Abstract
Fold recognition, or threading, is a popular protein structure modeling approach that uses known structure templates to build structures for those of unknown. The key to the success of fold recognition methods lies in the proper integration of sequence, physiochemical and structural information. Here we introduce another type of information, local structural preference potentials of 3-residue and 9-residue fragments, for fold recognition. By combining the two local structural preference potentials with the widely used sequence profile, secondary structure information and hydrophobic score, we have developed a new threading method called FR-t5 (fold recognition by use of 5 terms). In benchmark testings, we have found the consideration of local structural preference potentials in FR-t5 not only greatly enhances the alignment accuracy and recognition sensitivity, but also significantly improves the quality of prediction models.
Highlights
Modeling of protein structures based on structure templates found from experimentally determined structures, called templatebased modeling (TBM), is currently the most effective way to build a 3-D structure for a protein of unknown structure
We have shown that integration of these terms with the three widely used information, sequence profile, secondary structure and hydrophobic score allows us to develop an effective fold recognition method, called FR-t5, an abbreviation of fold recognition with 5 terms
By combining 3-residue and 9-residue local structure preference potentials (LSPPs) with the three widely used information, sequence profile, secondary structure and hydrophobic score, we further develop a new threading algorithm called FR-t5
Summary
Modeling of protein structures based on structure templates found from experimentally determined structures, called templatebased modeling (TBM), is currently the most effective way to build a 3-D structure for a protein of unknown structure. For target sequences with high sequence similarity to those of structure templates, the structural templates can be identified and the target sequences can be reliably aligned to the structural templates by those methods that use sequence information alone such as PSIBLAST [14] and HMMER [15]. For target sequences with low sequence similarity, the reliable identification of structural templates and accurate sequence-structure alignment requires a much more complex process called threading or fold recognition that integrates many other types of information with sequence profile information. Other types of structural information such as contact information, solvent accessibility, predicted backbone torsion angles and structure profiles have been explored to improve the accuracy of fold recognition [20,21,22,23,24,25]
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