Predicting the Structural Impact of Human Alternative Splicing

Abstract

SummaryProtein structure prediction with neural networks is a powerful new method for linking protein sequence, structure, and function, but structures have generally been predicted for only a single isoform of each gene, neglecting splice variants. To investigate the structural implications of alternative splicing, we used AlphaFold2 to predict the structures of more than 11,000 human isoforms. We employed multiple metrics to identify splicing-induced structural alterations, including template matching score, secondary structure composition, surface charge distribution, radius of gyration, accessibility of post-translational modification sites, and structure-based function prediction. We identified examples of how alternative splicing induced clear changes in each of these properties. Structural similarity between isoforms largely correlated with degree of sequence identity, but we identified a subset of isoforms with low structural similarity despite high sequence similarity. Exon skipping and alternative last exons tended to increase the surface charge and radius of gyration. Splicing also buried or exposed numerous post-translational modification sites, most notably among the isoforms ofBAX. Functional prediction nominated numerous functional differences among isoforms of the same gene, with loss of function compared to the reference predominating. Finally, we used single-cell RNA-seq data from the Tabula Sapiens to determine the cell types in which each structure is expressed. Our work represents an important resource for studying the structure and function of splice isoforms across the cell types of the human body.

A Reversible Spherical Geometric Conversion of Protein Backbone Structure Coordinate Matrix to Three Independent Vectors of ρ, θ and φ

“…This feature is crucial for maintaining prediction accuracy and enabling further analysis of the extracted structural features for its future application in protein structure prediction [13][14][15][16][17][18]101]. 2. the compact representation offered by spherical coordinates with Caenopore-5 as an example enhances computational efficiency, making our method suitable for large-scale (for both PDB and AlphaFoldDB) protein structure prediction tasks for the entire protein molecular space [47,[102][103][104][105][106][107][108][109]. 3. lastly, the intuitive nature of spherical coordinates also aids in the interpretation of structural features, potentially offering insights into the underlying principles governing protein folding and function [16,[110][111][112][113][114][115][116][117][118].…”

“…In particular, the key for machine learning-based protein structure prediction is the extraction of essential structural features from protein data, which serve as the basis for predicting the threedimensional arrangement of atoms in a protein molecule [44][45][46][47][48][49][50][51][52][53]. Take AlphaFold for example, which is an artificial intelligence system developed by DeepMind, a subsidiary of Alphabet Inc. (Google's parent company).…”