Smpred: A support vector machine approach to identify structural motifs in protein structure without using evolutionary information

Pugalenthi Ganesan, Krishna Kumar Kandaswamy, P. N. Suganthan, R. Sowdhamini, Thomas Martinetz, Prasanna R. Kolatkar*

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review

    4 Scopus citations


    Knowledge of three dimensional structure is essential to understand the function of a protein. Although the overall fold is made from the whole details of its sequence, a small group of residues, often called as structural motifs, play a crucial role in determining the protein fold and its stability. Identification of such structural motifs requires sufficient number of sequence and structural homologs to define conservation and evolutionary information. Unfortunately, there are many structures in the protein structure databases have no homologous structures or sequences. In this work, we report an SVM method, SMpred, to identify structural motifs from single protein structure without using sequence and structural homologs. SMpred method was trained and tested using 132 proteins domains containing 581 motifs. SMpred method achieved 78.79% accuracy with 79.06% sensitivity and 78.53% specificity. The performance of SMpred was evaluated with MegaMotifBase using 188 proteins containing 1161 motifs. Out of 1161 motifs, SMpred correctly identified 1503 structural motifs reported in MegaMotifBase. Further, we showed that SMpred is useful approach for the length deviant superfamilies and single member superfamilies. This result suggests the usefulness of our approach for facilitating the identification of structural motifs in protein structure in the absence of sequence and structural homologs. The dataset and executable for the SMpred algorithm is available at

    Original languageEnglish (US)
    Pages (from-to)405-414
    Number of pages10
    JournalJournal of Biomolecular Structure and Dynamics
    Issue number3
    StatePublished - Jan 1 2010


    • Fingerprint
    • Protein folding
    • Protein function
    • Structural motifs
    • Support vector machine

    ASJC Scopus subject areas

    • Structural Biology
    • Molecular Biology


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