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DeeplyTough: Learning Structural Comparison of Protein Binding Sites
journal contribution
posted on 2020-03-17, 19:04 authored by Martin Simonovsky, Joshua MeyersProtein pocket matching, or binding
site comparison, is of importance in drug discovery. Identification
of similar binding pockets can help guide efforts for hit-finding,
understanding polypharmacology, and characterization of protein function.
The design of pocket matching methods has traditionally involved much
intuition and has employed a broad variety of algorithms and representations
of the input protein structures. We regard the high heterogeneity
of past work and the recent availability of large-scale benchmarks
as an indicator that a data-driven approach may provide a new perspective.
We propose DeeplyTough, a convolutional neural network that encodes
a three-dimensional representation of protein pockets into descriptor
vectors that may be compared efficiently in an alignment-free manner
by computing pairwise Euclidean distances. The network is trained
with supervision (i) to provide similar pockets with similar descriptors,
(ii) to separate the descriptors of dissimilar pockets by a minimum
margin, and (iii) to achieve robustness to nuisance variations. We
evaluate our method using three large-scale benchmark datasets, on
which it demonstrates excellent performance for held-out data coming
from the training distribution and competitive performance when the
trained network is required to generalize to datasets constructed
independently. DeeplyTough is available at https://github.com/BenevolentAI/DeeplyTough.
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protein functionDeeplyToughnuisance variationsbinding pocketsdescriptor vectorsprotein pocketstraining distributionheld-out datadata-driven approachdrug discoveryLearning Structural Comparisoninput protein structuresbenchmark datasetsguide effortspairwise Euclidean distancesbinding site comparisonunderstanding polypharmacologyalignment-free mannerProtein Binding Sites Protein pocket
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