High Resolution Crystal Structures Leverage Protein Binding Affinity Predictions
Résumé
Predicting protein binding affinities from structural data has remained elusive, a
difficulty owing to the variety of protein binding modes. Using the structure-affinity-benchmark
(SAB, 144 cases with bound/unbound crystal structures and experimental affinity measurements),
prediction has been undertaken either by fitting a model using a handfull of pre-defined variables,
or by training a complex model from a large pool of parameters (typically hundreds). The former
route unnecessarily restricts the model space, while the latter is prone to overfitting.
We design models in a third tier, using twelve variables describing enthalpic and entropic variations
upon binding, and a model selection procedure identifying the best sparse model built from a subset
of these variables. Using these models, we report three main results. First, we present models
yielding a marked improvement of affinity predictions. For the whole dataset, we present a model
predicting Kd within one and two orders of magnitude for 48% and 79% of cases, respectively.
These statistics jump to 62% and 89% respectively, for the subset of the SAB consisting of high
resolution structures. Second, we show that these performances owe to a new parameter encoding
interface morphology and packing properties of interface atoms. Third, we argue that interface
flexibility and prediction hardness do not correlate, and that for flexible cases, a performance
matching that of the whole SAB can be achieved. Overall, our work suggests that the affinity
prediction problem could be partly solved using databases of high resolution complexes whose
affinity is known.
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