Structure-Function Linkage Research Articles

Radical SAM Enzymes and the Unexplored Chemistry of RiPP Biosynthesis

Radical S‐adenosylmethionine‐dependent (rSAM) enzymes comprise one of the largest and most functionally diverse protein superfamilies known. InterPro 87.0 (Nov‐2021) classifies >0.7M sequences in the UniProt database as containing a rSAM domain with the overwhelming majority having unknown and unpredictable functions. To accelerate the discovery of new enzymatic reactions, we have created an open‐access web resource, RadicalSAM.org, that applies modern genomic enzymology approaches to analyze the rSAM superfamily. This resource updates and considerably expands upon the archival Structure‐Function Linkage Database. RadicalSAM.org includes pre‐computed sequence similarity networks, domain architecture analysis, sequence logos, length histograms, multiple sequence alignments, hidden Markov models, genomic neighborhood diagrams, and taxonomical information for any desired grouping of rSAM proteins. Also included are relevant links to journal articles, UniProt, KEGG, Swiss‐Prot, PDB, and other external resources. This talk will briefly highlight examples of how we are leveraging insights provided by RadicalSAM.org, with our specific interests pertaining to the ribosomally synthesized and post‐translationally modified peptides (RiPPs).

Eco-geomorphological connectivity and coupling interactions at hillslope scale in drylands: Concepts and critical examples

The diagnosis of land degradation requires a deep understanding of ecosystem functioning and evolution. In dryland systems, in particular, research efforts must address the redistribution of scarce resources for vegetation, in a context of high spatial heterogeneity and non-linear response. This fact explains the prevalence of eco-hydrological perspectives interested in runoff processes and, the more recent, focused on connectivity as an indicator of system resource optimisation. From a geomorphological perspective and reviewing the concepts of eco-hydro-geomorphological interactions operating in ecosystems, this paper explores the effects of erosion on vegetation configuration through two case studies at different spatio-temporal scales. We focus on the structure-function linkage, specifically on how morphological traits relate with different stages in the erosional sequence, both in the abiotic and the biotic domain. Results suggest that vegetation dynamics are affected by structural boundary conditions at both scales, i.e. by surface armouring related with rock fragments at the patch scale, and by the degree of hillslope-channel coupling at the hillslope scale. Our preliminary results can serve as new working hypotheses about the structure-function interplay on hillslopes. All this, taking advantage of the recent technological achievements for acquiring very high-resolution geospatial data that offer new analytical possibilities in a range of scales.

A strategy for large-scale comparison of evolutionary- and reaction-based classifications of enzyme function.

Determining the molecular function of enzymes discovered by genome sequencing represents a primary foundation for understanding many aspects of biology. Historically, classification of enzyme reactions has used the enzyme nomenclature system developed to describe the overall reactions performed by biochemically characterized enzymes, irrespective of their associated sequences. In contrast, functional classification and assignment for the millions of protein sequences of unknown function now available is largely done in two computational steps, first by similarity-based assignment of newly obtained sequences to homologous groups, followed by transferring to them the known functions of similar biochemically characterized homologs. Due to the fundamental differences in their etiologies and practice, `how’ these chemistry- and evolution-centric functional classification systems relate to each other has been difficult to explore on a large scale. To investigate this issue in a new way, we integrated two published ontologies that had previously described each of these classification systems independently. The resulting infrastructure was then used to compare the functional assignments obtained from each classification system for the well-studied and functionally diverse enolase superfamily. Mapping these function assignments to protein structure and reaction similarity networks shows a profound and complex disconnect between the homology- and chemistry-based classification systems. This conclusion mirrors previous observations suggesting that except for closely related sequences, facile annotation transfer from small numbers of characterized enzymes to the huge number uncharacterized homologs to which they are related is problematic. Our extension of these comparisons to large enzyme superfamilies in a computationally intelligent manner provides a foundation for new directions in protein function prediction for the huge proportion of sequences of unknown function represented in major databases. Interactive sequence, reaction, substrate and product similarity networks computed for this work for the enolase and two other superfamilies are freely available for download from the Structure Function Linkage Database Archive (http://sfld.rbvi.ucsf.edu).

Biosynthesis of the nitrogenase active-site cofactor precursor NifB-co in Saccharomyces cerevisiae

The radical S-adenosylmethionine (SAM) enzyme NifB occupies a central and essential position in nitrogenase biogenesis. NifB catalyzes the formation of an [8Fe-9S-C] cluster, called NifB-co, which constitutes the core of the active-site cofactors for all 3 nitrogenase types. Here, we produce functional NifB in aerobically cultured Saccharomyces cerevisiae Combinatorial pathway design was employed to construct 62 strains in which transcription units driving different expression levels of mitochondria-targeted nif genes (nifUSXB and fdxN) were integrated into the chromosome. Two combinatorial libraries totaling 0.7 Mb were constructed: An expression library of 6 partial clusters, including nifUSX and fdxN, and a library consisting of 28 different nifB genes mined from the Structure-Function Linkage Database and expressed at different levels according to a factorial design. We show that coexpression in yeast of the nitrogenase maturation proteins NifU, NifS, and FdxN from Azotobacter vinelandii with NifB from the archaea Methanocaldococcus infernus or Methanothermobacter thermautotrophicus yields NifB proteins equipped with [Fe-S] clusters that, as purified, support in vitro formation of NifB-co. Proof of in vivo NifB-co formation was additionally obtained. NifX as purified from aerobically cultured S. cerevisiae coexpressing M. thermautotrophicus NifB with A. vinelandii NifU, NifS, and FdxN, and engineered yeast SAM synthase supported FeMo-co synthesis, indicative of NifX carrying in vivo-formed NifB-co. This study defines the minimal genetic determinants for the formation of the key precursor in the nitrogenase cofactor biosynthetic pathway in a eukaryotic organism.

Deciphering Molecular Virulence Mechanism of Mycobacterium tuberculosis Dop isopeptidase Based on Its Sequence-Structure-Function Linkage.

The pupylation pathway marks proteins for prokaryotic ubiquitin-like protein (Pup)-proteasomal degradation and survival strategy of mycobacteria inside of the host macrophages. Deamidase of Pup (Dop) plays a central role in the pupylation pathway. It is still a matter of investigation to know the function of Dop in virulence of mycobacterial lineage. Hence, the present study was intended to describe the sequence-structure-function-virulence link of Dop for understanding the molecular virulence mechanism of Mycobacterium tuberculosis H37Rv (Mtb). Phylogenetic analysis of this study indicated that Dop has extensively diverged across the proteasome-harboring bacteria. The functional part of Dop was converged across the pathogenic mycobacterial lineage. The genome-wide analysis pointed out that the pupylation gene locus was identical to each other, but its genome neighborhood differed from species to species. Molecular modeling and dynamic studies proved that the predicted structure of Mtb Dop was energetically stable and low conformational freedom. Moreover, evolutionary constraints in Mtb Dop were intensively analyzed for inferring its sequence-structure-function relationships for the full virulence of Mtb. It indicated that evolutionary optimization was extensively required to stabilize its local structural environment at the side chains of mutable residues. The sequence-structure-function-virulence link of Dop might have retained in Mtb by reordering hydrophobic and hydrogen bonding patterns in the local structural environment. Thus, the results of our study provide a quest to understand the molecular virulence and pathogenesis mechanisms of Mtb during the infection process.

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.

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

Author(s): Holliday, Gemma L; Brown, Shoshana D; Akiva, Eyal; Mischel, David; Hicks, Michael A; Morris, John H; Huang, Conrad C; Meng, Elaine C; Pegg, Scott C-H; Ferrin, Thomas E; Babbitt, Patricia C | Abstract: 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 urlhttp://sfld.rbvi.ucsf.edu/.

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/

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.

Enzyme ‘Social’ Networks: Tracking Sequence / Function Relationships to Elucidate Determinants of Enzyme Specificity and Mechanism

Integrated analysis of sequence, structure and functional properties of enzymes is an established approach for determining their mechanisms and specificities. As sequence data continue to mount, analyzing the natural variation of homologs offers a powerful context for guiding experiments, while turning studies of single enzymes or large superfamilies into bioinformatic 'big data' problems. Many current tools designed to tease out potential functional residues are not sensitive enough to accommodate the specific intricacies of enzyme superfamilies, yielding biased predictions and misannotation. Here, we present the Structure‐Function Linkage Database (http://sfld.rbvi.ucsf.edu), a hierarchical classification resource and bioinformatic infrastructure that enables research of structure‐function relationships in functionally diverse enzyme superfamilies. It offers manually curated and automatic classification of enzymes, informed by the mechanistic blueprint of their corresponding superfamilies. Importantly, annotation and data exploration in the database takes advantage of sequence similarity networks to visualize functional trends mapped to the context of sequence similarity. We will illustrate how networks are used for enzyme classification in the glutathione transferase superfamily and show how mapping active site residue variation across a large number of proteins aids in elucidating specificity determinants in the Sirtuin superfamily of deacetylases. In summary, our approach allows a unique way to effectively utilize massive multidimensional data for generating hypotheses about enzyme mechanism and specificity.

Quantitative assessment of protein function prediction programs.

Fast prediction of protein function is essential for high-throughput sequencing analysis. Bioinformatic resources provide cheaper and faster techniques for function prediction and have helped to accelerate the process of protein sequence characterization. In this study, we assessed protein function prediction programs that accept amino acid sequences as input. We analyzed the classification, equality, and similarity between programs, and, additionally, compared program performance. The following programs were selected for our assessment: Blast2GO, InterProScan, PANTHER, Pfam, and ScanProsite. This selection was based on the high number of citations (over 500), fully automatic analysis, and the possibility of returning a single best classification per sequence. We tested these programs using 12 gold standard datasets from four different sources. The gold standard classification of the databases was based on expert analysis, the Protein Data Bank, or the Structure-Function Linkage Database. We found that the miss rate among the programs is globally over 50%. Furthermore, we observed little overlap in the correct predictions from each program. Therefore, a combination of multiple types of sources and methods, including experimental data, protein-protein interaction, and data mining, may be the best way to generate more reliable predictions and decrease the miss rate.

Using the structure-function linkage database to characterize functional domains in enzymes.

The Structure-Function Linkage Database (SFLD; http://sfld.rbvi.ucsf.edu/) is a Web-accessible database designed to link enzyme sequence, structure, and functional information. This unit describes the protocols by which a user may query the database to predict the function of uncharacterized enzymes and to correct misannotated functional assignments. The information in this unit is especially useful in helping a user discriminate functional capabilities of a sequence that is only distantly related to characterized sequences in publicly available databases.

From sequence to enzyme mechanism using multi-label machine learning.

BackgroundIn this work we predict enzyme function at the level of chemical mechanism, providing a finer granularity of annotation than traditional Enzyme Commission (EC) classes. Hence we can predict not only whether a putative enzyme in a newly sequenced organism has the potential to perform a certain reaction, but how the reaction is performed, using which cofactors and with susceptibility to which drugs or inhibitors, details with important consequences for drug and enzyme design. Work that predicts enzyme catalytic activity based on 3D protein structure features limits the prediction of mechanism to proteins already having either a solved structure or a close relative suitable for homology modelling.ResultsIn this study, we evaluate whether sequence identity, InterPro or Catalytic Site Atlas sequence signatures provide enough information for bulk prediction of enzyme mechanism. By splitting MACiE (Mechanism, Annotation and Classification in Enzymes database) mechanism labels to a finer granularity, which includes the role of the protein chain in the overall enzyme complex, the method can predict at 96% accuracy (and 96% micro-averaged precision, 99.9% macro-averaged recall) the MACiE mechanism definitions of 248 proteins available in the MACiE, EzCatDb (Database of Enzyme Catalytic Mechanisms) and SFLD (Structure Function Linkage Database) databases using an off-the-shelf K-Nearest Neighbours multi-label algorithm.ConclusionWe find that InterPro signatures are critical for accurate prediction of enzyme mechanism. We also find that incorporating Catalytic Site Atlas attributes does not seem to provide additional accuracy. The software code (ml2db), data and results are available online at http://sourceforge.net/projects/ml2db/ and as supplementary files.

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

Author SummaryCytosolic glutathione transferases (cytGSTs) are a large and diverse superfamily of enzymes that have important roles in metabolism and defense against oxidative damage. They have been studied for several decades but because of the synthetic nature of the chemicals used to test these proteins to determine if they have cytGST activity, little is known about the physiological reactions and roles of cytGSTs. In this large, collaborative study, we constructed networks where more than 13,000 cytGST sequences were grouped by sequence similarity and then used these networks to prioritize new targets for experimental characterization in relatively unexplored regions of the superfamily. We report here experimental results confirming GST-like activity for 82 of them, along with 37 new three-dimensional molecular structures determined for 27 targets. These new data, along with experimental data previously 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 illuminate the complex ways in which their variations in sequence and structure affect our ability to predict unknown functional properties.

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.

Assessing functional novelty of PSI structures via structure-function analysis of large and diverse superfamilies

AbstractThe structural genomics initiatives have had as one of their aims to improve our understanding of protein function by providing representative structures for many structurally uncharacterised protein families. As suggested by the recent assessment of the Protein Structure Initiative (Structural Genomics Initiative, funded by the NIH), doubts have arisen as to whether Structural Genomics as initially planned were really beneficial to our understanding of biological issues, and in particular of protein function.A few protein domain superfamilies have been shown to account for unexpectedly large numbers of proteins encoded in fully sequenced genomes. These large superfamilies are generally very diverse, spanning a wide range of functions, both in terms of molecular activities and biological processes. Some of these superfamilies, such as the Rossmann-fold P-loop nucleotide hydrolases or the TIM-barrel glycosidases, have been the subject of extensive structural studies which in turn have shed light on how evolution of the sequence and structure properties produce functional diversity amongst homologues. Recently, the Structure-Function Linkage Database (SFLD) has been setup with the aim of helping the study of structure-function correlations in such superfamilies. Since the evolutionary success of these large superfamilies suggests biological importance, several Structural Genomics Centers have focused on providing full structural coverage for representatives of all sequence families in these superfamilies.In this work we evaluate structure/function diversity in a set of these large superfamilies and attempt to assess the quality and quantity of biological information gained from Structural Genomics.

Assessing functional novelty of PSI structures via structure-function analysis of large and diverse superfamilies

The structural genomics initiatives have had as one of their aims to improve our understanding of protein function by providing representative structures for many structurally uncharacterised protein families. As suggested by the recent assessment of the Protein Structure Initiative (Structural Genomics Initiative, funded by the NIH), doubts have arisen as to whether Structural Genomics as initially planned were really beneficial to our understanding of biological issues, and in particular of protein function.A few protein domain superfamilies have been shown to account for unexpectedly large numbers of proteins encoded in fully sequenced genomes. These large superfamilies are generally very diverse, spanning a wide range of functions, both in terms of molecular activities and biological processes. Some of these superfamilies, such as the Rossmann-fold P-loop nucleotide hydrolases or the TIM-barrel glycosidases, have been the subject of extensive structural studies which in turn have shed light on how evolution of the sequence and structure properties produce functional diversity amongst homologues. Recently, the Structure-Function Linkage Database (SFLD) has been setup with the aim of helping the study of structure-function correlations in such superfamilies. Since the evolutionary success of these large superfamilies suggests biological importance, several Structural Genomics Centers have focused on providing full structural coverage for representatives of all sequence families in these superfamilies.In this work we evaluate structure/function diversity in a set of these large superfamilies and attempt to assess the quality and quantity of biological information gained from Structural Genomics.

The Structure‐Function Linkage Database

The Structure-Function Linkage Database (SFLD) provides highly curated information about the relationships between protein structure and function. The SFLD currently focuses on enzymes, using a superfamily-centric organization designed to allow users to easily investigate how conserved folds and active sites are able to perform a wide variety of chemical reactions. The information within the SFLD includes sequences, structures, enzyme reactions (including partial reactions), the specific residues of each enzyme which participate in the reaction (and their roles when known), hidden Markov models based on family and superfamily classifications, and literature references. In an effort to make assignments of function and superfamily transparent, evidence codes and extensive metadata are easily accessible. Users can search the database using a sequence (useful in determining the function of a newly sequenced enzyme), a full or partial reaction (useful in identifying a template for enzyme engineering), or through a variety of keyword searches. The SFLD is freely available via a web interface at http://sfld.rbvi.ucsf.edu. This work was supported by NIH GM60595, NIH RR01081, and NSF 0234768.

Using the Structure‐Function Linkage Database to Characterize Functional Domains in Enzymes

The Structure-Function Linkage Database (SFLD; http://sfld.rbvi.ucsf.edu/) is a Web-accessible database designed to link enzyme sequence, structure, and functional information. This unit describes the protocols by which a user may query the database to predict the function of newly sequenced enzymes and to correct misannotated functional assignments for enzymes currently in public databases. It is especially useful in helping a user discriminate functional capabilities of a sequence that is only distantly related to characterized sequences in publicly available databases.

Leveraging Enzyme Structure−Function Relationships for Functional Inference and Experimental Design: The Structure−Function Linkage Database

The study of mechanistically diverse enzyme superfamilies-collections of enzymes that perform different overall reactions but share both a common fold and a distinct mechanistic step performed by key conserved residues-helps elucidate the structure-function relationships of enzymes. We have developed a resource, the structure-function linkage database (SFLD), to analyze these structure-function relationships. Unique to the SFLD is its hierarchical classification scheme based on linking the specific partial reactions (or other chemical capabilities) that are conserved at the superfamily, subgroup, and family levels with the conserved structural elements that mediate them. We present the results of analyses using the SFLD in correcting misannotations, guiding protein engineering experiments, and elucidating the function of recently solved enzyme structures from the structural genomics initiative. The SFLD is freely accessible at http://sfld.rbvi.ucsf.edu.