Improved modeling of side‐chains in proteins with rotamer‐based methods: A flexible rotamer model

Mendes, Jorge M.; Baptista, Antoniò M.; Carrondo, M.A.; Soares, Cláudio M.

doi:10.1002/(sici)1097-0134(19991201)37:4<530::aid-prot4>3.3.co;2-8

Cited by 28 publications

(67 citation statements)

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“…Twenty-five of the 30 carefully selected proteins, which could be operated by all programs, were calculated and averaged. The computational results of Holm and Sander (1991), SCWRL (Bower et al 1997), Mendes et al (1999), and this work were equally evaluated as described earlier. The computing time was counted on a Silicon graphic 400MHZ IP30 processor.…”

Section: Modeling the Side Chains For A Whole Proteinmentioning

confidence: 52%

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Side‐chain modeling with an optimized scoring function

Liang

Grishin²

2002

Protein Science

118

108

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Modeling side-chain conformations on a fixed protein backbone has a wide application in structure prediction and molecular design. Each effort in this field requires decisions about a rotamer set, scoring function, and search strategy. We have developed a new and simple scoring function, which operates on side-chain rotamers and consists of the following energy terms: contact surface, volume overlap, backbone dependency, electrostatic interactions, and desolvation energy. The weights of these energy terms were optimized to achieve the minimal average root mean square (rms) deviation between the lowest energy rotamer and real side-chain conformation on a training set of high-resolution protein structures. In the course of optimization, for every residue, its side chain was replaced by varying rotamers, whereas conformations for all other residues were kept as they appeared in the crystal structure. We obtained prediction accuracy of 90.4% for 1 , 78.3% for 1 + 2 , and 1.18 Å overall rms deviation. Furthermore, the derived scoring function combined with a Monte Carlo search algorithm was used to place all side chains onto a protein backbone simultaneously. The average prediction accuracy was 87.9% for 1 , 73.2% for 1 + 2 , and 1.34 Å rms deviation for 30 protein structures. Our approach was compared with available side-chain construction methods and showed improvement over the best among them: 4.4% for 1 , 4.7% for 1 + 2 , and 0.21 Å for rms deviation. We hypothesize that the scoring function instead of the search strategy is the main obstacle in side-chain modeling. Additionally, we show that a more detailed rotamer library is expected to increase 1 + 2 prediction accuracy but may have little effect on 1 prediction accuracy.

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Section: Modeling the Side Chains For A Whole Proteinmentioning

confidence: 52%

“…We compared our program with the torso program from the MAXSPROUT package (Holm and Sander 1991), SCWRL2.2 (Bower et al 1997), and that of Mendes et al (1999). Like our method, torso was based on the Monte Carlo algorithm.…”

Section: Modeling the Side Chains For A Whole Proteinmentioning

confidence: 99%

Side‐chain modeling with an optimized scoring function

Liang

Grishin²

2002

Protein Science

118

108

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“…Indeed, the current rotamer library's best possible RMSD for the tested proteins (found by positioning the rotamer that is closest to the x-ray structure) is between 0.94 Å to 1.52 Å. A way to overcome the search-space limitation was suggested by Mendes et al (52); it presents a rotamer as a continuous ensemble of conformations that cluster around the classic rigid rotamer. A different approach to expanding the search space was recently devised by Honig and coworkers (19), which achieved accurate predictions by using an extensive rotamer library containing over 7,560 members, in which bond lengths and bond angles were taken from the database rather than simply assuming idealized values.…”

Section: Discussionmentioning

confidence: 99%

A stochastic algorithm for global optimization and for best populations: A test case of side chains in proteins

Glick

Rayan

Goldblum

2002

Proc. Natl. Acad. Sci. U.S.A.

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The problem of global optimization is pivotal in a variety of scientific fields. Here, we present a robust stochastic search method that is able to find the global minimum for a given cost function, as well as, in most cases, any number of best solutions for very large combinatorial ''explosive'' systems. The algorithm iteratively eliminates variable values that contribute consistently to the highest end of a cost function's spectrum of values for the full system. Values that have not been eliminated are retained for a full, exhaustive search, allowing the creation of an ordered population of best solutions, which includes the global minimum. We demonstrate the ability of the algorithm to explore the conformational space of side chains in eight proteins, with 54 to 263 residues, to reproduce a population of their low energy conformations. The 1,000 lowest energy solutions are identical in the stochastic (with two different seed numbers) and full, exhaustive searches for six of eight proteins. The others retain the lowest 141 and 213 (of 1,000) conformations, depending on the seed number, and the maximal difference between stochastic and exhaustive is only about 0.15 Kcal͞mol. The energy gap between the lowest and highest of the 1,000 low-energy conformers in eight proteins is between 0.55 and 3.64 Kcal͞mol. This algorithm offers real opportunities for solving problems of high complexity in structural biology and in other fields of science and technology.M any problems in life sciences and in other fields of science and technology are of high complexity, thus requiring sophisticated methods of searching and scoring to achieve the ability to study and to simulate them by means of a computer simulation. An excellent search method coupled with a highly reliable scoring method should allow comparisons to some natural phenomena. In this article, we have taken the approach of comparing best populations found by a stochastic search method to a full, exhaustive search, as the crucial test of this method. However, comparisons to experimental results also are included. The problem chosen to exemplify this method is the positions of side chains in proteins, which is essential for both theoretical and experimental purposes. On the theoretical side, it is a subproblem in de novo protein structure prediction. It is essential for structure-based drug design (1), for inverse folding and threading algorithms (2), for predicting the effect of mutations on structure (3), for ab initio predictions of tertiary structure (4), for homology-based modeling (5), and others. From the x-ray crystallographer's point of view, it could speed the placement of side chains using the electron density maps of the main chain before refinement calculations. The main limitation is the large amount of possible conformations that each side chain may adopt (6). An exhaustive search of all possible conformations is beyond the scope of state of the art computers.Current strategies for side chain addition to a given backbone differ in three categories. The ...

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“…Several representations of side-chain flexibility incorporating multiple higher-energy conformations have been used in prediction and design [20][21][22][23] . However, the predictions resulting from models commonly used in protein design simulations have not been directly compared to experimental data on the amplitude of side-chain motion.…”

Section: Introductionmentioning

confidence: 99%

A Simple Model of Backbone Flexibility Improves Modeling of Side-chain Conformational Variability

Friedland

Linares

Smith

et al. 2008

Journal of Molecular Biology

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The considerable flexibility of side-chains in folded proteins is important for protein stability and function, and may play a role in mediating pathways of energetic connectivity between allosteric sites. While sampling side-chain degrees of freedom has been an integral part of several successful computational protein design methods, the predictions of these approaches have not been directly compared to experimental measurements of side-chain motional amplitudes. In addition, protein design methods generally keep the backbone fixed, an approximation that may substantially limit the ability to accurately model side-chain flexibility. Here we describe a Monte Carlo approach to modeling side-chain conformational variability and validate our method against a large dataset of methyl relaxation order parameters derived from Nuclear Magnetic Resonance experiments (17 proteins and a total of 530 data points). We also evaluate a model of backbone flexibility based on Backrub motions, a type of conformational change frequently observed in ultra-high resolution Xray structures that accounts for correlated side-chain backbone movements. The fixed-backbone model performs reasonably well with an overall rmsd between computed and predicted side-chain order parameters of 0.26. Notably, including backbone flexibility leads to significant © 2008 Elsevier Ltd. All rights reserved. * Corresponding author: Kortemme@cgl.ucsf.edu. AUTHOR CONTRIBUTIONSGDF and TK conceived and designed the experiments. GDF and AJL performed the experiments. GDF, TK, and AJL analyzed the data and wrote the paper. CAS contributed reagents/materials/analysis tools. SUPPORTING INFORMATION AVAILABLEOne figure with population distributions of ubiquitin I13 and L15 chi 1 and chi 2. One table with detailed results from Model 1; one table with results from Models 2 and 3 for nonpolar methyl groups only; and one table with detailed parameter sensitivity results from Model 2.Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. NIH Public Access

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Improved modeling of side‐chains in proteins with rotamer‐based methods: A flexible rotamer model

Cited by 28 publications

References 0 publications

Side‐chain modeling with an optimized scoring function

Side‐chain modeling with an optimized scoring function

A stochastic algorithm for global optimization and for best populations: A test case of side chains in proteins

A Simple Model of Backbone Flexibility Improves Modeling of Side-chain Conformational Variability

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