Proteins are considered the central compound necessary for life, as they play a crucial role in governing several life processes by performing the most essential biological and chemical functions in every living cell. Understanding protein structure will lead to a significant advance in life sciences and biology. The experimental methods for protein 3-dimensional structure determination are time-consuming, costly, and not feasible for some proteins. Accordingly, research efforts focus more and more on computational approaches to predict protein 3-dimensional structure. Template-based modeling is considered the most accurate protein structure prediction method. The success of template-based modeling relies on correctly identifying one or a few experimentally determined protein structures as templates that are likely to resemble the structure of the target sequence as well as accurately producing a sequence alignment that maps the residues of the target sequence to those of the template.
This talk will present improving the fold sensitivity in template-based protein structure modeling by correctly identifying the most appropriate template protein structures and precisely aligning the target and template sequences. Firstly, I will show that deploying three-dimensional information of protein along with structural information to measure the favorability of a target sequence fitting in the folding topology of a certain template, shall improve the sensitivity of protein modeling. Secondly, I will present a multi-objective alignment algorithm that extends the Needleman-Wunsch algorithm, which obtains a set of diversified alignments yielding Pareto optimality. The alignments obtained by the multi-objective alignment algorithm enable one to analyze the trade-offs among potentially conflicting objective functions, which allows us to pick more suitable alignments for protein modeling.