Hot spots for allosteric regulation on protein surfaces

Statistical Coupling Analysis Predicts Correlated Motions in Dihydrofolate Reductase

The role of dynamics in enzymatic function is a highly debated topic. Dihydrofolate reductase (DHFR), due to its universality and the depth with which it has been studied, is a model system in this debate. Myriad previous works have identified networks of residues in positions near to and remote from the active site that are involved in dynamics and others that are important for catalysis. For example, specific mutations on the Met20 loop in E. coli DHFR (N23PP/S148A) are known to disrupt millisecond-timescale motions and reduce catalytic activity. However, how and if networks of dynamically coupled residues influence the evolution of DHFR is still an unanswered question. In this study, we first identify, by statistical coupling analysis and molecular dynamic simulations, a network of coevolving residues, which possess increased correlated motions. We then go on to show that allosteric communication in this network is selectively knocked down in N23PP/S148A mutant E. coli DHFR. Finally, we identify two sites in the human DHFR sector which may accommodate the Met20 loop double proline mutation while preserving dynamics. These findings strongly implicate protein dynamics as a driving force for evolution.

Engineering of Halide Methyltransferase BxHMT through Dynamic Cross-Correlation Network Analysis

Halide methyltransferases (HMTs) provide an effective way to regenerate S-adenosyl methionine (SAM) from S-adenosyl homocysteine and reactive electrophiles, such as methyl iodide (MeI) and methyl toluene sulfonate (MeOTs). As compared with MeI, the cost-effective unnatural substrate MeOTs can be accessed directly from cheap and abundant alcohols, but shows only limited reactivity in SAM production. In this study, we developed a dynamic cross-correlation network analysis (DCCNA) strategy for quickly identifying hot spots influencing the catalytic efficiency of the enzyme, and applied it to the evolution of HMT from Paraburkholderia xenovorans. Finally, the optimal mutant, M4 (V55T/C125S/L127T/L129P), exhibited remarkable improvement, with a specific activity of 4.08 U/mg towards MeOTs, representing an 82-fold increase as compared to the wild-type (WT) enzyme. Notably, M4 also demonstrated a positive impact on the catalytic ability with other methyl donors. The structural mechanism behind the enhanced enzyme activity was uncovered by molecular dynamics simulations. Our work not only contributes a promising biocatalyst for the regeneration of SAM, but also offers a strategy for efficient enzyme engineering.

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.

Conformational Dynamics, Energetics, and the Divergent Evolution of Allosteric Regulation: The Case of the Yeast MAPK Family

Allosteric mechanisms provide finely-tuned control over signalling proteins. Proteins of the same family may share high sequence identity and structural similarity but show distinct traits of allosteric control and evolutionary divergent regulation. Revealing the determinants of such properties may be important to understand the molecular bases of different regulatory pathways. Herein, we investigate whether and how evolutionarily-divergent traits of allosteric regulation in homologous proteins can be decoded in terms of internal dynamics and interaction networks that support functionally oriented conformations. In this framework, we start from the comparative analysis of the dynamics and energetics of the yeast MAP Kinases (MAPKs) Fus3 and Kss1 in their native basins. Importantly, distinctive dynamic and energetic stabilization features emerge, which can be related to the two proteins' differential ability to be phosphorylated and engage with the allosteric activator Ste5. We then expanded our study to other evolutionarily-related MAPKs. We show that the dynamical and energetical traits defining the distinct regulatory profiles of Fus3 and Kss1 can be traced along their evolutionary tree. Overall, our approach is able to reconnect (latent) allostery with the principal elements of protein structural stabilization and dynamics, showing how allosteric regulation was encrypted in MAPKs structure well before Ste5 appearance.

The genetic landscape of a metabolic interaction

While much prior work has explored the constraints on protein sequence and evolution induced by physical protein-protein interactions, the sequence-level constraints emerging from non-binding functional interactions in metabolism remain unclear. To quantify how variation in the activity of one enzyme constrains the biochemical parameters and sequence of another, we focus on dihydrofolate reductase (DHFR) and thymidylate synthase (TYMS), a pair of enzymes catalyzing consecutive reactions in folate metabolism. We use deep mutational scanning to quantify the growth rate effect of 2696 DHFR single mutations in 3 TYMS backgrounds under conditions selected to emphasize biochemical epistasis. Our data are well-described by a relatively simple enzyme velocity to growth rate model that quantifies how metabolic context tunes enzyme mutational tolerance. Together our results reveal the structural distribution of epistasis in a metabolic enzyme and establish a foundation for the design of multi-enzyme systems.

Deep mutational scanning of Pneumocystis jirovecii dihydrofolate reductase reveals allosteric mechanism of resistance to an antifolate

Pneumocystis jirovecii is a fungal pathogen that causes pneumocystis pneumonia, a disease that mainly affects immunocompromised individuals. This fungus has historically been hard to study because of our inability to grow it in vitro. One of the main drug targets in P. jirovecii is its dihydrofolate reductase (PjDHFR). Here, by using functional complementation of the baker's yeast ortholog, we show that PjDHFR can be inhibited by the antifolate methotrexate in a dose-dependent manner. Using deep mutational scanning of PjDHFR, we identify mutations conferring resistance to methotrexate. Thirty-one sites spanning the protein have at least one mutation that leads to resistance, for a total of 355 high-confidence resistance mutations. Most resistance-inducing mutations are found inside the active site, and many are structurally equivalent to mutations known to lead to resistance to different antifolates in other organisms. Some sites show specific resistance mutations, where only a single substitution confers resistance, whereas others are more permissive, as several substitutions at these sites confer resistance. Surprisingly, one of the permissive sites (F199) is without direct contact to either ligand or cofactor, suggesting that it acts through an allosteric mechanism. Modeling changes in binding energy between F199 mutants and drug shows that most mutations destabilize interactions between the protein and the drug. This evidence points towards a more important role of this position in resistance than previously estimated and highlights potential unknown allosteric mechanisms of resistance to antifolate in DHFRs. Our results offer unprecedented resources for the interpretation of mutation effects in the main drug target of an uncultivable fungal pathogen.

Key interaction networks: Identifying evolutionarily conserved non-covalent interaction networks across protein families

Protein structure (and thus function) is dictated by non-covalent interaction networks. These can be highly evolutionarily conserved across protein families, the members of which can diverge in sequence and evolutionary history. Here we present KIN, a tool to identify and analyze conserved non-covalent interaction networks across evolutionarily related groups of proteins. KIN is available for download under a GNU General Public License, version 2, from https://www.github.com/kamerlinlab/KIN. KIN can operate on experimentally determined structures, predicted structures, or molecular dynamics trajectories, providing insight into both conserved and missing interactions across evolutionarily related proteins. This provides useful insight both into protein evolution, as well as a tool that can be exploited for protein engineering efforts. As a showcase system, we demonstrate applications of this tool to understanding the evolutionary-relevant conserved interaction networks across the class A β-lactamases.

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.

Allosteric communication and signal transduction in proteins

Allostery is one of the cornerstones of biological function, as it plays a fundamental role in regulating protein activity. The modelling of allostery has gradually moved from a conformation-based framework, linked to structural changes, to dynamics-based allostery, whereby the effects of ligand binding propagate via signal transduction from the allosteric site to other regions of the protein via inter-residue interactions. Characterising such allosteric signalling pathways, which do not necessarily lead to conformational changes, has been pursued experimentally and complemented by computational analysis of protein networks to detect subtle dynamic propagation paths. Considering allostery from the perspective of signal transduction broadens the understanding of allosteric mechanisms, underscores the importance of protein topology, and can provide insights into allosteric drug design.

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.

Mechanisms for cardiac calcium pump activation by its substrate and a synthetic allosteric modulator using fluorescence lifetime imaging

The discovery of allosteric modulators is an emerging paradigm in drug discovery, and signal transduction is a subtle and dynamic process that is challenging to characterize. We developed a time-correlated single photon-counting imaging approach to investigate the structural mechanisms for small-molecule activation of the cardiac sarcoplasmic reticulum Ca2+-ATPase, a pharmacologically important pump that transports Ca2+ at the expense of adenosine triphosphate (ATP) hydrolysis. We first tested whether the dissociation of sarcoplasmic reticulum Ca2+-ATPase from its regulatory protein phospholamban is required for small-molecule activation. We found that CDN1163, a validated sarcoplasmic reticulum Ca2+-ATPase activator, does not have significant effects on the stability of the sarcoplasmic reticulum Ca2+-ATPase-phospholamban complex. Time-correlated single photon-counting imaging experiments using the nonhydrolyzable ATP analog β,γ-Methyleneadenosine 5'-triphosphate (AMP-PCP) showed ATP is an allosteric modulator of sarcoplasmic reticulum Ca2+-ATPase, increasing the fraction of catalytically competent structures at physiologically relevant Ca2+ concentrations. Unlike ATP, CDN1163 alone has no significant effects on the Ca2+-dependent shifts in the structural populations of sarcoplasmic reticulum Ca2+-ATPase, and it does not increase the pump's affinity for Ca2+ ions. However, we found that CDN1163 enhances the ATP-mediated modulatory effects to increase the population of catalytically competent sarcoplasmic reticulum Ca2+-ATPase structures. Importantly, this structural shift occurs within the physiological window of Ca2+ concentrations at which sarcoplasmic reticulum Ca2+-ATPase operates. We demonstrated that ATP is both a substrate and modulator of sarcoplasmic reticulum Ca2+-ATPase and showed that CDN1163 and ATP act synergistically to populate sarcoplasmic reticulum Ca2+-ATPase structures that are primed for phosphorylation. This study provides novel insights into the structural mechanisms for sarcoplasmic reticulum Ca2+-ATPase activation by its substrate and a synthetic allosteric modulator.

Pervasive epistasis exposes intramolecular networks in adaptive enzyme evolution

Enzyme evolution is characterized by constant alterations of the intramolecular residue networks supporting their functions. The rewiring of these network interactions can give rise to epistasis. As mutations accumulate, the epistasis observed across diverse genotypes may appear idiosyncratic, that is, exhibit unique effects in different genetic backgrounds. Here, we unveil a quantitative picture of the prevalence and patterns of epistasis in enzyme evolution by analyzing 41 fitness landscapes generated from seven enzymes. We show that >94% of all mutational and epistatic effects appear highly idiosyncratic, which greatly distorted the functional prediction of the evolved enzymes. By examining seemingly idiosyncratic changes in epistasis along adaptive trajectories, we expose several instances of higher-order, intramolecular rewiring. Using complementary structural data, we outline putative molecular mechanisms explaining higher-order epistasis along two enzyme trajectories. Our work emphasizes the prevalence of epistasis and provides an approach to exploring this phenomenon through a molecular lens.

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.

Allosteric modulation of SHP2: Quest from known to unknown

Src homology-2 domain-containing protein tyrosine phosphatase-2 (SHP2) is a key regulatory factor in the cell cycle and its activating mutations play an important role in the development of various cancers, making it an important target for antitumor drugs. Due to the highly conserved amino acid sequence and positively charged nature of the active site of SHP2, it is difficult to discover inhibitors with high affinity for the catalytic site of SHP2 and sufficient cell permeability, making it considered an "undruggable" target. However, the discovery of allosteric regulation mechanisms provides new opportunities for transforming undruggable targets into druggable ones. Given the limitations of orthosteric inhibitors, SHP2 allosteric inhibitors have become a more selective and safer research direction. In this review, we elucidate the oncogenic mechanism of SHP2 and summarize the discovery methods of SHP2 allosteric inhibitors, providing new strategies for the design and improvement of SHP2 allosteric inhibitors.

Kinetic and thermodynamic allostery in the Ras protein family

Allostery, the transfer of information between distant parts of a macromolecule, is a fundamental feature of protein function and regulation. However, allosteric mechanisms are usually not explained by protein structure, requiring information on correlated fluctuations uniquely accessible to molecular simulation. Existing work to extract allosteric pathways from molecular dynamics simulations has focused on thermodynamic correlations. Here, we show how kinetic correlations encode complementary information essential to explain observed variations in allosteric regulation. We applied kinetic and thermodynamic correlation analysis on atomistic simulations of H, K, and NRas isoforms in the apo, GTP, and GDP-bound states of Ras protein, with and without complexing to its downstream effector, Raf. We show that switch I and switch II are the primary components of thermodynamic and kinetic allosteric networks, consistent with the key roles of these two motifs. These networks connect the switches to an allosteric loop recently discovered from a crystal structure of HRas. This allosteric loop is inactive in KRas, but is coupled to the hydrolysis arm switch II in NRas and HRas. We find that the mechanism in the latter two isoforms are thermodynamic and kinetic, respectively. Binding of Raf-RBD further activates thermodynamic allostery in HRas and KRas but has limited effect on NRas. These results indicate that kinetic and thermodynamic correlations are both needed to explain protein function and allostery. These two distinct channels of allosteric regulation, and their combinatorial variability, may explain how subtle mutational differences can lead to diverse regulatory profiles among enzymatic proteins.

Dissecting the Determinants of Domain Insertion Tolerance and Allostery in Proteins

Domain insertion engineering is a promising approach to recombine the functions of evolutionarily unrelated proteins. Insertion of light-switchable receptor domains into a selected effector protein, for instance, can yield allosteric effectors with light-dependent activity. However, the parameters that determine domain insertion tolerance and allostery are poorly understood. Here, an unbiased screen is used to systematically assess the domain insertion permissibility of several evolutionary unrelated proteins. Training machine learning models on the resulting data allow to dissect features informative for domain insertion tolerance and revealed sequence conservation statistics as the strongest indicators of suitable insertion sites. Finally, extending the experimental pipeline toward the identification of switchable hybrids results in opto-chemogenetic derivatives of the transcription factor AraC that function as single-protein Boolean logic gates. The study reveals determinants of domain insertion tolerance and yielded multimodally switchable proteins with unique functional properties.

The Genetic Landscape of a Metabolic Interaction

Enzyme abundance, catalytic activity, and ultimately sequence are all shaped by the need of growing cells to maintain metabolic flux while minimizing accumulation of deleterious intermediates. While much prior work has explored the constraints on protein sequence and evolution induced by physical protein-protein interactions, the sequence-level constraints emerging from non-binding functional interactions in metabolism remain unclear. To quantify how variation in the activity of one enzyme constrains the biochemical parameters and sequence of another, we focused on dihydrofolate reductase (DHFR) and thymidylate synthase (TYMS), a pair of enzymes catalyzing consecutive reactions in folate metabolism. We used deep mutational scanning to quantify the growth rate effect of 2,696 DHFR single mutations in 3 TYMS backgrounds under conditions selected to emphasize biochemical epistasis. Our data are well-described by a relatively simple enzyme velocity to growth rate model that quantifies how metabolic context tunes enzyme mutational tolerance. Together our results reveal the structural distribution of epistasis in a metabolic enzyme and establish a foundation for the design of multi-enzyme systems.

Kinetic Barrier to Enzyme Inhibition Is Manipulated by Dynamical Local Interactions in E. coli DHFR

Dihydrofolate reductase (DHFR) is an important drug target and a highly studied model protein for understanding enzyme dynamics. DHFR's crucial role in folate synthesis renders it an ideal candidate to understand protein function and protein evolution mechanisms. In this study, to understand how a newly proposed DHFR inhibitor, 4'-deoxy methyl trimethoprim (4'-DTMP), alters evolutionary trajectories, we studied interactions that lead to its superior performance over that of trimethoprim (TMP). To elucidate the inhibition mechanism of 4'-DTMP, we first confirmed, both computationally and experimentally, that the relative binding free energy cost for the mutation of TMP and 4'-DTMP is the same, pointing the origin of the characteristic differences to be kinetic rather than thermodynamic. We then employed an interaction-based analysis by focusing first on the active site and then on the whole enzyme. We confirmed that the polar modification in 4'-DTMP induces additional local interactions with the enzyme, particularly, the M20 loop. These changes are propagated to the whole enzyme as shifts in the hydrogen bond networks. To shed light on the allosteric interactions, we support our analysis with network-based community analysis and show that segmentation of the loop domain of inhibitor-bound DHFR must be avoided by a successful inhibitor.

Combining structural and coevolution information to unveil allosteric sites

Understanding allosteric regulation in biomolecules is of great interest to pharmaceutical research and computational methods emerged during the last decades to characterize allosteric coupling. However, the prediction of allosteric sites in a protein structure remains a challenging task. Here, we integrate local binding site information, coevolutionary information, and information on dynamic allostery into a structure-based three-parameter model to identify potentially hidden allosteric sites in ensembles of protein structures with orthosteric ligands. When tested on five allosteric proteins (LFA-1, p38-α, GR, MAT2A, and BCKDK), the model successfully ranked all known allosteric pockets in the top three positions. Finally, we identified a novel druggable site in MAT2A confirmed by X-ray crystallography and SPR and a hitherto unknown druggable allosteric site in BCKDK validated by biochemical and X-ray crystallography analyses. Our model can be applied in drug discovery to identify allosteric pockets.

Light-switchable transcription factors obtained by direct screening in mammalian cells

Optogenetic tools can provide fine spatial and temporal control over many biological processes. Yet the development of new light-switchable protein variants remains challenging, and the field still lacks general approaches to engineering or discovering protein variants with light-switchable biological functions. Here, we adapt strategies for protein domain insertion and mammalian-cell expression to generate and screen a library of candidate optogenetic tools directly in mammalian cells. The approach is based on insertion of the AsLOV2 photoswitchable domain at all possible positions in a candidate protein of interest, introduction of the library into mammalian cells, and light/dark selection for variants with photoswitchable activity. We demonstrate the approach's utility using the Gal4-VP64 transcription factor as a model system. Our resulting LightsOut transcription factor exhibits a > 150-fold change in transcriptional activity between dark and blue light conditions. We show that light-switchable function generalizes to analogous insertion sites in two additional Cys6Zn2 and C2H2 zinc finger domains, providing a starting point for optogenetic regulation of a broad class of transcription factors. Our approach can streamline the identification of single-protein optogenetic switches, particularly in cases where structural or biochemical knowledge is limited.

Engineered allostery in light-regulated LOV-Turbo enables precise spatiotemporal control of proximity labeling in living cells

The incorporation of light-responsive domains into engineered proteins has enabled control of protein localization, interactions and function with light. We integrated optogenetic control into proximity labeling, a cornerstone technique for high-resolution proteomic mapping of organelles and interactomes in living cells. Through structure-guided screening and directed evolution, we installed the light-sensitive LOV domain into the proximity labeling enzyme TurboID to rapidly and reversibly control its labeling activity with low-power blue light. 'LOV-Turbo' works in multiple contexts and dramatically reduces background in biotin-rich environments such as neurons. We used LOV-Turbo for pulse-chase labeling to discover proteins that traffic between endoplasmic reticulum, nuclear and mitochondrial compartments under cellular stress. We also showed that instead of external light, LOV-Turbo can be activated by bioluminescence resonance energy transfer from luciferase, enabling interaction-dependent proximity labeling. Overall, LOV-Turbo increases the spatial and temporal precision of proximity labeling, expanding the scope of experimental questions that can be addressed with proximity labeling.

Turning up the heat mimics allosteric signaling in imidazole-glycerol phosphate synthase

Allosteric drugs have the potential to revolutionize biomedicine due to their enhanced selectivity and protection against overdosage. However, we need to better understand allosteric mechanisms in order to fully harness their potential in drug discovery. In this study, molecular dynamics simulations and nuclear magnetic resonance spectroscopy are used to investigate how increases in temperature affect allostery in imidazole glycerol phosphate synthase. Results demonstrate that temperature increase triggers a cascade of local amino acid-to-amino acid dynamics that remarkably resembles the allosteric activation that takes place upon effector binding. The differences in the allosteric response elicited by temperature increase as opposed to effector binding are conditional to the alterations of collective motions induced by either mode of activation. This work provides an atomistic picture of temperature-dependent allostery, which could be harnessed to more precisely control enzyme function.

Undersampling and the inference of coevolution in proteins

Protein structure, function, and evolution depend on local and collective epistatic interactions between amino acids. A powerful approach to defining these interactions is to construct models of couplings between amino acids that reproduce the empirical statistics (frequencies and correlations) observed in sequences comprising a protein family. The top couplings are then interpreted. Here, we show that as currently implemented, this inference unequally represents epistatic interactions, a problem that fundamentally arises from limited sampling of sequences in the context of distinct scales at which epistasis occurs in proteins. We show that these issues explain the ability of current approaches to predict tertiary contacts between amino acids and the inability to obviously expose larger networks of functionally relevant, collectively evolving residues called sectors. This work provides a necessary foundation for more deeply understanding and improving evolution-based models of proteins.

Engineered allostery in light-regulated LOV-Turbo enables precise spatiotemporal control of proximity labeling in living cells

The incorporation of light-responsive domains into engineered proteins has enabled control of protein localization, interactions, and function with light. We integrated optogenetic control into proximity labeling (PL), a cornerstone technique for high-resolution proteomic mapping of organelles and interactomes in living cells. Through structure-guided screening and directed evolution, we installed the light-sensitive LOV domain into the PL enzyme TurboID to rapidly and reversibly control its labeling activity with low-power blue light. "LOV-Turbo" works in multiple contexts and dramatically reduces background in biotin-rich environments such as neurons. We used LOV-Turbo for pulse-chase labeling to discover proteins that traffick between endoplasmic reticulum, nuclear, and mitochondrial compartments under cellular stress. We also showed that instead of external light, LOV-Turbo can be activated by BRET from luciferase, enabling interaction-dependent PL. Overall, LOV-Turbo increases the spatial and temporal precision of PL, expanding the scope of experimental questions that can be addressed with PL.

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.

Protein dynamics: The future is bright and complicated!

Biological life depends on motion, and this manifests itself in proteins that display motion over a formidable range of time scales spanning from femtoseconds vibrations of atoms at enzymatic transition states, all the way to slow domain motions occurring on micro to milliseconds. An outstanding challenge in contemporary biophysics and structural biology is a quantitative understanding of the linkages among protein structure, dynamics, and function. These linkages are becoming increasingly explorable due to conceptual and methodological advances. In this Perspective article, we will point toward future directions of the field of protein dynamics with an emphasis on enzymes. Research questions in the field are becoming increasingly complex such as the mechanistic understanding of high-order interaction networks in allosteric signal propagation through a protein matrix, or the connection between local and collective motions. In analogy to the solution to the "protein folding problem," we argue that the way forward to understanding these and other important questions lies in the successful integration of experiment and computation, while utilizing the present rapid expansion of sequence and structure space. Looking forward, the future is bright, and we are in a period where we are on the doorstep to, at least in part, comprehend the importance of dynamics for biological function.

Simple mechanisms for the evolution of protein complexity

Proteins are tiny models of biological complexity: specific interactions among their many amino acids cause proteins to fold into elaborate structures, assemble with other proteins into higher-order complexes, and change their functions and structures upon binding other molecules. These complex features are classically thought to evolve via long and gradual trajectories driven by persistent natural selection. But a growing body of evidence from biochemistry, protein engineering, and molecular evolution shows that naturally occurring proteins often exist at or near the genetic edge of multimerization, allostery, and even new folds, so just one or a few mutations can trigger acquisition of these properties. These sudden transitions can occur because many of the physical properties that underlie these features are present in simpler proteins as fortuitous by-products of their architecture. Moreover, complex features of proteins can be encoded by huge arrays of sequences, so they are accessible from many different starting points via many possible paths. Because the bridges to these features are both short and numerous, random chance can join selection as a key factor in explaining the evolution of molecular complexity.

GCAT: A network model of mutational influences between amino acid positions in PSD95pdz3

Proteins exist for more than 3 billion years: proof of a sustainable design. They have mechanisms coping with internal perturbations (e.g., amino acid mutations), which tie genetic backgrounds to diseases or drug therapy failure. One difficulty to grasp these mechanisms is the asymmetry of amino acid mutational impact: a mutation at position i in the sequence, which impact a position j does not imply that the mutation at position j impacts the position i. Thus, to distinguish the influence of the mutation of i on j from the influence of the mutation of j on i, position mutational influences must be represented with directions. Using the X ray structure of the third PDZ domain of PDS-95 (Protein Data Bank 1BE9) and in silico mutations, we build a directed network called GCAT that models position mutational influences. In the GCAT, a position is a node with edges that leave the node (out-edges) for the influences of the mutation of the position on other positions and edges that enter the position (in-edges) for the influences of the mutation of other positions on the position. 1BE9 positions split into four influence categories called G, C, A and T going from positions influencing on average less other positions and influenced on average by less other positions (category C) to positions influencing on average more others positions and influenced on average by more other positions (category T). The four categories depict position neighborhoods in the protein structure with different tolerance to mutations.

Variable Regions of p53 Isoforms Allosterically Hard Code DNA Interaction

Allosteric regulation of protein activity pervades biology as the "second secret of life." We have been examining the allosteric regulation and mutant reactivation of the tumor suppressor protein p53. We have found that generalizing the definition of allosteric effector to include entire proteins and expanding the meaning of binding site to include the interface of a transcription factor with its DNA to be useful in understanding the modulation of protein activity. Here, we cast the variable regions of p53 isoforms as allosteric regulators of p53 interactions with its consensus DNA. We implemented molecular dynamics simulations and our lab's new techniques of molecular dynamics (MD) sectors and MD-Markov state models to investigate the effects of nine naturally occurring splice variant isoforms of p53. We find that all of the isoforms differ from wild type in their dynamic properties and how they interact with the DNA. We consider the implications of these findings on allostery and cancer treatment.

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.

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.

Mutagenic Activation of Glutathione Peroxidase-4: Approaches toward Rational Design of Allosteric Drugs

Glutathione peroxidase 4 (GPX4) reduces lipid hydroperoxides in lipid membranes, effectively inhibiting iron-dependent cell death or ferroptosis. The upregulation of the enzyme by the mutations at residues D21 and D23 has been suggested to be associated with higher protein activity, which confers more protection against neurodegenerative diseases such as Alzheimer's, Parkinson's, and Huntington's diseases. Therefore, it has become an attractive target for treating and preventing neurodegenerative diseases. However, identifying means of mimicking the beneficial effects of these mutations distant from the active site constitutes a formidable challenge in moving toward therapeutics. In this study, we explore using molecular dynamics simulations to computationally map the conformational and energetic landscape of the wild-type GPX4 protein and three mutant variants to identify the allosteric networks of the enzyme. We present the conformational dynamic profile providing the desired signature behavior of the enzyme. We also discuss the implications of these findings for drug design efforts.

Mechanistic Insights into the Long-range Allosteric Regulation of KRAS Via Neurofibromatosis Type 1 (NF1) Scaffold Upon SPRED1 Loading

Allosteric regulation is the most direct and efficient way of regulating protein function, wherein proteins transmit the perturbations at one site to another distinct functional site. Deciphering the mechanism of allosteric regulation is of vital importance for the comprehension of both physiological and pathological events in vivo as well as the rational allosteric drug design. However, it remains challenging to elucidate dominant allosteric signal transduction pathways, especially for large and multi-component protein machineries where long-range allosteric regulation exits. One of the quintessential examples having long-range allosteric regulation is the ternary complex, SPRED1-RAS-neurofibromin type 1 (NF1, a RAS GTPase-activating protein), in which SPRED1 facilitates RAS-GTP hydrolysis by interacting with NF1 at a distal, allosteric site from the RAS binding site. To address the underlying mechanism, we performed extensive Gaussian accelerated molecular dynamics simulations and Markov state model analysis of KRAS-NF1 complex in the presence and absence of SPRED1. Our findings suggested that SPRED1 loading allosterically enhanced KRAS-NF1 binding, but hindered conformational transformation of the NF1 catalytic center for RAS hydrolysis. Moreover, we unveiled the possible allosteric pathways upon SPRED1 binding through difference contact network analysis. This study not only provided an in-depth mechanistic insight into the allosteric regulation of KRAS by SPRED1, but also shed light on the investigation of long-range allosteric regulation among complex macromolecular systems.

Enhancing and inhibitory motifs regulate CD4 activity

CD4⁺ T cells use T cell receptor (TCR)–CD3 complexes, and CD4, to respond to peptide antigens within MHCII molecules (pMHCII). We report here that, through ~435 million years of evolution in jawed vertebrates, purifying selection has shaped motifs in the extracellular, transmembrane, and intracellular domains of eutherian CD4 that enhance pMHCII responses, and covary with residues in an intracellular motif that inhibits responses. Importantly, while CD4 interactions with the Src kinase, Lck, are viewed as key to pMHCII responses, our data indicate that CD4–Lck interactions derive their importance from the counterbalancing activity of the inhibitory motif, as well as motifs that direct CD4–Lck pairs to specific membrane compartments. These results have implications for the evolution and function of complex transmembrane receptors and for biomimetic engineering.

Edge weights in a protein elastic network reorganize collective motions and render long-range sensitivity responses

The effects of inter-residue interactions on protein collective motions are analyzed by comparing two elastic network models (ENM)—structural contact ENM (SC-ENM) and molecular dynamics (MD)-ENM—with the edge weights computed from an all-atom MD trajectory by structure-mechanics statistical learning. A theoretical framework is devised to decompose the eigenvalues of ENM Hessian into contributions from individual springs and to compute the sensitivities of positional fluctuations and covariances to spring constant variation. Our linear perturbation approach quantifies the response mechanisms as softness modulation and orientation shift. All contacts of C α positions in SC-ENM have an identical spring constant by fitting the profile of root-of-mean-squared-fluctuation calculated from an all-atom MD simulation, and the same trajectory data are also used to compute the specific spring constant of each contact as an MD-ENM edge weight. We illustrate that the soft-mode reorganization can be understood in terms of gaining weights along the structural contacts of low elastic strengths and loosing magnitude along those of high rigidities. With the diverse mechanical strengths encoded in protein dynamics, MD-ENM is found to have more pronounced long-range couplings and sensitivity responses with orientation shift identified as a key player in driving the specific residues to have high sensitivities. Furthermore, the responses of perturbing the springs of different residues are found to have asymmetry in the action–reaction relationship. In understanding the mutation effects on protein functional properties, such as long-range communications, our results point in the directions of collective motions as a major effector.

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 evolution of the ancient enzyme, dissimilatory sulfite reductase

Dissimilatory sulfite reductase is an ancient enzyme that has linked the global sulfur and carbon biogeochemical cycles since at least 3.47 Gya. While much has been learned about the phylogenetic distribution and diversity of DsrAB across environmental gradients, far less is known about the structural changes that occurred to maintain DsrAB function as the enzyme accompanied diversification of sulfate/sulfite reducing organisms (SRO) into new environments. Analyses of available crystal structures of DsrAB from Archaeoglobus fulgidus and Desulfovibrio vulgaris, representing early and late evolving lineages, respectively, show that certain features of DsrAB are structurally conserved, including active siro-heme binding motifs. Whether such structural features are conserved among DsrAB recovered from varied environments, including hot spring environments that host representatives of the earliest evolving SRO lineage (e.g., MV2-Eury), is not known. To begin to overcome these gaps in our understanding of the evolution of DsrAB, structural models from MV2.Eury were generated and evolutionary sequence co-variance analyses were conducted on a curated DsrAB database. Phylogenetically diverse DsrAB harbor many conserved functional residues including those that ligate active siro-heme(s). However, evolutionary co-variance analysis of monomeric DsrAB subunits revealed several False Positive Evolutionary Couplings (FPEC) that correspond to residues that have co-evolved despite being too spatially distant in the monomeric structure to allow for direct contact. One set of FPECs corresponds to residues that form a structural path between the two active siro-heme moieties across the interface between heterodimers, suggesting the potential for allostery or electron transfer within the enzyme complex. Other FPECs correspond to structural loops and gaps that may have been selected to stabilize enzyme function in different environments. These structural bioinformatics results suggest that DsrAB has maintained allosteric communication pathways between subunits as SRO diversified into new environments. The observations outlined here provide a framework for future biochemical and structural analyses of DsrAB to examine potential allosteric control of this enzyme.

Targeting allosteric regulation of cancer metabolism

Metabolic reprogramming is observed across all cancer types. Indeed, the success of many classic chemotherapies stems from their targeting of cancer metabolism. Contemporary research in this area has refined our understanding of tumor-specific metabolic mechanisms and has revealed strategies for exploiting these vulnerabilities selectively. Based on this growing understanding, new small-molecule tools and drugs have been developed to study and target tumor metabolism. Here, we highlight allosteric modulation of metabolic enzymes as an attractive mechanism of action for small molecules that target metabolic enzymes. We then discuss the mechanistic insights garnered from their application in cancer studies and highlight the achievements of this approach in targeting cancer metabolism. Finally, we discuss technological advances in drug discovery for allosteric modulators of enzyme activity.

Mapping the energetic and allosteric landscapes of protein binding domains

Allosteric communication between distant sites in proteins is central to biological regulation but still poorly characterized, limiting understanding, engineering and drug development^1-6. An important reason for this is the lack of methods to comprehensively quantify allostery in diverse proteins. Here we address this shortcoming and present a method that uses deep mutational scanning to globally map allostery. The approach uses an efficient experimental design to infer en masse the causal biophysical effects of mutations by quantifying multiple molecular phenotypes-here we examine binding and protein abundance-in multiple genetic backgrounds and fitting thermodynamic models using neural networks. We apply the approach to two of the most common protein interaction domains found in humans, an SH3 domain and a PDZ domain, to produce comprehensive atlases of allosteric communication. Allosteric mutations are abundant, with a large mutational target space of network-altering 'edgetic' variants. Mutations are more likely to be allosteric closer to binding interfaces, at glycine residues and at specific residues connecting to an opposite surface within the PDZ domain. This general approach of quantifying mutational effects for multiple molecular phenotypes and in multiple genetic backgrounds should enable the energetic and allosteric landscapes of many proteins to be rapidly and comprehensively mapped.

A guide to designing photocontrol in proteins: methods, strategies and applications

Light is essential for various biochemical processes in all domains of life. In its presence certain proteins inside a cell are excited, which either stimulates or inhibits subsequent cellular processes. The artificial photocontrol of specifically proteins is of growing interest for the investigation of scientific questions on the organismal, cellular and molecular level as well as for the development of medicinal drugs or biocatalytic tools. For the targeted design of photocontrol in proteins, three major methods have been developed over the last decades, which employ either chemical engineering of small-molecule photosensitive effectors (photopharmacology), incorporation of photoactive non-canonical amino acids by genetic code expansion (photoxenoprotein engineering), or fusion with photoreactive biological modules (hybrid protein optogenetics). This review compares the different methods as well as their strategies and current applications for the light-regulation of proteins and provides background information useful for the implementation of each technique.

Combinatorial Approaches for Efficient Design of Photoswitchable Protein-Protein Interactions as In Vivo Actuators

Light switchable two-component protein dimerization systems offer versatile manipulation and dissection of cellular events in living systems. Over the past 20 years, the field has been driven by the discovery of photoreceptor-based interaction systems, the engineering of light-actuatable binder proteins, and the development of photoactivatable compounds as dimerization inducers. This perspective is to categorize mechanisms and design approaches of these dimerization systems, compare their advantages and limitations, and bridge them to emerging applications. Our goal is to identify new opportunities in combinatorial protein design that can address current engineering challenges and expand in vivo applications.

Known allosteric proteins have central roles in genetic disease

Allostery is a form of protein regulation, where ligands that bind sites located apart from the active site can modify the activity of the protein. The molecular mechanisms of allostery have been extensively studied, because allosteric sites are less conserved than active sites, and drugs targeting them are more specific than drugs binding the active sites. Here we quantify the importance of allostery in genetic disease. We show that 1) known allosteric proteins are central in disease networks, contribute to genetic disease and comorbidities much more than non-allosteric proteins, and there is an association between being allosteric and involvement in disease; 2) they are enriched in many major disease types like hematopoietic diseases, cardiovascular diseases, cancers, diabetes, or diseases of the central nervous system; 3) variants from cancer genome-wide association studies are enriched near allosteric proteins, indicating their importance to polygenic traits; and 4) the importance of allosteric proteins in disease is due, at least partly, to their central positions in protein-protein interaction networks, and less due to their dynamical properties.

Druggable Lipid Binding Sites in Pentameric Ligand-Gated Ion Channels and Transient Receptor Potential Channels

Lipids modulate the function of many ion channels, possibly through direct lipid-protein interactions. The recent outpouring of ion channel structures by cryo-EM has revealed many lipid binding sites. Whether these sites mediate lipid modulation of ion channel function is not firmly established in most cases. However, it is intriguing that many of these lipid binding sites are also known sites for other allosteric modulators or drugs, supporting the notion that lipids act as endogenous allosteric modulators through these sites. Here, we review such lipid-drug binding sites, focusing on pentameric ligand-gated ion channels and transient receptor potential channels. Notable examples include sites for phospholipids and sterols that are shared by anesthetics and vanilloids. We discuss some implications of lipid binding at these sites including the possibility that lipids can alter drug potency or that understanding protein-lipid interactions can guide drug design. Structures are only the first step toward understanding the mechanism of lipid modulation at these sites. Looking forward, we identify knowledge gaps in the field and approaches to address them. These include defining the effects of lipids on channel function in reconstituted systems using asymmetric membranes and measuring lipid binding affinities at specific sites using native mass spectrometry, fluorescence binding assays, and computational approaches.

Extracting phylogenetic dimensions of coevolution reveals hidden functional signals

Despite the structural and functional information contained in the statistical coupling between pairs of residues in a protein, coevolution associated with function is often obscured by artifactual signals such as genetic drift, which shapes a protein's phylogenetic history and gives rise to concurrent variation between protein sequences that is not driven by selection for function. Here, we introduce a background model for phylogenetic contributions of statistical coupling that separates the coevolution signal due to inter-clade and intra-clade sequence comparisons and demonstrate that coevolution can be measured on multiple phylogenetic timescales within a single protein. Our method, nested coevolution (NC), can be applied as an extension to any coevolution metric. We use NC to demonstrate that poorly conserved residues can nonetheless have important roles in protein function. Moreover, NC improved the structural-contact predictions of several coevolution-based methods, particularly in subsampled alignments with fewer sequences. NC also lowered the noise in detecting functional sectors of collectively coevolving residues. Sectors of coevolving residues identified after application of NC were more spatially compact and phylogenetically distinct from the rest of the protein, and strongly enriched for mutations that disrupt protein activity. Thus, our conceptualization of the phylogenetic separation of coevolution provides the potential to further elucidate relationships among protein evolution, function, and genetic diseases.

Microfluidic deep mutational scanning of the human executioner caspases reveals differences in structure and regulation

The human caspase family comprises 12 cysteine proteases that are centrally involved in cell death and inflammation responses. The members of this family have conserved sequences and structures, highly similar enzymatic activities and substrate preferences, and overlapping physiological roles. In this paper, we present a deep mutational scan of the executioner caspases CASP3 and CASP7 to dissect differences in their structure, function, and regulation. Our approach leverages high-throughput microfluidic screening to analyze hundreds of thousands of caspase variants in tightly controlled in vitro reactions. The resulting data provides a large-scale and unbiased view of the impact of amino acid substitutions on the proteolytic activity of CASP3 and CASP7. We use this data to pinpoint key functional differences between CASP3 and CASP7, including a secondary internal cleavage site, CASP7 Q196 that is not present in CASP3. Our results will open avenues for inquiry in caspase function and regulation that could potentially inform the development of future caspase-specific therapeutics.

Physics of biomolecular recognition and conformational dynamics

Biomolecular recognition usually leads to the formation of binding complexes, often accompanied by large-scale conformational changes. This process is fundamental to biological functions at the molecular and cellular levels. Uncovering the physical mechanisms of biomolecular recognition and quantifying the key biomolecular interactions are vital to understand these functions. The recently developed energy landscape theory has been successful in quantifying recognition processes and revealing the underlying mechanisms. Recent studies have shown that in addition to affinity, specificity is also crucial for biomolecular recognition. The proposed physical concept of intrinsic specificity based on the underlying energy landscape theory provides a practical way to quantify the specificity. Optimization of affinity and specificity can be adopted as a principle to guide the evolution and design of molecular recognition. This approach can also be used in practice for drug discovery using multidimensional screening to identify lead compounds. The energy landscape topography of molecular recognition is important for revealing the underlying flexible binding or binding-folding mechanisms. In this review, we first introduce the energy landscape theory for molecular recognition and then address four critical issues related to biomolecular recognition and conformational dynamics: (1) specificity quantification of molecular recognition; (2) evolution and design in molecular recognition; (3) flexible molecular recognition; (4) chromosome structural dynamics. The results described here and the discussions of the insights gained from the energy landscape topography can provide valuable guidance for further computational and experimental investigations of biomolecular recognition and conformational dynamics.

High throughput and quantitative enzymology in the genomic era

Accurate predictions from models based on physical principles are the ultimate metric of our biophysical understanding. Although there has been stunning progress toward structure prediction, quantitative prediction of enzyme function has remained challenging. Realizing this goal will require large numbers of quantitative measurements of rate and binding constants and the use of these ground-truth data sets to guide the development and testing of these quantitative models. Ground truth data more closely linked to the underlying physical forces are also desired. Here, we describe technological advances that enable both types of ground truth measurements. These advances allow classic models to be tested, provide novel mechanistic insights, and place us on the path toward a predictive understanding of enzyme structure and function.

Switching an active site helix in dihydrofolate reductase reveals limits to subdomain modularity

To what degree are individual structural elements within proteins modular such that similar structures from unrelated proteins can be interchanged? We study subdomain modularity by creating 20 chimeras of an enzyme, Escherichia coli dihydrofolate reductase (DHFR), in which a catalytically important, 10-residue α-helical sequence is replaced by α-helical sequences from a diverse set of proteins. The chimeras stably fold but have a range of diminished thermal stabilities and catalytic activities. Evolutionary coupling analysis indicates that the residues of this α-helix are under selection pressure to maintain catalytic activity in DHFR. Reversion to phenylalanine at key position 31 was found to partially restore catalytic activity, which could be explained by evolutionary coupling values. We performed molecular dynamics simulations using replica exchange with solute tempering. Chimeras with low catalytic activity exhibit nonhelical conformations that block the binding site and disrupt the positioning of the catalytically essential residue D27. Simulation observables and in vitro measurements of thermal stability and substrate-binding affinity are strongly correlated. Several E. coli strains with chromosomally integrated chimeric DHFRs can grow, with growth rates that follow predictions from a kinetic flux model that depends on the intracellular abundance and catalytic activity of DHFR. Our findings show that although α-helices are not universally substitutable, the molecular and fitness effects of modular segments can be predicted by the biophysical compatibility of the replacement segment.