High precision protein functional site detection using 3D convolutional neural networks

By Wen Torng, Russ B. Altman. 2018

72 citations recorded by the source corpus.

Abstract Motivation Accurate annotation of protein functions is fundamental for understanding molecular and cellular physiology. Data-driven methods hold promise for systematically deriving rules underlying the relationship between protein structure and function. However, the choice of protein structural representation is critical. Pre-defined biochemical features emphasize certain aspects of protein properties while ignoring others, and therefore may fail to capture critical information in complex protein sites. Results In this paper, we present a general framework that applies 3D convolutional neural networks (3DCNNs) to structure-based protein functional site detection. The framework can extract task-dependent features automatically from the raw atom distributions. We benchmarked our method against other methods and demonstrate better or comparable performance for site detection. Our deep 3DCNNs achieved an average recall of 0.955 at a precision threshold of 0.99 on PROSITE families, detected 98.89 and 92.88% of nitric oxide synthase and TRYPSIN-like enzyme sites in Catalytic Site Atlas, and showed good performance on challenging cases where sequence motifs are absent but a function is known to exist. Finally, we inspected the individual contributions of each atom to the classification decisions and show that our models successfully recapitulate known 3D features within protein functional sites. Availability and implementation The 3DCNN models described in this paper are available at https://simtk.org/projects/fscnn. Supplementary information Supplementary data are available at Bioinformatics online.

Subfields: Materials Chemistry, Molecular Biology

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Citations in the corpus

EquiBind: Geometric Deep Learning for Drug Binding Structure Prediction (2022, methodology): “Recently, DL changed this paradigm, e.g., using 3D CNNs (Aggarwal et al., 2021; Jiménez et al., 2017; Torng & Altman, 2019b) or sequence models (Sankararaman et al., 2010).”
Predicting the locations of cryptic pockets from single protein structures using the PocketMiner graph neural network (2022, methodology): “The 3D CNN model we trained is similar to the model from Torng et al. 2019 73 , with three 3D convolutional layers with filters of size 3x3x3 and the following numbers of filters for each of the three layers: 32, 64 128.”
Predicting locations of cryptic pockets from single protein structures using the PocketMiner graph neural network (2023, methodology): “We then used this data to train a graph neural network based on the geometric vector perceptron graph neural network (GVP-GNN) 31 as well as a 3D convolutional neural network (3D-CNN) 32 .”