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1. Implementing and Assessing an Alchemical Method for Calculating Protein–Protein Binding Free Energy

   https://doi.org/doi: 10.1021/acs.jctc.0c01045  

   Patel, Dharmeshkumar ; Patel, Jagdish S. ; Ytreberg, F. Marty ( March 2021 , Journal of chemical theory and computation)

   null (Ed.)

   Protein–protein binding is fundamental to most biological processes. It is important to be able to use computation to accurately estimate the change in protein–protein binding free energy due to mutations in order to answer biological questions that would be experimentally challenging, laborious, or time-consuming. Although nonrigorous free-energy methods are faster, rigorous alchemical molecular dynamics-based methods are considerably more accurate and are becoming more feasible with the advancement of computer hardware and molecular simulation software. Even with sufficient computational resources, there are still major challenges to using alchemical free-energy methods for protein–protein complexes, such as generating hybrid structures and topologies, maintaining a neutral net charge of the system when there is a charge-changing mutation, and setting up the simulation. In the current study, we have used the pmx package to generate hybrid structures and topologies, and a double-system/single-box approach to maintain the net charge of the system. To test the approach, we predicted relative binding affinities for two protein–protein complexes using a nonequilibrium alchemical method based on the Crooks fluctuation theorem and compared the results with experimental values. The method correctly identified stabilizing from destabilizing mutations for a small protein–protein complex, and a larger, more challenging antibody complex. Strong correlations were obtained between predicted and experimental relative binding affinities for both protein–protein systems. 

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2. To probe the binding pathway of a selective compound (D089-0563) to c-MYC Pu24 G-quadruplex using free ligand binding simulations and Markov state model analysis

   https://doi.org/10.1039/d0cp03863f  

   Chen, Brian ; Fountain, Griffin ; Sullivan, Holli-Joi ; Paradis, Nicholas ; Wu, Chun ( October 2020 , Physical Chemistry Chemical Physics)

   null (Ed.)

   D089-0563 is a highly promising anti-cancer compound that selectively binds the transcription-silencing G-quadruplex element (Pu27) at the promoter region of the human c-MYC oncogene; however, its binding mechanism remains elusive. The structure of Pu27 is not available due to its polymorphism, but the G-quadruplex structures of its two shorter derivatives in complex with a ligand (Pu24/Phen-DC3 and Pu22/DC-34) are available and show significant structural variance as well as different ligand binding patterns in the 3′ region. Because D089-0563 shares the same scaffold as DC34 while having a significantly different scaffold from Phen-DC3, we picked Pu24 instead of Pu22 for this study in order to gain additional ligand binding insight. Using free ligand molecular dynamics binding simulations (33 μs), we probed the binding of D089-0563 to Pu24. Our clustering analysis identified three binding modes (top, side, and bottom) and subsequent MMPBSA binding energy analysis identified the top mode as the most thermodynamically stable. Our Markov State Model (MSM) analysis revealed that there are three parallel pathways for D089-0563 to the top mode from unbound state and that the ligand binding follows the conformational selection mechanism. Combining our predicted complex structures with the two experimental structures, it is evident that structural differences in the 3′ region between Pu24 and Pu22 lead to different binding behaviors despite having similar ligands; this also explains the different promoter activity caused by the two G-quadruplex sequences observed in a recent synthetic biology study. Based on interaction insights, 625 D089-0563 derivatives were designed and docked; 59 of these showed slightly improved docking scores. 

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3. Evaluation and intercomparison of wildfire smoke forecasts from multiple modeling systems for the 2019 Williams Flats fire

   https://doi.org/10.5194/acp-21-14427-2021  

   Ye, Xinxin ; Arab, Pargoal ; Ahmadov, Ravan ; James, Eric ; Grell, Georg A. ; Pierce, Bradley ; Kumar, Aditya ; Makar, Paul ; Chen, Jack ; Davignon, Didier ; et al ( January 2021 , Atmospheric Chemistry and Physics)

   Abstract. Wildfire smoke is one of the most significant concerns ofhuman and environmental health, associated with its substantial impacts onair quality, weather, and climate. However, biomass burning emissions andsmoke remain among the largest sources of uncertainties in air qualityforecasts. In this study, we evaluate the smoke emissions and plumeforecasts from 12 state-of-the-art air quality forecasting systemsduring the Williams Flats fire in Washington State, US, August 2019, whichwas intensively observed during the Fire Influence on Regional to GlobalEnvironments and Air Quality (FIREX-AQ) field campaign. Model forecasts withlead times within 1 d are intercompared under the same framework basedon observations from multiple platforms to reveal their performanceregarding fire emissions, aerosol optical depth (AOD), surface PM2.5,plume injection, and surface PM2.5 to AOD ratio. The comparison ofsmoke organic carbon (OC) emissions suggests a large range of daily totalsamong the models, with a factor of 20 to 50. Limited representations of thediurnal patterns and day-to-day variations of emissions highlight the needto incorporate new methodologies to predict the temporal evolution andreduce uncertainty of smoke emission estimates. The evaluation of smoke AOD(sAOD) forecasts suggests overall underpredictions in both the magnitude andsmoke plume area for nearly all models, although the high-resolution modelshave a better representation of the fine-scale structures of smoke plumes.The models driven by fire radiativepower (FRP)-based fire emissions or assimilating satellite AODdata generally outperform the others. Additionally, limitations of thepersistence assumption used when predicting smoke emissions are revealed bysubstantial underpredictions of sAOD on 8 August 2019, mainly over thetransported smoke plumes, owing to the underestimated emissions on7 August. In contrast, the surface smoke PM2.5 (sPM2.5) forecastsshow both positive and negative overall biases for these models, with mostmembers presenting more considerable diurnal variations of sPM2.5.Overpredictions of sPM2.5 are found for the models driven by FRP-basedemissions during nighttime, suggesting the necessity to improve verticalemission allocation within and above the planetary boundary layer (PBL).Smoke injection heights are further evaluated using the NASA LangleyResearch Center's Differential Absorption High Spectral Resolution Lidar(DIAL-HSRL) data collected during the flight observations. As the firebecame stronger over 3–8 August, the plume height became deeper, with aday-to-day range of about 2–9 km a.g.l. However, narrower ranges arefound for all models, with a tendency of overpredicting the plume heights forthe shallower injection transects and underpredicting for the days showingdeeper injections. The misrepresented plume injection heights lead toinaccurate vertical plume allocations along the transects corresponding totransported smoke that is 1 d old. Discrepancies in model performance forsurface PM2.5 and AOD are further suggested by the evaluation of theirratio, which cannot be compensated for by solely adjusting the smoke emissionsbut are more attributable to model representations of plume injections,besides other possible factors including the evolution of PBL depths andaerosol optical property assumptions. By consolidating multiple forecastsystems, these results provide strategic insight on pathways to improvesmoke forecasts. 

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4. Validation and parameterization of a novel physics-constrained neural dynamics model applied to turbulent fluid flow

   https://doi.org/10.1063/5.0122115  

   Shankar, Varun ; Portwood, Gavin D. ; Mohan, Arvind T. ; Mitra, Peetak P. ; Krishnamurthy, Dilip ; Rackauckas, Christopher ; Wilson, Lucas A. ; Schmidt, David P. ; Viswanathan, Venkatasubramanian ( November 2022 , Physics of Fluids)

   In fluid physics, data-driven models to enhance or accelerate time to solution are becoming increasingly popular for many application domains, such as alternatives to turbulence closures, system surrogates, or for new physics discovery. In the context of reduced order models of high-dimensional time-dependent fluid systems, machine learning methods grant the benefit of automated learning from data, but the burden of a model lies on its reduced-order representation of both the fluid state and physical dynamics. In this work, we build a physics-constrained, data-driven reduced order model for Navier–Stokes equations to approximate spatiotemporal fluid dynamics in the canonical case of isotropic turbulence in a triply periodic box. The model design choices mimic numerical and physical constraints by, for example, implicitly enforcing the incompressibility constraint and utilizing continuous neural ordinary differential equations for tracking the evolution of the governing differential equation. We demonstrate this technique on a three-dimensional, moderate Reynolds number turbulent fluid flow. In assessing the statistical quality and characteristics of the machine-learned model through rigorous diagnostic tests, we find that our model is capable of reconstructing the dynamics of the flow over large integral timescales, favoring accuracy at the larger length scales. More significantly, comprehensive diagnostics suggest that physically interpretable model parameters, corresponding to the representations of the fluid state and dynamics, have attributable and quantifiable impact on the quality of the model predictions and computational complexity.

    

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5. E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials

   https://doi.org/10.1038/s41467-022-29939-5  

   Batzner, Simon ; Musaelian, Albert ; Sun, Lixin ; Geiger, Mario ; Mailoa, Jonathan P. ; Kornbluth, Mordechai ; Molinari, Nicola ; Smidt, Tess E. ; Kozinsky, Boris ( May 2022 , Nature Communications)

   This work presents Neural Equivariant Interatomic Potentials (NequIP), an E(3)-equivariant neural network approach for learning interatomic potentials from ab-initio calculations for molecular dynamics simulations. While most contemporary symmetry-aware models use invariant convolutions and only act on scalars, NequIP employs E(3)-equivariant convolutions for interactions of geometric tensors, resulting in a more information-rich and faithful representation of atomic environments. The method achieves state-of-the-art accuracy on a challenging and diverse set of molecules and materials while exhibiting remarkable data efficiency. NequIP outperforms existing models with up to three orders of magnitude fewer training data, challenging the widely held belief that deep neural networks require massive training sets. The high data efficiency of the method allows for the construction of accurate potentials using high-order quantum chemical level of theory as reference and enables high-fidelity molecular dynamics simulations over long time scales.

    

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