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    • Keywords


      Gaussian mixture modelling; geometric invariants; local conformation library; principal component analysis, sequence structure relation

    • Abstract


      The classical approaches for protein structure prediction rely either on homology of the protein sequence with a template structure or on ab initio calculations for energy minimization. These methods suffer from disadvantages such as the lack of availability of homologous template structures or intractably large conformational search space, respectively. The recently proposed fragment library based approaches first predict the local structures, which can be used in conjunction with the classical approaches of protein structure prediction. The accuracy of the predictions is dependent on the quality of the fragment library. In this work, we have constructed a library of local conformation classes purely based on geometric similarity. The local conformations are represented using Geometric Invariants, properties that remain unchanged under transformations such as translation and rotation, followed by dimension reduction via principal component analysis. The local conformations are then modeled as a mixture of Gaussian probability distribution functions (PDF). Each one of the Gaussian PDF’s corresponds to a conformational class with the centroid representing the average structure of that class. We find 46 classes when we use an octapeptide as a unit of local conformation. The protein 3-D structure can now be described as a sequence of local conformational classes. Further, it was of interest to see whether the local conformations can be predicted from the amino acid sequences. To that end, we have analyzed the correlation between sequence features and the conformational classes.

    • Author Affiliations


      Ashish V Tendulkar1 Babatunde Ogunnaike2 Pramod P Wangikar3

      1. Kanwal Rekhi School of Information Technology, Indian Institute of Technology Bombay, Powai, Mumbai 400 076, India
      2. Chemical Engineering Department, University of Delaware, Newark, DE-19716, USA
      3. Department of Chemical Engineering, Indian Institute of Technology Bombay, Powai, Mumbai 400 076, India
    • Dates

  • Journal of Biosciences | News

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      Posted on July 25, 2019

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