Study of the interaction between a novel, protein-stabilizing dipeptide and Interferon-alpha-2a by construction of a Markov state model from molecular dynamics simulations

Markov State Models: To Optimize or Not to Optimize

Markov state models (MSM) are a popular statistical method for analyzing the conformational dynamics of proteins including protein folding. With all statistical and machine learning (ML) models, choices must be made about the modeling pipeline that cannot be directly learned from the data. These choices, or hyperparameters, are often evaluated by expert judgment or, in the case of MSMs, by maximizing variational scores such as the VAMP-2 score. Modern ML and statistical pipelines often use automatic hyperparameter selection techniques ranging from the simple, choosing the best score from a random selection of hyperparameters, to the complex, optimization via, e.g., Bayesian optimization. In this work, we ask whether it is possible to automatically select MSM models this way by estimating and analyzing over 16,000,000 observations from over 280,000 estimated MSMs. We find that differences in hyperparameters can change the physical interpretation of the optimization objective, making automatic selection difficult. In addition, we find that enforcing conditions of equilibrium in the VAMP scores can result in inconsistent model selection. However, other parameters that specify the VAMP-2 score (lag time and number of relaxation processes scored) have only a negligible influence on model selection. We suggest that model observables and variational scores should be only a guide to model selection and that a full investigation of the MSM properties should be undertaken when selecting hyperparameters.

Uncertainties in Markov State Models of Small Proteins

Markov state models are widely used to describe and analyze protein dynamics based on molecular dynamics simulations, specifically to extract functionally relevant characteristic time scales and motions. Particularly for larger biomolecules such as proteins, however, insufficient sampling is a notorious concern and often the source of large uncertainties that are difficult to quantify. Furthermore, there are several other sources of uncertainty, such as choice of the number of Markov states and lag time, choice and parameters of dimension reduction preprocessing step, and uncertainty due to the limited number of observed transitions; the latter is often estimated via a Bayesian approach. Here, we quantified and ranked all of these uncertainties for four small globular test proteins. We found that the largest uncertainty is due to insufficient sampling and initially increases with the total trajectory length T up to a critical tipping point, after which it decreases as 1 / T , thus providing guidelines for how much sampling is required for given accuracy. We also found that single long trajectories yielded better sampling accuracy than many shorter trajectories starting from the same structure. In comparison, the remaining sources of the above uncertainties are generally smaller by a factor of about 5, rendering them less of a concern but certainly not negligible. Importantly, the Bayes uncertainty, commonly used as the only uncertainty estimate, captures only a relatively small part of the true uncertainty, which is thus often drastically underestimated.

Biophysical insight into the interaction of levocabastine with human serum albumin: spectroscopy and molecular docking approach

Interaction of levocabastine with human serum albumin (HSA) is investigated by applying fluorescence spectroscopy, circular dichroism spectroscopy and molecular docking methods. Levocabastine is an important drug in treatment of allergy and currently a target drug for drug repurposing to treat other diseases like vernal keratoconjuctivitis. Fluorescence quenching data revealed that levocabastine bind weakly to protein with binding constant in the order of 10³ M^-1. Förster resonance energy transfer results indicated the binding distance of 2.28 nm for levocabastine. Synchronous fluorescence result suggest slight blue shift for tryptophan upon levocabastine binding, binding of levocabastine impelled rise in α-helical structure in protein, while there are minimal changes in tertiary structure in protein. Moreover, docking results indicate levocabastine binds to pocket near to the drug site-I in HSA via hydrogen bonding and hydrophobic interactions. Understanding the interaction of levocabastine with HSA is significant for the advancement of therapeutic and diagnostic strategies for optimal treatment results.Communicated by Ramaswamy H. Sarma.