High-Performance Deep Learning Toolbox for Genome-Scale Prediction of Protein Structure and Function

Abstract

Computational biology is one of many scientific disciplines ripe for innovation and acceleration with the advent of high-performance computing (HPC). In recent years, the field of machine learning has also seen significant benefits from adopting HPC practices. In this work, we present a novel HPC pipeline that incorporates various machine-learning approaches for structure-based functional annotation of proteins on the scale of whole genomes. Our pipeline makes extensive use of deep learning and provides computational insights into best practices for training advanced deep-learning models for high-throughput data such as proteomics data. We showcase methodologies our pipeline currently supports and detail future tasks for our pipeline to envelop, including large-scale sequence comparison using SAdLSA and prediction of protein tertiary structures using AlphaFold2.

Keywords: computational biology; deep learning; high-performance computing; machine learning; protein sequence alignment; protein structure prediction.

Fig. 1.

Overview of SAdLSA, a deep-learning algorithm for protein sequence alignment. The symbol ⊗ denotes outer concatenation of two embedding vectors

Fig. 2.

Scheme for SAdLSA deployment at scale on Summit

Fig. 3.

Performance of SAdLSA on large sets of proteins and scaling across Summit. A: Runtime, in seconds, against the Pfam database for each sequence using one node. B: Runtime, in seconds, against the Pfam database for each sequence using one thousand nodes. Parallelization was over the Pfam database for each sequence input for inference calculation.

Fig. 4.

Performance of SAdLSA on the PDB70 database on Summit. Top: Weak scaling– total time in minutes for a given number of sequences to be aligned to the PDB70 using 12 sequences, 96 nodes, and 120 sequences, 1000 nodes. The set of 12 was chosen to have a similar length distribution ad the set of 120. Bottom: Runtime, in seconds, against the PDB70 database for each sequence in the 120 sequence set used in Figure 3 above, using one thousand nodes. Parallelization was over the PDB70 database for each sequence input for inference calculation.

Fig. 5.

Scheme of the model parallelization method on a Summit node for domain boundary prediction

Fig. 6.

Simplified scheme for annotation pipeline with consensus using our toolbox.