Point mutations in protein globular domains: contributions from function, stability and misfolding

Molecular Information Theory Meets Protein Folding

We propose an application of molecular information theory to analyze the folding of single domain proteins. We analyze results from various areas of protein science, such as sequence-based potentials, reduced amino acid alphabets, backbone configurational entropy, secondary structure content, residue burial layers, and mutational studies of protein stability changes. We found that the average information contained in the sequences of evolved proteins is very close to the average information needed to specify a fold ∼2.2 ± 0.3 bits/(site·operation). The effective alphabet size in evolved proteins equals the effective number of conformations of a residue in the compact unfolded state at around 5. We calculated an energy-to-information conversion efficiency upon folding of around 50%, lower than the theoretical limit of 70%, but much higher than human-built macroscopic machines. We propose a simple mapping between molecular information theory and energy landscape theory and explore the connections between sequence evolution, configurational entropy, and the energetics of protein folding.

Protein structure and aggregation: a marriage of necessity ruled by aggregation gatekeepers

Protein aggregation propensity is a pervasive and seemingly inescapable property of proteomes. Strikingly, a significant fraction of the proteome is supersaturated, meaning that, for these proteins, their native conformation is less stable than the aggregated state. Maintaining the integrity of a proteome under such conditions is precarious and requires energy-consuming proteostatic regulation. Why then is aggregation propensity maintained at such high levels over long evolutionary timescales? Here, we argue that the conformational stability of the native and aggregated states are correlated thermodynamically and that codon usage strengthens this correlation. As a result, the folding of stable proteins requires kinetic control to avoid aggregation, provided by aggregation gatekeepers. These unique residues are evolutionarily selected to kinetically favor native folding, either on their own or by coopting chaperones.

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.

ProTstab - predictor for cellular protein stability

BACKGROUND

Stability is one of the most fundamental intrinsic characteristics of proteins and can be determined with various methods. Characterization of protein properties does not keep pace with increase in new sequence data and therefore even basic properties are not known for far majority of identified proteins. There have been some attempts to develop predictors for protein stabilities; however, they have suffered from small numbers of known examples.

RESULTS

We took benefit of results from a recently developed cellular stability method, which is based on limited proteolysis and mass spectrometry, and developed a machine learning method using gradient boosting of regression trees. ProTstab method has high performance and is well suited for large scale prediction of protein stabilities.

CONCLUSIONS

The Pearson's correlation coefficient was 0.793 in 10-fold cross validation and 0.763 in independent blind test. The corresponding values for mean absolute error are 0.024 and 0.036, respectively. Comparison with a previously published method indicated ProTstab to have superior performance. We used the method to predict stabilities of all the remaining proteins in the entire human proteome and then correlated the predicted stabilities to protein chain lengths of isoforms and to localizations of proteins.

Compensated pathogenic variants in coagulation factors VIII and IX present complex mapping between molecular impact and hemophilia severity

Compensated pathogenic deviations (CPDs) are sequence variants that are pathogenic in humans but neutral in other species. In recent years, our molecular understanding of CPDs has advanced substantially. For example, it is known that their impact on human proteins is generally milder than that of average pathogenic mutations and that their impact is suppressed in non-human carriers by compensatory mutations. However, prior studies have ignored the evolutionarily relevant relationship between molecular impact and organismal phenotype. Here, we explore this topic using CPDs from FVIII and FIX and data concerning carriers' hemophilia severity. We find that, regardless of their molecular impact, these mutations can be associated with either mild or severe disease phenotypes. Only a weak relationship is found between protein stability changes and severity. We also characterize the population variability of hemostasis proteins, which constitute the genetic background of FVIII and FIX, using data from the 1000 Genome project. We observe that genetic background can vary substantially between individuals in terms of both the amount and nature of genetic variants. Finally, we discuss how these results highlight the need to include new terms in present models of protein evolution to explain the origin of CPDs.

A Shift in Aggregation Avoidance Strategy Marks a Long-Term Direction to Protein Evolution

To detect a direction to evolution, without the pitfalls of reconstructing ancestral states, we need to compare "more evolved" to "less evolved" entities. But because all extant species have the same common ancestor, none are chronologically more evolved than any other. However, different gene families were born at different times, allowing us to compare young protein-coding genes to those that are older and hence have been evolving for longer. To be retained during evolution, a protein must not only have a function, but must also avoid toxic dysfunction such as protein aggregation. There is conflict between the two requirements: hydrophobic amino acids form the cores of protein folds, but also promote aggregation. Young genes avoid strongly hydrophobic amino acids, which is presumably the simplest solution to the aggregation problem. Here we show that young genes' few hydrophobic residues are clustered near one another along the primary sequence, presumably to assist folding. The higher aggregation risk created by the higher hydrophobicity of older genes is counteracted by more subtle effects in the ordering of the amino acids, including a reduction in the clustering of hydrophobic residues until they eventually become more interspersed than if distributed randomly. This interspersion has previously been reported to be a general property of proteins, but here we find that it is restricted to old genes. Quantitatively, the index of dispersion delineates a gradual trend, i.e., a decrease in the clustering of hydrophobic amino acids over billions of years.

Local frustration around enzyme active sites

Conflicting biological goals often meet in the specification of protein sequences for structure and function. Overall, strong energetic conflicts are minimized in folded native states according to the principle of minimal frustration, so that a sequence can spontaneously fold, but local violations of this principle open up the possibility to encode the complex energy landscapes that are required for active biological functions. We survey the local energetic frustration patterns of all protein enzymes with known structures and experimentally annotated catalytic residues. In agreement with previous hypotheses, the catalytic sites themselves are often highly frustrated regardless of the protein oligomeric state, overall topology, and enzymatic class. At the same time a secondary shell of more weakly frustrated interactions surrounds the catalytic site itself. We evaluate the conservation of these energetic signatures in various family members of major enzyme classes, showing that local frustration is evolutionarily more conserved than the primary structure itself.

FoldX as Protein Engineering Tool: Better Than Random Based Approaches?

Improving protein stability is an important goal for basic research as well as for clinical and industrial applications but no commonly accepted and widely used strategy for efficient engineering is known. Beside random approaches like error prone PCR or physical techniques to stabilize proteins, e.g. by immobilization, in silico approaches are gaining more attention to apply target-oriented mutagenesis. In this review different algorithms for the prediction of beneficial mutation sites to enhance protein stability are summarized and the advantages and disadvantages of FoldX are highlighted. The question whether the prediction of mutation sites by the algorithm FoldX is more accurate than random based approaches is addressed.

Improvement in the Thermostability of a β-Amino Acid Converting ω-Transaminase by Using FoldX

ω-Transaminases (ω-TAs) are important biocatalysts for the synthesis of active, chiral pharmaceutical ingredients containing amino groups, such as β-amino acids, which are important in peptidomimetics and as building blocks for drugs. However, the application of ω-TAs is limited by the availability and stability of enzymes with high conversion rates. One strategy for the synthesis and optical resolution of β-phenylalanine and other important aromatic β-amino acids is biotransformation by utilizing an ω-transaminase from Variovorax paradoxus. We designed variants of this ω-TA to gain higher process stability on the basis of predictions calculated by using the FoldX software. We herein report the first thermostabilization of a nonthermostable S-selective ω-TA by FoldX-guided site-directed mutagenesis. The melting point (T_m ) of our best-performing mutant was increased to 59.3 °C, an increase of 4.0 °C relative to the T_m value of the wild-type enzyme, whereas the mutant fully retained its specific activity.

Enzyme Efficiency but Not Thermostability Drives Cefotaxime Resistance Evolution in TEM-1 β-Lactamase

A leading intellectual challenge in evolutionary genetics is to identify the specific phenotypes that drive adaptation. Enzymes offer a particularly promising opportunity to pursue this question, because many enzymes' contributions to organismal fitness depend on a comparatively small number of experimentally accessible properties. Moreover, on first principles the demands of enzyme thermostability stand in opposition to the demands of catalytic activity. This observation, coupled with the fact that enzymes are only marginally thermostable, motivates the widely held hypothesis that mutations conferring functional improvement require compensatory mutations to restore thermostability. Here, we explicitly test this hypothesis for the first time, using four missense mutations in TEM-1 β-lactamase that jointly increase cefotaxime Minimum Inhibitory Concentration (MIC) ∼1500-fold. First, we report enzymatic efficiency (kcat/KM) and thermostability (Tm, and thence ΔG of folding) for all combinations of these mutations. Next, we fit a quantitative model that predicts MIC as a function of kcat/KM and ΔG. While kcat/KM explains ∼54% of the variance in cefotaxime MIC (∼92% after log transformation), ΔG does not improve explanatory power of the model. We also find that cefotaxime MIC rises more slowly in kcat/KM than predicted. Several explanations for these discrepancies are suggested. Finally, we demonstrate substantial sign epistasis in MIC and kcat/KM, and antagonistic pleiotropy between phenotypes, in spite of near numerical additivity in the system. Thus constraints on selectively accessible trajectories, as well as limitations in our ability to explain such constraints in terms of underlying mechanisms are observed in a comparatively "well-behaved" system.

Circumventing the stability-function trade-off in an engineered FN3 domain

The favorable biophysical attributes of non-antibody scaffolds make them attractive alternatives to monoclonal antibodies. However, due to the well-known stability-function trade-off, these gains tend to be marginal after functional selection. A notable example is the fibronectin Type III (FN3) domain, FNfn10, which has been previously evolved to bind lysozyme with 1 pM affinity (FNfn10-α-lys), but suffers from poor thermodynamic and kinetic stability. To explore this stability-function compromise further, we grafted the lysozyme-binding loops from FNfn10-α-lys onto our previously engineered, ultra-stable FN3 scaffold, FN3con. The resulting variant (FN3con-α-lys) bound lysozyme with a markedly reduced affinity, but retained high levels of thermal stability. The crystal structure of FNfn10-α-lys in complex with lysozyme revealed unanticipated interactions at the protein-protein interface involving framework residues of FNfn10-α-lys, thus explaining the failure to transfer binding via loop grafting. Utilizing this structural information, we redesigned FN3con-α-lys and restored picomolar binding affinity to lysozyme, while maintaining thermodynamic stability (with a thermal melting temperature 2-fold higher than that of FNfn10-α-lys). FN3con therefore provides an exceptional window of stability to tolerate deleterious mutations, resulting in a substantial advantage for functional design. This study emphasizes the utility of consensus design for the generation of highly stable scaffolds for downstream protein engineering studies.

The effect of chirality and steric hindrance on intrinsic backbone conformational propensities: tools for protein design

The conformational propensities of amino acids are an amalgamation of sequence effects, environmental effects and underlying intrinsic behavior. Many have attempted to investigate neighboring residue effects to aid in our understanding of protein folding and improve structure prediction efforts, especially with respect to difficult to characterize states, such as disordered or unfolded states. Host-guest peptide series are a useful tool in examining the propensities of the amino acids free from the surrounding protein structure. Here, we compare the distributions of the backbone dihedral angles (φ/ψ) of the 20 proteogenic amino acids in two different sequence contexts using the AAXAA and GGXGG host-guest pentapeptide series. We further examine their intrinsic behaviors across three environmental contexts: water at 298 K, water at 498 K, and 8 M urea at 298 K. The GGXGG systems provide the intrinsic amino acid propensities devoid of any conformational context. The alanine residues in the AAXAA series enforce backbone chirality, thereby providing a model of the intrinsic behavior of amino acids in a protein chain. Our results show modest differences in φ/ψ distributions due to the steric constraints of the Ala side chains, the magnitudes of which are dependent on the denaturing conditions. One of the strongest factors modulating φ/ψ distributions was the protonation of titratable side chains, and the largest differences observed were in the amino acid propensities for the rarely sampled αL region.

Selection for Protein Kinetic Stability Connects Denaturation Temperatures to Organismal Temperatures and Provides Clues to Archaean Life

The relationship between the denaturation temperatures of proteins (T_m values) and the living temperatures of their host organisms (environmental temperatures: T_ENV values) is poorly understood. Since different proteins in the same organism may show widely different T_m’s, no simple universal relationship between T_m and T_ENV should hold, other than T_m≥T_ENV. Yet, when analyzing a set of homologous proteins from different hosts, T_m’s are oftentimes found to correlate with T_ENV’s but this correlation is shifted upward on the T_m axis. Supporting this trend, we recently reported T_m’s for resurrected Precambrian thioredoxins that mirror a proposed environmental cooling over long geological time, while remaining a shocking ~50°C above the proposed ancestral ocean temperatures. Here, we show that natural selection for protein kinetic stability (denaturation rate) can produce a T_m↔T_ENV correlation with a large upward shift in T_m. A model for protein stability evolution suggests a link between the T_m shift and the in vivo lifetime of a protein and, more specifically, allows us to estimate ancestral environmental temperatures from experimental denaturation rates for resurrected Precambrian thioredoxins. The T_ENV values thus obtained match the proposed ancestral ocean cooling, support comparatively high Archaean temperatures, and are consistent with a recent proposal for the environmental temperature (above 75°C) that hosted the last universal common ancestor. More generally, this work provides a framework for understanding how features of protein stability reflect the environmental temperatures of the host organisms.

Effect of salt on the formation of salt-bridges in β-hairpin peptides

The formation of salt-bridges in β-hairpin peptides is measured in increasing salt concentrations, indicating a decrease in the salt-bridged population due to charge–charge screening, as well as non-cooperative salt-bridge triads.

Neutral and weakly nonneutral sequence variants may define individuality

Large-scale computational analyses of the growing wealth of genome-variation data consistently tell two distinct stories. The first is expected: coding variants reported in disease-related databases significantly alter the function of affected proteins. The second is surprising: the genomes of healthy individuals appear to carry many variants that are predicted to have some effect on function. As long as the complete experimental analysis of all human genome variants remains impossible, computational methods, such as PolyPhen, SNAP, and SIFT, might provide important insights. These methods capture the effects of particular variants very well and can highlight trends in populations of variants. Diseases are, arguably, extreme phenotypic variations and are often attributable to one or a few severely functionally disruptive variants. Our findings suggest a genomic basis of the different nondisease phenotypes. Prediction methods indicate that variants in seemingly healthy individuals tend to be neutral or weakly disruptive for protein molecular function. These variant effects are predicted to be largely either experimentally undetectable or are not deemed significant enough to be published. This may suggest that nondisease phenotypes arise through combinations of many variants whose effects are weakly nonneutral (damaging or enhancing) to the molecular protein function but fall within the wild-type range of overall physiological function.

Evolutionary selection for protein aggregation

Protein aggregation is being found to be associated with an increasing number of human diseases. Aggregation can lead to a loss of function (lack of active protein) or to a toxic gain of function (cytotoxicity associated with protein aggregates). Although potentially harmful, protein sequences predisposed to aggregation seem to be ubiquitous in all kingdoms of life, which suggests an evolutionary advantage to having such segments in polypeptide sequences. In fact, aggregation-prone segments are essential for protein folding and for mediating certain protein-protein interactions. Moreover, cells use protein aggregates for a wide range of functions. Against this background, life has adapted to tolerate the presence of potentially dangerous aggregation-prone sequences by constraining and counteracting the aggregation process. In the present review, we summarize the current knowledge of the advantages associated with aggregation-prone stretches in proteomes and the strategies that cellular systems have developed to control the aggregation process.

PROTS-RF: a robust model for predicting mutation-induced protein stability changes

The ability to improve protein thermostability via protein engineering is of great scientific interest and also has significant practical value. In this report we present PROTS-RF, a robust model based on the Random Forest algorithm capable of predicting thermostability changes induced by not only single-, but also double- or multiple-point mutations. The model is built using 41 features including evolutionary information, secondary structure, solvent accessibility and a set of fragment-based features. It achieves accuracies of 0.799,0.782, 0.787, and areas under receiver operating characteristic (ROC) curves of 0.873, 0.868 and 0.862 for single-, double- and multiple- point mutation datasets, respectively. Contrary to previous suggestions, our results clearly demonstrate that a robust predictive model trained for predicting single point mutation induced thermostability changes can be capable of predicting double and multiple point mutations. It also shows high levels of robustness in the tests using hypothetical reverse mutations. We demonstrate that testing datasets created based on physical principles can be highly useful for testing the robustness of predictive models.

Conditional disorder in chaperone action

Protein disorder remains an intrinsically fuzzy concept. Its role in protein function is difficult to conceptualize and its experimental study is challenging. Although a wide variety of roles for protein disorder have been proposed, establishing that disorder is functionally important, particularly in vivo, is not a trivial task. Several molecular chaperones have now been identified as conditionally disordered proteins; fully folded and chaperone-inactive under non-stress conditions, they adopt a partially disordered conformation upon exposure to distinct stress conditions. This disorder appears to be vital for their ability to bind multiple aggregation-sensitive client proteins and to protect cells against the stressors. The study of these conditionally disordered chaperones should prove useful in understanding the functional role for protein disorder in molecular recognition.

PoPMuSiC 2.1: a web server for the estimation of protein stability changes upon mutation and sequence optimality

Background

The rational design of modified proteins with controlled stability is of extreme importance in a whole range of applications, notably in the biotechnological and environmental areas, where proteins are used for their catalytic or other functional activities. Future breakthroughs in medical research may also be expected from an improved understanding of the effect of naturally occurring disease-causing mutations on the molecular level.

Results

PoPMuSiC-2.1 is a web server that predicts the thermodynamic stability changes caused by single site mutations in proteins, using a linear combination of statistical potentials whose coefficients depend on the solvent accessibility of the mutated residue. PoPMuSiC presents good prediction performances (correlation coefficient of 0.8 between predicted and measured stability changes, in cross validation, after exclusion of 10% outliers). It is moreover very fast, allowing the prediction of the stability changes resulting from all possible mutations in a medium size protein in less than a minute. This unique functionality is user-friendly implemented in PoPMuSiC and is particularly easy to exploit. Another new functionality of our server concerns the estimation of the optimality of each amino acid in the sequence, with respect to the stability of the structure. It may be used to detect structural weaknesses, i.e. clusters of non-optimal residues, which represent particularly interesting sites for introducing targeted mutations. This sequence optimality data is also expected to have significant implications in the prediction and the analysis of particular structural or functional protein regions. To illustrate the interest of this new functionality, we apply it to a dataset of known catalytic sites, and show that a much larger than average concentration of structural weaknesses is detected, quantifying how these sites have been optimized for function rather than stability.

Conclusion

The freely available PoPMuSiC-2.1 web server is highly useful for identifying very rapidly a list of possibly relevant mutations with the desired stability properties, on which subsequent experimental studies can be focused. It can also be used to detect sequence regions corresponding to structural weaknesses, which could be functionally important or structurally delicate regions, with obvious applications in rational protein design.

Bacterial protein structures reveal phylum dependent divergence

Protein sequence space is vast compared to protein fold space. This raises important questions about how structures adapt to evolutionary changes in protein sequences. A growing trend is to regard protein fold space as a continuum rather than a series of discrete structures. From this perspective, homologous protein structures within the same functional classification should reveal a constant rate of structural drift relative to sequence changes. The clusters of orthologous groups (COG) classification system was used to annotate homologous bacterial protein structures in the Protein Data Bank (PDB). The structures and sequences of proteins within each COG were compared against each other to establish their relatedness. As expected, the analysis demonstrates a sharp structural divergence between the bacterial phyla Firmicutes and Proteobacteria. Additionally, each COG had a distinct sequence/structure relationship, indicating that different evolutionary pressures affect the degree of structural divergence. However, our analysis also shows the relative drift rate between sequence identity and structure divergence remains constant.

Thermal adaptation of viruses and bacteria

A previously established multiscale population genetics model posits that fitness can be inferred from the physical properties of proteins under the physiological assumption that a loss of stability by any protein confers the lethal phenotype to an organism. Here, we develop this model further by positing that replication rate (fitness) of a bacterial or viral strain directly depends on the copy number of folded proteins, which determine its replication rate. Using this model, and both numerical and analytical approaches, we studied the adaptation process of bacteria and viruses at varied environmental temperatures. We found that a broad distribution of protein stabilities observed in the model and in experiment is the key determinant of thermal response for viruses and bacteria. Our results explain most of the earlier experimental observations: the striking asymmetry of thermal response curves; the absence of evolutionary tradeoff, which was expected but not found in experiments; correlation between denaturation temperature for several protein families and the optimal growth temperature of their carrier organisms; and proximity of bacterial or viral optimal growth temperatures to their evolutionary temperatures. Our theory quantitatively and with high accuracy described thermal response curves for 35 bacterial species using, for each species, only two adjustable parameters-the number of rate-determining genes and the energy barrier for metabolic reactions.

Optimizing protein stability in vivo

Identifying mutations that stabilize proteins is challenging because most substitutions are destabilizing. In addition to being of immense practical utility, the ability to evolve protein stability in vivo may indicate how evolution has formed today's protein sequences. Here we describe a genetic selection that directly links the in vivo stability of proteins to antibiotic resistance. It allows the identification of stabilizing mutations within proteins. The large majority of mutants selected for improved antibiotic resistance are stabilized both thermodynamically and kinetically, indicating that similar principles govern stability in vivo and in vitro. The approach requires no prior structural or functional knowledge and allows selection for stability without a need to maintain function. Mutations that enhance thermodynamic stability of the protein Im7 map overwhelmingly to surface residues involved in binding to colicin E7, showing how the evolutionary pressures that drive Im7-E7 complex formation have compromised the stability of the isolated Im7 protein.

Inferring stabilizing mutations from protein phylogenies: application to influenza hemagglutinin

One selection pressure shaping sequence evolution is the requirement that a protein fold with sufficient stability to perform its biological functions. We present a conceptual framework that explains how this requirement causes the probability that a particular amino acid mutation is fixed during evolution to depend on its effect on protein stability. We mathematically formalize this framework to develop a Bayesian approach for inferring the stability effects of individual mutations from homologous protein sequences of known phylogeny. This approach is able to predict published experimentally measured mutational stability effects (ΔΔG values) with an accuracy that exceeds both a state-of-the-art physicochemical modeling program and the sequence-based consensus approach. As a further test, we use our phylogenetic inference approach to predict stabilizing mutations to influenza hemagglutinin. We introduce these mutations into a temperature-sensitive influenza virus with a defect in its hemagglutinin gene and experimentally demonstrate that some of the mutations allow the virus to grow at higher temperatures. Our work therefore describes a powerful new approach for predicting stabilizing mutations that can be successfully applied even to large, complex proteins such as hemagglutinin. This approach also makes a mathematical link between phylogenetics and experimentally measurable protein properties, potentially paving the way for more accurate analyses of molecular evolution.

Mutating a protein frequently causes a change in its stability. As scientists, we often care about these changes because we would like to engineer a protein's stability or understand how its stability is impacted by a naturally occurring mutation. Evolution also cares about mutational stability changes, because a basic evolutionary requirement is that proteins remain sufficiently stable to perform their biological functions. Our work is based on the idea that it should be possible to use the fact that evolution selects for stability to infer from related proteins the effects of specific mutations. We show that we can indeed use protein evolutionary histories to computationally predict previously measured mutational stability changes more accurately than methods based on either of the two main existing strategies. We then test whether we can predict mutations that increase the stability of hemagglutinin, an influenza protein whose rapid evolution is partly responsible for the ability of this virus to cause yearly epidemics. We experimentally create viruses carrying predicted stabilizing mutations and find that several do in fact improve the virus's ability to grow at higher temperatures. Our computational approach may therefore be of use in understanding the evolution of this medically important virus.

Relative tolerance of an enzymatic molten globule and its thermostable counterpart to point mutation

Enzyme structures reflect the complex interplay between the free energy of unfolding (DeltaG) and catalytic efficiency. Consequently, the effects of point mutations on structure, stability, and function are difficult to predict. It has been proposed that the mutational robustness of homologous enzymes correlates with a higher initial DeltaG. To examine this issue, we compared the tolerance of a natural thermostable chorismate mutase and an engineered molten globular variant to targeted mutation. These mutases possess similar sequence, structure, and catalytic efficiency but dramatically different DeltaG values. We find that analogous point mutations can have widely divergent effects on catalytic activity in these scaffolds. In a set of five rationally designed single-amino acid changes, the thermostable scaffold suffers activity losses ranging from 50-fold smaller, for an aspartate-to-glycine substitution at the active site, to 2-fold greater, for a phenylalanine-to-tryptophan substitution in the hydrophobic core, versus that of the molten globular scaffold. However, biophysical characterization indicates that the variations in catalytic efficiency are not caused by losses of either secondary structural integrity or thermodynamic stability. Rather, the activity differences between variant pairs are very much context-dependent and likely stem from subtle changes in the fine structure of the active site. Thus, in many cases, it may be more productive to focus on changes in local conformation than on global stability when attempting to understand and predict how enzymes respond to point mutations.

Aggregation propensity of the human proteome

Formation of amyloid-like fibrils is involved in numerous human protein deposition diseases, but is also an intrinsic property of polypeptide chains in general. Progress achieved recently now allows the aggregation propensity of proteins to be analyzed over large scales. In this work we used a previously developed predictive algorithm to analyze the propensity of the 34,180 protein sequences of the human proteome to form amyloid-like fibrils. We show that long proteins have, on average, less intense aggregation peaks than short ones. Human proteins involved in protein deposition diseases do not differ extensively from the rest of the proteome, further demonstrating the generality of protein aggregation. We were also able to reproduce some of the results obtained with other algorithms, demonstrating that they do not depend on the type of computational tool employed. For example, proteins with different subcellular localizations were found to have different aggregation propensities, in relation to the various efficiencies of quality control mechanisms. Membrane proteins, intrinsically disordered proteins, and folded proteins were confirmed to have very different aggregation propensities, as a consequence of their different structures and cellular microenvironments. In addition, gatekeeper residues at strategic positions of the sequences were found to protect human proteins from aggregation. The results of these comparative analyses highlight the existence of intimate links between the propensity of proteins to form aggregates with β-structure and their biology. In particular, they emphasize the existence of a negative selection pressure that finely modulates protein sequences in order to adapt their aggregation propensity to their biological context.

Amyloid-like fibrils are insoluble proteinaceous fibrillar aggregates with a characteristic structure (the cross-β core) that form and deposit in more than 40 pathological conditions in humans. These include Alzheimer's disease, Parkinson's disease, type II diabetes, and the spongiform encephalopathies. A number of proteins not involved in any disease can also form amyloid-like fibrils in vitro, suggesting that amyloid fibril formation is an intrinsic property of proteins in general. Recent efforts in understanding the physico-chemical grounds of amyloid fibril formation has led to the development of several algorithms, capable of predicting a number of aggregation-related parameters of a protein directly from its amino acid sequence. In order to study the predicted aggregation behavior of the human proteome, we have run one of these algorithms on the 34,180 human protein sequences. Our results demonstrate that molecular evolution has acted on protein sequences to finely modulate their aggregation propensities, depending on different parameters related to their in vivo environment. Together with cellular control mechanisms, this natural selection protects proteins from aggregation during their lifetime.

How protein stability and new functions trade off

Numerous studies have noted that the evolution of new enzymatic specificities is accompanied by loss of the protein's thermodynamic stability (ΔΔG), thus suggesting a tradeoff between the acquisition of new enzymatic functions and stability. However, since most mutations are destabilizing (ΔΔG>0), one should ask how destabilizing mutations that confer new or altered enzymatic functions relative to all other mutations are. We applied ΔΔG computations by FoldX to analyze the effects of 548 mutations that arose from the directed evolution of 22 different enzymes. The stability effects, location, and type of function-altering mutations were compared to ΔΔG changes arising from all possible point mutations in the same enzymes. We found that mutations that modulate enzymatic functions are mostly destabilizing (average ΔΔG = +0.9 kcal/mol), and are almost as destabilizing as the “average” mutation in these enzymes (+1.3 kcal/mol). Although their stability effects are not as dramatic as in key catalytic residues, mutations that modify the substrate binding pockets, and thus mediate new enzymatic specificities, place a larger stability burden than surface mutations that underline neutral, non-adaptive evolutionary changes. How are the destabilizing effects of functional mutations balanced to enable adaptation? Our analysis also indicated that many mutations that appear in directed evolution variants with no obvious role in the new function exert stabilizing effects that may compensate for the destabilizing effects of the crucial function-altering mutations. Thus, the evolution of new enzymatic activities, both in nature and in the laboratory, is dependent on the compensatory, stabilizing effect of apparently “silent” mutations in regions of the protein that are irrelevant to its function.

To perform its function, a protein must fold into a complex, three-dimensional structure that is maintained by a network of interactions between its amino acid residues. Evolution of a new protein function will be driven by mutation of amino acids in key positions (new-function mutations). Such mutation can also hamper interactions that ensure the stability of a protein's fold—sometimes to a degree that renders the protein non-functional. Indeed, previous studies have noted that the evolution of new enzymatic functions is accompanied by significant losses in protein stability, suggesting a “tradeoff” between acquisition of new enzymatic functions and stability. But since most mutations are destabilizing, we sought to compare new-function mutations with other types of mutations. We performed a comprehensive analysis of the type, location, and stability effects of mutations that have conferred new enzymatic functions in laboratory evolution experiments. We found that stability changes (ΔΔG) of new-function mutations are similar to those of all other mutations, but are weaker than those of mutations that characterize neutral evolutionary changes (mutations that accumulate with no change of structure and function). Our analysis also revealed the important role of neutral (i.e., “non-functional”) mutations in compensating for the destabilizing effects of the “new-function” mutations.

Prevention of amyloid-like aggregation as a driving force of protein evolution

Uncontrolled protein aggregation is a constant challenge in all compartments of living organisms. The failure of a peptide or protein to remain soluble often results in pathology. So far, more than 40 human diseases have been associated with the formation of extracellular fibrillar aggregates - known as amyloid fibrils - or structurally related intracellular deposits. It is well known that molecular chaperones and elaborate quality control mechanisms exist in the cell to counteract aggregation. However, an increasing number of reports during the past few years indicate that proteins have also evolved structural and sequence-based strategies to prevent aggregation. This review describes these strategies and the selection pressures that exist on protein sequences to combat their uncontrolled aggregation. We will describe the different types of mechanism evolved by proteins that adopt different conformational states including normally folded proteins, intrinsically disordered polypeptide chains, elastomeric systems and multimodular proteins.

Protein stability imposes limits on organism complexity and speed of molecular evolution

Classical population genetics a priori assigns fitness to alleles without considering molecular or functional properties of proteins that these alleles encode. Here we study population dynamics in a model where fitness can be inferred from physical properties of proteins under a physiological assumption that loss of stability of any protein encoded by an essential gene confers a lethal phenotype. Accumulation of mutations in organisms containing Gamma genes can then be represented as diffusion within the Gamma-dimensional hypercube with adsorbing boundaries determined, in each dimension, by loss of a protein's stability and, at higher stability, by lack of protein sequences. Solving the diffusion equation whose parameters are derived from the data on point mutations in proteins, we determine a universal distribution of protein stabilities, in agreement with existing data. The theory provides a fundamental relation between mutation rate, maximal genome size, and thermodynamic response of proteins to point mutations. It establishes a universal speed limit on rate of molecular evolution by predicting that populations go extinct (via lethal mutagenesis) when mutation rate exceeds approximately six mutations per essential part of genome per replication for mesophilic organisms and one to two mutations per genome per replication for thermophilic ones. Several RNA viruses function close to the evolutionary speed limit, whereas error correction mechanisms used by DNA viruses and nonmutant strains of bacteria featuring various genome lengths and mutation rates have brought these organisms universally approximately 1,000-fold below the natural speed limit.

The distribution of residues in a polypeptide sequence is a determinant of aggregation optimized by evolution

It has been shown that the propensity of a protein to form amyloid-like fibrils can be predicted with high accuracy from the knowledge of its amino acid sequence. It has also been suggested, however, that some regions of the sequences are more important than others in determining the aggregation process. Here, we have addressed this issue by constructing a set of "sequence scrambled" variants of the first 29 residues of horse heart apomyoglobin (apoMb(1-29)), in which the sequence was modified while maintaining the same amino acid composition. The clustering of the most amyloidogenic residues in one region of the sequence was found to cause a marked increase of the elongation rate (k(agg)) and a remarkable shortening of the lag phase (t(lag)) of the fibril growth, as determined by far-UV circular dichroism and thioflavin T fluorescence. We also show that taking explicitly into consideration the presence of aggregation-promoting regions in the predictive methods results in a quantitative agreement between the theoretical and observed k(agg) and t(lag) values of the apoMb(1-29) variants. These results, together with a comparison between homologous segments from the family of globins, indicate the existence of a negative selection against the clustering of highly amyloidogenic residues in one or few regions of polypeptide sequences.

Thermodynamics of neutral protein evolution

Naturally evolving proteins gradually accumulate mutations while continuing to fold to stable structures. This process of neutral evolution is an important mode of genetic change and forms the basis for the molecular clock. We present a mathematical theory that predicts the number of accumulated mutations, the index of dispersion, and the distribution of stabilities in an evolving protein population from knowledge of the stability effects (delta deltaG values) for single mutations. Our theory quantitatively describes how neutral evolution leads to marginally stable proteins and provides formulas for calculating how fluctuations in stability can overdisperse the molecular clock. It also shows that the structural influences on the rate of sequence evolution observed in earlier simulations can be calculated using just the single-mutation delta deltaG values. We consider both the case when the product of the population size and mutation rate is small and the case when this product is large, and show that in the latter case the proteins evolve excess mutational robustness that is manifested by extra stability and an increase in the rate of sequence evolution. All our theoretical predictions are confirmed by simulations with lattice proteins. Our work provides a mathematical foundation for understanding how protein biophysics shapes the process of evolution.