A Universal Pattern in the Percolation and Dissipation of Protein Structural Perturbations

Engineering the native ensemble to tune protein function: Diverse mutational strategies and interlinked molecular mechanisms

Natural proteins are fragile entities, intrinsically sensitive to perturbations both at the level of sequence and their immediate environment. Here, we highlight the diverse strategies available for engineering function through mutations influencing backbone conformational entropy, charge-charge interactions, and in the loops and hinge regions, many of which are located far from the active site. It thus appears that there are potentially numerous ways to microscopically vary the identity of residues and the constituent interactions to tune function. Functional modulation could occur via changes in native-state stability, altered thermodynamic coupling extents within the folded structure, redistributed dynamics, or through modulation of the population of conformational substates. As these mechanisms are intrinsically linked and given the pervasive long-range effects of mutations, it is crucial to consider the interaction network as a whole and fully map the native conformational landscape to place mutational effects in the context of allostery and protein evolution.

Phosphorylation of cytosolic hPGK1 affects protein stability and ligand binding: implications for its subcellular targeting in cancer

Human phosphoglycerate kinase 1(hPGK1) is a key glycolytic enzyme that regulates the balance between ADP and ATP concentrations inside the cell. Phosphorylation of hPGK1 at S203 and S256 has been associated with enzyme import from the cytosol to the mitochondria and the nucleus respectively. These changes in subcellular locations drive tumorigenesis and are likely associated with site-specific changes in protein stability. In this work, we investigate the effects of site-specific phosphorylation on thermal and kinetic stability and protein structural dynamics by hydrogen-deuterium exchange (HDX) and molecular dynamics (MD) simulations. We also investigate the binding of 3-phosphoglycerate and Mg-ADP using these approaches. We show that the phosphomimetic mutation S256D reduces hPGK1 kinetic stability by 50-fold, with no effect of the mutation S203D. Calorimetric studies of ligand binding show a large decrease in affinity for Mg-ADP in the S256D variant, whereas Mg-ADP binding to the WT and S203D can be accurately investigated using protein kinetic stability and binding thermodynamic models. HDX and MD simulations confirmed the destabilization caused by the mutation S256D (with some long-range effects on stability) and its reduced affinity for Mg-ADP due to the strong destabilization of its binding site (particularly in the apo-state). Our research provides evidence suggesting that modifications in protein stability could potentially enhance the translocation of hPGK1 to the nucleus in cancer. While the structural and energetic basis of its mitochondrial import remain unknown.

A novel pathological mutant reveals the role of torsional flexibility in the serpin breach in adoption of an aggregation-prone intermediate

Mutants of alpha-1-antitrypsin cause the protein to self-associate and form ordered aggregates ('polymers') that are retained within hepatocytes, resulting in a predisposition to the development of liver disease. The associated reduction in secretion, and for some mutants, impairment of function, leads to a failure to protect lung tissue against proteases released during the inflammatory response and an increased risk of emphysema. We report here a novel deficiency mutation (Gly192Cys), that we name the Sydney variant, identified in a patient in heterozygosity with the Z allele (Glu342Lys). Cellular analysis revealed that the novel variant was mostly retained as insoluble polymers within the endoplasmic reticulum. The basis for this behaviour was investigated using biophysical and structural techniques. The variant showed a 40% reduction in inhibitory activity and a reduced stability as assessed by thermal unfolding experiments. Polymerisation involves adoption of an aggregation-prone intermediate and paradoxically the energy barrier for transition to this state was increased by 16% for the Gly192Cys variant with respect to the wild-type protein. However, with activation to the intermediate state, polymerisation occurred at a 3.8-fold faster rate overall. X-ray crystallography provided two crystal structures of the Gly192Cys variant, revealing perturbation within the 'breach' region with Cys192 in two different orientations: in one structure it faces towards the hydrophobic core while in the second it is solvent-exposed. This orientational heterogeneity was confirmed by PEGylation. These data show the critical role of the torsional freedom imparted by Gly192 in inhibitory activity and stability against polymerisation.

Insights into the pathogenesis of primary hyperoxaluria type I from the structural dynamics of alanine:glyoxylate aminotransferase variants

Primary hyperoxaluria type I (PH1) is caused by deficient alanine:glyoxylate aminotransferase (AGT) activity. PH1-causing mutations in AGT lead to protein mistargeting and aggregation. Here, we use hydrogen-deuterium exchange (HDX) to characterize the wild-type (WT), the LM (a polymorphism frequent in PH1 patients) and the LM G170R (the most common mutation in PH1) variants of AGT. We provide the first experimental analysis of AGT structural dynamics, showing that stability is heterogeneous in the native state and providing a blueprint for frustrated regions with potentially functional relevance. The LM and LM G170R variants only show local destabilization. Enzymatic transamination of the pyridoxal 5-phosphate cofactor bound to AGT hardly affects stability. Our study, thus, supports that AGT misfolding is not caused by dramatic effects on structural dynamics.

Molecular Mechanisms, Genotype-Phenotype Correlations and Patient-Specific Treatments in Inherited Metabolic Diseases

Advances in DNA sequencing technologies are revealing a vast genetic heterogeneity in human population, which may predispose to metabolic alterations if the activity of metabolic enzymes is affected [...].

Loss of stability and unfolding cooperativity in hPGK1 upon gradual structural perturbation of its N-terminal domain hydrophobic core

Phosphoglycerate kinase has been a model for the stability, folding cooperativity and catalysis of a two-domain protein. The human isoform 1 (hPGK1) is associated with cancer development and rare genetic diseases that affect several of its features. To investigate how mutations affect hPGK1 folding landscape and interaction networks, we have introduced mutations at a buried site in the N-terminal domain (F25 mutants) that either created cavities (F25L, F25V, F25A), enhanced conformational entropy (F25G) or introduced structural strain (F25W) and evaluated their effects using biophysical experimental and theoretical methods. All F25 mutants folded well, but showed reduced unfolding cooperativity, kinetic stability and altered activation energetics according to the results from thermal and chemical denaturation analyses. These alterations correlated well with the structural perturbation caused by mutations in the N-terminal domain and the destabilization caused in the interdomain interface as revealed by H/D exchange under native conditions. Importantly, experimental and theoretical analyses showed that these effects are significant even when the perturbation is mild and local. Our approach will be useful to establish the molecular basis of hPGK1 genotype-phenotype correlations due to phosphorylation events and single amino acid substitutions associated with disease.

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

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

Topological bio-scaling analysis as a universal measure of protein folding

Scaling relationships for polymeric molecules establish power law dependencies between the number of molecular segments and linear dimensions, such as the radius of gyration. They also establish spatial topological properties of the chains, such as their dimensionality. In the spatial domain, power exponents α = 1 (linear stretched molecule), α = 0.5 (the ideal chain) and α = 0.333 (compact globule) are significant. During folding, the molecule undergoes the transition from the one-dimensional linear to the three-dimensional globular state within a very short time. However, intermediate states with fractional dimensions can be stabilized by modifying the solubility (e.g. by changing the solution temperature). Topological properties, such as dimension, correlate with the interaction energy, and thus by tuning the solubility one can control molecular interaction. We investigate these correlations using the example of a well-studied short model of Trp-cage protein. The radius of gyration is used to estimate the fractal dimension of the chain at different stages of folding. It is expected that the same principle is applicable to much larger molecules and that topological (dimensional) characteristics can provide insights into molecular folding and interactions.

Allosteric Communication in the Multifunctional and Redox NQO1 Protein Studied by Cavity-Making Mutations

Allosterism is a common phenomenon in protein biochemistry that allows rapid regulation of protein stability; dynamics and function. However, the mechanisms by which allosterism occurs (by mutations or post-translational modifications (PTMs)) may be complex, particularly due to long-range propagation of the perturbation across protein structures. In this work, we have investigated allosteric communication in the multifunctional, cancer-related and antioxidant protein NQO1 by mutating several fully buried leucine residues (L7, L10 and L30) to smaller residues (V, A and G) at sites in the N-terminal domain. In almost all cases, mutated residues were not close to the FAD or the active site. Mutations L→G strongly compromised conformational stability and solubility, and L30A and L30V also notably decreased solubility. The mutation L10A, closer to the FAD binding site, severely decreased FAD binding affinity (≈20 fold vs. WT) through long-range and context-dependent effects. Using a combination of experimental and computational analyses, we show that most of the effects are found in the apo state of the protein, in contrast to other common polymorphisms and PTMs previously characterized in NQO1. The integrated study presented here is a first step towards a detailed structural–functional mapping of the mutational landscape of NQO1, a multifunctional and redox signaling protein of high biomedical relevance.

Predicting and Simulating Mutational Effects on Protein Folding Kinetics

Mutational perturbations of protein structures, i.e., phi-value analysis, are commonly employed to probe the extent of involvement of a particular residue in the rate-determining step(s) of folding. This generally involves the measurement of folding thermodynamic parameters and kinetic rate constants for the wild-type and mutant proteins. While computational approaches have been reasonably successful in understanding and predicting the effect of mutations on folding thermodynamics, it has been challenging to explore the same on kinetics due to confounding structural, energetic, and dynamic factors. Accordingly, the frequent observation of fractional phi-values (mean of ~0.3) has resisted a precise and consistent interpretation. Here, we describe how to construct, parameterize, and employ a simple one-dimensional free energy surface model that is grounded in the basic tenets of the energy landscape theory to predict and simulate the effect of mutations on folding kinetics. As a proof of principle, we simulate one-dimensional free energy profiles of 806 mutations from 24 different proteins employing just the experimental destabilization as input, reproduce the relative unfolding activation free energies with a correlation of 0.91, and show that the mean phi-value of 0.3 essentially corresponds to the extent of stabilization energy gained at the barrier top while folding.

A hierarchy of coupling free energies underlie the thermodynamic and functional architecture of protein structures

Protein sequences and structures evolve by satisfying varied physical and biochemical constraints. This multi-level selection is enabled not just by the patterning of amino acids on the sequence, but also via coupling between residues in the native structure. Here, we employ an energetically detailed statistical mechanical model with millions of microstates to extract such long-range structural correlations, i.e. thermodynamic coupling free energies, from a diverse family of protein structures. We find that despite the intricate and anisotropic distribution of coupling patterns, the majority of residues (>70%) are only marginally coupled contributing to functional motions and catalysis. Physical origins of ‘sectors’, determinants of native ensemble heterogeneity in extant, ancient and designed proteins, and the basis for allostery emerge naturally from coupling free energies. The statistical framework highlights how evolutionary selection and optimization occur at the level of global interaction network for a given protein fold impacting folding, function, and allosteric outputs.

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Evolution of protein structures occurs at the level of global interaction network.

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More than 70% of the protein residues are weakly or marginally coupled.

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Functional ‘sector’ regions are a manifestation of marginal coupling.

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Coupling indices vary across the entire proteins in extant-ancient and natural-designed pairs.

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The proposed methodology can be used to understand allostery and epistasis.

Evolutionary coupling range varies widely among enzymes depending on selection pressure

Recent studies proposed that enzyme-active sites induce evolutionary constraints at long distances. The physical origin of such long-range evolutionary coupling is unknown. Here, I use a recent biophysical model of evolution to study the relationship between physical and evolutionary couplings on a diverse data set of monomeric enzymes. I show that evolutionary coupling is not universally long-range. Rather, range varies widely among enzymes, from 2 to 20 Å. Furthermore, the evolutionary coupling range of an enzyme does not inform on the underlying physical coupling, which is short range for all enzymes. Rather, evolutionary coupling range is determined by functional selection pressure.

Diverse Native Ensembles Dictate the Differential Functional Responses of Nuclear Receptor Ligand-Binding Domains

Native states of folded proteins are characterized by a large ensemble of conformations whose relative populations and interconversion dynamics determine the functional output. This is more apparent in transcription factors that have evolved to be inherently sensitive to small perturbations, thus fine-tuning gene expression. To explore the extent to which such functional features are imprinted on the folding landscape of transcription factor ligand-binding domains (LBDs), we characterize paralogous LBDs of the nuclear receptor (NR) family employing an energetically detailed and ensemble-based Ising-like statistical mechanical model. We find that the native ensembles of the LBDs from glucocorticoid receptor, PPAγ, and thyroid hormone receptor display a remarkable diversity in the width of the native wells, the number and nature of partially structured states, and hence the degree of conformational order. Monte Carlo simulations employing the full state representation of the ensemble highlight that many of the functional conformations coexist in equilibrium, whose relative populations are sensitive to both temperature and the strength of ligand binding. Allosteric modulation of the degree of structure at a coregulator binding site on ligand binding is shown to arise via a redistribution of populations in the native ensembles of glucocorticoid and PPAγ LBDs. Our results illustrate how functional requirements can drive the evolution of conformationally diverse native ensembles in paralogs.

Double Mutant Cycles as a Tool to Address Folding, Binding, and Allostery

Quantitative measurement of intramolecular and intermolecular interactions in protein structure is an elusive task, not easy to address experimentally. The phenomenon denoted 'energetic coupling' describes short- and long-range interactions between two residues in a protein system. A powerful method to identify and quantitatively characterize long-range interactions and allosteric networks in proteins or protein-ligand complexes is called double-mutant cycles analysis. In this review we describe the thermodynamic principles and basic equations that underlie the double mutant cycle methodology, its fields of application and latest employments, and caveats and pitfalls that the experimentalists must consider. In particular, we show how double mutant cycles can be a powerful tool to investigate allosteric mechanisms in protein binding reactions as well as elusive states in protein folding pathways.

Toward Developing Intuitive Rules for Protein Variant Effect Prediction Using Deep Mutational Scanning Data

Protein structure and function can be severely altered by even a single amino acid mutation. Predictions of mutational effects using extensive artificial intelligence (AI)-based models, although accurate, remain as enigmatic as the experimental observations in terms of improving intuitions about the contributions of various factors. Inspired by Lipinski's rules for drug-likeness, we devise simple thresholding criteria on five different descriptors such as conservation, which have so far been limited to qualitative interpretations such as high conservation implies high mutational effect. We analyze systematic deep mutational scanning data of all possible single amino acid substitutions on seven proteins (25153 mutations) to first define these thresholds and then to evaluate the scope and limits of the predictions. At this stage, the approach allows us to comment easily and with a low error rate on the subset of mutations classified as neutral or deleterious by all of the descriptors. We hope that complementary to the accurate AI predictions, these thresholding rules or their subsequent modifications will serve the purpose of codifying the knowledge about the effects of mutations.

Naturally-Occurring Rare Mutations Cause Mild to Catastrophic Effects in the Multifunctional and Cancer-Associated NQO1 Protein

The functional and pathological implications of the enormous genetic diversity of the human genome are mostly unknown, primarily due to our unability to predict pathogenicity in a high-throughput manner. In this work, we characterized the phenotypic consequences of eight naturally-occurring missense variants on the multifunctional and disease-associated NQO1 protein using biophysical and structural analyses on several protein traits. Mutations found in both exome-sequencing initiatives and in cancer cell lines cause mild to catastrophic effects on NQO1 stability and function. Importantly, some mutations perturb functional features located structurally far from the mutated site. These effects are well rationalized by considering the nature of the mutation, its location in protein structure and the local stability of its environment. Using a set of 22 experimentally characterized mutations in NQO1, we generated experimental scores for pathogenicity that correlate reasonably well with bioinformatic scores derived from a set of commonly used algorithms, although the latter fail to semiquantitatively predict the phenotypic alterations caused by a significant fraction of mutations individually. These results provide insight into the propagation of mutational effects on multifunctional proteins, the implementation of in silico approaches for establishing genotype-phenotype correlations and the molecular determinants underlying loss-of-function in genetic diseases.

Protein-driven mechanism of multiorgan damage in COVID-19

We propose a new plausible mechanism by mean of which SARS-CoV-2 produces extrapulmonary damages in severe COVID-19 patients. The mechanism consist on the existence of vulnerable proteins (VPs), which are (i) mainly expressed outside the lungs; (ii) their perturbations is known to produce human diseases; and (iii) can be perturbed directly or indirectly by SARS-CoV-2 proteins. These VPs are perturbed by other proteins, which are: (i) mainly expressed in the lungs, (ii) are targeted directly by SARS-CoV-2 proteins, (iii) can navigate outside the lungs as cargo of extracellular vesicles (EVs); and (iv) can activate VPs via subdiffusive processes inside the target organ. Using bioinformatic tools and mathematical modeling we identifies 26 VPs and their 38 perturbators, which predict extracellular damages in the immunologic endocrine, cardiovascular, circulatory, lymphatic, musculoskeletal, neurologic, dermatologic, hepatic, gastrointestinal, and metabolic systems, as well as in the eyes. The identification of these VPs and their perturbators allow us to identify 27 existing drugs which are candidates to be repurposed for treating extrapulmonary damage in severe COVID-19 patients. After removal of drugs having undesirable drug-drug interactions we select 7 drugs and one natural product: apabetalone, romidepsin, silmitasertib, ozanezumab, procaine, azacitidine, amlexanox, volociximab, and ellagic acid, whose combinations can palliate the organs and systems found to be damaged by COVID-19. We found that at least 4 drugs are needed to treat all the multiorgan damages, for instance: the combination of romidepsin, silmitasertib, apabetalone and azacitidine.

Supertertiary protein structure affects an allosteric network

Protein function can be allosterically regulated by changes in structure or dynamics. PDZ domains are classic examples for studies of allostery in single protein domains. However, PDZ domains are often found in multidomain proteins; in particular, PDZ3 is located in a supramodule containing three domains. The allosteric network in PDZ3 has never been studied in the presence of the adjacent domains. Here we map the allosteric network for a PDZ3:ligand complex, both in isolation and in the context of a supramodule. We demonstrate that the allosteric network is highly dependent on this supertertiary structure, with broad implications for studies of allostery in single domains.

The notion that protein function is allosterically regulated by structural or dynamic changes in proteins has been extensively investigated in several protein domains in isolation. In particular, PDZ domains have represented a paradigm for these studies, despite providing conflicting results. Furthermore, it is still unknown how the association between protein domains in supramodules, consitituting so-called supertertiary structures, affects allosteric networks. Here, we experimentally mapped the allosteric network in a PDZ:ligand complex, both in isolation and in the context of a supramodular structure, and show that allosteric networks in a PDZ domain are highly dependent on the supertertiary structure in which they are present. This striking sensitivity of allosteric networks to the presence of adjacent protein domains is likely a common property of supertertiary structures in proteins. Our findings have general implications for prediction of allosteric networks from primary and tertiary structures and for quantitative descriptions of allostery.

Predicting the viability of beta-lactamase: How folding and binding free energies correlate with beta-lactamase fitness

One of the long-standing holy grails of molecular evolution has been the ability to predict an organism's fitness directly from its genotype. With such predictive abilities in hand, researchers would be able to more accurately forecast how organisms will evolve and how proteins with novel functions could be engineered, leading to revolutionary advances in medicine and biotechnology. In this work, we assemble the largest reported set of experimental TEM-1 β-lactamase folding free energies and use this data in conjunction with previously acquired fitness data and computational free energy predictions to determine how much of the fitness of β-lactamase can be directly predicted by thermodynamic folding and binding free energies. We focus upon β-lactamase because of its long history as a model enzyme and its central role in antibiotic resistance. Based upon a set of 21 β-lactamase single and double mutants expressly designed to influence protein folding, we first demonstrate that modeling software designed to compute folding free energies such as FoldX and PyRosetta can meaningfully, although not perfectly, predict the experimental folding free energies of single mutants. Interestingly, while these techniques also yield sensible double mutant free energies, we show that they do so for the wrong physical reasons. We then go on to assess how well both experimental and computational folding free energies explain single mutant fitness. We find that folding free energies account for, at most, 24% of the variance in β-lactamase fitness values according to linear models and, somewhat surprisingly, complementing folding free energies with computationally-predicted binding free energies of residues near the active site only increases the folding-only figure by a few percent. This strongly suggests that the majority of β-lactamase's fitness is controlled by factors other than free energies. Overall, our results shed a bright light on to what extent the community is justified in using thermodynamic measures to infer protein fitness as well as how applicable modern computational techniques for predicting free energies will be to the large data sets of multiply-mutated proteins forthcoming.

pPerturb: A Server for Predicting Long-Distance Energetic Couplings and Mutation-Induced Stability Changes in Proteins via Perturbations

The strength of intraprotein interactions or contact network is one of the dominant factors determining the thermodynamic stabilities of proteins. The nature and the extent of connectivity of this network also play a role in allosteric signal propagation characteristics upon ligand binding to a protein domain. Here, we develop a server for rapid quantification of the strength of an interaction network by employing an experimentally consistent perturbation approach previously validated against a large data set of 375 mutations in 19 different proteins. The web server can be employed to predict the extent of destabilization of proteins arising from mutations in the protein interior in experimentally relevant units. Moreover, coupling distances-a measure of the extent of percolation on perturbation-and overall perturbation magnitudes are predicted in a residue-specific manner, enabling a first look at the distribution of energetic couplings in a protein or its changes upon ligand binding. We show specific examples of how the server can be employed to probe for the distribution of local stabilities in a protein, to examine changes in side chain orientations or packing before and after ligand binding, and to predict changes in stabilities of proteins upon mutations of buried residues. The web server is freely available at http://pbl.biotech.iitm.ac.in/pPerturb and supports recent versions of all major browsers.

NQO1: A target for the treatment of cancer and neurological diseases, and a model to understand loss of function disease mechanisms

NAD(P)H quinone oxidoreductase 1 (NQO1) is a multi-functional protein that catalyses the reduction of quinones (and other molecules), thus playing roles in xenobiotic detoxification and redox balance, and also has roles in stabilising apoptosis regulators such as p53. The structure and enzymology of NQO1 is well-characterised, showing a substituted enzyme mechanism in which NAD(P)H binds first and reduces an FAD cofactor in the active site, assisted by a charge relay system involving Tyr-155 and His-161. Protein dynamics play important role in physio-pathological aspects of this protein. NQO1 is a good target to treat cancer due to its overexpression in cancer cells. A polymorphic form of NQO1 (p.P187S) is associated with increased cancer risk and certain neurological disorders (such as multiple sclerosis and Alzheimer´s disease), possibly due to its roles in the antioxidant defence. p.P187S has greatly reduced FAD affinity and stability, due to destabilization of the flavin binding site and the C-terminal domain, which leading to reduced activity and enhanced degradation. Suppressor mutations partially restore the activity of p.P187S by local stabilization of these regions, and showing long-range allosteric communication within the protein. Consequently, the correction of NQO1 misfolding by pharmacological chaperones is a viable strategy, which may be useful to treat cancer and some neurological conditions, targeting structural spots linked to specific disease-mechanisms. Thus, NQO1 emerges as a good model to investigate loss of function mechanisms in genetic diseases as well as to improve strategies to discriminate between neutral and pathogenic variants in genome-wide sequencing studies.

Effect of site-directed point mutations on protein misfolding: A simulation study

A Monte Carlo simulation based sequence design method is proposed to investigate the role of site-directed point mutations in protein misfolding. Site-directed point mutations are incorporated in the designed sequences of selected proteins. While most mutated sequences correctly fold to their native conformation, some of them stabilize in other nonnative conformations and thus misfold/unfold. The results suggest that a critical number of hydrophobic amino acid residues must be present in the core of the correctly folded proteins, whereas proteins misfold/unfold if this number of hydrophobic residues falls below the critical limit. A protein can accommodate only a particular number of hydrophobic residues at the surface, provided a large number of hydrophilic residues are present at the surface and critical hydrophobicity of the core is preserved. Some surface sites are observed to be equally sensitive toward site-directed point mutations as the core sites. Point mutations with highly polar and charged amino acids increases the misfold/unfold propensity of proteins. Substitution of natural amino acids at sites with different number of nonbonded contacts suggests that both amino acid identity and its respective site-specificity determine the stability of a protein. A clash-match method is developed to calculate the number of matching and clashing interactions in the mutated protein sequences. While misfolded/unfolded sequences have a higher number of clashing and a lower number of matching interactions, the correctly folded sequences have a lower number of clashing and a higher number of matching interactions. These results are valid for different SCOP classes of proteins.

Double-mutant cycles: new directions and applications

Double-mutant cycle (DMC) analysis is a powerful approach for detecting and quantifying the energetics of both direct and long-range interactions in proteins and other chemical systems. It can also be used to unravel higher-order interactions (e.g. three-body effects) that lead to cooperativity in protein folding and function. In this review, we describe new applications of DMC analysis based on advances in native mass spectrometry and high-throughput methods such as next generation sequencing and protein complementation assays. These developments have facilitated carrying out high-throughput DMC analysis, which can be used to characterize increasingly higher-order interactions and very large interaction networks in proteins. Such studies have provided insights into the extent of cooperativity (epistasis) in protein structures. High-throughput DMC studies have also been used to validate correlated mutation analysis and can provide restraints for protein docking.

Toward Comprehensive Allosteric Control over Protein Activity

Universality of allosteric signaling in proteins, molecular machines, and receptors complemented by the great advantages of prospected allosteric drugs in the highly specific, non-competitive, and modulatory nature of their actions calls for deeper theoretical understanding of allosteric communication. We present a computational model that makes it possible to tackle the problem of modulating the energetics of protein allosteric communication. In the context of the energy landscape paradigm, allosteric signaling is always a result of perturbations, such as ligand binding, mutations, and intermolecular interactions. The calculation of local partition functions in the protein harmonic model with perturbations allows us to evaluate the energetics of allosteric communication at the single-residue level. In this framework, Allosteric Signaling Maps are proposed as a tool to exhaustively describe allosteric communication in the protein, to tune already existing signaling, and to design new elements of regulation for taking the protein activity under allosteric control.

Fluctuation correlations as major determinants of structure- and dynamics-driven allosteric effects

Both structure- and dynamics-driven allosteric effects are determined by the correlation of distance fluctuations in proteins.

Modulation of allosteric coupling by mutations: from protein dynamics and packing to altered native ensembles and function

A large body of work has gone into understanding the effect of mutations on protein structure and function. Conventional treatments have involved quantifying the change in stability, activity and relaxation rates of the mutants with respect to the wild-type protein. However, it is now becoming increasingly apparent that mutational perturbations consistently modulate the packing and dynamics of a significant fraction of protein residues, even those that are located >10–15 Å from the mutated site. Such long-range modulation of protein features can distinctly tune protein stability and the native conformational ensemble contributing to allosteric modulation of function. In this review, I summarize a series of experimental and computational observations that highlight the incredibly pliable nature of proteins and their response to mutational perturbations manifested via the intra-protein interaction network. I highlight how an intimate understanding of mutational effects could pave the way for integrating stability, folding, cooperativity and even allostery within a single physical framework.

Insight into the specificity and severity of pathogenic mechanisms associated with missense mutations through experimental and structural perturbation analyses

Most pathogenic missense mutations cause specific molecular phenotypes through protein destabilization. However, how protein destabilization is manifested as a given molecular phenotype is not well understood. We develop here a structural and energetic approach to describe mutational effects on specific traits such as function, regulation, stability, subcellular targeting or aggregation propensity. This approach is tested using large-scale experimental and structural perturbation analyses in over thirty mutations in three different proteins (cancer-associated NQO1, transthyretin related with amyloidosis and AGT linked to primary hyperoxaluria type I) and comprising five very common pathogenic mechanisms (loss-of-function and gain-of-toxic function aggregation, enzyme inactivation, protein mistargeting and accelerated degradation). Our results revealed that the magnitude of destabilizing effects and, particularly, their propagation through the structure to promote disease-associated conformational states largely determine the severity and molecular mechanisms of disease-associated missense mutations. Modulation of the structural perturbation at a mutated site is also shown to cause switches between different molecular phenotypes. When very common disease-associated missense mutations were investigated, we also found that they were not among the most deleterious possible missense mutations at those sites, and required additional contributions from codon bias and effects of CpG sites to explain their high frequency in patients. Our work sheds light on the molecular basis of pathogenic mechanisms and genotype–phenotype relationships, with implications for discriminating between pathogenic and neutral changes within human genome variability from whole genome sequencing studies.

Switching Protein Conformational Substates by Protonation and Mutation

Protein modules that regulate the availability and conformational status of transcription factors determine the rapidity, duration, and magnitude of cellular response to changing conditions. One such system is the single-gene product Cnu, a four-helix bundle transcription co-repressor, which acts as a molecular thermosensor regulating the expression of virulence genes in enterobacteriaceae through modulation of its native conformational ensemble. Cnu and related genes have also been implicated in pH-dependent expression of virulence genes. We hypothesize that protonation of a conserved buried histidine (H45) in Cnu promotes large electrostatic frustration, thus disturbing the H-NS, a transcription factor, binding face. Spectroscopic and calorimetric methods reveal that H45 exhibits a suppressed p K_a of ∼5.1, the protonation of which switches the conformation to an alternate native ensemble in which the fourth helix is disordered. The population redistribution can also be achieved through a mutation H45V, which does not display any switching behavior at pH values greater than 4. The Wako-Saitô-Muñoz-Eaton (WSME) statistical mechanical model predicts specific differences in the conformations and fluctuations of the fourth and first helices of Cnu determining the observed pH response. We validate these predictions through fluorescence lifetime measurements of a sole tryptophan, highlighting the presence of both native and non-native interactions in the regions adjoining the binding face of Cnu. Our combined experimental-computational study thus shows that Cnu acts both as a thermo- and pH-sensor orchestrated via a subtle but quantifiable balance between the weak packing of a structural element and protonation of a buried histidine that promotes electrostatic frustration.

Biophysical and functional perturbation analyses at cancer-associated P187 and K240 sites of the multifunctional NADP(H):quinone oxidoreductase 1

Once whole-genome sequencing has reached the clinical practice, a main challenge ahead is the high-throughput and accurate prediction of the pathogenicity of genetic variants. However, current prediction tools do not consider explicitly a well-known property of disease-causing mutations: their ability to affect multiple functional sites distant in the protein structure. Here we carried out an extensive biophysical characterization of fourteen mutant variants at two cancer-associated sites of the enzyme NQO1, a paradigm of multi-functional protein. We showed that the magnitude of destabilizing effects, their molecular origins (structural vs. dynamic) and their efficient propagation through the protein structure gradually led to functional perturbations at different sites. Modulation of these structural perturbations also led to switches between molecular phenotypes. Our work supports that experimental and computational perturbation analyses would improve our understanding of the molecular basis of many loss-of-function genetic diseases as well as our ability to accurately predict the pathogenicity of genetic variants in a high-throughput fashion.

Evolutionary Divergent Suppressor Mutations in Conformational Diseases

Neutral and adaptive mutations are key players in the evolutionary dynamics of proteins at molecular, cellular and organismal levels. Conversely, largely destabilizing mutations are rarely tolerated by evolution, although their occurrence in diverse human populations has important roles in the pathogenesis of conformational diseases. We have recently proposed that divergence at certain sites from the consensus (amino acid) state during mammalian evolution may have rendered some human proteins more vulnerable towards disease-associated mutations, primarily by decreasing their conformational stability. We herein extend and refine this hypothesis discussing results from phylogenetic and structural analyses, structure-based energy calculations and structure-function studies at molecular and cellular levels. As proof-of-principle, we focus on different mammalian orthologues of the NQO1 (NAD(P)H:quinone oxidoreductase 1) and AGT (alanine:glyoxylate aminotransferase) proteins. We discuss the different loss-of-function pathogenic mechanisms associated with diseases involving the two enzymes, including enzyme inactivation, accelerated degradation, intracellular mistargeting, and aggregation. Last, we take into account the potentially higher robustness of mammalian orthologues containing certain consensus amino acids as suppressors of human disease, and their relation with different intracellular post-translational modifications and protein quality control capacities, to be discussed as sources of phenotypic variability between human and mammalian models of disease and as tools for improving current therapeutic approaches.

Structure and energy based quantitative missense variant effect analysis provides insights into drug resistance mechanisms of anaplastic lymphoma kinase mutations

Anaplastic lymphoma kinase (ALK) is considered as a validated molecular target in multiple malignancies, such as non-small cell lung cancer (NSCLC). However, the effectiveness of molecularly targeted therapies using ALK inhibitors is almost universally limited by drug resistance. Drug resistance to molecularly targeted therapies has now become a major obstacle to effective cancer treatment and personalized medicine. It is of particular importance to provide an improved understanding on the mechanisms of resistance of ALK inhibitors, thus rational new therapeutic strategies can be developed to combat resistance. We used state-of-the-art computational approaches to systematically explore the mutational effects of ALK mutations on drug resistance properties. We found the activation of ALK was increased by substitution with destabilizing mutations, creating the capacity to confer drug resistance to inhibitors. In addition, results implied that evolutionary constraints might affect the drug resistance properties. Moreover, an extensive profile of drugs against ALK mutations was constructed to give better understanding of the mechanism of drug resistance based on structural transitions and energetic variation. Our work hopes to provide an up-to-date mechanistic framework for understanding the mechanisms of drug resistance induced by ALK mutations, thus tailor treatment decisions after the emergence of resistance in ALK-dependent diseases.

In proteins, the structural responses of a position to mutation rely on the Goldilocks principle: not too many links, not too few

A disease has distinct genetic and molecular hallmarks such as sequence variants that are likely to produce the alternative protein structures accountable for individual responses to drugs and disease development.