Folding the human proteome using BioNeMo: A fused dataset of structural models for machine learning purposes.

Human proteins are crucial players in both health and disease. Understanding their molecular landscape is a central topic in biological research. Here, we present an extensive dataset of predicted protein structures for 42,042 distinct human proteins, including splicing variants, derived from the UniProt reference proteome UP000005640. To ensure high quality and comparability, the dataset was generated by combining state-of-the-art modeling-tools AlphaFold 2, OpenFold, and ESMFold, provided within NVIDIA's BioNeMo platform, as well as homology modeling using Innophore's CavitomiX platform. Our dataset is offered in both unedited and edited formats for diverse research requirements. The unedited version contains structures as generated by the different prediction methods, whereas the edited version contains refinements, including a dataset of structures without low prediction-confidence regions and structures in complex with predicted ligands based on homologs in the PDB. We are confident that this dataset represents the most comprehensive collection of human protein structures available today, facilitating diverse applications such as structure-based drug design and the prediction of protein function and interactions.

#COVIDisAirborne: AI-enabled multiscale computational microscopy of delta SARS-CoV-2 in a respiratory aerosol.

We seek to completely revise current models of airborne transmission of respiratory viruses by providing never-before-seen atomic-level views of the SARS-CoV-2 virus within a respiratory aerosol. Our work dramatically extends the capabilities of multiscale computational microscopy to address the significant gaps that exist in current experimental methods, which are limited in their ability to interrogate aerosols at the atomic/molecular level and thus obscure our understanding of airborne transmission. We demonstrate how our integrated data-driven platform provides a new way of exploring the composition, structure, and dynamics of aerosols and aerosolized viruses, while driving simulation method development along several important axes. We present a series of initial scientific discoveries for the SARS-CoV-2 Delta variant, noting that the full scientific impact of this work has yet to be realized.

Artificial intelligence-enabled virtual screening of ultra-large chemical libraries with deep docking.

With the recent explosion of chemical libraries beyond a billion molecules, more efficient virtual screening approaches are needed. The Deep Docking (DD) platform enables up to 100-fold acceleration of structure-based virtual screening by docking only a subset of a chemical library, iteratively synchronized with a ligand-based prediction of the remaining docking scores. This method results in hundreds- to thousands-fold virtual hit enrichment (without significant loss of potential drug candidates) and hence enables the screening of billion molecule-sized chemical libraries without using extraordinary computational resources. Herein, we present and discuss the generalized DD protocol that has been proven successful in various computer-aided drug discovery (CADD) campaigns and can be applied in conjunction with any conventional docking program. The protocol encompasses eight consecutive stages: molecular library preparation, receptor preparation, random sampling of a library, ligand preparation, molecular docking, model training, model inference and the residual docking. The standard DD workflow enables iterative application of stages 3-7 with continuous augmentation of the training set, and the number of such iterations can be adjusted by the user. A predefined recall value allows for control of the percentage of top-scoring molecules that are retained by DD and can be adjusted to control the library size reduction. The procedure takes 1-2 weeks (depending on the available resources) and can be completely automated on computing clusters managed by job schedulers. This open-source protocol, at https://github.com/jamesgleave/DD_protocol , can be readily deployed by CADD researchers and can significantly accelerate the effective exploration of ultra-large portions of a chemical space.

DD-GUI: a graphical user interface for deep learning-accelerated virtual screening of large chemical libraries (Deep Docking).

SUMMARY: Deep learning (DL) can significantly accelerate virtual screening of ultra-large chemical libraries, enabling the evaluation of billions of compounds at a fraction of the computational cost and time required by conventional docking. Here, we introduce DD-GUI, the graphical user interface for such DL approach we have previously developed, termed Deep Docking (DD). The DD-GUI allows for quick setups of large-scale virtual screens in an intuitive way, and provides convenient tools to track the progress and analyze the outcomes of a drug discovery project. AVAILABILITY AND IMPLEMENTATION: DD-GUI is freely available with an MIT license on GitHub at https://github.com/jamesgleave/DeepDockingGUI. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

#COVIDisAirborne: AI-Enabled Multiscale Computational Microscopy of Delta SARS-CoV-2 in a Respiratory Aerosol.

We seek to completely revise current models of airborne transmission of respiratory viruses by providing never-before-seen atomic-level views of the SARS-CoV-2 virus within a respiratory aerosol. Our work dramatically extends the capabilities of multiscale computational microscopy to address the significant gaps that exist in current experimental methods, which are limited in their ability to interrogate aerosols at the atomic/molecular level and thus ob-scure our understanding of airborne transmission. We demonstrate how our integrated data-driven platform provides a new way of exploring the composition, structure, and dynamics of aerosols and aerosolized viruses, while driving simulation method development along several important axes. We present a series of initial scientific discoveries for the SARS-CoV-2 Delta variant, noting that the full scientific impact of this work has yet to be realized. ACM REFERENCE FORMAT: Abigail Dommer 1† , Lorenzo Casalino 1† , Fiona Kearns 1† , Mia Rosenfeld 1 , Nicholas Wauer 1 , Surl-Hee Ahn 1 , John Russo, 2 Sofia Oliveira 3 , Clare Morris 1 , AnthonyBogetti 4 , AndaTrifan 5,6 , Alexander Brace 5,7 , TerraSztain 1,8 , Austin Clyde 5,7 , Heng Ma 5 , Chakra Chennubhotla 4 , Hyungro Lee 9 , Matteo Turilli 9 , Syma Khalid 10 , Teresa Tamayo-Mendoza 11 , Matthew Welborn 11 , Anders Christensen 11 , Daniel G. A. Smith 11 , Zhuoran Qiao 12 , Sai Krishna Sirumalla 11 , Michael O'Connor 11 , Frederick Manby 11 , Anima Anandkumar 12,13 , David Hardy 6 , James Phillips 6 , Abraham Stern 13 , Josh Romero 13 , David Clark 13 , Mitchell Dorrell 14 , Tom Maiden 14 , Lei Huang 15 , John McCalpin 15 , Christo- pherWoods 3 , Alan Gray 13 , MattWilliams 3 , Bryan Barker 16 , HarindaRajapaksha 16 , Richard Pitts 16 , Tom Gibbs 13 , John Stone 6 , Daniel Zuckerman 2 *, Adrian Mulholland 3 *, Thomas MillerIII 11,12 *, ShantenuJha 9 *, Arvind Ramanathan 5 *, Lillian Chong 4 *, Rommie Amaro 1 *. 2021. #COVIDisAirborne: AI-Enabled Multiscale Computational Microscopy ofDeltaSARS-CoV-2 in a Respiratory Aerosol. In Supercomputing '21: International Conference for High Perfor-mance Computing, Networking, Storage, and Analysis . ACM, New York, NY, USA, 14 pages. https://doi.org/finalDOI.

Automated discovery of noncovalent inhibitors of SARS-CoV-2 main protease by consensus Deep Docking of 40 billion small molecules.

Recent explosive growth of 'make-on-demand' chemical libraries brought unprecedented opportunities but also significant challenges to the field of computer-aided drug discovery. To address this expansion of the accessible chemical universe, molecular docking needs to accurately rank billions of chemical structures, calling for the development of automated hit-selecting protocols to minimize human intervention and error. Herein, we report the development of an artificial intelligence-driven virtual screening pipeline that utilizes Deep Docking with Autodock GPU, Glide SP, FRED, ICM and QuickVina2 programs to screen 40 billion molecules against SARS-CoV-2 main protease (Mpro). This campaign returned a significant number of experimentally confirmed inhibitors of Mpro enzyme, and also enabled to benchmark the performance of twenty-eight hit-selecting strategies of various degrees of stringency and automation. These findings provide new starting scaffolds for hit-to-lead optimization campaigns against Mpro and encourage the development of fully automated end-to-end drug discovery protocols integrating machine learning and human expertise.

AI-driven multiscale simulations illuminate mechanisms of SARS-CoV-2 spike dynamics.

We develop a generalizable AI-driven workflow that leverages heterogeneous HPC resources to explore the time-dependent dynamics of molecular systems. We use this workflow to investigate the mechanisms of infectivity of the SARS-CoV-2 spike protein, the main viral infection machinery. Our workflow enables more efficient investigation of spike dynamics in a variety of complex environments, including within a complete SARS-CoV-2 viral envelope simulation, which contains 305 million atoms and shows strong scaling on ORNL Summit using NAMD. We present several novel scientific discoveries, including the elucidation of the spike's full glycan shield, the role of spike glycans in modulating the infectivity of the virus, and the characterization of the flexible interactions between the spike and the human ACE2 receptor. We also demonstrate how AI can accelerate conformational sampling across different systems and pave the way for the future application of such methods to additional studies in SARS-CoV-2 and other molecular systems.

AI-Driven Multiscale Simulations Illuminate Mechanisms of SARS-CoV-2 Spike Dynamics.

We develop a generalizable AI-driven workflow that leverages heterogeneous HPC resources to explore the time-dependent dynamics of molecular systems. We use this workflow to investigate the mechanisms of infectivity of the SARS-CoV-2 spike protein, the main viral infection machinery. Our workflow enables more efficient investigation of spike dynamics in a variety of complex environments, including within a complete SARS-CoV-2 viral envelope simulation, which contains 305 million atoms and shows strong scaling on ORNL Summit using NAMD. We present several novel scientific discoveries, including the elucidation of the spike's full glycan shield, the role of spike glycans in modulating the infectivity of the virus, and the characterization of the flexible interactions between the spike and the human ACE2 receptor. We also demonstrate how AI can accelerate conformational sampling across different systems and pave the way for the future application of such methods to additional studies in SARS-CoV-2 and other molecular systems.

Specific cation effects at aqueous solution-vapor interfaces: Surfactant-like behavior of Li+ revealed by experiments and simulations.

It is now well established by numerous experimental and computational studies that the adsorption propensities of inorganic anions conform to the Hofmeister series. The adsorption propensities of inorganic cations, such as the alkali metal cations, have received relatively little attention. Here we use a combination of liquid-jet X-ray photoelectron experiments and molecular dynamics simulations to investigate the behavior of K+ and Li+ ions near the interfaces of their aqueous solutions with halide ions. Both the experiments and the simulations show that Li+ adsorbs to the aqueous solution-vapor interface, while K+ does not. Thus, we provide experimental validation of the "surfactant-like" behavior of Li+ predicted by previous simulation studies. Furthermore, we use our simulations to trace the difference in the adsorption of K+ and Li+ ions to a difference in the resilience of their hydration shells.

Use of a spinal cage for creating stable constructs in ankle and subtalar fusion.

In complicated foot surgery with reconstruction of the hindfoot, a gap will sometimes be present between the bones that must be filled and stabilized. Bone grafting with structural bone graft is 1 alternative; however, it can collapse and must be stabilized with screws or a nail. A locking intramedullary nail can be used but could lead to nonunion owing to distraction. Newer nails include a compression device but that can result in shortening. We developed a technique that includes distraction of the fusion area with a spinal cage and then compression of the construct by inserting a compression screw through the cage. We present our experience with this technique.We reviewed the data from 7 patients who had undergone surgery using this technique. The technique included distraction of the fusion area and insertion of a titanium cylindrical spinal cage filled with autologous cancellous bone graft. A cannulated compression screw was then inserted through the cage, creating compression of the fusion area against the cage and achieving stabilization of the fusion area. Postoperatively, a non-weightbearing cast was applied for 3 months, followed by a full weightbearing cast until radiographic fusion was apparent. Complete radiographic union was observed in all 7 patients within 6 to 12 months postoperatively. At the latest follow-up visit, the mean American Orthopaedic Foot and Ankle Society scale score was 54 ± 16 (range 30 to 71) points. The use of a cylindrical titanium cage with a local bone graft and stabilization by distraction and compression provided a stable construct, avoided shortening, and led to good fusion. In addition, donor site complications and unpredictable strength loss and lysis of bone allograft were avoided.

The Sterile Elastic Exsanguination Tourniquet vs. the Pneumatic Tourniquet for Total Knee Arthroplasty.

We compared the sterile elastic exsanguination tourniquet and the pneumatic tourniquet for total knee arthroplasty. 145 patients were operated on using a pneumatic tourniquet and 166 with the sterile elastic exsanguination tourniquet. Patients with the sterile elastic exsanguination tourniquet had a smaller decrease in hemoglobin on post-operative days one (P<0.028) and three (P<0.045). The amount of blood collected from drains at 24h was significantly lower in the sterile elastic exsanguination group. A trend towards a higher rate of wound complications within 3months following the operation was found in the pneumatic tourniquet group. The sterile elastic exsanguination tourniquet works at least as good as the pneumatic one.

Interactions of gaseous HNO3 and water with individual and mixed alkyl self-assembled monolayers at room temperature.

The major removal processes for gaseous nitric acid (HNO3) in the atmosphere are dry and wet deposition onto various surfaces. The surface in the boundary layer is often covered with organic films, but the interaction of gaseous HNO3 with them is not well understood. To better understand the factors controlling the uptake of gaseous nitric acid and its dissociation in organic films, studies were carried out using single component and mixtures of C8 and C18 alkyl self-assembled monolayers (SAMs) attached to a germanium (Ge) attenuated total reflectance (ATR) crystal upon which a thin layer of SiOx had been deposited. For comparison, diffuse reflectance infrared Fourier transform spectrometry (DRIFTS) studies were also carried out using a C18 SAM attached to the native oxide layer on the surface of silicon powder. These studies show that the alkyl chain length and order/disorder of the SAMs does not significantly affect the uptake or dissociation/recombination of molecular HNO3. Thus, independent of the nature of the SAM, molecular HNO3 is observed up to 70-90% relative humidity. After dissociation, molecular HNO3 is regenerated on all SAM surfaces when water is removed. Results of molecular dynamics simulations are consistent with experiments and show that defects and pores on the surfaces control the uptake, dissociation and recombination of molecular HNO3. Organic films on surfaces in the boundary layer will certainly be more irregular and less ordered than SAMs studied here, therefore undissociated HNO3 may be present on surfaces in the boundary layer to a greater extent than previously thought. The combination of this observation with the results of recent studies showing enhanced photolysis of nitric acid on surfaces suggests that renoxification of deposited nitric acid may need to be taken into account in atmospheric models.

Thermodynamics of iodide adsorption at the instantaneous air-water interface.

We performed molecular dynamics simulations using both polarizable and non-polarizable force fields to study the adsorption of iodide to the air-water interface. A novel aspect of our analysis is that the progress of ion adsorption is measured as the distance from the instantaneous interface, which is defined by a coarse-graining scheme proposed recently by Willard and Chandler ["Instantaneous liquid interfaces," J. Phys. Chem. B 114, 1954-1958 (2010)]. Referring structural and thermodynamic quantities to the instantaneous interface unmasks molecular-scale details that are obscured by thermal fluctuations when the same quantities are referred to an average measure of the position of the interface, such as the Gibbs dividing surface. Our results suggest that an ion adsorbed at the interface resides primarily in the topmost water layer, and the interfacial location of the ion is favored by enthalpy and opposed by entropy.

Simulation and theory of ions at atmospherically relevant aqueous liquid-air interfaces.

Chemistry occurring at or near the surface of aqueous droplets and thin films in the atmosphere influences air quality and climate. Molecular dynamics simulations are becoming increasingly useful for gaining atomic-scale insight into the structure and reactivity of aqueous interfaces in the atmosphere. Here we review simulation studies of atmospherically relevant aqueous liquid-air interfaces, with an emphasis on ions that play important roles in the chemistry of atmospheric aerosols. In addition to surveying results from simulation studies, we discuss challenges to the refinement and experimental validation of the methodology for simulating ion adsorption to the air-water interface and recent advances in elucidating the driving forces for adsorption. We also review the recent development of a dielectric continuum theory capable of reproducing simulation and experimental data on ion behavior at aqueous interfaces.

Understanding hydrogen sorption in a polar metal-organic framework with constricted channels.

A high fidelity molecular model is developed for a metal-organic framework (MOF) with narrow (approximately 7.3 Å) nearly square channels. MOF potential models, both with and neglecting explicit polarization, are constructed. Atomic partial point charges for simulation are derived from both fragment-based and fully periodic electronic structure calculations. The molecular models are designed to accurately predict and retrodict material gas sorption properties while assessing the role of induction for molecular packing in highly restricted spaces. Thus, the MOF is assayed via grand canonical Monte Carlo (GCMC) for its potential in hydrogen storage. The confining channels are found to typically accommodate between two to three hydrogen molecules in close proximity to the MOF framework at or near saturation pressures. Further, the net attractive potential energy interactions are dominated by van der Waals interactions in the highly polar MOF - induction changes the structure of the sorbed hydrogen but not the MOF storage capacity. Thus, narrow channels, while providing reasonably promising isosteric heat values, are not the best choice of topology for gas sorption applications from both a molecular and gravimetric perspective.

Electrochemical Surface Potential Due to Classical Point Charge Models Drives Anion Adsorption to the Air-Water Interface.

We demonstrate that the driving forces for ion adsorption to the air-water interface for point charge models result from both cavitation and a term that is of the form of a negative electrochemical surface potential. We carefully characterize the role of the free energy due to the electrochemical surface potential computed from simple empirical models and its role in ionic adsorption within the context of dielectric continuum theory. Our research suggests that the electrochemical surface potential due to point charge models provides anions with a significant driving force for adsoprtion to the air-water interface. This is contrary to the results of ab initio simulations that indicate that the average electrostatic surface potential should favor the desorption of anions at the air-water interface. The results have profound implications for the studies of ionic distributions in the vicinity of hydrophobic surfaces and proteins.

Dissociation of strong acid revisited: X-ray photoelectron spectroscopy and molecular dynamics simulations of HNO3 in water.

Molecular-level insight into the dissociation of nitric acid in water is obtained from X-ray photoelectron spectroscopy and first-principles molecular dynamics (MD) simulations. Our combined studies reveal surprisingly abrupt changes in solvation configurations of undissociated nitric acid at approximately 4 M concentration. Experimentally, this is inferred from shifts of the N1s binding energy of HNO(3)(aq) as a function of concentration and is associated with variations in the local electronic structure of the nitrogen atom. It also shows up as a discontinuity in the degree of dissociation as a function of concentration, determined here from the N1s photoelectron signal intensity, which can be separately quantified for undissociated HNO(3)(aq) and dissociated NO(3)(-)(aq). Intermolecular interactions within the nitric acid solution are discussed on the basis of MD simulations, which reveal that molecular HNO(3) interacts remarkably weakly with solvating water molecules at low concentration; around 4 M there is a turnover to a more structured solvation shell, accompanied by an increase in hydrogen bonding between HNO(3) and water. We suggest that the driving force behind the more structured solvent configuration of HNO(3) is the overlap of nitric acid solvent shells that sets in around 4 M concentration.

Atomic charges derived from electrostatic potentials for molecular and periodic systems.

We present a method for fitting atomic charges to the electrostatic potential (ESP) of periodic and nonperiodic systems. This method is similar to the method of Campa et al. [ J. Chem. Theory Comput. 2009, 5, 2866]. We compare the Wolf and Ewald long-range electrostatic summation methods in calculating the ESP for periodic systems. We find that the Wolf summation is computationally more efficient than the Ewald summation by about a factor of 5 with comparable accuracy. Our analysis shows that the choice of grid mesh size influences the fitted atomic charges, especially for systems with buried (highly coordinated) atoms. We find that a maximum grid spacing of 0.2−0.3 A is required to obtain reliable atomic charges. The effect of the exclusion radius for point selection is assessed; we find that the common choice of using the van der Waals (vdW) radius as the exclusion radius for each atom may result in large deviations between the ESP generated from the ab initio calculations and that computed from the fitted charges, especially for points closest to the exclusion radii. We find that a larger value of exclusion radius than commonly used, 1.3 times the vdW radius, provides more reliable results. We find that a penalty function approach for fitting charges for buried atoms, with the target charge taken from Bader charge analysis, gives physically reasonable results.

An Accurate and Transferable Intermolecular Diatomic Hydrogen Potential for Condensed Phase Simulation.

An anisotropic many-body H2 potential energy function has been developed for use in heterogeneous systems. The intermolecular potential has been derived from first principles and expressed in a form that is readily transferred to exogenous systems, e.g. in modeling H2 sorption in solid-state materials. Explicit many-body polarization effects, known to be important in simulating hydrogen at high density, are incorporated. The analytic form of the potential energy function is suitable for methods of statistical physics, such as Monte Carlo or Molecular Dynamics simulation. The model has been validated on dense supercritical hydrogen and demonstrated to reproduce the experimental data with high accuracy.

On the mechanism of hydrogen storage in a metal-organic framework material.

Monte Carlo simulations were performed modeling hydrogen sorption in a recently synthesized metal-organic framework material (MOF) that exhibits large molecular hydrogen uptake capacity. The MOF is remarkable because at 78 K and 1.0 atm it sorbs hydrogen at a density near that of liquid hydrogen (at 20 K and 1.0 atm) when considering H2 density in the pores. Unlike most other MOFs that have been investigated for hydrogen storage, it has a highly ionic framework and many relatively small channels. The simulations demonstrate that it is both of these physical characteristics that lead to relatively strong hydrogen interactions in the MOF and ultimately large hydrogen uptake. Microscopically, hydrogen interacts with the MOF via three principle attractive potential energy contributions: Van der Waals, charge-quadrupole, and induction. Previous simulations of hydrogen storage in MOFs and other materials have not focused on the role of polarization effects, but they are demonstrated here to be the dominant contribution to hydrogen physisorption. Indeed, polarization interactions in the MOF lead to two distinct populations of dipolar hydrogen that are identified from the simulations that should be experimentally discernible using, for example, Raman spectroscopy. Since polarization interactions are significantly enhanced by the presence of a charged framework with narrow pores, MOFs are excellent hydrogen storage candidates.