What is the best reference state for designing statistical atomic potentials in protein structure prediction?

Deng, Haiyou; Jia, Ya; Wei, Yanyu; Zhang, Yang

doi:10.1002/prot.24121

Cited by 30 publications

(43 citation statements)

References 49 publications

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“…Similar success rates for Prosa, Dfire and Rosetta are observed (columns 5, 6 & 7 in Table S2) with a slight edge when using a consensus scoring. This suggests that scoring functions deliver comparable performance despite the large differences in their complexity and style (Deng et al, 2012; Rykunov and Fiser, 2010). …”

Section: Resultsmentioning

confidence: 96%

Modeling Proteins Using a Super-Secondary Structure Library and NMR Chemical Shift Information

et al. 2013

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Summary A remaining challenge in protein modeling is to predict structures for sequences that do not share recognizable sequence similarity to any experimentally solved structure. This challenge can be addressed by hybrid algorithms that utilize easily obtainable experimental data and carry a limited amount of indirect structural information. Based on earlier observations, the library of protein super-secondary structure motifs (Smotifs) saturated about a decade ago, and new folds discovered since then are novel combinations of existing Smotifs. This observation suggests that it should be possible to build any structure, of either a known or yet to be discovered fold, from a combination of existing Smotifs derived from already known structures. In the absence of any sequence similarity signal, limited experimental data can be used to relate the backbone conformations of Smotifs between target proteins and known experimental structures. Here we present a modeling algorithm that relies on an exhaustive Smotif library and on NMR chemical shift patterns without any input of primary sequence information. In a test of 102 proteins with unique folds, the algorithm delivered 90 homology model quality models, among them 24 high quality ones, and a topologically correct solution for almost all cases. Detailed analysis of the method’s performance suggests that further improvement can be achieved by improving sampling algorithms and developing more precise tools that predict dihedral angle preferences from chemical shift assignments. The current approach opens a venue to address the modeling of larger protein structures for which chemical shifts are available.

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Section: Resultsmentioning

confidence: 96%

Modeling Proteins Using a Super-Secondary Structure Library and NMR Chemical Shift Information

et al. 2013

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“…And MC-fold, which can predict RNA 3D structures, also use an all-atom score function that embedded in the RNA tertiary structure prediction programs to rank RNA tertiary structures [9]. However, for protein, it is the problem that it is the distinction on universality and pertinence that makes the potentials perform diversely in different decoy sets [25]. There is a problem which is the same as protein in RNA, which means that there is a contradiction between universality and pertinence.…”

Section: Introductionmentioning

confidence: 99%

PTRNAmark: an all-atomic distance-dependent knowledge-based potential for 3D RNA structure evaluation

Yang¹,

Shi

2016

Preprint

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RNA molecules play vital biological roles, and understanding their structures gives us crucial insights into their biological functions. Model evaluation is a necessary step for better prediction and design of 3D RNA structures.Knowledge-based statistical potential has been proved to be a powerful approach for evaluating models of protein tertiary structures. In present, several knowledge-based potentials have also been proposed to assess models of RNA 3D structures. However, further amelioration is required to rank near-native structures and pick out the native structure from near-native structures, which is crucial in the prediction of RNA tertiary structures. In this work, we built a novel RNA knowledge-based potential-PTRNAmark, which not only combines nucleotides' mutual and self energies but also fully considers the specificity of every RNA. The benchmarks on different testing data sets all show that PTRNAmark are more efficient than existing evaluation methods in recognizing native state from a pool of near-native states of RNAs as well as in ranking near-native states of RNA models.

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“…Although this concept is old and originates from the work of Tanaka and Scheraga, 3 Miyazawa and Jernigan, 4 and Sippl, [5][6][7] it is still under debates. [8][9][10][11] Particularly, the computation of the reference state is a challenging problem. 12 Although some assumptions were made to ease the expression of the reference state for protein monomers, 5,[13][14][15] to deduce scoring functions for the proteinprotein docking, one usually computes the reference state based on a large set of generated non-native conformations of protein complexes (decoys).…”

Section: Introductionmentioning

confidence: 99%

Knowledge of Native Protein–Protein Interfaces Is Sufficient To Construct Predictive Models for the Selection of Binding Candidates

Popov

Grudinin

2015

J. Chem. Inf. Model.

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Selection of putative binding poses is a challenging part of virtual screening for protein-protein interactions. Predictive models to filter out binding candidates with the highest binding affinities comprise scoring functions that assign a score to each binding pose. Existing scoring functions are typically deduced by collecting statistical information about interfaces of native conformations of protein complexes along with interfaces of a large generated set of non-native conformations. However, the obtained scoring functions become biased toward the method used to generate the non-native conformations, i.e., they may not recognize near-native interfaces generated with a different method. The present study demonstrates that knowledge of only native protein-protein interfaces is sufficient to construct well-discriminative predictive models for the selection of binding candidates. Here we introduce a new scoring method that comprises a knowledge-based potential called KSENIA deduced from structural information about the native interfaces of 844 crystallographic protein-protein complexes. We derive KSENIA using convex optimization with a training set composed of native protein complexes and their near-native conformations obtained using deformations along the low-frequency normal modes. As a result, our knowledge-based potential has only marginal bias toward a method used to generate putative binding poses. Furthermore, KSENIA is smooth by construction, which allows it to be used along with rigid-body optimization to refine the binding poses. Using several test benchmarks, we demonstrate that our method discriminates well native and near-native conformations of protein complexes from non-native ones. Our methodology can be easily adapted to the recognition of other types of molecular interactions, such as protein-ligand, protein-RNA, etc. KSENIA will be made publicly available as a part of the SAMSON software platform at https://team.inria.fr/nano-d/software .

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What is the best reference state for designing statistical atomic potentials in protein structure prediction?

Cited by 30 publications

References 49 publications

Modeling Proteins Using a Super-Secondary Structure Library and NMR Chemical Shift Information

Modeling Proteins Using a Super-Secondary Structure Library and NMR Chemical Shift Information

PTRNAmark: an all-atomic distance-dependent knowledge-based potential for 3D RNA structure evaluation

Knowledge of Native Protein–Protein Interfaces Is Sufficient To Construct Predictive Models for the Selection of Binding Candidates

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