• Use of secondary structural information and C𝛼-C𝛼 distance restraints to model protein structures with MODELLER

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    • Keywords


      Model evolution; protein modelling; residue contact prediction; secondary structure prediction

    • Abstract


      Protein secondary structure predictions and amino acid long range contact map predictions from primary sequence of proteins have been explored to aid in modelling protein tertiary structures. In order to evaluate the usefulness of secondary structure and 3D-residue contact prediction methods to model protein structures we have used the known Q3 (alpha-helix, beta-strands and irregular turns/loops) secondary structure information, along with residue-residue contact information as restraints for MODELLER. We present here results of our modelling studies on 30 best resolved single domain protein structures of varied lengths. The results shows that it is very difficult to obtain useful models even with 100% accurate secondary structure predictions and accurate residue contact predictions for up to 30% of residues in a sequence. The best models that we obtained for proteins of lengths 37, 70, 118, 136 and 193 amino acid residues are of RMSDs 4.17, 5.27, 9.12, 7.89 and 9.69, respectively. The results show that one can obtain better models for the proteins which have high percent of alpha-helix content. This analysis further shows that MODELLER restrain optimization program can be useful only if we have truly homologous structure(s) as a template where it derives numerous restraints, almost identical to the templates used. This analysis also clearly indicates that even if we satisfy several true residue-residue contact distances, up to 30% of their sequence length with fully known secondary structural information, we end up predicting model structures much distant from their corresponding native structures.

    • Author Affiliations


      Boojala V B Reddy1 Yiannis N Kaznessis2

      1. Laboratory of Bioinformatics and In Silico Drug Design, Department of Computer Science, Queens College, CUNY 65-30 Kissena Blvd, Flushing, NY 11367, USA
      2. Digital Technology Center and Department of Chemical Engineering and Materials Science, University of Minnesota Minneapolis, MN 55455, USA
    • Dates

  • Journal of Biosciences | News

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      Posted on July 25, 2019

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