CAD‐score: A new contact area difference‐based function for evaluation of protein structural models

Olechnovič, Kliment; Kulberkytė, Eleonora; Venclovas, C̆eslovas

doi:10.1002/prot.24172

Cited by 140 publications

(167 citation statements)

References 33 publications

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“…23) For the official CASP assessment, the quality of a model was evaluated by assigning the model a GDT-TS value. GDT-TS is an averaged fraction percentage of residues in the predicted models that deviate from the native structure after four different sequence-dependent superpositions using Cα distance thresholds of 1, 2, 4 and 8 Å.…”

Section: Resultsmentioning

confidence: 99%

Quality Assessment Methods for 3D Protein Structure Models Based on a Residue–Residue Distance Matrix Prediction

Terashi

Nakamura

Shimoyama

et al. 2014

CHEMICAL & PHARMACEUTICAL BULLETIN

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In the absence of experimentally determined three dimensional (3D) structures of proteins, the prediction of protein structures using computational methods is a standard alternative approach in bioinformatics. When using the predicted protein models to compute the native structure of an unknown target protein, estimating the actual quality of the protein models is important for selecting the best or near-best model. Moreover, estimates of the differences between the protein models and the native protein structure are obviously useful to end users who can then decide on the utility of the models for their specific problems. This article describes two new single-model quality assessment (QA) programs, pure single-model QA method (psQA) and a template based QA method (tbQA), that we developed. psQA is a pure single-model QA program that uses a neural network method to predict residue-residue distance matrices of the native protein structures. tbQA is a quasi-single-model QA program that mainly uses target-template sequence alignments and template structures. The performance of these two model QA programs was analyzed in a data set of 24022 models for 94 targets from the 10th critical assessment of protein structure prediction (CASP10) experiment.Key words model quality assessment; protein structure prediction; model accuracy; Critical Assessment of protein Structure Prediction When experimental three dimensional (3D) structures of proteins are not available, protein structure prediction using a computational method is widely used in biology and medical research, and has proven to be a useful tool. In the tenth community-wide Critical Assessment of Structure Prediction ( CASP10 1) ), various protein structure prediction methods, including the methods of 69 individual servers, were tested and showed significant progress over the past CASP experiments.2-4) Currently, protein structure prediction methods are still being developed and a significant progress has been made to improve their accuracy.In general, protein structure prediction methods consist of two steps: (1) generation of many candidate models from different alignments and templates, and (2) estimation of the quality of the candidate models to select the best or near best model. For protein structure prediction, the usefulness of a predicted protein model depends on its quality (i.e., similarity with the native protein structure); however, the quality of the model cannot be ascertained when the experimental protein structure is unknown. At present, many protein structure prediction methods can generate high quality models as candidates, but it remains difficult to identify the best or near best model. Therefore, computational biologists have developed various quality assessment (QA) methods to estimate the quality of predicted models when the experimental structure is not available. Accurate QA methods that are based on the coordinates of predicted models will contribute to the improvement of protein structure prediction strategies. Moreover, estimates of ...

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Section: Resultsmentioning

confidence: 99%

Quality Assessment Methods for 3D Protein Structure Models Based on a Residue–Residue Distance Matrix Prediction

Terashi

Nakamura

Shimoyama

et al. 2014

CHEMICAL & PHARMACEUTICAL BULLETIN

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“…If C(Si, Sj) = 1 for all i, j ∈ {S1, S2, ..., SN } and i, j ∈ {SN+1, SN+2, ..., S2N }, contact groups {S1, S2, ..., SN } and {SN+1, SN+2, ..., S2N } are called contact cliques of size N . Here, any common contact definition (e.g., based on atomatom distances [8] or contact areas [17]) can be adopted. Using above notations, six kinds of small subsets are defined:…”

Section: Initial Superposition Samplingmentioning

confidence: 99%

Compare local pocket and global protein structure models by small structure patterns

Cui

Kuwahara

et al. 2015

Proceedings of the 6th ACM Conference on Bioinformatics, Computational Biology and Health Informatics

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Researchers proposed several criteria to assess the quality of predicted protein structures because it is one of the essential tasks in the Critical Assessment of Techniques for Protein Structure Prediction (CASP) competitions. Popular criteria include root mean squared deviation (RMSD), MaxSub score, TM-score, GDT-TS and GDT-HA scores. All these criteria require calculation of rigid transformations to superimpose the the predicted protein structure to the native protein structure. Yet, how to obtain the rigid transformations is unknown or with high time complexity, and, hence, heuristic algorithms were proposed.In this work, we carefully design various small structure patterns, including the ones specifically tuned for local pockets. Such structure patterns are biologically meaningful, and address the issue of relying on a sufficient number of backbone residue fragments for existing methods. We sample the rigid transformations from these small structure patterns; and the optimal superpositions yield by these small structures are refined and reported. As a result, among 11, 669 pairs of predicted and native local protein pocket models * To whom all correspondence should be addressed (email: shuaicli@cityu.edu.hk). † To whom all correspondence should be addressed (email: xin.gao@kaust.edu.sa).Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org. BCB '15, September 9-12, 2015 from the CASP10 dataset, the GDT-TS scores calculated by our method are significantly higher than those calculated by LGA. Moreover, our program is computationally much more efficient.Source codes and executables are publicly available at

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“…Selecting correct rotamers [5][6][7][8] is another aspect. Other measures have also been proposed [13][14][15][16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

A Critical Note on Symmetry Contact Artifacts and the Evaluation of the Quality of Homology Models

et al. 2018

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Abstract:It is much easier to determine a protein's sequence than to determine its three dimensional structure and consequently homology modeling will be an essential aspect of most studies that require 3D protein structure data. Homology modeling templates tend to be PDB files. About 88% of all protein structures in the PDB have been determined with X-ray crystallography, and thus are based on crystals that by necessity hold non-natural packing contacts in accordance with the crystal symmetry. Active site residues, residues involved in intermolecular interactions, residues that get post-translationally modified, or other sites of interest, normally are located at the protein surface so that it is particularly important to correctly model surface-located residues. Unfortunately, surface residues are just those that suffer most from crystal packing artifacts. Our study of the influence of crystal packing artifacts on the quality of homology models reveals that this influence is much larger than generally assumed, and that the evaluation of the quality of homology models should properly account for these artifacts.

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CAD‐score: A new contact area difference‐based function for evaluation of protein structural models

Cited by 140 publications

References 33 publications

Quality Assessment Methods for 3D Protein Structure Models Based on a Residue–Residue Distance Matrix Prediction

Quality Assessment Methods for 3D Protein Structure Models Based on a Residue–Residue Distance Matrix Prediction

Compare local pocket and global protein structure models by small structure patterns

A Critical Note on Symmetry Contact Artifacts and the Evaluation of the Quality of Homology Models

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