Allostery and the Monod-Wyman-Changeux model after 50 years

Catalytic Conversion of NO and CO by Noble-Metal-Free Copper-Vanadium Oxide Cluster Anions CuVO3,4. J Phys Chem Lett 2024;15:9043-9050. [PMID: 39194150 DOI: 10.1021/acs.jpclett.4c01965] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

Herein, by using state-of-the-art mass spectrometry, we demonstrated experimentally that the bimetallic copper-vanadium oxide cluster anions CuVO3,4- can catalyze the reduction of NO by CO into N2O and CO2. Note that the catalysis of NO reduction by CO has been rarely established in the gas phase and noble-metal containing clusters were commonly emphasized. Benefiting from quantum-chemical calculations, the Cu-V synergistic effect that both metal atoms work energetically to favor NO adsorption, N-N coupling, and CO oxidation by facilitating electron transfer can be understood at a strictly molecular level. Theoretical results demonstrated that the precaptured NO molecule encourages the adsorption of the second NO by electron donation. This finding deepens our understanding on NO reduction that NO functions not only as a reactant but also as a promoter during the reactions. This discovery could be helpful to permeate the nature and mechanism of active sites on related copper-vanadium heterogeneous catalyst used in real-life NO reduction.

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.

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.

Cooperative Conformational Transitions Underpin the Activation Heat Capacity in the Temperature Dependence of Enzyme Catalysis

Many enzymes display non-Arrhenius behavior with curved Arrhenius plots in the absence of denaturation. There has been significant debate about the origin of this behavior and recently the role of the activation heat capacity (ΔCP⧧) has been widely discussed. If enzyme-catalyzed reactions occur with appreciable negative values of ΔCP⧧ (arising from narrowing of the conformational space along the reaction coordinate), then curved Arrhenius plots are a consequence. To investigate these phenomena in detail, we have collected high precision temperature-rate data over a wide temperature interval for a model glycosidase enzyme MalL, and a series of mutants that change the temperature-dependence of the enzyme-catalyzed rate. We use these data to test a range of models including macromolecular rate theory (MMRT) and an equilibrium model. In addition, we have performed extensive molecular dynamics (MD) simulations to characterize the conformational landscape traversed by MalL in the enzyme-substrate complex and an enzyme-transition state complex. We have crystallized the enzyme in a transition state-like conformation in the absence of a ligand and determined an X-ray crystal structure at very high resolution (1.10 Å). We show (using simulation) that this enzyme-transition state conformation has a more restricted conformational landscape than the wildtype enzyme. We coin the term "transition state-like conformation (TLC)" to apply to this state of the enzyme. Together, these results imply a cooperative conformational transition between an enzyme-substrate conformation (ES) and a transition-state-like conformation (TLC) that precedes the chemical step. We present a two-state model as an extension of MMRT (MMRT-2S) that describes the data along with a convenient approximation with linear temperature dependence of the activation heat capacity (MMRT-1L) that can be used where fewer data points are available. Our model rationalizes disparate behavior seen for MalL and previous results for a thermophilic alcohol dehydrogenase and is consistent with a raft of data for other enzymes. Our model can be used to characterize the conformational changes required for enzyme catalysis and provides insights into the role of cooperative conformational changes in transition state stabilization that are accompanied by changes in heat capacity for the system along the reaction coordinate. TLCs are likely to be of wide importance in understanding the temperature dependence of enzyme activity and other aspects of enzyme catalysis.

A three-level regulatory mechanism of the aldo-keto reductase subfamily AKR12D

Modulation of protein function through allosteric regulation is central in biology, but biomacromolecular systems involving multiple subunits and ligands may exhibit complex regulatory mechanisms at different levels, which remain poorly understood. Here, we discover an aldo-keto reductase termed AKRtyl and present its three-level regulatory mechanism. Specifically, by combining steady-state and transient kinetics, X-ray crystallography and molecular dynamics simulation, we demonstrate that AKRtyl exhibits a positive synergy mediated by an unusual Monod-Wyman-Changeux (MWC) paradigm of allosteric regulation at low concentrations of the cofactor NADPH, but an inhibitory effect at high concentrations is observed. While the substrate tylosin binds at a remote allosteric site with positive cooperativity. We further reveal that these regulatory mechanisms are conserved in AKR12D subfamily, and that substrate cooperativity is common in AKRs across three kingdoms of life. This work provides an intriguing example for understanding complex allosteric regulatory networks.

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.

Why classical receptor theory, which ignores allostery, can effectively measure the strength of an allosteric effect as agonist's efficacy

BACKGROUND AND PURPOSE

The classical theory of receptor action has been used for decades as a powerful tool to estimate molecular determinants of ligand-induced receptor activation (i.e., affinity and efficacy) from experimentally observable biological responses. However, it is also a well-recognized fact that the receptor-binding and activation mechanisms, and the parameters thereof, described in the classical theory contradict with the modern view of receptor activation based on allosteric principles.

EXPERIMENTAL APPROACH

We used mathematical analysis, along with some numerical simulations, to answer the key question as to what extent the classical theory is compatible-if at all-with the modern understanding of receptor activation.

KEY RESULTS

Here, we showed conclusively that (1) receptor activation equations based on allosteric principles contain the logic of the classical theory in disguise, and therefore, (2) estimates of "intrinsic efficacy" (ε) obtained by means of classical techniques (i.e., null methods or fitting the operational model to concentration-response data) are equivalent to the allosteric coupling factors that represent the molecular efficacy of ligands.

CONCLUSION AND IMPLICATIONS

Thus, we conclude that despite the justified criticisms it has received so far, the classical theory may continue to be useful in estimating ligand efficacy from experimental data, if used properly. Here, we also provide rigorous criteria for the proper use of the theory. These findings not only have implications for ligand classification but also resolve some long lasting discussions in the field of bias agonism in GPCR, which requires reasonable estimates of relative ligand efficacies at different signalling pathways.

Evaluating sheep hemoglobins with MD simulations as an animal model for sickle cell disease

Sickle cell disease (SCD) affects millions worldwide, yet there are few therapeutic options. To develop effective treatments, preclinical models that recapitulate human physiology and SCD pathophysiology are needed. SCD arises from a single Glu-to-Val substitution at position 6 in the β subunit of hemoglobin (Hb), promoting Hb polymerization and subsequent disease. Sheep share important physiological and developmental characteristics with humans, including the same developmental pattern of fetal to adult Hb switching. Herein, we investigated whether introducing the SCD mutation into the sheep β-globin locus would recapitulate SCD's complex pathophysiology by generating high quality SWISS-MODEL sheep Hb structures and performing MD simulations of normal/sickle human (huHbA/huHbS) and sheep (shHbB/shHbS) Hb, establishing how accurately shHbS mimics huHbS behavior. shHbS, like huHbS, remained stable with low RMSD, while huHbA and shHbB had higher and fluctuating RMSD. shHbB and shHbS also behaved identically to huHbA and huHbS with respect to β2-Glu6 and β1-Asp73 (β1-Asn72 in sheep) solvent interactions. These data demonstrate that introducing the single SCD-causing Glu-to-Val substitution into sheep β-globin causes alterations consistent with the Hb polymerization that drives RBC sickling, supporting the development of a SCD sheep model to pave the way for alternative cures for this debilitating, globally impactful disease.

Computing allostery: from the understanding of biomolecular regulation and the discovery of cryptic sites to molecular design

The concept of allostery has become a central tenet in the study of biological systems. In parallel, the discovery of allosteric drugs is generating new opportunities to selectively modulate difficult targets involved in pathologic mechanisms. Molecular simulations can provide atomistically detailed insight into the processes involved in allosteric regulation and signaling, and at the same time, they have the potential to unveil regulatory hotspots or cryptic sites that are not immediately evident from the analysis of static structures. In this context, computational approaches should be able to connect the study of allosteric regulation at different scales to the possibility of leveraging this knowledge to expand the chemical space of new, active drugs. Here, we will discuss recent advances in the study of allosteric regulation via computational methods and connect the mechanistic knowledge generated to the possibility of designing new small molecules that can tweak the functions of their receptors.

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.

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.

Single-Molecule Reaction-Diffusion

We propose to capture reaction-diffusion on a molecule-by-molecule basis from the fastest acquirable timescale, namely individual photon arrivals. We illustrate our method on intrinsically disordered human proteins, the linker histone H1.0 as well as its chaperone prothymosin α , as these diffuse through an illuminated confocal spot and interact forming larger ternary complexes on millisecond timescales. Most importantly, single-molecule reaction-diffusion, smRD, reveals single molecule properties without trapping or otherwise confining molecules to surfaces. We achieve smRD within a Bayesian paradigm and term our method Bayes-smRD. Bayes-smRD is further free of the average, bulk, results inherent to the analysis of long photon arrival traces by fluorescence correlation spectroscopy. In learning from thousands of photon arrivals continuous spatial positions and discrete conformational and photophysical state changes, Bayes-smRD estimates kinetic parameters on a molecule-by-molecule basis with two to three orders of magnitude less data than tools such as fluorescence correlation spectroscopy thereby also dramatically reducing sample photodamage.

New Insights into the Cooperativity and Dynamics of Dimeric Enzymes

A survey of protein databases indicates that the majority of enzymes exist in oligomeric forms, with about half of those found in the UniProt database being homodimeric. Understanding why many enzymes are in their dimeric form is imperative. Recent developments in experimental and computational techniques have allowed for a deeper comprehension of the cooperative interactions between the subunits of dimeric enzymes. This review aims to succinctly summarize these recent advancements by providing an overview of experimental and theoretical methods, as well as an understanding of cooperativity in substrate binding and the molecular mechanisms of cooperative catalysis within homodimeric enzymes. Focus is set upon the beneficial effects of dimerization and cooperative catalysis. These advancements not only provide essential case studies and theoretical support for comprehending dimeric enzyme catalysis but also serve as a foundation for designing highly efficient catalysts, such as dimeric organic catalysts. Moreover, these developments have significant implications for drug design, as exemplified by Paxlovid, which was designed for the homodimeric main protease of SARS-CoV-2.

Insight into the role of the Bateman domain at the molecular and physiological levels through engineered IMP dehydrogenases

Inosine 5'-monophosphate (IMP) dehydrogenase (IMPDH) is an ubiquitous enzyme that catalyzes the NAD+ -dependent oxidation of inosine 5'-monophosphate into xanthosine 5'-monophosphate. This enzyme is formed of two distinct domains, a core domain where the catalytic reaction occurs, and a less-conserved Bateman domain. Our previous studies gave rise to the classification of bacterial IMPDHs into two classes, according to their oligomeric and kinetic properties. MgATP is a common effector but cause to different effects when it binds within the Bateman domain: it is either an allosteric activator for Class I IMPDHs or a modulator of the oligomeric state for Class II IMPDHs. To get insight into the role of the Bateman domain in the dissimilar properties of the two classes, deleted variants of the Bateman domain and chimeras issued from the interchange of the Bateman domain between the three selected IMPDHs have been generated and characterized using an integrative structural biology approach. Biochemical, biophysical, structural, and physiological studies of these variants unveil the Bateman domain as being the carrier of the molecular behaviors of both classes.

From Microstates to Macrostates in the Conformational Dynamics of GroEL: A Single-Molecule Förster Resonance Energy Transfer Study

The chaperonin GroEL is a multisubunit molecular machine that assists in protein folding in the Escherichia coli cytosol. Past studies have shown that GroEL undergoes large allosteric conformational changes during its reaction cycle. Here, we report single-molecule Förster resonance energy transfer measurements that directly probe the conformational transitions of one subunit within GroEL and its single-ring variant under equilibrium conditions. We find that four microstates span the conformational manifold of the protein and interconvert on the submillisecond time scale. A unique set of relative populations of these microstates, termed a macrostate, is obtained by varying solution conditions, e.g., adding different nucleotides or the cochaperone GroES. Strikingly, ATP titration studies demonstrate that the partition between the apo and ATP-ligated conformational macrostates traces a sigmoidal response with a Hill coefficient similar to that obtained in bulk experiments of ATP hydrolysis. These coinciding results from bulk measurements for an entire ring and single-molecule measurements for a single subunit provide new evidence for the concerted allosteric transition of all seven subunits.

Shadow enhancers mediate trade-offs between transcriptional noise and fidelity

Enhancers are stretches of regulatory DNA that bind transcription factors (TFs) and regulate the expression of a target gene. Shadow enhancers are two or more enhancers that regulate the same target gene in space and time and are associated with most animal developmental genes. These multi-enhancer systems can drive more consistent transcription than single enhancer systems. Nevertheless, it remains unclear why shadow enhancer TF binding sites are distributed across multiple enhancers rather than within a single large enhancer. Here, we use a computational approach to study systems with varying numbers of TF binding sites and enhancers. We employ chemical reaction networks with stochastic dynamics to determine the trends in transcriptional noise and fidelity, two key performance objectives of enhancers. This reveals that while additive shadow enhancers do not differ in noise and fidelity from their single enhancer counterparts, sub- and superadditive shadow enhancers have noise and fidelity trade-offs not available to single enhancers. We also use our computational approach to compare the duplication and splitting of a single enhancer as mechanisms for the generation of shadow enhancers and find that the duplication of enhancers can decrease noise and increase fidelity, although at the metabolic cost of increased RNA production. A saturation mechanism for enhancer interactions similarly improves on both of these metrics. Taken together, this work highlights that shadow enhancer systems may exist for several reasons: genetic drift or the tuning of key functions of enhancers, including transcription fidelity, noise and output.

Perspective: How Fast Dynamics Affect Slow Function in Protein Machines

Internal motions in proteins take place on a broad range of time- and space-scales. The potential roles of these dynamics in the biochemical functions of proteins have intrigued biophysicists for many years, and multiple mechanisms to couple motions to function have been proposed. Some of these mechanisms have relied on equilibrium concepts. For example, the modulation of dynamics was proposed to change the entropy of a protein, hence affecting processes such as binding. This so-called dynamic allostery scenario has been demonstrated in several recent experiments. Perhaps even more intriguing may be models that involve out-of-equilibrium operation, which by necessity require the input of energy. We discuss several recent experimental studies that expose such potential mechanisms for coupling dynamics and function. In Brownian ratchets, for example, directional motion is promoted by switching a protein between two free energy surfaces. An additional example involves the effect of microsecond domain-closure dynamics of an enzyme on its much slower chemical cycle. These observations lead us to propose a novel two-time-scale paradigm for the activity of protein machines: fast equilibrium fluctuations take place on the microsecond-millisecond time scale, while on a slower time scale, free energy is invested in order to push the system out of equilibrium and drive functional transitions. Motions on the two time scales affect each other and are essential for the overall function of these machines.

Catalytic NO Reduction by Noble-Metal-Free Vanadium-Aluminum Oxide Cluster Anions

By using state-of-the-art mass spectrometry and guided by the newly discovered single-electron mechanism (SEM; e.g., Ti³⁺ + 2NO → Ti⁴⁺-O^•- + N₂O), we determined experimentally that the vanadium-aluminum oxide clusters V_4-xAl_xO_10-x^- (x = 1-3) can catalyze the reduction of NO by CO and substantiated theoretically that the SEM still prevails in driving the catalysis. This finding marks an important step in cluster science in which a noble metal had been demonstrated to be indispensable in NO activation mediated by heteronuclear metal clusters. The results provide new insights into the SEM in which active V-Al cooperative communication favors the transfer of an unpaired electron from the V atom to NO attached to the Al atom on which the reduction reaction actually takes place. This study provides a clear picture for improving our understanding of related heterogeneous catalysis, and the electron hopping behavior induced by NO adsorption could be a fundamental chemistry for driving NO reduction.

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.

Biochemical methods to map and quantify allosteric motions in human glucokinase

Allosteric regulation of protein function is ubiquitous in biology. Allostery originates from ligand-mediated alterations in polypeptide structure and/or dynamics, which produce a cooperative kinetic or thermodynamic response to changing ligand concentrations. Establishing a mechanistic description of individual allosteric events requires both mapping the relevant changes in protein structure and quantifying the rates of differential conformational dynamics in the absence and presence of effectors. In this chapter, we describe three biochemical approaches to understand the dynamic and structural signatures of protein allostery using the well-established cooperative enzyme glucokinase as a case study. The combined application of pulsed proteolysis, biomolecular nuclear magnetic resonance spectroscopy and hydrogen-deuterium exchange mass spectrometry offers complementary information that can used to establish molecular models for allosteric proteins, especially when differential protein dynamics are involved.

Straightforward model construction and analysis of multicomponent biomolecular systems in equilibrium

Mathematical modelling of molecular systems can be extremely helpful in elucidating complex phenomena in (bio)chemistry. However, equilibrium conditions in systems consisting of more than two components, such as for molecular glues bound to two proteins, can typically not be analytically determined without assumptions and (semi-)numerical models are not trivial to derive by the non-expert. Here we present a framework for equilibrium models, geared towards molecular glues and other contemporary multicomponent chemical biology challenges. The framework utilizes a general derivation method capable of generating custom mass-balance models for equilibrium conditions of complex molecular systems, based on the simple, reversible biomolecular reactions describing these systems. Several chemical biology concepts are revisited via the framework to demonstrate the simplicity, generality and validity of the approach. The ease of use of the framework and the ability to both analyze systems and gain additional insights in the underlying parameters driving equilibria formation strongly aids the analysis and understanding of biomolecular systems. New directions for research and analysis are brought forward based on the model formation and system and parameter analysis. This conceptual framework severely reduces the time and expertise requirements which currently impede the broad integration of such valuable equilibrium models into molecular glue development and chemical biology research.

A biophysical framework for double-drugging kinases

Orthosteric inhibition of kinases has been challenging due to the conserved active site architecture of kinases and emergence of resistance mutants. Simultaneous inhibition of distant orthosteric and allosteric sites, which we refer to as "double-drugging", has recently been shown to be effective in overcoming drug resistance. However, detailed biophysical characterization of the cooperative nature between orthosteric and allosteric modulators has not been undertaken. Here, we provide a quantitative framework for double-drugging of kinases employing isothermal titration calorimetry, Förster resonance energy transfer, coupled-enzyme assays, and X-ray crystallography. We discern positive and negative cooperativity for Aurora A kinase (AurA) and Abelson kinase (Abl) with different combinations of orthosteric and allosteric modulators. We find that a conformational equilibrium shift is the main principle governing this cooperative effect. Notably, for both kinases, we find a synergistic decrease of the required orthosteric and allosteric drug dosages when used in combination to inhibit kinase activities to clinically relevant inhibition levels. X-ray crystal structures of the doubledrugged kinase complexes reveal the molecular principles underlying the cooperative nature of double-drugging AurA and Abl with orthosteric and allosteric inhibitors. Finally, we observe the first fully-closed conformation of Abl when bound to a pair of positively cooperative orthosteric and allosteric modulators, shedding light onto the puzzling abnormality of previously solved closed Abl structures. Collectively, our data provide mechanistic and structural insights into rational design and evaluation of doubledrugging strategies.

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.

Glycomimetic Peptides as Therapeutic Tools

The entry of peptides into glycobiology has led to the development of a unique class of therapeutic tools. Although numerous and well-known peptides are active as endocrine regulatory factors that bind to specific receptors, and peptides have been used extensively as epitopes for vaccine production, the use of peptides that mimic sugars as ligands of lectin-type receptors has opened a unique approach to modulate activity of immune cells. Ground-breaking work that initiated the use of peptides as tools for therapy identified sugar mimetics by screening phage display libraries. The peptides that have been discovered show significant potential as high-avidity, therapeutic tools when synthesized as multivalent structures. Advantages of peptides over sugars as drugs for immune modulation will be illustrated in this review.

Correlated Evolution of Low-Frequency Vibrations and Function in Enzymes

Previous studies of the flexibility of ancestral proteins suggest that proteins evolve their function by altering their native state ensemble. Here, we propose a more direct method to analyze such changes during protein evolution by comparing thermally activated vibrations at frequencies below 6 THz, which report on the dynamics of collective protein modes. We analyzed the backbone vibrational density of states of ancestral and extant β-lactamases and thioredoxins and observed marked changes in the vibrational spectrum in response to evolution. Coupled with previously observed changes in protein flexibility, the observed shifts of vibrational mode densities suggest that protein dynamics and dynamical allostery are critical factors for the evolution of enzymes with specialized catalytic and biophysical properties.

Exploration of Computational Aids for Effective Drug Designing and Management of Viral Diseases: A Comprehensive Review

BACKGROUND

Microbial diseases, specifically originating from viruses are the major cause of human mortality all over the world. The current COVID-19 pandemic is a case in point, where the dynamics of the viral-human interactions are still not completely understood, making its treatment a case of trial and error. Scientists are struggling to devise a strategy to contain the pandemic for over a year and this brings to light the lack of understanding of how the virus grows and multiplies in the human body.

METHODS

This paper presents the perspective of the authors on the applicability of computational tools for deep learning and understanding of host-microbe interaction, disease progression and management, drug resistance and immune modulation through in silico methodologies which can aid in effective and selective drug development. The paper has summarized advances in the last five years. The studies published and indexed in leading databases have been included in the review.

RESULTS

Computational systems biology works on an interface of biology and mathematics and intends to unravel the complex mechanisms between the biological systems and the inter and intra species dynamics using computational tools, and high-throughput technologies developed on algorithms, networks and complex connections to simulate cellular biological processes.

CONCLUSION

Computational strategies and modelling integrate and prioritize microbial-host interactions and may predict the conditions in which the fine-tuning attenuates. These microbial-host interactions and working mechanisms are important from the aspect of effective drug designing and fine- tuning the therapeutic interventions.

Fullerene Complexation in a Hydrogen-Bonded Porphyrin Receptor via Induced-Fit: Cooperative Action of Tautomerization and C-H···π Interactions

A supramolecular chiral hydrogen-bonded tetrameric aggregate possessing a large cavity and tetraarylporphyrin substituents was assembled using alternating 4H- and 2H-bonds between ureidopyrimidinone and isocytosine units, respectively. The aggregation mode was rationally shifted from social to narcissistic self-sorting by changing urea substituent size only. The H-bonded tetramer forms a strong complex with C₆₀ guest, at the same time undergoing remarkable structural changes. Namely, the cavity adjusts to the guest via keto-to-enol tautomerization of the ureidopyrimidinone unit and as a result, porphyrin substituents move apart from each other in a scissor blade-like opening fashion. The rearrangement is accompanied by C-H···π interaction between the alkyl solubilizing groups and the nearby placed porphyrin π-systems. The latter interaction was found to be crucial for the guest complexation event, providing energetic compensation for otherwise costly tautomerization. We showed that only the systems possessing sufficiently long alkyl chains capable of interacting with a porphyrin ring are able to form a complex with C₆₀. The structural rearrangement of the tetramer was quantitatively characterized by electron paramagnetic resonance pulsed dipolar spectroscopy measurements using photogenerated triplets of porphyrin and C₆₀ as spin probes. Further exploring the C-H···π interaction as a decisive element for the C₆₀ recognition, we investigated the guest-induced self-sorting phenomenon using scrambled tetramer assemblies composed of two types of monomers possessing alkyl chains of different lengths. The presence of the fullerene guest has enabled the selective scavenging of monomers capable of C-H···π interaction to form homo-tetrameric aggregates.

A perspective on the discovery of enzyme activators

Enzyme activation remains a largely under-represented and poorly exploited area of drug discovery despite some key literature examples of the successful application of enzyme activators by various mechanisms and their importance in a wide range of therapeutic interventions. Here we describe the background nomenclature, present the current position of this field of drug discovery and discuss the challenges of hit identification for enzyme activation, as well as our perspectives on the approaches needed to overcome these challenges in early drug discovery.

Nonequilibrium Modeling of the Elementary Step in PDZ3 Allosteric Communication

While allostery is of paramount importance for protein signaling and regulation, the underlying dynamical process of allosteric communication is not well understood. The PDZ3 domain represents a prime example of an allosteric single-domain protein, as it features a well-established long-range coupling between the C-terminal α₃-helix and ligand binding. In an intriguing experiment, Hamm and co-workers employed photoswitching of the α₃-helix to initiate a conformational change of PDZ3 that propagates from the C-terminus to the bound ligand within 200 ns. Performing extensive nonequilibrium molecular dynamics simulations, the modeling of the experiment reproduces the measured time scales and reveals a detailed picture of the allosteric communication in PDZ3. In particular, a correlation analysis identifies a network of contacts connecting the α₃-helix and the core of the protein, which move in a concerted manner. Representing a one-step process and involving direct α₃-ligand contacts, this cooperative transition is considered as the elementary step in the propagation of conformational change.

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.

Contact-Mediated Retinal-Opsin Coupling Enables Proton Pumping in Gloeobacter Rhodopsin

When a chromophore embedded in a photoreceptive protein undergoes a reaction upon photoexcitation, the photoreaction triggers structural changes in the protein moiety that are necessary for the function of the protein. It is thus essential to elucidate the coupling between the chromophore and protein moiety to understand the functional mechanism for photoreceptive proteins, but the mechanism by which this coupling occurs remains poorly understood. Here, we show that nonbonded atomic contacts play an essential role in driving functionally important structural changes following photoisomerization of the chromophore in Gloeobacter rhodopsin (GR). Time-resolved ultraviolet resonance Raman spectroscopy revealed that the substitution of Trp222, which contacts with methyl groups of the retinal chromophore, with a Phe residue reduced the extent of structural change. The proton-pumping activity of the GR mutant was as small as 9% of that of the wild type. Time-resolved visible absorption and resonance Raman spectra showed that the photocycle of the mutant proceeded to the L intermediate following the all-trans to 13-cis photoisomerization step but did not result in the deprotonation of the chromophore. The present results demonstrate that the atomic contacts between the chromophore and the Trp222 side chain induce the structural changes necessary for proton transfer. The requirement for dense atomic packing in a protein structure for the efficient propagation of structural changes through a coupling mechanism is discussed.

CoDNaS-Q: a database of conformational diversity of the native state of proteins with quaternary structure

Summary

A collection of conformers that exist in a dynamical equilibrium defines the native state of a protein. The structural differences between them describe their conformational diversity, a defining characteristic of the protein with an essential role in multiple cellular processes. Since most proteins carry out their functions by assembling into complexes, we have developed CoDNaS-Q, the first online resource to explore conformational diversity in homooligomeric proteins. It features a curated collection of redundant protein structures with known quaternary structure. CoDNaS-Q integrates relevant annotations that allow researchers to identify and explore the extent and possible reasons of conformational diversity in homooligomeric protein complexes.

Availability and Implementation

CoDNaS-Q is freely accessible at http://ufq.unq.edu.ar/codnasq/ or https://codnas-q.bioinformatica.org/home. The data can be retrieved from the website. The source code of the database can be downloaded from https://github.com/SfrRonaldo/codnas-q.

Supplementary information

Supplementary data are available at Bioinformatics online.

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.

Xenobiotics Delivered by Electronic Nicotine Delivery Systems: Potential Cellular and Molecular Mechanisms on the Pathogenesis of Chronic Kidney Disease

Chronic kidney disease (CKD) is characterized as sustained damage to the renal parenchyma, leading to impaired renal functions and gradually progressing to end-stage renal disease (ESRD). Diabetes mellitus (DM) and arterial hypertension (AH) are underlying diseases of CKD. Genetic background, lifestyle, and xenobiotic exposures can favor CKD onset and trigger its underlying diseases. Cigarette smoking (CS) is a known modified risk factor for CKD. Compounds from tobacco combustion act through multi-mediated mechanisms that impair renal function. Electronic nicotine delivery systems (ENDS) consumption, such as e-cigarettes and heated tobacco devices, is growing worldwide. ENDS release mainly nicotine, humectants, and flavorings, which generate several byproducts when heated, including volatile organic compounds and ultrafine particles. The toxicity assessment of these products is emerging in human and experimental studies, but data are yet incipient to achieve truthful conclusions about their safety. To build up the knowledge about the effect of currently employed ENDS on the pathogenesis of CKD, cellular and molecular mechanisms of ENDS xenobiotic on DM, AH, and kidney functions were reviewed. Unraveling the toxic mechanisms of action and endpoints of ENDS exposures will contribute to the risk assessment and implementation of proper health and regulatory interventions.

Large-Scale Ligand Perturbations of the Protein Conformational Landscape Reveal State-Specific Interaction Hotspots

Protein flexibility is important for ligand binding but often ignored in drug design. Considering proteins as ensembles rather than static snapshots creates opportunities to target dynamic proteins that lack FDA-approved drugs, such as the human chaperone, heat shock protein 90 (Hsp90). Hsp90α accommodates ligands with a dynamic lid domain, yet no comprehensive analysis relating lid conformations to ligand properties is available. To date, ∼300 ligand-bound Hsp90α crystal structures are deposited in the Protein Data Bank, which enables us to consider ligand binding as a perturbation of the protein conformational landscape. By estimating binding site volumes, we classified structures into distinct major and minor lid conformations. Supported by retrospective docking, each conformation creates unique hotspots that bind chemically distinguishable ligands. Clustering revealed insightful exceptions and the impact of crystal packing. Overall, Hsp90α’s plasticity provides a cautionary tale of overinterpreting individual crystal structures and motivates an ensemble-based view of drug design.

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.

Prediction and confirmation of a switch-like region within the N-terminal domain of hSIRT1

Many proteins display conformational changes resulting from allosteric regulation. Often only a few residues are crucial in conveying these structural and functional allosteric changes. These regions that undergo a significant change in structure upon receiving an input signal, such as molecular recognition, are defined as switch-like regions. Identifying these key residues within switch-like regions can help elucidate the mechanism of allosteric regulation and provide guidance for synthetic regulation. In this study, we combine a novel computational workflow with biochemical methods to identify a switch-like region in the N-terminal domain of human SIRT1 (hSIRT1), a lysine deacetylase that plays important roles in regulating cellular pathways. Based on primary sequence, computational methods predicted a region between residues 186-193 in hSIRT1 to exhibit switch-like behavior. Mutations were then introduced in this region and the resulting mutants were tested for allosteric reactions to resveratrol, a known hSIRT1 allosteric regulator. After fine-tuning the mutations based on comparison of known secondary structures, we were able to pinpoint M193 as the residue essential for allosteric regulation, likely by communicating the allosteric signal. Mutation of this residue maintained enzyme activity but abolished allosteric regulation by resveratrol. Our findings suggest a method to predict switch-like regions in allosterically regulated enzymes based on the primary sequence. If further validated, this could be an efficient way to identify key residues in enzymes for therapeutic drug targeting and other applications.

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.

Structural-Energetic Basis for Coupling between Equilibrium Fluctuations and Phosphorylation in a Protein Native Ensemble

The functioning of proteins is intimately tied to their fluctuations in the native ensemble. The structural-energetic features that determine fluctuation amplitudes and hence the shape of the underlying landscape, which in turn determine the magnitude of the functional output, are often confounded by multiple variables. Here, we employ the FF1 domain from human p190A RhoGAP protein as a model system to uncover the molecular basis for phosphorylation of a buried tyrosine, which is crucial to the transcriptional activity associated with transcription factor TFII-I. Combining spectroscopy, calorimetry, statistical-mechanical modeling, molecular simulations, and in vitro phosphorylation assays, we show that the FF1 domain samples a diverse array of conformations in its native ensemble, some of which are phosphorylation-competent. Upon eliminating unfavorable charge-charge interactions through a single charge-reversal (K53E) or charge-neutralizing (K53Q) mutation, we observe proportionately lower phosphorylation extents due to the altered structural coupling, damped equilibrium fluctuations, and a more compact native ensemble. We thus establish a conformational selection mechanism for phosphorylation in the FF1 domain with K53 acting as a "gatekeeper", modulating the solvent exposure of the buried tyrosine. Our work demonstrates the role of unfavorable charge-charge interactions in governing functional events through the modulation of native ensemble characteristics, a feature that could be prevalent in ordered protein domains.

A Framework for Investigating Rules of Life by Establishing Zones of Influence

The incredible complexity of biological processes across temporal and spatial scales hampers defining common underlying mechanisms driving the patterns of life. However, recent advances in sequencing, big data analysis, machine learning, and molecular dynamics simulation have renewed the hope and urgency of finding potential hidden rules of life. There currently exists no framework to develop such synoptic investigations. Some efforts aim to identify unifying rules of life across hierarchical levels of time, space, and biological organization, but not all phenomena occur across all the levels of these hierarchies. Instead of identifying the same parameters and rules across levels, we posit that each level of a temporal and spatial scale and each level of biological organization has unique parameters and rules that may or may not predict outcomes in neighboring levels. We define this neighborhood, or the set of levels, across which a rule functions as the zone of influence. Here, we introduce the zone of influence framework and explain using three examples: (a) randomness in biology, where we use a Poisson process to describe processes from protein dynamics to DNA mutations to gene expressions, (b) island biogeography, and (c) animal coloration. The zone of influence framework may enable researchers to identify which levels are worth investigating for a particular phenomenon and reframe the narrative of searching for a unifying rule of life to the investigation of how, when, and where various rules of life operate.

Allostery and protein plasticity: the keystones for bacterial signaling and regulation

Bacteria sense intracellular and environmental signals using an array of proteins as antennas. The information is transmitted from such sensory modules to other protein domains that act as output effectors. Sensor and effector can be part of the same polypeptide or instead be separate diffusible proteins that interact specifically. The output effector modules regulate physiologic responses, allowing the cells to adapt to the varying conditions. These biological machineries are known as signal transduction systems (STSs). Despite the captivating architectural diversity exhibited by STS proteins, a universal feature is their allosteric regulation: signal binding at one site modifies the activity at a physically distant site. Allostery requires protein plasticity, precisely encoded within their 3D structures, and implicating programmed molecular motions. This review summarizes how STS proteins connect stimuli to specific responses by exploiting allostery and protein plasticity. Illustrative examples spanning a wide variety of protein folds will focus on one- and two-component systems (TCSs). The former encompass the entire transmission route within a single polypeptide, whereas TCSs have evolved as separate diffusible proteins that interact specifically, sometimes including additional intermediary proteins in the pathway. Irrespective of their structural diversity, STS proteins are able to modulate their own molecular motions, which can be relatively slow, rigid-body movements, all the way to fast fluctuations in the form of macromolecular flexibility, thus spanning a continuous protein dynamics spectrum. In sum, STSs rely on allostery to steer information transmission, going from simple two-state switching to rich multi-state conformational order/disorder transitions.

Symmetry of Post-Translational Modifications in a Human Enzyme

Paraoxonase 2 (PON2) is a member of a small family of human lactonases. Recently, post-translational modifications (PTMs) of PON2 were highlighted, one of which involved the modulation of the enzyme activity. Furthermore, two important single nucleotide polymorphisms (SNPs) involved in type 2 diabetes and its consequences, were found to modulate the enzyme activity as well. The position on the PON2 structural model of both residues corresponding to SNPs and PTMs suggested a symmetry of the molecule. By sequence and structure superposition we were able to confirm this finding. The result will be discussed in light of the evolution of symmetry in biological molecules and their function.

An empirical energy landscape reveals mechanism of proteasome in polypeptide translocation

The ring-like ATPase complexes in the AAA+ family perform diverse cellular functions that require coordination between the conformational transitions of their individual ATPase subunits (Erzberger and Berger, 2006; Puchades et al., 2020). How the energy from ATP hydrolysis is captured to perform mechanical work by these coordinated movements is unknown. In this study, we developed a novel approach for delineating the nucleotide-dependent free-energy landscape (FEL) of the proteasome’s heterohexameric ATPase complex based on complementary structural and kinetic measurements. We used the FEL to simulate the dynamics of the proteasome and quantitatively evaluated the predicted structural and kinetic properties. The FEL model predictions are consistent with a wide range of experimental observations in this and previous studies and suggested novel mechanistic features of the proteasomal ATPases. We find that the cooperative movements of the ATPase subunits result from the design of the ATPase hexamer entailing a unique free-energy minimum for each nucleotide-binding status. ATP hydrolysis dictates the direction of substrate translocation by triggering an energy-dissipating conformational transition of the ATPase complex.

In cells, many biological processes are carried out by large complexes made up of different proteins. These macromolecules act like miniature machines, flexing and moving their various parts to perform their cellular roles. One such complex is the 26S proteasome, which is responsible for recycling other proteins in the cell. The proteasome consists of approximately 31 subunits, including a ring of six ATPase enzymes that provide the complex with the energy it needs to mechanically unfold proteins.

To understand how the proteasome and other large complexes work, researchers need to be able to monitor how their structure changes over time. These dynamics are challenging to probe directly with experiments, but can be assessed using computer simulations which track the movement of individual molecules and atoms. However, currently available computer systems do not have enough power to simulate the dynamics of large protein assemblies, like the 26S proteasome: for example, it would take longer than a thousand years to model how each atom in the complex moves over a timescale in which a biological change would happen (roughly 100ms).

Here, Fang, Hon et al. have developed a new approach to simulate the structural dynamics of the proteasome’s ring of ATPase enzymes. Different known structures of the proteasome were used to identify the range of possible movements and shapes the complex can make. Fang, Hon et al. then used this data to calculate the energy level of each structure – also known as the ‘free energy landscape’ – and the rate of transition between them. This made it possible to simulate how the different ATPase enzymes move within the ring under a wide range of conditions.

The simulated ATPase movements predicted how the proteasome machine would behave during various tasks, including degrading other proteins. Fan, Hon et al. carefully examined these predictions and found that they were consistent with experimental observations, validating their new simulation method.

This work demonstrates the feasibility of simulating the actions of a large protein complex based on its free energy landscape. The results offer important insights into the functional mechanics of the 26S proteasome and related protein machines. Further work may help to simplify this process so the approach can be used to investigate the dynamics of other protein assemblies.

Suppression of cell-cell variation by cooperative interaction of phosphatase and response regulator

In bacterial chemotaxis, the output of chemosensing, the concentration of the response regulator CheY-P that was constantly adjusted by the opposing action of the kinase CheA and the phosphatase CheZ, serves as the input of the ultrasensitive flagellar motor that drives bacterial motility. The steady-state kinase activity exhibits large cell-to-cell variation that may result in similar variation in CheY-P concentration. Here, we found that the in vivo phosphatase activity is highly cooperative with respect to CheY-P concentration, and this suppresses the cell-to-cell variation of CheY-P concentration so that it falls within the operational range of the flagellar motor. Therefore, the cooperativity of the CheZ and CheY-P interaction we identified here provided a mechanism of robust coupling between the output of chemosensing and the input of the flagellar motor. Suppression of cell heterogeneity by cooperativity of protein-protein interaction is likely a common feature in many biological signaling systems.