The urgent search for drugs to combat SARS-CoV-2 has included the use of supercomputers. The use of general-purpose graphical processing units (GPUs), massive parallelism, and new software for high performance computing (HPC) has allowed researchers to search the vast chemical space of potential drugs faster than ever before. We developed a new drug discovery pipeline using the Summit supercomputer at Oak Ridge National Laboratory to help pioneer this effort, with new platforms that incorporate GPU-accelerated simulation and allow for the virtual screening of billions of potential drug compounds in days compared to weeks or months for their ability to inhibit SARS-COV-2 proteins. This effort will accelerate the process of developing drugs to combat the current COVID-19 pandemic and other diseases. XX 2020 1

In this work, we expand on a dataset recently introduced for protein interface prediction (PIP), the Database of Interacting Protein Structures (DIPS), to present DIPS-Plus, an enhanced, feature-rich dataset of 42,112 complexes for machine learning of protein interfaces. While the original DIPS dataset contains only the Cartesian coordinates for atoms contained in the protein complex along with their types, DIPS-Plus contains multiple residue-level features including surface proximities, half-sphere amino acid compositions, and new profile hidden Markov model (HMM)-based sequence features for each amino acid, providing researchers a curated feature bank for training protein interface prediction methods. We demonstrate through rigorous benchmarks that training an existing state-of-the-art (SOTA) model for PIP on DIPS-Plus yields new SOTA results, surpassing the performance of some of the latest models trained on residue-level and atom-level encodings of protein complexes to date.

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.

This work describes an analytical procedure, single particle-inductively coupled plasma-time-of-flight-mass spectrometry (SP-ICP-TOF-MS), that was developed to determine the platinum binding efficiency of protein-coated magnetic microparticles. SP-ICP-TOF-MS is advantageous due to its ability to quasi-simultaneously detect all nuclides (⁷Li–²⁴²Pu), allowing for both platinum and iron (composition of magnetic microparticles) to be measured concurrently. This method subsequently allows for the differentiation between bound and unbound platinum. The 1 μm magnetic microparticles were fully characterized for their iron concentration, particle concentration, and trace element composition by bulk digestion-ICP-MS and SP-ICP-TOF-MS. The results of both approaches agreed with the certificate values. Using the single particle methodology the platinum loading was quantified to be to 0.18 ± 0.02 fg per particle and 0.32 ± 0.02 fg per particle, for the streptavidin-coated and azurin-coated microparticles, respectively. Both streptavidin-coated and the azurin-coated microparticles had a particle–platinum association of >65%. Platinum bound samples were also analyzed via bulk digestion-based ICP-MS. The bulk ICP-MS results overestimated platinum loading due to free platinum in the samples. This highlights the importance of single particle analysis for a closer inspection of platinum binding performance. The SP-ICP-TOF-MS approach offers advantages over typical bulk digestion methods by eliminating laborious sample preparation, enabling differentiation between bound/unbound platinum in a solution, and quantification of platinum on a particle-by-particle basis. The procedure presented here enables quantification of metal content per particle, which could be broadly implemented for other single particle applications.