Ring Closure and Local Conformational Deformations of Chain Molecules
About: This article is published in Macromolecules.The article was published on 1970-03-01. It has received 414 citations till now. The article focuses on the topics: Ring (chemistry) & Closure (topology).
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TL;DR: This review presents a brief introduction of the available molecular docking methods, and their development and applications in drug discovery, and a recently developed local move Monte Carlo based approach is introduced.
Abstract: Molecular docking has become an increasingly important tool for drug discovery. In this review, we present a brief introduction of the available molecular docking methods, and their development and applications in drug discovery. The relevant basic theories, including sampling algorithms and scoring functions, are summarized. The differences in and performance of available docking software are also discussed. Flexible receptor molecular docking approaches, especially those including backbone flexibility in receptors, are a challenge for available docking methods. A recently developed Local Move Monte Carlo (LMMC) based approach is introduced as a potential solution to flexible receptor docking problems. Three application examples of molecular docking approaches for drug discovery are provided.
1,787 citations
Cites background from "Ring Closure and Local Conformation..."
...The pioneering work on local move was done by Go and Scheraga [119], who developed a solution for the system of equations defining the values of the six torsion angles that preserve the backbone bond lengths and angles....
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TL;DR: The overall results are the best reported to date, and the combination of an accurate all‐atom energy function, efficient methods for loop buildup and side‐chain optimization, and, especially for the longer loops, the hierarchical refinement protocol is attributed.
Abstract: The application of all-atom force fields (and explicit or implicit solvent models) to protein homology-modeling tasks such as side-chain and loop prediction remains challenging both because of the expense of the individual energy calculations and because of the difficulty of sampling the rugged all-atom energy surface. Here we address this challenge for the problem of loop prediction through the development of numerous new algorithms, with an emphasis on multiscale and hierarchical techniques. As a first step in evaluating the performance of our loop prediction algorithm, we have applied it to the problem of reconstructing loops in native structures; we also explicitly include crystal packing to provide a fair comparison with crystal structures. In brief, large numbers of loops are generated by using a dihedral angle-based buildup procedure followed by iterative cycles of clustering, side-chain optimization, and complete energy minimization of selected loop structures. We evaluate this method by using the largest test set yet used for validation of a loop prediction method, with a total of 833 loops ranging from 4 to 12 residues in length. Average/median backbone root-mean-square deviations (RMSDs) to the native structures (superimposing the body of the protein, not the loop itself) are 0.42/0.24 A for 5 residue loops, 1.00/0.44 A for 8 residue loops, and 2.47/1.83 A for 11 residue loops. Median RMSDs are substantially lower than the averages because of a small number of outliers; the causes of these failures are examined in some detail, and many can be attributed to errors in assignment of protonation states of titratable residues, omission of ligands from the simulation, and, in a few cases, probable errors in the experimentally determined structures. When these obvious problems in the data sets are filtered out, average RMSDs to the native structures improve to 0.43 A for 5 residue loops, 0.84 A for 8 residue loops, and 1.63 A for 11 residue loops. In the vast majority of cases, the method locates energy minima that are lower than or equal to that of the minimized native loop, thus indicating that sampling rarely limits prediction accuracy. The overall results are, to our knowledge, the best reported to date, and we attribute this success to the combination of an accurate all-atom energy function, efficient methods for loop buildup and side-chain optimization, and, especially for the longer loops, the hierarchical refinement protocol.
1,774 citations
TL;DR: It is concluded that the most promising detailed approach to the protein‐folding problem would consist of some coarse global sampling strategy combined with the local energy minimization in the torsion coordinate space.
Abstract: An efficient methodology, further referred to as ICM, for versatile modeling operations and global energy optimization on arbitrarily fixed multimolecular systems is described. It is aimed at protein structure prediction, homology modeling, molecular docking, nuclear magnetic resonance (NMR) structure determination, and protein design. The method uses and further develops a previously introduced approach to model biomolecular structures in which bond lengths, bond angles, and torsion angles are considered as independent variables, any subset of them being fixed. Here we simplify and generalize the basic description of the system, introduce the variable dihedral phase angle, and allow arbitrary connections of the molecules and conventional definition of the torsion angles. Algorithms for calculation of energy derivatives with respect to internal variables in the topological tree of the system and for rapid evaluation of accessible surface are presented. Multidimensional variable restraints are proposed to represent the statistical information about the torsion angle distributions in proteins. To incorporate complex energy terms as solvation energy and electrostatics into a structure prediction procedure, a “double-energy” Monte Carlo minimization procedure in which these terms are omitted during the minimization stage of the random step and included for the comparison with the previous conformation in a Markov chain is proposed and justified. The ICM method is applied successfully to a molecular docking problem. The procedure finds the correct parallel arrangement of two rigid helixes from a leucine zipper domain as the lowest-energy conformation (0.5 A root mean square, rms, deviation from the native structure) starting from completely random configuration. Structures with antiparallel helixes or helixes staggered by one helix turn had energies higher by about 7 or 9 kcal/mol, respectively. Soft docking was also attempted. A docking procedure allowing side-chain flexibility also converged to the parallel configuration starting from the helixes optimized individually. To justdy an internal coordinate approach to the structure prediction as opposed to a Cartesian one, energy hypersurfaces around the native structure of the squash seeds trypsin inhibitor were studied. Torsion angle minimization from the optimal conformation randomly distorted up to the rms deviation of 2.2 A or angular rms deviation of l0° restored the native conformation in most cases. In contrast, Cartesian coordinate minimization did not reach the minimum from deviations as small as 0.3 A or 2°. We conclude that the most promising detailed approach to the protein-folding problem would consist of some coarse global sampling strategy combined with the local energy minimization in the torsion coordinate space. © 1994 by John Wiley & Sons, Inc.
1,569 citations
TL;DR: This work has shown that sub-angstrom accuracy in protein loop reconstruction by robotics-inspired conformational sampling can be improved by incorporating carrier error correction into the sampling procedure.
Abstract: Sub-angstrom accuracy in protein loop reconstruction by robotics-inspired conformational sampling
427 citations
TL;DR: A procedure, CONGEN, for uniformly sampling the conformational space of short polypeptide segments in proteins has been implemented and is generally capable of generating conformations where the lowest energy conformation matches the known structure within a rms deviation of 1 Å.
Abstract: A procedure, CONGEN, for uniformly sampling the conformational spaceof short polypeptide segments in proteins has been implemented. Because thetime required for this sampling grows exponentially with the number of residues, parameters are introduced to limit the conformational space that has to be explored. This is done by the use of the empirical energy function ofCHARMM [B. R. Brooks, R. E. Bruccoleri, B. D. Olafson, D. J. States, S. Swaminathan and M. Karplus (1983) J. Comput. Chem.4, 187-217] and truncating the search when conformations of grossly unfavorable energy are sampled. Tests are made to determine control parameters that optimize the search without excluding important configurations. When applied to known protein structures, the resulting procedure is generally capable of generating conformations where the lowest energy conformation matches the known structure within a rms deviation of 1 A.
406 citations