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Significance Antibiotic resistance in Gram-negative pathogens has been identified as an urgent threat to human health by the World Health Organization. The major challenge with treating infections by these pathogens is developing antibiotics that can traverse the dense bacterial outer membrane (OM) formed by a mesh of lipopolysaccharides. Effective antibiotics permeate through OM porins, which have evolved for nutrient diffusion; however, the conformational states of these porins regulating permeation are still unclear. Here, we used molecular dynamics simulations, free energy calculations, Markov-state modeling, and whole-cell accumulation assays to provide mechanistic insight on how a porin shifts between open and closed states. We provide a mechanism of how Gram-negative bacteria confer resistance to antibiotics. 

Efficient sampling in biomolecular simulations is critical for accurately capturing the complex dynamic behaviors of biological systems. Adaptive sampling techniques aim to improve efficiency by focusing computational resources on the most relevant regions of the phase space. In this work, we present a framework for identifying the optimal sampling policy through metric-driven ranking. Our approach systematically evaluates the policy ensemble and ranks the policies based on their ability to explore the conformational space effectively. Through a series of biomolecular simulation case studies, we demonstrate that the choice of a different adaptive sampling policy at each round significantly outperforms single policy sampling, leading to faster convergence and improved sampling performance. This approach takes an ensemble of adaptive sampling policies and identifies the optimal policy for the next round based on current data. Beyond presenting this ensemble view of adaptive sampling, we also propose two sampling algorithms that approximate this ranking framework on the fly. The modularity of this framework allows incorporation of any adaptive sampling policy, making it versatile and suitable as a comprehensive adaptive sampling scheme. 

Cyanobacteria are responsible for up to 80% of aquatic carbon dioxide fixation and have evolved a specialized carbon concentrating mechanism to increase photosynthetic yield. As such, cyanobacteria are attractive targets for synthetic biology and engineering approaches to address the demands of global energy security, food production, and climate change for an increasing world’s population. The bicarbonate transporter BicA is a sodium-dependent, low-affinity, high-flux bicarbonate symporter expressed in the plasma membrane of cyanobacteria. Despite extensive biochemical characterization of BicA, including the resolution of the BicA crystal structure, the dynamic understanding of the bicarbonate transport mechanism remains elusive. To this end, we have collected over 1 ms of all-atom molecular dynamics simulation data of the BicA dimer to elucidate the structural rearrangements involved in the substrate transport process. We further characterized the energetics of the transition of BicA protomers and investigated potential mutations that are shown to decrease the free energy barrier of conformational transitions. In all, our study illuminates a detailed mechanistic understanding of the conformational dynamics of bicarbonate transporters and provides atomistic insights to engineering these transporters for enhanced photosynthetic production. 

Endocannabinoids are naturally occurring lipid-like molecules that bind to cannabinoid receptors (CB₁and CB₂) and regulate many of human bodily functions via the endocannabinoid system. There is a tremendous interest in developing selective drugs that target the CB receptors. However, the biophysical mechanisms responsible for the subtype selectivity for endocannbinoids have not been established. Recent experimental structures of CB receptors show that endocannbinoids potentially bind via membrane using the lipid access channel in the transmembrane region of the receptors. Furthermore, the N-terminus of the receptor could move in and out of the binding pocket thereby modulating both the pocket volume and its residue composition. On the basis of these observations, we propose two hypothesis to explain the selectivity of the endocannabinoid, anandamide for CB₁receptor. First, the selectivity arises from distinct enthalpic ligand-protein interactions along the ligand binding pathway formed due to the movement of N-terminus and subsequent shifts in the binding pocket composition. Second, selectivity arises from the volumetric differences in the binding pocket allowing for differences in ligand conformational entropy. To quantitatively test these hypothesis, we perform extensive molecular dynamics simulations (∼0.9 milliseconds) along with Markov state modeling and deep learning-based VAMPnets to provide an interpretable characterization of the anandamide binding process to cannabinoid receptors and explain its selectivity for CB₁. Our findings reveal that the distinct N-terminus positions along lipid access channels between TM1 and TM7 lead to different binding mechanisms and interactions between anandamide and the binding pocket residues. To validate the critical stabilizing interactions along the binding pathway, relative free energy calculations of anandamide analogs are used. Moreover, the larger CB₂pocket volume increases the entropic effects of ligand binding by allowing higher ligand fluctuations but reduced stable interactions. Therefore, the opposing enthalpy and entropy effects between the receptors shape the endocannabinoid selectivity. Overall, the CB₁selectivity of anandamide is explained by the dominant enthalpy contributions due to ligand-protein interactions in stable binding poses. This study shed lights on potential selectivity mechanisms for endocannabinoids that would aid in the discovery of CB selective drugs 

New psychoactive substances (NPS) targeting cannabinoid receptor 1 pose a significant threat to society as recreational abusive drugs that have pronounced physiological side effects. These greater adverse effects compared to classical cannabinoids have been linked to the higher downstream β-arrestin signaling. Thus, understanding the mechanism of differential signaling will reveal important structure-activity relationship essential for identifying and potentially regulating NPS molecules. In this study, we simulate the slow (un)binding process of NPS MDMB-Fubinaca and classical cannabinoid HU-210 from CB1 using multi-ensemble simulation to decipher the effects of ligand binding dynamics on downstream signaling. The transition-based reweighing method is used for the estimation of transition rates and underlying thermodynamics of (un)binding processes of ligands with nanomolar affinities. Our analyses reveal major interaction differences with transmembrane TM7 between NPS and classical cannabinoids. A variational autoencoder-based approach, neural relational inference (NRI), is applied to assess the allosteric effects on intracellular regions attributable to variations in binding pocket interactions. NRI analysis indicate a heightened level of allosteric control of NPxxY motif for NPS-bound receptors, which contributes to the higher probability of formation of a crucial triad interaction (Y7.53-Y5.58-T3.46) necessary for stronger β-arrestin signaling. Hence, in this work, MD simulation, data-driven statistical methods, and deep learning point out the structural basis for the heightened physiological side effects associated with NPS, contributing to efforts aimed at mitigating their public health impact. 

Molecular dynamics (MD) simulations are fundamental computational tools for the study of proteins and their free energy landscapes. However, sampling protein conformational changes through MD simulations is challenging due to the relatively long time scales of these processes. Many enhanced sampling approaches have emerged to tackle this problem, including biased sampling and path-sampling methods. In this Perspective, we focus on adaptive sampling algorithms. These techniques differ from other approaches because the thermodynamic ensemble is preserved and the sampling is enhanced solely by restarting MD trajectories at particularly chosen seeds rather than introducing biasing forces. We begin our treatment with an overview of theoretically transparent methods, where we discuss principles and guidelines for adaptive sampling. Then, we present a brief summary of select methods that have been applied to realistic systems in the past. Finally, we discuss recent advances in adaptive sampling methodology powered by deep learning techniques, as well as their shortcomings. 

Rapid computational exploration of the free energy landscape of biological molecules remains an active area of research due to the difficulty of sampling rare state transitions in molecular dynamics (MD) simulations. In recent years, an increasing number of studies have exploited machine learning (ML) models to enhance and analyze MD simulations. Notably, unsupervised models that extract kinetic information from a set of parallel trajectories have been proposed including the variational approach for Markov processes (VAMP), VAMPNets, and time-lagged variational autoencoders (TVAE). In this work, we propose a combination of adaptive sampling with active learning of kinetic models to accelerate the discovery of the conformational landscape of biomolecules. In particular, we introduce and compare several techniques that combine kinetic models with two adaptive sampling regimes (least counts and multiagent reinforcement learning- based adaptive sampling) to enhance the exploration of conformational ensembles without introducing biasing forces. Moreover, inspired by the active learning approach of uncertainty-based sampling, we also present MaxEnt VAMPNet. This technique consists of restarting simulations from the microstates that maximize the Shannon entropy of a VAMPNet trained to perform the soft discretization of metastable states. By running simulations on two test systems, the WLALL pentapeptide and the villin headpiece subdomain, we empirically demonstrate that MaxEnt VAMPNet results in faster exploration of conformational landscapes compared with the baseline and other proposed methods. 

Membrane transporters of the solute carrier 6 (SLC6) family mediate various physiological processes by facilitating the translocation of amino acids, neurotransmitters, and other metabolites. In the body, the activity of these transporters is tightly controlled through various post-translational modifications with implications on protein expression, stability, membrane trafficking, and dynamics. While N-linked glycosylation is a universal regulatory mechanism among eukaryotes, a consistent mechanism of how glycosylation affects the SLC6 transporter family remains elusive. It is generally believed that glycans influence transporter stability and membrane trafficking; however, the role of glycosylation on transporter dynamics remains disputable, with differing conclusions among individual transporters across the SLC6 family. In this study, we collected over 1 ms of aggregated all-atom molecular dynamics (MD) simulation data to systematically identify the impact of N-glycans on SLC6 transporter dynamics. We modeled four human SLC6 transporters, the serotonin, dopamine, glycine, and B0AT1 transporters, by first simulating all possible combinations of a glycan attached to each glycosylation site followed by investigating the effect of larger, oligo-N-linked glycans to each transporter. The simulations reveal that glycosylation does not significantly affect the transporter structure but alters the dynamics of the glycosylated extracellular loop and surrounding regions. The structural consequences of glycosylation on the loop dynamics are further emphasized with larger glycan molecules attached. However, no apparent differences in ligand stability or movement of the gating helices were observed, and as such, the simulations suggest that glycosylation does not have a profound effect on conformational dynamics associated with substrate transport. 

Reconciliation of experimental and computational spectroscopic observables is critical for understanding protein dynamics.