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ImportanceDespite the importance of clinician-scientists in propelling biomedical advances, the proportion of physicians engaged in both hypothesis-driven research and clinical care continues to decline. Recently, multiple institutions have developed programs that promote MD-only physicians pursuing careers in science, but few reports on the impact of these are available.ObjectiveTo assess if a cohort-based training program for MD-only physician-scientists that includes didactic and experiential curricula favorably informs participants’ scientific development.DesignThe Chan Zuckerberg Biohub (CZB) Physician-Scientist Fellowship Program (PSFP) conducted a study from July 2020 to August 2022.Participants24 inaugural program participants at UCSF and Stanford University (median postgraduate year at program start, 5.5; 17 clinical specialties represented; 10 [42%] identified as female; 7 [29%] identified as underrepresented in medicine).ExposuresThe CZB PSFP is a selective two-year career development program for MD-only physicians. Participants attended a two-week immersive training at the program outset, and subsequently, weekly curricular and scientific meetings throughout the program while conducting research.Main Outcomes and MeasuresPrimary outcome measurements included pre-, 1-month, and 12-month assessments of confidence in research skills, career skills, and self-identification as scientists. Program satisfaction and feedback related to program curriculum and community were collected at 1 month, 6 months, and 12 months.ResultsAfter 12 months, 94% reported satisfaction with the program and participants demonstrated increased confidence in research skills (mean [SD] pre vs. post, 3.79 [0.59] vs. 5.09 [0.42], PConclusion and RelevanceParticipants demonstrated significant gains in confidence in core research and career skills as well as personal identification as scientists, demonstrating the efficacy of a longitudinal curriculum, peer support, and community building in fostering development as an investigator. The highly portable nature of this strategy may facilitate ready adoption and implementation at other institutions.

Behavioral medicine scientists, practitioners, and educators can engage in evidence-based science communication strategies to amplify the science and combat misinformation. Such efforts are critical to protect public health during crises such as the COVID-19 pandemic and to promote overall well-being.

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.

An internship program model that supports life sciences doctoral students’ pursuit of a broad range of careers is described. Evaluation of the program model at two institutions finds that participation increases students’ confidence in career decision making without extending time to degree and may help some trainees avoid “default postdocs.”, The Graduate Student Internships for Career Exploration (GSICE) program at the University of California, San Francisco (UCSF), offers structured training and hands-on experience through internships for a broad range of PhD-level careers. The GSICE program model was successfully replicated at the University of California, Davis (UC Davis). Here, we present outcome data for a total of 217 PhD students participating in the UCSF and UC Davis programs from 2010 to 2015 and 2014 to 2015, respectively. The internship programs at the two sites demonstrated comparable participation, internship completion rates, and overall outcomes. Using survey, focus group, and individual interview data, we find that the programs provide students with career development skills, while increasing students’ confidence in career exploration and decision making. Internships, in particular, were perceived by students to increase their ability to discern a career area of choice and to increase confidence in pursuing that career. We present data showing that program participation does not change median time to degree and may help some trainees avoid “default postdocs.” Our findings suggest important strategies for institutions developing internship programs for PhD students, namely: including a structured training component, allowing postgraduation internships, and providing a central organization point for internship programs.

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.

Bacterial chemotactic responses are initiated when certain small molecules (i.e., carbohydrates, amino acids) interact with bacterial chemoreceptors. Although bacterial chemotaxis has been the subject of intense investigations, few have explored the influence of attractant structure on signal generation and chemotaxis. Previously, we found that polymers bearing multiple copies of galactose interact with the chemoreceptor Trg via the periplasmic binding protein glucose/galactose binding protein (GGBP). These synthetic multivalent ligands were potent agonists of Escherichia coli chemotaxis. Here, we report on the development of a second generation of multivalent attractants that possess increased chemotactic activities. Strikingly, the new ligands can alter bacterial behavior at concentrations 10-fold lower than those required with the original displays; thus, they are some of the most potent synthetic chemoattractants known. The potency depends on the number of galactose moieties attached to the oligomer backbone and the length of the linker tethering these carbohydrates. Our investigations reveal the plasticity of GGBP; it can bind and mediate responses to several carbohydrates and carbohydrate derivatives. These attributes of GGBP may underlie the ability of bacteria to sense a variety of ligands with relatively few receptors. Our results provide insight into the design and development of compounds that can modulate bacterial chemotaxis and pathogenicity.

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.