GOAP: A Generalized Orientation-Dependent, All-Atom Statistical Potential for Protein Structure Prediction

Zhou, Hongyi; Skolnick, Jeffrey

doi:10.1016/j.bpj.2011.09.012

Cited by 259 publications

(300 citation statements)

References 56 publications

(85 reference statements)

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“…While we trust the general trends highlighted by these results, we tone down the importance of In extensive testing on available decoy sets and models from the Critical Assessment of protheir exceptional character as they may only reflect the specificity of the Titan-HRD data set. tein Structure Prediction (CASP) experiments we find that PPD yields better energydistance correlations than one of the state of the art single model potentials, GOAP [85]. We note however that the sophisticated distance-based and orientation-based statistical potential GOAP is better at picking the best decoys and has a better though comparable performance for fixed energy-distance correlation.…”

Section: Resultsmentioning

confidence: 74%

“…a All-atom statistical distance-based potential [116]. b All-atom orientation-dependent statistical potential [85]. c The semi-empirical physical potential AMBER99SB-ILDN [117].…”

Section: Training Knowledge-based Potentials With Different Distancementioning

confidence: 99%

“…We have compared the two energy functions PPD and PPE with two well established all-atom statistical potentials RAPDF [116] and GOAP [85] and with a semi-empirical physical potential, AMBER99SB-ILDN [117], for all decoy sets in Titan-HRD, CASP-HRD, and TSA. Results for correlations between energy and distance measures and for R scores are given in Tables 6.4 and 6.5, respectively.…”

Section: Comparison With Other Energy Functionsmentioning

confidence: 99%

“…GOAP is an all-atom orientation-dependent knowledge-based statistical potential that includes a distance-based term and an angle-dependent contribution [85]. The distance-based term is an all-atom statistical potential that is based on the reference state that was introduced with the DFIRE potential [118].…”

Section: Comparison With Other Energy Functionsmentioning

confidence: 99%

“…Semiempirical methods are derived from knowledge of the basic physical principles whereas statistical potentials are based on the nonrandom statistics of known protein structures [84]. Statistical energy functions are either residue based or atom based and the most recent statistical potentials include pairwise interactions, orientations of side-chains [85], secondary structural preferences, solvent-exposure, and other geometric properties of proteins [86]. We note that there have been attempts to combine physics-based and statistics-based potentials to improve protein structure refinement [26,87,27,88,28].…”

Section: Introductionmentioning

confidence: 99%

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Protein structure refinement by optimization

Carlsen

Røgen

2015

Proteins

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SummaryProteins are the main active elements of life whose chemical activities regulate cellular activities. A protein is characterized by having a sequence of amino acids and a three dimensional structure. The three-dimensional structure has only been determined experimentally for 50000 of the seven million sequences that are known. Determining the protein structure from its sequence of amino acids is therefore a major problem in computational structural biology and is referred to as the protein folding problem. The folding problem is solved using de novo methods or comparative methods depending on whether the three-dimensional structure of a homologous sequence is known. Whether or not a protein model can be used for industrial purposes depends on the quality of the predicted structure. A model can be used to design a drug when the quality is high.The overall goal of this project is to assess and improve the quality of a predicted structure. The starting point of this work is a technique called metric training where a knowledge-based protein potential, for a fixed set of native protein structures and a set of deformed decoys for each native structure, is designed to have native-decoy energy gabs that correlates maximally to a native-decoy distance. The main contribution of this thesis is methods developed for analyzing the performance of metrically trained knowledge-based potentials and for optimizing their performance while making them less dependent on the decoy set used to define them. We focus on using the gradient and the Hessian in the analysis and present a novel smooth solvation potential but otherwise the studied potential is kept close to standard coarse grained potentials.We analyze the importance of the choice of metric both when used in metric training and when used in the evaluation of the performance of the resulting potential and find a significant improvement by using a metric based on intrinsic geometry. It is well-known that energy minimization of a potential that is efficient in ordering a fixed set of decoys need not bring the decoys closer to the native state. The next part of the work is focused on improving the convergence of decoy structures and we present a method that significantly improves the results of shorter energy minimizations of a metrically trained potential and discuss its limitations. In an ideal potential all nearnative decoys will converge toward the native structure being at-least a local minimum of the potential. To address how far the current functional form of the potential is from an ideal potential we present two methods for finding the optimal metrically trained potential that simultaneous has a number of native structures as a local minimum. Our results generally indicate that a more fine-grained potential is needed to meet desired model accuracies but even with our coarse-grained model we obtain good results and there is an unexplored possibility to combine it with comparative modeling.To allow fast energy minimization in Matlab a new set of more sparse formulas...

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Section: Resultsmentioning

confidence: 74%

“…a All-atom statistical distance-based potential [116]. b All-atom orientation-dependent statistical potential [85]. c The semi-empirical physical potential AMBER99SB-ILDN [117].…”

Section: Training Knowledge-based Potentials With Different Distancementioning

confidence: 99%

Section: Comparison With Other Energy Functionsmentioning

confidence: 99%

Section: Comparison With Other Energy Functionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Protein structure refinement by optimization

Carlsen

Røgen

2015

Proteins

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A distance‐ and orientation‐dependent energy function of amino acid key blocks

Chen

2014

Biopolymers

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Blocks are the selected portions of amino acids. They have been used effectively to represent amino acids in distinguishing the native conformation from the decoys. Although many statistical energy functions exist, most of them rely on the distances between two or more amino acids. In this study, the authors have developed a pairwise energy function "DOKB" that is both distance and orientation dependent, and it is based on the key blocks that bias the distal ends of side chains. The results suggest that both the distance and the orientation are needed to distinguish the fine details of the packing geometry. DOKB appears to perform well in recognizing native conformations when compared with six other energy functions. Highly packed clusters play important roles in stabilizing the structure. The investigation about the highly packed clusters at the residue level suggests that certain residue pairs in a low-energy region have lower probability to appear in the highly packed clusters than in the entire protein. The cluster energy term appears to significantly improve the recognition of the native conformations in ig_structal decoy set, in which more highly packed clusters are contained than in other decoy sets.

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sDFIRE: Sequence‐specific statistical energy function for protein structure prediction by decoy selections

et al. 2016

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An important unsolved problem in molecular and structural biology is the protein folding and structure prediction problem. One major bottleneck for solving this is the lack of an accurate energy to discriminate near-native conformations against other possible conformations. Here we have developed sDFIRE energy function, which is an optimized linear combination of DFIRE (the Distance-scaled Finite Ideal gas Reference state based Energy), the orientation dependent (polar-polar and polar-nonpolar) statistical potentials, and the matching scores between predicted and model structural properties including predicted main-chain torsion angles and solvent accessible surface area. The weights for these scoring terms are optimized by three widely used decoy sets consisting of a total of 134 proteins. Independent tests on CASP8 and CASP9 decoy sets indicate that sDFIRE outperforms other state-of-the-art energy functions in selecting near native structures and in the Pearson's correlation coefficient between the energy score and structural accuracy of the model (measured by TM-score).

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GOAP: A Generalized Orientation-Dependent, All-Atom Statistical Potential for Protein Structure Prediction

Cited by 259 publications

References 56 publications

Protein structure refinement by optimization

Protein structure refinement by optimization

A distance‐ and orientation‐dependent energy function of amino acid key blocks

sDFIRE: Sequence‐specific statistical energy function for protein structure prediction by decoy selections

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