Computational Methods for Protein Structure Prediction and by Ying Xu, Dong Xu, Jie Liang

By Ying Xu, Dong Xu, Jie Liang

Quantity of this two-volume series provides a complete review of protein constitution prediction equipment and contains protein threading, De novo tools, functions to membrane proteins and protein complexes, structure-based drug layout, in addition to constitution prediction as a platforms challenge. a chain of appendices assessment the organic and chemical fundamentals relating to protein constitution, desktop technological know-how for structural informatics, and prerequisite arithmetic and facts.

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Extra info for Computational Methods for Protein Structure Prediction and Modeling: Volume 2: Structure Prediction (Biological and Medical Physics, Biomedical Engineering)

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The maximum score over all combinations in the table ni; for the left child node k which are consistent with combination f; and 3. The maximum score over all combinations in the table m j for the right child node j which are consistent with combination f. The optimality column indicates if the score for each combination f is optimal over all combinations with the same candidate choices as f for the subset of cores of Xi that also occur in its parent node bag. 6 illustrates the computation for a row in the table of an internal node that has two child nodes.

12. 5 Assessing Statistical Significance of Threading Alignments Protein threading is used mainly for two purposes: (a) identification of the correct structural fold or folds from a collection ofprotein structures for a query protein and (b) prediction of the backbone structure through identifying the correct assignment (or alignment) of the amino acids of a query sequence to the structural positions in the correct structural fold. In a way, protein structure prediction through protein threading could be considered as a problem to find a global optimal threading result among many sequence-structure alignments.

The energy term could be designed so that the bigger the violation, the larger the penalty. Similarly, if a residue X is known to be on the surface of a protein structure, an energy term could be specifically designed for this knowledge so that it penalizes threading alignments that do not put X into a surface position in the template structure, and the amount of penalty could be designed to reflect the degree of violation of this particular knowledge. To deal with all geometric constraints, we can design a new energy term E G ,which is the sum of the individual penalty functions for all the specific geometric constraints.

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