Finding the breakthrough compounds hiding in chemical space
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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.
Download the guide and learn what generative chemistry is, where it fits in the discovery workflow, and best practices to avoid common pitfalls.
Why traditional ligand-based QSAR methods have fallen short You might be sceptical about ligand-based QSAR approaches; many researchers are. We discussed why there has…
From X-ray refinement to lead optimisation Conventional ligand-fitting and refinement methods in X-ray electron density maps often yield models with…