warsaw, Poland
| |
Collapse
|
45
|
De Ruvo M, Giuliani A, Paci P, Santoni D, Di Paola L. Shedding light on protein-ligand binding by graph theory: the topological nature of allostery. Biophys Chem 2012; 165-166:21-9. [PMID: 22464849 DOI: 10.1016/j.bpc.2012.03.001] [Citation(s) in RCA: 35] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2012] [Revised: 03/02/2012] [Accepted: 03/02/2012] [Indexed: 11/17/2022]
Abstract
Allostery is a very important feature of proteins; we propose a mesoscopic approach to allosteric mechanisms elucidation, based on protein contact matrices. The application of graph theory methods to the characterization of the allosteric process and, more broadly, to obtain the conformational changes upon binding, reveals key features of the protein function. The proposed method highlights the leading role played by topological over geometrical changes in allosteric transitions. Topological invariants were able to discriminate between true allosteric motions and generic protein motions upon binding.
Collapse
Affiliation(s)
- Micol De Ruvo
- Faculty of Engineering, Università CAMPUS BioMedico, Via A. del Portillo, 21, 00128 Roma, Italy
| | | | | | | | | |
Collapse
|
46
|
Park IH, Gangupomu V, Wagner J, Jain A, Vaidehi N. Structure refinement of protein low resolution models using the GNEIMO constrained dynamics method. J Phys Chem B 2012; 116:2365-75. [PMID: 22260550 PMCID: PMC3377353 DOI: 10.1021/jp209657n] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
The challenge in protein structure prediction using homology modeling is the lack of reliable methods to refine the low resolution homology models. Unconstrained all-atom molecular dynamics (MD) does not serve well for structure refinement due to its limited conformational search. We have developed and tested the constrained MD method, based on the generalized Newton-Euler inverse mass operator (GNEIMO) algorithm for protein structure refinement. In this method, the high-frequency degrees of freedom are replaced with hard holonomic constraints and a protein is modeled as a collection of rigid body clusters connected by flexible torsional hinges. This allows larger integration time steps and enhances the conformational search space. In this work, we have demonstrated the use of torsional GNEIMO method without using any experimental data as constraints, for protein structure refinement starting from low-resolution decoy sets derived from homology methods. In the eight proteins with three decoys for each, we observed an improvement of ~2 Å in the rmsd in coordinates to the known experimental structures of these proteins. The GNEIMO trajectories also showed enrichment in the population density of native-like conformations. In addition, we demonstrated structural refinement using a "freeze and thaw" clustering scheme with the GNEIMO framework as a viable tool for enhancing localized conformational search. We have derived a robust protocol based on the GNEIMO replica exchange method for protein structure refinement that can be readily extended to other proteins and possibly applicable for high throughput protein structure refinement.
Collapse
Affiliation(s)
- In-Hee Park
- Division of Immunology, Beckman Research Institute of the City of Hope, Duarte, California 91010, USA
| | | | | | | | | |
Collapse
|
47
|
Homeyer N, Gohlke H. Free Energy Calculations by the Molecular Mechanics Poisson−Boltzmann Surface Area Method. Mol Inform 2012; 31:114-22. [DOI: 10.1002/minf.201100135] [Citation(s) in RCA: 603] [Impact Index Per Article: 50.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2011] [Accepted: 11/26/2011] [Indexed: 11/06/2022]
|
48
|
Terashi G, Oosawa M, Nakamura Y, Kanou K, Takeda-Shitaka M. United3D: A Protein Model Quality Assessment Program That Uses Two Consensus Based Methods. Chem Pharm Bull (Tokyo) 2012; 60:1359-65. [DOI: 10.1248/cpb.c12-00287] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
|
49
|
Pritchard-Bell A, Shell MS. Smoothing protein energy landscapes by integrating folding models with structure prediction. Biophys J 2011; 101:2251-9. [PMID: 22067165 DOI: 10.1016/j.bpj.2011.09.036] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2011] [Revised: 09/13/2011] [Accepted: 09/19/2011] [Indexed: 10/15/2022] Open
Abstract
Decades of work has investigated the energy landscapes of simple protein models, but what do the landscapes of real, large, atomically detailed proteins look like? We explore an approach to this problem that systematically extracts simple funnel models of actual proteins using ensembles of structure predictions and physics-based atomic force fields and sampling. Central to our effort are calculations of a quantity called the relative entropy, which quantifies the extent to which a given set of structure decoys and a putative native structure can be projected onto a theoretical funnel description. We examine 86 structure prediction targets and one coupled folding-binding system, and find that in a majority of cases the relative entropy robustly signals which structures are nearest to native (i.e., which appear to lie closest to a funnel bottom). Importantly, the landscape model improves substantially upon purely energetic measures in scoring decoys. Our results suggest that physics-based models-including both folding theories and all-atom force fields-may be successfully integrated with structure prediction efforts. Conversely, detailed predictions of structures and the relative entropy approach enable one to extract coarse topographic features of protein landscapes that may enhance the development and application of simpler folding models.
Collapse
Affiliation(s)
- Ari Pritchard-Bell
- Department of Chemical Engineering, University of California Santa Barbara, Santa Barbara, California, USA
| | | |
Collapse
|
50
|
MacCallum JL, Pérez A, Schnieders MJ, Hua L, Jacobson MP, Dill KA. Assessment of protein structure refinement in CASP9. Proteins 2011; 79 Suppl 10:74-90. [PMID: 22069034 DOI: 10.1002/prot.23131] [Citation(s) in RCA: 78] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2011] [Revised: 06/15/2011] [Accepted: 07/03/2011] [Indexed: 11/06/2022]
Abstract
We assess performance in the structure refinement category in CASP9. Two years after CASP8, the performance of the best groups has not improved. There are few groups that improve any of our assessment scores with statistical significance. Some predictors, however, are able to consistently improve the physicality of the models. Although we cannot identify any clear bottleneck in improving refinement, several points arise: (1) The refinement portion of CASP has too few targets to make many statistically meaningful conclusions. (2) Predictors are usually very conservative, limiting the possibility of large improvements in models. (3) No group is actually able to correctly rank their five submissions-indicating that potentially better models may be discarded. (4) Different sampling strategies work better for different refinement problems; there is no single strategy that works on all targets. In general, conservative strategies do better, while the greatest improvements come from more adventurous sampling-at the cost of consistency. Comparison with experimental data reveals aspects not captured by comparison to a single structure. In particular, we show that improvement in backbone geometry does not always mean better agreement with experimental data. Finally, we demonstrate that even given the current challenges facing refinement, the refined models are useful for solving the crystallographic phase problem through molecular replacement. Proteins 2011;. © 2011 Wiley-Liss, Inc.
Collapse
Affiliation(s)
- Justin L MacCallum
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York, USA.
| | | | | | | | | | | |
Collapse
|