Parallel molecular mechanisms for enzyme temperature adaptation.

The mechanisms that underly the adaptation of enzyme activities and stabilities to temperature are fundamental to our understanding of molecular evolution and how enzymes work. Here, we investigate the molecular and evolutionary mechanisms of enzyme temperature adaption, combining deep mechanistic studies with comprehensive sequence analyses of thousands of enzymes. We show that temperature adaptation in ketosteroid isomerase (KSI) arises primarily from one residue change with limited, local epistasis, and we establish the underlying physical mechanisms. This residue change occurs in diverse KSI backgrounds, suggesting parallel adaptation to temperature. We identify residues associated with organismal growth temperature across 1005 diverse bacterial enzyme families, suggesting widespread parallel adaptation to temperature. We assess the residue properties, molecular interactions, and interaction networks that appear to underly temperature adaptation.

Human SIRT1 Multispecificity Is Modulated by Active-Site Vicinity Substitutions during Natural Evolution.

Many enzymes that catalyze protein post-translational modifications can specifically modify multiple target proteins. However, little is known regarding the molecular basis and evolution of multispecificity in these enzymes. Here, we used a combined bioinformatics and experimental approaches to investigate the evolution of multispecificity in the sirtuin-1 (SIRT1) deacetylase. Guided by bioinformatics analysis of SIRT1 orthologs and substrates, we identified and examined important amino acid substitutions that have occurred during the evolution of sirtuins in Metazoa and Fungi. We found that mutation of human SIRT1 at these positions, based on sirtuin orthologs from Fungi, could alter its substrate specificity. These substitutions lead to reduced activity toward K382 acetylated p53 protein, which is only present in Metazoa, without affecting the high activity toward the conserved histone substrates. Results from ancestral sequence reconstruction are consistent with a model in which ancestral sirtuin proteins exhibited multispecificity, suggesting that the multispecificity of some metazoan sirtuins, such as hSIRT1, could be a relatively ancient trait.

Exploring the sequence, function, and evolutionary space of protein superfamilies using sequence similarity networks and phylogenetic reconstructions.

Integrative computational methods can facilitate the discovery of new protein functions and enzymatic reactions by enabling the observation and investigation of complex sequence-structure-function and evolutionary relationships within protein superfamilies. Here, we highlight the use of sequence similarity networks (SSNs) and phylogenetic reconstructions to map the functional divergence and evolutionary history of protein superfamilies. We exemplify this approach using the nitroreductase (NTR) flavoenzyme superfamily, demonstrating that SSN investigations can provide a rapid and effective means to classify groups of proteins, expose sequence similarity relationships across the global scale of a protein superfamily, and efficiently support detailed phylogenetic analyses. Integration of such approaches with systematic experimental characterization will expand our understanding of the functional diversity of enzymes, their evolution, and their associated physiological roles.

Structural, Kinetic, and Mechanistic Analysis of an Asymmetric 4-Oxalocrotonate Tautomerase Trimer.

A 4-oxalocrotonate tautomerase (4-OT) trimer has been isolated from Burkholderia lata, and a kinetic, mechanistic, and structural analysis has been performed. The enzyme is the third described oligomer state for 4-OT along with a homo- and heterohexamer. The 4-OT trimer is part of a small subset of sequences (133 sequences) within the 4-OT subgroup of the tautomerase superfamily (TSF). The TSF has two distinct features: members are composed of a single ß-α-ß unit (homo- and heterohexamer) or two consecutively joined ß-α-ß units (trimer) and generally have a catalytic amino-terminal proline. The enzyme, designated as fused 4-OT, functions as a 4-OT where the active site groups (Pro-1, Arg-39, Arg-76, Phe-115, Arg-127) mirror those in the canonical 4-OT from Pseudomonas putida mt-2. Inactivation by 2-oxo-3-pentynoate suggests that Pro-1 of fused 4-OT has a low p Ka enabling the prolyl nitrogen to function as a general base. A remarkable feature of the fused 4-OT is the absence of P3 rotational symmetry in the structure (1.5 Å resolution). The asymmetric arrangement of the trimer is not due to the fusion of the two ß-α-ß building blocks because an engineered "unfused" variant that breaks the covalent bond between the two units (to generate a heterohexamer) assumes the same asymmetric oligomerization state. It remains unknown how the different active site configurations contribute to the observed overall activities and whether the asymmetry has a biological purpose or role in the evolution of TSF members.

Atlas of the Radical SAM Superfamily: Divergent Evolution of Function Using a "Plug and Play" Domain.

The radical SAM superfamily contains over 100,000 homologous enzymes that catalyze a remarkably broad range of reactions required for life, including metabolism, nucleic acid modification, and biogenesis of cofactors. While the highly conserved SAM-binding motif responsible for formation of the key 5'-deoxyadenosyl radical intermediate is a key structural feature that simplifies identification of superfamily members, our understanding of their structure-function relationships is complicated by the modular nature of their structures, which exhibit varied and complex domain architectures. To gain new insight about these relationships, we classified the entire set of sequences into similarity-based subgroups that could be visualized using sequence similarity networks. This superfamily-wide analysis reveals important features that had not previously been appreciated from studies focused on one or a few members. Functional information mapped to the networks indicates which members have been experimentally or structurally characterized, their known reaction types, and their phylogenetic distribution. Despite the biological importance of radical SAM chemistry, the vast majority of superfamily members have never been experimentally characterized in any way, suggesting that many new reactions remain to be discovered. In addition to 20 subgroups with at least one known function, we identified additional subgroups made up entirely of sequences of unknown function. Importantly, our results indicate that even general reaction types fail to track well with our sequence similarity-based subgroupings, raising major challenges for function prediction for currently identified and new members that continue to be discovered. Interactive similarity networks and other data from this analysis are available from the Structure-Function Linkage Database.

Revealing Unexplored Sequence-Function Space Using Sequence Similarity Networks.

The rapidly expanding number of protein sequences found in public databases can improve our understanding of how protein functions evolve. However, our current knowledge of protein function likely represents a small fraction of the diverse repertoire that exists in nature. Integrative computational methods can facilitate the discovery of new protein functions and enzymatic reactions through the observation and investigation of the complex sequence-structure-function relationships within protein superfamilies. Here, we highlight the use of sequence similarity networks (SSNs) to identify previously unexplored sequence and function space. We exemplify this approach using the nitroreductase (NTR) superfamily. We demonstrate that SSN investigations can provide a rapid and effective means to classify groups of proteins, therefore exposing experimentally unexplored sequences that may exhibit novel functionality. Integration of such approaches with systematic experimental characterization will expand our understanding of the functional diversity of enzymes and their associated physiological roles.

A global view of structure-function relationships in the tautomerase superfamily.

The tautomerase superfamily (TSF) consists of more than 11,000 nonredundant sequences present throughout the biosphere. Characterized members have attracted much attention because of the unusual and key catalytic role of an N-terminal proline. These few characterized members catalyze a diverse range of chemical reactions, but the full scale of their chemical capabilities and biological functions remains unknown. To gain new insight into TSF structure-function relationships, we performed a global analysis of similarities across the entire superfamily and computed a sequence similarity network to guide classification into distinct subgroups. Our results indicate that TSF members are found in all domains of life, with most being present in bacteria. The eukaryotic members of the cis-3-chloroacrylic acid dehalogenase subgroup are limited to fungal species, whereas the macrophage migration inhibitory factor subgroup has wide eukaryotic representation (including mammals). Unexpectedly, we found that 346 TSF sequences lack Pro-1, of which 85% are present in the malonate semialdehyde decarboxylase subgroup. The computed network also enabled the identification of similarity paths, namely sequences that link functionally diverse subgroups and exhibit transitional structural features that may help explain reaction divergence. A structure-guided comparison of these linker proteins identified conserved transitions between them, and kinetic analysis paralleled these observations. Phylogenetic reconstruction of the linker set was consistent with these findings. Our results also suggest that contemporary TSF members may have evolved from a short 4-oxalocrotonate tautomerase-like ancestor followed by gene duplication and fusion. Our new linker-guided strategy can be used to enrich the discovery of sequence/structure/function transitions in other enzyme superfamilies.

Evolutionary and molecular foundations of multiple contemporary functions of the nitroreductase superfamily.

Insight regarding how diverse enzymatic functions and reactions have evolved from ancestral scaffolds is fundamental to understanding chemical and evolutionary biology, and for the exploitation of enzymes for biotechnology. We undertook an extensive computational analysis using a unique and comprehensive combination of tools that include large-scale phylogenetic reconstruction to determine the sequence, structural, and functional relationships of the functionally diverse flavin mononucleotide-dependent nitroreductase (NTR) superfamily (>24,000 sequences from all domains of life, 54 structures, and >10 enzymatic functions). Our results suggest an evolutionary model in which contemporary subgroups of the superfamily have diverged in a radial manner from a minimal flavin-binding scaffold. We identified the structural design principle for this divergence: Insertions at key positions in the minimal scaffold that, combined with the fixation of key residues, have led to functional specialization. These results will aid future efforts to delineate the emergence of functional diversity in enzyme superfamilies, provide clues for functional inference for superfamily members of unknown function, and facilitate rational redesign of the NTR scaffold.

Biocuration in the structure-function linkage database: the anatomy of a superfamily.

With ever-increasing amounts of sequence data available in both the primary literature and sequence repositories, there is a bottleneck in annotating molecular function to a sequence. This article describes the biocuration process and methods used in the structure-function linkage database (SFLD) to help address some of the challenges. We discuss how the hierarchy within the SFLD allows us to infer detailed functional properties for functionally diverse enzyme superfamilies in which all members are homologous, conserve an aspect of their chemical function and have associated conserved structural features that enable the chemistry. Also presented is the Enzyme Structure-Function Ontology (ESFO), which has been designed to capture the relationships between enzyme sequence, structure and function that underlie the SFLD and is used to guide the biocuration processes within the SFLD. Database URL: http://sfld.rbvi.ucsf.edu/.

Mast cells are directly activated by contact with cancer cells by a mechanism involving autocrine formation of adenosine and autocrine/paracrine signaling of the adenosine A3 receptor.

Mast cells (MCs) constitute an important part of the tumor microenvironment (TME). However, their underlying mechanisms of activation within the TME remain poorly understood. Here we show that recapitulating cell-to-cell contact interactions by exposing MCs to membranes derived from a number of cancer cell types, results in MC activation, evident by the increased phosphorylation of the ERK1/2 MAP kinases and Akt, in a phosphatidylinositol 3-kinase dependent fashion. Activation is unidirectional since MC derived membranes do not activate cancer cells. Stimulated ERK1/2 phosphorylation is strictly dependent on the ecto enzyme CD73 that mediates autocrine formation of adenosine, and is inhibited by knockdown of the A3 adenosine receptor (A3R) as well as by an A3R antagonist or by agonist-stimulated down-regulation of the A3R. We also show that cancer cell mediated triggering upregulates expression and stimulates secretion of interleukin 8 from the activated MCs. These findings provide evidence for a novel mode of unidirectional crosstalk between MCs and cancer cells implicating direct activation by cancer cells in MC reprogramming into a pro tumorigenic profile.

Evaluating Functional Annotations of Enzymes Using the Gene Ontology.

The Gene Ontology (GO) (Ashburner et al., Nat Genet 25(1):25-29, 2000) is a powerful tool in the informatics arsenal of methods for evaluating annotations in a protein dataset. From identifying the nearest well annotated homologue of a protein of interest to predicting where misannotation has occurred to knowing how confident you can be in the annotations assigned to those proteins is critical. In this chapter we explore what makes an enzyme unique and how we can use GO to infer aspects of protein function based on sequence similarity. These can range from identification of misannotation or other errors in a predicted function to accurate function prediction for an enzyme of entirely unknown function. Although GO annotation applies to any gene products, we focus here a describing our approach for hierarchical classification of enzymes in the Structure-Function Linkage Database (SFLD) (Akiva et al., Nucleic Acids Res 42(Database issue):D521-530, 2014) as a guide for informed utilisation of annotation transfer based on GO terms.

Evolutionary reprograming of protein-protein interaction specificity.

Using mutation libraries and deep sequencing, Aakre et al. study the evolution of protein-protein interactions using a toxin-antitoxin model. The results indicate probable trajectories via "intermediate" proteins that are promiscuous, thus avoiding transitions via non-interactions. These results extend observations about other biological interactions and enzyme evolution, suggesting broadly general principles.

[FeFe]-hydrogenase maturation: insights into the role HydE plays in dithiomethylamine biosynthesis.

HydE and HydG are radical S-adenosyl-l-methionine enzymes required for the maturation of [FeFe]-hydrogenase (HydA) and produce the nonprotein organic ligands characteristic of its unique catalytic cluster. The catalytic cluster of HydA (the H-cluster) is a typical [4Fe-4S] cubane bridged to a 2Fe-subcluster that contains two carbon monoxides, three cyanides, and a bridging dithiomethylamine as ligands. While recent studies have shed light on the nature of diatomic ligand biosynthesis by HydG, little information exists on the function of HydE. Herein, we present biochemical, spectroscopic, bioinformatic, and molecular modeling data that together map the active site and provide significant insight into the role of HydE in H-cluster biosynthesis. Electron paramagnetic resonance and UV-visible spectroscopic studies demonstrate that reconstituted HydE binds two [4Fe-4S] clusters and copurifies with S-adenosyl-l-methionine. Incorporation of deuterium from D2O into 5'-deoxyadenosine, the cleavage product of S-adenosyl-l-methionine, coupled with molecular docking experiments suggests that the HydE substrate contains a thiol functional group. This information, along with HydE sequence similarity and genome context networks, has allowed us to redefine the presumed mechanism for HydE away from BioB-like sulfur insertion chemistry; these data collectively suggest that the source of the sulfur atoms in the dithiomethylamine bridge of the H-cluster is likely derived from HydE's thiol containing substrate.

Prediction and characterization of enzymatic activities guided by sequence similarity and genome neighborhood networks.

Metabolic pathways in eubacteria and archaea often are encoded by operons and/or gene clusters (genome neighborhoods) that provide important clues for assignment of both enzyme functions and metabolic pathways. We describe a bioinformatic approach (genome neighborhood network; GNN) that enables large scale prediction of the in vitro enzymatic activities and in vivo physiological functions (metabolic pathways) of uncharacterized enzymes in protein families. We demonstrate the utility of the GNN approach by predicting in vitro activities and in vivo functions in the proline racemase superfamily (PRS; InterPro IPR008794). The predictions were verified by measuring in vitro activities for 51 proteins in 12 families in the PRS that represent ∼85% of the sequences; in vitro activities of pathway enzymes, carbon/nitrogen source phenotypes, and/or transcriptomic studies confirmed the predicted pathways. The synergistic use of sequence similarity networks3 and GNNs will facilitate the discovery of the components of novel, uncharacterized metabolic pathways in sequenced genomes.

Large-scale determination of sequence, structure, and function relationships in cytosolic glutathione transferases across the biosphere.

The cytosolic glutathione transferase (cytGST) superfamily comprises more than 13,000 nonredundant sequences found throughout the biosphere. Their key roles in metabolism and defense against oxidative damage have led to thousands of studies over several decades. Despite this attention, little is known about the physiological reactions they catalyze and most of the substrates used to assay cytGSTs are synthetic compounds. A deeper understanding of relationships across the superfamily could provide new clues about their functions. To establish a foundation for expanded classification of cytGSTs, we generated similarity-based subgroupings for the entire superfamily. Using the resulting sequence similarity networks, we chose targets that broadly covered unknown functions and report here experimental results confirming GST-like activity for 82 of them, along with 37 new 3D structures determined for 27 targets. These new data, along with experimentally known GST reactions and structures reported in the literature, were painted onto the networks to generate a global view of their sequence-structure-function relationships. The results show how proteins of both known and unknown function relate to each other across the entire superfamily and reveal that the great majority of cytGSTs have not been experimentally characterized or annotated by canonical class. A mapping of taxonomic classes across the superfamily indicates that many taxa are represented in each subgroup and highlights challenges for classification of superfamily sequences into functionally relevant classes. Experimental determination of disulfide bond reductase activity in many diverse subgroups illustrate a theme common for many reaction types. Finally, sequence comparison between an enzyme that catalyzes a reductive dechlorination reaction relevant to bioremediation efforts with some of its closest homologs reveals differences among them likely to be associated with evolution of this unusual reaction. Interactive versions of the networks, associated with functional and other types of information, can be downloaded from the Structure-Function Linkage Database (SFLD; http://sfld.rbvi.ucsf.edu).

The Structure-Function Linkage Database.

The Structure-Function Linkage Database (SFLD, http://sfld.rbvi.ucsf.edu/) is a manually curated classification resource describing structure-function relationships for functionally diverse enzyme superfamilies. Members of such superfamilies are diverse in their overall reactions yet share a common ancestor and some conserved active site features associated with conserved functional attributes such as a partial reaction. Thus, despite their different functions, members of these superfamilies 'look alike', making them easy to misannotate. To address this complexity and enable rational transfer of functional features to unknowns only for those members for which we have sufficient functional information, we subdivide superfamily members into subgroups using sequence information, and lastly into families, sets of enzymes known to catalyze the same reaction using the same mechanistic strategy. Browsing and searching options in the SFLD provide access to all of these levels. The SFLD offers manually curated as well as automatically classified superfamily sets, both accompanied by search and download options for all hierarchical levels. Additional information includes multiple sequence alignments, tab-separated files of functional and other attributes, and sequence similarity networks. The latter provide a new and intuitively powerful way to visualize functional trends mapped to the context of sequence similarity.

Prediction of mutational tolerance in HIV-1 protease and reverse transcriptase using flexible backbone protein design.

Predicting which mutations proteins tolerate while maintaining their structure and function has important applications for modeling fundamental properties of proteins and their evolution; it also drives progress in protein design. Here we develop a computational model to predict the tolerated sequence space of HIV-1 protease reachable by single mutations. We assess the model by comparison to the observed variability in more than 50,000 HIV-1 protease sequences, one of the most comprehensive datasets on tolerated sequence space. We then extend the model to a second protein, reverse transcriptase. The model integrates multiple structural and functional constraints acting on a protein and uses ensembles of protein conformations. We find the model correctly captures a considerable fraction of protease and reverse-transcriptase mutational tolerance and shows comparable accuracy using either experimentally determined or computationally generated structural ensembles. Predictions of tolerated sequence space afforded by the model provide insights into stability-function tradeoffs in the emergence of resistance mutations and into strengths and limitations of the computational model.

A dynamic view of domain-motif interactions.

Many protein-protein interactions are mediated by domain-motif interaction, where a domain in one protein binds a short linear motif in its interacting partner. Such interactions are often involved in key cellular processes, necessitating their tight regulation. A common strategy of the cell to control protein function and interaction is by post-translational modifications of specific residues, especially phosphorylation. Indeed, there are motifs, such as SH2-binding motifs, in which motif phosphorylation is required for the domain-motif interaction. On the contrary, there are other examples where motif phosphorylation prevents the domain-motif interaction. Here we present a large-scale integrative analysis of experimental human data of domain-motif interactions and phosphorylation events, demonstrating an intriguing coupling between the two. We report such coupling for SH3, PDZ, SH2 and WW domains, where residue phosphorylation within or next to the motif is implied to be associated with switching on or off domain binding. For domains that require motif phosphorylation for binding, such as SH2 domains, we found coupled phosphorylation events other than the ones required for domain binding. Furthermore, we show that phosphorylation might function as a double switch, concurrently enabling interaction of the motif with one domain and disabling interaction with another domain. Evolutionary analysis shows that co-evolution of the motif and the proximal residues capable of phosphorylation predominates over other evolutionary scenarios, in which the motif appeared before the potentially phosphorylated residue, or vice versa. Our findings provide strengthening evidence for coupled interaction-regulation units, defined by a domain-binding motif and a phosphorylated residue.

Global landscape of HIV-human protein complexes.

Human immunodeficiency virus (HIV) has a small genome and therefore relies heavily on the host cellular machinery to replicate. Identifying which host proteins and complexes come into physical contact with the viral proteins is crucial for a comprehensive understanding of how HIV rewires the host's cellular machinery during the course of infection. Here we report the use of affinity tagging and purification mass spectrometry to determine systematically the physical interactions of all 18 HIV-1 proteins and polyproteins with host proteins in two different human cell lines (HEK293 and Jurkat). Using a quantitative scoring system that we call MiST, we identified with high confidence 497 HIV-human protein-protein interactions involving 435 individual human proteins, with ∼40% of the interactions being identified in both cell types. We found that the host proteins hijacked by HIV, especially those found interacting in both cell types, are highly conserved across primates. We uncovered a number of host complexes targeted by viral proteins, including the finding that HIV protease cleaves eIF3d, a subunit of eukaryotic translation initiation factor 3. This host protein is one of eleven identified in this analysis that act to inhibit HIV replication. This data set facilitates a more comprehensive and detailed understanding of how the host machinery is manipulated during the course of HIV infection.