Modularity of protein folds as a tool for template free modeling of structures
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The document discusses protein structural organization and the challenges of predicting three-dimensional structures from amino acid sequences. It highlights fragment assembly methods that utilize libraries of protein fragments to model structures, particularly for proteins with weak sequence similarities. The effectiveness of the smotiftf prediction algorithm and factors influencing model quality, such as secondary structure prediction methods and protein size, are also addressed.
Modularity of protein folds as a tool for template free modeling of structures
- 1. Modularity of Protein Folds as a Tool for Template-Free Modeling of Structures Brinda Vallat, Carlos Madrid-Aliste, Andras Fiser Department of Systems and Computational Biology, Albert Einstein College of Medicine, Bronx, New York, USA Presentation by Pranavathiyani G M.Sc Bioinformatics Centre for Bioinformatics Pondicherry University
- 2. IF 4.829 Introduction Proteins are made up of amino acids. Protein Structural Organization. i) Primary ii) Secondary iii)Tertiary iv)Quaternary Each protein folds into a unique three-dimensional structure that enables it to carry out its biological function. Predicting the 3D structure of proteins from their amino acid sequences remains a challenging problem.
- 5. Computational protein structure prediction
- 8. Fragment assembly based methods Fragment assembly based methods use a library of protein fragments obtained from known protein structures to explore the structure space accessible to the query protein. The fragments themselves may be obtained from remote homologs that share very weak sequence similarity with the query protein and are typically not good enough to be used directly for homology modelling. The quality of prediction drops significantly for larger proteins since the conformational search becomes tedious and less accurate for larger proteins.
- 9. Flowchart of the SmotifTF prediction algorithm.
- 10. GDT_TS values of top scoring models obtained with SmotifTF method using dynamic Smotif library generated at different e-value cutoffs.
- 11. Performance of SmotifTF on the benchmarking test set in comparison to other methods
- 12. Factors affecting model quality Secondary structure prediction method Size of the protein. Smotif ranking using HHalign.