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A comparison of different functions for predicted protein model quality assessment

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Abstract

In protein structure prediction, a considerable number of models are usually produced by either the Template-Based Method (TBM) or the ab initio prediction. The purpose of this study is to find the critical parameter in assessing the quality of the predicted models. A non-redundant template library was developed and 138 target sequences were modeled. The target sequences were all distant from the proteins in the template library and were aligned with template library proteins on the basis of the transformation matrix. The quality of each model was first assessed with QMEAN and its six parameters, which are C_β interaction energy (C_beta), all-atom pairwise energy (PE), solvation energy (SE), torsion angle energy (TAE), secondary structure agreement (SSA), and solvent accessibility agreement (SAE). Finally, the alignment score (score) was also used to assess the quality of model. Hence, a total of eight parameters (i.e., QMEAN, C_beta, PE, SE, TAE, SSA, SAE, score) were independently used to assess the quality of each model. The results indicate that SSA is the best parameter to estimate the quality of the model.

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Abbreviations

C_beta:

C_β interaction energy

PE:

All-atom pairwise energy

SE:

Solvation energy

TAE:

Torsion angle energy

SSA:

Secondary structure agreement

SAA:

Solvent accessibility agreement

TBM:

Template-based method

CASP:

Critical assessment of protein structure prediction

References

  1. Murzin AG (2001) Progress in protein structure prediction. Nat Struct Biol 8:110–112

    Article  CAS  Google Scholar 

  2. Guex N, Peitsch MC (1997) SWISS-MODEL and the Swiss-PdbViewer: an environment for comparative protein modeling. Electrophoresis 18:2714–2723

    Article  CAS  Google Scholar 

  3. Sánchez R, Sali A (1997) Advances in comparative protein-structure modelling. Curr Opin Struct Biol 7:206–214

    Article  Google Scholar 

  4. Bowie JU, Luthy R, Eisenberg D (1991) A method to identify protein sequences that fold into a known three-dimensional structure. Science 253:164–170

    Article  CAS  Google Scholar 

  5. Panchenko AR, Marchler-Bauer A, Bryant SH (2000) Combination of threading potentials and sequence profiles improves fold recognition. J Mol Biol 296:1319–1331

    Article  CAS  Google Scholar 

  6. Xu D, Zhang J, Roy A, Zhang Y (2011) Automated protein structure modeling in CASP9 by I-TASSER pipeline combined with QUARK-based ab initio folding and FG-MD-based structure refinement. Proteins: Struct Funct Bioinform 79:147–160

    Article  CAS  Google Scholar 

  7. Pillardy J, Czaplewski C, Liwo A, Lee J, Ripoll DR, Kaźmierkiewicz R, Oldziej S, Wedemeyer WJ, Gibson KD, Arnautova YA, Saunders J, Ye YJ, Scheraga HA (2001) Recent improvements in prediction of protein structure by global optimization of a potential energy function. Proc Natl Acad Sci USA 98:2329–2333

    Article  CAS  Google Scholar 

  8. Simons KT, Strauss C, Baker D (2001) Prospects for ab initio protein structural genomics. J Mol Biol 306:1191–1199

    Article  CAS  Google Scholar 

  9. Kolinski A, Skolnick J (1998) Assembly of protein structure from sparse experimental data: an efficient Monte Carlo model. Proteins 32:475–494

    Article  CAS  Google Scholar 

  10. Elofsson A (2002) A study on protein sequence alignment quality. Proteins 46:330–339

    Article  CAS  Google Scholar 

  11. Pandit SB, Zhang Y, Skolnick J (2006) TASSER-Lite: an automated tool for protein comparative modeling. Biophys J 91:4180–4190

    Article  CAS  Google Scholar 

  12. Kryshtafovych A, Fidelis K (2009) Protein structure prediction and model quality assess. Drug Discovery Today 14:386–393

    Article  CAS  Google Scholar 

  13. Pearson WR, Sierk ML (2005) The limits of protein sequence comparison. Curr Opin Struct Biol 15:254–260

    Article  CAS  Google Scholar 

  14. Levitt M, Gerstein M (1998) A unified statistical framework for sequence comparison and structure comparison. Proc Natl Acad Sci USA 95:5913–5920

    Article  CAS  Google Scholar 

  15. Siew N, Elofsson A, Rychlewski L, Fischer D (2000) MaxSub: an automated measure for the assessment of protein structure prediction quality. Bioinformatics 16:776–785

    Article  CAS  Google Scholar 

  16. Rohl CA, Strauss CEM, Misura KMS, Baker D (2004) Protein structure prediction using Rosetta. Methods Enzymol 383:66–93

    Article  CAS  Google Scholar 

  17. Zhang Y, Skolnick J (2004) Automated structure prediction of weakly homologous proteins on a genomic scale. Proc Natl Acad Sci USA 101:7594–7599

    Article  CAS  Google Scholar 

  18. Contreras-Moreira B, Fitzjohn PW, Bates PA (2003) In silico protein recombination:enhancing template and sequence alignment selection for comparative protein modelling. J Mol Biol 328:593–608

    Article  CAS  Google Scholar 

  19. John B, Sali A (2003) Comparative protein structure modeling by iterative alignment, model building and model assessment. Nucleic Acids Res 31:3982–3992

    Article  CAS  Google Scholar 

  20. Benkert P et al (2008) QMEAN: a comprehensive scoring function for model quality assessment. Proteins 71:261–277

    Article  CAS  Google Scholar 

  21. Wallner B, Fang H, Elofsson A (2003) Automatic consensus-based fold recognition using Pcons, ProQ, and Pmodeller. Proteins 53:534–541

    Article  CAS  Google Scholar 

  22. Li J, Fang H (2012) Substitution transformation of score matrix for improving alignment quality of local sequence of distantly related proteins. Current Bioinformtics 7:35–42

    Article  Google Scholar 

  23. Murzin AG, Brenner SE, Hubbard T (1995) SCOP: a structural classification of proteins database for the investigation of sequences and structures. J Mol Biol 247:536–540

    CAS  Google Scholar 

  24. Fang H, Wallner B, Lundström J, von Wowern C, Elofsson A (2001) Improved fold recognition by using the Pcons consensus approach. Protein Struct Prediction: Bioinform Approach 1:397–416

    Google Scholar 

  25. Chen H, Kihara D (2008) Estimating quality of template-based protein models by alignment stability. Proteins 71:1255–1274

    Article  CAS  Google Scholar 

  26. Rost B (2001) Review: protein secondary structure prediction continues to rise. J Struct Biol 134:204–218

    Article  CAS  Google Scholar 

  27. Ortiz AR, Kolinski A, Rotkiewicz P, Ilkowski B, Skolnick J (1999) Ab initio folding of proteins using restraints derived from evolutionary information. Proteins 37:177–185

    Article  Google Scholar 

  28. Eyrich VA, Standley DM, Felts AK, Friesner RA (1999) Protein tertiary structure prediction using a branch and bound algorithm. Proteins 35:41–57

    Article  CAS  Google Scholar 

  29. Lomize AL, Pogozheva ID, Mosberg HI (1999) Prediction of protein structure: the problem of fold multiplicity. Proteins 37:199–203

    Article  Google Scholar 

  30. Chen CC, Singh JP, Altman RB (1999) Using imperfect secondary structure predictions to improve molecular structure computations. Bioinformatics 15:53–65

    Article  Google Scholar 

  31. Samudrala R, Xia Y, Huang E, Levitt M (1999) Ab initio protein structure prediction using a combined hierarchical approach. Proteins 37:194–198

    Article  Google Scholar 

  32. Samudrala R, Huang ES, Koehl P, Levitt M (2000) Constructing side chains on near-native main chains for ab initio protein structure prediction. Protein Eng 13:453–457

    Article  CAS  Google Scholar 

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Acknowledgments

This work was supported by Grants from the National Natural Science Foundation of China (No. 31171386 and 31371399), the Jiangsu Province Natural Science Foundation (No. BK2011093).

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Authors and Affiliations

  1. Department of Hematology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, Jiangsu, 210008, People’s Republic of China

    Juan Li

  2. School of Life Science and Technology, China Pharmaceutical University, Nanjing, Jiangsu, 210009, People’s Republic of China

    Huisheng Fang

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  1. Juan Li
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  2. Huisheng Fang
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Correspondence to Huisheng Fang.

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Li, J., Fang, H. A comparison of different functions for predicted protein model quality assessment. J Comput Aided Mol Des 30, 553–558 (2016). https://doi.org/10.1007/s10822-016-9924-1

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  • DOI: https://doi.org/10.1007/s10822-016-9924-1

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