John D. Russo

Weighted ensemble (WE) is a path-sampling method that is conceptually simple, widely applicable, and statistically unbiased. In a WE simulation, an ensemble of trajectories is periodically pruned or replicated to enhance the sampling of rare transitions and improve the estimation of mean first-passage times (MFPTs). However, poor choices of the parameters governing pruning and replication can lead to high variance in MFPT estimates. Our previous work [Aristoff et al., J. Chem. Phys. 158, 014108 (2023)] presented an optimal WE parameterization strategy and applied it to low-dimensional example systems. The strategy harnesses estimated local MFPTs from different initial configurations to a single target state. In the present work, we apply the optimal parameterization strategy to more challenging high-dimensional molecular models, namely, synthetic molecular dynamics (MD) models of Trp-cage folding and unfolding, as well as atomistic MD models of NTL9 folding in high-friction and low-friction continuum solvents. In each system, we use WE to estimate the MFPT for folding or unfolding events. We show that the optimal parameterization reduces the variance of MFPT estimates in three of four systems, with a dramatic improvement in the most challenging atomistic system. Overall, the parameterization strategy improves the accuracy and reliability of WE estimates for the kinetics of biophysical processes.

The weighted ensemble (WE) strategy has been demonstrated to be highly efficient in generating pathways and rate constants for rare events such as protein folding and protein binding using atomistic molecular dynamics simulations. Here we present two sets of tutorials instructing users in the best practices for preparing, carrying out, and analyzing WE simulations for various applications using the WESTPA software. The first set of more basic tutorials describes a range of simulation types, from a molecular association process in explicit solvent to more complex processes such as host-guest association, peptide conformational sampling, and protein folding. The second set ecompasses six advanced tutorials instructing users in the best practices of using key new features and plugins/extensions of the WESTPA 2.0 software package, which consists of major upgrades for larger systems and/or slower processes. The advanced tutorials demonstrate the use of the following key features: (i) a generalized resampler module for the creation of “binless” schemes, (ii) a minimal adaptive binning scheme for more efficient surmounting of free energy barriers, (iii) streamlined handling of large simulation datasets using an HDF5 framework, (iv) two different schemes for more efficient rate-constant estimation, (v) a Python API for simplified analysis of WE simulations, and (vi) plugins/extensions for Markovian Weighted Ensemble Milestoning and WE rule-based modeling for systems biology models. Applications of the advanced tutorials include atomistic and non-spatial models, and consist of complex processes such as protein folding and the membrane permeability of a drug-like molecule. Users are expected to already have significant experience with running conventional molecular dynamics or systems biology simulations.

We present six advanced tutorials instructing users in the best practices of using key new features and plugins/extensions of the WESTPA 2.0 software package, which consists of major upgrades for enabling applications of the weighted ensemble (WE) path sampling strategy to even larger systems and/or slower processes. The tutorials demonstrate the use of the following key features: (i) a generalized resampler module for the creation of “binless” schemes, (ii) a minimal adaptive binning scheme for more efficient surmounting of free energy barriers, (iii) streamlined handling of large simulation datasets using an HDF5 framework, (iv) two different schemes for more efficient rate-constant estimation, (v) a Python API for simplified analysis of WE simulations, and (vi) plugins/extensions for Markovian Weighted Ensemble Milestoning and WE rule-based modeling at the system biology level. Applications of the tutorials range from atomistic to residue-level to non-spatial models, and include complex processes such as protein folding and the membrane permeability of a drug-like molecule. Users are expected to already have significant experience with running conventional molecular dynamics simulations and completed the previous suite of WESTPA tutorials.

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.

The weighted ensemble (WE) family of methods is one of several statistical-mechanics based path sampling strategies that can provide estimates of key observables (rate constants, pathways) using a fraction of the time required by direct simulation methods such as molecular dynamics or discrete-state stochastic algorithms. WE methods oversee numerous parallel trajectories using intermittent overhead operations at fixed time intervals, enabling facile interoperability with any dynamics engine. Here, we report on major upgrades to the WESTPA software package, an open-source, high-performance framework that implements both basic and recently developed WE methods. These upgrades offer substantial improvements over traditional WE. Key features of the new WESTPA 2.0 software enhance efficiency and ease of use: an adaptive binning scheme for more efficient surmounting of large free energy barriers, streamlined handling of large simulation datasets, exponentially improved analysis of kinetics, and developer-friendly tools for creating new WE methods, including a Python API and resampler module for implementing both binned and “binless” WE strategies. Table of Contents/Abstract Image: For the manuscript “WESTPA 2.0: High-performance upgrades for weighted ensemble simulations and analysis of longer-timescale applications” by Russo et al .

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

Significance: Vascular K _ATP channels formed by the potassium channel Kir6.1 and its regulatory protein SUR2B maintain blood pressure in the physiological range. Overactivity of the channel due to genetic mutations in either Kir6.1 or SUR2B causes severe cardiovascular pathologies known as Cantú syndrome. The cryogenic electron microscopy structures of the vascular K _ATP channel reported here show multiple, dynamically related conformations of the regulatory subunit SUR2B. Molecular dynamics simulations reveal the negatively charged ED-domain in SUR2B, a stretch of 15 glutamate (E) and aspartate (D) residues not previously resolved, play a key MgADP-dependent role in mediating interactions at the interface between the SUR2B and Kir6.1 subunits. Our findings provide a mechanistic understanding of how channel activity is regulated by intracellular MgADP.

Markov state models (MSMs) have been broadly adopted for analyzing molecular dynamics trajectories, but the approximate nature of the models that results from coarse-graining into discrete states is a long-known limitation. We show theoretically that, despite the coarse graining, in principle MSM-like analysis can yield unbiased estimation of key observables. We describe unbiased estimators for equilibrium state populations, for the mean first-passage time (MFPT) of an arbitrary process, and for state committors - i.e., splitting probabilities. Generically, the estimators are only asymptotically unbiased but we describe how extension of a recently proposed reweighting scheme can accelerate relaxation to unbiased values. Exactly accounting for 'sliding window' averaging over finite-length trajectories is a key, novel element of our analysis. In general, our analysis indicates that coarse-grained MSMs are asymptotically unbiased for steady-state properties only when appropriate boundary conditions (e.g., source-sink for MFPT estimation) are applied directly to trajectories, prior to calculation of the appropriate transition matrix.