Markov state models of molecular kinetics (MSMs), in which the long-time statistical dynamics of a molecule is approximated by a Markov chain on a discrete partition of configuration space, have seen widespread use in recent years. This approach has many appealing characteristics compared to straightforward molecular dynamics simulation and analysis, including the potential to mitigate the sampling problem by extracting long-time kinetic information from short trajectories and the ability to straightforwardly calculate expectation values and statistical uncertainties of various stationary and dynamical molecular observables. In this paper, we summarize the current state of the art in generation and validation of MSMs and give some important new results. We describe an upper bound for the approximation error made by modeling molecular dynamics with a MSM and we show that this error can be made arbitrarily small with surprisingly little effort. In contrast to previous practice, it becomes clear that the best MSM is not obtained by the most metastable discretization, but the MSM can be much improved if non-metastable states are introduced near the transition states. Moreover, we show that it is not necessary to resolve all slow processes by the state space partitioning, but individual dynamical processes of interest can be resolved separately. We also present an efficient estimator for reversible transition matrices and a robust test to validate that a MSM reproduces the kinetics of the molecular dynamics data.

Molecular force fields have been approaching a generational transition over the past several years, moving away from well-established and well-tuned, but intrinsically limited, fixed point charge models towards more intricate and expensive polarizable models that should allow more accurate description of molecular properties. The recently introduced AMOEBA force field is a leading publicly available example of this next generation of theoretical model, but to date has only received relatively limited validation, which we address here. We show that the AMOEBA force field is in fact a significant improvement over fixed charge models for small molecule structural and thermodynamic observables in particular, although further fine-tuning is necessary to describe solvation free energies of drug-like small molecules, dynamical properties away from ambient conditions, and possible improvements in aromatic interactions. State of the art electronic structure calculations reveal generally very good agreement with AMOEBA for demanding problems such as relative conformational energies of the alanine tetrapeptide and isomers of water sulfate complexes. AMOEBA is shown to be especially successful on protein-ligand binding and computational X-ray crystallography where polarization and accurate electrostatics are critical.

To meet the challenge of modeling the conformational dynamics of biological macromolecules over long time scales, much recent effort has been devoted to constructing stochastic kinetic models, often in the form of discrete-state Markov models, from short molecular dynamics simulations. To construct useful models that faithfully represent dynamics at the time scales of interest, it is necessary to decompose configuration space into a set of kinetically metastable states. Previous attempts to define these states have relied upon either prior knowledge of the slow degrees of freedom or on the application of conformational clustering techniques which assume that conformationally distinct clusters are also kinetically distinct. Here, we present a first version of an automatic algorithm for the discovery of kinetically metastable states that is generally applicable to solvated macromolecules. Given molecular dynamics trajectories initiated from a well-defined starting distribution, the algorithm discovers long lived, kinetically metastable states through successive iterations of partitioning and aggregating conformation space into kinetically related regions. The authors apply this method to three peptides in explicit solvent-terminally blocked alanine, the 21-residue helical F(s) peptide, and the engineered 12-residue beta-hairpin trpzip2-to assess its ability to generate physically meaningful states and faithful kinetic models.

Highlights d The receptor-binding motif (RBM) is a highly variable region of SARS-CoV-2 spike d RBM mutation N439K has emerged independently in multiple lineages d N439K increases spike affinity for hACE2; viral fitness and disease are unchanged d N439K confers resistance to several mAbs and escapes some polyclonal responses

An ideal anti-SARS-CoV-2 antibody would resist viral escape [1][2][3] , have activity against diverse SARS-related coronaviruses (sarbecoviruses) [4][5][6][7] , and be highly protective through viral neutralization [8][9][10][11] and effector functions 12,13 . Understanding how these properties relate to each other and vary across epitopes would aid development of antibody therapeutics and guide vaccine design. Here, we comprehensively characterize escape, breadth, and potency across a panel of SARS-CoV-2 antibodies targeting the receptor-binding domain (RBD). Despite a tradeoff between in vitro neutralization potency and breadth of sarbecovirus binding, we identify neutralizing antibodies with exceptional sarbecovirus breadth and a corresponding resistance to SARS-CoV-2 escape. One of these antibodies, S2H97, binds with high affinity across all sarbecovirus clades to a previously undescribed cryptic epitope and prophylactically protects hamsters from viral challenge. Antibodies targeting the ACE2 receptor binding motif (RBM) typically have poor breadth and are readily escaped by mutations despite high neutralization potency. Nevertheless, we characterize one potent RBM antibody (S2E12 8 ) with breadth across sarbecoviruses related to SARS-CoV-2 and a high barrier to viral escape. These data highlight principles underlying variation in escape, breadth, and potency among antibodies targeting the RBD, and identify epitopes and features to prioritize for therapeutic development against the current and potential future pandemics.The most potently neutralizing antibodies to SARS-CoV-2-including those in clinical use 14 and dominant in polyclonal sera 15,16 -target the spike receptor-binding domain (RBD). Mutations in the RBD that reduce binding by antibodies have emerged among SARS-CoV-2 variants [17][18][19][20][21] , highlighting the need for antibodies and vaccines that are robust to viral escape. We have previously described an antibody, S309 4 , that exhibits potent effector functions and neutralizes all current SARS-CoV-2 variants 22,23 and the divergent sarbecovirus SARS-CoV-1. S309 forms the basis for an antibody therapy (VIR-7831, recently renamed sotrovimab) that has received Emergency Use Authorization from the FDA for treatment of COVID-19 24 . Longer term, antibodies with broad activity across SARS-related coronaviruses (sarbecoviruses) would be useful to combat potential future spillovers 6 . These efforts would be aided by a systematic understanding of the relationships among antibody epitope,