Computational approaches to detect allosteric pathways in transmembrane molecular machines

Dynamically driven correlations in elastic net models reveal sequence of events and causality in proteins

An explicit analytic solution is given for the Langevin equation applied to the Gaussian Network Model of a protein subjected to both a random and a deterministic periodic force. Synchronous and asynchronous components of time correlation functions are derived and an expression for phase differences in the time correlations of residue pairs is obtained. The synchronous component enables the determination of dynamic communities within the protein structure. The asynchronous component reveals causality, where the time correlation function between residues i and j differs depending on whether i is observed before j or vice versa, resulting in directional information flow. Driver and driven residues in the allosteric process of cyclophilin A and human NAD-dependent isocitrate dehydrogenase are determined by a perturbation-scanning technique. Factors affecting phase differences between fluctuations of residues, such as network topology, connectivity, and residue centrality, are identified. Within the constraints of the isotropic Gaussian Network Model, our results show that asynchronicity increases with viscosity and distance between residues, decreases with increasing connectivity, and decreases with increasing levels of eigenvector centrality.

19F-NMR Probing of Ion-Induced Conformational Changes in Detergent-Solubilized and Nanodisc-Reconstituted NCX_Mj

Consecutive interactions of 3Na+ or 1Ca2+ with the Na+/Ca2+ exchanger (NCX) result in an alternative exposure (access) of the cytosolic and extracellular vestibules to opposite sides of the membrane, where ion-induced transitions between the outward-facing (OF) and inward-facing (IF) conformational states drive a transport cycle. Here, we investigate sub-state populations of apo and ion-bound species in the OF and IF states by analyzing detergent-solubilized and nanodisc-reconstituted preparations of NCX_Mj with 19F-NMR. The 19F probe was covalently attached to the cysteine residues at entry locations of the cytosolic and extracellular vestibules. Multiple sub-states of apo and ion-bound species were observed in nanodisc-reconstituted (but not in detergent-solubilized) NCX_Mj, meaning that the lipid-membrane environment preconditions multiple sub-state populations toward the OF/IF swapping. Most importantly, ion-induced sub-state redistributions occur within each major (OF or IF) state, where sub-state interconversions may precondition the OF/IF swapping. In contrast with large changes in population redistributions, the sum of sub-state populations within each inherent state (OF or IF) remains nearly unchanged upon ion addition. The present findings allow the further elucidation of structure-dynamic modules underlying ion-induced conformational changes that determine a functional asymmetry of ion access/translocation at opposite sides of the membrane and ion transport rates concurring physiological demands.

The Structural Basis of the Activity Cliff in Modafinil-Based Dopamine Transporter Inhibitors

Modafinil analogs with either a sulfoxide or sulfide moiety have improved binding affinities at the human dopamine transporter (hDAT) compared to modafinil, with lead sulfoxide-substituted analogs showing characteristics of atypical inhibition (e.g., JJC8-091). Interestingly, the only distinction between sulfoxide and sulfide substitution is the presence of one additional oxygen atom. To elucidate why such a subtle difference in ligand structure can result in different typical or atypical profiles, we investigated two pairs of analogs. Our quantum mechanical calculations revealed a more negatively charged distribution of the electrostatic potential surface of the sulfoxide substitution. Using molecular dynamics simulations, we demonstrated that sulfoxide-substituted modafinil analogs have a propensity to attract more water into the binding pocket. They also exhibited a tendency to dissociate from Asp79 and form a new interaction with Asp421, consequently promoting an inward-facing conformation of hDAT. In contrast, sulfide-substituted analogs did not display these effects. These findings elucidate the structural basis of the activity cliff observed with modafinil analogs and also enhance our understanding of the functionally relevant conformational spectrum of hDAT.

Synergy and anti-cooperativity in allostery: Molecular dynamics study of WT and oncogenic KRAS-RGL1

This study focuses on investigating the effects of an oncogenic mutation (G12V) on the stability and interactions within the KRAS-RGL1 protein complex. The KRAS-RGL1 complex is of particular interest due to its relevance to KRAS-associated cancers and the potential for developing targeted drugs against the KRAS system. The stability of the complex and the allosteric effects of specific residues are examined to understand their roles as modulators of complex stability and function. Using molecular dynamics simulations, we calculate the mutual information, MI, between two neighboring residues at the interface of the KRAS-RGL1 complex, and employ the concept of interaction information, II, to measure the contribution of a third residue to the interaction between interface residue pairs. Negative II indicates synergy, where the presence of the third residue strengthens the interaction, while positive II suggests anti-cooperativity. Our findings reveal that MI serves as a dominant factor in determining the results, with the G12V mutation increasing the MI between interface residues, indicating enhanced correlations due to the formation of a more compact structure in the complex. Interestingly, although II plays a role in understanding three-body interactions and the impact of distant residues, it is not significant enough to outweigh the influence of MI in determining the overall stability of the complex. Nevertheless, II may nonetheless be a relevant factor to consider in future drug design efforts. This study provides valuable insights into the mechanisms of complex stability and function, highlighting the significance of three-body interactions and the impact of distant residues on the binding stability of the complex. Additionally, our findings demonstrate that constraining the fluctuations of a third residue consistently increases the stability of the G12V variant, making it challenging to weaken complex formation of the mutated species through allosteric manipulation. The novel perspective offered by this approach on protein dynamics, function, and allostery has potential implications for understanding and targeting other protein complexes involved in vital cellular processes. The results contribute to our understanding of the effects of oncogenic mutations on protein-protein interactions and provide a foundation for future therapeutic interventions in the context of KRAS-associated cancers and beyond.

Advances of Predicting Allosteric Mechanisms Through Protein Contact in New Technologies and Their Application

Allostery is an intriguing phenomenon wherein the binding activity of a biological macromolecule is modulated via non-canonical binding site, resulting in synchronized functional changes. The mechanics underlying allostery are relatively complex and this review is focused on common methodologies used to study allostery, such as X-ray crystallography, NMR spectroscopy, and HDXMS. Different methodological approaches are used to generate data in different scenarios. For example, X-ray crystallography provides high-resolution structural information, NMR spectroscopy offers dynamic insights into allosteric interactions in solution, and HDXMS provides information on protein dynamics. The residue transition state (RTS) approach has emerged as a critical tool in understanding the energetics and conformational changes associated with allosteric regulation. Allostery has significant implications in drug discovery, gene transcription, disease diagnosis, and enzyme catalysis. Enzymes' catalytic activity can be modulated by allosteric regulation, offering opportunities to develop novel therapeutic alternatives. Understanding allosteric mechanisms associated with infectious organisms like SARS-CoV and bacterial pathogens can aid in the development of new antiviral drugs and antibiotics. Allosteric mechanisms are crucial in the regulation of a variety of signal transduction and cell metabolism pathways, which in turn govern various cellular processes. Despite progress, challenges remain in identifying allosteric sites and characterizing their contribution to a variety of biological processes. Increased understanding of these mechanisms can help develop allosteric systems specifically designed to modulate key biological mechanisms, providing novel opportunities for the development of targeted therapeutics. Therefore, the current review aims to summarize common methodologies that are used to further our understanding of allosteric mechanisms. In conclusion, this review provides insights into the methodologies used for the study of allostery, its applications in in silico modeling, the mechanisms underlying antibody allostery, and the ongoing challenges and prospects in advancing our comprehension of this intriguing phenomenon.

Subtle Structural Modification of a Synthetic Cannabinoid Receptor Agonist Drastically Increases its Efficacy at the CB1 Receptor

The emergence of synthetic cannabinoid receptor agonists (SCRAs) as illicit psychoactive substances has posed considerable public health risks, including fatalities. Many SCRAs exhibit much higher efficacy and potency compared with the phytocannabinoid Δ9-tetrahydrocannabinol (THC) at the cannabinoid receptor 1 (CB1R), leading to dramatic differences in signaling levels that can be toxic. In this study, we investigated the structure-activity relationships of aminoalkylindole SCRAs at CB1Rs, focusing on 5F-pentylindoles containing an amide linker attached to different head moieties. Using in vitro bioluminescence resonance energy transfer assays, we identified a few SCRAs exhibiting significantly higher efficacy in engaging the Gi protein and recruiting β-arrestin than the reference CB1R full agonist CP55940. Importantly, the extra methyl group on the head moiety of 5F-MDMB-PICA, as compared to that of 5F-MMB-PICA, led to a large increase in efficacy and potency at the CB1R. This pharmacological observation was supported by the functional effects of these SCRAs on glutamate field potentials recorded in hippocampal slices. Molecular modeling and simulations of the CB1R models bound with both of the SCRAs revealed critical structural determinants contributing to the higher efficacy of 5F-MDMB-PICA and how these subtle differences propagated to the receptor-G protein interface. Thus, we find that apparently minor structural changes in the head moiety of SCRAs can cause major changes in efficacy. Our results highlight the need for close monitoring of the structural modifications of newly emerging SCRAs and their potential for toxic drug responses in humans.

Connected Component Analysis of Dynamical Perturbation Contact Networks

Describing protein dynamical networks through amino acid contacts is a powerful way to analyze complex biomolecular systems. However, due to the size of the systems, identifying the relevant features of protein-weighted graphs can be a difficult task. To address this issue, we present the connected component analysis (CCA) approach that allows for fast, robust, and unbiased analysis of dynamical perturbation contact networks (DPCNs). We first illustrate the CCA method as applied to a prototypical allosteric enzyme, the imidazoleglycerol phosphate synthase (IGPS) enzyme from Thermotoga maritima bacteria. This approach was shown to outperform the clustering methods applied to DPCNs, which could not capture the propagation of the allosteric signal within the protein graph. On the other hand, CCA reduced the DPCN size, providing connected components that nicely describe the allosteric propagation of the signal from the effector to the active sites of the protein. By applying the CCA to the IGPS enzyme in different conditions, i.e., at high temperature and from another organism (yeast IGPS), and to a different enzyme, i.e., a protein kinase, we demonstrated how CCA of DPCNs is an effective and transferable tool that facilitates the analysis of protein-weighted networks.

A Novel "Activation Switch" Motif Common to All Aminergic Receptors

Aminergic receptors are G protein-coupled receptors (GPCRs) that transduce signals from small endogenous biogenic amines to regulate intracellular signaling pathways. Agonist binding in the ligand binding pocket on the extracellular side opens and prepares a cavity on the intracellular face of the receptors to interact with and activate G proteins and β-arrestins. Here, by reviewing and analyzing all available aminergic receptor structures, we seek to identify activation-related conformational changes that are independent of the specific scaffold of the bound agonist, which we define as "activation conformational changes" (ACCs). While some common intracellular ACCs have been well-documented, identifying common extracellular ACCs, including those in the ligand binding pocket, is complicated by local adjustments to different ligand scaffolds. Our analysis shows no common ACCs at the extracellular ends of the transmembrane helices. Furthermore, the restricted access to the ligand binding pocket identified previously in some receptors is not universal. Notably, the Trp6.48 toggle switch and the Pro5.50-Ile3.40-Phe6.44 (PIF) motif at the bottom of the ligand binding pocket have previously been proposed to mediate the conformational consequences of ligand binding to the intracellular side of the receptors. Our analysis shows that common ACCs in the ligand binding pocket are associated with the PIF motif and nearby residues, including Trp6.48, but fails to support a shared rotamer toggle associated with activation. However, we identify two common rearrangements between the extracellular and middle subsegments, and propose a novel "activation switch" motif common to all aminergic receptors. This motif includes the middle subsegments of transmembrane helices 3, 5, and 6 and integrates both the PIF motif and Trp6.48.

The Molecular Mechanism of Positive Allosteric Modulation at the Dopamine D1 Receptor

The dopamine D1 receptor (D1R) is a promising target for treating various psychiatric disorders. While upregulation of D1R activity has shown potential in alleviating motor and cognitive symptoms, orthosteric agonists have limitations, restricting their clinical applications. However, the discovery of several allosteric compounds specifically targeting the D1R, such as LY3154207, has opened new therapeutic avenues. Based on the cryo-EM structures of the D1R, we conducted molecular dynamics simulations to investigate the binding and allosteric mechanisms of LY3154207. Our simulations revealed that LY3154207 preferred the horizontal orientation above intracellular loop 2 (IL2) and stabilized the helical conformation of IL2. Moreover, LY3154207 binding induced subtle yet significant changes in key structural motifs and their neighboring residues. Notably, a cluster of residues centered around the Na+-binding site became more compact, while interactions involving the PIF motif and its neighboring residues were loosened upon LY3154207 binding, consistent with their role in opening the intracellular crevice for receptor activation. Additionally, we identified an allosteric pathway likely responsible for the positive allosteric effect of LY3154207 in enhancing Gs protein coupling. This mechanistic understanding of LY3154207's allosteric action at the D1R paves the way for the rational design of more potent and effective allosteric modulators.

A subtle structural modification of a synthetic cannabinoid receptor agonist drastically increases its efficacy at the CB1 receptor

The emergence of synthetic cannabinoid receptor agonists (SCRAs) as illicit psychoactive substances has posed considerable public health risks that include fatalities. Many SCRAs exhibit much higher efficacy and potency, compared with the phytocannabinoid Δ9-tetrahydrocannabinol (THC), at the cannabinoid receptor 1 (CB1R), a G protein-coupled receptor involved in modulating neurotransmitter release. In this study, we investigated structure activity relationships (SAR) of aminoalkylindole SCRAs at CB1Rs, focusing on 5F-pentylindoles containing an amide linker attached to different head moieties. Using in vitro bioluminescence resonance energy transfer (BRET) assays, we identified a few of SCRAs exhibiting significantly higher efficacy in engaging the Gi protein and recruiting β-arrestin than the reference CB1R full agonist CP55940. Importantly, adding a methyl group at the head moiety of 5F-MMB-PICA yielded 5F-MDMB-PICA, an agonist exhibiting a large increase in efficacy and potency at the CB1R. This pharmacological observation was supported by a functional assay of the effects of these SCRAs on glutamate field potentials recorded in hippocampal slices. Molecular modeling and simulations of the CB1R bound with either of the SCRAs revealed critical structural determinants contributing to the higher efficacy of 5F-MDMB-PICA, and how these subtle differences propagated to the receptor-G protein interface. Thus, we find that apparently minor structural changes in the head moiety of SCRAs can cause major changes in efficacy. Our results highlight the need for close monitoring of structural modifications of newly emerging SCRAs and their potential for toxic drug responses in humans.

Binding preference at the μ-opioid receptor underlies distinct pharmacology of cyclopropyl versus valeryl analogs of fentanyl

Illicitly manufactured fentanyl is driving the current opioid crisis, and various fentanyl analogs are appearing in recreational drug markets worldwide. To assess the potential health risks posed by fentanyl analogs, it is necessary to understand structure-activity relationships for these compounds. Here we compared the pharmacology of two structurally related fentanyl analogs implicated in opioid overdose: cyclopropylfentanyl and valerylfentanyl. Cyclopropylfentanyl has a three-carbon ring attached to the carbonyl group on the fentanyl scaffold, whereas valerylfentanyl has a four-carbon chain at the same position. In vitro assays examining μ-opioid receptor (MOR) coupling to G proteins in CHO cells showed that cyclopropylfentanyl is a full agonist (EC50 = 8.6 nM, %Emax = 113%), with potency and efficacy similar to fentanyl (EC50 = 10.3 nM, %Emax = 113%). By contrast, valerylfentanyl is a partial agonist at MOR (EC50 = 179.8 nM, %Emax = 60%). Similar results were found in assays assessing MOR-mediated β-arrestin recruitment in HEK cells. In vivo studies in male CD-1 mice demonstrated that both fentanyl analogs induce naloxone-reversible antinociception and respiratory suppression, but cyclopropylfentanyl is 100-times more potent as an antinociceptive agent (ED50 = 0.04 mg/kg, s. c.) than valerylfentanyl (ED50 = 4.0 mg/kg, s. c.). Molecular simulation results revealed that the alkyl chain of valerylfentanyl cannot be well accommodated by the active state of MOR and may transition the receptor toward an inactive state, converting the fentanyl scaffold to a partial agonist. Taken together, our results suggest that cyclopropylfentanyl presents much greater risk of adverse effects when compared to valerylfentanyl. Moreover, the summed findings may provide clues to the design of therapeutic opioids with reduced adverse side effects.

Network Analysis of Molecular Dynamics Sectors in the p53 Protein

Design of allosteric regulators is an emergent field in the area of drug discovery holding promise for currently untreated diseases. Allosteric regulators bind to a protein in one location and affect a distant site. The ubiquitous presence of allosteric effectors in biology and the success of serendipitously identified allosteric compounds point to the potential they hold. Although the mechanism of transmission of an allosteric signal is not unequivocally determined, one hypothesis suggests that groups of evolutionarily covarying residues within a protein, termed sectors, are conduits. A long-term goal of our lab is to allosterically modulate the activity of proteins by binding small molecules at points of allosteric control. However, methods to consistently identify such points remain unclear. Sector residues on the surfaces of proteins are a promising source of allosteric targets. Recently, we introduced molecular dynamics (MD)-based sectors; MD sectors capitalize on covariance of motion, in place of evolutionary covariance. By focusing on motional covariance, MD sectors tap into the framework of statistical mechanics afforded by the Boltzmann ensemble of structural conformations comprising the underlying data set. We hypothesized that the method of MD sectors can be used to identify a cohesive network of motionally covarying residues capable of transmitting an allosteric signal in a protein. While our initial qualitative results showed promise for the method to predict sectors, that a network of cohesively covarying residues had been produced remained an untested assumption. In this work, we apply network theory to rigorously analyze MD sectors, allowing us to quantitatively assess the biologically relevant property of network cohesiveness of sectors in the context of the tumor suppressor protein, p53. We revised the methodology for assessing and improving MD sectors. Specifically, we introduce a metric to calculate the cohesive properties of the network. Our new approach separates residues into two categories: sector residues and non-sector residues. The relatedness within each respective group is computed with a distance metric. Cohesive sector networks are identified as those that have high relatedness among the sector residues which exceeds the relatedness of the residues to the non-sector residues in terms of the correlation of motions. Our major finding was that the revised means of obtaining sectors was more efficacious than previous iterations, as evidenced by the greater cohesion of the networks. These results are discussed in the context of the development of allosteric regulators of p53 in particular and the expected applicability of the method to the drug design field in general.

Sparse group selection and analysis of function-related residue for protein-state recognition

Machine learning methods have helped to advance wide range of scientific and technological field in recent years, including computational chemistry. As the chemical systems could become complex with high dimension, feature selection could be critical but challenging to develop reliable machine learning based prediction models, especially for proteins as bio-macromolecules. In this study, we applied sparse group lasso (SGL) method as a general feature selection method to develop classification model for an allosteric protein in different functional states. This results into a much improved model with comparable accuracy (Acc) and only 28 selected features comparing to 289 selected features from a previous study. The Acc achieves 91.50% with 1936 selected feature, which is far higher than that of baseline methods. In addition, grouping protein amino acids into secondary structures provides additional interpretability of the selected features. The selected features are verified as associated with key allosteric residues through comparison with both experimental and computational works about the model protein, and demonstrate the effectiveness and necessity of applying rigorous feature selection and evaluation methods on complex chemical systems.

A comprehensive evaluation of the potential binding poses of fentanyl and its analogs at the µ-opioid receptor

Fentanyl and its analogs are selective agonists of the µ-opioid receptor (MOR). Among novel synthetic opioids (NSOs), they dominate the recreational drug market and are the main culprits for the opioid crisis, which has been exacerbated by the COVID-19 pandemic. By taking advantage of the crystal structures of the MOR, several groups have investigated the binding mechanism of fentanyl, but have not reached a consensus, in terms of both the binding orientation and the fentanyl conformation. Thus, the binding mechanism of fentanyl at the MOR remains an unsolved and challenging question. Here, we carried out a systematic computational study to investigate the preferred fentanyl conformations, and how these conformations are being accommodated in the MOR binding pocket. We characterized the free energy landscape of fentanyl conformations with metadynamics simulations, and compared and evaluated several possible fentanyl binding conditions in the MOR with long-timescale molecular dynamics simulations. Our results indicate that the most preferred binding pose in the MOR binding pocket corresponds well with the global minimum on the energy landscape of fentanyl in the absence of the receptor, while the energy landscape can be reconfigured by modifying the fentanyl scaffold. The interactions with the receptor may stabilize a slightly unfavored fentanyl conformation in an alternative binding pose. By extending similar investigations to fentanyl analogs, our findings establish a structure–activity relationship of fentanyl binding at the MOR. In addition to providing a structural basis to understand the potential toxicity of the emerging NSOs, such insights will contribute to developing new, safer analgesics.

Exploring the putative mechanism of allosteric modulations by mixed-action kappa/mu opioid receptor bitopic modulators

The modulation and selectivity mechanisms of seven mixed-action kappa opioid receptor (KOR)/mu opioid receptor (MOR) bitopic modulators were explored. Molecular modeling results indicated that the 'message' moiety of seven bitopic modulators shared the same binding mode with the orthosteric site of the KOR and MOR, whereas the 'address' moiety bound with different subdomains of the allosteric site of the KOR and MOR. The 'address' moiety of seven bitopic modulators bound to different subdomains of the allosteric site of the KOR and MOR may exhibit distinguishable allosteric modulations to the binding affinity and/or efficacy of the 'message' moiety. Moreover, the 3-hydroxy group on the phenolic moiety of the seven bitopic modulators induced selectivity to the KOR over the MOR.

Community Network Analysis of Allosteric Proteins

Community network analysis (CNA) of correlated protein motions allows modeling of signals propagation in allosteric proteic systems. From standard classical molecular dynamics (MD) simulations, protein motions can be analysed by means of mutual information between pairs of amino acid residues, providing dynamical weighted networks that contains fundamental information of the communication among amino acids. The CNA method has been successfully applied to a variety of allosteric systems including an enzyme, a nuclear receptor and a bacterial adaptive immune system, providing characterization of the allosteric pathways. This method is complementary to network analyses based on different metrics and it is particularly powerful for studying large proteic systems, as it provides a coarse-grained view of the communication flows within large and complex networks.

Data-guided Multi-Map variables for ensemble refinement of molecular movies

Driving molecular dynamics simulations with data-guided collective variables offer a promising strategy to recover thermodynamic information from structure-centric experiments. Here, the three-dimensional electron density of a protein, as it would be determined by cryo-EM or x-ray crystallography, is used to achieve simultaneously free-energy costs of conformational transitions and refined atomic structures. Unlike previous density-driven molecular dynamics methodologies that determine only the best map-model fits, our work employs the recently developed Multi-Map methodology to monitor concerted movements within equilibrium, non-equilibrium, and enhanced sampling simulations. Construction of all-atom ensembles along the chosen values of the Multi-Map variable enables simultaneous estimation of average properties, as well as real-space refinement of the structures contributing to such averages. Using three proteins of increasing size, we demonstrate that biased simulation along the reaction coordinates derived from electron densities can capture conformational transitions between known intermediates. The simulated pathways appear reversible with minimal hysteresis and require only low-resolution density information to guide the transition. The induced transitions also produce estimates for free energy differences that can be directly compared to experimental observables and population distributions. The refined model quality is superior compared to those found in the Protein Data Bank. We find that the best quantitative agreement with experimental free-energy differences is obtained using medium resolution density information coupled to comparatively large structural transitions. Practical considerations for probing the transitions between multiple intermediate density states are also discussed.

GDP Release from the Open Conformation of Gα Requires Allosteric Signaling from the Agonist-Bound Human β2 Adrenergic Receptor

G-protein-coupled receptors (GPCRs) transmit signals into the cell in response to ligand binding at its extracellular domain, which is characterized by the coupling of agonist-induced receptor conformational change to guanine nucleotide (GDP) exchange with guanosine triphosphate on a heterotrimeric (αβγ) guanine nucleotide-binding protein (G-protein), leading to the activation of the G-protein. The signal transduction mechanisms have been widely researched in vivo and in silico. However, coordinated communication from stimulating ligands to the bound GDP still remains elusive. In the present study, we used microsecond (μS) molecular dynamic (MD) simulations to directly probe the communication from the β₂ adrenergic receptor (β₂AR) with an agonist or an antagonist or no ligand to GDP bound to the open conformation of the Gα protein. Molecular mechanism-general Born surface area calculation results indicated either the agonist or the antagonist destabilized the binding between the receptor and the G-protein but the agonist caused a higher level of destabilization than the antagonist. This is consistent with the role of agonist in the activation of the G-protein. Interestingly, while GDP remained bound with the Gα-protein for the two inactive systems (antagonist-bound and apo form), GDP dissociated from the open conformation of the Gα protein for the agonist activated system. Data obtained from MD simulations indicated that the receptor and the Gα subunit play a big role in coordinated communication and nucleotide exchange. Based on residue interaction network analysis, we observed that engagement of agonist-bound β₂AR with an α5 helix of G_α is essential for the GDP release and the residues in the phosphate-binding loop, α1 helix, and α5 helix play very important roles in the GDP release. The insights on GPCR-G-protein communication will facilitate the rational design of agonists and antagonists that target both active and inactive GPCR binding pockets, leading to more precise drugs.

Allosteric Regulation at the Crossroads of New Technologies: Multiscale Modeling, Networks, and Machine Learning

Allosteric regulation is a common mechanism employed by complex biomolecular systems for regulation of activity and adaptability in the cellular environment, serving as an effective molecular tool for cellular communication. As an intrinsic but elusive property, allostery is a ubiquitous phenomenon where binding or disturbing of a distal site in a protein can functionally control its activity and is considered as the "second secret of life." The fundamental biological importance and complexity of these processes require a multi-faceted platform of synergistically integrated approaches for prediction and characterization of allosteric functional states, atomistic reconstruction of allosteric regulatory mechanisms and discovery of allosteric modulators. The unifying theme and overarching goal of allosteric regulation studies in recent years have been integration between emerging experiment and computational approaches and technologies to advance quantitative characterization of allosteric mechanisms in proteins. Despite significant advances, the quantitative characterization and reliable prediction of functional allosteric states, interactions, and mechanisms continue to present highly challenging problems in the field. In this review, we discuss simulation-based multiscale approaches, experiment-informed Markovian models, and network modeling of allostery and information-theoretical approaches that can describe the thermodynamics and hierarchy allosteric states and the molecular basis of allosteric mechanisms. The wealth of structural and functional information along with diversity and complexity of allosteric mechanisms in therapeutically important protein families have provided a well-suited platform for development of data-driven research strategies. Data-centric integration of chemistry, biology and computer science using artificial intelligence technologies has gained a significant momentum and at the forefront of many cross-disciplinary efforts. We discuss new developments in the machine learning field and the emergence of deep learning and deep reinforcement learning applications in modeling of molecular mechanisms and allosteric proteins. The experiment-guided integrated approaches empowered by recent advances in multiscale modeling, network science, and machine learning can lead to more reliable prediction of allosteric regulatory mechanisms and discovery of allosteric modulators for therapeutically important protein targets.

Unveiling functional motions based on point mutations in biased signaling systems: A normal mode study on nerve growth factor bound to TrkA

Many receptors elicit signal transduction by activating multiple intracellular pathways. This transduction can be triggered by a non-specific ligand, which simultaneously activates all the signaling pathways of the receptors. However, the binding of one biased ligand preferentially trigger one pathway over another, in a process called biased signaling. The identification the functional motions related to each of these distinct pathways has a direct impact on the development of new effective and specific drugs. We show here how to detect specific functional motions by considering the case of the NGF/TrkA-Ig2 complex. NGF-mediated TrkA receptor activation is dependent on specific structural motions that trigger the neuronal growth, development, and survival of neurons in nervous system. The R221W mutation in the ngf gene impairs nociceptive signaling. We discuss how the large-scale structural effects of this mutation lead to the suppression of collective motions necessary to induce TrkA activation of nociceptive signaling. Our results suggest that subtle changes in the NGF interaction network due to the point mutation are sufficient to inhibit the motions of TrkA receptors putatively linked to nociception. The methodological approach presented in this article, based jointly on the normal mode analysis and the experimentally observed functional alterations due to point mutations provides an essential tool to reveal the structural changes and motions linked to the disease, which in turn could be necessary for a drug design study.

PySFD: comprehensive molecular insights from significant feature differences detected among many simulated ensembles

Motivation

Many modeling analyses of molecular dynamics (MD) simulations are based on a definition of states that can be (groups of) clusters of simulation frames in a feature space composed of molecular coordinates. With increasing dimension of this feature space (due to the increasing size or complexity of a simulated molecule), it becomes very difficult to cluster the underlying MD data and estimate a statistically robust model. To mitigate this “curse of dimensionality”, one can reduce the feature space, e.g., with principal component or time-lagged independent component analysis transformations, focusing the analysis on the most important modes of transitions. In practice, however, all these reduction strategies may neglect important molecular details that are susceptible to experimental verification.

Results

To recover such molecular details, I have developed PySFD (Significant Feature Differences analyzer for Python), a multi-processing software package that efficiently selects significantly different features of any user-defined feature type among potentially many different simulated state ensembles, such as meta-stable states of a Markov State Model (MSM). Applying PySFD on MSMs of an aggregate of 300 microseconds MD simulations recently performed on the major histocompatibility complex class II (MHCII) protein, I demonstrate how this toolkit can extract and visualize valuable mechanistic information from big MD simulation data, e.g., in form of networks of dynamic interaction changes connecting functionally relevant sites of a protein complex.

Availability and implementation

PySFD is freely available under the L-GPL license at https://github.com/markovmodel/PySFD.

Supplementary information

Supplementary data are available at Bioinformatics online.

Residue-Level Allostery Propagates through the Effective Coarse-Grained Hessian

The long-ranged coupling between residues that gives rise to allostery in a protein is built up from short-ranged physical interactions. Computational tools used to predict this coupling and its functional relevance have relied on the application of graph theoretical metrics to residue-level correlations measured from all-atom molecular dynamics simulations. The short-ranged interactions that yield these long-ranged residue-level correlations are quantified by the effective coarse-grained Hessian. Here we compute an effective harmonic coarse-grained Hessian from simulations of a benchmark allosteric protein, IGPS, and demonstrate the improved locality of this graph Laplacian over two other connectivity matrices. Additionally, two centrality metrics are developed that indicate the direct and indirect importance of each residue at producing the covariance between the effector binding pocket and the active site. The residue importance indicated by these two metrics is corroborated by previous mutagenesis experiments and leads to unique functional insights; in contrast to previous computational analyses, our results suggest that fP76-hK181 is the most important contact for conveying direct allosteric paths across the HisF-HisH interface. The connectivity around fD98 is found to be important at affecting allostery through indirect means.

Binding Interactions of Ergotamine and Dihydroergotamine to 5-Hydroxytryptamine Receptor 1B (5-HT1b) Using Molecular Dynamics Simulations and Dynamic Network Analysis

Ergotamine (ERG) and dihydroergotamine (DHE), common migraine drugs, have small structural differences but lead to clinically important distinctions in their pharmacological profiles. For example, DHE is less potent than ERG by about 10-fold at the 5-hydroxytrptamine receptor 1B (5-HT_1B). Although the high-resolution crystal structures of the 5-HT_1B receptor with both ligands have been solved, the high similarity between these two complex structures does not sufficiently explain their activity differences and the activation mechanism of the receptor. Hence, an examination of the dynamic motion of both drugs with the receptor is required. In this study, we ran a total of 6.0 μs molecular dynamics simulations on each system. Our simulation data show the subtle variations between the two systems in terms of the ligand-receptor interactions and receptor secondary structures. More importantly, the ligand and protein root-mean-square fluctuations (RMSFs) for the two systems were distinct, with ERG having a trend of lower RMSF values, indicating it to be bound tighter to 5-HT_1B with less fluctuations. The molecular mechanism-general born surface area (MM-GBSA) binding energies illustrate this further, proving ERG has an overall stronger MM-GBSA binding energy. Analysis of several different microswitches has shown that the 5-HT_1B-ERG complex is in a more active conformation state than 5-HT_1B-DHE, which is further supported by the dynamic network model, with reference to mutagenesis data with the critical nodes and the first three low-energy modes from the normal mode analysis. We also identify Trp327^6.48 and Phe331^6.52 as key residues involved in the active state 5-HT_1B for both ligands. Using the detailed dynamic information from our analysis, we made predictions for possible modifications to DHE and ERG that yielded five derivatives that might have more favorable binding energies and reduced structural fluctuations.

A structural basis for how ligand binding site changes can allosterically regulate GPCR signaling and engender functional selectivity

Signaling bias is the propensity for some agonists to preferentially stimulate G protein-coupled receptor (GPCR) signaling through one intracellular pathway versus another. We previously identified a G protein-biased agonist of the D₂ dopamine receptor (D2R) that results in impaired β-arrestin recruitment. This signaling bias was predicted to arise from unique interactions of the ligand with a hydrophobic pocket at the interface of the second extracellular loop and fifth transmembrane segment of the D2R. Here, we showed that residue Phe189 within this pocket (position 5.38 using Ballesteros-Weinstein numbering) functions as a microswitch for regulating receptor interactions with β-arrestin. This residue is relatively conserved among class A GPCRs, and analogous mutations within other GPCRs similarly impaired β-arrestin recruitment while maintaining G protein signaling. To investigate the mechanism of this signaling bias, we used an active-state structure of the β₂-adrenergic receptor (β2R) to build β2R-WT and β2R-Y199^5.38A models in complex with the full β2R agonist BI-167107 for molecular dynamics simulations. These analyses identified conformational rearrangements in β2R-Y199^5.38A that propagated from the extracellular ligand binding site to the intracellular surface, resulting in a modified orientation of the second intracellular loop in β2R-Y199^5.38A, which is predicted to affect its interactions with β-arrestin. Our findings provide a structural basis for how ligand binding site alterations can allosterically affect GPCR-transducer interactions and result in biased signaling.

Integrated Computational Approaches and Tools forAllosteric Drug Discovery

Understanding molecular mechanisms underlying the complexity of allosteric regulationin proteins has attracted considerable attention in drug discovery due to the benefits and versatilityof allosteric modulators in providing desirable selectivity against protein targets while minimizingtoxicity and other side effects. The proliferation of novel computational approaches for predictingligand-protein interactions and binding using dynamic and network-centric perspectives has ledto new insights into allosteric mechanisms and facilitated computer-based discovery of allostericdrugs. Although no absolute method of experimental and in silico allosteric drug/site discoveryexists, current methods are still being improved. As such, the critical analysis and integration ofestablished approaches into robust, reproducible, and customizable computational pipelines withexperimental feedback could make allosteric drug discovery more efficient and reliable. In this article,we review computational approaches for allosteric drug discovery and discuss how these tools can beutilized to develop consensus workflows for in silico identification of allosteric sites and modulatorswith some applications to pathogen resistance and precision medicine. The emerging realization thatallosteric modulators can exploit distinct regulatory mechanisms and can provide access to targetedmodulation of protein activities could open opportunities for probing biological processes and insilico design of drug combinations with improved therapeutic indices and a broad range of activities.

The allosteric mechanism of substrate-specific transport in SLC6 is mediated by a volumetric sensor

Neurotransmitter:sodium symporters (NSSs) in the SLC6 family terminate neurotransmission by coupling the thermodynamically favorable transport of ions to the thermodynamically unfavorable transport of neurotransmitter back into presynaptic neurons. Results from many structural, functional, and computational studies on LeuT, a bacterial NSS homolog, have provided critical insight into the mechanism of sodium-coupled transport, but the mechanism underlying substrate-specific transport rates is still not understood. We present a combination of molecular dynamics simulations, single-molecule fluorescence resonance energy transfer (smFRET) imaging, and measurements of Na+ binding and substrate transport that reveals an allosteric substrate specificity mechanism. In this mechanism, residues F259 and I359 in the substrate binding pocket couple the binding of substrate to Na+ release from the Na2 site by allosterically modulating the stability of a partially open, inward-facing state. We propose a model for transport selectivity in which residues F259 and I359 act as a volumetric sensor that inhibits the transport of bulky amino acids.

Data-driven computational analysis of allosteric proteins by exploring protein dynamics, residue coevolution and residue interaction networks

BACKGROUND

Computational studies of allosteric interactions have witnessed a recent renaissance fueled by the growing interest in modeling of the complex molecular assemblies and biological networks. Allosteric interactions in protein structures allow for molecular communication in signal transduction networks.

METHODS

In this work, we performed a large scale comprehensive and multi-faceted analysis of >300 diverse allosteric proteins and complexes with allosteric modulators. By modeling and exploring coarse-grained dynamics, residue coevolution, and residue interaction networks for allosteric proteins, we have determined unifying molecular signatures shared by allosteric systems.

RESULTS

The results of this study have suggested that allosteric inhibitors and allosteric activators may differentially affect global dynamics and network organization of protein systems, leading to diverse allosteric mechanisms. By using structural and functional data on protein kinases, we present a detailed case study that that included atomic-level analysis of coevolutionary networks in kinases bound with allosteric inhibitors and activators.

CONCLUSIONS

We have found that coevolutionary networks can form direct communication pathways connecting functional regions and can recapitulate key regulatory sites and interactions responsible for allosteric signaling in the studied protein systems. The results of this computational investigation are compared with the experimental studies and reveal molecular signatures of known regulatory hotspots in protein kinases.

GENERAL SIGNIFICANCE

This study has shown that allosteric inhibitors and allosteric activators can have a different effect on residue interaction networks and can exploit distinct regulatory mechanisms, which could open up opportunities for probing allostery and new drug combinations with broad range of activities.

Using Graphs of Dynamic Hydrogen-Bond Networks To Dissect Conformational Coupling in a Protein Motor

DExD/H-box proteins are soluble enzymes that couple binding and hydrolysis of adenosine triphosphate (ATP) with reactions involving RNA metabolism or bind and push newly synthesized proteins across bacterial cell membranes. Knowledge of the reaction mechanism of these enzymes could help the development of new therapeutics. In order to explore the mechanism of long-distance conformational coupling in SecA, the DEAD-box motor of the Sec protein secretion in bacteria, we implemented algorithms that provide simplified graph representations of the protein's dynamic hydrogen-bond networks. We find that mutations near the nucleotide-binding site or changes of the nucleotide-binding state of SecA associate with altered dynamics at the preprotein binding domain and identify extended networks of hydrogen bonds that connect the active site of SecA to the region where SecA binds newly synthesized secretory proteins. Water molecules participate in hydrogen-bonded water chains that bridge functional domains of SecA and could contribute to long-distance conformational coupling.

Exploring Allosteric Pathways of a V-Type Enzyme with Dynamical Perturbation Networks

Elucidation of the allosteric pathways in proteins is a computational challenge that strongly benefits from combination of atomistic molecular dynamics (MD) simulations and coarse-grained analysis of the complex dynamical network of chemical interactions based on graph theory. Here, we introduce and assess the performances of the dynamical perturbation network analysis of allosteric pathways in a prototypical V-type allosteric enzyme. Dynamical atomic contacts obtained from MD simulations are used to weight the allosteric protein graph, which involves an extended network of contacts perturbed by the effector binding in the allosteric site. The outcome showed good agreement with previously reported theoretical and experimental extended studies and it provided recognition of new potential allosteric spots that can be exploited in future mutagenesis experiments. Overall, the dynamical perturbation network analysis proved to be a powerful computational tool, complementary to other network-based approaches that can assist the full exploitation of allosteric phenomena for advances in protein engineering and rational drug design.

Integration of network models and evolutionary analysis into high-throughput modeling of protein dynamics and allosteric regulation: theory, tools and applications

Proteins are dynamical entities that undergo a plethora of conformational changes, accomplishing their biological functions. Molecular dynamics simulation and normal mode analysis methods have become the gold standard for studying protein dynamics, analyzing molecular mechanism and allosteric regulation of biological systems. The enormous amount of the ensemble-based experimental and computational data on protein structure and dynamics has presented a major challenge for the high-throughput modeling of protein regulation and molecular mechanisms. In parallel, bioinformatics and systems biology approaches including genomic analysis, coevolution and network-based modeling have provided an array of powerful tools that complemented and enriched biophysical insights by enabling high-throughput analysis of biological data and dissection of global molecular signatures underlying mechanisms of protein function and interactions in the cellular environment. These developments have provided a powerful interdisciplinary framework for quantifying the relationships between protein dynamics and allosteric regulation, allowing for high-throughput modeling and engineering of molecular mechanisms. Here, we review fundamental advances in protein dynamics, network theory and coevolutionary analysis that have provided foundation for rapidly growing computational tools for modeling of allosteric regulation. We discuss recent developments in these interdisciplinary areas bridging computational biophysics and network biology, focusing on promising applications in allosteric regulations, including the investigation of allosteric communication pathways, protein–DNA/RNA interactions and disease mutations in genomic medicine. We conclude by formulating and discussing future directions and potential challenges facing quantitative computational investigations of allosteric regulatory mechanisms in protein systems.

Computation-guided analysis of paroxetine binding to hSERT reveals functionally important structural elements and dynamics

The serotonin transporter (SERT) is one of the primary targets for medications to treat neuropsychiatric disorders and functions by exploiting pre-existing ion gradients of Na⁺, Cl^-, and K⁺ to translocate serotonin from the synaptic cleft into the presynaptic neuron. Although recent hSERT crystal structures represent a milestone for structure-function analyses of mammalian neurotransmitter:sodium symporters, they are all derived from thermostabilized but transport-deficient constructs. Two of these structures are in complex with paroxetine, the most potent selective serotonin reuptake inhibitor known. In this study, by carrying out and analyzing the results of extensive and comparative molecular dynamics simulations while also re-evaluating the transport and binding properties of the thermostabilized constructs, we identified functionally important structural elements that are perturbed by these mutations, revealed unexpected dynamics in the central primary binding site of SERT, and uncovered a conceivable ambiguity in paroxetine's binding orientation. We propose that the favored entropy contribution plays a significant role in paroxetine's extraordinarily high affinity for SERT. Our findings lay the foundation for future mechanistic studies and rational design of high-affinity SERT inhibitors. This article is part of the issue entitled 'Special Issue on Neurotransmitter Transporters'.

The E2.65A mutation disrupts dynamic binding poses of SB269652 at the dopamine D2 and D3 receptors

The dopamine D2 and D3 receptors (D2R and D3R) are important targets for antipsychotics and for the treatment of drug abuse. SB269652, a bitopic ligand that simultaneously binds both the orthosteric binding site (OBS) and a secondary binding pocket (SBP) in both D2R and D3R, was found to be a negative allosteric modulator. Previous studies identified Glu^2.65 in the SBP to be a key determinant of both the affinity of SB269652 and the magnitude of its cooperativity with orthosteric ligands, as the E^2.65A mutation decreased both of these parameters. However, the proposed hydrogen bond (H-bond) between Glu^2.65 and the indole moiety of SB269652 is not a strong interaction, and a structure activity relationship study of SB269652 indicates that this H-bond may not be the only element that determines its allosteric properties. To understand the structural basis of the observed phenotype of E^2.65A, we carried out molecular dynamics simulations with a cumulative length of ~77 μs of D2R and D3R wild-type and their E^2.65A mutants bound to SB269652. In combination with Markov state model analysis and by characterizing the equilibria of ligand binding modes in different conditions, we found that in both D2R and D3R, whereas the tetrahydroisoquinoline moiety of SB269652 is stably bound in the OBS, the indole-2-carboxamide moiety is dynamic and only intermittently forms H-bonds with Glu^2.65. Our results also indicate that the E^2.65A mutation significantly affects the overall shape and size of the SBP, as well as the conformation of the N terminus. Thus, our findings suggest that the key role of Glu^2.65 in mediating the allosteric properties of SB269652 extends beyond a direct interaction with SB269652, and provide structural insights for rational design of SB269652 derivatives that may retain its allosteric properties.

G protein-coupled receptors (GPCRs) are targets of more than 25% of prescription drugs on the market. Due to their critical roles in human physiology, competitive modulation of these receptors has been found to be associated with many undesired side effects. Allosteric modulation holds the promise of retaining normal receptor function and improving selectivity. However, the underlying molecular mechanisms of the allosteric modulation of GPCRs have remained largely uncharted. The dopamine D2-like receptors have been implicated in voluntary movement, reward, sleep, learning, and memory. Based on previous experimental findings, we computationally characterized the binding of a negative allosteric modulator of dopamine D2 and D3 receptors, and revealed the dynamic binding mode of this modulator in a secondary binding pocket (SBP) of the receptors. Our results highlight the key role of a Glu in mediating the allosteric properties of the modulator by shaping the dynamically formed SBP, and shed light on rational design and optimization of allosteric modulators of GPCRs.

A partially-open inward-facing intermediate conformation of LeuT is associated with Na+ release and substrate transport

Neurotransmitter:sodium symporters (NSS), targets of antidepressants and psychostimulants, clear neurotransmitters from the synaptic cleft through sodium (Na+)-coupled transport. Substrate and Na+ are thought to be transported from the extracellular to intracellular space through an alternating access mechanism by coordinated conformational rearrangements in the symporter that alternately expose the binding sites to each side of the membrane. However, the mechanism by which the binding of ligands coordinates conformational changes occurring on opposite sides of the membrane is not well understood. Here, we report the use of single-molecule fluorescence resonance energy transfer (smFRET) techniques to image transitions between distinct conformational states on both the extracellular and intracellular sides of the prokaryotic NSS LeuT, including partially open intermediates associated with transport activity. The nature and functional context of these hitherto unidentified intermediate states shed new light on the allosteric mechanism that couples substrate and Na+ symport by the NSS family through conformational dynamics.

Investigating Small-Molecule Ligand Binding to G Protein-Coupled Receptors with Biased or Unbiased Molecular Dynamics Simulations

An increasing number of G protein-coupled receptor (GPCR) crystal structures provide important-albeit static-pictures of how small molecules or peptides interact with their receptors. These high-resolution structures represent a tremendous opportunity to apply molecular dynamics (MD) simulations to capture atomic-level dynamical information that is not easy to obtain experimentally. Understanding ligand binding and unbinding processes, as well as the related responses of the receptor, is crucial to the design of better drugs targeting GPCRs. Here, we discuss possible ways to study the dynamics involved in the binding of small molecules to GPCRs, using long timescale MD simulations or metadynamics-based approaches.

Dependence of prevalence of contiguous pathways in proteins on structural complexity

Allostery is a regulatory mechanism in proteins where an effector molecule binds distal from an active site to modulate its activity. Allosteric signaling may occur via a continuous path of residues linking the active and allosteric sites, which has been suggested by large conformational changes evident in crystal structures. An alternate possibility is that the signal occurs in the realm of ensemble dynamics via an energy landscape change. While the latter was first proposed on theoretical grounds, increasing evidence suggests that such a control mechanism is plausible. A major difficulty for testing the two methods is the ability to definitively determine that a residue is directly involved in allosteric signal transduction. Statistical Coupling Analysis (SCA) is a method that has been successful at predicting pathways, and experimental tests involving mutagenesis or domain substitution provide the best available evidence of signaling pathways. However, ascertaining energetic pathways which need not be contiguous is far more difficult. To date, simple estimates of the statistical significance of a pathway in a protein remain to be established. The focus of this work is to estimate such benchmarks for the statistical significance of contiguous pathways for the null model of selecting residues at random. We found that when 20% of residues in proteins are randomly selected, contiguous pathways at the 6 Å cutoff level were found with success rates of 51% in PDZ, 30% in p53, and 3% in MutS. The results suggest that the significance of pathways may have system specific factors involved. Furthermore, the possible existence of false positives for contiguous pathways implies that signaling could be occurring via alternate routes including those consistent with the energetic landscape model.

An optimal distance cutoff for contact-based Protein Structure Networks using side-chain centers of mass

Proteins are highly dynamic entities attaining a myriad of different conformations. Protein side chains change their states during dynamics, causing clashes that are propagated at distal sites. A convenient formalism to analyze protein dynamics is based on network theory using Protein Structure Networks (PSNs). Despite their broad applicability, few efforts have been devoted to benchmarking PSN methods and to provide the community with best practices. In many applications, it is convenient to use the centers of mass of the side chains as nodes. It becomes thus critical to evaluate the minimal distance cutoff between the centers of mass which will provide stable network properties. Moreover, when the PSN is derived from a structural ensemble collected by molecular dynamics (MD), the impact of the MD force field has to be evaluated. We selected a dataset of proteins with different fold and size and assessed the two fundamental properties of the PSN, i.e. hubs and connected components. We identified an optimal cutoff of 5 Å that is robust to changes in the force field and the proteins. Our study builds solid foundations for the harmonization and standardization of the PSN approach.

The role of transmembrane segment 5 (TM5) in Na2 release and the conformational transition of neurotransmitter:sodium symporters toward the inward-open state

Neurotransmitter:sodium symporters (NSSs) terminate neurotransmission by the reuptake of released neurotransmitters. This active accumulation of substrate against its concentration gradient is driven by the transmembrane Na⁺ gradient and requires that the transporter traverses several conformational states. LeuT, a prokaryotic NSS homolog, has been crystallized in outward-open, outward-occluded, and inward-open states. Two crystal structures of another prokaryotic NSS homolog, the multihydrophobic amino acid transporter (MhsT) from Bacillus halodurans, have been resolved in novel inward-occluded states, with the extracellular vestibule closed and the intracellular portion of transmembrane segment 5 (TM5i) in either an unwound or a helical conformation. We have investigated the potential involvement of TM5i in binding and unbinding of Na2, i.e. the Na⁺ bound in the Na2 site, by carrying out comparative molecular dynamics simulations of the models derived from the two MhsT structures. We find that the helical TM5i conformation is associated with a higher propensity for Na2 release, which leads to the repositioning of the N terminus and transition to an inward-open state. By using comparative interaction network analysis, we also identify allosteric pathways connecting TM5i and the Na2 binding site to the extracellular and intracellular regions. Based on our combined computational and mutagenesis studies of MhsT and LeuT, we propose that TM5i plays a key role in Na2 binding and release associated with the conformational transition toward the inward-open state, a role that is likely to be shared across the NSS family.

Detection of Side Chain Rearrangements Mediating the Motions of Transmembrane Helices in Molecular Dynamics Simulations of G Protein-Coupled Receptors

Structure and dynamics are essential elements of protein function. Protein structure is constantly fluctuating and undergoing conformational changes, which are captured by molecular dynamics (MD) simulations. We introduce a computational framework that provides a compact representation of the dynamic conformational space of biomolecular simulations. This method presents a systematic approach designed to reduce the large MD simulation spatiotemporal datasets into a manageable set in order to guide our understanding of how protein mechanics emerge from side chain organization and dynamic reorganization. We focus on the detection of side chain interactions that undergo rearrangements mediating global domain motions and vice versa. Side chain rearrangements are extracted from side chain interactions that undergo well-defined abrupt and persistent changes in distance time series using Gaussian mixture models, whereas global domain motions are detected using dynamic cross-correlation. Both side chain rearrangements and global domain motions represent the dynamic components of the protein MD simulation, and are both mapped into a network where they are connected based on their degree of coupling. This method allows for the study of allosteric communication in proteins by mapping out the protein dynamics into an intramolecular network to reduce the large simulation data into a manageable set of communities composed of coupled side chain rearrangements and global domain motions. This computational framework is suitable for the study of tightly packed proteins, such as G protein-coupled receptors, and we present an application on a seven microseconds MD trajectory of CC chemokine receptor 7 (CCR7) bound to its ligand CCL21.

A Markov State-based Quantitative Kinetic Model of Sodium Release from the Dopamine Transporter

The dopamine transporter (DAT) belongs to the neurotransmitter:sodium symporter (NSS) family of membrane proteins that are responsible for reuptake of neurotransmitters from the synaptic cleft to terminate a neuronal signal and enable subsequent neurotransmitter release from the presynaptic neuron. The release of one sodium ion from the crystallographically determined sodium binding site Na2 had been identified as an initial step in the transport cycle which prepares the transporter for substrate translocation by stabilizing an inward-open conformation. We have constructed Markov State Models (MSMs) from extensive molecular dynamics simulations of human DAT (hDAT) to explore the mechanism of this sodium release. Our results quantify the release process triggered by hydration of the Na2 site that occurs concomitantly with a conformational transition from an outward-facing to an inward-facing state of the transporter. The kinetics of the release process are computed from the MSM, and transition path theory is used to identify the most probable sodium release pathways. An intermediate state is discovered on the sodium release pathway, and the results reveal the importance of various modes of interaction of the N-terminus of hDAT in controlling the pathways of release.

Toward Understanding the Structural Basis of Partial Agonism at the Dopamine D3 Receptor

Both dopamine D₃ receptor (D₃R) partial agonists and antagonists have been implicated as potential medications for substance use disorders. In contrast to antagonists, partial agonists may cause fewer side effects since they maintain some dopaminergic tone and may be less disruptive to normal neuronal functions. Here, we report three sets of 4-phenylpiperazine stereoisomers that differ considerably in efficacy: the (R)-enantiomers are antagonists/weak partial agonists, whereas the (S)-enantiomers are much more efficacious. To investigate the structural basis of partial agonism, we performed comparative microsecond-scale molecular dynamics simulations starting from the inactive state of D₃R in complex with these enantiomers. Analysis of the simulation results reveals common structural rearrangements near the ligand binding site induced by the bound (S)-enantiomers, but not by the (R)-enantiomers, that are features of partially activated receptor conformations. These receptor models bound with partial agonists may be useful for structure-based design of compounds with tailored efficacy profiles.

How Do Short Chain Nonionic Detergents Destabilize G-Protein-Coupled Receptors?

Stability of detergent-solubilized G-protein-coupled receptors (GPCRs) is crucial for their purification in a biologically relevant state, and it is well-known that short chain detergents such as octylglucoside are more denaturing than long chain detergents such as dodecylmaltoside. However, the molecular basis for this phenomenon is poorly understood. To gain insights into the mechanism of detergent destabilization of GPCRs, we used atomistic molecular dynamics simulations of thermostabilized adenosine receptor (A2AR) mutants embedded in either a lipid bilayer or detergent micelles of alkylmaltosides and alkylglucosides. A2AR mutants in dodecylmaltoside or phospholipid showed low flexibility and good interhelical packing. In contrast, A2AR mutants in either octylglucoside or nonylglucoside showed decreased α-helicity in transmembrane regions, decreased α-helical packing, and the interpenetration of detergent molecules between transmembrane α-helices. This was not observed in octylglucoside containing phospholipid. Cholesteryl hemisuccinate in dodecylmaltoside increased the energetic stability of the receptor by wedging into crevices on the hydrophobic surface of A2AR, increasing packing interactions within the receptor and stiffening the detergent micelle. The data suggest a three-stage process for the initial events in the destabilization of GPCRs by octylglucoside: (i) highly mobile detergent molecules form small micelles around the receptor; (ii) loss of α-helicity and decreased interhelical packing interactions in transmembrane regions are promoted by increased receptor thermal motion; (iii) transient separation of transmembrane helices allowed penetration of detergent molecules into the core of the receptor. The relative hydration of the headgroup and alkyl chain correlates with detergent harshness and suggests new avenues to develop milder versions of octylglucoside for receptor crystallization.

MHC class II complexes sample intermediate states along the peptide exchange pathway

The presentation of peptide-MHCII complexes (pMHCIIs) for surveillance by T cells is a well-known immunological concept in vertebrates, yet the conformational dynamics of antigen exchange remain elusive. By combining NMR-detected H/D exchange with Markov modelling analysis of an aggregate of 275 microseconds molecular dynamics simulations, we reveal that a stable pMHCII spontaneously samples intermediate conformations relevant for peptide exchange. More specifically, we observe two major peptide exchange pathways: the kinetic stability of a pMHCII's ground state defines its propensity for intrinsic peptide exchange, while the population of a rare, intermediate conformation correlates with the propensity of the HLA-DM-catalysed pathway. Helix-destabilizing mutants designed based on our model shift the exchange behaviour towards the HLA-DM-catalysed pathway and further allow us to conceptualize how allelic variation can shape an individual's MHC restricted immune response.

MHCII proteins bind and present both foreign and self-antigens to potentially activate CD4+ T cells via cognate T cell receptors (TCRs) during the adaptive immune response. Here, the authors combine NMR-detected H/D exchange with Markov modelling analysis to shed light on the dynamics of MHCII peptide exchange.

New perspectives on the computational characterization of the kinetics of binding-unbinding in drug design: implications for novel therapies

The efficiency and the propensity of a drug to be bound to its target protein have been inseparable concepts for decades now. The correlation between the pharmacological activity and the binding affinity has been the first rule to design and optimize a new drug rationally. However, this argument does not prove to be infallible when the results of in vivo assays have to be confronted. Only recently, we understand that other magnitudes as the kinetic rates of binding and unbinding, or the mean residence time of the complex drug-protein, are equally relevant to draw a more accurate model of the mechanism of action of a drug. It is in this scenario where new computational techniques to simulate the all-atom dynamics of the biomolecular system find its valuable place on the challenge of designing new molecules for more effective and less toxic therapies.

Conformational Dynamics on the Extracellular Side of LeuT Controlled by Na+ and K+ Ions and the Protonation State of Glu290

Ions play key mechanistic roles in the gating dynamics of neurotransmitter:sodium symporters (NSSs). In recent microsecond scale molecular dynamics simulations of a complete model of the dopamine transporter, a NSS protein, we observed a partitioning of K(+) ions from the intracellular side toward the unoccupied Na2 site of dopamine transporter following the release of the Na2-bound Na(+) Here we evaluate with computational simulations and experimental measurements of ion affinities under corresponding conditions, the consequences of K(+) binding in the Na2 site of LeuT, a bacterial homolog of NSS, when both Na(+) ions and substrate have left, and the transporter prepares for a new cycle. We compare the results with the consequences of binding Na(+) in the same apo system. Analysis of >50-μs atomistic molecular dynamics and enhanced sampling trajectories of constructs with Glu(290), either charged or neutral, point to the Glu(290) protonation state as a main determinant in the structural reconfiguration of the extracellular vestibule of LeuT in which a "water gate" opens through coordinated motions of residues Leu(25), Tyr(108), and Phe(253) The resulting water channel enables the binding/dissociation of the Na(+) and K(+) ions that are prevalent, respectively, in the extracellular and intracellular environments.