Machine Learning 101: How to optimise hyperparameters
What are parameters in machine learning models? The regular (non-hyper) parameters of an ML model are the numbers that it…
Summary
This paper demonstrates how the Surflex-PSIM method can help investigate hypotheses around protein function in cases where function cannot be determined by sequence similarity.
Surflex-PSIM is a surface-based protein similarity algorithm, which supports fully automatic binding site detection and is fast enough to screen comprehensive databases of protein binding sites.
In this study, binding site detection was validated on apo/holo cognate protein pairs, and used to screen a set of 8 proteins that had poorly characterized functions at the time of crystallization, but were later biochemically annotated. It showed excellent performance identifying binding site matches, and for a panel of 12 unannotated proteins, supported suggestion of likely functions.
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