IFF: Identifying key residues in intrinsically disordered regions of proteins using machine learning

Abstract

Conserved residues in protein homolog sequence alignments are structurally or functionally important. For intrinsically disordered proteins or proteins with intrinsically disordered regions (IDRs), however, alignment often fails because they lack a steric structure to constrain evolution. Although sequences vary, the physicochemical features of IDRs may be preserved in maintaining function. Therefore, a method to retrieve common IDR features may help identify functionally important residues. We applied unsupervised contrastive learning to train a model with self-attention neuronal networks on human IDR orthologs. Parameters in the model were trained to match sequences in ortholog pairs but not in other IDRs. The trained model successfully identifies previously reported critical residues from experimental studies, especially those with an overall pattern (e.g., multiple aromatic residues or charged blocks) rather than short motifs. This predictive model can be used to identify potentially important residues in other proteins, improving our understanding of their functions. The trained model can be run directly from the Jupyter Notebook in the GitHub repository using Binder (mybinder.org). The only required input is the primary sequence. The training scripts are available on GitHub (https://github.com/allmwh/IFF). The training datasets have been deposited in an Open Science Framework repository (https://osf.io/jk29b).

Keywords: intrinsically disordered proteins; liquid-liquid phase separation; unsupervised contrastive machine learning.

FIGURE 1

Flowchart of the training scheme. (a) Schematic representation of how the training datasets were constructed from human sequences (orange lines) and orthologs (green lines). (b) A training batch made up of 50 randomly selected subgroups. (c) Embedding of the human sequence and one of its orthologs from the same subgroup (selection probability weighted by dissimilarity) to different dimensions (as a tensor for each sequence). (d) The architecture of the training model. The steps in panels (b)–(d) were repeated 580 times to cover all subgroups in the training set, and the whole process (a training epoch) was repeated 400 times.

FIGURE 2

Results of the trained model for reference proteins and attention score distributions for individual amino acids. (a–d) Sequences and attention scores for the intrinsically disordered regions of (a) the RNA‐binding proteins TDP‐43, FUS, and hnRNP‐A1, (b) human and zebrafish galectin‐3, (c) NPM1, FMRP, and Caprin‐1, and (d) Pbp‐1. The attention scores appear as heatmaps from high (red) to low (gray) in the top row of each protein along with residue numbers. Amino acids with different physical properties are shown on separate rows as indicated in panel (a). Purple arrows indicate amino acids of known functional importance. (e) Half‐violin plots of the distribution of attention scores in human IDRs for each amino acid, sorted by median value from high (tryptophan, W) to low (alanine, A). IDRs, intrinsically disordered regions.