Abstract
In silico protein structure prediction using efficient fully automated servers continues to remain a challenging problem. While many of these servers can generate near-native structures, the lack of reliable structure quality assessment methods makes the identification of these structures problematic. The most common way of discriminating between predicted structures of a given protein is to employ either knowledge or physics based energy functions. Our recently developed MUFOLD-MD server uses an alternative ranking method of the predicted protein structures by testing their relative stability against gradual heating during all atom molecular dynamics (MD) simulations.
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