» Articles » PMID: 16522791

Identification of Correct Regions in Protein Models Using Structural, Alignment, and Consensus Information

Overview
Journal Protein Sci
Specialty Biochemistry
Date 2006 Mar 9
PMID 16522791
Citations 82
Authors
Affiliations
Soon will be listed here.
Abstract

In this study we present two methods to predict the local quality of a protein model: ProQres and ProQprof. ProQres is based on structural features that can be calculated from a model, while ProQprof uses alignment information and can only be used if the model is created from an alignment. In addition, we also propose a simple approach based on local consensus, Pcons-local. We show that all these methods perform better than state-of-the-art methodologies and that, when applicable, the consensus approach is by far the best approach to predict local structure quality. It was also found that ProQprof performed better than other methods for models based on distant relationships, while ProQres performed best for models based on closer relationship, i.e., a model has to be reasonably good to make a structural evaluation useful. Finally, we show that a combination of ProQprof and ProQres (ProQlocal) performed better than any other nonconsensus method for both high- and low-quality models. Additional information and Web servers are available at: http://www.sbc.su.se/~bjorn/ProQ/.

Citing Articles

AFsample2 predicts multiple conformations and ensembles with AlphaFold2.

Kalakoti Y, Wallner B Commun Biol. 2025; 8(1):373.

PMID: 40045015 PMC: 11882827. DOI: 10.1038/s42003-025-07791-9.


Recent advances and challenges in protein complex model accuracy estimation.

Liang F, Sun M, Xie L, Zhao X, Liu D, Zhao K Comput Struct Biotechnol J. 2024; 23:1824-1832.

PMID: 38707538 PMC: 11066466. DOI: 10.1016/j.csbj.2024.04.049.


Combining pairwise structural similarity and deep learning interface contact prediction to estimate protein complex model accuracy in CASP15.

Roy R, Liu J, Giri N, Guo Z, Cheng J Proteins. 2023; 91(12):1889-1902.

PMID: 37357816 PMC: 10749984. DOI: 10.1002/prot.26542.


Deciphering extract as electron shuttles with anti-COVID-19 activity and its performance in microbial fuel cells.

Lin C, Chen B, Ting J, Rogio K, Tsai P, Liu Y J Taiwan Inst Chem Eng. 2023; 145:104838.

PMID: 37051508 PMC: 10068517. DOI: 10.1016/j.jtice.2023.104838.


Combining pairwise structural similarity and deep learning interface contact prediction to estimate protein complex model accuracy in CASP15.

Roy R, Liu J, Giri N, Guo Z, Cheng J bioRxiv. 2023; .

PMID: 36945536 PMC: 10028888. DOI: 10.1101/2023.03.08.531814.


References
1.
Fischer D, Elofsson A, Rychlewski L, Pazos F, Valencia A, Rost B . CAFASP2: the second critical assessment of fully automated structure prediction methods. Proteins. 2002; Suppl 5:171-83. DOI: 10.1002/prot.10036. View

2.
Felts A, Gallicchio E, Wallqvist A, Levy R . Distinguishing native conformations of proteins from decoys with an effective free energy estimator based on the OPLS all-atom force field and the Surface Generalized Born solvent model. Proteins. 2002; 48(2):404-22. DOI: 10.1002/prot.10171. View

3.
Emanuelsson O, Nielsen H, von Heijne G . ChloroP, a neural network-based method for predicting chloroplast transit peptides and their cleavage sites. Protein Sci. 1999; 8(5):978-84. PMC: 2144330. DOI: 10.1110/ps.8.5.978. View

4.
Siew N, Elofsson A, Rychlewski L, Fischer D . MaxSub: an automated measure for the assessment of protein structure prediction quality. Bioinformatics. 2000; 16(9):776-85. DOI: 10.1093/bioinformatics/16.9.776. View

5.
Cristobal S, Zemla A, Fischer D, Rychlewski L, Elofsson A . A study of quality measures for protein threading models. BMC Bioinformatics. 2001; 2:5. PMC: 55330. DOI: 10.1186/1471-2105-2-5. View