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The inhibition of protein–protein interactions is a growing strategy in drug development. In addition to structured regions, many protein loop regions are involved in protein–protein interactions and thus have been identified as potential drug targets. To effectively target such regions, protein structure is critical. Loop structure prediction is a challenging subgroup in the field of protein structure prediction because of the reduced level of conservation in protein sequences compared to the secondary structure elements. AlphaFold 2 has been suggested to be one of the greatest achievements in the field of protein structure prediction. The AlphaFold 2 predicted protein structures near the X-ray resolution in the Critical Assessment of protein Structure Prediction (CASP 14) competition in 2020. The purpose of this work is to survey the performance of AlphaFold 2 in specifically predicting protein loop regions. We have constructed an independent dataset of 31,650 loop regions from 2613 proteins (deposited after the AlphaFold 2 was trained) with both experimentally determined structures and AlphaFold 2 predicted structures. With extensive evaluation using our dataset, the results indicate that AlphaFold 2 is a good predictor of the structure of loop regions, especially for short loop regions. Loops less than 10 residues in length have an average Root Mean Square Deviation (RMSD) of 0.33 Å and an average the Template Modeling score (TM-score) of 0.82. However, we see that as the number of residues in a given loop increases, the accuracy of AlphaFold 2’s prediction decreases. Loops more than 20 residues in length have an average RMSD of 2.04 Å and an average TM-score of 0.55. Such a correlation between accuracy and length of the loop is directly linked to the increase in flexibility. Moreover, AlphaFold 2 does slightly over-predict α-helices and β-strands in proteins.
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Initial GPU Optimization of Template Modeling Score
Accuracy in the prediction of protein structures is key in understanding the biological functions of different proteins. Numerous measures of similarity tools for protein structures have been developed over the years, and these include Root Mean Square Deviation (RMSD), as well as Template Modeling Score (TM-score). While RMSD is influenced by the length of the protein and therefore the similarity between superimposed models can be affected by divergent loops in the models, TM-score is rather a robust and a more accurate method. TM-score, however, is much slower than RMSD in terms of calculations for the optimal superimposed model. Here, we present initial optimization work on GPU-TM-score, a GPU accelerated Template Modeling Score for fast and accurate measuring of similarity between protein structures. Our optimization is based on OpenACC parallelization and performance analysis of bottleneck regions and the KABSCH algorithm for the calculation of optimal superimposition within parallel architectures. Our initial results indicate an average 3.14× speedup compared to original TM-score on a benchmark set of 20 protein structures. This speedup is recorded on an Nvidia Volta V100 GPU compared to an AMD EPYC 7742 64-core processor.
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- PAR ID:
- 10392948
- Date Published:
- Journal Name:
- Chronicle of computing
- ISSN:
- 2831-350X
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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