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Rackers JA, Wang Z, Lu C, Laury ML, Lagardère L, Schnieders MJ, Piquemal JP, Ren P, Ponder JW. Tinker 8: Software Tools for Molecular Design. J Chem Theory Comput 2018; 14:5273-5289. [PMID: 30176213 PMCID: PMC6335969 DOI: 10.1021/acs.jctc.8b00529] [Citation(s) in RCA: 301] [Impact Index Per Article: 50.2] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
The Tinker software, currently released as version 8, is a modular molecular mechanics and dynamics package written primarily in a standard, easily portable dialect of Fortran 95 with OpenMP extensions. It supports a wide variety of force fields, including polarizable models such as the Atomic Multipole Optimized Energetics for Biomolecular Applications (AMOEBA) force field. The package runs on Linux, macOS, and Windows systems. In addition to canonical Tinker, there are branches, Tinker-HP and Tinker-OpenMM, designed for use on message passing interface (MPI) parallel distributed memory supercomputers and state-of-the-art graphical processing units (GPUs), respectively. The Tinker suite also includes a tightly integrated Java-based graphical user interface called Force Field Explorer (FFE), which provides molecular visualization capabilities as well as the ability to launch and control Tinker calculations.
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Affiliation(s)
- Joshua A. Rackers
- Program in Computational & Molecular Biophysics, Washington University School of Medicine, Saint Louis, Missouri 63110, United States
| | - Zhi Wang
- Department of Chemistry, Washington University in Saint Louis, Saint Louis, Missouri 63130, United States
| | - Chao Lu
- Department of Chemistry, Washington University in Saint Louis, Saint Louis, Missouri 63130, United States
| | - Marie L. Laury
- Department of Chemistry, Washington University in Saint Louis, Saint Louis, Missouri 63130, United States
| | - Louis Lagardère
- Laboratoire de Chimie Théorique, Sorbonne Universités, UPMC Paris 06, UMR 7616, case courrier 137, 4 place Jussieu, F-75005, Paris, France
| | - Michael J. Schnieders
- Department of Biomedical Engineering, The University of Iowa, Iowa City, IA 52242, United States
| | - Jean-Philip Piquemal
- Laboratoire de Chimie Théorique, Sorbonne Universités, UPMC Paris 06, UMR 7616, case courrier 137, 4 place Jussieu, F-75005, Paris, France
| | - Pengyu Ren
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, Texas 78712, United States
| | - Jay W. Ponder
- Program in Computational & Molecular Biophysics, Washington University School of Medicine, Saint Louis, Missouri 63110, United States
- Department of Chemistry, Washington University in Saint Louis, Saint Louis, Missouri 63130, United States
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Three-dimensional protein structure prediction: Methods and computational strategies. Comput Biol Chem 2014; 53PB:251-276. [DOI: 10.1016/j.compbiolchem.2014.10.001] [Citation(s) in RCA: 121] [Impact Index Per Article: 12.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2014] [Revised: 10/03/2014] [Accepted: 10/07/2014] [Indexed: 01/01/2023]
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3
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Agrafiotis DK, Xu H, Zhu F, Bandyopadhyay D, Liu P. Stochastic Proximity Embedding: Methods and Applications. Mol Inform 2010; 29:758-70. [DOI: 10.1002/minf.201000134] [Citation(s) in RCA: 26] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2010] [Accepted: 10/28/2010] [Indexed: 11/07/2022]
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Narang P, Bhushan K, Bose S, Jayaram B. A computational pathway for bracketing native-like structures fo small alpha helical globular proteins. Phys Chem Chem Phys 2009; 7:2364-75. [PMID: 19785123 DOI: 10.1039/b502226f] [Citation(s) in RCA: 48] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Impressive advances in the applications of bioinformatics for protein structure prediction coupled with growing structural databases on one hand and the insurmountable time-scale problem with ab initio computational methods on the other continue to raise doubts whether a computational solution to the protein folding problem--categorized as an NP-hard problem--is within reach in the near future. Combining some specially designed biophysical filters and vector algebra tools with ab initio methods, we present here a promising computational pathway for bracketing native-like structures of small alpha helical globular proteins departing from secondary structural information. The automated protocol is initiated by generating multiple structures around the loops between secondary structural elements. A set of knowledge-based biophysical filters namely persistence length and radius of gyration, developed and calibrated on approximately 1000 globular proteins, is introduced to screen the trial structures to filter out improbable candidates for the native and reduce the size of the library of probable structures. The ensemble so generated encompasses a few structures with native-like topology. Monte Carlo optimizations of the loop dihedrals are then carried out to remove steric clashes. The resultant structures are energy minimized and ranked according to a scoring function tested previously on a series of decoy sets vis-a-vis their corresponding natives. We find that the 100 lowest energy structures culled from the ensemble of energy optimized trial structures comprise at least a few to within 3-5 angstroms of the native. Thus the formidable "needle in a haystack" problem is narrowed down to finding an optimal solution amongst a computationally tractable number of alternatives. Encouraging results obtained on twelve small alpha helical globular proteins with the above outlined pathway are presented and discussed.
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Affiliation(s)
- Pooja Narang
- Department of Chemistry, Indian Institute of Technology, Hauz Khas, New Delhi 110016, India
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5
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Liu T, Horst JA, Samudrala R. A novel method for predicting and using distance constraints of high accuracy for refining protein structure prediction. Proteins 2009; 77:220-34. [PMID: 19422061 DOI: 10.1002/prot.22434] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
The principal bottleneck in protein structure prediction is the refinement of models from lower accuracies to the resolution observed by experiment. We developed a novel constraints-based refinement method that identifies a high number of accurate input constraints from initial models and rebuilds them using restrained torsion angle dynamics (rTAD). We previously created a Bayesian statistics-based residue-specific all-atom probability discriminatory function (RAPDF) to discriminate native-like models by measuring the probability of accuracy for atom type distances within a given model. Here, we exploit RAPDF to score (i.e., filter) constraints from initial predictions that may or may not be close to a native-like state, obtain consensus of top scoring constraints amongst five initial models, and compile sets with no redundant residue pair constraints. We find that this method consistently produces a large and highly accurate set of distance constraints from which to build refinement models. We further optimize the balance between accuracy and coverage of constraints by producing multiple structure sets using different constraint distance cutoffs, and note that the cutoff governs spatially near versus distant effects in model generation. This complete procedure of deriving distance constraints for rTAD simulations improves the quality of initial predictions significantly in all cases evaluated by us. Our procedure represents a significant step in solving the protein structure prediction and refinement problem, by enabling the use of consensus constraints, RAPDF, and rTAD for protein structure modeling and refinement.
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Affiliation(s)
- Tianyun Liu
- Department of Genetics, Stanford University, Stanford, California, USA
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6
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Dukka BKC. Improving consensus structure by eliminating averaging artifacts. BMC STRUCTURAL BIOLOGY 2009; 9:12. [PMID: 19267905 PMCID: PMC2662860 DOI: 10.1186/1472-6807-9-12] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/03/2008] [Accepted: 03/06/2009] [Indexed: 11/29/2022]
Abstract
Background Common structural biology methods (i.e., NMR and molecular dynamics) often produce ensembles of molecular structures. Consequently, averaging of 3D coordinates of molecular structures (proteins and RNA) is a frequent approach to obtain a consensus structure that is representative of the ensemble. However, when the structures are averaged, artifacts can result in unrealistic local geometries, including unphysical bond lengths and angles. Results Herein, we describe a method to derive representative structures while limiting the number of artifacts. Our approach is based on a Monte Carlo simulation technique that drives a starting structure (an extended or a 'close-by' structure) towards the 'averaged structure' using a harmonic pseudo energy function. To assess the performance of the algorithm, we applied our approach to Cα models of 1364 proteins generated by the TASSER structure prediction algorithm. The average RMSD of the refined model from the native structure for the set becomes worse by a mere 0.08 Å compared to the average RMSD of the averaged structures from the native structure (3.28 Å for refined structures and 3.36 A for the averaged structures). However, the percentage of atoms involved in clashes is greatly reduced (from 63% to 1%); in fact, the majority of the refined proteins had zero clashes. Moreover, a small number (38) of refined structures resulted in lower RMSD to the native protein versus the averaged structure. Finally, compared to PULCHRA [1], our approach produces representative structure of similar RMSD quality, but with much fewer clashes. Conclusion The benchmarking results demonstrate that our approach for removing averaging artifacts can be very beneficial for the structural biology community. Furthermore, the same approach can be applied to almost any problem where averaging of 3D coordinates is performed. Namely, structure averaging is also commonly performed in RNA secondary prediction [2], which could also benefit from our approach.
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Affiliation(s)
- B K C Dukka
- Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, Charlotte, NC, USA.
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Ganguly P, Desiraju GR. Van der waals and polar intermolecular contact distances: quantifying supramolecular synthons. Chem Asian J 2008; 3:868-80. [PMID: 18386268 DOI: 10.1002/asia.200700343] [Citation(s) in RCA: 41] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Crystal structures are viewed as being determined by ranges and constraints on interatomic contact distances between neighboring molecules. These distances are considered to arise from environment-dependent atomic sizes, that is, larger sizes for isotropic, van der Waals type contacts and smaller sizes for more-polar, possibly ionic contacts. Although the idea of different, or anisotropic, radii for atoms is not new, we developed a method of obtaining atomic sizes that is based on a theoretical framework. Using different atomic sizes for the same atom in different environments, we were able to rationalize some structural observations and anomalies. For example, benzene with the Pbca structure may be described in terms of two types of CH interactions: a longer contact largely of the van der Waals type, and a shorter, structure-determining type (C(delta-)H(delta+)), which we term "n-polar". Our approach is illustrated with three examples: 1) the equivalence in crystal packing of fluorobenzene, benzonitrile, pyridine N-oxide, and pyridine/HF 1:1 molecular complex, all of which take the not-so-common tetragonal P4(1)2(1)2 space group and are practically isomorphous; 2) the similarity of the Pa3 acetylene and Pbca benzene crystal structures; and 3) the equivalence between an increase in pressure and an increase in the "n-polar" contacts in Pbca benzene; in other words, the equivalence between hydrostatic pressure and chemical pressure. In the context of crystal engineering, we describe a method whereby the topological information conveyed in a supramolecular synthon is recast in a more quantitative manner. A particular synthon, and in turn the crystal structure to which it leads, is viable within small ranges of distances of its constituent atoms, and these distances are determined by chemical factors.
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Izrailev S, Zhu F, Agrafiotis DK. A distance geometry heuristic for expanding the range of geometries sampled during conformational search. J Comput Chem 2006; 27:1962-9. [PMID: 17031897 DOI: 10.1002/jcc.20506] [Citation(s) in RCA: 34] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
A recent study of crystal structures of protein-ligand complexes has shown that bioactive conformations tend to be more extended than random ones (Diller and Merz, J. Comput. Aid. Mol. Des. 2002, 16, 105-112). Existing conformational sampling techniques produce molecular conformations with a distribution of geometric sizes that may not cover that of the bioactive conformations. Here, we describe a simple heuristic for biasing the conformational search toward more extended or compact conformations, while maintaining excellent sampling. The method uses a boosting strategy to generate a series of conformations, each of which is at least as extended (or compact) as the previous one. We demonstrate that this method significantly expands the range of geometric sizes generated during the search and thus increases the efficiency of sampling bioactive conformations.
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Affiliation(s)
- Sergei Izrailev
- Johnson & Johnson Pharmaceutical Research and Development, L.L.C, 665 Stockton Drive, Exton, Pennsylvania 19341, USA
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Li X, Jacobson MP, Friesner RA. High-resolution prediction of protein helix positions and orientations. Proteins 2004; 55:368-82. [PMID: 15048828 DOI: 10.1002/prot.20014] [Citation(s) in RCA: 50] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
We have developed a new method for predicting helix positions in globular proteins that is intended primarily for comparative modeling and other applications where high precision is required. Unlike helix packing algorithms designed for ab initio folding, we assume that knowledge is available about the qualitative placement of all helices. However, even among homologous proteins, the corresponding helices can demonstrate substantial differences in positions and orientations, and for this reason, improperly positioned helices can contribute significantly to the overall backbone root-mean-square deviation (RMSD) of comparative models. A helix packing algorithm for use in comparative modeling must obtain high precision to be useful, and for this reason we utilize an all-atom protein force field (OPLS) and a Generalized Born continuum solvent model. To reduce the computational expense associated with using a detailed, physics-based energy function, we have developed new hierarchical and multiscale algorithms for sampling the helices and flanking loops. We validate the method using a test suite of 33 cases, which are drawn from a diverse set of high-resolution crystal structures. The helix positions are reproduced with an average backbone RMSD of 0.6 A, while the average backbone RMSD of the complete loop-helix-loop region (i.e., the helix with the surrounding loops, which are also repredicted) is 1.3 A.
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Affiliation(s)
- Xin Li
- Department of Chemistry, Columbia University, New York, New York, USA
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Zhu J, Zhu Q, Shi Y, Liu H. How well can we predict native contacts in proteins based on decoy structures and their energies? Proteins 2003; 52:598-608. [PMID: 12910459 DOI: 10.1002/prot.10444] [Citation(s) in RCA: 22] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
Abstract
One strategy for ab initio protein structure prediction is to generate a large number of possible structures (decoys) and select the most fitting ones based on a scoring or free energy function. The conformational space of a protein is huge, and chances are rare that any heuristically generated structure will directly fall in the neighborhood of the native structure. It is desirable that, instead of being thrown away, the unfitting decoy structures can provide insights into native structures so prediction can be made progressively. First, we demonstrate that a recently parameterized physics-based effective free energy function based on the GROMOS96 force field and a generalized Born/surface area solvent model is, as several other physics-based and knowledge-based models, capable of distinguishing native structures from decoy structures for a number of widely used decoy databases. Second, we observe a substantial increase in correlations of the effective free energies with the degree of similarity between the decoys and the native structure, if the similarity is measured by the content of native inter-residue contacts in a decoy structure rather than its root-mean-square deviation from the native structure. Finally, we investigate the possibility of predicting native contacts based on the frequency of occurrence of contacts in decoy structures. For most proteins contained in the decoy databases, a meaningful amount of native contacts can be predicted based on plain frequencies of occurrence at a relatively high level of accuracy. Relative to using plain frequencies, overwhelming improvements in sensitivity of the predictions are observed for the 4_state_reduced decoy sets by applying energy-dependent weighting of decoy structures in determining the frequency. There, approximately 80% native contacts can be predicted at an accuracy of approximately 80% using energy-weighted frequencies. The sensitivity of the plain frequency approach is much lower (20% to 40%). Such improvements are, however, not observed for the other decoy databases. The rationalization and implications of the results are discussed.
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Affiliation(s)
- Jiang Zhu
- Key Laboratory of Structural Biology, University of Science and Technology of China, Chinese Academy of Sciences, School of Life Sciences, Hefei, Anhui, 230026, China.
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Xu H, Izrailev S, Agrafiotis DK. Conformational sampling by self-organization. JOURNAL OF CHEMICAL INFORMATION AND COMPUTER SCIENCES 2003; 43:1186-91. [PMID: 12870910 DOI: 10.1021/ci0340557] [Citation(s) in RCA: 30] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
A new stochastic algorithm for conformational sampling is described. The algorithm generates molecular conformations that are consistent with a set of geometric constraints, which include interatomic distance bounds and chiral volumes derived from the molecular connectivity table. The algorithm repeatedly selects individual geometric constraints at random and updates the respective atomic coordinates toward satisfying the chosen constraint. When compared to a conventional distance geometry algorithm based on the same set of geometric constraints, our method is faster and generates conformations that are more diverse and more energetically favorable.
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Affiliation(s)
- Huafeng Xu
- 3-Dimensional Pharmaceuticals, Inc., 665 Stockton Drive, Exton, Pennsylvania 19341, USA
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12
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Zagrovic B, Snow CD, Khaliq S, Shirts MR, Pande VS. Native-like mean structure in the unfolded ensemble of small proteins. J Mol Biol 2002; 323:153-64. [PMID: 12368107 DOI: 10.1016/s0022-2836(02)00888-4] [Citation(s) in RCA: 144] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
Abstract
The nature of the unfolded state plays a great role in our understanding of proteins. However, accurately studying the unfolded state with computer simulation is difficult, due to its complexity and the great deal of sampling required. Using a supercluster of over 10,000 processors we have performed close to 800 micros of molecular dynamics simulation in atomistic detail of the folded and unfolded states of three polypeptides from a range of structural classes: the all-alpha villin headpiece molecule, the beta hairpin tryptophan zipper, and a designed alpha-beta zinc finger mimic. A comparison between the folded and the unfolded ensembles reveals that, even though virtually none of the individual members of the unfolded ensemble exhibits native-like features, the mean unfolded structure (averaged over the entire unfolded ensemble) has a native-like geometry. This suggests several novel implications for protein folding and structure prediction as well as new interpretations for experiments which find structure in ensemble-averaged measurements.
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Affiliation(s)
- Bojan Zagrovic
- Biophysics Program, Stanford University, Stanford, CA 94305-5080, USA
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Betancourt MR, Skolnick J. Finding the needle in a haystack: educing native folds from ambiguousab initio protein structure predictions. J Comput Chem 2001. [DOI: 10.1002/1096-987x(200102)22:3<339::aid-jcc1006>3.0.co;2-r] [Citation(s) in RCA: 60] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
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Samudrala R, Huang ES, Koehl P, Levitt M. Constructing side chains on near-native main chains for ab initio protein structure prediction. PROTEIN ENGINEERING 2000; 13:453-7. [PMID: 10906341 DOI: 10.1093/protein/13.7.453] [Citation(s) in RCA: 27] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
Is there value in constructing side chains while searching protein conformational space during an ab initio simulation? If so, what is the most computationally efficient method for constructing these side chains? To answer these questions, four published approaches were used to construct side chain conformations on a range of near-native main chains generated by ab initio protein structure prediction methods. The accuracy of these approaches was compared with a naive approach that selects the most frequently observed rotamer for a given amino acid to construct side chains. An all-atom conditional probability discriminatory function is useful at selecting conformations with overall low all-atom root mean square deviation (r.m.s.d.) and the discrimination improves on sets that are closer to the native conformation. In addition, the naive approach performs as well as more sophisticated methods in terms of the percentage of chi(1) angles built accurately and the all-atom r. m.s.d., between the native and near-native conformations. The results suggest that the naive method would be extremely useful for fast and efficient side chain construction on vast numbers of conformations for ab initio prediction of protein structure.
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Affiliation(s)
- R Samudrala
- Department of Structural Biology, Stanford University School of Medicine, Stanford, CA 94305, USA.
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15
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Xia Y, Huang ES, Levitt M, Samudrala R. Ab initio construction of protein tertiary structures using a hierarchical approach. J Mol Biol 2000; 300:171-85. [PMID: 10864507 DOI: 10.1006/jmbi.2000.3835] [Citation(s) in RCA: 141] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
We present a hierarchical method to predict protein tertiary structure models from sequence. We start with complete enumeration of conformations using a simple tetrahedral lattice model. We then build conformations with increasing detail, and at each step select a subset of conformations using empirical energy functions with increasing complexity. After enumeration on lattice, we select a subset of low energy conformations using a statistical residue-residue contact energy function, and generate all-atom models using predicted secondary structure. A combined knowledge-based atomic level energy function is then used to select subsets of the all-atom models. The final predictions are generated using a consensus distance geometry procedure. We test the feasibility of the procedure on a set of 12 small proteins covering a wide range of protein topologies. A rigorous double-blind test of our method was made under the auspices of the CASP3 experiment, where we did ab initio structure predictions for 12 proteins using this approach. The performance of our methodology at CASP3 is reasonably good and completely consistent with our initial tests.
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Affiliation(s)
- Y Xia
- Department of Structural Biology, Stanford University School of Medicine, Stanford, CA, 94305, USA
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Huang ES, Samudrala R, Ponder JW. Ab initio fold prediction of small helical proteins using distance geometry and knowledge-based scoring functions. J Mol Biol 1999; 290:267-81. [PMID: 10388572 DOI: 10.1006/jmbi.1999.2861] [Citation(s) in RCA: 67] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
The problem of protein tertiary structure prediction from primary sequence can be separated into two subproblems: generation of a library of possible folds and specification of a best fold given the library. A distance geometry procedure based on random pairwise metrization with good sampling properties was used to generate a library of 500 possible structures for each of 11 small helical proteins. The input to distance geometry consisted of sets of restraints to enforce predicted helical secondary structure and a generic range of 5 to 11 A between predicted contact residues on all pairs of helices. For each of the 11 targets, the resulting library contained structures with low RMSD versus the native structure. Near-native sampling was enhanced by at least three orders of magnitude compared to a random sampling of compact folds. All library members were scored with a combination of an all-atom distance-dependent function, a residue pair-potential, and a hydrophobicity function. In six of the 11 cases, the best-ranking fold was considered to be near native. Each library was also reduced to a final ab initio prediction via consensus distance geometry performed over the 50 best-ranking structures from the full set of 500. The consensus results were of generally higher quality, yielding six predictions within 6.5 A of the native fold. These favorable predictions corresponded to those for which the correlation between the RMSD and the scoring function were highest. The advantage of the reported methodology is its extreme simplicity and potential for including other types of structural restraints.
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Affiliation(s)
- E S Huang
- Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, Saint Louis, MO, 63110, USA
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Orengo C, Bray J, Hubbard T, LoConte L, Sillitoe I. Analysis and assessment of ab initio three-dimensional prediction, secondary structure, and contacts prediction. Proteins 1999. [DOI: 10.1002/(sici)1097-0134(1999)37:3+<149::aid-prot20>3.0.co;2-h] [Citation(s) in RCA: 85] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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