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

Mechanistic insight into the mode of inhibition of dietary flavonoids; targeting macrophage migration inhibitory factor

Introduction: The Macrophage Migration Inhibitory Factor (MIF), a key pro-inflammatory mediator, is responsible for modulating immune responses. An array of inflammatory and autoimmune diseases has been linked to the dysregulated activity of MIF. The significance in physiological as well as pathophysiological phenomena underscores the potential of MIF as an attractive target with pharmacological relevance. Extensive research in past has uncovered a number of inhibitors, while the ISO-1, or (S, R)-3-(4-hydroxyphenyl)-4,5-dihydro-5-isoxazole acetic acid methyl ester being recognized as a benchmark standard so far. Recent work by Yang and coworkers identified five promising dietary flavonoids, with superior activity compared to the standard ISO-1. Nevertheless, the exact atomic-level inhibitory mechanism is still elusive. Methods: To improve the dynamic research, and rigorously characterize, and compare molecular signatures of MIF complexes with ISO-1 and flavonoids, principal component analysis (PCA) was linked with molecular dynamics (MD) simulations and binding free energy calculations. Results: The results suggest that by blocking the tautomerase site these small molecule inhibitors could modify the MIF activity by disrupting the intrinsic dynamics in particular functional areas. The stability matrices revealed the average deviation values ranging from 0.27-0.32 nm while the residue level fluctuations indicated that binding of the selected flavonoids confer enhanced stability relative to the ISO-1. Furthermore, the gyration values extracted from the simulated trajectories were found in the range of 1.80-1.83 nm. Discussion: Although all the tested flavonoids demonstrated remarkable results, the one obtained for the potent inhibitors, particularly Morin and Amentoflavone exhibited a good correlation with biological activity. The PCA results featured relatively less variance and constricted conformational landscape than others. The stable ensembles and reduced variation in turns might be the possible reasons for their outstanding performance documented previously. The results from the present exploration provide a comprehensive understanding of the molecular complexes formed by flavonoids and MIF, shedding light on their potential roles and impacts. Future studies on MIF inhibitors may benefit from the knowledge gathered from this investigation.

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.

Exploring the molecular mechanisms of Lrp/AsnC-type transcription regulator DecR, an L-cysteine-responsive feast/famine regulatory protein

The Lrp/AsnC family of transcriptional regulators is commonly found in prokaryotes and is associated with the regulation of amino acid metabolism. However, it remains unclear how the L-cysteine-responsive Lrp/AsnC family regulator perceives and responds to L-cysteine. Here, we try to elucidate the molecular mechanism of the L-cysteine-responsive transcriptional regulator. Through 5'RACE and EMSA, we discovered a 15 bp incompletely complementary pair palindromic sequence essential for DecR binding, which differed slightly from the binding sequence of other Lrp/AsnC transcription regulators. Using alanine scanning, we identified the L-cysteine binding site on DecR and found that different Lrp/AsnC regulators adjust their binding pocket's side-chain residues to accommodate their specific effector. MD simulations were then conducted to explore how ligand binding influences the allosteric behavior of the protein. PCA and in silico docking revealed that ligand binding induced perturbations in the linker region, triggering conformational alterations and leading to the relocalization of the DNA-binding domains, enabling the embedding of the DNA-binding region of DecR into the DNA molecule, thereby enhancing DNA-binding affinity. Our findings can broaden the understanding of the recognition and regulatory mechanisms of the Lrp/AsnC-type transcription factors, providing a theoretical basis for further investigating the molecular mechanisms of other transcription factors.

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

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

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

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

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.

Searching for Structure: Characterizing the Protein Conformational Landscape with Clustering-Based Algorithms

The identification and characterization of the main conformations from a protein population are a challenging and inherently high-dimensional problem. Here, we evaluate the performance of the Secondary sTructural Ensembles with machine LeArning (StELa) double-clustering method, which clusters protein structures based on the relationship between the φ and ψ dihedral angles in a protein backbone and the secondary structure of the protein, thus focusing on the local properties of protein structures. The classification of states as vectors composed of the clusters' indices arising naturally from the Ramachandran plot is followed by the hierarchical clustering of the vectors to allow for the identification of the main features of the corresponding free energy landscape (FEL). We compare the performance of StELa with the established root-mean-squared-deviation (RMSD)-based clustering algorithm, which focuses on global properties of protein structures and with Combinatorial Averaged Transient Structure (CATS), the combinatorial averaged transient structure clustering method based on distributions of the φ and ψ dihedral angle coordinates. Using ensembles of conformations from molecular dynamics simulations of intrinsically disordered proteins (IDPs) of various lengths (tau protein fragments) or short fragments from a globular protein, we show that StELa is the clustering method that identifies many of the minima and relevant energy states around the minima from the corresponding FELs. In contrast, the RMSD-based algorithm yields a large number of clusters that usually cover most of the FEL, thus being unable to distinguish between states, while CATS does not sample well the FELs for long IDPs and fragments from globular proteins.

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.

Exploring the viral protease inhibitor space driven by consensus scoring-based virtual screening

Dengue fever presents a major health concern, and the lack of an effective vaccine or definite therapeutic regimen has led the research community to identify safe-by-design potential targets for drug discovery. Since the association of the NS2B co-factor with the protease domain of NS3 is imperative for the catalytic activity of the enzyme complex, inhibitors blocking their interaction could provide an alternative strategy to combat the dengue virus. In this context, the present study is aimed at exploring computer-assisted modeling of significant physicochemical features required for the inhibition of the dengue virus protease complex. First of all, alanine scanning was utilized to map hot spot residues critical for the association of the two subunits, NS2B and NS3pro, by studying their energy profiles. Then, consensus score-based virtual screening was performed to search through the commercially available chemical datasets. After screening, 1,575 small molecules were moved forward into docking studies to investigate their interactions with crucial interfacial residues (i.e., Tyr23, Lys26, Phe46, and Leu58), with only 233 molecules passing that stage. The top 30 molecules were selected based on a detailed profile of intermolecular interactions. After that, the top five molecules were selected for detailed mechanistic studies via molecular dynamics simulations followed by subsequent binding free energy calculations, principal component analysis in conjunction with free energy landscape. To the best of our knowledge, this is the first systematic and comprehensive investigation to identify protein-protein interaction blockers against the target protein at such a large scale, using integrated computational tools. Our results highlight the enhanced stability and good binding affinities towards the target protein of these compounds, which might act as new scaffolds for NS2B-NS3 protease inhibition. Future studies will be directed to explore the detailed atomistic-based structural and energetic framework of the mutation-induced affinity change between the protease domain of the DENV-2 NS3 protein and its cofactor NS2B. The detailed insight in turn might suggest precise and focused targeted points for the structure-based drug design but the computational cost may be a challenge.

Graphical abstract

Supplementary Information

The online version contains supplementary material available at 10.1007/s40203-023-00174-0.

Activation Loop Plasticity and Active Site Coupling in the MAP Kinase, ERK2

Previous studies of the protein kinase, ERK2, using NMR and hydrogen-exchange measurements have shown changes in dynamics accompanying its activation by phosphorylation. However, knowledge about the conformational motions involved is incomplete. Here, we examined ERK2 using long conventional molecular dynamics (MD) simulations starting from crystal structures of phosphorylated (2P) and unphosphorylated (0P) forms. Individual trajectories were run for (5 to 25) μs, totaling 727 μs. The results show unexpected flexibility of the A-loop, with multiple long-lived (>5 μs) conformational states in both 2P- and 0P-ERK2. Differential contact network and principal component analyses reveal coupling between the A-loop fold and active site dynamics, with evidence for conformational selection in the kinase core of 2P-ERK2 but not 0P-ERK2. Simulations of 2P-ERK2 show A-loop states corresponding to restrained dynamics within the N-lobe, including regions around catalytic residues. One A-loop conformer forms lasting interactions with the L16 segment, leading to reduced RMSF and greater compaction in the active site. By contrast, simulations of 0P-ERK2 reveal excursions of A-loop residues away from the C-lobe, leading to greater active site mobility. Thus, the A-loop in ERK2 switches between distinct conformations that reflect coupling with the active site, possibly via the L16 segment. Crystal packing interactions suggest that lattice contacts with the A-loop may restrain its structural variation in X-ray structures of ERK2. The novel conformational states identified by MD expand our understanding of ERK2 regulation, by linking the activated state of the kinase to reduced dynamics and greater compaction surrounding the catalytic site.

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.

Disease mutations and phosphorylation alter the allosteric pathways involved in autoinhibition of protein phosphatase 2A

Mutations in protein phosphatase 2A (PP2A) are connected to intellectual disability and cancer. It has been hypothesized that these mutations might disrupt the autoinhibition and phosphorylation-induced activation of PP2A. Since they are located far from both the active and substrate binding sites, it is unclear how they exert their effect. We performed allosteric pathway analysis based on molecular dynamics simulations and combined it with biochemical experiments to investigate the autoinhibition of PP2A. In the wild type (WT), the C-arm of the regulatory subunit B56δ obstructs the active and substrate binding sites exerting a dual autoinhibition effect. We find that the disease mutant, E198K, severely weakens the allosteric pathways that stabilize the C-arm in the WT. Instead, the strongest allosteric pathways in E198K take a different route that promotes exposure of the substrate binding site. To facilitate the allosteric pathway analysis, we introduce a path clustering algorithm for lumping pathways into channels. We reveal remarkable similarities between the allosteric channels of E198K and those in phosphorylation-activated WT, suggesting that the autoinhibition can be alleviated through a conserved mechanism. In contrast, we find that another disease mutant, E200K, which is in spatial proximity of E198, does not repartition the allosteric pathways leading to the substrate binding site; however, it may still induce exposure of the active site. This finding agrees with our biochemical data, allowing us to predict the activity of PP2A with the phosphorylated B56δ and provide insight into how disease mutations in spatial proximity alter the enzymatic activity in surprisingly different mechanisms.

The quaternary question: Determining allostery in spastin through dynamics classification learning and bioinformatics

The nanomachine from the ATPases associated with various cellular activities superfamily, called spastin, severs microtubules during cellular processes. To characterize the functionally important allostery in spastin, we employed methods from evolutionary information, to graph-based networks, to machine learning applied to atomistic molecular dynamics simulations of spastin in its monomeric and the functional hexameric forms, in the presence or absence of ligands. Feature selection, using machine learning approaches, for transitions between spastin states recognizes all the regions that have been proposed as allosteric or functional in the literature. The analysis of the composition of the Markov State Model macrostates in the spastin monomer, and the analysis of the direction of change in the top machine learning features for the transitions, indicate that the monomer favors the binding of ATP, which primes the regions involved in the formation of the inter-protomer interfaces for binding to other protomer(s). Allosteric path analysis of graph networks, built based on the cross-correlations between residues in simulations, shows that perturbations to a hub specific for the pre-hydrolysis hexamer propagate throughout the structure by passing through two obligatory regions: the ATP binding pocket, and pore loop 3, which connects the substrate binding site to the ATP binding site. Our findings support a model where the changes in the terminal protomers due to the binding of ligands play an active role in the force generation in spastin. The secondary structures in spastin, which are found to be highly degenerative within the network paths, are also critical for feature transitions of the classification models, which can guide the design of allosteric effectors to enhance or block allosteric signaling.

Piericones A and B as Potent Antithrombotics: Nanomolar Noncompetitive Protein Disulfide Isomerase Inhibitors with an Unexpected Chemical Architecture

Extracellular protein disulfide isomerase (PDI) is a promising target for thrombotic-related diseases. Four potent PDI inhibitors with unprecedented chemical architectures, piericones A-D (1-4), were isolated from Pieris japonica. Their structures were elucidated by spectroscopic data analysis, chemical methods, quantum ¹³C nuclear magnetic resonance DP4+ and electronic circular dichroism calculations, and single-crystal X-ray diffraction analysis. Piericones A (1) and B (2) were nanomolar noncompetitive PDI inhibitors possessing an unprecedented 3,6,10,15-tetraoxatetracyclo[7.6.0.0^4,9.0^1,12]pentadecane motif with nine contiguous stereogenic centers. Their biosynthetic pathways were proposed to include a key intermolecular aldol reaction and an intramolecular 1,2-migration reaction. Piericone A (1) significantly inhibited in vitro platelet aggregation and fibrin formation and in vivo thrombus formation via the inhibition of extracellular PDI without increasing the bleeding risk. The molecular docking and dynamics simulation of 1 and 2 provided a novel structure basis to develop PDI inhibitors as potent antithrombotics.

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.