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Population growth and climate change will impact food security and potentially exacerbate the environmental toll that agriculture has taken on our planet. These existential concerns demand that a passionate, interdisciplinary, and diverse community of plant science professionals is trained during the 21st century. Furthermore, societal trends that question the importance of science and expert knowledge highlight the need to better communicate the value of rigorous fundamental scientific exploration. Engaging students and the general public in the wonder of plants, and science in general, requires renewed efforts that take advantage of advances in technology and new models of funding and knowledge dissemination. In November 2018, funded by the National Science Foundation through the Arabidopsis Research and Training for the 21st century (ART 21) research coordination network, a symposium and workshop were held that included a diverse panel of students, scientists, educators, and administrators from across the US. The purpose of the workshop was to re‐envision how outreach programs are funded, evaluated, acknowledged, and shared within the plant science community. One key objective was to generate a roadmap for future efforts. We hope that this document will serve as such, by providing a comprehensive resource for students and young faculty interested in developing effective outreach programs. We also anticipate that this document will guide the formation of community partnerships to scale up currently successful outreach programs, and lead to the design of future programs that effectively engage with a more diverse student body and citizenry.

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. © 2013 The Author(s). Published by Oxford University Press.

The mechanistically diverse enolase superfamily is a paradigm for elucidating Nature's strategies for divergent evolution of enzyme function. Each of the different reactions catalyzed by members of the superfamily is initiated by abstraction of the alpha-proton of a carboxylate substrate that is coordinated to an essential Mg(2+). The muconate lactonizing enzyme (MLE) from Pseudomonas putida, a member of a family that catalyzes the syn-cycloisomerization of cis,cis-muconate to (4S)-muconolactone in the beta-ketoadipate pathway, has provided critical insights into the structural bases for evolution of function within the superfamily. A second, divergent family of homologous MLEs that catalyzes anti-cycloisomerization has been identified. Structures of members of both families liganded with the common (4S)-muconolactone product (syn, Pseudomonas fluorescens, gi 70731221 ; anti, Mycobacterium smegmatis, gi 118470554 ) document that the conserved Lys at the end of the second beta-strand in the (beta/alpha)(7)beta-barrel domain serves as the acid catalyst in both reactions. The different stereochemical courses (syn and anti) result from different structural strategies for determining substrate specificity: although the distal carboxylate group of the cis,cis-muconate substrate attacks the same face of the proximal double bond, opposite faces of the resulting enolate anion intermediate are presented to the conserved Lys acid catalyst. The discovery of two families of homologous, but stereochemically distinct, MLEs likely provides an example of "pseudoconvergent" evolution of the same function from different homologous progenitors within the enolase superfamily, in which different spatial arrangements of active site functional groups and substrate specificity determinants support catalysis of the same reaction.

The ongoing functional annotation of proteins relies upon the work of curators to capture experimental findings from scientific literature and apply them to protein sequence and structure data. However, with the increasing use of high-throughput experimental assays, a small number of experimental studies dominate the functional protein annotations collected in databases. Here we investigate just how prevalent is the "few articles -- many proteins" phenomenon. We examine the experimentally validated annotation of proteins provided by several groups in the GO Consortium, and show that the distribution of proteins per published study is exponential, with 0.14% of articles providing the source of annotations for 25% of the proteins in the UniProt-GOA compilation. Since each of the dominant articles describes the use of an assay that can find only one function or a small group of functions, this leads to substantial biases in what we know about the function of many proteins. Mass-spectrometry, microscopy and RNAi experiments dominate high throughput experiments. Consequently, the functional information derived from these experiments is mostly of the subcellular location of proteins, and of the participation of proteins in embryonic developmental pathways. For some organisms, the information provided by different studies overlap by a large amount. We also show that the information provided by high throughput experiments is less specific than those provided by low throughput experiments. Given the experimental techniques available, certain biases in protein function annotation due to high-throughput experiments are unavoidable. Knowing that these biases exist and understanding their characteristics and extent is important for database curators, developers of function annotation programs, and anyone who uses protein function annotation data to plan experiments., Accepted to PLoS Computational Biology. Press embargo applies. v4: text corrected for style and supplementary material inserted

Automated annotation of protein function is challenging. As the number of sequenced genomes rapidly grows, the overwhelming majority of protein products can only be annotated computationally. If computational predictions are to be relied upon, it is crucial that the accuracy of these methods be high. Here we report the results from the first large-scale community-based Critical Assessment of protein Function Annotation (CAFA) experiment. Fifty-four methods representing the state-of-the-art for protein function prediction were evaluated on a target set of 866 proteins from eleven organisms. Two findings stand out: (i) today’s best protein function prediction algorithms significantly outperformed widely-used first-generation methods, with large gains on all types of targets; and (ii) although the top methods perform well enough to guide experiments, there is significant need for improvement of currently available tools.

Due to the rapid release of new data from genome sequencing projects, the majority of protein sequences in public databases have not been experimentally characterized; rather, sequences are annotated using computational analysis. The level of misannotation and the types of misannotation in large public databases are currently unknown and have not been analyzed in depth. We have investigated the misannotation levels for molecular function in four public protein sequence databases (UniProtKB/Swiss-Prot, GenBank NR, UniProtKB/TrEMBL, and KEGG) for a model set of 37 enzyme families for which extensive experimental information is available. The manually curated database Swiss-Prot shows the lowest annotation error levels (close to 0% for most families); the two other protein sequence databases (GenBank NR and TrEMBL) and the protein sequences in the KEGG pathways database exhibit similar and surprisingly high levels of misannotation that average 5%–63% across the six superfamilies studied. For 10 of the 37 families examined, the level of misannotation in one or more of these databases is >80%. Examination of the NR database over time shows that misannotation has increased from 1993 to 2005. The types of misannotation that were found fall into several categories, most associated with “overprediction” of molecular function. These results suggest that misannotation in enzyme superfamilies containing multiple families that catalyze different reactions is a larger problem than has been recognized. Strategies are suggested for addressing some of the systematic problems contributing to these high levels of misannotation., Author Summary One of the core elements of modern biological scientific investigation is the universal availability of millions of protein sequences from thousands of different organisms, allowing for exciting new investigations into biological questions. These sequences, found in large primary sequence databases such as GenBank NR or UniProt/TrEMBL, in secondary databases such as the valuable pathways database KEGG, or in highly curated databases such as UniProt/Swiss-Prot, are often annotated by computationally predicted protein functions. The scale of the available predicted function information is enormous but the accuracy of these predictions is essentially unknown. We investigate the critical question of the accuracy of functional predictions in these four public databases. We used 37 well-characterized enzyme families as a gold standard for comparing the accuracy of functional annotations in these databases. We find that function prediction error (i.e., misannotation) is a serious problem in all but the manually curated database Swiss-Prot. We discuss several approaches for mitigating the consequences of these high levels of misannotation.