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

Hinge-shift mechanism as a protein design principle for the evolution of β-lactamases from substrate promiscuity to specificity

TEM-1 β-lactamase degrades β-lactam antibiotics with a strong preference for penicillins. Sequence reconstruction studies indicate that it evolved from ancestral enzymes that degraded a variety of β-lactam antibiotics with moderate efficiency. This generalist to specialist conversion involved more than 100 mutational changes, but conserved fold and catalytic residues, suggesting a role for dynamics in enzyme evolution. Here, we develop a conformational dynamics computational approach to rationally mold a protein flexibility profile on the basis of a hinge-shift mechanism. By deliberately weighting and altering the conformational dynamics of a putative Precambrian β-lactamase, we engineer enzyme specificity that mimics the modern TEM-1 β-lactamase with only 21 amino acid replacements. Our conformational dynamics design thus re-enacts the evolutionary process and provides a rational allosteric approach for manipulating function while conserving the enzyme active site.

Ancestral sequence reconstruction - An underused approach to understand the evolution of gene function in plants?

Whilst substantial research effort has been placed on understanding the interactions of plant proteins with their molecular partners, relatively few studies in plants - by contrast to work in other organisms - address how these interactions evolve. It is thought that ancestral proteins were more promiscuous than modern proteins and that specificity often evolved following gene duplication and subsequent functional refining. However, ancestral protein resurrection studies have found that some modern proteins have evolved de novo from ancestors lacking those functions. Intriguingly, the new interactions evolved as a consequence of just a few mutations and, as such, acquisition of new functions appears to be neither difficult nor rare, however, only a few of them are incorporated into biological processes before they are lost to subsequent mutations. Here, we detail the approach of ancestral sequence reconstruction (ASR), providing a primer to reconstruct the sequence of an ancestral gene. We will present case studies from a range of different eukaryotes before discussing the few instances where ancestral reconstructions have been used in plants. As ASR is used to dig into the remote evolutionary past, we will also present some alternative genetic approaches to investigate molecular evolution on shorter timescales. We argue that the study of plant secondary metabolism is particularly well suited for ancestral reconstruction studies. Indeed, its ancient evolutionary roots and highly diverse landscape provide an ideal context in which to address the focal issue around the emergence of evolutionary novelties and how this affects the chemical diversification of plant metabolism.

In vitro selected GUAA tetraloop-binding receptors with structural plasticity and evolvability towards natural RNA structural modules

GNRA tetraloop-binding receptor interactions are key components in the macromolecular assembly of a variety of functional RNAs. In nature, there is an apparent bias for GAAA/11nt receptor and GYRA/helix interactions, with the former interaction being thermodynamically more stable than the latter. While past in vitro selections allowed isolation of novel GGAA and GUGA receptors, we report herein an in vitro selection that revealed several novel classes of specific GUAA receptors with binding affinities comparable to those from natural GAAA/11nt interactions. These GUAA receptors have structural homology with double-locked bulge RNA modules naturally occurring in ribosomal RNAs. They display mutational robustness that enables exploration of the sequence/phenotypic space associated to GNRA/receptor interactions through epistasis. Their thermodynamic self-assembly fitness landscape is characterized by a rugged neutral network with possible evolutionary trajectories toward natural GNRA/receptor interactions. High throughput sequencing analysis revealed synergetic mutations located away from the tertiary interactions that positively contribute to assembly fitness. Our study suggests that the repertoire of GNRA/receptor interactions is much larger than initially thought from the analysis of natural stable RNA molecules and also provides clues for their evolution towards natural GNRA/receptors.

Can constraint network analysis guide the identification phase of KnowVolution? A case study on improved thermostability of an endo-β-glucanase

Cellulases are industrially important enzymes, e.g., in the production of bioethanol, in pulp and paper industry, feedstock, and textile. Thermostability is often a prerequisite for high process stability and improving thermostability without affecting specific activities at lower temperatures is challenging and often time-consuming. Protein engineering strategies that combine experimental and computational are emerging in order to reduce experimental screening efforts and speed up enzyme engineering campaigns. Constraint Network Analysis (CNA) is a promising computational method that identifies beneficial positions in enzymes to improve thermostability. In this study, we compare CNA and directed evolution in the identification of beneficial positions in order to evaluate the potential of CNA in protein engineering campaigns (e.g., in the identification phase of KnowVolution). We engineered the industrially relevant endoglucanase EGLII from Penicillium verruculosum towards increased thermostability. From the CNA approach, six variants were obtained with an up to 2-fold improvement in thermostability. The overall experimental burden was reduced to 40% utilizing the CNA method in comparison to directed evolution. On a variant level, the success rate was similar for both strategies, with 0.27% and 0.18% improved variants in the epPCR and CNA-guided library, respectively. In essence, CNA is an effective method for identification of positions that improve thermostability.

Funneled energy landscape unifies principles of protein binding and evolution

Most proteins have evolved to spontaneously fold into native structure and specifically bind with their partners for the purpose of fulfilling biological functions. According to Darwin, protein sequences evolve through random mutations, and only the fittest survives. The understanding of how the evolutionary selection sculpts the interaction patterns for both biomolecular folding and binding is still challenging. In this study, we incorporated the constraint of functional binding into the selection fitness based on the principle of minimal frustration for the underlying biomolecular interactions. Thermodynamic stability and kinetic accessibility were derived and quantified from a global funneled energy landscape that satisfies the requirements of both the folding into the stable structure and binding with the specific partner. The evolution proceeds via a bowl-like evolution energy landscape in the sequence space with a closed-ring attractor at the bottom. The sequence space is increasingly reduced until this ring attractor is reached. The molecular-interaction patterns responsible for folding and binding are identified from the evolved sequences, respectively. The residual positions participating in the interactions responsible for folding are highly conserved and maintain the hydrophobic core under additional evolutionary constraints of functional binding. The positions responsible for binding constitute a distributed network via coupling conservations that determine the specificity of binding with the partner. This work unifies the principles of protein binding and evolution under minimal frustration and sheds light on the evolutionary design of proteins for functions.

Contingency between Historical Substitutions in the Acetylcholine Receptor Pore

Human adult muscle-type acetylcholine receptors incorporating a reconstructed ancestral β-subunit exhibit reduced single-channel conductance when compared to wild-type. The ancestral and wild-type β-subunits differ by 132 amino acids, including substitution of residues that line the lumen of the channel pore, near its narrowest constriction. Here we show that a single historical substitution in this region of the ancestral β-subunit accounts for the difference in conductance. Furthermore, the contribution of the substituted residue to conductance is dependent upon its ancestral or wild-type background, and it can be modulated by a neighboring residue that has also evolved throughout the β-subunit history. Using an expanded molecular phylogeny, we track the order in which these two mutations occurred and then show that the order in which they are installed upon the ancestral, but not the human, background determines their individual contribution to conductance. Our results show how the contribution of amino acids to acetylcholine receptor conductance is contingent upon their evolutionary history and that the order in which substitutions occurred was important for shaping conductance in the modern-day receptor.

What Has a Century of Quantitative Genetics Taught Us About Nature's Genetic Tool Kit?

The complexity of heredity has been appreciated for decades: Many traits are controlled not by a single genetic locus but instead by polymorphisms throughout the genome. The importance of complex traits in biology and medicine has motivated diverse approaches to understanding their detailed genetic bases. Here, we focus on recent systematic studies, many in budding yeast, which have revealed that large numbers of all kinds of molecular variation, from noncoding to synonymous variants, can make significant contributions to phenotype. Variants can affect different traits in opposing directions, and their contributions can be modified by both the environment and the epigenetic state of the cell. The integration of prospective (synthesizing and analyzing variants) and retrospective (examining standing variation) approaches promises to reveal how natural selection shapes quantitative traits. Only by comprehensively understanding nature's genetic tool kit can we predict how phenotypes arise from the complex ensembles of genetic variants in living organisms.

Deep Mutational Scanning of SARS-CoV-2 Receptor Binding Domain Reveals Constraints on Folding and ACE2 Binding

The receptor binding domain (RBD) of the SARS-CoV-2 spike glycoprotein mediates viral attachment to ACE2 receptor and is a major determinant of host range and a dominant target of neutralizing antibodies. Here, we experimentally measure how all amino acid mutations to the RBD affect expression of folded protein and its affinity for ACE2. Most mutations are deleterious for RBD expression and ACE2 binding, and we identify constrained regions on the RBD's surface that may be desirable targets for vaccines and antibody-based therapeutics. But a substantial number of mutations are well tolerated or even enhance ACE2 binding, including at ACE2 interface residues that vary across SARS-related coronaviruses. However, we find no evidence that these ACE2-affinity-enhancing mutations have been selected in current SARS-CoV-2 pandemic isolates. We present an interactive visualization and open analysis pipeline to facilitate use of our dataset for vaccine design and functional annotation of mutations observed during viral surveillance.

The Ingenuity of Bacterial Genomes

The genomes of bacteria contain fewer genes and substantially less noncoding DNA than those of eukaryotes, and as a result, they have much less raw material to invent new traits. Yet, bacteria are vastly more taxonomically diverse, numerically abundant, and globally successful in colonizing new habitats compared to eukaryotes. Although bacterial genomes are generally considered to be optimized for efficient growth and rapid adaptation, nonadaptive processes have played a major role in shaping the size, contents, and compact organization of bacterial genomes and have allowed the establishment of deleterious traits that serve as the raw materials for genetic innovation.

Genome-wide and structural analyses of pseudokinases encoded in the genome of Arabidopsis thaliana provide functional insights

Protein Kinase-Like Non-Kinases (PKLNKs), commonly known as "pseudokinases", are homologous to eukaryotic Ser/Thr/Tyr protein kinases (PKs) but lack the crucial aspartate residue in the catalytic loop, indispensable for phosphotransferase activity. Therefore, they are predicted to be "catalytically inactive" enzyme homologs. Analysis of protein-kinase like sequences from Arabidopsis thaliana led to the identification of more than 120 pseudokinases lacking catalytic aspartate, majority of which are closely related to the plant-specific receptor-like kinase family. These pseudokinases engage in different biological processes, enabled by their diverse domain architectures and specific subcellular localizations. Structural comparison of pseudokinases with active and inactive conformations of canonical PKs, belonging to both plant and animal origin, revealed unique structural differences. The currently available crystal structures of pseudokinases show that the loop topologically equivalent to activation segment of PKs adopts a distinct-folded conformation, packing against the pseudoenzyme core, in contrast to the extended and inhibitory geometries observed for active and inactive states, respectively, of catalytic PKs. Salt-bridge between ATP-binding Lys and DFG-Asp as well as hydrophobic interactions between the conserved nonpolar residue C-terminal to the equivalent DFG motif and nonpolar residues in C-helix mediate such a conformation in pseudokinases. This results in enhanced solvent accessibility of the pseudocatalytic loop in pseudokinases that can possibly serve as an interacting surface while associating with other proteins. Specifically, our analysis identified several residues that may be involved in pseudokinase regulation and hints at the repurposing of pseudocatalytic residues to achieve mechanistic control over noncatalytic functions of pseudoenzymes.

Resurrection of ancestral effector caspases identifies novel networks for evolution of substrate specificity

Apoptotic caspases evolved with metazoans more than 950 million years ago (MYA), and a series of gene duplications resulted in two subfamilies consisting of initiator and effector caspases. The effector caspase genes (caspases-3, -6, and -7) were subsequently fixed into the Chordata phylum more than 650 MYA when the gene for a common ancestor (CA) duplicated, and the three effector caspases have persisted throughout mammalian evolution. All caspases prefer an aspartate residue at the P1 position of substrates, so each caspase evolved discrete cellular roles through changes in substrate recognition at the P4 position combined with allosteric regulation. We examined the evolution of substrate specificity in caspase-6, which prefers valine at the P4 residue, compared with caspases-3 and -7, which prefer aspartate, by reconstructing the CA of effector caspases (AncCP-Ef1) and the CA of caspase-6 (AncCP-6An). We show that AncCP-Ef1 is a promiscuous enzyme with little distinction between Asp, Val, or Leu at P4. The specificity of caspase-6 was defined early in its evolution, where AncCP-6An demonstrates a preference for Val over Asp at P4. Structures of AncCP-Ef1 and of AncCP-6An show a network of charged amino acids near the S4 pocket that, when combined with repositioning a flexible active site loop, resulted in a more hydrophobic binding pocket in AncCP-6An. The ancestral protein reconstructions show that the caspase-hemoglobinase fold has been conserved for over 650 million years and that only three substitutions in the scaffold are necessary to shift substrate selection toward Val over Asp.

Harnessing Conformational Plasticity to Generate Designer Enzymes

Recent years have witnessed an explosion of interest in understanding the role of conformational dynamics both in the evolution of new enzymatic activities from existing enzymes and in facilitating the emergence of enzymatic activity de novo on scaffolds that were previously non-catalytic. There are also an increasing number of examples in the literature of targeted engineering of conformational dynamics being successfully used to alter enzyme selectivity and activity. Despite the obvious importance of conformational dynamics to both enzyme function and evolvability, many (although not all) computational design approaches still focus either on pure sequence-based approaches or on using structures with limited flexibility to guide the design. However, there exist a wide variety of computational approaches that can be (re)purposed to introduce conformational dynamics as a key consideration in the design process. Coupled with laboratory evolution and more conventional existing sequence- and structure-based approaches, these techniques provide powerful tools for greatly expanding the protein engineering toolkit. This Perspective provides an overview of evolutionary studies that have dissected the role of conformational dynamics in facilitating the emergence of novel enzymes, as well as advances in computational approaches that allow one to target conformational dynamics as part of enzyme design. Harnessing conformational dynamics in engineering studies is a powerful paradigm with which to engineer the next generation of designer biocatalysts.

Deep mutational scanning of SARS-CoV-2 receptor binding domain reveals constraints on folding and ACE2 binding

The receptor binding domain (RBD) of the SARS-CoV-2 spike glycoprotein mediates viral attachment to ACE2 receptor, and is a major determinant of host range and a dominant target of neutralizing antibodies. Here we experimentally measure how all amino-acid mutations to the RBD affect expression of folded protein and its affinity for ACE2. Most mutations are deleterious for RBD expression and ACE2 binding, and we identify constrained regions on the RBD's surface that may be desirable targets for vaccines and antibody-based therapeutics. But a substantial number of mutations are well tolerated or even enhance ACE2 binding, including at ACE2 interface residues that vary across SARS-related coronaviruses. However, we find no evidence that these ACE2-affinity enhancing mutations have been selected in current SARS-CoV-2 pandemic isolates. We present an interactive visualization and open analysis pipeline to facilitate use of our dataset for vaccine design and functional annotation of mutations observed during viral surveillance.

Computer simulations explain the anomalous temperature optimum in a cold-adapted enzyme

Cold-adapted enzymes from psychrophilic species show the general characteristics of being more heat labile, and having a different balance between enthalpic and entropic contributions to free energy barrier of the catalyzed reaction compared to mesophilic orthologs. Among cold-adapted enzymes, there are also examples that show an enigmatic inactivation at higher temperatures before unfolding of the protein occurs. Here, we analyze these phenomena by extensive computer simulations of the catalytic reactions of psychrophilic and mesophilic α-amylases. The calculations yield temperature dependent reaction rates in good agreement with experiment, and also elicit the anomalous rate optimum for the cold-adapted enzyme, which occurs about 15 °C below the melting point. This result allows us to examine the structural basis of thermal inactivation, which turns out to be caused by breaking of a specific enzyme-substrate interaction. This type of behaviour is also likely to be relevant for other enzymes displaying such anomalous temperature optima.

Quantitative mapping of binding specificity landscapes for homologous targets by using a high-throughput method

To facilitate investigations of protein-protein interactions (PPIs), we developed a novel platform for quantitative mapping of protein binding specificity landscapes, which combines the multi-target screening of a mutagenesis library into high- and low-affinity populations with sophisticated next-generation sequencing analysis. Importantly, this method generates accurate models to predict affinity and specificity values for any mutation within a protein complex, and requires only a few experimental binding affinity measurements using purified proteins for calibration. We demonstrated the utility of the approach by mapping quantitative landscapes for interactions between the N-terminal domain of the tissue inhibitor of metalloproteinase 2 (N-TIMP2) and three matrix metalloproteinases (MMPs) having homologous structures but different affinities (MMP-1, MMP-3, and MMP-14). The binding landscapes for N-TIMP2/MMP-1 and N-TIMP2/MMP-3 showed the PPIs to be almost fully optimized, with most single mutations giving a loss of affinity. In contrast, the non-optimized PPI for N-TIMP2/MMP-14 was reflected in a wide range of binding affinities, where single mutations exhibited a far more attenuated effect on the PPI. Our new platform reliably and comprehensively identified not only hot- and cold-spot residues, but also specificity-switch mutations that shape target affinity and specificity. Thus, our approach provides a methodology giving an unprecedentedly rich quantitative analysis of the binding specificity landscape, which will broaden the understanding of the mechanisms and evolutionary origins of specific PPIs and facilitate the rational design of specific inhibitors for structurally similar target proteins.

The interplay of spatial organization and biochemistry in building blocks of cellular signalling pathways

Biochemical pathways and networks are central to cellular information processing. While a broad range of studies have dissected multiple aspects of information processing in biochemical pathways, the effect of spatial organization remains much less understood. It is clear that space is central to intracellular organization, plays important roles in cellular information processing and has been exploited in evolution; additionally, it is being increasingly exploited in synthetic biology through the development of artificial compartments, in a variety of ways. In this paper, we dissect different aspects of the interplay between spatial organization and biochemical pathways, by focusing on basic building blocks of these pathways: covalent modification cycles and two-component systems, with enzymes which may be monofunctional or bifunctional. Our analysis of spatial organization is performed by examining a range of 'spatial designs': patterns of localization or non-localization of enzymes/substrates, theoretically and computationally. Using these well-characterized in silico systems, we analyse the following. (i) The effect of different types of spatial organization on the overall kinetics of modification, and the role of distinct modification mechanisms therein. (ii) How different information processing characteristics seen experimentally and studied from the viewpoint of kinetics are perturbed, or generated. (iii) How the activity of enzymes (bifunctional enzymes in particular) may be spatially manipulated, and the relationship between localization and activity. (iv) How transitions in spatial organization (encountered either through evolution or through the lifetime of cells, as seen in multiple model organisms) impacts the kinetic module (and pathway) behaviour, and how transitions in chemistry may be impacted by prior spatial organization. The basic insights which emerge are central to understanding the role of spatial organization in biochemical pathways in both bacteria and eukaryotes, and are of direct relevance to engineering spatial organization of pathways in bottom-up synthetic biology in cellular and cell-free systems.

Ancient origins of allosteric activation in a Ser-Thr kinase

A myriad of cellular events are regulated by allostery; therefore, evolution of this process is of fundamental interest. Here, we use ancestral sequence reconstruction to resurrect ancestors of two colocalizing proteins, Aurora A kinase and its allosteric activator TPX2 (targeting protein for Xklp2), to experimentally characterize the evolutionary path of allosteric activation. Autophosphorylation of the activation loop is the most ancient activation mechanism; it is fully developed in the oldest kinase ancestor and has remained stable over 1 billion years of evolution. As the microtubule-associated protein TPX2 appeared, efficient kinase binding to TPX2 evolved, likely owing to increased fitness by virtue of colocalization. Subsequently, TPX2-mediated allosteric kinase regulation gradually evolved. Surprisingly, evolution of this regulation is encoded in the kinase and did not arise by a dominating mechanism of coevolution.

Co-evolution in the Jungle: From Leafcutter Ant Colonies to Chromosomal Ends

Biological entities are multicomponent systems where each part is directly or indirectly dependent on the others. In effect, a change in a single component might have a consequence on the functioning of its partners, thus affecting the fitness of the entire system. In this article, we provide a few examples of such complex biological systems, ranging from ant colonies to a population of amino acids within a single-polypeptide chain. Based on these examples, we discuss one of the central and still challenging questions in biology: how do such multicomponent consortia co-evolve? More specifically, we ask how telomeres, nucleo-protein complexes protecting the integrity of linear DNA chromosomes, originated from the ancestral organisms having circular genomes and thus not dealing with end-replication and end-protection problems. Using the examples of rapidly evolving topologies of mitochondrial genomes in eukaryotic microorganisms, we show what means of co-evolution were employed to accommodate various types of telomere-maintenance mechanisms in mitochondria. We also describe an unprecedented runaway evolution of telomeric repeats in nuclei of ascomycetous yeasts accompanied by co-evolution of telomere-associated proteins. We propose several scenarios derived from research on telomeres and supported by other studies from various fields of biology, while emphasizing that the relevant answers are still not in sight. It is this uncertainty and a lack of a detailed roadmap that makes the journey through the jungle of biological systems still exciting and worth undertaking.

Probing biophysical sequence constraints within the transmembrane domains of rhodopsin by deep mutational scanning

Membrane proteins must balance the sequence constraints associated with folding and function against the hydrophobicity required for solvation within the bilayer. We recently found the expression and maturation of rhodopsin are limited by the hydrophobicity of its seventh transmembrane domain (TM7), which contains polar residues that are essential for function. On the basis of these observations, we hypothesized that rhodopsin's expression should be less tolerant of mutations in TM7 relative to those within hydrophobic TM domains. To test this hypothesis, we used deep mutational scanning to compare the effects of 808 missense mutations on the plasma membrane expression of rhodopsin in HEK293T cells. Our results confirm that a higher proportion of mutations within TM7 (37%) decrease rhodopsin's plasma membrane expression relative to those within a hydrophobic TM domain (TM2, 25%). These results in conjunction with an evolutionary analysis suggest solvation energetics likely restricts the evolutionary sequence space of polar TM domains.

Modeling Structural Constraints on Protein Evolution via Side-Chain Conformational States

Few models of sequence evolution incorporate parameters describing protein structure, despite its high conservation, essential functional role and increasing availability. We present a structurally aware empirical substitution model for amino acid sequence evolution in which proteins are expressed using an expanded alphabet that relays both amino acid identity and structural information. Each character specifies an amino acid as well as information about the rotamer configuration of its side-chain: the discrete geometric pattern of permitted side-chain atomic positions, as defined by the dihedral angles between covalently linked atoms. By assigning rotamer states in 251,194 protein structures and identifying 4,508,390 substitutions between closely related sequences, we generate a 55-state “Dayhoff-like” model that shows that the evolutionary properties of amino acids depend strongly upon side-chain geometry. The model performs as well as or better than traditional 20-state models for divergence time estimation, tree inference, and ancestral state reconstruction. We conclude that not only is rotamer configuration a valuable source of information for phylogenetic studies, but that modeling the concomitant evolution of sequence and structure may have important implications for understanding protein folding and function.

High-throughput competitive fluorescence polarization assay reveals functional redundancy in the S100 protein family

The calcium-binding, vertebrate-specific S100 protein family consists of 20 paralogs in humans (referred as the S100ome), with several clinically important members. To explore their protein-protein interactions (PPIs) quantitatively, we have chosen an unbiased, high-throughput, competitive fluorescence polarization (FP) assay that revealed a partial functional redundancy when the complete S100ome (n = 20) was tested against numerous model partners (n = 13). Based on their specificity, the S100ome can be grouped into two distinct classes: promiscuous and orphan. In the first group, members bound to several ligands (> 4-5) with comparable high affinity, while in the second one, the paralogs bound only one partner weakly, or no ligand was identified. Our results demonstrate that FP assays are highly suitable for quantitative interaction profiling of selected protein families. Moreover, we provide evidence that PPI-based phenotypic characterization can complement or even exceed the information obtained from the sequence-based phylogenetic analysis of the S100ome, an evolutionary young protein family.

Enzyme Evolution: An Epistatic Ratchet versus a Smooth Reversible Transition

Evolutionary trajectories are deemed largely irreversible. In a newly diverged protein, reversion of mutations that led to the functional switch typically results in loss of both the new and the ancestral functions. Nonetheless, evolutionary transitions where reversions are viable have also been described. The structural and mechanistic causes of reversion compatibility versus incompatibility therefore remain unclear. We examined two laboratory evolution trajectories of mammalian paraoxonase-1, a lactonase with promiscuous organophosphate hydrolase (OPH) activity. Both trajectories began with the same active-site mutant, His115Trp, which lost the native lactonase activity and acquired higher OPH activity. A neo-functionalization trajectory amplified the promiscuous OPH activity, whereas the re-functionalization trajectory restored the native activity, thus generating a new lactonase that lacks His115. The His115 revertants of these trajectories indicated opposite trends. Revertants of the neo-functionalization trajectory lost both the evolved OPH and the original lactonase activity. Revertants of the trajectory that restored the original lactonase function were, however, fully active. Crystal structures and molecular simulations show that in the newly diverged OPH, the reverted His115 and other catalytic residues are displaced, thus causing loss of both the original and the new activity. In contrast, in the re-functionalization trajectory, reversion compatibility of the original lactonase activity derives from mechanistic versatility whereby multiple residues can fulfill the same task. This versatility enables unique sequence-reversible compositions that are inaccessible when the active site was repurposed toward a new function.

ASPEN, a methodology for reconstructing protein evolution with improved accuracy using ensemble models

Evolutionary reconstruction algorithms produce models of the evolutionary history of proteins or species. Such algorithms are highly sensitive to their inputs: the sequences used and their alignments. Here, we asked whether the variance introduced by selecting different input sequences could be used to better identify accurate evolutionary models. We subsampled from available ortholog sequences and measured the distribution of observed relationships between paralogs produced across hundreds of models inferred from the subsamples. We observed two important phenomena. First, the reproducibility of an all-sequence, single-alignment reconstruction, measured by comparing topologies inferred from 90% subsamples, directly correlates with the accuracy of that single-alignment reconstruction, producing a measurable value for something that has been traditionally unknowable. Second, topologies that are most consistent with the observations made in the ensemble are more accurate and we present a meta algorithm that exploits this property to improve model accuracy.

Evolutionary Analysis of JAZ Proteins in Plants: An Approach in Search of the Ancestral Sequence

Jasmonates are phytohormones that regulate development, metabolism and immunity. Signal transduction is critical to activate jasmonate responses, but the evolution of some key regulators such as jasmonate-ZIM domain (JAZ) repressors is not clear. Here, we identified 1065 JAZ sequence proteins in 66 lower and higher plants and analyzed their evolution by bioinformatics methods. We found that the TIFY and Jas domains are highly conserved along the evolutionary scale. Furthermore, the canonical degron sequence LPIAR(R/K) of the Jas domain is conserved in lower and higher plants. It is noteworthy that degron sequences showed a large number of alternatives from gymnosperms to dicots. In addition, ethylene-responsive element binding factor-associated amphiphilic repression (EAR) motifs are displayed in all plant lineages from liverworts to angiosperms. However, the cryptic MYC2-interacting domain (CMID) domain appeared in angiosperms for the first time. The phylogenetic analysis performed using the Maximum Likelihood method indicated that JAZ ortholog proteins are grouped according to their similarity and plant lineage. Moreover, ancestral JAZ sequences were constructed by PhyloBot software and showed specific changes in the TIFY and Jas domains during evolution from liverworts to dicots. Finally, we propose a model for the evolution of the ancestral sequences of the main eight JAZ protein subgroups. These findings contribute to the understanding of the JAZ family origin and expansion in land plants.

Harnessing evolutionary diversification of primary metabolism for plant synthetic biology

Plants produce numerous natural products that are essential to both plant and human physiology. Recent identification of genes and enzymes involved in their biosynthesis now provides exciting opportunities to reconstruct plant natural product pathways in heterologous systems through synthetic biology. The use of plant chassis, although still in infancy, can take advantage of plant cells' inherent capacity to synthesize and store various phytochemicals. Also, large-scale plant biomass production systems, driven by photosynthetic energy production and carbon fixation, could be harnessed for industrial-scale production of natural products. However, little is known about which plants could serve as ideal hosts and how to optimize plant primary metabolism to efficiently provide precursors for the synthesis of desirable downstream natural products or specialized (secondary) metabolites. Although primary metabolism is generally assumed to be conserved, unlike the highly-diversified specialized metabolism, primary metabolic pathways and enzymes can differ between microbes and plants and also among different plants, especially at the interface between primary and specialized metabolisms. This review highlights examples of the diversity in plant primary metabolism and discusses how we can utilize these variations in plant synthetic biology. I propose that understanding the evolutionary, biochemical, genetic, and molecular bases of primary metabolic diversity could provide rational strategies for identifying suitable plant hosts and for further optimizing primary metabolism for sizable production of natural and bio-based products in plants.

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.

Chimeric dihydrofolate reductases display properties of modularity and biophysical diversity

While reverse genetics and functional genomics have long affirmed the role of individual mutations in determining protein function, there have been fewer studies addressing how large-scale changes in protein sequences, such as in entire modular segments, influence protein function and evolution. Given how recombination can reassort protein sequences, these types of changes may play an underappreciated role in how novel protein functions evolve in nature. Such studies could aid our understanding of whether certain organismal phenotypes related to protein function-such as growth in the presence or absence of an antibiotic-are robust with respect to the identity of certain modular segments. In this study, we combine molecular genetics with biochemical and biophysical methods to gain a better understanding of protein modularity in dihydrofolate reductase (DHFR), an enzyme target of antibiotics also widely used as a model for protein evolution. We replace an integral α-helical segment of Escherichia coli DHFR with segments from a number of different organisms (many nonmicrobial) and examine how these chimeric enzymes affect organismal phenotypes (e.g., resistance to an antibiotic) as well as biophysical properties of the enzyme (e.g., thermostability). We find that organismal phenotypes and enzyme properties are highly sensitive to the identity of DHFR modules, and that this chimeric approach can create enzymes with diverse biophysical characteristics.

Structure and evolution of the 4-helix bundle domain of Zuotin, a J-domain protein co-chaperone of Hsp70

The J-domain protein Zuotin is a multi-domain eukaryotic Hsp70 co-chaperone. Though it is primarily ribosome-associated, positioned at the exit of the 60S subunit tunnel where it promotes folding of nascent polypeptide chains, Zuotin also has off-ribosome functions. Domains of Zuotin needed for 60S association and interaction with Hsp70 are conserved in eukaryotes. However, whether the 4-helix bundle (4HB) domain is conserved remains an open question. We undertook evolutionary and structural approaches to clarify this issue. We found that the 4HB segment of human Zuotin also forms a bundle of 4 helices. The positive charge of Helix I, which in Saccharomyces cerevisiae is responsible for interaction with the 40S subunit, is particularly conserved. However, the C-termini of fungal and human 4HBs are not similar. In fungi the C-terminal segment forms a plug that folds back into the bundle; in S. cerevisiae it plays an important role in bundle stability and, off the ribosome, in transcriptional activation. In human, C-terminal helix IV of the 4HB is extended, protruding from the bundle. This extension serves as a linker to the regulatory SANT domains, which are present in animals, plants and protists, but not fungi. Further analysis of Zuotin sequences revealed that the plug likely arose as a result of genomic rearrangement upon SANT domain loss early in the fungal lineage. In the lineage leading to S. cerevisiae, the 4HB was subjected to positive selection with the plug becoming increasingly hydrophobic. Eventually, these hydrophobic plug residues were coopted for a novel regulatory function—activation of a recently emerged transcription factor, Pdr1. Our data suggests that Zuotin evolved off-ribosome functions twice—once involving SANT domains, then later in fungi, after SANT domain loss, by coopting the hydrophobic plug. Zuotin serves as an example of complex intertwining of molecular chaperone function and cell regulation.

Experimental Evolution of Innovation and Novelty

How does novelty, a new, genetically based function, evolve? A compelling answer has been elusive because there are few model systems where both the genetic mechanisms generating novel functions and the ecological conditions that govern their origin and spread can be studied in detail. This review article considers what we have learned about the evolution of novelty from microbial selection experiments. This work reveals that the genetic routes to novelty can be more highly variable than standard models have led us to believe and underscores the importance of considering both genetics and ecology in this process.

UNVEILing connections between genotype, phenotype, and fitness in natural populations

Understanding the links between genetic variation and fitness in natural populations is a central goal of evolutionary genetics. This monumental task spans the fields of classical and molecular genetics, population genetics, biochemistry, physiology, developmental biology, and ecology. Advances to our molecular and developmental toolkits are facilitating integrative approaches across these traditionally separate fields, providing a more complete picture of the genotype-phenotype map in natural and non-model systems. Here, we summarize research presented at the first annual symposium of the UNVEIL Network, an NSF-funded collaboration between the University of Montana and the University of Nebraska, Lincoln, which took place from the 1st to the 3rd of June, 2018. We discuss how this body of work advances basic evolutionary science, what it implies for our ability to predict evolutionary change, and how it might inform novel conservation strategies.

Pervasive Pairwise Intragenic Epistasis among Sequential Mutations in TEM-1 β-Lactamase

Interactions between mutations play a central role in shaping the fitness landscape, but a clear picture of intragenic epistasis has yet to emerge. To further reveal the prevalence and patterns of intragenic epistasis, we present a survey of epistatic interactions between sequential mutations in TEM-1 β-lactamase. We measured the fitness effect of ~12,000 pairs of consecutive amino acid substitutions and used our previous study of the fitness effects of single amino acid substitutions to calculate epistasis for over 8000 mutation pairs. Since sequential mutations are prone to physically interact, we postulated that our study would be surveying specific epistasis instead of nonspecific epistasis. We found widespread negative epistasis, especially in beta-strands, and a high frequency of negative sign epistasis among individually beneficial mutations. Negative epistasis (52%) occurred 7.6 times as frequently as positive epistasis (6.8%). Buried residues experienced more negative epistasis that surface-exposed residues. However, TEM-1 exhibited a couple of hotspots for positive epistasis, most notably L221/ R222 at which many combinations of mutations positively interacted. This study is the first to systematically examine pairwise epistasis throughout an entire protein performing its native function in its native host.

SodaPop: a forward simulation suite for the evolutionary dynamics of asexual populations on protein fitness landscapes

Motivation

Protein evolution is determined by forces at multiple levels of biological organization. Random mutations have an immediate effect on the biophysical properties, structure and function of proteins. These same mutations also affect the fitness of the organism. However, the evolutionary fate of mutations, whether they succeed to fixation or are purged, also depends on population size and dynamics. There is an emerging interest, both theoretically and experimentally, to integrate these two factors in protein evolution. Although there are several tools available for simulating protein evolution, most of them focus on either the biophysical or the population-level determinants, but not both. Hence, there is a need for a publicly available computational tool to explore both the effects of protein biophysics and population dynamics on protein evolution.

Results

To address this need, we developed SodaPop, a computational suite to simulate protein evolution in the context of the population dynamics of asexual populations. SodaPop accepts as input several fitness landscapes based on protein biochemistry or other user-defined fitness functions. The user can also provide as input experimental fitness landscapes derived from deep mutational scanning approaches or theoretical landscapes derived from physical force field estimates. Here, we demonstrate the broad utility of SodaPop with different applications describing the interplay of selection for protein properties and population dynamics. SodaPop is designed such that population geneticists can explore the influence of protein biochemistry on patterns of genetic variation, and that biochemists and biophysicists can explore the role of population size and demography on protein evolution.

Availability and implementation

Source code and binaries are freely available at https://github.com/louisgt/SodaPop under the GNU GPLv3 license. The software is implemented in C++ and supported on Linux, Mac OS/X and Windows.

Supplementary information

Supplementary data are available at Bioinformatics online.

Biochemical and structural analyses suggest that plasminogen activators coevolved with their cognate protein substrates and inhibitors

Protein sequences of members of the plasminogen activation system are present throughout the entire vertebrate phylum. This important and well-described proteolytic cascade is governed by numerous protease-substrate and protease-inhibitor interactions whose conservation is crucial to maintaining unchanged protein function throughout evolution. The pressure to preserve protein-protein interactions may lead to either co-conservation or covariation of binding interfaces. Here, we combined covariation analysis and structure-based prediction to analyze the binding interfaces of urokinase (uPA):plasminogen activator inhibitor-1 (PAI-1) and uPA:plasminogen complexes. We detected correlated variation between the S3-pocket-lining residues of uPA and the P3 residue of both PAI-1 and plasminogen. These residues are known to form numerous polar interactions in the human uPA:PAI-1 Michaelis complex. To test the effect of mutations that correlate with each other and have occurred during mammalian diversification on protein-protein interactions, we produced uPA, PAI-1, and plasminogen from human and zebrafish to represent mammalian and nonmammalian orthologs. Using single amino acid point substitutions in these proteins, we found that the binding interfaces of uPA:plasminogen and uPA:PAI-1 may have coevolved to maintain tight interactions. Moreover, we conclude that although the interaction areas between protease-substrate and protease-inhibitor are shared, the two interactions are mechanistically different. Compared with a protease cleaving its natural substrate, the interaction between a protease and its inhibitor is more complex and involves a more fine-tuned mechanism. Understanding the effects of evolution on specific protein interactions may help further pharmacological interventions of the plasminogen activation system and other proteolytic systems.

A Single Mutation Unlocks Cascading Exaptations in the Origin of a Potent Pitviper Neurotoxin

Evolutionary innovations and complex phenotypes seemingly require an improbable amount of genetic change to evolve. Rattlesnakes display two dramatically different venom phenotypes. Type I venoms are hemorrhagic with low systemic toxicity and high expression of tissue-destroying snake venom metalloproteinases. Type II venoms are highly neurotoxic and lack snake venom metalloproteinase expression and associated hemorrhagic activity. This dichotomy hinges on Mojave toxin (MTx), a phospholipase A2 (PLA2) based β-neurotoxin expressed in Type II venoms. MTx is comprised of a nontoxic acidic subunit that undergoes extensive proteolytic processing and allosterically regulates activity of a neurotoxic basic subunit. Evolution of the acidic subunit presents an evolutionary challenge because the need for high expression of a nontoxic venom component and the proteolytic machinery required for processing suggests genetic changes of seemingly little immediate benefit to fitness. We showed that MTx evolved through a cascading series of exaptations unlocked by a single nucleotide change. The evolution of one new cleavage site in the acidic subunit unmasked buried cleavage sites already present in ancestral PLA2s, enabling proteolytic processing. Snake venom serine proteases, already present in the venom to disrupt prey hemostasis, possess the requisite specificities for MTx acidic subunit proteolysis. The dimerization interface between MTx subunits evolved by exploiting a latent, but masked, hydrophobic interaction between ancestral PLA2s. The evolution of MTx through exaptation of existing functional and structural features suggests complex phenotypes that depend on evolutionary innovations can arise from minimal genetic change enabled by prior evolution.

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.

High-Throughput Reconstruction of Ancestral Protein Sequence, Structure, and Molecular Function

Ancestral protein sequence reconstruction is a powerful technique for explicitly testing hypotheses about the evolution of molecular function, allowing researchers to meticulously dissect how historical changes in protein sequence impacted functional repertoire by altering the protein's 3D structure. These techniques have provided concrete, experimentally validated insights into ancient evolutionary processes and help illuminate the complex relationship between protein sequence, structure, and function. Inferring the protein family phylogenies on which ancestral sequence reconstruction depends and reconstructing the sequences, themselves, are amenable to high-throughput computational analysis. However, determining the structures of ancestral-reconstructed proteins and characterizing their functions typically rely on time-consuming and expensive laboratory analyses, limiting most current studies to examining a relatively small number of specific hypotheses. For this reason, we have little detailed, unbiased information about how molecular function evolves across large protein family phylogenies. Here we describe a generalized protocol that integrates ancestral sequence reconstruction with structural homology modeling and structure-based molecular affinity prediction to characterize historical changes in protein function across families with thousands of individual sequences. We highlight key steps in the analysis protocol requiring particularly careful attention to avoid introducing potential errors as well as steps for which computationally efficient subroutines can be substituted for more intensive approaches, allowing researchers to scale the analysis up or down, depending on available resources and requirements for reproducibility and scientific rigor. In our view, this approach provides a compelling compliment to more laboratory-intensive procedures, generating important contextual information that can help guide detailed experiments.

Neutron scattering in the biological sciences: progress and prospects

The scattering of neutrons can be used to provide information on the structure and dynamics of biological systems on multiple length and time scales. Pursuant to a National Science Foundation-funded workshop in February 2018, recent developments in this field are reviewed here, as well as future prospects that can be expected given recent advances in sources, instrumentation and computational power and methods. Crystallography, solution scattering, dynamics, membranes, labeling and imaging are examined. For the extraction of maximum information, the incorporation of judicious specific deuterium labeling, the integration of several types of experiment, and interpretation using high-performance computer simulation models are often found to be particularly powerful.

Improving cytidine and adenine base editors by expression optimization and ancestral reconstruction

Base editors enable targeted single-nucleotide conversions in genomic DNA. Here we show that expression levels are a bottleneck in base-editing efficiency. We optimize cytidine (BE4) and adenine (ABE7.10) base editors by modification of nuclear localization signals (NLS) and codon usage, and ancestral reconstruction of the deaminase component. The resulting BE4max, AncBE4max, and ABEmax editors correct pathogenic SNPs with substantially increased efficiency in a variety of mammalian cell types.

The CaspBase: a curated database for evolutionary biochemical studies of caspase functional divergence and ancestral sequence inference

Sequence databases are powerful tools for the contemporary scientists' toolkit. However, most functional annotations in public databases are determined computationally and are not verified by a human expert. While hypotheses generated from computational studies are now amenable to experimentation, the quality of the results relies on the quality of input data. We developed the CaspBase to expedite high-quality dataset compilation of annotated caspase sequences, to maximize phylogenetic signal, and to reduce the noise contributed from public databanks. We describe our methods of curation for the CaspBase and how researchers can acquire sequences from CaspBase.org. Our immediate goal for developing the CaspBase was to optimize the ancestral protein reconstruction (APR) of caspases, and we demonstrate the utility of the CaspBase in APR studies. We also developed the Common Position (CP) system for comparing human caspase family paralogs and suggest the CP system as an update to current reporting methods of caspase amino acid positions. We present a standardized multiple sequence alignment (MSA) for the CP system and show the advantage of using large databases such as the CaspBase in defining structural positions in proteins. Although the results described here pertain to caspase evolution and structure-function studies, the methods can be adapted to any gene family.

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.

The Src module: an ancient scaffold in the evolution of cytoplasmic tyrosine kinases

Tyrosine kinases were first discovered as the protein products of viral oncogenes. We now know that this large family of metazoan enzymes includes nearly one hundred structurally diverse members. Tyrosine kinases are broadly classified into two groups: the transmembrane receptor tyrosine kinases, which sense extracellular stimuli, and the cytoplasmic tyrosine kinases, which contain modular ligand-binding domains and propagate intracellular signals. Several families of cytoplasmic tyrosine kinases have in common a core architecture, the "Src module," composed of a Src-homology 3 (SH3) domain, a Src-homology 2 (SH2) domain, and a kinase domain. Each of these families is defined by additional elaborations on this core architecture. Structural, functional, and evolutionary studies have revealed a unifying set of principles underlying the activity and regulation of tyrosine kinases built on the Src module. The discovery of these conserved properties has shaped our knowledge of the workings of protein kinases in general, and it has had important implications for our understanding of kinase dysregulation in disease and the development of effective kinase-targeted therapies.

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.

Synthetic circuits reveal how mechanisms of gene regulatory networks constrain evolution

Phenotypic variation is the raw material of adaptive Darwinian evolution. The phenotypic variation found in organismal development is biased towards certain phenotypes, but the molecular mechanisms behind such biases are still poorly understood. Gene regulatory networks have been proposed as one cause of constrained phenotypic variation. However, most pertinent evidence is theoretical rather than experimental. Here, we study evolutionary biases in two synthetic gene regulatory circuits expressed in Escherichia coli that produce a gene expression stripe-a pivotal pattern in embryonic development. The two parental circuits produce the same phenotype, but create it through different regulatory mechanisms. We show that mutations cause distinct novel phenotypes in the two networks and use a combination of experimental measurements, mathematical modelling and DNA sequencing to understand why mutations bring forth only some but not other novel gene expression phenotypes. Our results reveal that the regulatory mechanisms of networks restrict the possible phenotypic variation upon mutation. Consequently, seemingly equivalent networks can indeed be distinct in how they constrain the outcome of further evolution.