Large-scale Conformational Transitions Research Articles

Switching Go̅-Martini for Investigating Protein Conformational Transitions and Associated Protein-Lipid Interactions.

Proteins are dynamic biomolecules that can transform between different conformational states when exerting physiological functions, which is difficult to simulate using all-atom methods. Coarse-grained (CG) Go̅-like models are widely used to investigate large-scale conformational transitions, which usually adopt implicit solvent models and therefore cannot explicitly capture the interaction between proteins and surrounding molecules, such as water and lipid molecules. Here, we present a new method, named Switching Go̅-Martini, to simulate large-scale protein conformational transitions between different states, based on the switching Go̅ method and the CG Martini 3 force field. The method is straightforward and efficient, as demonstrated by the benchmarking applications for multiple protein systems, including glutamine binding protein (GlnBP), adenylate kinase (AdK), and β2-adrenergic receptor (β2AR). Moreover, by employing the Switching Go̅-Martini method, we can not only unveil the conformational transition from the E2Pi-PL state to E1 state of the type 4 P-type ATPase (P4-ATPase) flippase ATP8A1-CDC50 but also provide insights into the intricate details of lipid transport.

NMR and Single-Molecule FRET Insights into Fast Protein Motions and Their Relation to Function.

Proteins often undergo large-scale conformational transitions, in which secondary and tertiary structure elements (loops, helices, and domains) change their structures or their positions with respect to each other. Simple considerations suggest that such dynamics should be relatively fast, but the functional cycles of many proteins are often relatively slow. Sophisticated experimental methods are starting to tackle this dichotomy and shed light on the contribution of large-scale conformational dynamics to protein function. In this review, we focus on the contribution of single-molecule Förster resonance energy transfer and nuclear magnetic resonance (NMR) spectroscopies to the study of conformational dynamics. We briefly describe the state of the art in each of each of these techniques and then point out their similarities and differences, as well as the relative strengths and weaknesses of each. Several case studies, in which the connection between fast conformational dynamics and slower function has been demonstrated, are then introduced and discussed. These examples include both enzymes and large protein machines, some of which have been studied by both NMR and fluorescence spectroscopies. Expected final online publication date for the Annual Review of Biophysics, Volume 53 is May 2024. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Energetic and structural insights behind calcium induced conformational transition in calmodulin.

Calmodulin (CaM) is a key signaling protein that triggers several cellular and physiological processes inside the cell. Upon binding with calcium ion, CaM undergoes large scale conformational transition from a closed state to an open state that facilitates its interaction with various target protein and regulates their activity. This work explores the origin of the energetic and structural variation of the wild type and mutated CaM and explores the molecular origin for the structural differences between them. We first calculated the sequential calcium binding energy to CaM using the PDLD/S-LRA/β approach. This study shows a very good correlation with experimental calcium binding energies. Next we calculated the calcium binding energies to the wild type CaM and several mutated CaM systems which were reported experimentally. On the structural aspect, it has been reported experimentally that certain mutation (Q41L-K75I) in calcium bound CaM leads to complete conformational transition from an open to a closed state. By using equilibrium molecular dynamics simulation, free energy calculation and contact frequency map analysis, we have shown that the formation of a cluster of long-range hydrophobic contacts, initiated by the Q41L-K75I CaM variant is the driving force behind its closing motion. This study unravels the energetics and structural aspects behind calcium ion induced conformational changes in wild type CaM and its variant.

Machine Learning Generation of Dynamic Protein Conformational Ensembles.

Machine learning has achieved remarkable success across a broad range of scientific and engineering disciplines, particularly its use for predicting native protein structures from sequence information alone. However, biomolecules are inherently dynamic, and there is a pressing need for accurate predictions of dynamic structural ensembles across multiple functional levels. These problems range from the relatively well-defined task of predicting conformational dynamics around the native state of a protein, which traditional molecular dynamics (MD) simulations are particularly adept at handling, to generating large-scale conformational transitions connecting distinct functional states of structured proteins or numerous marginally stable states within the dynamic ensembles of intrinsically disordered proteins. Machine learning has been increasingly applied to learn low-dimensional representations of protein conformational spaces, which can then be used to drive additional MD sampling or directly generate novel conformations. These methods promise to greatly reduce the computational cost of generating dynamic protein ensembles, compared to traditional MD simulations. In this review, we examine recent progress in machine learning approaches towards generative modeling of dynamic protein ensembles and emphasize the crucial importance of integrating advances in machine learning, structural data, and physical principles to achieve these ambitious goals.

Swing-out opening of stromal interaction molecule 1.

Stromal interaction molecule 1 (STIM1) resides in the endoplasmic reticulum (ER) membrane and senses luminal calcium (Ca2+ ) concentration. STIM1 activation involves a large-scale conformational transition that exposes a STIM1 domain termed "CAD/SOAR", - which is required for activation of the calcium channel Orai. Under resting cell conditions, STIM1 assumes a quiescent state where CAD/SOAR is suspended in an intramolecular clamp formed by the coiled-coil 1 domain (CC1) and CAD/SOAR. Here, we present a structural model of the cytosolic part of the STIM1 resting state using molecular docking simulations that take into account previously reported interaction sites between the CC1α1 and CAD/SOAR domains. We corroborate and refine previously reported interdomain coiled-coil contacts. Based on our model, we provide a detailed analysis of the CC1-CAD/SOAR binding interface using molecular dynamics simulations. We find a very similar binding interface for a proposed domain-swapped configuration of STIM1, where the CAD/SOAR domain of one monomer interacts with the CC1α1 domain of another monomer of STIM1. The rich structural and dynamical information obtained from our simulations reveals novel interaction sites such as M244, I409, or E370, which are crucial for STIM1 quiescent state stability. We tested our predictions by electrophysiological and Förster resonance energy transfer experiments on corresponding single-point mutants. These experiments provide compelling support for the structural model of the STIM1 quiescent state reported here. Based on transitions observed in enhanced-sampling simulations paired with an analysis of the quiescent STIM1 conformational dynamics, our work offers a first atomistic model for CC1α1-CAD/SOAR detachment.

Theory and computation early career award talk: unraveling the CRISPR-Cas revolution through the lens of computational biophysics.

The clustered regularly interspaced short palindromic repeat (CRISPR) genome-editing revolution established the beginning of a new era in life sciences. I will report the role of state-of-the-art computations in the CRISPR-Cas9 revolution, from the early refinement of cryo-EM data to enhanced simulations of large-scale conformational transitions. Molecular simulations reported a mechanism for RNA binding and the formation of a catalytically competent Cas9 enzyme, corroborated by recent cryo-EM data of the activated state. Inspired by single-molecule experiments, molecular dynamics offered a rationale for the onset of off-target effects, while graph theory unveiled the allosteric regulation. Finally, the use of a mixed quantum-classical approach established the catalytic mechanism of DNA cleavage. Overall, molecular simulations have been instrumental in understanding the dynamics and mechanism of CRISPR-Cas9, contributing to understanding function, catalysis, allostery, and specificity. Future computational studies will bridge the scales from quantum mechanical approaches to enhanced simulations and cryo-EM processing methods to push the boundaries of molecular simulations and tackle the most exciting problems in the biophysics of genome editing.

Markov State Models and Molecular Dynamics Simulations Reveal the Conformational Transition of the Intrinsically Disordered Hypervariable Region of K-Ras4B to the Ordered Conformation.

K-Ras4B, the most frequently mutated Ras isoform in human tumors, plays a vital part in cell growth, differentiation, and survival. Its tail, the C-terminal hypervariable region (HVR), is involved in anchoring K-Ras4B at the cellular plasma membrane and in isoform-specific protein-protein interactions and signaling. In the inactive guanosine diphosphate-bound state, the intrinsically disordered HVR interacts with the catalytic domain at the effector-binding region, rendering K-Ras4B in its autoinhibited state. Activation releases the HVR from the catalytic domain, with its ensemble favoring an ordered α-helical structure. The large-scale conformational transition of the HVR from the intrinsically disordered to the ordered conformation remains poorly understood. Here, we deploy a computational scheme that integrates a transition path-generation algorithm, extensive molecular dynamics simulation, and Markov state model analysis to investigate the conformational landscape of the HVR transition pathway. Our findings reveal a stepwise pathway for the HVR transition and uncover several key conformational substates along the transition pathway. Importantly, key interactions between the HVR and the catalytic domain are unraveled, highlighting the pathogenesis of K-Ras4B mild mutations in several congenital developmental anomaly syndromes. Together, these findings provide a deeper understanding of the HVR transition mechanism and the regulation of K-Ras4B activity at an atomic level.

Study of Allosteric Transitions of Human P-Glycoprotein by Using the Two-State Anisotropic Network Model.

Human P-glycoprotein (P-gp) is a kind of ATP-binding cassette (ABC) transporters. Once human P-gp is overexpressed in tumor cells, which can lead to tumor multidrug resistance (MDR). However, the present experimental methods are difficult to obtain the large-scale conformational transition process of human P-gp. In this work, we explored the allosteric pathway of human P-gp from the inward-facing (IF) to the outward-facing (OF) state in the substrate transport process with the two-state anisotropic network model (tANM). These results suggest that the allosteric transitions proceed in a coupled way. The conformational changes of nucleotide-binding domains (NBDs) finally make the transmembrane domains (TMDs) to the OF state via the role of the allosteric propagation of the intracellular helices IH1 and IH2. Additionally, this allosteric pathway is advantageous in energy compared with other methods. This study reveals the conformational transition of P-gp, which contributes to an understanding of the allosteric mechanism of ABC exporters.

An implicit solvent model for accurate simulation of large-scale protein conformational transitions

Implicit solvent models capture the average influence of solvent by direct estimation of the solvation free energy. They reduce the system size ∼10-fold compared to explicit solvent models and could allow more efficient simulation of protein conformational transitions. We have previously implemented the generalized Born with molecular volume (GBMV2) implicit solvent model on GPU and achieved ∼60-fold speed up. Even though GBMV2 is considered one of most accurate implicit solvent models, its suitability for simulating large-scale conformational fluctuations, especially those of intrinsically disordered proteins (IDPs) has not been carefully evaluated due to the computational cost.

Integrin-based mechanosensing through conformational deformation

Conversion of integrins from low to high affinity states, termed activation, is important in biological processes, including immunity, hemostasis, angiogenesis, and embryonic development. Integrin activation is regulated by large-scale conformational transitions from closed, low affinity states to open, high affinity states. Although it has been suggested that substrate stiffness shifts the conformational equilibrium of integrin and governs its unbinding, here, we address the role of integrin conformational activation in cellular mechanosensing. Comparison of wild-type versus activating mutants of integrin αVβ3 show that activating mutants shift cell spreading, focal adhesion kinase activation, traction stress, and force on talin toward high stiffness values at lower stiffness. Although all activated integrin mutants showed equivalent binding affinity for soluble ligands, the β3 S243E mutant showed the strongest shift in mechanical responses. To understand this behavior, we used coarse-grained computational models derived from molecular level information. The models predicted that wild-type integrin αVβ3 displaces under force and that activating mutations shift the required force toward lower values, with S243E showing the strongest effect. Cellular stiffness sensing thus correlates with computed effects of force on integrin conformation. Together, these data identify a role for force-induced integrin conformational deformation in cellular mechanosensing.

Activation pathway of a G protein-coupled receptor uncovers conformational intermediates as targets for allosteric drug design

G protein-coupled receptors (GPCRs) are the most common proteins targeted by approved drugs. A complete mechanistic elucidation of large-scale conformational transitions underlying the activation mechanisms of GPCRs is of critical importance for therapeutic drug development. Here, we apply a combined computational and experimental framework integrating extensive molecular dynamics simulations, Markov state models, site-directed mutagenesis, and conformational biosensors to investigate the conformational landscape of the angiotensin II (AngII) type 1 receptor (AT1 receptor) — a prototypical class A GPCR—activation. Our findings suggest a synergistic transition mechanism for AT1 receptor activation. A key intermediate state is identified in the activation pathway, which possesses a cryptic binding site within the intracellular region of the receptor. Mutation of this cryptic site prevents activation of the downstream G protein signaling and β-arrestin-mediated pathways by the endogenous AngII octapeptide agonist, suggesting an allosteric regulatory mechanism. Together, these findings provide a deeper understanding of AT1 receptor activation at an atomic level and suggest avenues for the design of allosteric AT1 receptor modulators with a broad range of applications in GPCR biology, biophysics, and medicinal chemistry.

Enhanced sampling protocol to elucidate fusion peptide opening of SARS-CoV-2 spike protein

Large-scale conformational transitions in the spike protein S2 domain are required during host-cell infection of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus. Although conventional molecular dynamics simulations have been extensively used to study therapeutic targets of SARS-CoV-2, it is still challenging to gain molecular insight into the key conformational changes because of the size of the spike protein and the long timescale required to capture these transitions. In this work, we have developed an efficient simulation protocol that leverages many short simulations, a dynamic selection algorithm, and Markov state models to interrogate the structural changes of the S2 domain. We discovered that the conformational flexibility of the dynamic region upstream of the fusion peptide in S2 is coupled to the proteolytic cleavage state of the spike protein. These results suggest that opening of the fusion peptide likely occurs on a submicrosecond timescale after cleavage at the S2′ site. Building on the structural and dynamical information gained to date about S2 domain dynamics, we provide proof of principle that a small molecule bound to a seam neighboring the fusion peptide can slow the opening of the fusion peptide, leading to a new inhibition strategy for experiments to confirm. In aggregate, these results will aid the development of drug cocktails to inhibit infections caused by SARS-CoV-2 and other coronaviruses.

Coarse-Grained Modeling of Multiple Pathways in Conformational Transitions of Multi-Domain Proteins.

Large-scale conformational transitions in multi-domain proteins are often essential for their functions. To investigate the transitions, it is necessary to explore multiple potential pathways, which involve different intermediate structures. Here, we present a multi-basin (MB) coarse-grained (CG) structure-based Go̅ model for describing transitions in proteins with more than two moving domains. This model is an extension of our dual-basin Go̅ model in which system-dependent parameters are determined systematically using the multistate Bennett acceptance ratio method. In the MB Go̅ model for multi-domain proteins, we assume that intermediate structures may have partial inter-domain native contacts. This approach allows us to search multiple transition pathways that involve distinct intermediate structures using the CG molecular dynamics (MD) simulations. We apply this scheme to an enzyme, adenylate kinase (AdK), which has three major domains and can move along two different pathways. Using the optimized mixing parameters for each pathway, AdK shows frequent transitions between the Open, Closed, and the intermediate basins and samples a wide variety of conformations within each basin. The explored multiple transition pathways could be compared with experimental data and examined in more detail by atomistic MD simulations.

Mechanistic Insights from Molecular Dynamics Simulations of Large-Scale Conformational Transitions in the INDY Transport Cycle

Mechanistic Insights from Molecular Dynamics Simulations of Large-Scale Conformational Transitions in the INDY Transport Cycle

Elucidation of interactions regulating conformational stability and dynamics of SARS-CoV-2 S-protein

The ongoing COVID-19 pandemic caused by the new coronavirus, SARS-CoV-2, calls for urgent developments of vaccines and antiviral drugs. The spike protein of SARS-CoV-2 (S-protein), which consists of trimeric polypeptide chains with glycosylated residues on the surface, triggers the virus entry into a host cell. Extensive structural and functional studies on this protein have rapidly advanced our understanding of the S-protein structure at atomic resolutions, although most of these structural studies overlook the effect of glycans attached to the S-protein on the conformational stability and functional motions between the inactive down and active up forms. Here, we performed all-atom molecular dynamics simulations of both down and up forms of a fully glycosylated S-protein in solution as well as targeted molecular dynamics simulations between them to elucidate key interdomain interactions for stabilizing each form and inducing the large-scale conformational transitions. The residue-level interaction analysis of the simulation trajectories detects distinct amino acid residues and N-glycans as determinants on conformational stability of each form. During the conformational transitions between them, interdomain interactions mediated by glycosylated residues are switched to play key roles on the stabilization of another form. Electrostatic interactions, as well as hydrogen bonds between the three receptor binding domains, work as driving forces to initiate the conformational transitions toward the active form. This study sheds light on the mechanisms underlying conformational stability and functional motions of the S-protein, which are relevant for vaccine and antiviral drug developments.

Deciphering collaborative sidechain motions in proteins during molecular dynamics simulations

The dynamic structure of proteins is essential for their functions and may include large conformational transitions which can be studied by molecular dynamics (MD) simulations. However, details of these transitions are difficult to automatically track. To facilitate their analysis, we developed two scores of correlation between sidechain dihedral angles. The CIRCULAR and OMES scores are computed from, respectively, dihedral angle values and rotamer distributions. As a case study, we applied our methods to an activation-like transition of the chemokine receptor CXCR4, observed during accelerated MD simulations. The principal component analysis of the correlation matrices was consistent with the networking structure of the top ranking pairs. Both scores identify a set of residues whose “collaborative” sidechain rotamerization immediately preceded or accompanied the conformational transition of CXCR4. Detailed analysis of the sequential order of these rotamerizations suggests that an allosteric mechanism, involving the outward motion of an asparagine residue in transmembrane helix 3, might be a prerequisite to the large scale conformational transition of CXCR4. This case study provides the proof-of-concept that the correlation methods developed here are valuable exploratory techniques to help decipher complex reactional pathways.

Precisely Quantifying the Energetics of the Ribosome

The elongation cycle of the ribosome requires rapid and regulated dynamics of numerous large-scale conformational rearrangements. While these are high-dimensional processes, single-molecule experiments typically measure a small number of interatomic distances. Thus, an outstanding challenge is to identify the most kinetically-relevant degrees of freedom. Our strategy for addressing this gap is to use simplified theoretical models and molecular dynamics simulations to simulate many (100-1000) spontaneous large-scale (∼30-50 Å) conformational transitions for each step (accommodation, A/P and P/E formation, translocation, subunit rotation). These vast data sets then allow us to identify suitable experimental strategies for precisely probing the free-energy landscape and rate-limiting barriers in the ribosome. In addition to having utility in experimental studies, these simulations are also revealing the pronounced influence of molecular sterics and flexibility on the kinetics of elongation.

Quantifying the Effects of Charge Regulation on Disorder-Order Transitions of Highly Charged Protein Sequences

Charge regulation, which refers to the modulation of charge states of ionizable residues due to shifted pKa values, is an important albeit often overlooked determinant of sequence-ensemble and sequence-structure relationships of intrinsically disordered and intrinsically ordered proteins. We have developed complimentary computational and experimental approaches that account for the complete ensemble of charge states for a given sequence. This combined approach enables the computational calculation of conformational ensembles as a function of pH, and experimental measurement of charge states as a function of pH, allowing us to quantify the effects of charge regulation on archetypal sequences that are predicted to be intrinsically disordered. Here, we present results from a joint experimental and computational analysis of the effects of charge regulation on block-copolypeptides composed entirely of acidic and basic residues, (E4K4)n. These sequences form stable single alpha helices, although they are erroneously predicted to form disordered ensembles if one assumes fixed charge states for the ionizable residues. We employ a novel capillary electrophoresis method for measuring charge as a function of pH. These data, combined with potentiometric analysis, show that glutamate residues have highly shifted pKa values when compared to those of model compounds. This preferential neutralization of glutamate residues that are internal to the block co-polypeptide sequence engenders alpha helix formation by lowering the free energy of desolvation and enabling the favorable propagation of the alpha helix. These findings are readily explained using simulations based on the recently developed q-canonical Monte Carlo sampling method. Our results suggest that sequence-specific charge regulation can engender large-scale conformational transitions and even underlie the driving forces for phase separation. Further, our results highlight the need for refining disorder predictors based on an improved understanding of context dependent charge regulation effects.

Large-Scale Biomolecular Conformational Transitions Explored by a Combined Elastic Network Model and Enhanced Sampling Molecular Dynamics.

Biomolecules often undergo large-scale conformational transitions when carrying out their functions. However, it is still challenging for conventional molecular dynamics simulations to provide adequate structural dynamics information to interpret associated mechanisms. Here, we present a combined elastic network model and enhanced sampling-based strategy (iterANM-IaMD) by adopting iterANM to construct initial conformation space and enhanced sampling IaMD to explore the free energy landscape along specific large-scale conformational transitions. We applied this strategy to three functionally and structurally distinct proteins (adenylate kinase, calmodulin, and p38α kinase), which undergo striking conformational change upon ligand binding. The simulation results for both free and ligand-bound proteins show qualitative and quantitative agreement with existing studies, suggesting iterANM-IaMD as an accurate and efficient tool to investigate structural dynamics involved in complicated biological processes. Our work also provides insights into the relationship between the dynamics and functionality of biomolecules.

NMR Analysis of Apo Glutamine-Binding Protein Exposes Challenges in the Study of Interdomain Dynamics.

Glutamine-binding protein (GlnBP) displays an apo, "open" and a holo, "closed" crystal form, mutually related by a rigid-body reorientation of its domains. A fundamental question about such large-scale conformational transitions, whether the closed state exists in the absence of ligand, is controversial in the case of GlnBP. NMR observations have indicated no evidence of the closed form, whereas experimentally validated computations have suggested a remarkable ca. 40 % population. Herein, a paramagnetic NMR strategy designed to detect the putative apo-closed species shows that a major population of the latter is highly improbable. Further, NMR residual dipolar couplings collected under three anisotropic conditions do not reveal differential domain alignment and establish that the average solution conformation is satisfied by the apo-open crystal structure. Our results indicate that the computational prediction of large-scale interdomain motions is not trivial and may lead to erroneous conclusions without proper experimental validation.