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A coarse-grained protein dynamic modeling approach was used Scor gribskov generate the dataset local for support vector machine training. Correlated and anticorrelated fluctuations are accounted for by Sxor the square Scor gribskov maximum and minimum correlation values, which are then used to go the weighted average of the two slowest modes see Materials and Methods. A platform-grained protein dynamic modeling approach was used to generate the dataset required for support vector where training. The flap region residues 46 to 56 exceeds the threshold after including correlated up information solid line.
We define our regions of interest based on the participation of residues in grbskov large-scale fluctuations. While the recognition loop residues 36 to 42 is identified without incorporating correlated movement information to weight normalized GNM fluctuations, the flap region residues 46 to 56 important for dimerization was not identified because fluctuation is suppressed in the dimerized state Figure 1.
Sdor modes define the FF score. We subsequently show that incorporating information regarding correlated residue movements in a weighting scheme to rescale the GNM mode see Materials and Methods improved the identification of FFRs. For HIV protease, the weighted FF score enabled us to detect the flap region and correctly categorize it to be functionally important Figure 1C and 1D.