Structural and energetic basis of allostery

Ternary π-π Stacking Complexes by Allosteric Regulation in Multilayer Nanographenes

Construction of π-π stacking supramolecular complexes with more than two different components is challenging due to the weak and directionless nature of dispersion interactions. Here, we report ternary complexes of a ditopic nanographene tetraimide (1), α-substituted phthalocyanine (Pc), and polyaromatic hydrocarbons (PAHs) in solution and the crystalline state via allosteric regulation. Binding of one Pc gives rise to significant distortion and conformational changes in 1 that in turn lead to the inhibition of the second binding of Pc. The conformational changes associated with first binding allowed an allosteric binding of a third component (PAHs) to form ternary complexes in solution. 1H NMR titration revealed moderately high thermodynamic stability for the ternary complexes in CDCl3. Competition between allosterically regulated ternary complexes ([Pc·1·PAH]) and 1:2 stoichiometric binary complexes of 1 with PAHs ([PAH·1·PAH]) was elucidated. Further, the selective formation of ternary complexes in solution led to the generation of ternary cocrystals from a 1:1:1 mixture of three components in solution. Our work shows that large π-conjugated nanographenes designed with allosteric recognition sites allow the construction of multilayer ternary complexes in solution and the solid state even with dispersive π-π interactions.

MEF-AlloSite: an accurate and robust Multimodel Ensemble Feature selection for the Allosteric Site identification model

A crucial mechanism for controlling the actions of proteins is allostery. Allosteric modulators have the potential to provide many benefits compared to orthosteric ligands, such as increased selectivity and saturability of their effect. The identification of new allosteric sites presents prospects for the creation of innovative medications and enhances our comprehension of fundamental biological mechanisms. Allosteric sites are increasingly found in different protein families through various techniques, such as machine learning applications, which opens up possibilities for creating completely novel medications with a diverse variety of chemical structures. Machine learning methods, such as PASSer, exhibit limited efficacy in accurately finding allosteric binding sites when relying solely on 3D structural information.Scientific ContributionPrior to conducting feature selection for allosteric binding site identification, integration of supporting amino-acid-based information to 3D structural knowledge is advantageous. This approach can enhance performance by ensuring accuracy and robustness. Therefore, we have developed an accurate and robust model called Multimodel Ensemble Feature Selection for Allosteric Site Identification (MEF-AlloSite) after collecting 9460 relevant and diverse features from the literature to characterise pockets. The model employs an accurate and robust multimodal feature selection technique for the small training set size of only 90 proteins to improve predictive performance. This state-of-the-art technique increased the performance in allosteric binding site identification by selecting promising features from 9460 features. Also, the relationship between selected features and allosteric binding sites enlightened the understanding of complex allostery for proteins by analysing selected features. MEF-AlloSite and state-of-the-art allosteric site identification methods such as PASSer2.0 and PASSerRank have been tested on three test cases 51 times with a different split of the training set. The Student's t test and Cohen's D value have been used to evaluate the average precision and ROC AUC score distribution. On three test cases, most of the p-values ( < 0.05 ) and the majority of Cohen's D values ( > 0.5 ) showed that MEF-AlloSite's 1-6% higher mean of average precision and ROC AUC than state-of-the-art allosteric site identification methods are statistically significant.

Illuminating Protein Allostery by Chemically Accurate Contact Response Analysis (ChACRA)

Decoding allostery at the atomic level is essential for understanding the relationship between a protein's sequence, structure, and dynamics. Recently, we have shown that decomposing temperature responses of inter-residue contacts can reveal allosteric couplings and provide useful insight into the functional dynamics of proteins. The details of this Chemically Accurate Contact Response Analysis (ChACRA) are presented here along with its application to two well-known allosteric proteins. The first protein, IGPS, is a model of ensemble allostery that lacks clear structural differences between the active and inactive states. We show that the application of ChACRA reveals the experimentally identified allosteric coupling between effector and active sites of IGPS. The second protein, ATCase, is a classic example of allostery with distinct active and inactive structural states. Using ChACRA, we directly identify the most significant residue level interactions underlying the enzyme's cooperative behavior. Both test cases demonstrate the utility of ChACRA's unsupervised machine learning approach for dissecting allostery at the residue level.

Probing conformational dynamics of DNA binding by CO-sensing transcription factor, CooA

The transcription factor CooA is a CRP/FNR (cAMP receptor protein/ fumarate and nitrate reductase) superfamily protein that uses heme to sense carbon monoxide (CO). Allosteric activation of CooA in response to CO binding is currently described as a series of discrete structural changes, without much consideration for the potential role of protein dynamics in the process of DNA binding. This work uses site-directed spin-label electron paramagnetic resonance spectroscopy (SDSL-EPR) to probe slow timescale (μs-ms) conformational dynamics of CooA with a redox-stable nitroxide spin label, and IR spectroscopy to probe the environment at the CO-bound heme. A series of cysteine substitution variants were created to selectively label CooA in key functional regions, the heme-binding domain, the 4/5-loop, the hinge region, and the DNA binding domain. The EPR spectra of labeled CooA variants are compared across three functional states: Fe(III) "locked off", Fe(II)-CO "on", and Fe(II)-CO bound to DNA. We observe changes in the multicomponent EPR spectra at each location; most notably in the hinge region and DNA binding domain, broadening the description of the CooA allosteric mechanism to include the role of protein dynamics in DNA binding. DNA-dependent changes in IR vibrational frequency and band broadening further suggest that there is conformational heterogeneity in the active WT protein and that DNA binding alters the environment of the heme-bound CO.

Cation Dependence of Enniatin B/Membrane-Interactions Assessed Using Surface-Enhanced Infrared Absorption (SEIRA) Spectroscopy

Enniatins are mycotoxins with well-known antibacterial, antifungal, antihelmintic and antiviral activity, which have recently come to attention as potential mitochondriotoxic anticancer agents. The cytotoxicity of enniatins is traced back to ionophoric properties, in which the cyclodepsipeptidic structure results in enniatin:cation-complexes of various stoichiometries proposed as membrane-active species. In this work, we employed a combination of surface-enhanced infrared absorption (SEIRA) spectroscopy, tethered bilayer lipid membranes (tBLMs) and density functional theory (DFT)-based computational spectroscopy to monitor the cation-dependence (Mz+=Na+, K+, Cs+, Li+, Mg2+, Ca2+) on the mechanism of enniatin B (EB) incorporation into membranes and identify the functionally relevant EBn : Mz+ complexes formed. We find that Na+ promotes a cooperative incorporation, modelled via an autocatalytic mechanism and mediated by a distorted 2 : 1-EB2 : Na+ complex. K+ (and Cs+) leads to a direct but less efficient insertion into membranes due to the adoption of "ideal" EB2 : K+ sandwich complexes. In contrast, the presence of Li+, Mg2+, and Ca2+ causes a (partial) extraction of EB from the membrane via the formation of "belted" 1 : 1-EB : Mz+ complexes, which screen the cationic charge less efficiently. Our results point to a relevance of the cation dependence for the transport into the malignant cells where the mitochondriotoxic anticancer activity is exerted.

Theory vs. experiment: The rise of the dynamic view of proteins

Over the past century, the scientific conception of the protein has evolved significantly. This paper focuses on the most recent stage of this evolution, namely, the origin of the dynamic view of proteins and the challenge it posed to the static view of classical molecular biology. Philosophers and scientists have offered two hypotheses to explain the origin of the dynamic view and its slow reception by structural biologists. Some have argued that the shift from the static to the dynamic view was a Kuhnian revolution, driven by the accumulation of dynamic anomalies, while others have argued that the shift was caused by new empirical findings made possible by technological advances. I analyze this scientific episode and ultimately reject both of these empiricist accounts. I argue that focusing primarily on technological advances and empirical discoveries overlooks the important role of theory in driving this scientific change. I show how the application of general thermodynamic principles to proteins gave rise to the dynamic view, and a commitment to these principles then led early adopters to seek out the empirical examples of protein dynamics, which would eventually convince their peers. My analysis of this historical case shows that empiricist accounts of modern scientific progress-at least those that aim to explain developments in the molecular life sciences-need to be tempered in order to capture the interplay between theory and experiment.

Is allostery a fuzzy concept?

Allostery is an important property of biological macromolecules which regulates diverse biological functions such as catalysis, signal transduction, transport, and molecular recognition. However, the concept was expressed using two different definitions by J. Monod and, over time, more have been added by different authors, making it fuzzy. Here, we reviewed the different meanings of allostery in the current literature and found that it has been used to indicate that the function of a protein is regulated by heterotropic ligands, and/or that the binding of ligands and substrates presents homotropic positive or negative cooperativity, whatever the hypothesized or demonstrated reaction mechanism might be. Thus, proteins defined to be allosteric include not only those that obey the two-state concerted model, but also those that obey different reaction mechanisms such as ligand-induced fit, possibly coupled to sequential structure changes, and ligand-linked dissociation-association. Since each reaction mechanism requires its own mathematical description and is defined by it, there are many possible 'allosteries'. This lack of clarity is made even fuzzier by the fact that the reaction mechanism is often assigned imprecisely and/or implicitly in the absence of the necessary experimental evidence. In this review, we examine a list of proteins that have been defined to be allosteric and attempt to assign a reaction mechanism to as many as possible.

A parameterized two-domain thermodynamic model explains diverse mutational effects on protein allostery

New experimental findings continue to challenge our understanding of protein allostery. Recent deep mutational scanning study showed that allosteric hotspots in the tetracycline repressor (TetR) and its homologous transcriptional factors are broadly distributed rather than spanning well-defined structural pathways as often assumed. Moreover, hotspot mutation-induced allostery loss was rescued by distributed additional mutations in a degenerate fashion. Here, we develop a two-domain thermodynamic model for TetR, which readily rationalizes these intriguing observations. The model accurately captures the in vivo activities of various mutants with changes in physically transparent parameters, allowing the data-based quantification of mutational effects using statistical inference. Our analysis reveals the intrinsic connection of intra- and inter-domain properties for allosteric regulation and illustrate epistatic interactions that are consistent with structural features of the protein. The insights gained from this study into the nature of two-domain allostery are expected to have broader implications for other multi-domain allosteric proteins.

Nano-optogenetics for Disease Therapies

Optogenetic, known as the method of 21 centuries, combines optic and genetic engineering to precisely control photosensitive proteins for manipulation of a broad range of cellular functions, such as flux of ions, protein oligomerization and dissociation, cellular intercommunication, and so on. In this technique, light is conventionally delivered to targeted cells through optical fibers or micro light-emitting diodes, always suffering from high invasiveness, wide-field illumination facula, strong absorption, and scattering by nontargeted endogenous substance. Light-transducing nanomaterials with advantages of high spatiotemporal resolution, abundant wireless-excitation manners, and easy functionalization for recognition of specific cells, recently have been widely explored in the field of optogenetics; however, there remain a few challenges to restrain its clinical applications. This review summarized recent progress on light-responsive genetically encoded proteins and the myriad of activation strategies by use of light-transducing nanomaterials and their disease-treatment applications, which is expected for sparking helpful thought to push forward its preclinical and translational uses.

A structure-based computational model of IP3R1 incorporating Ca and IP3 regulation

The inositol 1,4,5-triphosphate receptor (IP3R) mediates Ca release in many cell types and is pivotal to a wide range of cellular processes. High-resolution cryoelectron microscopy studies have provided new structural details of IP3R type 1 (IP3R1), showing that channel function is determined by the movement of various domains within and between each of its four subunits. Channel properties are regulated by ligands, such as Ca and IP3, which bind at specific sites and control the interactions between these domains. However, it is not known how the various ligand-binding sites on IP3R1 interact to control the opening of the channel. In this study, we present a coarse-grained model of IP3R1 that accounts for the channel architecture and the location of specific Ca- and IP3-binding sites. This computational model accounts for the domain-domain interactions within and between the four subunits that form IP3R1, and it also describes how ligand binding regulates these interactions. Using a kinetic model, we explore how two Ca-binding sites on the cytosolic side of the channel interact with the IP3-binding site to regulate the channel open probability. Our primary finding is that the bell-shaped open probability of IP3R1 provides constraints on the relative strength of these regulatory binding sites. In particular, we argue that a specific Ca-binding site, whose function has not yet been established, is very likely a channel antagonist. Additionally, we apply our model to show that domain-domain interactions between neighboring subunits exert control over channel cooperativity and dictate the nonlinear response of the channel to Ca concentration. This suggests that specific domain-domain interactions play a pivotal role in maintaining the channel's stability, and a disruption of these interactions may underlie disease states associated with Ca dysregulation.

Structural basis for the allosteric behaviour and substrate specificity of Lactococcus lactis Prolidase

Prolidase (EC 3.4.13.9) is an enzyme that specifically hydrolyzes Xaa-Pro dipeptides into free amino acids. We previously studied kinetic behaviours and solved the crystal structure of wild-type (WT) Lactococcus lactis prolidase (Llprol), showing that this homodimeric enzyme has unique characteristics: allosteric behaviour and substrate inhibition. In this study, we focused on solving the crystal structures of three Llprol mutants (D36S, H38S, and R293S) which behave differently in v-S plots. The D36S and R293S Llprol mutants do not show allosteric behaviour, and the Llprol mutant H38S has allosteric behaviour comparable to the WT enzyme (Hill constant 1.52 and 1.58, respectively). The crystal structures of Llprol variants suggest that the active site of Llprol formed with amino acid residues from both monomers, i.e., located in an interfacial area of dimer. The comparison between the structure models of Llprol indicated that the two monomers in the dimers of Llprol variants have different relative positions among Llprol variants. They showed different interatomic distances between the amino acid residues bridging the two monomers and varied sizes of the solvent-accessible interface areas in each Llprol variant. These observations indicated that Llprol could adapt to different conformational states with distinctive substrate affinities. It is strongly speculated that the domain movements required for productive substrate binding are restrained in allosteric Llprol (WT and H38S). At low substrate concentrations, only one out of the two active sites at the dimer interface could accept substrate; as a result, the asymmetrical activated dimer leads to allosteric behaviour.

Perspectives on Computational Enzyme Modeling: From Mechanisms to Design and Drug Development

Understanding enzyme mechanisms is essential for unraveling the complex molecular machinery of life. In this review, we survey the field of computational enzymology, highlighting key principles governing enzyme mechanisms and discussing ongoing challenges and promising advances. Over the years, computer simulations have become indispensable in the study of enzyme mechanisms, with the integration of experimental and computational exploration now established as a holistic approach to gain deep insights into enzymatic catalysis. Numerous studies have demonstrated the power of computer simulations in characterizing reaction pathways, transition states, substrate selectivity, product distribution, and dynamic conformational changes for various enzymes. Nevertheless, significant challenges remain in investigating the mechanisms of complex multistep reactions, large-scale conformational changes, and allosteric regulation. Beyond mechanistic studies, computational enzyme modeling has emerged as an essential tool for computer-aided enzyme design and the rational discovery of covalent drugs for targeted therapies. Overall, enzyme design/engineering and covalent drug development can greatly benefit from our understanding of the detailed mechanisms of enzymes, such as protein dynamics, entropy contributions, and allostery, as revealed by computational studies. Such a convergence of different research approaches is expected to continue, creating synergies in enzyme research. This review, by outlining the ever-expanding field of enzyme research, aims to provide guidance for future research directions and facilitate new developments in this important and evolving field.

A parametrized two-domain thermodynamic model explains diverse mutational effects on protein allostery

New experimental findings continue to challenge our understanding of protein allostery. Recent deep mutational scanning study showed that allosteric hotspots in the tetracycline repressor (TetR) and its homologous transcriptional factors are broadly distributed rather than spanning well-defined structural pathways as often assumed. Moreover, hotspot mutation-induced allostery loss was rescued by distributed additional mutations in a degenerate fashion. Here, we develop a two-domain thermodynamic model for TetR, which readily rationalizes these intriguing observations. The model accurately captures the in vivo activities of various mutants with changes in physically transparent parameters, allowing the data-based quantification of mutational effects using statistical inference. Our analysis reveals the intrinsic connection of intra- and inter-domain properties for allosteric regulation and illustrate epistatic interactions that are consistent with structural features of the protein. The insights gained from this study into the nature of two-domain allostery are expected to have broader implications for other multidomain allosteric proteins.

T-cell virtuosity in ''knowing thyself"

Major Histocompatibility Complex (MHC) I and II and the αβ T-cell antigen receptor (TCRαβ) govern fundamental traits of adaptive immunity. They form a membrane-borne ligand-receptor system weighing host proteome integrity to detect contamination by nonself proteins. MHC-I and -II exhibit the "MHC-fold", which is able to bind a large assortment of short peptides as proxies for self and nonself proteins. The ensuing varying surfaces are mandatory ligands for Ig-like TCRαβ highly mutable binding sites. Conserved molecular signatures guide TCRαβ ligand binding sites to focus on the MHC-fold (MHC-restriction) while leaving many opportunities for its most hypervariable determinants to contact the peptide. This riveting molecular strategy affords many options for binding energy compatible with specific recognition and signalling aimed to eradicated microbial pathogens and cancer cells. While the molecular foundations of αβ T-cell adaptive immunity are largely understood, uncertainty persists on how peptide-MHC binding induces the TCRαβ signals that instruct cell-fate decisions. Solving this mystery is another milestone for understanding αβ T-cells' self/nonself discrimination. Recent developments revealing the innermost links between TCRαβ structural dynamics and signalling modality should help dissipate this long-sought-after enigma.

Modulation of Allostery with Multiple Mechanisms by Hotspot Mutations in TetR

Modulating allosteric coupling offers unique opportunities for biomedical applications. Such efforts can benefit from efficient prediction and evaluation of allostery hotspot residues that dictate the degree of cooperativity between distant sites. We demonstrate that effects of allostery hotspot mutations can be evaluated qualitatively and semiquantitatively by molecular dynamics simulations in a bacterial tetracycline repressor (TetR). The simulations recapitulate the effects of these mutations on abolishing the induction function of TetR and provide a rationale for the different rescuabilities observed to restore allosteric coupling of the hotspot mutations. We demonstrate that the same noninducible phenotype could be the result of perturbations in distinct structural and energetic properties of TetR. Our work underscores the value of explicitly computing the functional free energy landscapes to effectively evaluate and rank hotspot mutations despite the prevalence of compensatory interactions and therefore provides quantitative guidance to allostery modulation for therapeutic and engineering applications.

Modulation of Allostery with Multiple Mechanisms by Hotspot Mutations in TetR

Modulating allosteric coupling offers unique opportunities for biomedical applications. Such efforts can benefit from efficient prediction and evaluation of allostery hotspot residues that dictate the degree of co-operativity between distant sites. We demonstrate that effects of allostery hotspot mutations can be evaluated qualitatively and semi-quantitatively by molecular dynamics simulations in a bacterial tetracycline repressor (TetR). The simulations recapitulate the effects of these mutations on abolishing the induction function of TetR and provide a rationale for the different degrees of rescuability observed to restore allosteric coupling of the hotspot mutations. We demonstrate that the same non-inducible phenotype could be the result of perturbations in distinct structural and energetic properties of TetR. Our work underscore the value of explicitly computing the functional free energy landscapes to effectively evaluate and rank hotspot mutations despite the prevalence of compensatory interactions, and therefore provide quantitative guidance to allostery modulation for therapeutic and engineering applications.

Genetic switching by the Lac repressor is based on two-state Monod-Wyman-Changeux allostery

High-resolution NMR spectroscopy enabled us to characterize allosteric transitions between various functional states of the dimeric Escherichia coli Lac repressor. In the absence of ligands, the dimer exists in a dynamic equilibrium between DNA-bound and inducer-bound conformations. Binding of either effector shifts this equilibrium toward either bound state. Analysis of the ternary complex between repressor, operator DNA, and inducer shows how adding the inducer results in allosteric changes that disrupt the interdomain contacts between the inducer binding and DNA binding domains and how this in turn leads to destabilization of the hinge helices and release of the Lac repressor from the operator. Based on our data, the allosteric mechanism of the induction process is in full agreement with the well-known Monod-Wyman-Changeux model.

All over or overall - Do we understand allostery?

Allostery is probably the most important concept in the regulation of cellular processes. Models to explain allostery are plenty. Each sheds light on different aspects but their entirety conveys an ambiguous feeling of comprehension and disappointment. Here, I discuss the most popular allostery models, their roots, similarities, and limitations. All of them are thermodynamic models. Naturally this bears a certain degree of redundancy, which forms the center of this review. After sixty years, many questions remain unanswered, mainly because our human longing for causality as base for understanding is not satisfied by thermodynamics alone. A description of allostery in terms of pathways, i.e., as a temporal chain of events, has been-, and still is-, a missing piece of the puzzle.

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

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

Garcinolic Acid Distinguishes Between GACKIX Domains and Modulates Interaction Networks

Natural products are often uniquely suited to modulate protein-protein interactions (PPIs) due to their architectural and functional group complexity relative to synthetic molecules. Here we demonstrate that the natural product garcinolic acid allosterically blocks the CBP/p300 KIX PPI network and displays excellent selectivity over related GACKIX motifs. It does so via a strong interaction (KD 1 μM) with a non-canonical binding site containing a structurally dynamic loop in CBP/p300 KIX. Garcinolic acid engages full-length CBP in the context of the proteome and in doing so effectively inhibits KIX-dependent transcription in a leukemia model. As the most potent small-molecule KIX inhibitor yet reported, garcinolic acid represents an important step forward in the therapeutic targeting of CBP/p300.

Protein conformational ensembles in function: roles and mechanisms

The sequence-structure-function paradigm has dominated twentieth century molecular biology. The paradigm tacitly stipulated that for each sequence there exists a single, well-organized protein structure. Yet, to sustain cell life, function requires (i) that there be more than a single structure, (ii) that there be switching between the structures, and (iii) that the structures be incompletely organized. These fundamental tenets called for an updated sequence-conformational ensemble-function paradigm. The powerful energy landscape idea, which is the foundation of modernized molecular biology, imported the conformational ensemble framework from physics and chemistry. This framework embraces the recognition that proteins are dynamic and are always interconverting between conformational states with varying energies. The more stable the conformation the more populated it is. The changes in the populations of the states are required for cell life. As an example, in vivo, under physiological conditions, wild type kinases commonly populate their more stable "closed", inactive, conformations. However, there are minor populations of the "open", ligand-free states. Upon their stabilization, e.g., by high affinity interactions or mutations, their ensembles shift to occupy the active states. Here we discuss the role of conformational propensities in function. We provide multiple examples of diverse systems, including protein kinases, lipid kinases, and Ras GTPases, discuss diverse conformational mechanisms, and provide a broad outlook on protein ensembles in the cell. We propose that the number of molecules in the active state (inactive for repressors), determine protein function, and that the dynamic, relative conformational propensities, rather than the rigid structures, are the hallmark of cell life.

Mixed, nonclassical behavior in a classic allosteric protein

Allostery is a major driver of biological processes requiring coordination. Thus, it is one of the most fundamental and remarkable phenomena in nature, and there is motivation to understand and manipulate it to a multitude of ends. Today, it is often described in terms of two phenomenological models proposed more than a half-century ago involving only T(tense) or R(relaxed) conformations. Here, methyl-based NMR provides extensive detail on a dynamic T to R switch in the classical dimeric allosteric protein, yeast chorismate mutase (CM), that occurs in the absence of substrate, but only with the activator bound. Switching of individual subunits is uncoupled based on direct observation of mixed TR states in the dimer. This unique finding excludes both classic models and solves the paradox of a coexisting hyperbolic binding curve and highly skewed substrate-free T-R equilibrium. Surprisingly, structures of the activator-bound and effector-free forms of CM appear the same by NMR, providing another example of the need to account for dynamic ensembles. The apo enzyme, which has a sigmoidal activity profile, is shown to switch, not to R, but to a related high-energy state. Thus, the conformational repertoire of CM does not just change as a matter of degree depending on the allosteric input, be it effector and/or substrate. Rather, the allosteric model appears to completely change in different contexts, which is only consistent with modern ensemble-based frameworks.

Allosteric Determinants in High Temperature Requirement A Enzymes Are Conserved and Regulate the Population of Active Conformations

High temperature requirement A (HtrA) are allosterically regulated enzymes wherein effector binding to the PDZ domain triggers proteolytic activity. Yet, it remains unclear if the inter-residue network governing allostery is conserved across HtrA enzymes. Here, we investigated and identified the inter-residue interaction networks by molecular dynamics simulations on representative HtrA proteases, Escherichia coli DegS and Mycobacterium tuberculosis PepD, in effector-bound and free forms. This information was used to engineer mutations that could potentially perturb allostery and conformational sampling in a different homologue, M. tuberculosis HtrA. Mutations in HtrA perturbed allosteric regulation─a finding consistent with the hypothesis that the inter-residue interaction network is conserved across HtrA enzymes. Electron density from data collected on cryo-protected HtrA crystals revealed that mutations altered the topology of the active site. Ensemble models fitted into electron density calculated from room-temperature diffraction data showed that only a fraction of these models had a catalytically competent active site conformation alongside a functional oxyanion hole thus providing experimental evidence that these mutations influenced conformational sampling. Mutations at analogous positions in the catalytic domain of DegS perturbed the coupling between effector binding and proteolytic activity, thus confirming the role of these residues in the allosteric response. The finding that a perturbation in the conserved inter-residue network alters conformational sampling and the allosteric response suggests that an ensemble allosteric model best describes regulated proteolysis in HtrA enzymes.

From Deep Mutational Mapping of Allosteric Protein Landscapes to Deep Learning of Allostery and Hidden Allosteric Sites: Zooming in on "Allosteric Intersection" of Biochemical and Big Data Approaches

The recent advances in artificial intelligence (AI) and machine learning have driven the design of new expert systems and automated workflows that are able to model complex chemical and biological phenomena. In recent years, machine learning approaches have been developed and actively deployed to facilitate computational and experimental studies of protein dynamics and allosteric mechanisms. In this review, we discuss in detail new developments along two major directions of allosteric research through the lens of data-intensive biochemical approaches and AI-based computational methods. Despite considerable progress in applications of AI methods for protein structure and dynamics studies, the intersection between allosteric regulation, the emerging structural biology technologies and AI approaches remains largely unexplored, calling for the development of AI-augmented integrative structural biology. In this review, we focus on the latest remarkable progress in deep high-throughput mining and comprehensive mapping of allosteric protein landscapes and allosteric regulatory mechanisms as well as on the new developments in AI methods for prediction and characterization of allosteric binding sites on the proteome level. We also discuss new AI-augmented structural biology approaches that expand our knowledge of the universe of protein dynamics and allostery. We conclude with an outlook and highlight the importance of developing an open science infrastructure for machine learning studies of allosteric regulation and validation of computational approaches using integrative studies of allosteric mechanisms. The development of community-accessible tools that uniquely leverage the existing experimental and simulation knowledgebase to enable interrogation of the allosteric functions can provide a much-needed boost to further innovation and integration of experimental and computational technologies empowered by booming AI field.

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.

The "violin model": Looking at community networks for dynamic allostery

Allosteric regulation of proteins continues to be an engaging research topic for the scientific community. Models describing allosteric communication have evolved from focusing on conformation-based descriptors of protein structural changes to appreciating the role of internal protein dynamics as a mediator of allostery. Here, we explain a "violin model" for allostery as a contemporary method for approaching the Cooper-Dryden model based on redistribution of protein thermal fluctuations. Based on graph theory, the violin model makes use of community network analysis to functionally cluster correlated protein motions obtained from molecular dynamics simulations. This Review provides the theory and workflow of the methodology and explains the application of violin model to unravel the workings of protein kinase A.

Coevolution-based prediction of key allosteric residues for protein function regulation

Allostery is fundamental to many biological processes. Due to the distant regulation nature, how allosteric mutations, modifications, and effector binding impact protein function is difficult to forecast. In protein engineering, remote mutations cannot be rationally designed without large-scale experimental screening. Allosteric drugs have raised much attention due to their high specificity and possibility of overcoming existing drug-resistant mutations. However, optimization of allosteric compounds remains challenging. Here, we developed a novel computational method KeyAlloSite to predict allosteric site and to identify key allosteric residues (allo-residues) based on the evolutionary coupling model. We found that protein allosteric sites are strongly coupled to orthosteric site compared to non-functional sites. We further inferred key allo-residues by pairwise comparing the difference of evolutionary coupling scores of each residue in the allosteric pocket with the functional site. Our predicted key allo-residues are in accordance with previous experimental studies for typical allosteric proteins like BCR-ABL1, Tar, and PDZ3, as well as key cancer mutations. We also showed that KeyAlloSite can be used to predict key allosteric residues distant from the catalytic site that are important for enzyme catalysis. Our study demonstrates that weak coevolutionary couplings contain important information of protein allosteric regulation function. KeyAlloSite can be applied in studying the evolution of protein allosteric regulation, designing and optimizing allosteric drugs, and performing functional protein design and enzyme engineering.

Multilayered allosteric modulation of coupled folding and binding by phosphorylation, peptidyl-prolyl cis/trans isomerization, and diversity of interaction partners

Intrinsically disordered proteins (IDPs) play key roles in cellular regulation, including signal transduction, transcription, and cell-cycle control. Accordingly, IDPs can commonly interact with numerous different target proteins, and their interaction networks are expected to be highly regulated. However, many of the underlying regulatory mechanisms have remained unclear. Here, we examine the representative case of the nuclear coactivator binding domain (NCBD) of the large multidomain protein CBP, a hub in transcriptional regulation, and the interaction with several of its binding partners. Single-molecule Förster resonance energy transfer measurements show that phosphorylation of NCBD reduces its binding affinity, with effects that vary depending on the binding partner and the site and number of modifications. The complexity of the interaction is further increased by the dependence of the affinities on peptidyl-prolyl cis/trans isomerization in NCBD. Overall, our results reveal the potential for allosteric regulation on at least three levels: the different affinities of NCBD for its different binding partners, the differential modulation of these affinities by phosphorylation, and the effect of peptidyl-prolyl cis/trans isomerization on binding.

Binding and Functional Folding (BFF): A Physiological Framework for Studying Biomolecular Interactions and Allostery

EF-hand Ca2+-binding proteins (CBPs), such as S100 proteins (S100s) and calmodulin (CaM), are signaling proteins that undergo conformational changes upon increasing intracellular Ca2+. Upon binding Ca2+, S100 proteins and CaM interact with protein targets and induce important biological responses. The Ca2+-binding affinity of CaM and most S100s in the absence of target is weak (CaKD > 1 μM). However, upon effector protein binding, the Ca2+ affinity of these proteins increases via heterotropic allostery (CaKD < 1 μM). Because of the high number and micromolar concentrations of EF-hand CBPs in a cell, at any given time, allostery is required physiologically, allowing for (i) proper Ca2+ homeostasis and (ii) strict maintenance of Ca2+-signaling within a narrow dynamic range of free Ca2+ ion concentrations, [Ca2+]free. In this review, mechanisms of allostery are coalesced into an empirical "binding and functional folding (BFF)" physiological framework. At the molecular level, folding (F), binding and folding (BF), and BFF events include all atoms in the biomolecular complex under study. The BFF framework is introduced with two straightforward BFF types for proteins (type 1, concerted; type 2, stepwise) and considers how homologous and nonhomologous amino acid residues of CBPs and their effector protein(s) evolved to provide allosteric tightening of Ca2+ and simultaneously determine how specific and relatively promiscuous CBP-target complexes form as both are needed for proper cellular function.

A gating lever and molecular logic gate that couple voltage and calcium sensor activation to opening in BK potassium channels

BK channels uniquely integrate voltage and calcium signaling in diverse cell types through allosteric activation of their K+-conducting pore by structurally distinct V and Ca2+ sensor domains. Here, we define mechanisms and interaction pathways that link V sensors to the pore by analyzing effects on allosteric coupling of point mutations in the context of Slo1 BK channel structure. A gating lever, mediated by S4/S5 segment interaction within the transmembrane domain, rotates to engage and stabilize the open conformation of the S6 inner pore helix upon V sensor activation. In addition, an indirect pathway, mediated by the carboxyl-terminal cytosolic domain (CTD) and C-linker that connects the CTD to S6, stabilizes the closed conformation when V sensors are at rest. Unexpectedly, this mechanism, which bypasses the covalent connections of C-linker to CTD and pore, also transduces Ca2+-dependent coupling in a manner that is completely nonadditive with voltage, analogous to the function of a digital logic (OR) gate.

An Experimental Framework for Developing Point-of-Need Biosensors: Connecting Bio-Layer Interferometry and Electrochemical Impedance Spectroscopy

Biolayer interferometry (BLI) is a well-established laboratory technique for studying biomolecular interactions important for applications such as drug development. Currently, there are interesting opportunities for expanding the use of BLI in other fields, including the development of rapid diagnostic tools. To date, there are no detailed frameworks for implementing BLI in target-recognition studies that are pivotal for developing point-of-need biosensors. Here, we attempt to bridge these domains by providing a framework that connects output(s) of molecular interaction studies with key performance indicators used in the development of point-of-need biosensors. First, we briefly review the governing theory for protein-ligand interactions, and we then summarize the approach for real-time kinetic quantification using various techniques. The 2020 PRISMA guideline was used for all governing theory reviews and meta-analyses. Using the information from the meta-analysis, we introduce an experimental framework for connecting outcomes from BLI experiments (KD, kon, koff) with electrochemical (capacitive) biosensor design. As a first step in the development of a larger framework, we specifically focus on mapping BLI outcomes to five biosensor key performance indicators (sensitivity, selectivity, response time, hysteresis, operating range). The applicability of our framework was demonstrated in a study of case based on published literature related to SARS-CoV-2 spike protein to show the development of a capacitive biosensor based on truncated angiotensin-converting enzyme 2 (ACE2) as the receptor. The case study focuses on non-specific binding and selectivity as research goals. The proposed framework proved to be an important first step toward modeling/simulation efforts that map molecular interactions to sensor design.

Deep mutational scanning and machine learning reveal structural and molecular rules governing allosteric hotspots in homologous proteins

A fundamental question in protein science is where allosteric hotspots - residues critical for allosteric signaling - are located, and what properties differentiate them. We carried out deep mutational scanning (DMS) of four homologous bacterial allosteric transcription factors (aTFs) to identify hotspots and built a machine learning model with this data to glean the structural and molecular properties of allosteric hotspots. We found hotspots to be distributed protein-wide rather than being restricted to 'pathways' linking allosteric and active sites as is commonly assumed. Despite structural homology, the location of hotspots was not superimposable across the aTFs. However, common signatures emerged when comparing hotspots coincident with long-range interactions, suggesting that the allosteric mechanism is conserved among the homologs despite differences in molecular details. Machine learning with our large DMS datasets revealed global structural and dynamic properties to be a strong predictor of whether a residue is a hotspot than local and physicochemical properties. Furthermore, a model trained on one protein can predict hotspots in a homolog. In summary, the overall allosteric mechanism is embedded in the structural fold of the aTF family, but the finer, molecular details are sequence-specific.

Thermodynamic coupling between neighboring binding sites in homo-oligomeric ligand sensing proteins from mass resolved ligand-dependent population distributions

Homo-oligomeric ligand-activated proteins are ubiquitous in biology. The functions of such molecules are commonly regulated by allosteric coupling between ligand-binding sites. Understanding the basis for this regulation requires both quantifying the free energy ΔG transduced between sites, and the structural basis by which it is transduced. We consider allostery in three variants of the model ring-shaped homo-oligomeric trp RNA-binding attenuation protein (TRAP). First, we developed a nearest-neighbor statistical thermodynamic binding model comprising microscopic free energies for ligand binding to isolated sites ΔG0 , and for coupling between adjacent sites, ΔGα . Using the resulting partition function (PF) we explored the effects of these parameters on simulated population distributions for the 2N possible liganded states. We then experimentally monitored ligand-dependent population shifts using conventional spectroscopic and calorimetric methods and using native mass spectrometry (MS). By resolving species with differing numbers of bound ligands by their mass, native MS revealed striking differences in their ligand-dependent population shifts. Fitting the populations to a binding polynomial derived from the PF yielded coupling free energy terms corresponding to orders of magnitude differences in cooperativity. Uniquely, this approach predicts which of the possible 2N liganded states are populated at different ligand concentrations, providing necessary insights into regulation. The combination of statistical thermodynamic modeling with native MS may provide the thermodynamic foundation for a meaningful understanding of the structure-thermodynamic linkage that drives cooperativity.

Allostery: Allosteric Cancer Drivers and Innovative Allosteric Drugs

Here, we discuss the principles of allosteric activating mutations, propagation downstream of the signals that they prompt, and allosteric drugs, with examples from the Ras signaling network. We focus on Abl kinase where mutations shift the landscape toward the active, imatinib binding-incompetent conformation, likely resulting in the high affinity ATP outcompeting drug binding. Recent pharmacological innovation extends to allosteric inhibitor (GNF-5)-linked PROTAC, targeting Bcr-Abl1 myristoylation site, and broadly, allosteric heterobifunctional degraders that destroy targets, rather than inhibiting them. Designed chemical linkers in bifunctional degraders can connect the allosteric ligand that binds the target protein and the E3 ubiquitin ligase warhead anchor. The physical properties and favored conformational state of the engineered linker can precisely coordinate the distance and orientation between the target and the recruited E3. Allosteric PROTACs, noncompetitive molecular glues, and bitopic ligands, with covalent links of allosteric ligands and orthosteric warheads, increase the effective local concentration of productively oriented and placed ligands. Through covalent chemical or peptide linkers, allosteric drugs can collaborate with competitive drugs, degrader anchors, or other molecules of choice, driving innovative drug discovery.

Protein Conformational Space at the Edge of Allostery: Turning a Non-allosteric Malate Dehydrogenase into an "Allosterized" Enzyme using Evolution Guided Punctual Mutations

We unveil the intimate relationship between protein dynamics and allostery by following the trajectories of model proteins in their conformational and sequence spaces. Starting from a nonallosteric hyperthermophilic malate dehydrogenase, we have tracked the role of protein dynamics in the evolution of the allosteric capacity. Based on a large phylogenetic analysis of the malate (MalDH) and lactate dehydrogenase (LDH) superfamily, we identified two amino acid positions that could have had a major role for the emergence of allostery in LDHs, which we targeted for investigation by site-directed mutagenesis. Wild-type MalDH and the single and double mutants were tested with respect to their substrate recognition profiles. The double mutant displayed a sigmoid-shaped profile typical of homotropic activation in LDH. By using molecular dynamics simulations, we showed that the mutations induce a drastic change in the protein sampling of its conformational landscape, making transiently T-like (inactive) conformers, typical of allosteric LDHs, accessible. Our data fit well with the seminal key concept linking protein dynamics and evolvability. We showed that the selection of a new phenotype can be achieved by a few key dynamics-enhancing mutations causing the enrichment of low-populated conformational substates.

Conformational fluctuations in the molten globule state of α-lactalbumin

A molten globule (MG) state is an intermediate state of a protein observed during the unfolding of the native structure. The MG state of the protein is induced by various denaturing agents (like urea), extreme pH, pressure, and heat. Experiments suggest that the MG state of some proteins is functionally relevant even if there is no well-defined tertiary structure. Earlier experimental and theoretical studies show that the MG state of a protein is dynamic in nature, where conformational states are interconverted on nanosecond time scales. These observations lead us to study and compare the conformational fluctuations of the MG state to those of intrinsic disordered proteins (IDPs). We consider a milk protein, α-lactalbumin (aLA), which shows an MG state at low pH upon removal of the calcium (Ca²⁺) ion. We use the constant pH molecular dynamics (CpHMD) simulation to maintain the protonation state of titratable residues at a low pH during the simulation. We use the dihedral principal component analysis, the density based clustering method, and the machine learning technique to identify the conformational fluctuations. We observe metastable states in the MG state. The residues containing the essential coordinates responsible for metastability belong to a stable helix in the crystal structure, but most of them prefer unstructured or bent conformation in the MG state. These residues control the exposure of the putative binding residues for fatty acids. Thus, the MG state of a protein behaves as an intrinsic disorder protein, although the disorder here is induced by external conditions.

Phenol sensing in nature is modulated via a conformational switch governed by dynamic allostery

The NtrC family of proteins senses external stimuli and accordingly stimulates stress and virulence pathways via activation of associated σ⁵⁴-dependent RNA polymerases. However, the structural determinants that mediate this activation are not well understood. Here, we establish using computational, structural, biochemical, and biophysical studies that MopR, an NtrC protein, harbors a dynamic bidirectional electrostatic network that connects the phenol pocket to two distal regions, namely the “G-hinge” and the “allosteric linker.” While the G-hinge influences the entry of phenol into the pocket, the allosteric linker passes the signal to the downstream ATPase domain. We show that phenol binding induces a rewiring of the electrostatic connections by eliciting dynamic allostery and demonstrates that perturbation of the core relay residues results in a complete loss of ATPase stimulation. Furthermore, we found a mutation of the G-hinge, ∼20 Å from the phenol pocket, promotes altered flexibility by shifting the pattern of conformational states accessed, leading to a protein with 7-fold enhanced phenol binding ability and enhanced transcriptional activation. Finally, we conducted a global analysis that illustrates that dynamic allostery-driven conserved community networks are universal and evolutionarily conserved across species. Taken together, these results provide insights into the mechanisms of dynamic allostery-mediated conformational changes in NtrC sensor proteins.

Optimising Elastic Network Models for Protein Dynamics and Allostery: Spatial and Modal Cut-offs and Backbone Stiffness

The family of coarse-grained models for protein dynamics known as Elastic Network Models (ENMs) require careful choice of parameters to represent well experimental measurements or fully-atomistic simulations. The most basic ENM that represents each protein residue by a node at the position of its C-alpha atom, all connected by springs of equal stiffness, up to a cut-off in distance. Even at this level a choice is required of the optimum cut-off distance and the upper limit of elastic normal modes taken in any sum for physical properties, such as dynamic correlation or allosteric effects on binding. Additionally, backbone-enhanced ENM (BENM) may improve the model by allocating a higher stiffness to springs that connect along the protein backbone. This work reports on the effect of varying these three parameters (distance and mode cutoffs, backbone stiffness) on the dynamical structure of three proteins, Catabolite Activator Protein (CAP), Glutathione S-transferase (GST), and the SARS-CoV-2 Main Protease (M pro ). Our main results are: (1) balancing B-factor and dispersion-relation predictions, a near-universal optimal value of 8.5 Å is advisable for ENMs; (2) inhomogeneity in elasticity brings the first mode containing spatial structure not well-resolved by the ENM typically within the first 20; (3) the BENM only affects modes in the upper third of the distribution, and, additionally to the ENM, is only able to model the dispersion curve better in this vicinity; (4) BENM does not typically affect fluctuation-allostery, which also requires careful treatment of the effector binding to the host protein to capture.

Carbon Monoxide Signaling: Examining Its Engagement with Various Molecular Targets in the Context of Binding Affinity, Concentration, and Biologic Response

Carbon monoxide (CO) has been firmly established as an endogenous signaling molecule with a variety of pathophysiological and pharmacological functions, including immunomodulation, organ protection, and circadian clock regulation, among many others. In terms of its molecular mechanism(s) of action, CO is known to bind to a large number of hemoproteins with at least 25 identified targets, including hemoglobin, myoglobin, neuroglobin, cytochrome c oxidase, cytochrome P450, soluble guanylyl cyclase, myeloperoxidase, and some ion channels with dissociation constant values spanning the range of sub-nM to high μM. Although CO's binding affinity with a large number of targets has been extensively studied and firmly established, there is a pressing need to incorporate such binding information into the analysis of CO's biologic response in the context of affinity and dosage. Especially important is to understand the reservoir role of hemoglobin in CO storage, transport, distribution, and transfer. We critically review the literature and inject a sense of quantitative assessment into our analyses of the various relationships among binding affinity, CO concentration, target occupancy level, and anticipated pharmacological actions. We hope that this review presents a picture of the overall landscape of CO's engagement with various targets, stimulates additional research, and helps to move the CO field in the direction of examining individual targets in the context of all of the targets and the concentration of available CO. We believe that such work will help the further understanding of the relationship of CO concentration and its pathophysiological functions and the eventual development of CO-based therapeutics. SIGNIFICANCE STATEMENT: The further development of carbon monoxide (CO) as a therapeutic agent will significantly rely on the understanding of CO's engagement with therapeutically relevant targets of varying affinity. This review critically examines the literature by quantitatively analyzing the intricate relationships among targets, target affinity for CO, CO level, and the affinity state of carboxyhemoglobin and provide a holistic approach to examining the molecular mechanism(s) of action for CO.

Molecular Dynamics Simulations Establish the Molecular Basis for the Broad Allostery Hotspot Distributions in the Tetracycline Repressor

It is imperative to identify the network of residues essential to the allosteric coupling for the purpose of rationally engineering allostery in proteins. Deep mutational scanning analysis has emerged as a function-centric approach for identifying such allostery hotspots in a comprehensive and unbiased fashion, leading to observations that challenge our understanding of allostery at the molecular level. Specifically, a recent deep mutational scanning study of the tetracycline repressor (TetR) revealed an unexpectedly broad distribution of allostery hotspots throughout the protein structure. Using extensive molecular dynamics simulations (up to 50 μs) and free energy computations, we establish the molecular and energetic basis for the strong anticooperativity between the ligand and DNA binding sites. The computed free energy landscapes in different ligation states illustrate that allostery in TetR is well described by a conformational selection model, in which the apo state samples a broad set of conformations, and specific ones are selectively stabilized by either ligand or DNA binding. By examining a range of structural and dynamic properties of residues at both local and global scales, we observe that various analyses capture different subsets of experimentally identified hotspots, suggesting that these residues modulate allostery in distinct ways. These results motivate the development of a thermodynamic model that qualitatively explains the broad distribution of hotspot residues and their distinct features in molecular dynamics simulations. The multifaceted strategy that we establish here for hotspot evaluations and our insights into their mechanistic contributions are useful for modulating protein allostery in mechanistic and engineering studies.

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.

Cellular sentience as the primary source of biological order and evolution

All life is cellular, starting some 4 billion years ago with the emergence of the first cells. In order to survive their early evolution in the face of an extremely challenging environment, the very first cells invented cellular sentience and cognition, allowing them to make relevant decisions to survive through creative adaptations in a continuously running evolutionary narrative. We propose that the success of cellular life has crucially depended on a biological version of Maxwell's demons which permits the extraction of relevant sensory information and energy from the cellular environment, allowing cells to sustain anti-entropic actions. These sensor-effector actions allowed for the creative construction of biological order in the form of diverse organic macromolecules, including crucial polymers such as DNA, RNA, and cytoskeleton. Ordered biopolymers store analogue (structures as templates) and digital (nucleotide sequences of DNA and RNA) information that functioned as a form memory to support the development of organisms and their evolution. Crucially, all cells are formed by the division of previous cells, and their plasma membranes are physically and informationally continuous across evolution since the beginning of cellular life. It is argued that life is supported through life-specific principles which support cellular sentience, distinguishing life from non-life. Biological order, together with cellular cognition and sentience, allow the creative evolution of all living organisms as the authentic authors of evolutionary novelty.

Identification of Core Allosteric Sites through Temperature- and Nucleus-Invariant Chemical Shift Covariance

Allosteric regulation is essential to control biological function. In addition, allosteric sites offer a promising venue for selective drug targeting. However, accurate mapping of allosteric sites remains challenging since allostery relies on often subtle, yet functionally relevant, structural and dynamical changes. A viable approach proposed to overcome such challenge is the chemical shift covariance analysis (CHESCA). Although CHESCA offers an exhaustive map of allosteric networks, it is critical to define the core allosteric sites to be prioritized in subsequent functional studies or the design of allosteric drugs. Here we propose two new CHESCA-based methodologies, called temperature CHESCA (T-CHESCA) and CLASS-CHESCA, aimed at narrowing down allosteric maps to the core allosteric residues. Both T- and CLASS-CHESCAs rely on the invariance of core inter-residue correlations to changes in the chemical shifts of the active and inactive conformations interconverting in fast exchange. In the T-CHESCA the chemical shifts of such states are modulated through temperature changes, while in the CLASS-CHESCA through variations in the spin-active nuclei involved in pairwise correlations. The T- and CLASS-CHESCAs as well as complete-linkage CHESCA were applied to the cAMP-binding domain of the exchange protein directly activated by cAMP (EPAC), which serves as a prototypical allosteric switch. Residues consistently identified by the three CHESCA methods were found in previously identified EPAC allosteric core sites. Hence, the T-, CLASS- and CL-CHESCA provide a toolset to establish allosteric site hierarchy and triage allosteric sites to be further analyzed by mutations and functional assays. Furthermore, the core allosteric networks selectively revealed through T- and CLASS-CHESCA are expected to facilitate the mechanistic understanding of disease-related mutations and the design of selective allosteric modulators.

Hysteresis in Hepatitis B Virus (HBV) Requires Assembly of Near-Perfect Capsids

The hepatitis B virus (HBV) must release its contents to initiate infection, making capsid disassembly critical to the viral life cycle. Capsid assembly proceeds through a cascade of weak interactions between copies of capsid protein (Cp) to yield uniform particles. However, there is a hysteresis to capsid dissociation that allows capsids to persist under conditions where they could not assemble. In this study, we have sought to define the basis of hysteresis by examining urea-induced dissociation of in vitro-assembled HBV capsids. In general, capsid samples show a mixture of two pools, differentiated by stability. Labile capsid dissociation corresponds to an ∼5 μM pseudocritical concentration of assembly (pcc), the same as that observed in assembly reactions. Dissociation of the stable pool corresponds to a subfemtomolar pcc, indicative of hysteresis. The fraction of stable capsids in an assembly reaction increases with the integrity of the Cp preparation and when association is performed at a higher ionic strength, which modifies the Cp conformation. Labile complexes are more prevalent when assembly conditions yield many kinetically trapped (incomplete and overgrown) products. Cp isolated from stable capsids reassembles into a mixture of stable and labile capsids. These results suggest that hysteresis arises from an ideal capsid lattice, even when some of the substituents in that lattice have defects. Consistent with structural studies that show a subtle difference between Cp dimers and Cp in capsid, we propose that hysteresis arises when HBV capsids undergo a lattice-dependent structural transition.

Landscape-Based Protein Stability Analysis and Network Modeling of Multiple Conformational States of the SARS-CoV-2 Spike D614G Mutant: Conformational Plasticity and Frustration-Induced Allostery as Energetic Drivers of Highly Transmissible Spike Variants

The structural and functional studies of the SARS-CoV-2 spike protein variants revealed an important role of the D614G mutation that is shared across many variants of concern (VOCs), suggesting the effect of this mutation on the enhanced virus infectivity and transmissibility. The recent structural and biophysical studies provided important evidence about multiple conformational substates of the D614G spike protein. The development of a plausible mechanistic model that can explain the experimental observations from a more unified thermodynamic perspective is an important objective of the current work. In this study, we employed efficient and accurate coarse-grained simulations of multiple structural substates of the D614G spike trimers together with the ensemble-based mutational frustration analysis to characterize the dynamics signatures of the conformational landscapes. By combining the local frustration profiling of the conformational states with residue-based mutational scanning of protein stability and network analysis of allosteric interactions and communications, we determine the patterns of mutational sensitivity in the functional regions and sites of variants. We found that the D614G mutation may induce a considerable conformational adaptability of the open states in the SARS-CoV-2 spike protein without compromising the folding stability and integrity of the spike protein. The results suggest that the D614G mutant may employ a hinge-shift mechanism in which the dynamic couplings between the site of mutation and the interprotomer hinge modulate the interdomain interactions, global mobility change, and the increased stability of the open form. This study proposes that mutation-induced modulation of the conformational flexibility and energetic frustration at the interprotomer interfaces may serve as an efficient mechanism for allosteric regulation of the SARS-CoV-2 spike proteins.

Allostery in the dynamic coactivator domain KIX occurs through minor conformational micro-states

The coactivator KIX of CBP uses two binding surfaces to recognize multiple activators and exhibits allostery in ternary complex formation. Activator•coactivator interactions are central to transcriptional regulation, yet the microscopic origins of allostery in dynamic proteins like KIX are largely unknown. Here, we investigate the molecular recognition and allosteric manifestations involved in two KIX ternary systems c-Myb•KIX•MLL and pKID•KIX•MLL. Exploring the hypothesis that binary complex formation prepays an entropic cost for positive cooperativity, we utilize molecular dynamics simulations, side chain methyl order parameters, and differential scanning fluorimetry (DSF) to explore conformational entropy changes in KIX. The protein's configurational micro-states from structural clustering highlight the utility of protein plasticity in molecular recognition and allostery. We find that apo KIX occupies a wide distribution of lowly-populated configurational states. Each binding partner has its own suite of KIX states that it selects, building a model of molecular recognition fingerprints. Allostery is maximized with MLL pre-binding, which corresponds to the observation of a significant reduction in KIX micro-states observed when MLL binds. With all binding partners, the changes in KIX conformational entropy arise predominantly from changes in the most flexible loop. Likewise, we find that a small molecule and mutations allosterically inhibit/enhance activator binding by tuning loop dynamics, suggesting that loop-targeting chemical probes could be developed to alter KIX•activator interactions. Experimentally capturing KIX stabilization is challenging, particularly because of the disordered nature of particular activators. However, DSF melting curves allow for inference of relative entropic changes that occur across complexes, which we compare to our computed entropy changes using simulation methyl order parameters.

Principles of Kinase Allosteric Inhibition and Pocket Validation

With several marketed drugs, allosteric inhibition of kinases has translated to pharmacological effects and clinical benefits comparable to those from orthosteric inhibition. However, despite much effort over more than 20 years, the number of kinase targets associated with FDA-approved allosteric drugs is limited, suggesting the challenges in identifying and validating allosteric inhibitors. Here we review the principles of allosteric inhibition, summarize the discovery of allosteric MEK1/2 and BCR-ABL1 inhibitors, and discuss the approaches to screening and demonstrating the functional activity of allosteric pocket ligands.

Structure and Cooperativity in Substrate-Enzyme Interactions: Perspectives on Enzyme Engineering and Inhibitor Design

Enzyme-based synthetic chemistry provides a green way to synthesize industrially important chemical scaffolds and provides incomparable substrate specificity and unmatched stereo-, regio-, and chemoselective product formation. However, using biocatalysts at an industrial scale has its challenges, like their narrow substrate scope, limited stability in large-scale one-pot reactions, and low expression levels. These limitations can be overcome by engineering and fine-tuning these biocatalysts using advanced protein engineering methods. A detailed understanding of the enzyme structure and catalytic mechanism and its structure-function relationship, cooperativity in binding of substrates, and dynamics of substrate-enzyme-cofactor complexes is essential for rational enzyme engineering for a specific purpose. This Review covers all these aspects along with an in-depth categorization of various industrially and pharmaceutically crucial bisubstrate enzymes based on their reaction mechanisms and their active site and substrate/cofactor-binding site structures. As the bisubstrate enzymes constitute around 60% of the known industrially important enzymes, studying their mechanism of actions and structure-activity relationship gives significant insight into deciding the targets for protein engineering for developing industrial biocatalysts. Thus, this Review is focused on providing a comprehensive knowledge of the bisubstrate enzymes' structure, their mechanisms, and protein engineering approaches to develop them into industrial biocatalysts.