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The string method with swarms of trajectories (SMwST) is an algorithm that identifies a physically meaningful transition pathwaya one-dimensional curve, embedded within a high-dimensional space of selected collective variables. The SMwST algorithm leans on a series of short, unbiased molecular dynamics simulations spawned at different locations of the discretized path, from whence an average dynamic drift is determined to evolve the string toward an optimal pathway. However conceptually simple in both its theoretical formulation and practical implementation, the SMwST algorithm is computationally intensive and requires a careful choice of parameters for optimal costeffectiveness in applications to challenging problems in chemistry and biology. In this contribution, the SMwST algorithm is presented in a self-contained manner, discussing with a critical eye its theoretical underpinnings, applicability, inherent limitations, and use in the context of path-following free-energy calculations and their possible extension to kinetics modeling. Through multiple simulations of a prototypical polypeptide, combining the search of the transition pathway and the computation of the potential of mean force along it, several practical aspects of the methodology are examined with the objective of optimizing the computational effort, yet without sacrificing accuracy. In light of the results reported here, we propose some general guidelines aimed at improving the efficiency and reliability of the computed pathways and free-energy profiles underlying the conformational transitions at hand.

Major facilitator superfamily (MFS) of transporters consists of three classes of membrane transporters: symporters, uniporters, and antiporters. Despite such diverse functions, MFS transporters are believed to undergo similar conformational changes within their distinct transport cycles. While the similarities between conformational changes are noteworthy, the differences are also important since they could potentially explain the distinct functions of symporters, uniporters, and antiporters of MFS superfamily.We have performed a variety of equilibrium, non-equilibrium, biased, and unbiased allatom molecular dynamics (MD) simulations of bacterial proton-coupled oligopeptide transporter GkPOT, glucose transporter 1 (GluT1), and glycerol-3-phosphate transporter (GlpT) to compare the similarities and differences of the conformational dynamics of three different classes of transporters. Here we have simulated the apo protein in an explicit membrane environment. Our results suggest a very similar conformational transition involving interbundle salt-bridge formation/disruption coupled with the orientation changes of transmembrane (TM) helices, specifically H1/H7 and H5/H11, resulting in an alternation in the accessibility of water at the cyto-and periplasmic gates.

Within the last two decades, severe acute respiratory syndrome (SARS) coronaviruses 1 and 2 (SARS-CoV-1 and SARS-CoV-2) have caused two major outbreaks. For reasons yet to be fully understood the COVID-19 outbreak caused by SARS-CoV-2 has been significantly more widespread than the 2003 SARS epidemic caused by SARS-CoV-1, despite striking similarities between the two viruses. One of the most variable genes differentiating SARS-CoV-1 and SARS-CoV-2 is the S gene that encodes the spike glycoprotein. This protein mediates a crucial step in the infection, i.e., host cell recognition and viral entry, which starts with binding to the host cell angiotensin converting enzyme 2 (ACE2) protein for both viruses. Recent structural and functional studies have shed light on the differential binding behavior of the SARS-CoV-1 and SARS-CoV-2 spike proteins. In particular, cryogenic electron microscopy (cryo-EM) studies show that ACE2 binding is preceded by a large-scale conformational change in the spike protein to expose the receptor binding domain (RBD) to its binding partner. Unfortunately, these studies do not provide detailed information on the dynamics of this activation process. Here, we have used an extensive set of unbiased and biased microsecond-timescale all-atom molecular dynamics (MD) simulations of SARS-CoV-1 and SARS-CoV-2 spike protein ectodomains in explicit solvent to determine the differential behavior of spike protein activation in the two viruses. Our results based on nearly 50 microseconds of equilibrium and nonequilibrium MD simulations indicate that the active form of the SARS-CoV-2 spike protein is considerably more stable than the active SARS-CoV-1 spike protein. Unlike the active SARS-CoV-2 spike, the active SARS-CoV-1 spike spontaneously undergoes a large-scale conformational transition to a pseudo-inactive state, which occurs in part due to interactions between the N-terminal domain (NTD) and RBD that are absent in the SARS-CoV-2 spike protein. Steered MD (SMD) simulations indicate that the energy barriers between active and inactive states are comparatively lower for the SARS-CoV-1 spike protein. Based on these results, we hypothesize that the greater propensity of the SARS-CoV-2 spike protein to remain in the active conformation contributes to the higher transmissibility of SARS-CoV-2 in comparison to SARS-CoV-1. These results strongly suggest that the differential binding behavior of the active SARS-CoV-1 and 2 spike proteins is not merely due to differences in their RBDs as other domains of the spike protein such as the NTD could play a crucial role in the effective binding process, which involves the prebinding activation. Therefore, our hypothesis predicts that mutations in regions such as the NTD, which is not directly involved in binding, may lead to a change in the effective binding behavior of the coronavirus.

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