Ancestral sequence reconstruction dissects structural and functional differences among eosinophil ribonucleases.

Evolutionarily conserved structural folds can give rise to diverse biological functions, yet predicting atomic-scale interactions that contribute to the emergence of novel activities within such folds remains challenging. Pancreatic-type ribonucleases illustrate this complexity, sharing a core structure that has evolved to accommodate varied functions. In this study, we used ancestral sequence reconstruction to probe evolutionary and molecular determinants that distinguish biological activities within eosinophil members of the RNase 2/3 subfamily. Our investigation unveils functional, structural, and dynamical behaviors that differentiate the evolved ancestral ribonuclease (AncRNase) from its contemporary eosinophil RNase orthologs. Leveraging the potential of ancestral reconstruction for protein engineering, we used AncRNase predictions to design a minimal 4-residue variant that transforms human RNase 2 into a chimeric enzyme endowed with the antimicrobial and cytotoxic activities of RNase 3 members. This work provides unique insights into mutational and evolutionary pathways governing structure, function, and conformational states within the eosinophil RNase subfamily, offering potential for targeted modulation of RNase-associated functions.

Evolution avoids a pathological stabilizing interaction in the immune protein S100A9.

Stability constrains evolution. While much is known about constraints on destabilizing mutations, less is known about the constraints on stabilizing mutations. We recently identified a mutation in the innate immune protein S100A9 that provides insight into such constraints. When introduced into human S100A9, M63F simultaneously increases the stability of the protein and disrupts its natural ability to activate Toll-like receptor 4. Using chemical denaturation, we found that M63F stabilizes a calcium-bound conformation of hS100A9. We then used NMR to solve the structure of the mutant protein, revealing that the mutation distorts the hydrophobic binding surface of hS100A9, explaining its deleterious effect on function. Hydrogen-deuterium exchange (HDX) experiments revealed stabilization of the region around M63F in the structure, notably Phe37. In the structure of the M63F mutant, the Phe37 and Phe63 sidechains are in contact, plausibly forming an edge-face π-stack. Mutating Phe37 to Leu abolished the stabilizing effect of M63F as probed by both chemical denaturation and HDX. It also restored the biological activity of S100A9 disrupted by M63F. These findings reveal that Phe63 creates a molecular staple with Phe37 that stabilizes a nonfunctional conformation of the protein, thus disrupting function. Using a bioinformatic analysis, we found that S100A9 proteins from different organisms rarely have Phe at both positions 37 and 63, suggesting that avoiding a pathological stabilizing interaction indeed constrains S100A9 evolution. This work highlights an important evolutionary constraint on stabilizing mutations, namely, that they must avoid inappropriately stabilizing nonfunctional protein conformations.

A conserved folding nucleus sculpts the free energy landscape of bacterial and archaeal orthologs from a divergent TIM barrel family.

The amino acid sequences of proteins have evolved over billions of years, preserving their structures and functions while responding to evolutionary forces. Are there conserved sequence and structural elements that preserve the protein folding mechanisms? The functionally diverse and ancient (ßα)1-8 TIM barrel motif may answer this question. We mapped the complex six-state folding free energy surface of a ∼3.6 billion y old, bacterial indole-3-glycerol phosphate synthase (IGPS) TIM barrel enzyme by equilibrium and kinetic hydrogen-deuterium exchange mass spectrometry (HDX-MS). HDX-MS on the intact protein reported exchange in the native basin and the presence of two thermodynamically distinct on- and off-pathway intermediates in slow but dynamic equilibrium with each other. Proteolysis revealed protection in a small (α1ß2) and a large cluster (ß5α5ß6α6ß7) and that these clusters form cores of stability in Ia and Ibp The strongest protection in both states resides in ß4α4 with the highest density of branched aliphatic side chain contacts in the folded structure. Similar correlations were observed previously for an evolutionarily distinct archaeal IGPS, emphasizing a key role for hydrophobicity in stabilizing common high-energy folding intermediates. A bioinformatics analysis of IGPS sequences from the three superkingdoms revealed an exceedingly high hydrophobicity and surprising α-helix propensity for ß4, preceded by a highly conserved ßα-hairpin clamp that links ß3 and ß4. The conservation of the folding mechanisms for archaeal and bacterial IGPS proteins reflects the conservation of key elements of sequence and structure that first appeared in the last universal common ancestor of these ancient proteins.

Disentangling contact and ensemble epistasis in a riboswitch.

Mutations introduced into macromolecules often exhibit epistasis, where the effect of one mutation alters the effect of another. Knowing the mechanisms that lead to epistasis is important for understanding how macromolecules work and evolve, as well as for effective macromolecular engineering. Here, we investigate the interplay between "contact epistasis" (epistasis arising from physical interactions between mutated residues) and "ensemble epistasis" (epistasis that occurs when a mutation redistributes the conformational ensemble of a macromolecule, thus changing the effect of the second mutation). We argue that the two mechanisms can be distinguished in allosteric macromolecules by measuring epistasis at differing allosteric effector concentrations. Contact epistasis manifests as nonadditivity in the microscopic equilibrium constants describing the conformational ensemble. This epistatic effect is independent of allosteric effector concentration. Ensemble epistasis manifests as nonadditivity in thermodynamic observables-such as ligand binding-that are determined by the distribution of ensemble conformations. This epistatic effect strongly depends on allosteric effector concentration. Using this framework, we experimentally investigated the origins of epistasis in three pairwise mutant cycles introduced into the adenine riboswitch aptamer domain by measuring ligand binding as a function of allosteric effector concentration. We found evidence for both contact and ensemble epistasis in all cycles. Furthermore, we found that the two mechanisms of epistasis could interact with each other. For example, in one mutant cycle we observed 6 kcal/mol of contact epistasis in a microscopic equilibrium constant. In that same cycle, the maximum epistasis in ligand binding was only 1.5 kcal/mol: shifts in the ensemble masked the contribution of contact epistasis. Finally, our work yields simple heuristics for identifying contact and ensemble epistasis based on measurements of a biochemical observable as a function of allosteric effector concentration.

Identification and Characterization of Zebrafish Tlr4 Coreceptor Md-2.

The zebrafish (Danio rerio) is a powerful model organism for studies of the innate immune system. One apparent difference between human and zebrafish innate immunity is the cellular machinery for LPS sensing. In amniotes, the protein complex formed by TLR4 and myeloid differentiation factor 2 (Tlr4/Md-2) recognizes the bacterial molecule LPS and triggers an inflammatory response. It is believed that zebrafish have neither Md-2 nor Tlr4; Md-2 has not been identified outside of amniotes, whereas the zebrafish tlr4 genes appear to be paralogs, not orthologs, of amniote TLR4s We revisited these conclusions. We identified a zebrafish gene encoding Md-2, ly96 Using single-cell RNA sequencing, we found that ly96 is transcribed in cells that also transcribe genes diagnostic for innate immune cells, including the zebrafish tlr4-like genes. In larval zebrafish, ly96 is expressed in a small number of macrophage-like cells. In a functional assay, zebrafish Md-2 and Tlr4ba form a complex that activates NF-κB signaling in response to LPS. In larval zebrafish ly96 loss-of-function mutations perturbed LPS-induced cytokine production but gave little protection against LPS toxicity. Finally, by analyzing the genomic context of tlr4 genes in 11 jawed vertebrates, we found that tlr4 arose prior to the divergence of teleosts and tetrapods. Thus, an LPS-sensitive Tlr4/Md-2 complex is likely an ancestral feature shared by mammals and zebrafish, rather than a de novo invention on the tetrapod lineage. We hypothesize that zebrafish retain an ancestral, low-sensitivity Tlr4/Md-2 complex that confers LPS responsiveness to a specific subset of innate immune cells.

Were Ancestral Proteins Less Specific?

Some have hypothesized that ancestral proteins were, on average, less specific than their descendants. If true, this would provide a universal axis along which to organize protein evolution and suggests that reconstructed ancestral proteins may be uniquely powerful tools for protein engineering. Ancestral sequence reconstruction studies are one line of evidence used to support this hypothesis. Previously, we performed such a study, investigating the evolution of peptide-binding specificity for the paralogs S100A5 and S100A6. The modern proteins appeared more specific than their last common ancestor (ancA5/A6), as each paralog bound a subset of the peptides bound by ancA5/A6. In this study, we revisit this transition, using quantitative phage display to measure the interactions of 30,533 random peptides with human S100A5, S100A6, and ancA5/A6. This unbiased screen reveals a different picture. While S100A5 and S100A6 do indeed bind to a subset of the peptides recognized by ancA5/A6, they also acquired new peptide partners outside of the set recognized by ancA5/A6. Our previous work showed that ancA5/A6 had lower specificity than its descendants when measured against biological targets; our new work shows that ancA5/A6 has similar specificity to the modern proteins when measured against a random set of peptide targets. This demonstrates that altered biological specificity does not necessarily indicate altered intrinsic specificity, and sounds a cautionary note for using ancestral reconstruction studies with biological targets as a means to infer global evolutionary trends in specificity.

Exploring the Evolutionary History of Kinetic Stability in the α-Lytic Protease Family.

In addition to encoding the tertiary fold and stability, the primary sequence of a protein encodes the folding trajectory and kinetic barriers that determine the speed of folding. How these kinetic barriers are encoded is not well understood. Here, we use evolutionary sequence variation in the α-lytic protease (αLP) protein family to probe the relationship between sequence and energy landscape. αLP has an unusual energy landscape: the native state of αLP is not the most thermodynamically favored conformation and, instead, remains folded due to a large kinetic barrier preventing unfolding. To fold, αLP utilizes an N-terminal pro region similar in size to the protease itself that functions as a folding catalyst. Once folded, the pro region is removed, and the native state does not unfold on a biologically relevant time scale. Without the pro region, αLP folds on the order of millennia. A phylogenetic search uncovers αLP homologs with a wide range of pro region sizes, including some with no pro region at all. In the resulting phylogenetic tree, these homologs cluster by pro region size. By studying homologs naturally lacking a pro region, we demonstrate they can be thermodynamically stable, fold much faster than αLP, yet retain the same fold as αLP. Key amino acids thought to contribute to αLP's extreme kinetic stability are lost in these homologs, supporting their role in kinetic stability. This study highlights how the entire energy landscape plays an important role in determining the evolutionary pressures on the protein sequence.

Inferring a complete genotype-phenotype map from a small number of measured phenotypes.

Understanding evolution requires detailed knowledge of genotype-phenotype maps; however, it can be a herculean task to measure every phenotype in a combinatorial map. We have developed a computational strategy to predict the missing phenotypes from an incomplete, combinatorial genotype-phenotype map. As a test case, we used an incomplete genotype-phenotype dataset previously generated for the malaria parasite's 'chloroquine resistance transporter' (PfCRT). Wild-type PfCRT (PfCRT3D7) lacks significant chloroquine (CQ) transport activity, but the introduction of the eight mutations present in the 'Dd2' isoform of PfCRT (PfCRTDd2) enables the protein to transport CQ away from its site of antimalarial action. This gain of a transport function imparts CQ resistance to the parasite. A combinatorial map between PfCRT3D7 and PfCRTDd2 consists of 256 genotypes, of which only 52 have had their CQ transport activities measured through expression in the Xenopus laevis oocyte. We trained a statistical model with these 52 measurements to infer the CQ transport activity for the remaining 204 combinatorial genotypes between PfCRT3D7 and PfCRTDd2. Our best-performing model incorporated a binary classifier, a nonlinear scale, and additive effects for each mutation. The addition of specific pairwise- and high-order-epistatic coefficients decreased the predictive power of the model. We evaluated our predictions by experimentally measuring the CQ transport activities of 24 additional PfCRT genotypes. The R2 value between our predicted and newly-measured phenotypes was 0.90. We then used the model to probe the accessibility of evolutionary trajectories through the map. Approximately 1% of the possible trajectories between PfCRT3D7 and PfCRTDd2 are accessible; however, none of the trajectories entailed eight successive increases in CQ transport activity. These results demonstrate that phenotypes can be inferred with known uncertainty from a partial genotype-phenotype dataset. We also validated our approach against a collection of previously published genotype-phenotype maps. The model therefore appears general and should be applicable to a large number of genotype-phenotype maps.

Historical contingency and its biophysical basis in glucocorticoid receptor evolution.

Understanding how chance historical events shape evolutionary processes is a central goal of evolutionary biology. Direct insights into the extent and causes of evolutionary contingency have been limited to experimental systems, because it is difficult to know what happened in the deep past and to characterize other paths that evolution could have followed. Here we combine ancestral protein reconstruction, directed evolution and biophysical analysis to explore alternative 'might-have-been' trajectories during the ancient evolution of a novel protein function. We previously found that the evolution of cortisol specificity in the ancestral glucocorticoid receptor (GR) was contingent on permissive substitutions, which had no apparent effect on receptor function but were necessary for GR to tolerate the large-effect mutations that caused the shift in specificity. Here we show that alternative mutations that could have permitted the historical function-switching substitutions are extremely rare in the ensemble of genotypes accessible to the ancestral GR. In a library of thousands of variants of the ancestral protein, we recovered historical permissive substitutions but no alternative permissive genotypes. Using biophysical analysis, we found that permissive mutations must satisfy at least three physical requirements--they must stabilize specific local elements of the protein structure, maintain the correct energetic balance between functional conformations, and be compatible with the ancestral and derived structures--thus revealing why permissive mutations are rare. These findings demonstrate that GR evolution depended strongly on improbable, non-deterministic events, and this contingency arose from intrinsic biophysical properties of the protein.

Molecular ensembles make evolution unpredictable.

Evolutionary prediction is of deep practical and philosophical importance. Here we show, using a simple computational protein model, that protein evolution remains unpredictable, even if one knows the effects of all mutations in an ancestral protein background. We performed a virtual deep mutational scan-revealing the individual and pairwise epistatic effects of every mutation to our model protein-and then used this information to predict evolutionary trajectories. Our predictions were poor. This is a consequence of statistical thermodynamics. Proteins exist as ensembles of similar conformations. The effect of a mutation depends on the relative probabilities of conformations in the ensemble, which in turn, depend on the exact amino acid sequence of the protein. Accumulating substitutions alter the relative probabilities of conformations, thereby changing the effects of future mutations. This manifests itself as subtle but pervasive high-order epistasis. Uncertainty in the effect of each mutation accumulates and undermines prediction. Because conformational ensembles are an inevitable feature of proteins, this is likely universal.

Evolutionary biochemistry: revealing the historical and physical causes of protein properties.

The repertoire of proteins and nucleic acids in the living world is determined by evolution; their properties are determined by the laws of physics and chemistry. Explanations of these two kinds of causality - the purviews of evolutionary biology and biochemistry, respectively - are typically pursued in isolation, but many fundamental questions fall squarely at the interface of fields. Here we articulate the paradigm of evolutionary biochemistry, which aims to dissect the physical mechanisms and evolutionary processes by which biological molecules diversified and to reveal how their physical architecture facilitates and constrains their evolution. We show how an integration of evolution with biochemistry moves us towards a more complete understanding of why biological molecules have the properties that they do.

Evolutionary trend toward kinetic stability in the folding trajectory of RNases H.

Proper folding of proteins is critical to producing the biological machinery essential for cellular function. The rates and energetics of a protein's folding process, which is described by its energy landscape, are encoded in the amino acid sequence. Over the course of evolution, this landscape must be maintained such that the protein folds and remains folded over a biologically relevant time scale. How exactly a protein's energy landscape is maintained or altered throughout evolution is unclear. To study how a protein's energy landscape changed over time, we characterized the folding trajectories of ancestral proteins of the ribonuclease H (RNase H) family using ancestral sequence reconstruction to access the evolutionary history between RNases H from mesophilic and thermophilic bacteria. We found that despite large sequence divergence, the overall folding pathway is conserved over billions of years of evolution. There are robust trends in the rates of protein folding and unfolding; both modern RNases H evolved to be more kinetically stable than their most recent common ancestor. Finally, our study demonstrates how a partially folded intermediate provides a readily adaptable folding landscape by allowing the independent tuning of kinetics and thermodynamics.

pytc: Open-Source Python Software for Global Analyses of Isothermal Titration Calorimetry Data.

Here we describe pytc, an open-source Python package for global fits of thermodynamic models to multiple isothermal titration calorimetry experiments. Key features include simplicity, the ability to implement new thermodynamic models, a robust maximum likelihood fitter, a fast Bayesian Markov-Chain Monte Carlo sampler, rigorous implementation, extensive documentation, and full cross-platform compatibility. pytc fitting can be done using an application program interface or via a graphical user interface. It is available for download at https://github.com/harmslab/pytc .

Conservation of Specificity in Two Low-Specificity Proteins.

Many regulatory proteins bind peptide regions of target proteins and modulate their activity. Such regulatory proteins can often interact with highly diverse target peptides. In many instances, it is not known if the peptide-binding interface discriminates targets in a biological context, or whether biological specificity is achieved exclusively through external factors such as subcellular localization. We used an evolutionary biochemical approach to distinguish these possibilities for two such low-specificity proteins: S100A5 and S100A6. We used isothermal titration calorimetry to study the binding of peptides with diverse sequence and biochemistry to human S100A5 and S100A6. These proteins bound distinct, but overlapping, sets of peptide targets. We then studied the peptide binding properties of orthologs sampled from across five amniote species. Binding specificity was conserved along all lineages, for the last 320 million years, despite the low specificity of each protein. We used ancestral sequence reconstruction to determine the binding specificity of the last common ancestor of the paralogs. The ancestor bound the entire set of peptides bound by modern S100A5 and S100A6 proteins, suggesting that paralog specificity evolved via subfunctionalization. To rule out the possibility that specificity is conserved because it is difficult to modify, we identified a single historical mutation that, when reverted in human S100A5, gave it the ability to bind an S100A6-specific peptide. These results reveal strong evolutionary constraints on peptide binding specificity. Despite being able to bind a large number of targets, the specificity of S100 peptide interfaces is likely important for the biology of these proteins.

Robustness of Reconstructed Ancestral Protein Functions to Statistical Uncertainty.

Hypotheses about the functions of ancient proteins and the effects of historical mutations on them are often tested using ancestral protein reconstruction (APR)-phylogenetic inference of ancestral sequences followed by synthesis and experimental characterization. Usually, some sequence sites are ambiguously reconstructed, with two or more statistically plausible states. The extent to which the inferred functions and mutational effects are robust to uncertainty about the ancestral sequence has not been studied systematically. To address this issue, we reconstructed ancestral proteins in three domain families that have different functions, architectures, and degrees of uncertainty; we then experimentally characterized the functional robustness of these proteins when uncertainty was incorporated using several approaches, including sampling amino acid states from the posterior distribution at each site and incorporating the alternative amino acid state at every ambiguous site in the sequence into a single "worst plausible case" protein. In every case, qualitative conclusions about the ancestral proteins' functions and the effects of key historical mutations were robust to sequence uncertainty, with similar functions observed even when scores of alternate amino acids were incorporated. There was some variation in quantitative descriptors of function among plausible sequences, suggesting that experimentally characterizing robustness is particularly important when quantitative estimates of ancient biochemical parameters are desired. The worst plausible case method appears to provide an efficient strategy for characterizing the functional robustness of ancestral proteins to large amounts of sequence uncertainty. Sampling from the posterior distribution sometimes produced artifactually nonfunctional proteins for sequences reconstructed with substantial ambiguity.

High-order epistasis shapes evolutionary trajectories.

High-order epistasis-where the effect of a mutation is determined by interactions with two or more other mutations-makes small, but detectable, contributions to genotype-fitness maps. While epistasis between pairs of mutations is known to be an important determinant of evolutionary trajectories, the evolutionary consequences of high-order epistasis remain poorly understood. To determine the effect of high-order epistasis on evolutionary trajectories, we computationally removed high-order epistasis from experimental genotype-fitness maps containing all binary combinations of five mutations. We then compared trajectories through maps both with and without high-order epistasis. We found that high-order epistasis strongly shapes the accessibility and probability of evolutionary trajectories. A closer analysis revealed that the magnitude of epistasis, not its order, predicts is effects on evolutionary trajectories. We further find that high-order epistasis makes it impossible to predict evolutionary trajectories from the individual and paired effects of mutations. We therefore conclude that high-order epistasis profoundly shapes evolutionary trajectories through genotype-fitness maps.

Thermodynamic system drift in protein evolution.

Proteins from thermophiles are generally more thermostable than their mesophilic homologs, but little is known about the evolutionary process driving these differences. Here we attempt to understand how the diverse thermostabilities of bacterial ribonuclease H1 (RNH) proteins evolved. RNH proteins from Thermus thermophilus (ttRNH) and Escherichia coli (ecRNH) share similar structures but differ in melting temperature (T(m)) by 20 °C. ttRNH's greater stability is caused in part by the presence of residual structure in the unfolded state, which results in a low heat capacity of unfolding (ΔCp) relative to ecRNH. We first characterized RNH proteins from a variety of extant bacteria and found that Tm correlates with the species' growth temperatures, consistent with environmental selection for stability. We then used ancestral sequence reconstruction to statistically infer evolutionary intermediates along lineages leading to ecRNH and ttRNH from their common ancestor, which existed approximately 3 billion years ago. Finally, we synthesized and experimentally characterized these intermediates. The shared ancestor has a melting temperature between those of ttRNH and ecRNH; the T(m)s of intermediate ancestors along the ttRNH lineage increased gradually over time, while the ecRNH lineage exhibited an abrupt drop in Tm followed by relatively little change. To determine whether the underlying mechanisms for thermostability correlate with the changes in T(m), we measured the thermodynamic basis for stabilization--ΔCp and other thermodynamic parameters--for each of the ancestors. We observed that, while the T(m) changes smoothly, the mechanistic basis for stability fluctuates over evolutionary time. Thus, even while overall stability appears to be strongly driven by selection, the proteins explored a wide variety of mechanisms of stabilization, a phenomenon we call "thermodynamic system drift." This suggests that even on lineages with strong selection to increase stability, proteins have wide latitude to explore sequence space, generating biophysical diversity and potentially opening new evolutionary pathways.

Biophysical mechanisms for large-effect mutations in the evolution of steroid hormone receptors.

The genetic and biophysical mechanisms by which new protein functions evolve is a central question in evolutionary biology, biochemistry, and biophysics. Of particular interest is whether major shifts in protein function are caused by a few mutations of large effect and, if they are, the mechanisms that mediate these changes. Here we combine ancestral protein reconstruction with genetic manipulation and explicit studies of protein structure and dynamics to dissect an ancient and discrete shift in ligand specificity in the steroid receptors, a family of biologically essential hormone-controlled transcription factors. We previously found that the ancestor of the entire steroid receptor family was highly specific for estrogens, but its immediate phylogenetic descendant was sensitive only to androgens, progestogens, and corticosteroids. Here we show that this shift in function was driven primarily by two historical amino acid changes, which caused a ∼70,000-fold change in the ancestral protein's specificity. These replacements subtly changed the chemistry of two amino acids, but they dramatically reduced estrogen sensitivity by introducing an excess of interaction partners into the receptor/estrogen complex, inducing a frustrated ensemble of suboptimal hydrogen bond networks unique to estrogens. This work shows how the protein's architecture and dynamics shaped its evolution, amplifying a few biochemically subtle mutations into major shifts in the energetics and function of the protein.

Evolution of minimal specificity and promiscuity in steroid hormone receptors.

Most proteins are regulated by physical interactions with other molecules; some are highly specific, but others interact with many partners. Despite much speculation, we know little about how and why specificity/promiscuity evolves in natural proteins. It is widely assumed that specific proteins evolved from more promiscuous ancient forms and that most proteins' specificity has been tuned to an optimal state by selection. Here we use ancestral protein reconstruction to trace the evolutionary history of ligand recognition in the steroid hormone receptors (SRs), a family of hormone-regulated animal transcription factors. We resurrected the deepest ancestral proteins in the SR family and characterized the structure-activity relationships by which they distinguished among ligands. We found that that the most ancient split in SR evolution involved a discrete switch from an ancient receptor for aromatized estrogens--including xenobiotics--to a derived receptor that recognized non-aromatized progestagens and corticosteroids. The family's history, viewed in relation to the evolution of their ligands, suggests that SRs evolved according to a principle of minimal specificity: at each point in time, receptors evolved ligand recognition criteria that were just specific enough to parse the set of endogenous substances to which they were exposed. By studying the atomic structures of resurrected SR proteins, we found that their promiscuity evolved because the ancestral binding cavity was larger than the primary ligand and contained excess hydrogen bonding capacity, allowing adventitious recognition of larger molecules with additional functional groups. Our findings provide an historical explanation for the sensitivity of modern SRs to natural and synthetic ligands--including endocrine-disrupting drugs and pollutants--and show that knowledge of history can contribute to ligand prediction. They suggest that SR promiscuity may reflect the limited power of selection within real biological systems to discriminate between perfect and "good enough."