Convergent Evolution of Enzyme Active Sites Is not a Rare Phenomenon

Replicating PET Hydrolytic Activity by Positioning Active Sites with Smaller Synthetic Protein Scaffolds

Evolutionary constraints significantly limit the diversity of naturally occurring enzymes, thereby reducing the sequence repertoire available for enzyme discovery and engineering. Recent breakthroughs in protein structure prediction and de novo design, powered by artificial intelligence, now enable to create enzymes with desired functions without solely relying on traditional genome mining. Here, a computational strategy is demonstrated for creating new-to-nature polyethylene terephthalate hydrolases (PET hydrolases) by leveraging the known catalytic mechanisms and implementing multiple deep learning algorithms and molecular computations. This strategy includes the extraction of functional motifs from a template enzyme (here leaf-branch compost cutinase, LCC, is used), regeneration of new protein sequences, computational screening, experimental validation, and sequence refinement. PET hydrolytic activity is successfully replicated with designer enzymes that are at least 30% shorter in sequence length than LCC. Among them, RsPETase1 stands out due to its robust expressibility. It exhibits comparable catalytic efficiency (kcat/Km) to LCC and considerable thermostability with a melting temperature of 56 °C, despite sharing only 34% sequence similarity with LCC. This work suggests that enzyme diversity can be expanded by recapitulating functional motifs with computationally built protein scaffolds, thus generating opportunities to acquire highly active and robust enzymes that do not exist in nature.

AmyA contributes to the glycogen synthesis in Sulfolobus acidocaldarius

Glycogen, an α-1,4 linked glucose polymer with α-1,6 linked branches, accumulates in Sulfolobus acidocaldarius in granular form and contributes to stress resistance. While the glg operon responsible for glycogen metabolism has been studied, the gene responsible for branch formation remained elusive. Interestingly, the ΔAmyA mutant failed to accumulate glycogen. We hypothesized that amyA is responsible for branch formation in glycogen. In this study, AmyA was characterized to have dual activities as an α-amylase and a glycogen-branching enzyme. Glycogen extracted from S. acidocaldarius exhibited α-1,6 linked glucose branches, with most branches containing 5-13 glucose units. AmyA showed a preference for synthesizing branches with a degree of polymerization of 6. Structural modeling of AmyA, in comparison with GH57 glycogen-branching enzymes (GBE), revealed the presence of key amino acids essential for branching activity, located in positions structurally analogous to those in GH57 GBEs, enabling AmyA to function as a glycogen-branching enzyme. Alignment of the glg operons showed that amyA is conserved, while glgB is absent in most Crenarchaeota. Based on these findings, we propose that AmyA synthesizes α-1,6 branches in glycogen, substituting for the role of GlgB in Crenarchaeota.

New insights into exploring new functional enzymes through the enzyme promiscuity

Enzyme promiscuity, defined as the ability of enzymes to catalyze reactions beyond their primary physiological functions, has emerged as a pivotal concept in modern enzyme engineering. This review provides a comprehensive exploration of enzyme promiscuity and its implications for the discovery and development of novel functional enzymes. Through targeted strategies such as (semi-)rational design, directed evolution, and de novo design, enzyme promiscuity has been harnessed to broaden substrate scopes, enhance catalytic efficiencies, and adapt enzymes to diverse reaction conditions. These modifications often involve subtle alterations to the active site, which impact catalytic mechanisms and open new pathways for the synthesis and degradation of complex organic compounds. Striking a balance between maintaining native activity and enhancing promiscuous functions remains a significant challenge in enzyme engineering. Nevertheless, advances in structural biology and computational modeling offer promising strategies to overcome these obstacles. By elucidating the mechanistic basis of enzyme promiscuity, this review aims to deepen our understanding of this phenomenon. It underscores the necessity of further investigating the mechanisms underlying promiscuous enzymatic activity and highlights the importance of leveraging promiscuous enzymes to address industrial application demands and drive the development of next-generation biocatalysts.

Functional profiling of the sequence stockpile: a protein pair-based assessment of in silico prediction tools

MOTIVATION

In silico functional annotation of proteins is crucial to narrowing the sequencing-accelerated gap in our understanding of protein activities. Numerous function annotation methods exist, and their ranks have been growing, particularly so with the recent deep learning-based developments. However, it is unclear if these tools are truly predictive. As we are not aware of any methods that can identify new terms in functional ontologies, we ask if they can, at least, identify molecular functions of proteins that are non-homologous to or far-removed from known protein families.

RESULTS

Here, we explore the potential and limitations of the existing methods in predicting the molecular functions of thousands of such proteins. Lacking the "ground truth" functional annotations, we transformed the assessment of function prediction into evaluation of functional similarity of protein pairs that likely share function but are unlike any of the currently functionally annotated sequences. Notably, our approach transcends the limitations of functional annotation vocabularies, providing a means to assess different-ontology annotation methods. We find that most existing methods are limited to identifying functional similarity of homologous sequences and fail to predict the function of proteins lacking reference. Curiously, despite their seemingly unlimited by-homology scope, deep learning methods also have trouble capturing the functional signal encoded in protein sequence. We believe that our work will inspire the development of a new generation of methods that push boundaries and promote exploration and discovery in the molecular function domain.

AVAILABILITY AND IMPLEMENTATION

The data underlying this article are available at https://doi.org/10.6084/m9.figshare.c.6737127.v3. The code used to compute siblings is available openly at https://bitbucket.org/bromberglab/siblings-detector/.

Paradigms of convergent evolution in enzymes

There are many occurrences of enzymes catalysing the same reaction but having significantly different structures. Leveraging the comprehensive information on enzymes stored in the Mechanism and Catalytic Site Atlas (M-CSA), we present a collection of 34 cases for which there is sufficient evidence of functional convergence without an evolutionary link. For each case, we compare enzymes which have identical Enzyme Commission numbers (i.e. catalyse the same reaction), but different identifiers in the CATH data resource (i.e. different folds). We focus on similarities between their sequences, structures, active site geometries, cofactors and catalytic mechanisms. These features are then assessed to evaluate whether all the evidence for these structurally diverse proteins supports their independent evolution to catalyse the same chemical reaction. Our approach combines published literature information with knowledge-based computational resources from, amongst others, M-CSA, PDBe and PDBsum, supported by tailor-made software to explore active site structures and assess similarities in mechanism. We find that there are multiple types of convergent functional evolution observed to date, and it is necessary to investigate sequence, structure, active site geometry and enzyme mechanisms to describe such convergence accurately.

Novel Insights on Extracellular Electron Transfer Networks in the Desulfovibrionaceae Family: Unveiling the Potential Significance of Horizontal Gene Transfer

Some sulfate-reducing bacteria (SRB), mainly belonging to the Desulfovibrionaceae family, have evolved the capability to conserve energy through microbial extracellular electron transfer (EET), suggesting that this process may be more widespread than previously believed. While previous evidence has shown that mobile genetic elements drive the plasticity and evolution of SRB and iron-reducing bacteria (FeRB), few have investigated the shared molecular mechanisms related to EET. To address this, we analyzed the prevalence and abundance of EET elements and how they contributed to their differentiation among 42 members of the Desulfovibrionaceae family and 23 and 59 members of Geobacteraceae and Shewanellaceae, respectively. Proteins involved in EET, such as the cytochromes PpcA and CymA, the outer membrane protein OmpJ, and the iron-sulfur cluster-binding CbcT, exhibited widespread distribution within Desulfovibrionaceae. Some of these showed modular diversification. Additional evidence revealed that horizontal gene transfer was involved in the acquiring and losing of critical genes, increasing the diversification and plasticity between the three families. The results suggest that specific EET genes were widely disseminated through horizontal transfer, where some changes reflected environmental adaptations. These findings enhance our comprehension of the evolution and distribution of proteins involved in EET processes, shedding light on their role in iron and sulfur biogeochemical cycling.

Genomic language model predicts protein co-regulation and function

Deciphering the relationship between a gene and its genomic context is fundamental to understanding and engineering biological systems. Machine learning has shown promise in learning latent relationships underlying the sequence-structure-function paradigm from massive protein sequence datasets. However, to date, limited attempts have been made in extending this continuum to include higher order genomic context information. Evolutionary processes dictate the specificity of genomic contexts in which a gene is found across phylogenetic distances, and these emergent genomic patterns can be leveraged to uncover functional relationships between gene products. Here, we train a genomic language model (gLM) on millions of metagenomic scaffolds to learn the latent functional and regulatory relationships between genes. gLM learns contextualized protein embeddings that capture the genomic context as well as the protein sequence itself, and encode biologically meaningful and functionally relevant information (e.g. enzymatic function, taxonomy). Our analysis of the attention patterns demonstrates that gLM is learning co-regulated functional modules (i.e. operons). Our findings illustrate that gLM's unsupervised deep learning of the metagenomic corpus is an effective and promising approach to encode functional semantics and regulatory syntax of genes in their genomic contexts and uncover complex relationships between genes in a genomic region.

Structure-guided metagenome mining to tap microbial functional diversity

Scientists now have access to millions of accurate three-dimensional (3D) models of protein structures. How do we leverage 3D structural models to learn about microbial functions encoded in metagenomes? Here, we review recent developments using protein structural features to mine metagenomes from diverse environments ranging from the human gut to soil and ocean viromes. We compare 3D protein structural methods to characterize antibiotic resistance phenotypes, nutrient cycling, and host-drug-microbe interactions. Broadly, we encourage the scientific community to look beyond global sequence and structure alignments by considering fine-grained descriptors such as distance to ligand, active site, and tertiary interactions between amino acid residues scaling to microbiomes. Finally, we highlight structure-inspired approaches to chart new areas of microbial protein-coding sequence space.

Structural similarities between SAM and ATP recognition motifs and detection of ATP binding in a SAM binding DNA methyltransferase

S-adenosylmethionine (SAM) is a ubiquitous co-factor that serves as a donor for methylation reactions and additionally serves as a donor of other functional groups such as amino and ribosyl moieties in a variety of other biochemical reactions. Such versatility in function is enabled by the ability of SAM to be recognized by a wide variety of protein molecules that vary in their sequences and structural folds. To understand what gives rise to specific SAM binding in diverse proteins, we set out to study if there are any structural patterns at their binding sites. A comprehensive analysis of structures of the binding sites of SAM by all-pair comparison and clustering, indicated the presence of 4 different site-types, only one among them being well studied. For each site-type we decipher the common minimum principle involved in SAM recognition by diverse proteins and derive structural motifs that are characteristic of SAM binding. The presence of the structural motifs with precise three-dimensional arrangement of amino acids in SAM sites that appear to have evolved independently, indicates that these are winning arrangements of residues to bring about SAM recognition. Further, we find high similarity between one of the SAM site types and a well known ATP binding site type. We demonstrate using in vitro experiments that a known SAM binding protein, HpyAII.M1, a type 2 methyltransferase can bind and hydrolyse ATP. We find common structural motifs that explain this, further supported through site-directed mutagenesis. Observation of similar motifs for binding two of the most ubiquitous ligands in multiple protein families with diverse sequences and structural folds presents compelling evidence at the molecular level in favour of convergent evolution.

New biochemistry in the Rhodanese-phosphatase superfamily: emerging roles in diverse metabolic processes, nucleic acid modifications, and biological conflicts

The protein-tyrosine/dual-specificity phosphatases and rhodanese domains constitute a sprawling superfamily of Rossmannoid domains that use a conserved active site with a cysteine to catalyze a range of phosphate-transfer, thiotransfer, selenotransfer and redox activities. While these enzymes have been extensively studied in the context of protein/lipid head group dephosphorylation and various thiotransfer reactions, their overall diversity and catalytic potential remain poorly understood. Using comparative genomics and sequence/structure analysis, we comprehensively investigate and develop a natural classification for this superfamily. As a result, we identified several novel clades, both those which retain the catalytic cysteine and those where a distinct active site has emerged in the same location (e.g. diphthine synthase-like methylases and RNA 2' OH ribosyl phosphate transferases). We also present evidence that the superfamily has a wider range of catalytic capabilities than previously known, including a set of parallel activities operating on various sugar/sugar alcohol groups in the context of NAD+-derivatives and RNA termini, and potential phosphate transfer activities involving sugars and nucleotides. We show that such activities are particularly expanded in the RapZ-C-DUF488-DUF4326 clade, defined here for the first time. Some enzymes from this clade are predicted to catalyze novel DNA-end processing activities as part of nucleic-acid-modifying systems that are likely to function in biological conflicts between viruses and their hosts.

Using mechanism similarity to understand enzyme evolution

Enzyme reactions take place in the active site through a series of catalytic steps, which are collectively termed the enzyme mechanism. The catalytic step is thereby the individual unit to consider for the purposes of building new enzyme mechanisms - i.e. through the mix and match of individual catalytic steps, new enzyme mechanisms and reactions can be conceived. In the case of natural evolution, it has been shown that new enzyme functions have emerged through the tweaking of existing mechanisms by the addition, removal, or modification of some catalytic steps, while maintaining other steps of the mechanism intact. Recently, we have extracted and codified the information on the catalytic steps of hundreds of enzymes in a machine-readable way, with the aim of automating this kind of evolutionary analysis. In this paper, we illustrate how these data, which we called the "rules of enzyme catalysis", can be used to identify similar catalytic steps across enzymes that differ in their overall function and/or structural folds. A discussion on a set of three enzymes that share part of their mechanism is used as an exemplar to illustrate how this approach can reveal divergent and convergent evolution of enzymes at the mechanistic level.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12551-022-01022-9.

A novel Tetrahymena thermophila sterol C-22 desaturase belongs to the Fatty Acid Hydroxylase/Desaturase superfamily

Sterols in eukaryotic cells play important roles in modulating membrane fluidity and in cell signaling and trafficking. During evolution, a combination of gene losses and acquisitions gave rise to an extraordinary diversity of sterols in different organisms. The sterol C-22 desaturase identified in plants and fungi as a cytochrome P-450 monooxygenase evolved from the first eukaryotic cytochrome P450 and was lost in many lineages. Although the ciliate Tetrahymena thermophila desaturates sterols at the C-22 position, no cytochrome P-450 orthologs are present in the genome. Here, we aim to identify the genes responsible for the desaturation as well as their probable origin. We used gene knockout and yeast heterologous expression approaches to identify two putative genes, retrieved from a previous transcriptomic analysis, as sterol C-22 desaturases. Furthermore, we demonstrate using bioinformatics and evolutionary analyses that both genes encode a novel type of sterol C-22 desaturase that belongs to the large fatty acid hydroxylase/desaturase superfamily and the genes originated by genetic duplication prior to functional diversification. These results stress the widespread existence of nonhomologous isofunctional enzymes among different lineages of the tree of life as well as the suitability for the use of T. thermophila as a valuable model to investigate the evolutionary process of large enzyme families.

Proprotein Convertases and the Complement System

Proteins destined for secretion - after removal of the signal sequence - often undergo further proteolytic processing by proprotein convertases (PCs). Prohormones are typically processed in the regulated secretory pathway, while most plasma proteins travel though the constitutive pathway. The complement system is a major proteolytic cascade in the blood, serving as a first line of defense against microbes and also contributing to the immune homeostasis. Several complement components, namely C3, C4, C5 and factor I (FI), are multi-chain proteins that are apparently processed by PCs intracellularly. Cleavage occurs at consecutive basic residues and probably also involves the action of carboxypeptidases. The most likely candidate for the intracellular processing of complement proteins is furin, however, because of the overlapping specificities of basic amino acid residue-specific proprotein convertases, other PCs might be involved. To our surprise, we have recently discovered that processing of another complement protein, mannan-binding lectin-associated serine protease-3 (MASP-3) occurs in the blood by PCSK6 (PACE4). A similar mechanism had been described for the membrane protease corin, which is also activated extracellularly by PCSK6. In this review we intend to point out that the proper functioning of the complement system intimately depends on the action of proprotein convertases. In addition to the non-enzymatic components (C3, C4, C5), two constitutively active complement proteases are directly activated by PCs either intracellularly (FI), or extracellularly (MASP-3), moreover indirectly, through the constitutive activation of pro-factor D by MASP-3, the activity of the alternative pathway also depends on a PC present in the blood.

Capturing the geometry, function, and evolution of enzymes with 3D templates

Structural templates are 3D signatures representing protein functional sites, such as ligand binding cavities, metal coordination motifs, or catalytic sites. Here we explore methods to generate template libraries and algorithms to query structures for conserved 3D motifs. Applications of templates are discussed, as well as some exemplar cases for examining evolutionary links in enzymes. We also introduce the concept of using more than one template per structure to represent flexible sites, as an approach to better understand catalysis through snapshots captured in enzyme structures. Functional annotation from structure is an important topic that has recently resurfaced due to the new more accurate methods of protein structure prediction. Therefore, we anticipate that template-based functional site detection will be a powerful tool in the task of characterizing a vast number of new protein models.

Conformational variation in enzyme catalysis: A structural study on catalytic residues

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We introduce a pipeline to compare and contrast active sites from homologous enzymes in 3D.

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Comprehensive structural study covering enzymes from a large functional space.

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High heterogeneity in magnitude of active site flexibililty between enzyme families.

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Diffferent catalytic residue types and functions relate to different degrees of flexibility.

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Four paradigms classify enzymes according to the structural behaviour during catalysis.

Conformational variation in catalytic residues can be captured as alternative snapshots in enzyme crystal structures. Addressing the question of whether active site flexibility is an intrinsic and essential property of enzymes for catalysis, we present a comprehensive study on the 3D variation of active sites of 925 enzyme families, using explicit catalytic residue annotations from the Mechanism and Catalytic Site Atlas and structural data from the Protein Data Bank. Through weighted pairwise superposition of the functional atoms of active sites, we captured structural variability at single-residue level and examined the geometrical changes as ligands bind or as mutations occur. We demonstrate that catalytic centres of enzymes can be inherently rigid or flexible to various degrees according to the function they perform, and structural variability most often involves a subset of the catalytic residues, usually those not directly involved in the formation or cleavage of bonds. Moreover, data suggest that 2/3 of active sites are flexible, and in half of those, flexibility is only observed in the side chain. The goal of this work is to characterise our current knowledge of the extent of flexibility at the heart of catalysis and ultimately place our findings in the context of the evolution of catalysis as enzymes evolve new functions and bind different substrates.

A unified evolutionary origin for the ubiquitous protein transporters SecY and YidC

BACKGROUND

Protein transporters translocate hydrophilic segments of polypeptide across hydrophobic cell membranes. Two protein transporters are ubiquitous and date back to the last universal common ancestor: SecY and YidC. SecY consists of two pseudosymmetric halves, which together form a membrane-spanning protein-conducting channel. YidC is an asymmetric molecule with a protein-conducting hydrophilic groove that partially spans the membrane. Although both transporters mediate insertion of membrane proteins with short translocated domains, only SecY transports secretory proteins and membrane proteins with long translocated domains. The evolutionary origins of these ancient and essential transporters are not known.

RESULTS

The features conserved by the two halves of SecY indicate that their common ancestor was an antiparallel homodimeric channel. Structural searches with SecY's halves detect exceptional similarity with YidC homologs. The SecY halves and YidC share a fold comprising a three-helix bundle interrupted by a helical hairpin. In YidC, this hairpin is cytoplasmic and facilitates substrate delivery, whereas in SecY, it is transmembrane and forms the substrate-binding lateral gate helices. In both transporters, the three-helix bundle forms a protein-conducting hydrophilic groove delimited by a conserved hydrophobic residue. Based on these similarities, we propose that SecY originated as a YidC homolog which formed a channel by juxtaposing two hydrophilic grooves in an antiparallel homodimer. We find that archaeal YidC and its eukaryotic descendants use this same dimerisation interface to heterodimerise with a conserved partner. YidC's sufficiency for the function of simple cells is suggested by the results of reductive evolution in mitochondria and plastids, which tend to retain SecY only if they require translocation of large hydrophilic domains.

CONCLUSIONS

SecY and YidC share previously unrecognised similarities in sequence, structure, mechanism, and function. Our delineation of a detailed correspondence between these two essential and ancient transporters enables a deeper mechanistic understanding of how each functions. Furthermore, key differences between them help explain how SecY performs its distinctive function in the recognition and translocation of secretory proteins. The unified theory presented here explains the evolution of these features, and thus reconstructs a key step in the origin of cells.

NICEdrug.ch, a workflow for rational drug design and systems-level analysis of drug metabolism

The discovery of a drug requires over a decade of intensive research and financial investments - and still has a high risk of failure. To reduce this burden, we developed the NICEdrug.ch resource, which incorporates 250,000 bioactive molecules, and studied their enzymatic metabolic targets, fate, and toxicity. NICEdrug.ch includes a unique fingerprint that identifies reactive similarities between drug-drug and drug-metabolite pairs. We validated the application, scope, and performance of NICEdrug.ch over similar methods in the field on golden standard datasets describing drugs and metabolites sharing reactivity, drug toxicities, and drug targets. We use NICEdrug.ch to evaluate inhibition and toxicity by the anticancer drug 5-fluorouracil, and suggest avenues to alleviate its side effects. We propose shikimate 3-phosphate for targeting liver-stage malaria with minimal impact on the human host cell. Finally, NICEdrug.ch suggests over 1300 candidate drugs and food molecules to target COVID-19 and explains their inhibitory mechanism for further experimental screening. The NICEdrug.ch database is accessible online to systematically identify the reactivity of small molecules and druggable enzymes with practical applications in lead discovery and drug repurposing.

CHARMM-GUI LBS Finder & Refiner for Ligand Binding Site Prediction and Refinement

A protein performs its task by binding a variety of ligands in its local region that is also known as the ligand-binding-site (LBS). Therefore, accurate prediction, characterization, and refinement of LBS can facilitate protein functional annotations and structure-based drug design. In this work, we present CHARMM-GUI LBS Finder & Refiner (https://www.charmm-gui.org/input/lbsfinder) that predicts potential LBS, offers interactive features for local LBS structure analysis, and prepares various molecular dynamics (MD) systems and inputs by setting up distance restraint potentials for LBS structure refinement. LBS Finder & Refiner supports 5 different commonly used simulation programs, such as NAMD, AMBER, GROMACS, GENESIS, and OpenMM, for LBS structure refinement together with hydrogen mass repartitioning. The capability of LBS Finder & Refiner is illustrated through LBS structure predictions and refinements of 48 modeled and 20 apo benchmark target proteins. Overall, successful LBS structure predictions and refinements are seen in our benchmark tests. We hope that LBS Finder & Refiner is useful to predict, characterize, and refine potential LBS on any given protein of interest.

Combined Theoretical and Experimental Study to Unravel the Differences in Promiscuous Amidase Activity of Two Nonhomologous Enzymes

Convergent evolution has resulted in nonhomologous enzymes that contain similar active sites that catalyze the same primary and secondary reactions. Comparing how these enzymes achieve their reaction promiscuity can yield valuable insights to develop functions from the optimization of latent activities. In this work, we have focused on the promiscuous amidase activity in the esterase from Bacillus subtilis (Bs2) and compared with the same activity in the promiscuous lipase B from Candida antarctica (CALB). The study, combining multiscale quantum mechanics/molecular mechanics (QM/MM) simulations, deep machine learning approaches, and experimental characterization of Bs2 kinetics, confirms the amidase activity of Bs2 and CALB. The computational results indicate that both enzymes offer a slightly different reaction environment reflected by electrostatic effects within the active site, thus resulting in a different reaction mechanism during the acylation step. A convolutional neural network (CNN) has been used to understand the conserved amino acids among the evolved protein family and suggest that Bs2 provides a more robust protein scaffold to perform future mutagenesis studies. Results derived from this work will help reveal the origin of enzyme promiscuity, which will find applications in enzyme (re)design, particularly in creating a highly active amidase.

The Convergence of the Hedgehog/Intein Fold in Different Protein Splicing Mechanisms

Protein splicing catalyzed by inteins utilizes many different combinations of amino-acid types at active sites. Inteins have been classified into three classes based on their characteristic sequences. We investigated the structural basis of the protein splicing mechanism of class 3 inteins by determining crystal structures of variants of a class 3 intein from Mycobacterium chimaera and molecular dynamics simulations, which suggested that the class 3 intein utilizes a different splicing mechanism from that of class 1 and 2 inteins. The class 3 intein uses a bond cleavage strategy reminiscent of proteases but share the same Hedgehog/INTein (HINT) fold of other intein classes. Engineering of class 3 inteins from a class 1 intein indicated that a class 3 intein would unlikely evolve directly from a class 1 or 2 intein. The HINT fold appears as structural and functional solution for trans-peptidyl and trans-esterification reactions commonly exploited by diverse mechanisms using different combinations of amino-acid types for the active-site residues.

Promiscuous Enzyme Activity as a Driver of Allo and Iso Convergent Evolution, Lessons from the β-Lactamases

The probability of the evolution of a character depends on two factors: the probability of moving from one character state to another character state and the probability of the new character state fixation. The more the evolution of a character is probable, the more the convergent evolution will be witnessed, and consequently, convergent evolution could mean that the convergent character evolution results as a combination of these two factors. We investigated this phenomenon by studying the convergent evolution of biochemical functions. For the investigation we used the case of β-lactamases. β-lactamases hydrolyze β-lactams, which are antimicrobials able to block the DD-peptidases involved in bacterial cell wall synthesis. β-lactamase activity is present in two different superfamilies: the metallo-β-lactamase and the serine β-lactamase. The mechanism used to hydrolyze the β-lactam is different for the two superfamilies. We named this kind of evolution an allo-convergent evolution. We further showed that the β-lactamase activity evolved several times within each superfamily, a convergent evolution type that we named iso-convergent evolution. Both types of convergent evolution can be explained by the two evolutionary mechanisms discussed above. The probability of moving from one state to another is explained by the promiscuous β-lactamase activity present in the ancestral sequences of each superfamily, while the probability of fixation is explained in part by positive selection, as the organisms having β-lactamase activity allows them to resist organisms that secrete β-lactams. Indeed, an organism that has a mutation that increases the β-lactamase activity will be selected, as the organisms having this activity will have an advantage over the others.

A single residue determines substrate preference in benzylisoquinoline alkaloid N-methyltransferases

N-methylation is a recurring feature in the biosynthesis of many plant specialized metabolites, including alkaloids. A crucial step in the conserved central pathway that provides intermediates for the biosynthesis of benzylisoquinoline alkaloids (BIAs) involves conversion of the secondary amine (S)-coclaurine into the tertiary amine (S)-N-methylcoclaurine by coclaurine N-methyltransferase (CNMT). Subsequent enzymatic steps yield the core intermediate (S)-reticuline, from which various branch pathways for the biosynthesis of major BIAs such as morphine, noscapine and sanguinarine diverge. An additional N-methylation yielding quaternary BIAs is catalyzed by reticuline N-methyltransferase (RNMT), such as in the branch pathway leading to the taxonomically widespread and ecologically significant alkaloid magnoflorine. Despite their functional differences, analysis of primary sequence information has been unable to accurately distinguish between CNMT-like and RNMT-like enzymes, necessitating laborious in vitro screening. Furthermore, despite a recent emphasis on structural characterization of BIA NMTs, the features and mechanisms underlying the CNMT-RNMT functional dichotomy were unknown. We report the identification of structural variants tightly correlated with function in known BIA NMTs and show through reciprocal mutagenesis that a single residue acts as a switch between CNMT- and RNMT-like functions. We use yeast in vivo screening to show that this discovery allows for accurate prediction of activity strictly from primary sequence information and, on this basis, improve the annotation of previously reported putative BIA NMTs. Our results highlight the unusually short mutational distance separating ancestral CNMT-like enzymes from more evolutionarily advanced RNMT-like enzymes, and thus help explain the widespread yet sporadic occurrence of quaternary BIAs in plants. While this is the first report of structural variants controlling mono-versus di-methylation activity among plant NMT enzymes, comparison with bacterial MT enzymes also suggests possible convergent evolution.

Computational methods and tools for binding site recognition between proteins and small molecules: from classical geometrical approaches to modern machine learning strategies

In the current "genomic era" the number of identified genes is growing exponentially. However, the biological function of a large number of the corresponding proteins is still unknown. Recognition of small molecule ligands (e.g., substrates, inhibitors, allosteric regulators, etc.) is pivotal for protein functions in the vast majority of the cases and knowledge of the region where these processes take place is essential for protein function prediction and drug design. In this regard, computational methods represent essential tools to tackle this problem. A significant number of software tools have been developed in the last few years which exploit either protein sequence information, structure information or both. This review describes the most recent developments in protein function recognition and binding site prediction, in terms of both freely-available and commercial solutions and tools, detailing the main characteristics of the considered tools and providing a comparative analysis of their performance.

The functional convergence of antibiotic resistance in β-lactamases is not conferred by a simple convergent substitution of amino acid

Bacterial resistance to antibiotics is a serious medical and public health concern worldwide. Such resistance is conferred by a variety of mechanisms, but the extensive variability in levels of resistance across bacteria is a common finding. Understanding the underlying evolutionary processes governing this functional variation in antibiotic resistance is important as it may allow the development of appropriate strategies to improve treatment options for bacterial infections. The main objective of this study was to examine the functional evolution of β-lactamases, a common mechanism of enzymatic resistance that inactivates a widely used class of antibiotics. We first obtained β-lactamase protein sequences and minimal inhibitory concentration (MIC), a measure of antibiotic function, from previously published literature. We then used a molecular phylogenetic framework to examine the evolution of β-lactamase functional activity. We found that the functional activity of antibiotic resistance mediated by β-lactamase has evolved in a convergent manner within molecular classes, but is not associated with any single amino acid substitution. This suggests that the dynamics of convergent evolution in this system can vary between the functional and molecular (sequence) levels. Such disassociation may hamper bioinformatic approaches to antibiotic resistance determination and underscore the need for (less efficient but more effective) activity assays as an essential step in evaluating resistance in a given case.

Rossmann-Fold Methyltransferases: Taking a "β-Turn" around Their Cofactor, S-Adenosylmethionine

Methyltransferases (MTases) are superfamilies of enzymes that catalyze the transfer of a methyl group from S-adenosylmethionine (SAM), a nucleoside-based cofactor, to a wide variety of substrates such as DNA, RNA, proteins, small molecules, and lipids. Depending upon their structural features, the MTases can be further classified into different classes; we consider exclusively the largest class of MTases, the Rossmann-fold MTases. It has been shown that the nucleoside cofactor-binding Rossmann enzymes, particularly the nicotinamide adenine dinucleotide (NAD)-, flavin adenine dinucleotide (FAD)-, and SAM-binding MTases enzymes, share common binding motifs that include a Gly-rich loop region that interacts with the cofactor and a highly conserved acidic residue (Asp/Glu) that interacts with the ribose moiety of the cofactor. Here, we observe that the Gly-rich loop region of the Rossmann MTases adapts a specific type II' β-turn in the proximity of the cofactor (<4 Å), and it appears to be a key feature of these superfamilies. Additionally, we demonstrate that the conservation of this β-turn could play a critical role in the enzyme-cofactor interaction, thereby shedding new light on the structural conformation of the Gly-rich loop region from Rossmann MTases.

Systematic Identification and Classification of β-Lactamases Based on Sequence Similarity Criteria: β-Lactamase Annotation

β-lactamases, the enzymes responsible for resistance to β-lactam antibiotics, are widespread among prokaryotic genera. However, current β-lactamase classification schemes do not represent their present diversity. Here, we propose a workflow to identify and classify β-lactamases. Initially, a set of curated sequences was used as a model for the construction of profiles Hidden Markov Models (HMM), specific for each β-lactamase class. An extensive, nonredundant set of β-lactamase sequences was constructed from 7 different resistance proteins databases to test the methodology. The profiles HMM were improved for their specificity and sensitivity and then applied to fully assembled genomes. Five hierarchical classification levels are described, and a new class of β-lactamases with fused domains is proposed. Our profiles HMM provide a better annotation of β-lactamases, with classes and subclasses defined by objective criteria such as sequence similarity. This classification offers a solid base to the elaboration of studies on the diversity, dispersion, prevalence, and evolution of the different classes and subclasses of this critical enzymatic activity.

Mining of potential drug targets through the identification of essential and analogous enzymes in the genomes of pathogens of Glycine max, Zea mays and Solanum lycopersicum

Pesticides are one of the most widely used pest and disease control measures in plant crops and their indiscriminate use poses a direct risk to the health of populations and environment around the world. As a result, there is a great need for the development of new, less toxic molecules to be employed against plant pathogens. In this work, we employed an in silico approach to study the genes coding for enzymes of the genomes of three commercially important plants, soybean (Glycine max), tomato (Solanum lycopersicum) and corn (Zea mays), as well as 15 plant pathogens (4 bacteria and 11 fungi), focusing on revealing a set of essential and non-homologous isofunctional enzymes (NISEs) that could be prioritized as drug targets. By combining sequence and structural data, we obtained an initial set of 568 cases of analogy, of which 97 were validated and further refined, revealing a subset of 29 essential enzymatic activities with a total of 119 different structural forms, most belonging to central metabolic routes, including the carbohydrate metabolism, the metabolism of amino acids, among others. Further, another subset of 26 enzymatic activities possess a tertiary structure specific for the pathogen, not present in plants, men and Apis mellifera, which may be of importance for the development of specific enzymatic inhibitors against plant diseases that are less harmful to humans and the environment.

Global organization of a binding site network gives insight into evolution and structure-function relationships of proteins

The global organization of protein binding sites is analyzed by constructing a weighted network of binding sites based on their structural similarities and detecting communities of structurally similar binding sites based on the minimum description length principle. The analysis reveals that there are two central binding site communities that play the roles of the network hubs of smaller peripheral communities. The sizes of communities follow a power-law distribution, which indicates that the binding sites included in larger communities may be older and have been evolutionary structural scaffolds of more recent ones. Structurally similar binding sites in the same community bind to diverse ligands promiscuously and they are also embedded in diverse domain structures. Understanding the general principles of binding site interplay will pave the way for improved drug design and protein design.

Understanding Russell's viper venom factor V activator's substrate specificity by surface plasmon resonance and in-silico studies

Blood coagulation factor V (FV) is activated either by Factor X or thrombin, cleaving at three different sites viz., Site I (Arg709-Ser710), site II (Arg1018-Thr1019), and site III (Arg1545-Ser1546). Russell's viper venom factor V activator (RVV-V) is a thrombin-like serine proteinase that activates FV with selective, single cleavage at site III. A long lasting effort is being pending in understanding the 'selective' binding specificity of the RVV-V towards site III. Here, we present the binding kinetic study of RVV-V with two designed peptides corresponding to the regions from site I (Gln699-Asn713) and site II (1008Lys-Pro1022), respectively, that include 15 amino acids. Our investigation for justifying the binding efficacy and kinetics of peptides includes SPR method, protein-peptide docking, molecular dynamics simulation, and principal component analysis (PCA). Surprisingly, the SPR experiment disclosed that the Peptide II showed a lower binding affinity with KD of 2.775 mM while the Peptide I showed none. Docking and simulation of both the peptides with RVV-V engaged either rooted or shallow binding for Peptide II and Peptide I respectively. The peptide binding resulted in global conformational changes in the native fold of RVV-V, whereas the similar studies for thrombin failed to make major changes in the native fold. In support, the PCA analysis for RVV-V showed the dislocation of catalytic triad upon binding both the peptides. Hence, RVV-V, a serine protease, is incompetent in cleaving these two sites. This study suggests a transition in RVV-V from the native rigid to the distorted flexible structure and paves a way to design a new peptide substrate/inhibitor.

Functional Analogy in Human Metabolism: Enzymes with Different Biological Roles or Functional Redundancy?

Since enzymes catalyze almost all chemical reactions that occur in living organisms, it is crucial that genes encoding such activities are correctly identified and functionally characterized. Several studies suggest that the fraction of enzymatic activities in which multiple events of independent origin have taken place during evolution is substantial. However, this topic is still poorly explored, and a comprehensive investigation of the occurrence, distribution, and implications of these events has not been done so far. Fundamental questions, such as how analogous enzymes originate, why so many events of independent origin have apparently occurred during evolution, and what are the reasons for the coexistence in the same organism of distinct enzymatic forms catalyzing the same reaction, remain unanswered. Also, several isofunctional enzymes are still not recognized as nonhomologous, even with substantial evidence indicating different evolutionary histories. In this work, we begin to investigate the biological significance of the cooccurrence of nonhomologous isofunctional enzymes in human metabolism, characterizing functional analogous enzymes identified in metabolic pathways annotated in the human genome. Our hypothesis is that the coexistence of multiple enzymatic forms might not be interpreted as functional redundancy. Instead, these enzymatic forms may be implicated in distinct (and probably relevant) biological roles.

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.

Sequence/structural analysis of xylem proteome emphasizes pathogenesis-related proteins, chitinases and β-1, 3-glucanases as key players in grapevine defense against Xylella fastidiosa

Background. Xylella fastidiosa, the causative agent of various plant diseases including Pierce’s disease in the US, and Citrus Variegated Chlorosis in Brazil, remains a continual source of concern and economic losses, especially since almost all commercial varieties are sensitive to this Gammaproteobacteria. Differential expression of proteins in infected tissue is an established methodology to identify key elements involved in plant defense pathways.

Methods. In the current work, we developed a methodology named CHURNER that emphasizes relevant protein functions from proteomic data, based on identification of proteins with similar structures that do not necessarily have sequence homology. Such clustering emphasizes protein functions which have multiple copies that are up/down-regulated, and highlights similar proteins which are differentially regulated. As a working example we present proteomic data enumerating differentially expressed proteins in xylem sap from grapevines that were infected with X. fastidiosa.

Results. Analysis of this data by CHURNER highlighted pathogenesis related PR-1 proteins, reinforcing this as the foremost protein function in xylem sap involved in the grapevine defense response to X. fastidiosa. β-1, 3-glucanase, which has both anti-microbial and anti-fungal activities, is also up-regulated. Simultaneously, chitinases are found to be both up and down-regulated by CHURNER, and thus the net gain of this protein function loses its significance in the defense response.

Discussion. We demonstrate how structural data can be incorporated in the pipeline of proteomic data analysis prior to making inferences on the importance of individual proteins to plant defense mechanisms. We expect CHURNER to be applicable to any proteomic data set.

An Ancient Fingerprint Indicates the Common Ancestry of Rossmann-Fold Enzymes Utilizing Different Ribose-Based Cofactors

Nucleoside-based cofactors are presumed to have preceded proteins. The Rossmann fold is one of the most ancient and functionally diverse protein folds, and most Rossmann enzymes utilize nucleoside-based cofactors. We analyzed an omnipresent Rossmann ribose-binding interaction: a carboxylate side chain at the tip of the second β-strand (β2-Asp/Glu). We identified a canonical motif, defined by the β2-topology and unique geometry. The latter relates to the interaction being bidentate (both ribose hydroxyls interacting with the carboxylate oxygens), to the angle between the carboxylate and the ribose, and to the ribose's ring configuration. We found that this canonical motif exhibits hallmarks of divergence rather than convergence. It is uniquely found in Rossmann enzymes that use different cofactors, primarily SAM (S-adenosyl methionine), NAD (nicotinamide adenine dinucleotide), and FAD (flavin adenine dinucleotide). Ribose-carboxylate bidentate interactions in other folds are not only rare but also have a different topology and geometry. We further show that the canonical geometry is not dictated by a physical constraint--geometries found in noncanonical interactions have similar calculated bond energies. Overall, these data indicate the divergence of several major Rossmann-fold enzyme classes, with different cofactors and catalytic chemistries, from a common pre-LUCA (last universal common ancestor) ancestor that possessed the β2-Asp/Glu motif.

Sequence/structural analysis of xylem proteome emphasizes pathogenesis-related proteins, chitinases and β-1, 3-glucanases as key players in grapevine defense against Xylella fastidiosa

Background. Xylella fastidiosa, the causative agent of various plant diseases including Pierce's disease in the US, and Citrus Variegated Chlorosis in Brazil, remains a continual source of concern and economic losses, especially since almost all commercial varieties are sensitive to this Gammaproteobacteria. Differential expression of proteins in infected tissue is an established methodology to identify key elements involved in plant defense pathways. Methods. In the current work, we developed a methodology named CHURNER that emphasizes relevant protein functions from proteomic data, based on identification of proteins with similar structures that do not necessarily have sequence homology. Such clustering emphasizes protein functions which have multiple copies that are up/down-regulated, and highlights similar proteins which are differentially regulated. As a working example we present proteomic data enumerating differentially expressed proteins in xylem sap from grapevines that were infected with X. fastidiosa. Results. Analysis of this data by CHURNER highlighted pathogenesis related PR-1 proteins, reinforcing this as the foremost protein function in xylem sap involved in the grapevine defense response to X. fastidiosa. β-1, 3-glucanase, which has both anti-microbial and anti-fungal activities, is also up-regulated. Simultaneously, chitinases are found to be both up and down-regulated by CHURNER, and thus the net gain of this protein function loses its significance in the defense response. Discussion. We demonstrate how structural data can be incorporated in the pipeline of proteomic data analysis prior to making inferences on the importance of individual proteins to plant defense mechanisms. We expect CHURNER to be applicable to any proteomic data set.

An assessment of catalytic residue 3D ensembles for the prediction of enzyme function

Background

The central element of each enzyme is the catalytic site, which commonly catalyzes a single biochemical reaction with high specificity. It was unclear to us how often sites that catalyze the same or highly similar reactions evolved on different, i. e. non-homologous protein folds and how similar their 3D poses are. Both similarities are key criteria for assessing the usability of pose comparison for function prediction.

Results

We have analyzed the SCOP database on the superfamily level in order to estimate the number of non-homologous enzymes possessing the same function according to their EC number. 89 % of the 873 substrate-specific functions (four digit EC number) assigned to mono-functional, single-domain enzymes were only found in one superfamily. For a reaction-specific grouping (three digit EC number), this value dropped to 35 %, indicating that in approximately 65 % of all enzymes the same function evolved in two or more non-homologous proteins.

For these isofunctional enzymes, structural similarity of the catalytic sites may help to predict function, because neither high sequence similarity nor identical folds are required for a comparison. To assess the specificity of catalytic 3D poses, we compiled the redundancy-free set ENZ_SITES, which comprises 695 sites, whose composition and function are well-defined. We compared their poses with the help of the program Superpose3D and determined classification performance. If the sites were from different superfamilies, the number of true and false positive predictions was similarly high, both for a coarse and a detailed grouping of enzyme function. Moreover, classification performance did not improve drastically, if we additionally used homologous sites to predict function.

Conclusions

For a large number of enzymatic functions, dissimilar sites evolved that catalyze the same reaction and it is the individual substrate that determines the arrangement of the catalytic site and its local environment. These substrate-specific requirements turn the comparison of catalytic residues into a weak classifier for the prediction of enzyme function.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-015-0807-6) contains supplementary material, which is available to authorized users.

Considerations of Protein Subpockets in Fragment-Based Drug Design

While the fragment-based drug design approach continues to gain importance, gaps in the tools and methods available in the identification and accurate utilization of protein subpockets have limited the scope. The importance of these features of small molecule-protein recognition is highlighted with several examples. A generalized solution for the identification of subpockets and corresponding chemical fragments remains elusive, but there are numerous advancements in methods that can be used in combination to address subpockets. Finally, additional examples of approaches that consider the relative importance of small-molecule co-dependence of protein conformations are highlighted to emphasize an increased significance of subpockets, especially at protein interfaces.

Isolation and in silico characterization of novel esterase gene with β-lactamase fold isolated from metagenome of north western Himalayas

An esterase-producing clone Aph2 was isolated from the Apharwat soil metagenomic library, a mountain peak in NW Himalayas. ORF 2 (Est Ac) of clone Aph2 corresponds to 271 aa protein and showed 26 % sequence similarity to carboxylesterase gene of Synechococcus sp. JA-2-3B. Est Ac contains nucleophilic Ser in S68-X-X-K71 motif of β-lactamases with Tyr Y103. The conserved sequences are common with family VIII carboxylesterase and class C β-lactamase sequences. Phylogenetic analysis revealed that Est Ac sequence is closely related to esterase than to β-lactamases. In silico 3D protein structure of Est Ac was generated using MODELLER software (9.10 version). Model was generated on the basis of carboxylesterase template (PDB:1CI8) of Est B (Burkholderia gladioli) and the stereochemical parameters of the model generated were satisfactory. Docking with diisopropyl-fluorophosphate confirmed catalytic activity of Ser68 present in S-X-X-K motif.

The Classification and Evolution of Enzyme Function

Enzymes are the proteins responsible for the catalysis of life. Enzymes sharing a common ancestor as defined by sequence and structure similarity are grouped into families and superfamilies. The molecular function of enzymes is defined as their ability to catalyze biochemical reactions; it is manually classified by the Enzyme Commission and robust approaches to quantitatively compare catalytic reactions are just beginning to appear. Here, we present an overview of studies at the interface of the evolution and function of enzymes.

Synthetic biology for the directed evolution of protein biocatalysts: navigating sequence space intelligently

The amino acid sequence of a protein affects both its structure and its function. Thus, the ability to modify the sequence, and hence the structure and activity, of individual proteins in a systematic way, opens up many opportunities, both scientifically and (as we focus on here) for exploitation in biocatalysis. Modern methods of synthetic biology, whereby increasingly large sequences of DNA can be synthesised de novo, allow an unprecedented ability to engineer proteins with novel functions. However, the number of possible proteins is far too large to test individually, so we need means for navigating the 'search space' of possible protein sequences efficiently and reliably in order to find desirable activities and other properties. Enzymologists distinguish binding (Kd) and catalytic (kcat) steps. In a similar way, judicious strategies have blended design (for binding, specificity and active site modelling) with the more empirical methods of classical directed evolution (DE) for improving kcat (where natural evolution rarely seeks the highest values), especially with regard to residues distant from the active site and where the functional linkages underpinning enzyme dynamics are both unknown and hard to predict. Epistasis (where the 'best' amino acid at one site depends on that or those at others) is a notable feature of directed evolution. The aim of this review is to highlight some of the approaches that are being developed to allow us to use directed evolution to improve enzyme properties, often dramatically. We note that directed evolution differs in a number of ways from natural evolution, including in particular the available mechanisms and the likely selection pressures. Thus, we stress the opportunities afforded by techniques that enable one to map sequence to (structure and) activity in silico, as an effective means of modelling and exploring protein landscapes. Because known landscapes may be assessed and reasoned about as a whole, simultaneously, this offers opportunities for protein improvement not readily available to natural evolution on rapid timescales. Intelligent landscape navigation, informed by sequence-activity relationships and coupled to the emerging methods of synthetic biology, offers scope for the development of novel biocatalysts that are both highly active and robust.

The enzymatic nature of an anonymous protein sequence cannot reliably be inferred from superfamily level structural information alone

As the largest fraction of any proteome does not carry out enzymatic functions, and in order to leverage 3D structural data for the annotation of increasingly higher volumes of sequence data, we wanted to assess the strength of the link between coarse grained structural data (i.e., homologous superfamily level) and the enzymatic versus non-enzymatic nature of protein sequences. To probe this relationship, we took advantage of 41 phylogenetically diverse (encompassing 11 distinct phyla) genomes recently sequenced within the GEBA initiative, for which we integrated structural information, as defined by CATH, with enzyme level information, as defined by Enzyme Commission (EC) numbers. This analysis revealed that only a very small fraction (about 1%) of domain sequences occurring in the analyzed genomes was found to be associated with homologous superfamilies strongly indicative of enzymatic function. Resorting to less stringent criteria to define enzyme versus non-enzyme biased structural classes or excluding highly prevalent folds from the analysis had only modest effect on this proportion. Thus, the low genomic coverage by structurally anchored protein domains strongly associated to catalytic activities indicates that, on its own, the power of coarse grained structural information to infer the general property of being an enzyme is rather limited.

Methods for predicting protein-ligand binding sites

Ligand binding is required for many proteins to function properly. A large number of bioinformatics tools have been developed to predict ligand binding sites as a first step in understanding a protein's function or to facilitate docking computations in virtual screening based drug design. The prediction usually requires only the three-dimensional structure (experimentally determined or computationally modeled) of the target protein to be searched for ligand binding site(s), and Web servers have been built, allowing the free and simple use of prediction tools. In this chapter, we review the underlying concepts of the methods used by various tools, and discuss their different features and the related issues of ligand binding site prediction. Some cautionary notes about the use of these tools are also provided.

Comparison of Metabolic Pathways in Escherichia coli by Using Genetic Algorithms

In order to understand how cellular metabolism has taken its modern form, the conservation and variations between metabolic pathways were evaluated by using a genetic algorithm (GA). The GA approach considered information on the complete metabolism of the bacterium Escherichia coli K-12, as deposited in the KEGG database, and the enzymes belonging to a particular pathway were transformed into enzymatic step sequences by using the breadth-first search algorithm. These sequences represent contiguous enzymes linked to each other, based on their catalytic activities as they are encoded in the Enzyme Commission numbers. In a posterior step, these sequences were compared using a GA in an all-against-all (pairwise comparisons) approach. Individual reactions were chosen based on their measure of fitness to act as parents of offspring, which constitute the new generation. The sequences compared were used to construct a similarity matrix (of fitness values) that was then considered to be clustered by using a k-medoids algorithm. A total of 34 clusters of conserved reactions were obtained, and their sequences were finally aligned with a multiple-sequence alignment GA optimized to align all the reaction sequences included in each group or cluster. From these comparisons, maps associated with the metabolism of similar compounds also contained similar enzymatic step sequences, reinforcing the Patchwork Model for the evolution of metabolism in E. coli K-12, an observation that can be expanded to other organisms, for which there is metabolism information. Finally, our mapping of these reactions is discussed, with illustrations from a particular case.

One origin for metallo-β-lactamase activity, or two? An investigation assessing a diverse set of reconstructed ancestral sequences based on a sample of phylogenetic trees

Bacteria use metallo-β-lactamase enzymes to hydrolyse lactam rings found in many antibiotics, rendering them ineffective. Metallo-β-lactamase activity is thought to be polyphyletic, having arisen on more than one occasion within a single functionally diverse homologous superfamily. Since discovery of multiple origins of enzymatic activity conferring antibiotic resistance has broad implications for the continued clinical use of antibiotics, we test the hypothesis of polyphyly further; if lactamase function has arisen twice independently, the most recent common ancestor (MRCA) is not expected to possess lactam-hydrolysing activity. Two major problems present themselves. Firstly, even with a perfectly known phylogeny, ancestral sequence reconstruction is error prone. Secondly, the phylogeny is not known, and in fact reconstructing a single, unambiguous phylogeny for the superfamily has proven impossible. To obtain a more statistical view of the strength of evidence for or against MRCA lactamase function, we reconstructed a sample of 98 MRCAs of the metallo-β-lactamases, each based on a different tree in a bootstrap sample of reconstructed phylogenies. InterPro sequence signatures and homology modelling were then used to assess our sample of MRCAs for lactamase functionality. Only 5 % of these models conform to our criteria for metallo-β-lactamase functionality, suggesting that the ancestor was unlikely to have been a metallo-β-lactamase. On the other hand, given that ancestral proteins may have had metallo-β-lactamase functionality with variation in sequence and structural properties compared with extant enzymes, our criteria are conservative, estimating a lower bound of evidence for metallo-β-lactamase functionality but not an upper bound.

The evolution of enzyme function in the isomerases

The advent of computational approaches to measure functional similarity between enzymes adds a new dimension to existing evolutionary studies based on sequence and structure. This paper reviews research efforts aiming to understand the evolution of enzyme function in superfamilies, presenting a novel strategy to provide an overview of the evolution of enzymes belonging to an individual EC class, using the isomerases as an exemplar.

Enzyme informatics

Over the last 50 years, sequencing, structural biology and bioinformatics have completely revolutionised biomolecular science, with millions of sequences and tens of thousands of three dimensional structures becoming available. The bioinformatics of enzymes is well served by, mostly free, online databases. BRENDA describes the chemistry, substrate specificity, kinetics, preparation and biological sources of enzymes, while KEGG is valuable for understanding enzymes and metabolic pathways. EzCatDB, SFLD and MACiE are key repositories for data on the chemical mechanisms by which enzymes operate. At the current rate of genome sequencing and manual annotation, human curation will never finish the functional annotation of the ever-expanding list of known enzymes. Hence there is an increasing need for automated annotation, though it is not yet widespread for enzyme data. In contrast, functional ontologies such as the Gene Ontology already profit from automation. Despite our growing understanding of enzyme structure and dynamics, we are only beginning to be able to design novel enzymes. One can now begin to trace the functional evolution of enzymes using phylogenetics. The ability of enzymes to perform secondary functions, albeit relatively inefficiently, gives clues as to how enzyme function evolves. Substrate promiscuity in enzymes is one example of imperfect specificity in protein-ligand interactions. Similarly, most drugs bind to more than one protein target. This may sometimes result in helpful polypharmacology as a drug modulates plural targets, but also often leads to adverse side-effects. Many chemoinformatics approaches can be used to model the interactions between druglike molecules and proteins in silico. We can even use quantum chemical techniques like DFT and QM/MM to compute the structural and energetic course of enzyme catalysed chemical reaction mechanisms, including a full description of bond making and breaking.

Characterization of bacterial NMN deamidase as a Ser/Lys hydrolase expands diversity of serine amidohydrolases

NMN deamidase (PncC) is a bacterial enzyme involved in NAD biosynthesis. We have previously demonstrated that PncC is structurally distinct from other known amidohydrolases. Here, we extended PncC characterization by mutating all potential catalytic residues and assessing their individual roles in catalysis through kinetic analyses. Inspection of these residues' spatial arrangement in the active site, allowed us to conclude that PncC is a serine-amidohydrolase, employing a Ser/Lys dyad for catalysis. Analysis of the PncC structure in complex with a modeled NMN substrate supported our conclusion, and enabled us to propose the catalytic mechanism.

The dipeptidyl peptidase IV inhibitors vildagliptin and K-579 inhibit a phospholipase C: a case of promiscuous scaffolds in proteins

The long term side effects of any newly introduced drug is a subject of intense research, and often raging controversies. One such example is the dipeptidyl peptidase-IV (DPP4) inhibitor used for treating type 2 diabetes, which is inconclusively implicated in increased susceptibility to acute pancreatitis. Previously, based on a computational analysis of the spatial and electrostatic properties of active site residues, we have demonstrated that phosphoinositide-specific phospholipase C (PI-PLC) from Bacillus cereus is a prolyl peptidase using in vivo experiments. In the current work, we first report the inhibition of the native activity of PI-PLC by two DPP4 inhibitors - vildagliptin (LAF-237) and K-579. While vildagliptin inhibited PI-PLC at micromolar concentrations, K-579 was a potent inhibitor even at nanomolar concentrations. Subsequently, we queried a comprehensive, non-redundant set of 5000 human proteins (50% similarity cutoff) with known structures using serine protease (SPASE) motifs derived from trypsin and DPP4. A pancreatic lipase and a gastric lipase are among the proteins that are identified as proteins having promiscuous SPASE scaffolds that could interact with DPP4 inhibitors. The presence of such scaffolds in human lipases is expected since they share the same catalytic mechanism with PI-PLC. However our methodology also detects other proteins, often with a completely different enzymatic mechanism, that have significantly congruent domains with the SPASE motifs. The reported elevated levels of serum lipase, although contested, could be rationalized by inhibition of lipases reported here. In an effort to further our understanding of the spatial and electrostatic basis of DPP4 inhibitors, we have also done a comprehensive analysis of all 76 known DPP4 structures liganded to inhibitors till date. Also, the methodology presented here can be easily adopted for other drugs, and provide the first line of filtering in the identification of pathways that might be inadvertently affected due to promiscuous scaffolds in proteins.