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Protein–Protein Interface Topology as a Predictor of Secondary Structure and Molecular Function Using Convolutional Deep Learning

Abstract : To power the specific recognition and binding of protein partners into functional complexes, a wealth of information about the structure and function of the partners is necessarily encoded into the global shape of protein-protein interfaces and their local topological features. To identify whether this is the case, this study uses convolutional deep learning methods (typically leveraged for 2D image recognition) on 3D voxel representations of protein-protein interfaces colored by burial depth. A novel twostage network, fed with voxelizations of each interface at two distinct resolutions, achieves balance between performance and computational cost. From the shape of the interfaces, the network tries to predict the presence of secondary structure motifs at the interface and the molecular function of the corresponding complex. Secondary structure and certain classes of function are found to be very well predicted, validating the hypothesis of interface shape as a conveyor of higher-level information. Interface patterns triggering the recognition of specific classes are also identified and described.
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https://hal.archives-ouvertes.fr/hal-03299619
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Soumis le : lundi 26 juillet 2021 - 15:42:56
Dernière modification le : mercredi 27 octobre 2021 - 09:58:10
Archivage à long terme le : : mercredi 27 octobre 2021 - 18:26:37

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Benjamin Bouvier. Protein–Protein Interface Topology as a Predictor of Secondary Structure and Molecular Function Using Convolutional Deep Learning. Journal of Chemical Information and Modeling, American Chemical Society, 2021, 61 (7), pp.3292-3303. ⟨10.1021/acs.jcim.1c00644⟩. ⟨hal-03299619⟩

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