Diversity in protein domain superfamilies

Key interaction networks: Identifying evolutionarily conserved non-covalent interaction networks across protein families

Protein structure (and thus function) is dictated by non-covalent interaction networks. These can be highly evolutionarily conserved across protein families, the members of which can diverge in sequence and evolutionary history. Here we present KIN, a tool to identify and analyze conserved non-covalent interaction networks across evolutionarily related groups of proteins. KIN is available for download under a GNU General Public License, version 2, from https://www.github.com/kamerlinlab/KIN. KIN can operate on experimentally determined structures, predicted structures, or molecular dynamics trajectories, providing insight into both conserved and missing interactions across evolutionarily related proteins. This provides useful insight both into protein evolution, as well as a tool that can be exploited for protein engineering efforts. As a showcase system, we demonstrate applications of this tool to understanding the evolutionary-relevant conserved interaction networks across the class A β-lactamases.

Computational Protein Design - Where it goes?

Proteins have been playing a critical role in the regulation of diverse biological processes related to human life. With the increasing demand, functional proteins are sparse in this immense sequence space. Therefore, protein design has become an important task in various fields, including medicine, food, energy, materials, etc. Directed evolution has recently led to significant achievements. Molecular modification of proteins through directed evolution technology has significantly advanced the fields of enzyme engineering, metabolic engineering, medicine, and beyond. However, it is impossible to identify desirable sequences from a large number of synthetic sequences alone. As a result, computational methods, including data-driven machine learning and physics-based molecular modeling, have been introduced to protein engineering to produce more functional proteins. This review focuses on recent advances in computational protein design, highlighting the applicability of different approaches as well as their limitations.

Retracing the evolution of a modern periplasmic binding protein

Investigating the evolution of structural features in modern multidomain proteins helps to understand their immense diversity and functional versatility. The class of periplasmic binding proteins (PBPs) offers an opportunity to interrogate one of the main processes driving diversification: the duplication and fusion of protein sequences to generate new architectures. The symmetry of their two-lobed topology, their mechanism of binding, and the organization of their operon structure led to the hypothesis that PBPs arose through a duplication and fusion event of a single common ancestor. To investigate this claim, we set out to reverse the evolutionary process and recreate the structural equivalent of a single-lobed progenitor using ribose-binding protein (RBP) as our model. We found that this modern PBP can be deconstructed into its lobes, producing two proteins that represent possible progenitor halves. The isolated halves of RBP are well folded and monomeric proteins, albeit with a lower thermostability, and do not retain the original binding function. However, the two entities readily form a heterodimer in vitro and in-cell. The x-ray structure of the heterodimer closely resembles the parental protein. Moreover, the binding function is fully regained upon formation of the heterodimer with a ligand affinity similar to that observed in the modern RBP. This highlights how a duplication event could have given rise to a stable and functional PBP-like fold and provides insights into how more complex functional structures can evolve from simpler molecular components.

Exploring the eukaryotic Yip and REEP/Yop superfamily of membrane-shaping adapter proteins (MSAPs): A cacophony or harmony of structure and function?

Polytopic cargo proteins are synthesized and exported along the secretory pathway from the endoplasmic reticulum (ER), through the Golgi apparatus, with eventual insertion into the plasma membrane (PM). While searching for proteins that could enhance cell surface expression of olfactory receptors, a new family of proteins termed “receptor expression-enhancing proteins” or REEPs were identified. These membrane-shaping hairpin proteins serve as adapters, interacting with intracellular transport machinery, to regulate cargo protein trafficking. However, REEPs belong to a larger family of proteins, the Yip (Ypt-interacting protein) family, conserved in yeast and higher eukaryotes. To date, eighteen mammalian Yip family members, divided into four subfamilies (Yipf, REEP, Yif, and PRAF), have been identified. Yeast research has revealed many intriguing aspects of yeast Yip function, functions that have not completely been explored with mammalian Yip family members. This review and analysis will clarify the different Yip family nomenclature that have encumbered prior comparisons between yeast, plants, and eukaryotic family members, to provide a more complete understanding of their interacting proteins, membrane topology, organelle localization, and role as regulators of cargo trafficking and localization. In addition, the biological role of membrane shaping and sensing hairpin and amphipathic helical domains of various Yip proteins and their potential cellular functions will be described. Lastly, this review will discuss the concept of Yip proteins as members of a larger superfamily of membrane-shaping adapter proteins (MSAPs), proteins that both shape membranes via membrane-sensing and hairpin insertion, and well as act as adapters for protein-protein interactions. MSAPs are defined by their localization to specific membranes, ability to alter membrane structure, interactions with other proteins via specific domains, and specific interactions/effects on cargo proteins.

SPLDExtraTrees: robust machine learning approach for predicting kinase inhibitor resistance

Drug resistance is a major threat to the global health and a significant concern throughout the clinical treatment of diseases and drug development. The mutation in proteins that is related to drug binding is a common cause for adaptive drug resistance. Therefore, quantitative estimations of how mutations would affect the interaction between a drug and the target protein would be of vital significance for the drug development and the clinical practice. Computational methods that rely on molecular dynamics simulations, Rosetta protocols, as well as machine learning methods have been proven to be capable of predicting ligand affinity changes upon protein mutation. However, the severely limited sample size and heavy noise induced overfitting and generalization issues have impeded wide adoption of machine learning for studying drug resistance. In this paper, we propose a robust machine learning method, termed SPLDExtraTrees, which can accurately predict ligand binding affinity changes upon protein mutation and identify resistance-causing mutations. Especially, the proposed method ranks training data following a specific scheme that starts with easy-to-learn samples and gradually incorporates harder and diverse samples into the training, and then iterates between sample weight recalculations and model updates. In addition, we calculate additional physics-based structural features to provide the machine learning model with the valuable domain knowledge on proteins for these data-limited predictive tasks. The experiments substantiate the capability of the proposed method for predicting kinase inhibitor resistance under three scenarios and achieve predictive accuracy comparable with that of molecular dynamics and Rosetta methods with much less computational costs.

Resurrecting Enzymes by Ancestral Sequence Reconstruction

Ancestral Sequence Reconstruction (ASR) allows one to infer the sequences of extinct proteins using the phylogeny of extant proteins. It consists of disclosing the evolutionary history-i.e., the phylogeny-of a protein family of interest and then inferring the sequences of its ancestors-i.e., the nodes in the phylogeny. Assisted by gene synthesis, the selected ancestors can be resurrected in the lab and experimentally characterized. The crucial step to succeed with ASR is starting from a reliable phylogeny. At the same time, it is of the utmost importance to have a clear idea on the evolutionary history of the family under study and the events that influenced it. This allows us to implement ASR with well-defined hypotheses and to apply the appropriate experimental methods. In the last years, ASR has become popular to test hypotheses about the origin of functionalities, changes in activities, understanding physicochemical properties of proteins, among others. In this context, the aim of this chapter is to present the ASR approach applied to the reconstruction of enzymes-i.e., proteins with catalytic roles. The spirit of this contribution is to provide a basic, hands-to-work guide for biochemists and biologists who are unfamiliar with molecular phylogenetics.

Three-dimensional Structure Databases of Biological Macromolecules

Databases of three-dimensional structures of proteins (and their associated molecules) provide: (a) Curated repositories of coordinates of experimentally determined structures, including extensive metadata; for instance information about provenance, details about data collection and interpretation, and validation of results. (b) Information-retrieval tools to allow searching to identify entries of interest and provide access to them. (c) Links among databases, especially to databases of amino-acid and genetic sequences, and of protein function; and links to software for analysis of amino-acid sequence and protein structure, and for structure prediction. (d) Collections of predicted three-dimensional structures of proteins. These will become more and more important after the breakthrough in structure prediction achieved by AlphaFold2. The single global archive of experimentally determined biomacromolecular structures is the Protein Data Bank (PDB). It is managed by wwPDB, a consortium of five partner institutions: the Protein Data Bank in Europe (PDBe), the Research Collaboratory for Structural Bioinformatics (RCSB), the Protein Data Bank Japan (PDBj), the BioMagResBank (BMRB), and the Electron Microscopy Data Bank (EMDB). In addition to jointly managing the PDB repository, the individual wwPDB partners offer many tools for analysis of protein and nucleic acid structures and their complexes, including providing computer-graphic representations. Their collective and individual websites serve as hubs of the community of structural biologists, offering newsletters, reports from Task Forces, training courses, and "helpdesks," as well as links to external software.Many specialized projects are based on the information contained in the PDB. Especially important are SCOP, CATH, and ECOD, which present classifications of protein domains.

Structural dynamics in the evolution of a bilobed protein scaffold

Proteins conduct numerous complex biological functions by use of tailored structural dynamics. The molecular details of how these emerged from ancestral peptides remains mysterious. How does nature utilize the same repertoire of folds to diversify function? To shed light on this, we analyzed bilobed proteins with a common structural core, which is spread throughout the tree of life and is involved in diverse biological functions such as transcription, enzymatic catalysis, membrane transport, and signaling. We show here that the structural dynamics of the structural core differentiate predominantly via terminal additions during a long-period evolution. This diversifies substrate specificity and, ultimately, biological function.

Novel biophysical tools allow the structural dynamics of proteins and the regulation of such dynamics by binding partners to be explored in unprecedented detail. Although this has provided critical insights into protein function, the means by which structural dynamics direct protein evolution remain poorly understood. Here, we investigated how proteins with a bilobed structure, composed of two related domains from the periplasmic-binding protein–like II domain family, have undergone divergent evolution, leading to adaptation of their structural dynamics. We performed a structural analysis on ∼600 bilobed proteins with a common primordial structural core, which we complemented with biophysical studies to explore the structural dynamics of selected examples by single-molecule Förster resonance energy transfer and Hydrogen–Deuterium exchange mass spectrometry. We show that evolutionary modifications of the structural core, largely at its termini, enable distinct structural dynamics, allowing the diversification of these proteins into transcription factors, enzymes, and extracytoplasmic transport-related proteins. Structural embellishments of the core created interdomain interactions that stabilized structural states, reshaping the active site geometry, and ultimately altered substrate specificity. Our findings reveal an as-yet-unrecognized mechanism for the emergence of functional promiscuity during long periods of evolution and are applicable to a large number of domain architectures.

The Power of Universal Contextualized Protein Embeddings in Cross-species Protein Function Prediction

Computationally annotating proteins with a molecular function is a difficult problem that is made even harder due to the limited amount of available labeled protein training data. Unsupervised protein embeddings partly circumvent this limitation by learning a universal protein representation from many unlabeled sequences. Such embeddings incorporate contextual information of amino acids, thereby modeling the underlying principles of protein sequences insensitive to the context of species. We used an existing pre-trained protein embedding method and subjected its molecular function prediction performance to detailed characterization, first to advance the understanding of protein language models, and second to determine areas of improvement. Then, we applied the model in a transfer learning task by training a function predictor based on the embeddings of annotated protein sequences of one training species and making predictions on the proteins of several test species with varying evolutionary distance. We show that this approach successfully generalizes knowledge about protein function from one eukaryotic species to various other species, outperforming both an alignment-based and a supervised-learning-based baseline. This implies that such a method could be effective for molecular function prediction in inadequately annotated species from understudied taxonomic kingdoms.

Navigating the amino acid sequence space between functional proteins using a deep learning framework

MOTIVATION

Shedding light on the relationships between protein sequences and functions is a challenging task with many implications in protein evolution, diseases understanding, and protein design. The protein sequence space mapping to specific functions is however hard to comprehend due to its complexity. Generative models help to decipher complex systems thanks to their abilities to learn and recreate data specificity. Applied to proteins, they can capture the sequence patterns associated with functions and point out important relationships between sequence positions. By learning these dependencies between sequences and functions, they can ultimately be used to generate new sequences and navigate through uncharted area of molecular evolution.

RESULTS

This study presents an Adversarial Auto-Encoder (AAE) approached, an unsupervised generative model, to generate new protein sequences. AAEs are tested on three protein families known for their multiple functions the sulfatase, the HUP and the TPP families. Clustering results on the encoded sequences from the latent space computed by AAEs display high level of homogeneity regarding the protein sequence functions. The study also reports and analyzes for the first time two sampling strategies based on latent space interpolation and latent space arithmetic to generate intermediate protein sequences sharing sequential properties of original sequences linked to known functional properties issued from different families and functions. Generated sequences by interpolation between latent space data points demonstrate the ability of the AAE to generalize and produce meaningful biological sequences from an evolutionary uncharted area of the biological sequence space. Finally, 3D structure models computed by comparative modelling using generated sequences and templates of different sub-families point out to the ability of the latent space arithmetic to successfully transfer protein sequence properties linked to function between different sub-families. All in all this study confirms the ability of deep learning frameworks to model biological complexity and bring new tools to explore amino acid sequence and functional spaces.

Tracing Evolution Through Protein Structures: Nature Captured in a Few Thousand Folds

This article is dedicated to the memory of Cyrus Chothia, who was a leading light in the world of protein structure evolution. His elegant analyses of protein families and their mechanisms of structural and functional evolution provided important evolutionary and biological insights and firmly established the value of structural perspectives. He was a mentor and supervisor to many other leading scientists who continued his quest to characterise structure and function space. He was also a generous and supportive colleague to those applying different approaches. In this article we review some of his accomplishments and the history of protein structure classifications, particularly SCOP and CATH. We also highlight some of the evolutionary insights these two classifications have brought. Finally, we discuss how the expansion and integration of protein sequence data into these structural families helps reveal the dark matter of function space and can inform the emergence of novel functions in Metazoa. Since we cover 25 years of structural classification, it has not been feasible to review all structure based evolutionary studies and hence we focus mainly on those undertaken by the SCOP and CATH groups and their collaborators.

The vaccinologist's "dirty little secret": a better understanding of structure-function relationships of viral immunogens might advance rational HIV vaccine design

I will offer a conceptual analysis of different notions of structure and function of viral immunogens and of different structure-function relationships. My focus will then be on the mechanisms by which the desired immune response is induced and why strategies based on three-dimensional molecular antigen structures and their rational design are limited in their ability to induce the desired immunogenicity. I will look at the mechanisms of action of adjuvants (thus the wordplay with Janeway’s “immunologist’s dirty little secret”). Strategies involving adjuvants and other (more successful) vaccination strategies rely on taking into account activities and functions (“what is going on”), and not just the structures involved (“who is there”), in binding in a “lock and key” fashion. Functional patterns as well as other organizational and temporal patterns, I will argue, are crucial for inducing the desired immune response and immunogenicity. The 3D structural approach by itself has its benefits – and its limits, which I want to highlight by this philosophical analysis, pointing out the importance of structure-function relationships. Different functional aspects such as antigenicity, immunogenicity, and immunity need to be kept separate and cannot be reduced to three-dimensional structures of vaccines. Taking into account different notions of structure and function and their relationships might thus advance our understanding of the immune system and rational HIV vaccine design, to which end philosophy can provide useful tools.

TREND: a platform for exploring protein function in prokaryotes based on phylogenetic, domain architecture and gene neighborhood analyses

Key steps in a computational study of protein function involve analysis of (i) relationships between homologous proteins, (ii) protein domain architecture and (iii) gene neighborhoods the corresponding proteins are encoded in. Each of these steps requires a separate computational task and sets of tools. Currently in order to relate protein features and gene neighborhoods information to phylogeny, researchers need to prepare all the necessary data and combine them by hand, which is time-consuming and error-prone. Here, we present a new platform, TREND (tree-based exploration of neighborhoods and domains), which can perform all the necessary steps in automated fashion and put the derived information into phylogenomic context, thus making evolutionary based protein function analysis more efficient. A rich set of adjustable components allows a user to run the computational steps specific to his task. TREND is freely available at http://trend.zhulinlab.org.

Choosing the Best Enzyme Complex Structure Made Easy

In this issue of Structure, Tyzack et al. (2018) present a study of enzyme-ligand complexes in the PDB and show that the molecular similarity of bound and cognate ligands can be used to choose the most biologically appropriate complex structure for analysis when multiple structures are available.

Considerations of AOX Functionality Revealed by Critical Motifs and Unique Domains

An understanding of the genes and mechanisms regulating environmental stress in crops is critical for boosting agricultural yield and safeguarding food security. Under adverse conditions, response pathways are activated for tolerance or resistance. In multiple species, the alternative oxidase (AOX) genes encode proteins which help in this process. Recently, this gene family has been extensively investigated in the vital crop plants, wheat, barley and rice. Cumulatively, these three species and/or their wild ancestors contain the genes for AOX1a, AOX1c, AOX1e, and AOX1d, and common patterns in the protein isoforms have been documented. Here, we add more information on these trends by emphasizing motifs that could affect expression, and by utilizing the most recent discoveries from the AOX isoform in Trypanosoma brucei to highlight clade-dependent biases. The new perspectives may have implications on how the AOX gene family has evolved and functions in monocots. The common or divergent amino acid substitutions between these grasses and the parasite are noted, and the potential effects of these changes are discussed. There is the hope that the insights gained will inform the way future AOX research is performed in monocots, in order to optimize crop production for food, feed, and fuel.

Most yeast SH3 domains bind peptide targets with high intrinsic specificity

A need exists to develop bioinformatics for predicting differences in protein function, especially for members of a domain family who share a common fold, yet are found in a diverse array of proteins. Many domain families have been conserved over large evolutionary spans and representative genomic data during these periods are now available. This allows a simple method for grouping domain sequences to reveal common and unique/specific binding residues. As such, we hypothesize that sequence alignment analysis of the yeast SH3 domain family across ancestral species in the fungal kingdom can determine whether each member encodes specific information to bind unique peptide targets. With this approach, we identify important specific residues for a given domain as those that show little conservation within an alignment of yeast domain family members (paralogs) but are conserved in an alignment of its direct relatives (orthologs). We find most of the yeast SH3 domain family members have maintained unique amino acid conservation patterns that suggest they bind peptide targets with high intrinsic specificity through varying degrees of non-canonical recognition. For a minority of domains, we predict a less diverse binding surface, likely requiring additional factors to bind targets specifically. We observe that our predictions are consistent with high throughput binding data, which suggests our approach can probe intrinsic binding specificity in any other interaction domain family that is maintained during evolution.

Crystal structure of an assembly intermediate of respiratory Complex II

Flavin is covalently attached to the protein scaffold in ~10% of flavoenzymes. However, the mechanism of covalent modification is unclear, due in part to challenges in stabilizing assembly intermediates. Here, we capture the structure of an assembly intermediate of the Escherichiacoli Complex II (quinol:fumarate reductase (FrdABCD)). The structure contains the E. coli FrdA subunit bound to covalent FAD and crosslinked with its assembly factor, SdhE. The structure contains two global conformational changes as compared to prior structures of the mature protein: the rotation of a domain within the FrdA subunit, and the destabilization of two large loops of the FrdA subunit, which may create a tunnel to the active site. We infer a mechanism for covalent flavinylation. As supported by spectroscopic and kinetic analyses, we suggest that SdhE shifts the conformational equilibrium of the FrdA active site to disfavor succinate/fumarate interconversion and enhance covalent flavinylation.

The mechanism for covalent flavinylation of flavoenzymes is still unclear. Here, the authors propose a mechanism based on the crystal structure of a flavinylation assembly intermediate of the E. coli respiratory Complex II comprising the E. coli FrdA subunit bound to covalent FAD and crosslinked with its assembly factor SdhE.

Enzyme function and its evolution

With rapid increases over recent years in the determination of protein sequence and structure, alongside knowledge of thousands of enzyme functions and hundreds of chemical mechanisms, it is now possible to combine breadth and depth in our understanding of enzyme evolution. Phylogenetics continues to move forward, though determining correct evolutionary family trees is not trivial. Protein function prediction has spawned a variety of promising methods that offer the prospect of identifying enzymes across the whole range of chemical functions and over numerous species. This knowledge is essential to understand antibiotic resistance, as well as in protein re-engineering and de novo enzyme design.

Differential catalytic promiscuity of the alkaline phosphatase superfamily bimetallo core reveals mechanistic features underlying enzyme evolution

Members of enzyme superfamilies specialize in different reactions but often exhibit catalytic promiscuity for one another's reactions, consistent with catalytic promiscuity as an important driver in the evolution of new enzymes. Wanting to understand how catalytic promiscuity and other factors may influence evolution across a superfamily, we turned to the well-studied alkaline phosphatase (AP) superfamily, comparing three of its members, two evolutionarily distinct phosphatases and a phosphodiesterase. We mutated distinguishing active-site residues to generate enzymes that had a common Zn²⁺ bimetallo core but little sequence similarity and different auxiliary domains. We then tested the catalytic capabilities of these pruned enzymes with a series of substrates. A substantial rate enhancement of ∼10¹¹-fold for both phosphate mono- and diester hydrolysis by each enzyme indicated that the Zn²⁺ bimetallo core is an effective mono/di-esterase generalist and that the bimetallo cores were not evolutionarily tuned to prefer their cognate reactions. In contrast, our pruned enzymes were ineffective sulfatases, and this limited promiscuity may have provided a driving force for founding the distinct one-metal-ion branch that contains all known AP superfamily sulfatases. Finally, our pruned enzymes exhibited 10⁷-10⁸-fold phosphotriesterase rate enhancements, despite absence of such enzymes within the AP superfamily. We speculate that the superfamily active-site architecture involved in nucleophile positioning prevents accommodation of the additional triester substituent. Overall, we suggest that catalytic promiscuity, and the ease or difficulty of remodeling and building onto existing protein scaffolds, have greatly influenced the course of enzyme evolution. Uncovering principles and properties of enzyme function, promiscuity, and repurposing provides lessons for engineering new enzymes.

Carbohydrate-active enzymes in Trichoderma harzianum: a bioinformatic analysis bioprospecting for key enzymes for the biofuels industry

BACKGROUND

Trichoderma harzianum is used in biotechnology applications due to its ability to produce powerful enzymes for the conversion of lignocellulosic substrates into soluble sugars. Active enzymes involved in carbohydrate metabolism are defined as carbohydrate-active enzymes (CAZymes), and the most abundant family in the CAZy database is the glycoside hydrolases. The enzymes of this family play a fundamental role in the decomposition of plant biomass.

RESULTS

In this study, the CAZymes of T. harzianum were identified and classified using bioinformatic approaches after which the expression profiles of all annotated CAZymes were assessed via RNA-Seq, and a phylogenetic analysis was performed. A total of 430 CAZymes (3.7% of the total proteins for this organism) were annotated in T. harzianum, including 259 glycoside hydrolases (GHs), 101 glycosyl transferases (GTs), 6 polysaccharide lyases (PLs), 22 carbohydrate esterases (CEs), 42 auxiliary activities (AAs) and 46 carbohydrate-binding modules (CBMs). Among the identified T. harzianum CAZymes, 47% were predicted to harbor a signal peptide sequence and were therefore classified as secreted proteins. The GH families were the CAZyme class with the greatest number of expressed genes, including GH18 (23 genes), GH3 (17 genes), GH16 (16 genes), GH2 (13 genes) and GH5 (12 genes). A phylogenetic analysis of the proteins in the AA9/GH61, CE5 and GH55 families showed high functional variation among the proteins.

CONCLUSIONS

Identifying the main proteins used by T. harzianum for biomass degradation can ensure new advances in the biofuel production field. Herein, we annotated and characterized the expression levels of all of the CAZymes from T. harzianum, which may contribute to future studies focusing on the functional and structural characterization of the identified proteins.

Lessons from making the Structural Classification of Proteins (SCOP) and their implications for protein structure modelling

The Structural Classification of Proteins (SCOP) database has facilitated the development of many tools and algorithms and it has been successfully used in protein structure prediction and large-scale genome annotations. During the development of SCOP, numerous exceptions were found to topological rules, along with complex evolutionary scenarios and peculiarities in proteins including the ability to fold into alternative structures. This article reviews cases of structural variations observed for individual proteins and among groups of homologues, knowledge of which is essential for protein structure modelling.

Resolving protein structure-function-binding site relationships from a binding site similarity network perspective

Functional annotation is seldom straightforward with complexities arising due to functional divergence in protein families or functional convergence between non-homologous protein families, leading to mis-annotations. An enzyme may contain multiple domains and not all domains may be involved in a given function, adding to the complexity in function annotation. To address this, we use binding site information from bound cognate ligands and catalytic residues, since it can help in resolving fold-function relationships at a finer level and with higher confidence. A comprehensive database of 2,020 fold-function-binding site relationships has been systematically generated. A network-based approach is employed to capture the complexity in these relationships, from which different types of associations are deciphered, that identify versatile protein folds performing diverse functions, same function associated with multiple folds and one-to-one relationships. Binding site similarity networks integrated with fold, function, and ligand similarity information are generated to understand the depth of these relationships. Apart from the observed continuity in the functional site space, network properties of these revealed versatile families with topologically different or dissimilar binding sites and structural families that perform very similar functions. As a case study, subtle changes in the active site of a set of evolutionarily related superfamilies are studied using these networks. Tracing of such similarities in evolutionarily related proteins provide clues into the transition and evolution of protein functions. Insights from this study will be helpful in accurate and reliable functional annotations of uncharacterized proteins, poly-pharmacology, and designing enzymes with new functional capabilities. Proteins 2017; 85:1319-1335. © 2017 Wiley Periodicals, Inc.

YcaO-Dependent Posttranslational Amide Activation: Biosynthesis, Structure, and Function

With advances in sequencing technology, uncharacterized proteins and domains of unknown function (DUFs) are rapidly accumulating in sequence databases and offer an opportunity to discover new protein chemistry and reaction mechanisms. The focus of this review, the formerly enigmatic YcaO superfamily (DUF181), has been found to catalyze a unique phosphorylation of a ribosomal peptide backbone amide upon attack by different nucleophiles. Established nucleophiles are the side chains of Cys, Ser, and Thr which gives rise to azoline/azole biosynthesis in ribosomally synthesized and posttranslationally modified peptide (RiPP) natural products. However, much remains unknown about the potential for YcaO proteins to collaborate with other nucleophiles. Recent work suggests potential in forming thioamides, macroamidines, and possibly additional post-translational modifications. This review covers all knowledge through mid-2016 regarding the biosynthetic gene clusters (BGCs), natural products, functions, mechanisms, and applications of YcaO proteins and outlines likely future research directions for this protein superfamily.