Improving AlphaFold predicted contacts in alpha-helical transmembrane proteins structures using structural features.

Background: Residue contacts maps offer a 2-d reduced representation of 3-d protein structures and constitute a structural constraint and scaffold in structural modeling. In addition, contact maps are also an effective tool in identifying interhelical binding sites and drawing insights about protein function. While most works predict contact maps using features derived from sequences, we believe information from known structures can be leveraged for a prediction improvement in unknown structures where decent approximate structures such as ones predicted by AlphaFold2 are available.

Results: Alphafold2's predicted structures are found to be quite accurate at inter-helical residue contact prediction task, achieving 83% average precision. We adopt an unconventional approach, using features extracted from atomic structures in the neighborhood of a residue pair and use them to predicting residue contact. We trained on features derived from experimentally determined structures and predicted on features derived from AlphaFold2's predicted structures. Our results demonstrate a remarkable improvement over AlphaFold2 achieving over 91.9% average precision for held-out and over 89.5% average precision in cross validation experiments.

Conclusion: Training on features generated from experimentally determined structures, we were able to leverage knowledge from known structures to significantly improve the contacts predicted using AlphaFold2 structures. We demonstrated that using coordinates directly (instead of the proposed features) does not lead to an improvement in contact prediction performance.