title: Modularity of Protein Folds as a Tool for Template-Free Modeling of Structures.
abstract: Predicting the three-dimensional structure of proteins from their amino acid sequences remains a challenging problem in molecular biology. While the current structural coverage of proteins is almost exclusively provided by template-based techniques, the modeling of the rest of the protein sequences increasingly require template-free methods. However, template-free modeling methods are much less reliable and are usually applicable for smaller proteins, leaving much space for improvement. We present here a novel computational method that uses a library of supersecondary structure fragments, known as Smotifs, to model protein structures. The library of Smotifs has saturated over time, providing a theoretical foundation for efficient modeling. The method relies on weak sequence signals from remotely related protein structures to create a library of Smotif fragments specific to the target protein sequence. This Smotif library is exploited in a fragment assembly protocol to sample decoys, which are assessed by a composite scoring function. Since the Smotif fragments are larger in size compared to the ones used in other fragment-based methods, the proposed modeling algorithm, SmotifTF, can employ an exhaustive sampling during decoy assembly. SmotifTF successfully predicts the overall fold of the target proteins in about 50% of the test cases and performs competitively when compared to other state of the art prediction methods, especially when sequence signal to remote homologs is diminishing. Smotif-based modeling is complementary to current prediction methods and provides a promising direction in addressing the structure prediction problem, especially when targeting larger proteins for modeling.
authors.affiliation: Department of Systems and Computational Biology, Albert Einstein College of Medicine, Bronx, New York, New York, United States of America.
authors.forename: Brinda
authors.initials: B
authors.lastname: Vallat
authors.affiliation: Department of Systems and Computational Biology, Albert Einstein College of Medicine, Bronx, New York, New York, United States of America.
authors.forename: Carlos
authors.initials: C
authors.lastname: Madrid-Aliste
authors.affiliation: Department of Systems and Computational Biology, Albert Einstein College of Medicine, Bronx, New York, New York, United States of America.
authors.forename: Andras
authors.initials: A
authors.lastname: Fiser
mesh_terms: D000465:Algorithms; D020816:Amino Acid Motifs; D019295:Computational Biology / Q000379:methods*; D008958:Models, Molecular; D011487:Protein Conformation; D017510:Protein Folding; D011506:Proteins / Q000737:chemistry* / Q000378:metabolism*; D020539:Sequence Analysis, Protein; D012984:Software
chemical_list: D011506:Proteins