Allosteric States Research Articles

Probing Functional Allosteric States and Conformational Ensembles of the Allosteric Protein Kinase States and Mutants: Atomistic Modeling and Comparative Analysis of AlphaFold2, OmegaFold, and AlphaFlow Approaches and Adaptations.

This study reports a comprehensive analysis and comparison of several AlphaFold2 adaptations and OmegaFold and AlphaFlow approaches in predicting distinct allosteric states, conformational ensembles, and mutation-induced structural effects for a panel of state-switching allosteric ABL mutants. The results revealed that the proposed AlphaFold2 adaptation with randomized alanine sequence scanning can generate functionally relevant allosteric states and conformational ensembles of the ABL kinase that qualitatively capture a unique pattern of population shifts between the active and inactive states in the allosteric ABL mutants. Consistent with the NMR experiments, the proposed AlphaFold2 adaptation predicted that G269E/M309L/T408Y mutant could induce population changes and sample a significant fraction of the fully inactive I2 form which is a low-populated, high-energy state for the wild-type ABL protein. We also demonstrated that other ABL mutants G269E/M309L/T334I and M309L/L320I/T334I that introduce a single activating T334I mutation can reverse equilibrium and populate exclusively the active ABL form. While the precise quantitative predictions of the relative populations of the active and various hidden inactive states in the ABL mutants remain challenging, our results provide evidence that AlphaFold2 adaptation with randomized alanine sequence scanning can adequately detect a spectrum of the allosteric ABL states and capture the equilibrium redistributions between structurally distinct functional ABL conformations. We further validated the robustness of the proposed AlphaFold2 adaptation for predicting the unique inactive architecture of the BSK8 kinase and structural differences between ligand-unbound apo and ATP-bound forms of BSK8. The results of this comparative study suggested that AlpahFold2, OmegaFold, and AlphaFlow approaches may be driven by structural memorization of existing protein folds and are strongly biased toward predictions of the thermodynamically stable ground states of the protein kinases, highlighting limitations and challenges of AI-based methodologies in detecting alternative functional conformations, accurate characterization of physically significant conformational ensembles, and prediction of mutation-induced allosteric structural changes.

Integration of a Randomized Sequence Scanning Approach in AlphaFold2 and Local Frustration Profiling of Conformational States Enable Interpretable Atomistic Characterization of Conformational Ensembles and Detection of Hidden Allosteric States in the ABL1 Protein Kinase.

Despite the success of AlphaFold methods in predicting single protein structures, these methods showed intrinsic limitations in the characterization of multiple functional conformations of allosteric proteins. The recent NMR-based structural determination of the unbound ABL kinase in the active state and discovery of the inactive low-populated functional conformations that are unique for ABL kinase present an ideal challenge for the AlphaFold2 approaches. In the current study, we employ several adaptations of the AlphaFold2 methodology to predict protein conformational ensembles and allosteric states of the ABL kinase including randomized alanine sequence scanning combined with the multiple sequence alignment subsampling proposed in this study. We show that the proposed new AlphaFold2 adaptation combined with local frustration profiling of conformational states enables accurate prediction of the protein kinase structures and conformational ensembles, also offering a robust approach for interpretable characterization of the AlphaFold2 predictions and detection of hidden allosteric states. We found that the large high frustration residue clusters are uniquely characteristic of the low-populated, fully inactive ABL form and can define energetically frustrated cracking sites of conformational transitions, presenting difficult targets for AlphaFold2. The results of this study uncovered previously unappreciated fundamental connections between local frustration profiles of the functional allosteric states and the ability of AlphaFold2 methods to predict protein structural ensembles of the active and inactive states. This study showed that integration of the randomized sequence scanning adaptation of AlphaFold2 with a robust landscape-based analysis allows for interpretable atomistic predictions and characterization of protein conformational ensembles, providing a physical basis for the successes and limitations of current AlphaFold2 methods in detecting functional allosteric states that play a significant role in protein kinase regulation.

Chemical screening by time-resolved X-ray scattering to discover allosteric probes

Drug discovery relies on efficient identification of small-molecule leads and their interactions with macromolecular targets. However, understanding how chemotypes impact mechanistically important conformational states often remains secondary among high-throughput discovery methods. Here, we present a conformational discovery pipeline integrating time-resolved, high-throughput small-angle X-ray scattering (TR-HT-SAXS) and classic fragment screening applied to allosteric states of the mitochondrial import oxidoreductase apoptosis-inducing factor (AIF). By monitoring oxidized and X-ray-reduced AIF states, TR-HT-SAXS leverages structure and kinetics to generate a multidimensional screening dataset that identifies fragment chemotypes allosterically stimulating AIF dimerization. Fragment-induced dimerization rates, quantified with time-resolved SAXS similarity analysis (kVR), capture structure–activity relationships (SAR) across the top-ranked 4-aminoquinoline chemotype. Crystallized AIF–aminoquinoline complexes validate TR-SAXS-guided SAR, supporting this conformational chemotype for optimization. AIF–aminoquinoline structures and mutational analysis reveal active site F482 as an underappreciated allosteric stabilizer of AIF dimerization. This conformational discovery pipeline illustrates TR-HT-SAXS as an effective technology for targeting chemical leads to important macromolecular states.

Binding site plasticity regulation of the FimH catch-bond mechanism

The bacterial fimbrial adhesin FimH is a remarkable and well-studied catch-bond protein found at the tip of E. coli type 1 pili, which allows pathogenic strains involved in urinary tract infections to bind high-mannose glycans exposed on human epithelia. The catch-bond behavior of FimH, where the strength of the interaction increases when a force is applied to separate the two partners, enables the bacteria to resist clearance when they are subjected to shear forces induced by urine flow. Two decades of experimental studies performed at the single-molecule level, as well as x-ray crystallography and modeling studies, have led to a consensus picture whereby force separates the binding domain from an inhibitor domain, effectively triggering an allosteric conformational change in the former. This force-induced allostery is thought to be responsible for an increased binding affinity at the core of the catch-bond mechanism. However, some important questions remain, the most challenging one being that the crystal structures corresponding to these two allosteric states show almost superimposable binding site geometries, which questions the molecular origin for the large difference in affinity. Using molecular dynamics with a combination of enhanced-sampling techniques, we demonstrate that the static picture provided by the crystal structures conceals a variety of binding site conformations that have a key impact on the apparent affinity. Crucially, the respective populations in each of these conformations are very different between the two allosteric states of the binding domain, which can then be related to experimental affinity measurements. We also evidence a previously unappreciated but important effect: in addition to the well-established role of the force as an allosteric regulator via domain separation, application of force tends to directly favor the high-affinity binding site conformations. We hypothesize that this additional “local” catch-bond effect could delay unbinding between the bacteria and the host cell before the “global” allosteric transition occurs, as well as stabilizing the complex even more once in the high-affinity allosteric state.

Introducing NMR strategies to define water molecules that drive metal binding in a transcriptional regulator

Staphylococcus aureus CzrA is a paradigmatic member of the ArsR family of transcriptional metalloregulators, which are critical for the bacterial response to stress. Zinc binding to CzrA, which induces DNA derepression, is entropically driven, as shown by calorimetry. A detailed equilibrium dynamics study of different allosteric states of CzrA revealed that zinc induces an entropy redistribution that controls for DNA binding regulation; however, this change in conformational entropy only accounts for a small net contribution to the total entropy. This difference between the change in conformational entropy vs. total entropy of zinc binding implies a significant contribution of solvent molecule rearrangements to this equilibrium. However, the absence of major structural changes suggests that solvent rearrangements occur mainly on the protein surface and/or from zinc desolvation, concomitant with a dynamical redistribution of conformational entropy. Previous results also suggest that zinc binding not only leads to a redistribution of protein internal dynamics, but also release of water molecules from the protein surface. In turn, these water molecules may make a significant contribution to the allosteric response that results in dissociation from the DNA.Quantifying the differential hydration of two conformational states that share very similar crystal structures and then correlating this with the protein's solvent entropy change constitutes an unresolved problem, even when thermodynamics suggest a significant contribution of solvent entropy. Here, we present different avenues to dissect hydration dynamics in a metal-binding transcriptional regulator that provide different insights into this complex problem. We explore primary solution NMR tools for probing protein–water interactions: the laboratory frame nuclear Overhauser effect (NOE) and its rotating frame counterpart (ROE) between long-lived water molecules and the protein residues. The wNOE/wROE ratio is a promising tool for the detection of hydration dynamics near the surface of a protein in a site-specific manner, minimizing contamination from bulk solvent. Molecular dynamics simulations and computational methods designed to provide a spatially resolved picture of solvent thermodynamics were also employed to provide a more complete panorama of solvent redistribution.

Exploring and Learning the Universe of Protein Allostery Using Artificial Intelligence Augmented Biophysical and Computational Approaches.

Allosteric mechanisms are commonly employed regulatory tools used by proteins to orchestrate complex biochemical processes and control communications in cells. The quantitative understanding and characterization of allosteric molecular events are among major challenges in modern biology and require integration of innovative computational experimental approaches to obtain atomistic-level knowledge of the allosteric states, interactions, and dynamic conformational landscapes. The growing body of computational and experimental studies empowered by emerging artificial intelligence (AI) technologies has opened up new paradigms for exploring and learning the universe of protein allostery from first principles. In this review we analyze recent developments in high-throughput deep mutational scanning of allosteric protein functions; applications and latest adaptations of Alpha-fold structural prediction methods for studies of protein dynamics and allostery; new frontiers in integrating machine learning and enhanced sampling techniques for characterization of allostery; and recent advances in structural biology approaches for studies of allosteric systems. We also highlight recent computational and experimental studies of the SARS-CoV-2 spike (S) proteins revealing an important and often hidden role of allosteric regulation driving functional conformational changes, binding interactions with the host receptor, and mutational escape mechanisms of S proteins which are critical for viral infection. We conclude with a summary and outlook of future directions suggesting that AI-augmented biophysical and computer simulation approaches are beginning to transform studies of protein allostery toward systematic characterization of allosteric landscapes, hidden allosteric states, and mechanisms which may bring about a new revolution in molecular biology and drug discovery.

Allosteric regulation of substrate channeling: Salmonella typhimurium tryptophan synthase.

The regulation of the synthesis of L-tryptophan (L-Trp) in enteric bacteria begins at the level of gene expression where the cellular concentration of L-Trp tightly controls expression of the five enzymes of the Trp operon responsible for the synthesis of L-Trp. Two of these enzymes, trpA and trpB, form an αββα bienzyme complex, designated as tryptophan synthase (TS). TS carries out the last two enzymatic processes comprising the synthesis of L-Trp. The TS α-subunits catalyze the cleavage of 3-indole D-glyceraldehyde 3′-phosphate to indole and D-glyceraldehyde 3-phosphate; the pyridoxal phosphate-requiring β-subunits catalyze a nine-step reaction sequence to replace the L-Ser hydroxyl by indole giving L-Trp and a water molecule. Within αβ dimeric units of the αββα bienzyme complex, the common intermediate indole is channeled from the α site to the β site via an interconnecting 25 Å-long tunnel. The TS system provides an unusual example of allosteric control wherein the structures of the nine different covalent intermediates along the β-reaction catalytic path and substrate binding to the α-site provide the allosteric triggers for switching the αββα system between the open (T) and closed (R) allosteric states. This triggering provides a linkage that couples the allosteric conformational coordinate to the covalent chemical reaction coordinates at the α- and β-sites. This coupling drives the α- and β-sites between T and R conformations to achieve regulation of substrate binding and/or product release, modulation of the α- and β-site catalytic activities, prevention of indole escape from the confines of the active sites and the interconnecting tunnel, and synchronization of the α- and β-site catalytic activities. Here we review recent advances in the understanding of the relationships between structure, function, and allosteric regulation of the complex found in Salmonella typhimurium.

Equilibria between conformational states of the Ras oncogene protein revealed by high pressure crystallography.

In this work, we experimentally investigate the allosteric transitions between conformational states on the Ras oncogene protein using high pressure crystallography. Ras protein is a small GTPase involved in central regulatory processes occurring in multiple conformational states. Ras acts as a molecular switch between active GTP-bound, and inactive GDP-bound states, controlling essential signal transduction pathways. An allosteric network of interactions between the effector binding regions and the membrane interacting regions is involved in Ras cycling. The conformational states which coexist simultaneously in solution possess higher Gibbs free energy than the ground state. Equilibria between these states can be shifted by applying pressure favouring conformations with lower partial molar volume, and has been previously analyzed by high-pressure NMR spectroscopy. High-pressure macromolecular crystallography (HPMX) is a powerful tool perfectly complementary to high-pressure NMR, allowing characterization at the molecular level with a high resolution the different allosteric states involved in the Ras cycling. We observe a transition above 300 MPa in the crystal leading to more stable conformers. Thus, we compare the crystallographic structures of Ras(wt)·Mg2+·GppNHp and Ras(D33K)·Mg2+·GppNHp at various high hydrostatic pressures. This gives insight into per-residue descriptions of the structural plasticity involved in allosteric equilibria between conformers. We have mapped out at atomic resolution the different segments of Ras protein which remain in the ground-state conformation or undergo structural changes, adopting excited-energy conformations corresponding to transient intermediate states. Such in crystallo phase transitions induced by pressure open the possibility to finely explore the structural determinants related to switching between Ras allosteric sub-states without any mutations nor exogenous partners.

Mutation of βGln114 to Ala Alters the Stabilities of Allosteric States in Tryptophan Synthase Catalysis.

The tryptophan synthase (TS) bienzyme complexes found in bacteria, yeasts, and molds are pyridoxal 5'-phosphate (PLP)-requiring enzymes that synthesize l-Trp. In the TS catalytic cycle, switching between the open and closed states of the α- and β-subunits via allosteric interactions is key to the efficient conversion of 3-indole-d-glycerol-3'-phosphate and l-Ser to l-Trp. In this process, the roles played by β-site residues proximal to the PLP cofactor have not yet been fully established. βGln114 is one such residue. To explore the roles played by βQ114, we conducted a detailed investigation of the βQ114A mutation on the structure and function of tryptophan synthase. Initial steady-state kinetic and static ultraviolet-visible spectroscopic analyses showed the Q to A mutation impairs catalytic activity and alters the stabilities of intermediates in the β-reaction. Therefore, we conducted X-ray structural and solid-state nuclear magnetic resonance spectroscopic studies to compare the wild-type and βQ114A mutant enzymes. These comparisons establish that the protein structural changes are limited to the Gln to Ala replacement, the loss of hydrogen bonds among the side chains of βGln114, βAsn145, and βArg148, and the inclusion of waters in the cavity created by substitution of the smaller Ala side chain. Because the conformations of the open and closed allosteric states are not changed by the mutation, we hypothesize that the altered properties arise from the lost hydrogen bonds that alter the relative stabilities of the open (βT state) and closed (βR state) conformations of the β-subunit and consequently alter the distribution of intermediates along the β-subunit catalytic path.

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.

HIV Infection and Neurocognitive Disorders in the Context of Chronic Drug Abuse: Evidence for Divergent Findings Dependent upon Prior Drug History.

The fronto-striatal circuitry, involving the nucleus accumbens, ventral tegmental area, and prefrontal cortex, mediates goal-directed behavior and is targeted by both drugs of abuse and HIV-1 infection. Acutely, both drugs and HIV-1 provoke increased dopamine activity within the circuit. However, chronic exposure to drugs or HIV-1 leads to dysregulation of the dopamine system as a result of fronto-striatal adaptations to oppose the effects of repeated instances of transiently increased dopamine. Specifically, chronic drug use leads to reduced dopaminergic tone, upregulation of dopamine transporters, and altered circuit connectivity, sending users into an allosteric state in which goal-directed behaviors are dysregulated (i.e., addiction). Similarly, chronic exposure to HIV-1, even with combination antiretroviral therapy (cART), dysregulates dopamine and dopamine transporter function and alters connectivity of the fronto-striatal circuit, contributing to apathy and clinical symptoms of HIV-1 associated neurocognitive disorders (HAND). Thus, in a drug user also exposed to HIV-1, dysregulation of the fronto-striatal dopamine circuit advances at an exacerbated rate and appears to be driven by mechanisms unique from those seen with chronic drug use or HIV-1 exposure alone. We posit that the effects of drug use and HIV-1 infection on microglia interact to drive the progression of motivational dysfunction at an accelerated rate. The current review will therefore explore how the fronto-striatal circuit adapts to drug use (using cocaine as an example), HIV-1 infection, and both together; emphasizing proper methods and providing future directions to develop treatments for pathologies disrupting goal-directed behaviors and improve clinical outcomes for affected patients. Graphical Abstract Drug use and HIV-1 in the fronto-striatal circuit. Drugs of abuse and HIV-1 infection both target the fronto-striatal circuit which mediates goal-directed behavior. Acutely, drugs and HIV-1 increase dopamine activity; in contrast chronic exposure produces circuit adaptions leading to dysregulation, addiction and/or apathy. Comorbid drug use and HIV-1 infection may interact with microglia to exacerbate motivational dysregulation.

Using a Network of Single Site Specific Mutations and Crosslinking Mass Spectrometry (CXMS) to Refine the Structure and Dynamics of the Human Alpha 1 Glycine Receptor (GLYR)

A network of site-specific single Cys-mutations coupled with CX-MS can be used to elucidate a more refined structure of GlyR and obtain a more definitive understanding of pentameric ligand-gated ion channel (pLGIC) allostery. Each Cys-mutant is introduced into an α1 homomeric Cys null background (C41S/C290A/C345S), or in the same background with F207G/A288G mutation that allows non-desensitizing GlyR activation by ivermectin (IVM). State-dependent crosslinking with methanethiosulfonate benzophenone to a single thiol of purified, vesicle reconstituted GlyR are conducted after enriching the receptor in different allosteric states: resting (no ligand), open (F207G/A288G + IVM), or desensitized (excess glycine). Digested peptides are analyzed via liquid chromatography mass spectrometry to identify sites of intra- and intermolecular crosslinking. Tandem MS of mass-shifted precursor ions further refine these distance constraints. Independent comparative studies targeting different single Cys GlyR (M287C, K116C, K206C) provides evidence of allosteric changes between the three states, as well as direct topological information of unresolved regions, most notably the M3-M4 loop, in other high-resolution structures of pLGICs.

Iterative annealing mechanism explains the functions of the GroEL and RNA chaperones.

Molecular chaperones are ATP-consuming machines, which facilitate the folding of proteins and RNA molecules that are kinetically trapped in misfolded states. Unassisted folding occurs by the kinetic partitioning mechanism according to which folding to the native state, with low probability as well as misfolding to one of the many metastable states, with high probability, occur rapidly. GroEL is an all-purpose stochastic machine that assists misfolded substrate proteins to fold. The RNA chaperones such as CYT-19, which are ATP-consuming enzymes, help the folding of ribozymes that get trapped in metastable states for long times. GroEL does not interact with the folded proteins but CYT-19 disrupts both the folded and misfolded ribozymes. The structures of GroEL and RNA chaperones are strikingly different. Despite these differences, the iterative annealing mechanism (IAM) quantitatively explains all the available experimental data for assisted folding of proteins and ribozymes. Driven by ATP binding and hydrolysis and GroES binding, GroEL undergoes a catalytic cycle during which it samples three allosteric states, T (apo), R (ATP bound), and R″ (ADP bound). Analyses of the experimental data show that the efficiency of the GroEL-GroES machinery and mutants is determined by the resetting rate k R ″ → T , which is largest for the wild-type (WT) GroEL. Generalized IAM accurately predicts the folding kinetics of Tetrahymena ribozyme and its variants. Chaperones maximize the product of the folding rate and the steady-state native state fold by driving the substrates out of equilibrium. Neither the absolute yield nor the folding rate is optimized.

Probing Protein Allostery as a Residue-Specific Concept via Residue Response Maps

Allosteric regulation is a well-established phenomenon defined as a distal conformational or dynamical change of the protein upon allosteric effector binding. Here, we developed a novel approach to delineate allosteric effects in proteins. In this approach, we applied robust machine learning methods, including deep neural network and random forest, on extensive molecular dynamics (MD) simulations to distinguish otherwise similar allosteric states of proteins. Using the PDZ3 domain of PDS-95 as a model protein, we demonstrated that the allosteric effects could be represented as residue-specific properties through two-dimensional property-residue maps, which we refer to as "residue response maps". These maps were constructed through two machine learning methods and could accurately describe how different properties of various residues are affected upon allosteric perturbation on protein. Based on the "residue response maps", we propose allostery as a residue-specific concept, suggesting that all residues could be considered as allosteric residues because each residue "senses" the allosteric events through changing its single or multiple attributes in a quantitatively unique way. The "residue response maps" could be used to fingerprint a protein based on the unique patterns of residue responses upon binding events, providing a novel way to systematically describe the protein allosteric effects of each residue upon perturbation.

Allosteric Regulation of Rod Photoreceptor Phosphodiesterase 6 (PDE6) Elucidated by Chemical Cross-Linking and Quantitative Mass Spectrometry

Photoreceptor phosphodiesterase (PDE6) is the central effector enzyme in the visual excitation pathway in rod and cone photoreceptors. Its tight regulation is essential for the speed, sensitivity, recovery, and adaptation of visual signaling. The rod PDE6 holoenzyme (Pαβγ2) is composed of a catalytic heterodimer (Pαβ) that binds two inhibitory γ subunits. Each of the two catalytic subunits (Pα and Pβ) contains a catalytic domain responsible for cGMP hydrolysis and two tandem GAF domains, one of which binds cGMP noncatalytically. Unlike related GAF-containing PDEs where cGMP binding allosterically activates catalysis, the physiological significance of cGMP binding to the GAF domains of PDE6 is unknown. To elucidate the structural determinants of PDE6 allosteric regulators, we biochemically characterized PDE6 complexes in various allosteric states (Pαβ, Pαβ–cGMP, Pαβγ2, and Pαβγ2–cGMP) with a quantitative cross-linking/mass spectrometry approach. We employed a normalization strategy to dissect the cross-linking reactivity of individual residues in order to assess the spatial cross-linking propensity of detected pairs. In addition to identifying cross-linked pairs that undergo conformational changes upon ligand binding, we observed an asymmetric binding of the inhibitory γ-subunit and the noncatalytic cGMP to the GAFa domains of rod PDE6, as well as a stable open conformation of Pαβ catalytic dimer in different allosteric states. These results advance our understanding of the exquisite regulatory control of the lifetime of rod PDE6 activation/deactivation during visual signaling, as well as providing a structural basis for interpreting how mutations in rod PDE6 subunits can lead to retinal diseases.

Abstract 2763: Raf-1 cysteine-rich domain (CRD) promotes active KRas4B membrane orientation facilitating dimerization through the allosteric lobe interface

Abstract Ras proteins are classical members of small GTPases, controlling signal transduction pathways and promoting cell proliferation. Ras consists of highly homologous catalytic domains and flexible C-terminal hypervariable regions (HVRs) that differ significantly across Ras isoforms. Ras activation is regulated by guanine nucleotide exchange factors that catalyze the exchange of GDP by GTP, and activation is terminated by GTPase-activating proteins that induce the GTP hydrolysis. Ras has multiple partners, signals through several key pathways and fulfills critical functions in the cell life. Mutations in Ras are common in a variety of cancers; yet it is still undruggable. KRas4B is frequently mutated in cancer. Elucidation of Ras conformational ensembles at the signaling platforms in the plasma membrane including the ligand-bound conformations, membrane orientations, the activated (or inactivated) allosteric modulated states, post-translationally modified states, mutational states, and transition states are essential for deciphering Ras functions from its conformational landscapes. In the MAPK pathway, KRas4B preferentially recruits Raf-1 and activates it. Raf kinases contain Ras binding domain (RBD) and cysteine-rich domain (CRD) at conserved region 1 (CR1), and kinase domain at CR3. The high-affinity interaction of Raf RBD with Ras was solved in the absence of CRD. It is known that Raf CRD play a key role in the membrane anchoring mechanism. Recently, pivotal roles of CRD in the membrane interactions of KRas4B-Raf-1 RBD-CRD complex were reported. Here, we employ all-atom molecular dynamics (MD) simulations to investigate dimeric KRas4B-Raf-1 complex at the anionic lipid bilayer composed of DOPC and DOPS (4:1). Active KRas4B dimer modulates Raf-1 activation, promoting dimerization of Raf-1’s kinase domain. In the dimeric complex of KRas4B-Raf-1, Raf-1 CRD stably binds anionic lipid bilayers inserting its positively-charged loop into the amphipathic interface. The key basic residues at the loop are responsible for the membrane association, suggesting that CRD is an intrinsic membrane binding domain of Raf kinase. Our simulations suggest that Raf-1 CRD not only offers an additional membrane anchor for the KRas4B-Raf-1 complex, but also reduces the fluctuations of Ras-RBD, enhancing the high affinity interaction between Ras and Raf. Raf-1 CRD supports the active-state Ras orientation, promoting Ras dimerization through the allosteric lobe helical interface at the membrane. The enhanced stability of the complex at the membrane rendered by CRD promotes Raf dimerization in the MAPK signaling, which is the key Ras proliferative pathway. Here we suggest these significant roles of CRD at the membrane in the Raf activation. Funded by Frederick National Laboratory for Cancer Research, National Institutes of Health, under contract HHSN261200800001E. Citation Format: Hyunbum Jang, Ruth Nussinov. Raf-1 cysteine-rich domain (CRD) promotes active KRas4B membrane orientation facilitating dimerization through the allosteric lobe interface [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2763.

Symmetry, Rigidity, and Allosteric Signaling: From Monomeric Proteins to Molecular Machines.

Allosteric signaling in biological molecules, which may be viewed as specific action at a distance due to localized perturbation upon binding of ligands or changes in environmental cues, is pervasive in biology. Insightful phenomenological Monod, Wyman, and Changeux (MWC) and Koshland, Nemethy, and Filmer (KNF) models galvanized research in describing allosteric transitions for over five decades, and these models continue to be the basis for describing the mechanisms of allostery in a bewildering array of systems. However, understanding allosteric signaling and the associated dynamics between distinct allosteric states at the molecular level is challenging and requires novel experiments complemented by computational studies. In this review, we first describe symmetry and rigidity as essential requirements for allosteric proteins or multisubunit structures. The general features, with MWC and KNF as two extreme scenarios, emerge when allosteric signaling is viewed from an energy landscape perspective. To go beyond the general theories, we describe computational tools that are either based solely on multiple sequences or their structures to predict the allostery wiring diagram. These methods could be used to predict the network of residues that carry allosteric signals. Methods to obtain molecular insights into the dynamics of allosteric transitions are briefly mentioned. The utility of the methods is illustrated by applications to systems ranging from monomeric proteins in which there is little conformational change in the transition between two allosteric states to membrane bound G-protein coupled receptors and multisubunit proteins. Finally, the role allostery plays in the functions of ATP-consuming molecular machines, bacterial chaperonin GroEL and molecular motors, is described. Although universal molecular principles governing allosteric signaling do not exist, we can draw the following general conclusions from a survey of different systems. (1) Multiple pathways connecting allosteric states are highly heterogeneous. (2) Allosteric signaling is exquisitely sensitive to the specific architecture of the system, which implies that the capacity for allostery is encoded in the structure itself. (3) The mechanical modes that connect distinct allosteric states are robust to sequence variations. (4) Extensive investigations of allostery in Hemoglobin and, more recently GroEL, show that to a large extent a network of salt bridge rearrangements serves as allosteric switches. In both these examples the dynamical changes in the allosteric switches are related to function.

A Local Allosteric Network in Heat Shock Protein 70 (Hsp70) Links Inhibitor Binding to Enzyme Activity and Distal Protein-Protein Interactions.

Allosteric inhibitors can be more difficult to optimize without an understanding of how their binding influences the conformational motions of the target. Here, we used an integrated computational and experimental approach to probe the molecular mechanism of an allosteric inhibitor of heat shock protein 70 (Hsp70). The anticancer compound, MKT-077, is known to bind a conserved site in members of the Hsp70 family, which favors the ADP-bound state and interferes with a protein-protein interaction (PPI) at long range. However, the binding site does not overlap with either the nucleotide-binding cleft or the PPI contact surface, so its mechanism is unclear. To this end, we modeled Hsp70's internal dynamics and studied how MKT-077 alters local sampling of its allosteric states. The results pointed to a set of concerted motions between five loops in Hsp70's nucleotide-binding domain (NBD), surrounding the MKT-077 binding site. To test this prediction, we mutated key residues and monitored chaperone activities in vitro. Together, the results indicate that MKT-077 interacts with loop222 to favor a pseudo-ADP bound conformer of Hsp70's NBD, even when ATP is present. We used this knowledge to synthesize an analog of MKT-077 that would better prevent motions of loop222 and confirmed that it had improved antiproliferative activity in breast cancer cells. These results provide an example of how to unlock and leverage the complex mechanisms of allosteric inhibitors.

Allostery and molecular machines.

Machines undergo repeated energy (fuel)-driven cycling between various functional and structural states. Such cycling involves motions of the machine parts, which take place in a highly coordinated manner in time and space. This description applies not only to man-made machines but also to the molecular machines that mediate many of the key processes in all forms of life [1]. Examples include protein synthesis by the ribosome, DNA unwinding by helicases during replication, protein degradation by the proteasome and protein folding by chaperones. The coordinated movements of most biomolecular machines are achieved by allosteric regulation of ATP binding and hydrolysis. Consequently, a molecular level understanding of how the essential machines of life work requires invoking and further developing allosteric theory. Hence, the motivation for the Royal Society Discussion Meeting entitled ‘Allostery and molecular machines' that took place in June 2017. The meeting brought together scientists interested in allostery, in general, with others who are studying specific biomolecular machines of interest. The meeting led to many interesting discussions and resulted in this special issue of the Philosophical Transactions B . In 1965, Monod, Wyman & Changeux published a seminal paper [2] that described a so-called ‘concerted model' (referred to here and generally as the MWC model) for cooperative ligand binding by oligomeric proteins. This issue contains a paper by one of the co-authors of this landmark paper in which the MWC model is explained in brief and its application to the nicotinic acetylcholine receptor is then described [3]. The MWC model remains important but its elegance has come at the price of several restrictive assumptions. One key assumption is that cooperativity is due to a shift in equilibrium between different quaternary …

Recognition of protein allosteric states and residues: Machine learning approaches.

Allostery is a process by which proteins transmit the effect of perturbation at one site to a distal functional site upon certain perturbation. As an intrinsically global effect of protein dynamics, it is difficult to associate protein allostery with individual residues, hindering effective selection of key residues for mutagenesis studies. The machine learning models including decision tree (DT) and artificial neural network (ANN) models were applied to develop classification model for a cell signaling allosteric protein with two states showing extremely similar tertiary structures in both crystallographic structures and molecular dynamics simulations. Both DT and ANN models were developed with 75% and 80% of predicting accuracy, respectively. Good agreement between machine learning models and previous experimental as well as computational studies of the same protein validates this approach as an alternative way to analyze protein dynamics simulations and allostery. In addition, the difference of distributions of key features in two allosteric states also underlies the population shift hypothesis of dynamics-driven allostery model. © 2018 Wiley Periodicals, Inc.