Quantifying platinum binding on protein-functionalized magnetic microparticles using single particle-ICP-TOF-MS.

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.

Tracing mechanistic pathways and reaction kinetics toward equilibrium in reactive molten salts.

In the dynamic environment of multi-component reactive molten salts, speciation unfolds as a complex process, involving multiple competing reaction pathways that are likely to face free energy barriers before reaching the reaction equilibria. Herein, we unravel intricate speciation in the AlCl3-KCl melt compositions with rate theory and ab initio molecular dynamics simulations. We find that the compositions with 100 and 50 mol% AlCl3 exclusively comprise neutral Al2Cl6 dimers and charged AlCl4- monomers, respectively. In intermediate AlCl3-KCl compositions, the chemical speciation proves to be a very complex process, requiring over 0.5 nanosecond to reach an equilibrium distribution of multiple species. It is a consequence of the competitive formation and dissociation of additional species, including charged Al dimers, trimers, and tetramers. Here, the species formation occurs through ion exchange events, which we explain by computing free energy landscapes and employing a Marcus-like rate theory. We show that both interspecies and intraspecies ion exchanges are probable and are dictated by the local structural reorganization reflected in the change of local coulombic fields. The species distributions are validated by comparing computed Raman spectra and neutron structure factors with the available experimental data. We find an excellent simulation-experiment agreement in both cases. Nevertheless, Raman spectroscopy turns out to be particularly advantageous for distinguishing between unique species distributions because of the distinct vibrational signatures of different species. The mechanistic insight into reaction dynamics gained in this study will be essential for the advancement of molten salts as reactive media in high-temperature energy applications.

Predicted structural proteome of Sphagnum divinum and proteome-scale annotation.

MOTIVATION: Sphagnum-dominated peatlands store a substantial amount of terrestrial carbon. The genus is undersampled and under-studied. No experimental crystal structure from any Sphagnum species exists in the Protein Data Bank and fewer than 200 Sphagnum-related genes have structural models available in the AlphaFold Protein Structure Database. Tools and resources are needed to help bridge these gaps, and to enable the analysis of other structural proteomes now made possible by accurate structure prediction. RESULTS: We present the predicted structural proteome (25 134 primary transcripts) of Sphagnum divinum computed using AlphaFold, structural alignment results of all high-confidence models against an annotated nonredundant crystallographic database of over 90,000 structures, a structure-based classification of putative Enzyme Commission (EC) numbers across this proteome, and the computational method to perform this proteome-scale structure-based annotation. AVAILABILITY AND IMPLEMENTATION: All data and code are available in public repositories, detailed at https://github.com/BSDExabio/SAFA. The structural models of the S. divinum proteome have been deposited in the ModelArchive repository at https://modelarchive.org/doi/10.5452/ma-ornl-sphdiv.

DIPS-Plus: The enhanced database of interacting protein structures for interface prediction.

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.

tinyIFD: A High-Throughput Binding Pose Refinement Workflow Through Induced-Fit Ligand Docking.

A critical step in structure-based drug discovery is predicting whether and how a candidate molecule binds to a model of a therapeutic target. However, substantial protein side chain movements prevent current screening methods, such as docking, from accurately predicting the ligand conformations and require expensive refinements to produce viable candidates. We present the development of a high-throughput and flexible ligand pose refinement workflow, called "tinyIFD". The main features of the workflow include the use of specialized high-throughput, small-system MD simulation code mdgx.cuda and an actively learning model zoo approach. We show the application of this workflow on a large test set of diverse protein targets, achieving 66% and 76% success rates for finding a crystal-like pose within the top-2 and top-5 poses, respectively. We also applied this workflow to the SARS-CoV-2 main protease (Mpro) inhibitors, where we demonstrate the benefit of the active learning aspect in this workflow.

SARS-CoV2 billion-compound docking.

This dataset contains ligand conformations and docking scores for 1.4 billion molecules docked against 6 structural targets from SARS-CoV2, representing 5 unique proteins: MPro, NSP15, PLPro, RDRP, and the Spike protein. Docking was carried out using the AutoDock-GPU platform on the Summit supercomputer and Google Cloud. The docking procedure employed the Solis Wets search method to generate 20 independent ligand binding poses per compound. Each compound geometry was scored using the AutoDock free energy estimate, and rescored using RFScore v3 and DUD-E machine-learned rescoring models. Input protein structures are included, suitable for use by AutoDock-GPU and other docking programs. As the result of an exceptionally large docking campaign, this dataset represents a valuable resource for discovering trends across small molecule and protein binding sites, training AI models, and comparing to inhibitor compounds targeting SARS-CoV-2. The work also gives an example of how to organize and process data from ultra-large docking screens.

OpenMDlr: parallel, open-source tools for general protein structure modeling and refinement from pairwise distances.

SUMMARY: Easy-to-use, open-source, general-purpose programs for modeling a protein structure from inter-atomic distances are needed for modeling from experimental data and refinement of predicted protein structures. OpenMDlr is an open-source Python package for modeling protein structures from pairwise distances between any atoms, and optionally, dihedral angles. We provide a user-friendly input format for harnessing modern biomolecular force fields in an easy-to-install package that can efficiently make use of multiple compute cores. AVAILABILITY AND IMPLEMENTATION: OpenMDlr is available at https://github.com/BSDExabio/OpenMDlr-amber. The package is written in Python (versions 3.x). All dependencies are open-source and can be installed with the Conda package management system. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Hit Expansion of a Noncovalent SARS-CoV-2 Main Protease Inhibitor.

Inhibition of the SARS-CoV-2 main protease (Mpro) is a major focus of drug discovery efforts against COVID-19. Here we report a hit expansion of non-covalent inhibitors of Mpro. Starting from a recently discovered scaffold (The COVID Moonshot Consortium. Open Science Discovery of Oral Non-Covalent SARS-CoV-2 Main Protease Inhibitor Therapeutics. bioRxiv 2020.10.29.339317) represented by an isoquinoline series, we searched a database of over a billion compounds using a cheminformatics molecular fingerprinting approach. We identified and tested 48 compounds in enzyme inhibition assays, of which 21 exhibited inhibitory activity above 50% at 20 µM. Among these, four compounds with IC50 values around 1 µM were found. Interestingly, despite the large search space, the isoquinolone motif was conserved in each of these four strongest binders. Room-temperature X-ray structures of co-crystallized protein-inhibitor complexes were determined up to 1.9 Å resolution for two of these compounds as well as one of the stronger inhibitors in the original isoquinoline series, revealing essential interactions with the binding site and water molecules. Molecular dynamics simulations and quantum chemical calculations further elucidate the binding interactions as well as electrostatic effects on ligand binding. The results help explain the strength of this new non-covalent scaffold for Mpro inhibition and inform lead optimization efforts for this series, while demonstrating the effectiveness of a high-throughput computational approach to expanding a pharmacophore library.

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

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.

Supercomputing Pipelines Search for Therapeutics Against COVID-19.

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.

GPU-Accelerated Drug Discovery with Docking on the Summit Supercomputer: Porting, Optimization, and Application to COVID-19 Research.

Protein-ligand docking is an in silico tool used to screen potential drug compounds for their ability to bind to a given protein receptor within a drug-discovery campaign. Experimental drug screening is expensive and time consuming, and it is desirable to carry out large scale docking calculations in a high-throughput manner to narrow the experimental search space. Few of the existing computational docking tools were designed with high performance computing in mind. Therefore, optimizations to maximize use of high-performance computational resources available at leadership-class computing facilities enables these facilities to be leveraged for drug discovery. Here we present the porting, optimization, and validation of the AutoDock-GPU program for the Summit supercomputer, and its application to initial compound screening efforts to target proteins of the SARS-CoV-2 virus responsible for the current COVID-19 pandemic.

Geometric Patterns for Neighboring Bases Near the Stacked State in Nucleic Acid Strands.

Structural variation in base stacking has been analyzed frequently in isolated double helical contexts for nucleic acids, but not as often in nonhelical geometries or in complex biomolecular environments. In this study, conformations of two neighboring bases near their stacked state in any environment are comprehensively characterized for single-strand dinucleotide (SSD) nucleic acid crystal structure conformations. An ensemble clustering method is used to identify a reduced set of representative stacking geometries based on pairwise distances between select atoms in consecutive bases, with multiple separable conformational clusters obtained for categories divided by nucleic acid type (DNA/RNA), SSD sequence, stacking face orientation, and the presence or absence of a protein environment. For both DNA and RNA, SSD conformations are observed that are either close to the A-form, or close to the B-form, or intermediate between the two forms, or further away from either form, illustrating the local structural heterogeneity near the stacked state. Among this large variety of distinct conformations, several common stacking patterns are observed between DNA and RNA, and between nucleic acids in isolation or in complex with proteins, suggesting that these might be stable stacking orientations. Noncanonical face/face orientations of the two bases are also observed for neighboring bases in the same strand, but their frequency is much lower, with multiple SSD sequences across categories showing no occurrences of such unusual stacked conformations. The resulting reduced set of stacking geometries is directly useful for stacking-energy comparisons between empirical force fields, prediction of plausible localized variations in single-strand structures near their canonical states, and identification of analogous stacking patterns in newly solved nucleic acid containing structures.

RNA approaches the B-form in stacked single strand dinucleotide contexts.

Duplex RNA adopts an A-form structure, while duplex DNA interconverts between the A- and B-forms depending on the environment. The C2'-endo sugar pucker seen in B-form DNA can occur infrequently in ribose sugars as well, but RNA is not understood to assume B-form conformations. Through analysis of over 45,000 stacked single strand dinucleotide (SSD) crystal structure conformations, this study demonstrates that RNA is capable of adopting a wide conformational range between the canonical A- and B-forms at the localized SSD level, including many B-form-like conformations. It does so through C2'-endo ribose conformations in one or both nucleotides, and B-form-like neighboring base stacking patterns. As chemical reactions on nucleic acids involve localized changes in chemical bonds, the understanding of how enzymes distinguish between DNA and RNA nucleotides is altered by the energetic accessibility of these rare B-form-like RNA SSD conformations. The existence of these conformations also has direct implications in parametrization of molecular mechanics energy functions used extensively to model nucleic acid behavior., 2016. © 2015 Wiley Periodicals, Inc. Biopolymers 105: 65-82, 2016.

Sequence and Temperature Influence on Kinetics of DNA Strand Displacement at Gold Electrode Surfaces.

Understanding complex contributions of surface environment to tethered nucleic acid sensing experiments has proven challenging, yet it is important because it is essential for interpretation and calibration of indispensable methods, such as microarrays. We investigate the effects of DNA sequence and solution temperature gradients on the kinetics of strand displacement at heated gold wire electrodes, and at gold disc electrodes in a heated solution. Addition of a terminal double mismatch (toehold) provides a reduction in strand displacement energy barriers sufficient to probe the secondary mechanisms involved in the hybridization process. In four different DNA capture probe sequences (relevant for the identification of genetically modified maize MON810), all but one revealed a high activation energy up to 200 kJ/mol during hybridization, that we attribute to displacement of protective strands by capture probes. Protective strands contain 4 to 5 mismatches to ease their displacement by the surface-confined probes at the gold electrodes. A low activation energy (30 kJ/mol) was observed for the sequence whose protective strand contained a toehold and one central mismatch, its kinetic curves displayed significantly different shapes, and we observed a reduced maximum signal intensity as compared to other sequences. These findings point to potential sequence-related contributions to oligonucleotide diffusion influencing kinetics. Additionally, for all sequences studied with heated wire electrodes, we observed a 23 K lower optimal hybridization temperature in comparison with disc electrodes in heated solution, and greatly reduced voltammetric signals after taking into account electrode surface area. We propose that thermodiffusion due to temperature gradients may influence both hybridization and strand displacement kinetics at heated microelectrodes, an explanation supported by computational fluid dynamics. DNA assays with surface-confined capture probes and temperature gradients should not neglect potential influences of thermodiffusion as well as sequence-related effects. Furthermore, studies attempting to characterize surface-tethered environments should consider thermodiffusion if temperature gradients are involved.