Improving Protein Template-based Modeling
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.
Maha M. Abdelrasoul is a Ph.D. candidate at Old Dominion University in the Computer Science Program. Maha received her Master and Bachelor of Science Degrees in Computer Engineering from the Arab Academy for Science and Technology in 2006, and 2011. Her research objectives are directed toward studying and implementing novel computational biology and machine learning algorithms to accommodate biological and chemical experiments on proteins. She has developed several computational methods for a set of fundamental and universal bioinformatics challenges, such as identifying conformational clusters of phosphorylated tyrosine, developing a multi-objective alignment algorithm to align biological sequences, and designing and implementing a template selection approach for protein template-based modeling.