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

Deciphering Structural Traits for Thermal and Kinetic Stability across Protein Family Evolution through Ancestral Sequence Reconstruction

Natural proteins are frequently marginally stable, and an increase in environmental temperature can easily lead to unfolding. As a result, protein engineering to improve protein stability is an area of intensive research. Nonetheless, since there is usually a high degree of structural homology between proteins from thermophilic organisms and their mesophilic counterparts, the identification of structural determinants for thermoadaptation is challenging. Moreover, in many cases, it has become clear that the success of stabilization strategies is often dependent on the evolutionary history of a protein family. In the last few years, the use of ancestral sequence reconstruction (ASR) as a tool for elucidation of the evolutionary history of functional traits of a protein family has gained strength. Here, we used ASR to trace the evolutionary pathways between mesophilic and thermophilic kinases that participate in the biosynthetic pathway of vitamin B1 in bacteria. By combining biophysics approaches, X-ray crystallography, and molecular dynamics simulations, we found that the thermal stability of these enzymes correlates with their kinetic stability, where the highest thermal/kinetic stability is given by an increase in small hydrophobic amino acids that allow a higher number of interatomic hydrophobic contacts, making this type of interaction the main support for stability in this protein architecture. The results highlight the potential benefits of using ASR to explore the evolutionary history of protein sequence and structure to identify traits responsible for the kinetic and thermal stability of any protein architecture.

The importance of input sequence set to consensus-derived proteins and their relationship to reconstructed ancestral proteins

A protein sequence encodes its energy landscape-all the accessible conformations, energetics, and dynamics. The evolutionary relationship between sequence and landscape can be probed phylogenetically by compiling a multiple sequence alignment of homologous sequences and generating common ancestors via Ancestral Sequence Reconstruction or a consensus protein containing the most common amino acid at each position. Both ancestral and consensus proteins are often more stable than their extant homologs-questioning the differences between them and suggesting that both approaches serve as general methods to engineer thermostability. We used the Ribonuclease H family to compare these approaches and evaluate how the evolutionary relationship of the input sequences affects the properties of the resulting consensus protein. While the consensus protein derived from our full Ribonuclease H sequence alignment is structured and active, it neither shows properties of a well-folded protein nor has enhanced stability. In contrast, the consensus protein derived from a phylogenetically-restricted set of sequences is significantly more stable and cooperatively folded, suggesting that cooperativity may be encoded by different mechanisms in separate clades and lost when too many diverse clades are combined to generate a consensus protein. To explore this, we compared pairwise covariance scores using a Potts formalism as well as higher-order sequence correlations using singular value decomposition (SVD). We find the SVD coordinates of a stable consensus sequence are close to coordinates of the analogous ancestor sequence and its descendants, whereas the unstable consensus sequences are outliers in SVD space.

Evolutionary analysis reveals the origin of sodium coupling in glutamate transporters

Secondary active membrane transporters harness the energy of ion gradients to concentrate their substrates. Homologous transporters evolved to couple transport to different ions in response to changing environments and needs. The bases of such diversification, and thus principles of ion coupling, are unexplored. Employing phylogenetics and ancestral protein reconstruction, we investigated sodium-coupled transport in prokaryotic glutamate transporters, a mechanism ubiquitous across life domains and critical to neurotransmitter recycling in humans. We found that the evolutionary transition from sodium-dependent to independent substrate binding to the transporter preceded changes in the coupling mechanism. Structural and functional experiments suggest that the transition entailed allosteric mutations, making sodium binding dispensable without affecting ion-binding sites. Allosteric tuning of transporters' energy landscapes might be a widespread route of their functional diversification.

Extant Sequence Reconstruction: The Accuracy of Ancestral Sequence Reconstructions Evaluated by Extant Sequence Cross-Validation

Ancestral sequence reconstruction (ASR) is a phylogenetic method widely used to analyze the properties of ancient biomolecules and to elucidate mechanisms of molecular evolution. Despite its increasingly widespread application, the accuracy of ASR is currently unknown, as it is generally impossible to compare resurrected proteins to the true ancestors. Which evolutionary models are best for ASR? How accurate are the resulting inferences? Here we answer these questions using a cross-validation method to reconstruct each extant sequence in an alignment with ASR methodology, a method we term "extant sequence reconstruction" (ESR). We thus can evaluate the accuracy of ASR methodology by comparing ESR reconstructions to the corresponding known true sequences. We find that a common measure of the quality of a reconstructed sequence, the average probability, is indeed a good estimate of the fraction of correct amino acids when the evolutionary model is accurate or overparameterized. However, the average probability is a poor measure for comparing reconstructions from different models, because, surprisingly, a more accurate phylogenetic model often results in reconstructions with lower probability. While better (more predictive) models may produce reconstructions with lower sequence identity to the true sequences, better models nevertheless produce reconstructions that are more biophysically similar to true ancestors. In addition, we find that a large fraction of sequences sampled from the reconstruction distribution may have fewer errors than the single most probable (SMP) sequence reconstruction, despite the fact that the SMP has the lowest expected error of all possible sequences. Our results emphasize the importance of model selection for ASR and the usefulness of sampling sequence reconstructions for analyzing ancestral protein properties. ESR is a powerful method for validating the evolutionary models used for ASR and can be applied in practice to any phylogenetic analysis of real biological sequences. Most significantly, ESR uses ASR methodology to provide a general method by which the biophysical properties of resurrected proteins can be compared to the properties of the true protein.

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

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

Selection pressures on evolution of ribonuclease H explored with rigorous free-energy-based design

Understanding natural protein evolution and designing novel proteins are motivating interest in development of high-throughput methods to explore large sequence spaces. In this work, we demonstrate the application of multisite λ dynamics (MSλD), a rigorous free energy simulation method, and chemical denaturation experiments to quantify evolutionary selection pressure from sequence-stability relationships and to address questions of design. This study examines a mesophilic phylogenetic clade of ribonuclease H (RNase H), furthering its extensive characterization in earlier studies, focusing on E. coli RNase H (ecRNH) and a more stable consensus sequence (AncCcons) differing at 15 positions. The stabilities of 32,768 chimeras between these two sequences were computed using the MSλD framework. The most stable and least stable chimeras were predicted and tested along with several other sequences, revealing a designed chimera with approximately the same stability increase as AncCcons, but requiring only half the mutations. Comparing the computed stabilities with experiment for 12 sequences reveals a Pearson correlation of 0.86 and root mean squared error of 1.18 kcal/mol, an unprecedented level of accuracy well beyond less rigorous computational design methods. We then quantified selection pressure using a simple evolutionary model in which sequences are selected according to the Boltzmann factor of their stability. Selection temperatures from 110 to 168 K are estimated in three ways by comparing experimental and computational results to evolutionary models. These estimates indicate selection pressure is high, which has implications for evolutionary dynamics and for the accuracy required for design, and suggests accurate high-throughput computational methods like MSλD may enable more effective protein design.

The importance of input sequence set to consensus-derived proteins and their relationship to reconstructed ancestral proteins

A protein sequence encodes its energy landscape - all the accessible conformations, energetics, and dynamics. The evolutionary relationship between sequence and landscape can be probed phylogenetically by compiling a multiple sequence alignment of homologous sequences and generating common ancestors via Ancestral Sequence Reconstruction or a consensus protein containing the most common amino acid at each position. Both ancestral and consensus proteins are often more stable than their extant homologs - questioning the differences and suggesting that both approaches serve as general methods to engineer thermostability. We used the Ribonuclease H family to compare these approaches and evaluate how the evolutionary relationship of the input sequences affects the properties of the resulting consensus protein. While the overall consensus protein is structured and active, it neither shows properties of a well-folded protein nor has enhanced stability. In contrast, the consensus protein derived from a phylogenetically-restricted region is significantly more stable and cooperatively folded, suggesting that cooperativity may be encoded by different mechanisms in separate clades and lost when too many diverse clades are combined to generate a consensus protein. To explore this, we compared pairwise covariance scores using a Potts formalism as well as higher-order couplings using singular value decomposition (SVD). We find the SVD coordinates of a stable consensus sequence are close to coordinates of the analogous ancestor sequence and its descendants, whereas the unstable consensus sequences are outliers in SVD space.

Probing the origin of prion protein misfolding via reconstruction of ancestral proteins

Prion diseases are fatal neurodegenerative diseases caused by pathogenic misfolding of the prion protein, PrP. They are transmissible between hosts, and sometimes between different species, as with transmission of bovine spongiform encephalopathy to humans. Although PrP is found in a wide range of vertebrates, prion diseases are seen only in certain mammals, suggesting that infectious misfolding was a recent evolutionary development. To explore when PrP acquired the ability to misfold infectiously, we reconstructed the sequences of ancestral versions of PrP from the last common primate, primate-rodent, artiodactyl, placental, bird, and amniote. Recombinant ancestral PrPs were then tested for their ability to form β-sheet aggregates, either spontaneously or when seeded with infectious prion strains from human, cervid, or rodent species. The ability to aggregate developed after the oldest ancestor (last common amniote), and aggregation capabilities diverged along evolutionary pathways consistent with modern-day susceptibilities. Ancestral bird PrP could not be seeded with modern-day prions, just as modern-day birds are resistant to prion disease. Computational modeling of structures suggested that differences in helix 2 could account for the resistance of ancestral bird PrP to seeding. Interestingly, ancestral primate PrP could be converted by all prion seeds, including both human and cervid prions, raising the possibility that species descended from an ancestral primate have retained the susceptibility to conversion by cervid prions. More generally, the results suggest that susceptibility to prion disease emerged prior to ~100 million years ago, with placental mammals possibly being generally susceptible to disease.

Evolution of the folding landscape of effector caspases

Caspases are a family of cysteinyl proteases that control programmed cell death and maintain homeostasis in multicellular organisms. The caspase family is an excellent model to study protein evolution because all caspases are produced as zymogens (procaspases [PCPs]) that must be activated to gain full activity; the protein structures are conserved through hundreds of millions of years of evolution; and some allosteric features arose with the early ancestor, whereas others are more recent evolutionary events. The apoptotic caspases evolved from a common ancestor (CA) into two distinct subfamilies: monomers (initiator caspases) or dimers (effector caspases). Differences in activation mechanisms of the two subfamilies, and their oligomeric forms, play a central role in the regulation of apoptosis. Here, we examine changes in the folding landscape by characterizing human effector caspases and their CA. The results show that the effector caspases unfold by a minimum three-state equilibrium model at pH 7.5, where the native dimer is in equilibrium with a partially folded monomeric (PCP-7, CA) or dimeric (PCP-6) intermediate. In comparison, the unfolding pathway of PCP-3 contains both oligomeric forms of the intermediate. Overall, the data show that the folding landscape was first established with the CA and was retained for >650 million years. Partially folded monomeric or dimeric intermediates in the ancestral ensemble provide mechanisms for evolutionary changes that affect stability of extant caspases. The conserved folding landscape allows for the fine-tuning of enzyme stability in a species-dependent manner while retaining the overall caspase–hemoglobinase fold.

Evolution, folding, and design of TIM barrels and related proteins

Proteins are chief actors in life that perform a myriad of exquisite functions. This diversity has been enabled through the evolution and diversification of protein folds. Analysis of sequences and structures strongly suggest that numerous protein pieces have been reused as building blocks and propagated to many modern folds. This information can be traced to understand how the protein world has diversified. In this review, we discuss the latest advances in the analysis of protein evolutionary units, and we use as a model system one of the most abundant and versatile topologies, the TIM-barrel fold, to highlight the existing common principles that interconnect protein evolution, structure, folding, function, and design.

Co-translational folding of nascent polypeptides: Multi-layered mechanisms for the efficient biogenesis of functional proteins

The coupling of protein synthesis and folding is a crucial yet poorly understood aspect of cellular protein folding. Over the past few years, it has become possible to experimentally follow and define protein folding on the ribosome, revealing principles that shape co-translational folding and distinguish it from refolding in solution. Here, we highlight some of these recent findings from biochemical and biophysical studies and their potential significance for cellular protein biogenesis. In particular, we focus on nascent chain interactions with the ribosome, interactions within the nascent protein, modulation of translation elongation rates, and the role of mechanical force that accompanies nascent protein folding. The ability to obtain mechanistic insight in molecular detail has set the stage for exploring the intricate process of nascent protein folding. We believe that the aspects discussed here will be generally important for understanding how protein synthesis and folding are coupled and regulated.

Avoidance of protein unfolding constrains protein stability in long-term evolution

Every amino acid residue can influence a protein's overall stability, making stability highly susceptible to change throughout evolution. We consider the distribution of protein stabilities evolutionarily permittable under two previously reported protein fitness functions: flux dynamics and misfolding avoidance. We develop an evolutionary dynamics theory and find that it agrees better with an extensive protein stability data set for dihydrofolate reductase orthologs under the misfolding avoidance fitness function rather than the flux dynamics fitness function. Further investigation with ribonuclease H data demonstrates that not any misfolded state is avoided; rather, it is only the unfolded state. At the end, we discuss how our work pertains to the universal protein abundance-evolutionary rate correlation seen across organisms' proteomes. We derive a closed-form expression relating protein abundance to evolutionary rate that captures Escherichia coli, Saccharomyces cerevisiae, and Homo sapiens experimental trends without fitted parameters.

Local Interactions That Contribute Minimal Frustration Determine Foldability

Earlier experiments suggest that the evolutionary information (conservation and coevolution) encoded in protein sequences is necessary and sufficient to specify the fold of a protein family. However, there is no computational work to quantify the effect of such evolutionary information on the folding process. Here we explore the role of early folding steps for sequences designed using coevolution and conservation through a combination of computational and experimental methods. We simulated a repertoire of native and designed WW domain sequences to analyze early local contact formation and found that the N-terminal β-hairpin turn would not form correctly due to strong non-native local contacts in unfoldable sequences. Through a maximum likelihood approach, we identified five local contacts that play a critical role in folding, suggesting that a small subset of amino acid pairs can be used to solve the "needle in the haystack" problem to design foldable sequences. Thus, using the contact probability of those five local contacts that form during the early stage of folding, we built a classification model that predicts the foldability of a WW sequence with 81% accuracy. This classification model was used to redesign WW domain sequences that could not fold due to frustration and make them foldable by introducing a few mutations that led to the stabilization of these critical local contacts. The experimental analysis shows that a redesigned sequence folds and binds to polyproline peptides with a similar affinity as those observed for native WW domains. Overall, our analysis shows that evolutionary-designed sequences should not only satisfy the folding stability but also ensure a minimally frustrated folding landscape.

DeCoDe: degenerate codon design for complete protein-coding DNA libraries

MOTIVATION

High-throughput protein screening is a critical technique for dissecting and designing protein function. Libraries for these assays can be created through a number of means, including targeted or random mutagenesis of a template protein sequence or direct DNA synthesis. However, mutagenic library construction methods often yield vastly more nonfunctional than functional variants and, despite advances in large-scale DNA synthesis, individual synthesis of each desired DNA template is often prohibitively expensive. Consequently, many protein-screening libraries rely on the use of degenerate codons (DCs), mixtures of DNA bases incorporated at specific positions during DNA synthesis, to generate highly diverse protein-variant pools from only a few low-cost synthesis reactions. However, selecting DCs for sets of sequences that covary at multiple positions dramatically increases the difficulty of designing a DC library and leads to the creation of many undesired variants that can quickly outstrip screening capacity.

RESULTS

We introduce a novel algorithm for total DC library optimization, degenerate codon design (DeCoDe), based on integer linear programming. DeCoDe significantly outperforms state-of-the-art DC optimization algorithms and scales well to more than a hundred proteins sharing complex patterns of covariation (e.g. the lab-derived avGFP lineage). Moreover, DeCoDe is, to our knowledge, the first DC design algorithm with the capability to encode mixed-length protein libraries. We anticipate DeCoDe to be broadly useful for a variety of library generation problems, ranging from protein engineering attempts that leverage mutual information to the reconstruction of ancestral protein states.

AVAILABILITY AND IMPLEMENTATION

github.com/OrensteinLab/DeCoDe.

CONTACT

yaronore@bgu.ac.il.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

The unfolding transition state of ubiquitin with charged residues has higher energy than that with hydrophobic residues

The native-state structure and folding pathways of a protein are encoded in its amino acid sequence.

Folding Rate Optimization Promotes Frustrated Interactions in Entangled Protein Structures

Many native structures of proteins accomodate complex topological motifs such as knots, lassos, and other geometrical entanglements. How proteins can fold quickly even in the presence of such topological obstacles is a debated question in structural biology. Recently, the hypothesis that energetic frustration might be a mechanism to avoid topological frustration has been put forward based on the empirical observation that loops involved in entanglements are stabilized by weak interactions between amino-acids at their extrema. To verify this idea, we use a toy lattice model for the folding of proteins into two almost identical structures, one entangled and one not. As expected, the folding time is longer when random sequences folds into the entangled structure. This holds also under an evolutionary pressure simulated by optimizing the folding time. It turns out that optmized protein sequences in the entangled structure are in fact characterized by frustrated interactions at the closures of entangled loops. This phenomenon is much less enhanced in the control case where the entanglement is not present. Our findings, which are in agreement with experimental observations, corroborate the idea that an evolutionary pressure shapes the folding funnel to avoid topological and kinetic traps.

Deciphering protein evolution and fitness landscapes with latent space models

Protein sequences contain rich information about protein evolution, fitness landscapes, and stability. Here we investigate how latent space models trained using variational auto-encoders can infer these properties from sequences. Using both simulated and real sequences, we show that the low dimensional latent space representation of sequences, calculated using the encoder model, captures both evolutionary and ancestral relationships between sequences. Together with experimental fitness data and Gaussian process regression, the latent space representation also enables learning the protein fitness landscape in a continuous low dimensional space. Moreover, the model is also useful in predicting protein mutational stability landscapes and quantifying the importance of stability in shaping protein evolution. Overall, we illustrate that the latent space models learned using variational auto-encoders provide a mechanism for exploration of the rich data contained in protein sequences regarding evolution, fitness and stability and hence are well-suited to help guide protein engineering efforts.

Weak Chemical Interactions That Drive Protein Evolution: Crowding, Sticking, and Quinary Structure in Folding and Function

In recent years, better instrumentation and greater computing power have enabled the imaging of elusive biomolecule dynamics in cells, driving many advances in understanding the chemical organization of biological systems. The focus of this Review is on interactions in the cell that affect both biomolecular stability and function and modulate them. The same protein or nucleic acid can behave differently depending on the time in the cell cycle, the location in a specific compartment, or the stresses acting on the cell. We describe in detail the crowding, sticking, and quinary structure in the cell and the current methods to quantify them both in vitro and in vivo. Finally, we discuss protein evolution in the cell in light of current biophysical evidence. We describe the factors that drive protein evolution and shape protein interaction networks. These interactions can significantly affect the free energy, ΔG, of marginally stable and low-population proteins and, due to epistasis, direct the evolutionary pathways in an organism. We finally conclude by providing an outlook on experiments to come and the possibility of collaborative evolutionary biology and biophysical efforts.

Computational Approaches and Challenges to Developing Universal Influenza Vaccines

The traditional design of effective vaccines for rapidly-evolving pathogens, such as influenza A virus, has failed to provide broad spectrum and long-lasting protection. With low cost whole genome sequencing technology and powerful computing capabilities, novel computational approaches have demonstrated the potential to facilitate the design of a universal influenza vaccine. However, few studies have integrated computational optimization in the design and discovery of new vaccines. Understanding the potential of computational vaccine design is necessary before these approaches can be implemented on a broad scale. This review summarizes some promising computational approaches under current development, including computationally optimized broadly reactive antigens with consensus sequences, phylogenetic model-based ancestral sequence reconstruction, and immunomics to compute conserved cross-reactive T-cell epitopes. Interactions between virus-host-environment determine the evolvability of the influenza population. We propose that with the development of novel technologies that allow the integration of data sources such as protein structural modeling, host antibody repertoire analysis and advanced phylodynamic modeling, computational approaches will be crucial for the development of a long-lasting universal influenza vaccine. Taken together, computational approaches are powerful and promising tools for the development of a universal influenza vaccine with durable and broad protection.

The Molecular Mechanisms Underlying Hidden Phenotypic Variation among Metallo-β-Lactamases

Genetic variation among orthologous genes has been largely formed through neutral genetic drift while maintaining the functional role of these genes. However, because the evolution of gene occurs in the context of each host organism, their sequence changes are also associated with adaptation to a specific environment. Thus, genetic variation can create critical phenotypic variation, particularly when genes are transferred to a new host by horizontal gene transfer. Unveiling "hidden phenotypic variation" is particularly important for genes that confer resistance to antibiotics. However, our understanding of the molecular mechanisms that underlie phenotypic variation remains limited. Here we sought to determine the extent of phenotypic variation in the B1 metallo-β-lactamase (MBL) family and its molecular basis by systematically characterizing eight MBL orthologs, including NDM-1 and VIM-2 and IMP-1. We found that these MBLs confer diverse levels of resistance. The phenotypic variation cannot be explained by variation in catalytic efficiency alone; rather, it is the combination of the catalytic efficiency and abundance of functional periplasmic enzyme that best predicts the observed variation in resistance. The level of functional periplasmic expression varied dramatically between MBL orthologs. This was the result of changes at multiple levels of each ortholog's: (1) quantity of mRNA, (2) amount of MBL expressed, and (3) efficacy of functional enzyme translocation to the periplasm. Overall, it is the interaction between each gene and the host's underlying cellular processes (transcription, translation, and translocation) that determines MBL genetic incompatibility through horizontal gene transfer. These host-specific processes may constrain the effective spread and deployment of MBLs to certain host species and could explain the current observed distribution bias.

Cryptic genetic variation shapes the adaptive evolutionary potential of enzymes

Genetic variation among orthologous proteins can cause cryptic phenotypic properties that only manifest in changing environments. Such variation may impact the evolvability of proteins, but the underlying molecular basis remains unclear. Here, we performed comparative directed evolution of four orthologous metallo-β-lactamases toward a new function and found that different starting genotypes evolved to distinct evolutionary outcomes. Despite a low initial fitness, one ortholog reached a significantly higher fitness plateau than its counterparts, via increasing catalytic activity. By contrast, the ortholog with the highest initial activity evolved to a less-optimal and phenotypically distinct outcome through changes in expression, oligomerization and activity. We show how cryptic molecular properties and conformational variation of active site residues in the initial genotypes cause epistasis, that could lead to distinct evolutionary outcomes. Our work highlights the importance of understanding the molecular details that connect genetic variation to protein function to improve the prediction of protein evolution.

Superfunneled Energy Landscape of Protein Evolution Unifies the Principles of Protein Evolution, Folding, and Design

Evolution is essential for shaping the biological functions. Darwin proposed the selection as the driving force for evolution upon mutations. While mutations are clear, the quantification of the selection force is still challenging. In this study, we identified and quantified both thermodynamic stability and kinetic accessibility as the selection forces for protein evolution. The protein evolution can be viewed and quantified as a trajectory moving along a superfunneled energy landscape with a line attractor at the bottom. The resulting evolved sequences and structures show strong protein characteristics including the hydrophobic core, high designability, and fast folding. The evolution principle uncovered here is validated on real proteins and sheds light on the protein design.

Structure and dynamics conspire in the evolution of affinity between intrinsically disordered proteins

In every established species, protein-protein interactions have evolved such that they are fit for purpose. However, the molecular details of the evolution of new protein-protein interactions are poorly understood. We have used nuclear magnetic resonance spectroscopy to investigate the changes in structure and dynamics during the evolution of a protein-protein interaction involving the intrinsically disordered CREBBP (CREB-binding protein) interaction domain (CID) and nuclear coactivator binding domain (NCBD) from the transcriptional coregulators NCOA (nuclear receptor coactivator) and CREBBP/p300, respectively. The most ancient low-affinity "Cambrian-like" [540 to 600 million years (Ma) ago] CID/NCBD complex contained less secondary structure and was more dynamic than the complexes from an evolutionarily younger "Ordovician-Silurian" fish ancestor (ca. 440 Ma ago) and extant human. The most ancient Cambrian-like CID/NCBD complex lacked one helix and several interdomain interactions, resulting in a larger solvent-accessible surface area. Furthermore, the most ancient complex had a high degree of millisecond-to-microsecond dynamics distributed along the entire sequences of both CID and NCBD. These motions were reduced in the Ordovician-Silurian CID/NCBD complex and further redistributed in the extant human CID/NCBD complex. Isothermal calorimetry experiments show that complex formation is enthalpically favorable and that affinity is modulated by a largely unfavorable entropic contribution to binding. Our data demonstrate how changes in structure and motion conspire to shape affinity during the evolution of a protein-protein complex and provide direct evidence for the role of structural, dynamic, and frustrational plasticity in the evolution of interactions between intrinsically disordered proteins.

Tracing a protein's folding pathway over evolutionary time using ancestral sequence reconstruction and hydrogen exchange

The conformations populated during protein folding have been studied for decades; yet, their evolutionary importance remains largely unexplored. Ancestral sequence reconstruction allows access to proteins across evolutionary time, and new methods such as pulsed-labeling hydrogen exchange coupled with mass spectrometry allow determination of folding intermediate structures at near amino-acid resolution. Here, we combine these techniques to monitor the folding of the ribonuclease H family along the evolutionary lineages of T. thermophilus and E. coli RNase H. All homologs and ancestral proteins studied populate a similar folding intermediate despite being separated by billions of years of evolution. Even though this conformation is conserved, the pathway leading to it has diverged over evolutionary time, and rational mutations can alter this trajectory. Our results demonstrate that evolutionary processes can affect the energy landscape to preserve or alter specific features of a protein’s folding pathway.

The burst-phase folding intermediate of ribonuclease H changes conformation over evolutionary history

The amino acid sequence encodes the energy landscape of a protein. Therefore, we expect evolutionary mutations to change features of the protein energy landscape, including the conformations adopted by a polypeptide as it folds to its native state. Ribonucleases H (RNase H) from Escherichia coli and Thermus thermophilus both fold via a partially folded intermediate in which the core region of the protein (helices A-D and strands 4-5) is structured. Strand 1, however, uniquely contributes to the T. thermophilus RNase H folding intermediate (Icore+1 ), but not the E. coli RNase H intermediate (Icore ) (Rosen & Marqusee, PLoS One 2015). We explore the origin of this difference by characterizing the folding intermediate of seven ancestral RNases H spanning the evolutionary history of these two homologs. Using fragment models with or without strand 1 and FRET probes to characterize the folding intermediate of each ancestor, we find a distinct evolutionary trend across the family-the involvement of strand 1 in the folding intermediate is an ancestral feature that is maintained in the thermophilic lineage and is gradually lost in the mesophilic lineage. Evolutionary sequence changes indeed modulate the conformations present on the folding landscape and altered the folding trajectory of RNase H.

Position-Specific contribution of interface tryptophans on membrane protein energetics

Interface tryptophans are key residues that facilitate the folding and stability of membrane proteins. Escherichia coli OmpX possesses two unique interface tryptophans, namely Trp76, which is present at the interface and is solvent-exposed, and Trp140, which is relatively more lipid solvated than Trp76 in symmetric lipid membranes. Here, we address the requirement for tryptophan and the consequences of aromatic amino acid substitutions on the folding and stability of OmpX. Using spectroscopic measurements of OmpX-Trp/Tyr/Phe mutants, we show that the specific mutation W76→Y allows barrel assembly >1.5-fold faster than native OmpX, and increases stability by ~0.4kcalmol^-1. In contrast, mutating W140→F/Y lowers OmpX thermodynamic stability by ~0.4kcalmol^-1, without affecting the folding kinetics. We conclude that the stabilizing effect of tryptophan at the membrane interface can be position-and local environment-specific. We propose that the thermodynamic contributions for interface residues be interpreted with caution.