AIM for Allostery: Using the Ising Model to Understand Information Processing and Transmission in Allosteric Biomolecular Systems

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.

Prediction of Allosteric Communication Pathways in Proteins

MOTIVATION

Allostery in proteins is an essential phenomenon in biological processes. In this paper, we present a computational model to predict paths of maximum information transfer between active and allosteric sites. In this information theoretic study, we use mutual information as the measure of information transfer, where transition probability of information from one residue to its contacting neighbors is proportional to the magnitude of mutual information between the two residues. Starting from a given residue and using a Hidden Markov Model, we successively determine the neighboring residues that eventually lead to a path of optimum information transfer. The Gaussian approximation of mutual information between residue pairs is adopted. The limits of validity of this approximation are discussed in terms of a nonlinear theory of mutual information and its reduction to the Gaussian form.

RESULTS

Predictions of the model are tested on six widely studied cases, CheY Bacterial Chemotaxis, B-cell Lymphoma extra-large Bcl-xL, Human proline isomerase cyclophilin A (CypA), Dihydrofolate reductase DHFR, HRas GTPase, and Caspase-1. The communication transmission rendering the propagation of local fluctuations from the active sites throughout the structure in multiple paths correlate well with the known experimental data. Distinct paths originating from the active site may likely represent a multi functionality such as involving more than one allosteric site and/or preexistence of some other functional states. Our model is computationally fast and simple, and can give allosteric communication pathways, which are crucial for the understanding and control of protein functionality.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Information Flow and Allosteric Communication in Proteins

Based on Schreiber's work on transfer entropy, a molecular theory of nonlinear information transfer in proteins is developed. The joint distribution function for residue fluctuations is expressed in terms of tensor Hermite polynomials which conveniently separate harmonic and nonlinear contributions to information transfer. The harmonic part of information transfer is expressed as the difference between time dependent and independent mutual information. Third order nonlinearities are discussed in detail. Amount and speed of information transfer between residues, important for understanding allosteric activity in proteins, are discussed. While mutual information shows the maximum amount of information that may be transferred between two residues, it does not explain the actual amount of transfer nor the transfer rate of information. For this, dynamic equations of the system are needed. The solution of the Langevin equation and molecular dynamics trajectories are used in the present work for this purpose. Allosteric communication in Human NAD-dependent isocitrate dehydrogenase is studied as an example. Calculations show that several paths contribute collectively to information transfer. Important residues on these paths are identified. Time resolved information transfer between these residues, their amplitudes and transfer rates, which are in agreement with time resolved ultraviolet resonance Raman measurements in general, are estimated. Estimated transfer rates are in the order of 1-20 megabits per second. Information transfer from third order contributions are one to two orders of magnitude smaller than the harmonic terms, showing that harmonic analysis is a good approximation to information transfer.

Allosteric conformational ensembles have unlimited capacity for integrating information

Integration of binding information by macromolecular entities is fundamental to cellular functionality. Recent work has shown that such integration cannot be explained by pairwise cooperativities, in which binding is modulated by binding at another site. Higher-order cooperativities (HOCs), in which binding is collectively modulated by multiple other binding events, appear to be necessary but an appropriate mechanism has been lacking. We show here that HOCs arise through allostery, in which effective cooperativity emerges indirectly from an ensemble of dynamically interchanging conformations. Conformational ensembles play important roles in many cellular processes but their integrative capabilities remain poorly understood. We show that sufficiently complex ensembles can implement any form of information integration achievable without energy expenditure, including all patterns of HOCs. Our results provide a rigorous biophysical foundation for analysing the integration of binding information through allostery. We discuss the implications for eukaryotic gene regulation, where complex conformational dynamics accompanies widespread information integration.

Ligand-Dependent Conformational Transitions in Molecular Dynamics Trajectories of GPCRs Revealed by a New Machine Learning Rare Event Detection Protocol

Central among the tools and approaches used for ligand discovery and design are Molecular Dynamics (MD) simulations, which follow the dynamic changes in molecular structure in response to the environmental condition, interactions with other proteins, and the effects of ligand binding. The need for, and successes of, MD simulations in providing this type of essential information are well documented, but so are the challenges presented by the size of the resulting datasets encoding the desired information. The difficulty of extracting information on mechanistically important state-to-state transitions in response to ligand binding and other interactions is compounded by these being rare events in the MD trajectories of complex molecular machines, such as G-protein-coupled receptors (GPCRs). To address this problem, we have developed a protocol for the efficient detection of such events. We show that the novel Rare Event Detection (RED) protocol reveals functionally relevant and pharmacologically discriminating responses to the binding of different ligands to the 5-HT_2AR orthosteric site in terms of clearly defined, structurally coherent, and temporally ordered conformational transitions. This information from the RED protocol offers new insights into specific ligand-determined functional mechanisms encoded in the MD trajectories, which opens a new and rigorously reproducible path to understanding drug activity with application in drug discovery.

A quantum biochemistry investigation of the protein-protein interactions for the description of allosteric modulation on biomass-degrading chimera

The worldwide dependence of population on fossil fuels continues to have several harmful implications for the environment. Bioethanol is an excellent option for renewable fuel to replace the current greenhouse gas emitters. In addition, its production by enzymatic route has gained space among the industrial processes because it replaces the traditional acid treatment. Due to its high versatility, the xylanase family is used in this process as an accessory enzyme for degrading the lignocellulosic substrate of biomass. A chimera built by a xylanolytic domain (Xyl) and a xylose-binding protein (XBP) showed an experimentally improved catalytic efficiency and interdomain allosteric modulation after xylose binding. In this context, we performed a quantum biochemistry characterization of the interactions between these domains and dynamic cross-correlation (DCC) analysis after performing molecular dynamics (DM) simulations of the systems in the presence and absence of xylose in the XBP active site. We used the density functional theory (DFT) within the molecular fractionation with the conjugated caps (MFCC) approach to describe the pair energies, and the corresponding energy difference between the chimera domains responsible for the allosteric effect and amino acid DCC to evaluate the interdomain coupling differences between the energy states. The detailed energetic investigation together with the related structural and dynamics counterparts revealed the molecular mechanisms of chimeric improvement of the xylanase activity observed experimentally. This mechanism was correlated with greater stability and high connectivity at the interdomain interface in the xylose bound relative to the free chimera. We identify the contributions of hydrogen bonds, hydrophobic interactions and water-mediated interactions in the interdomain region responsible for stability together with the structural and dynamical elements related to the allosteric effect. Taken together, these observations led to a comprehensive understanding of the chimera's modulatory action that occurs through the formation of a highly connected interface that makes the essential movements related to xylanolytic activity in xylanase correlated to those of the xylose-binding protein.

Allosteric Modulation of Neurotransmitter Transporters as a Therapeutic Strategy

Neurotransmitter transporters (NTTs) are involved in the fine-tuning of brain neurotransmitter homeostasis. As such, they are implicated in a plethora of complex behaviors, including reward, movement, and cognition. During recent decades, compounds that modulate NTT functions have been developed. Some of them are in clinical use for the management of different neuropsychiatric conditions. The majority of these compounds have been found to selectively interact with the orthosteric site of NTTs. Recently, diverse allosteric sites have been described in a number of NTTs, modulating their function. A more complex NTT pharmacology may be useful in the development of novel therapeutics. Here, we summarize current knowledge on such modulatory allosteric sites, with specific focus on their pharmacological and therapeutic potential.

Directional allosteric regulation of protein filament length

Cofilin and ADF are cytoskeleton remodeling proteins that cooperatively bind and fragment actin filaments. Bound cofilin molecules do not directly interact with each other, indicating that cooperative binding of cofilin is mediated by the actin filament lattice. Cofilactin is therefore a model system for studying allosteric regulation of self-assembly. How cofilin binding changes structural and mechanical properties of actin filaments is well established. Less is known about the interaction energies and the thermodynamics of filament fragmentation, which describes the collective manner in which the cofilin concentration controls mean actin filament length. Here, we provide a general thermodynamic framework for allosteric regulation of self-assembly, and we use the theory to predict the interaction energies of experimental actin filament length distributions over a broad range of cofilin binding densities and for multiple cofilactin variants. We find that bound cofilin induces changes in nearby actin-actin interactions, and that these allosteric effects are propagated along the filament to affect up to four neighboring cofilin-binding sites (i.e., beyond nearest-neighbor allostery). The model also predicts that cofilin differentially stabilizes and destabilizes longitudinal versus lateral actin-actin interactions, and that the magnitude, range, asymmetry, and even the sign of these interaction energies can be altered using different actin and cofilin mutational variants. These results demonstrate that the theoretical framework presented here can provide quantitative thermodynamic information governing cooperative protein binding and filament length regulation, thus revealing nanometer length-scale interactions from micron length-scale "wet-lab" measurements.

Dissipation in Non-Steady State Regulatory Circuits

In order to respond to environmental signals, cells often use small molecular circuits to transmit information about their surroundings. Recently, motivated by specific examples in signaling and gene regulation, a body of work has focused on the properties of circuits that function out of equilibrium and dissipate energy. We briefly review the probabilistic measures of information and dissipation and use simple models to discuss and illustrate trade-offs between information and dissipation in biological circuits. We find that circuits with non-steady state initial conditions can transmit more information at small readout delays than steady state circuits. The dissipative cost of this additional information proves marginal compared to the steady state dissipation. Feedback does not significantly increase the transmitted information for out of steady state circuits but does decrease dissipative costs. Lastly, we discuss the case of bursty gene regulatory circuits that, even in the fast switching limit, function out of equilibrium.

Computational design of G Protein-Coupled Receptor allosteric signal transductions

Membrane receptors sense and transduce extracellular stimuli into intracellular signaling responses but the molecular underpinnings remain poorly understood. We report a computational approach for designing protein allosteric signaling functions. By combining molecular dynamics simulations and design calculations, the method engineers amino-acid 'microswitches' at allosteric sites that modulate receptor stability or long-range coupling, to reprogram specific signaling properties. We designed 36 dopamine D2 receptor variants, whose constitutive and ligand-induced signaling agreed well with our predictions, repurposed the D2 receptor into a serotonin biosensor and predicted the signaling effects of more than 100 known G-protein-coupled receptor (GPCR) mutations. Our results reveal the existence of distinct classes of allosteric microswitches and pathways that define an unforeseen molecular mechanism of regulation and evolution of GPCR signaling. Our approach enables the rational design of allosteric receptors with enhanced stability and function to facilitate structural characterization, and reprogram cellular signaling in synthetic biology and cell engineering applications.

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.

A comprehensive ensemble model for comparing the allosteric effect of ordered and disordered proteins

Intrinsically disordered proteins/regions (IDPs/IDRs) are prevalent in allosteric regulation. It was previously thought that intrinsic disorder is favorable for maximizing the allosteric coupling. Here, we propose a comprehensive ensemble model to compare the roles of both order-order transition and disorder-order transition in allosteric effect. It is revealed that the MWC pathway (order-order transition) has a higher probability than the EAM pathway (disorder-order transition) in allostery, suggesting a complicated role of IDPs/IDRs in regulatory proteins. In addition, an analytic formula for the maximal allosteric coupling response is obtained, which shows that too stable or too unstable state is unfavorable to endow allostery, and is thus helpful for rational design of allosteric drugs.

Allosteric effect is an important regulation mechanism in biological processes, where the binding of a ligand at one site of a protein influences the function of a distant site. Conventionally, allostery was thought to originate from structural transition. However, in recent years, intrinsically disordered proteins (IDPs) were found to be widely involved in allosteric regulation in despite of their lack of ordered structure under physiological condition. It is still a mystery why IDPs are prevalent in allosteric proteins and how they differ from ordered proteins in allostery. Here, we propose a comprehensive ensemble model which includes both ordered and disordered states of a two-domain protein, and investigate the role of various state combinations in allosteric effect. By sampling the parameter space, we conclude that disordered proteins are less competitive than ordered proteins in performing allostery from a thermodynamic point of view. The prevalence of IDPs in allosteric regulation is likely determined by all their advantage, but not only by their capacity in endowing allostery.

The Allostery Landscape: Quantifying Thermodynamic Couplings in Biomolecular Systems

Allostery plays a fundamental role in most biological processes. However, little theory is available to describe it outside of two-state models. Here we use a statistical mechanical approach to show that the allosteric coupling between two collective variables is not a single number, but instead a two-dimensional thermodynamic coupling function that is directly related to the mutual information from information theory and the copula density function from probability theory. On this basis, we demonstrate how to quantify the contribution of specific energy terms to this thermodynamic coupling function, enabling an approximate decomposition that reveals the mechanism of allostery. We illustrate the thermodynamic coupling function and its use by showing how allosteric coupling in the alanine dipeptide molecule contributes to the overall shape of the Φ/Ψ free energy surface, and by identifying the interactions that are necessary for this coupling.

Allosteric Mechanisms of Molecular Machines at the Membrane: Transport by Sodium-Coupled Symporters

Solute transport across cell membranes is ubiquitous in biology as an essential physiological process. Secondary active transporters couple the unfavorable process of solute transport against its concentration gradient to the energetically favorable transport of one or several ions. The study of such transporters over several decades indicates that their function involves complex allosteric mechanisms that are progressively being revealed in atomistic detail. We focus on two well-characterized sodium-coupled symporters: the bacterial amino acid transporter LeuT, which is the prototype for the "gated pore" mechanism in the mammalian synaptic monoamine transporters, and the archaeal GltPh, which is the prototype for the "elevator" mechanism in the mammalian excitatory amino acid transporters. We present the evidence for the role of allostery in the context of a quantitative formalism that can reconcile biochemical and biophysical data and thereby connects directly to recent insights into the molecular structure and dynamics of these proteins. We demonstrate that, while the structures and mechanisms of these transporters are very different, the available data suggest a common role of specific models of allostery in their functions. We argue that such allosteric mechanisms appear essential not only for sodium-coupled symport in general but also for the function of other types of molecular machines in the membrane.

Computational approaches to detect allosteric pathways in transmembrane molecular machines

Many of the functions of transmembrane proteins involved in signal processing and transduction across the cell membrane are determined by allosteric couplings that propagate the functional effects well beyond the original site of activation. Data gathered from breakthroughs in biochemistry, crystallography, and single molecule fluorescence have established a rich basis of information for the study of molecular mechanisms in the allosteric couplings of such transmembrane proteins. The mechanistic details of these couplings, many of which have therapeutic implications, however, have only become accessible in synergy with molecular modeling and simulations. Here, we review some recent computational approaches that analyze allosteric coupling networks (ACNs) in transmembrane proteins, and in particular the recently developed Protein Interaction Analyzer (PIA) designed to study ACNs in the structural ensembles sampled by molecular dynamics simulations. The power of these computational approaches in interrogating the functional mechanisms of transmembrane proteins is illustrated with selected examples of recent experimental and computational studies pursued synergistically in the investigation of secondary active transporters and GPCRs. This article is part of a Special Issue entitled: Membrane Proteins edited by J.C. Gumbart and Sergei Noskov.