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Understanding structure-guided variant effect predictions using 3D convolutional neural networks.

Gayatri Ramakrishnan¹, Coos Baakman¹, Stephan Heijl²

¹Department of Medical Biosciences, Radboud University Medical Center, Nijmegen, Netherlands.

Frontiers in Molecular Biosciences

|July 21, 2023

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DeepRank-Mut predicts missense variant pathogenicity using 3D convolutional neural networks (3D-CNNs) and structural features. This approach enhances variant classification accuracy in molecular diagnostics.

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Predicting missense variant pathogenicity is crucial for molecular diagnostics but remains challenging.
Existing tools often struggle to fully integrate diverse data types like evolutionary information and structural features.

Purpose of the Study:

To introduce DeepRank-Mut, a framework for predicting missense variant pathogenicity.
To leverage physicochemical features of amino acids in 3D structural environments using deep learning.

Main Methods:

Extraction of atomic and residue-level features from the variant's 3D structural environment.
Representation of features in multi-channel 3D voxel grids.
Training a 3D convolutional neural network (3D-CNN) for pathogenicity prediction.

Keywords:

3D CNN gain-of-function loss-of-function machine learning missense variant protein structure

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Main Results:

DeepRank-Mut achieves performance comparable to existing resources combining sequence and structural data.
An average accuracy of 0.77 was obtained on independent test datasets using 10-fold cross-validation.
Evolutionary information and solvent accessibility of neighboring residues significantly influence predictions.

Conclusions:

DeepRank-Mut offers a robust deep learning approach for protein structure-guided pathogenicity prediction.
Understanding variant neighborhood contributions and disease mechanisms is key to improving predictive models.
The study provides insights for adopting deep learning in variant pathogenicity assessment.