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Creators/Authors contains: "Gray, Jeffrey J."

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  1. ; ; ; ; ; ; ; ; ; ; et al ( , CBE—Life Sciences Education)
    Frantz, Kyle (Ed.)
    In-person undergraduate research experiences (UREs) promote students’ integration into careers in life science research. In 2020, the COVID-19 pandemic prompted institutions hosting summer URE programs to offer them remotely, raising questions about whether undergraduates who participate in remote research can experience scientific integration and whether they might perceive doing research less favorably (i.e., not beneficial or too costly). To address these questions, we examined indicators of scientific integration and perceptions of the benefits and costs of doing research among students who participated in remote life science URE programs in Summer 2020. We found that students experienced gains in scientific self-efficacy pre- to post-URE, similar to results reported for in-person UREs. We also found that students experienced gains in scientific identity, graduate and career intentions, and perceptions of the benefits of doing research only if they started their remote UREs at lower levels on these variables. Collectively, students did not change in their perceptions of the costs of doing research despite the challenges of working remotely. Yet students who started with low cost perceptions increased in these perceptions. These findings indicate that remote UREs can support students’ self-efficacy development, but may otherwise be limited in their potential to promote scientific integration. 
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    Free, publicly-accessible full text available June 1, 2024
  2. ; ; ; ; ( , ACS Catalysis)
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  3. ; ; ; ; ; ; ; ; ( , Journal of Chemical Education)
    null (Ed.)
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  4. ; ; ; ; ; ; ( , Proteins: Structure, Function, and Bioinformatics)
    Abstract

    The continued emergence of new SARS‐CoV‐2 variants has accentuated the growing need for fast and reliable methods for the design of potentially neutralizing antibodies (Abs) to counter immune evasion by the virus. Here, we report on the de novo computational design of high‐affinity Ab variable regions (Fv) through the recombination of VDJ genes targeting the most solvent‐exposed hACE2‐binding residues of the SARS‐CoV‐2 spike receptor binding domain (RBD) protein using the software toolOptMAVEn‐2.0. Subsequently, we carried out computational affinity maturation of the designed variable regions through amino acid substitutions for improved binding with the target epitope. Immunogenicity of designs was restricted by preferring designs that match sequences from a 9‐mer library of “human Abs” based on a human string content score. We generated 106 different antibody designs and reported in detail on the top five that trade‐off the greatest computational binding affinity for the RBD with human string content scores. We further describe computational evaluation of the top five designs produced byOptMAVEn‐2.0using a Rosetta‐based approach. We used RosettaSnugDockfor local docking of the designs to evaluate their potential to bind the spike RBD and performed “forward folding” withDeepAbto assess their potential to fold into the designed structures. Ultimately, our results identified one designed Ab variable region, P1.D1, as a particularly promising candidate for experimental testing. This effort puts forth a computational workflow for the de novo design and evaluation of Abs that can quickly be adapted to target spike epitopes of emerging SARS‐CoV‐2 variants or other antigenic targets.

     
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  5. ; ; ; ; ; ; ; ; ; ; et al ( , The Biophysicist)
    ABSTRACT Biomolecular structure drives function, and computational capabilities have progressed such that the prediction and computational design of biomolecular structures is increasingly feasible. Because computational biophysics attracts students from many different backgrounds and with different levels of resources, teaching the subject can be challenging. One strategy to teach diverse learners is with interactive multimedia material that promotes self-paced, active learning. We have created a hands-on education strategy with a set of 16 modules that teach topics in biomolecular structure and design, from fundamentals of conformational sampling and energy evaluation to applications, such as protein docking, antibody design, and RNA structure prediction. Our modules are based on PyRosetta, a Python library that encapsulates all computational modules and methods in the Rosetta software package. The workshop-style modules are implemented as Jupyter Notebooks that can be executed in the Google Colaboratory, allowing learners access with just a Web browser. The digital format of Jupyter Notebooks allows us to embed images, molecular visualization movies, and interactive coding exercises. This multimodal approach may better reach students from different disciplines and experience levels, as well as attract more researchers from smaller labs and cognate backgrounds to leverage PyRosetta in science and engineering research. All materials are freely available at https://github.com/RosettaCommons/PyRosetta.notebooks. 
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  6. ; ; ; ; ; ; ; ; ; ; et al ( , CBE—Life Sciences Education)
    Braun, Derek (Ed.)
    The COVID-19 pandemic shut down undergraduate research programs across the United States. A group of 23 colleges, universities, and research institutes hosted remote undergraduate research programs in the life sciences during Summer 2020. Given the unprecedented offering of remote programs, we carried out a study to describe and evaluate them. Using structured templates, we documented how programs were designed and implemented, including who participated. Through focus groups and surveys, we identified programmatic strengths and shortcomings as well as recommendations for improvements from students’ perspectives. Strengths included the quality of mentorship, opportunities for learning and professional development, and a feeling of connection with a larger community. Weaknesses included limited cohort building, challenges with insufficient structure, and issues with technology. Although all programs had one or more activities related to diversity, equity, inclusion, and justice, these topics were largely absent from student reports even though programs coincided with a peak in national consciousness about racial inequities and structural racism. Our results provide evidence for designing remote Research Experiences for Undergraduates (REUs) that are experienced favorably by students. Our results also indicate that remote REUs are sufficiently positive to further investigate their affordances and constraints, including the potential to scale up offerings, with minimal concern about disenfranchising students. 
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  7. ; ; ; ; ; ; ; ; ; ; et al ( , Nature Communications)
    Abstract Each year vast international resources are wasted on irreproducible research. The scientific community has been slow to adopt standard software engineering practices, despite the increases in high-dimensional data, complexities of workflows, and computational environments. Here we show how scientific software applications can be created in a reproducible manner when simple design goals for reproducibility are met. We describe the implementation of a test server framework and 40 scientific benchmarks, covering numerous applications in Rosetta bio-macromolecular modeling. High performance computing cluster integration allows these benchmarks to run continuously and automatically. Detailed protocol captures are useful for developers and users of Rosetta and other macromolecular modeling tools. The framework and design concepts presented here are valuable for developers and users of any type of scientific software and for the scientific community to create reproducible methods. Specific examples highlight the utility of this framework, and the comprehensive documentation illustrates the ease of adding new tests in a matter of hours. 
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  8. ; ; ; ; ( , PLOS Computational Biology)
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  9. ; ; ; ; ( , Bioinformatics)
    Abstract Motivation

    Binding-induced conformational changes challenge current computational docking algorithms by exponentially increasing the conformational space to be explored. To restrict this search to relevant space, some computational docking algorithms exploit the inherent flexibility of the protein monomers to simulate conformational selection from pre-generated ensembles. As the ensemble size expands with increased flexibility, these methods struggle with efficiency and high false positive rates.

     Results

    Here, we develop and benchmark RosettaDock 4.0, which efficiently samples large conformational ensembles of flexible proteins and docks them using a novel, six-dimensional, coarse-grained score function. A strong discriminative ability allows an eight-fold higher enrichment of near-native candidate structures in the coarse-grained phase compared to RosettaDock 3.2. It adaptively samples 100 conformations each of the ligand and the receptor backbone while increasing computational time by only 20–80%. In local docking of a benchmark set of 88 proteins of varying degrees of flexibility, the expected success rate (defined as cases with ≥50% chance of achieving 3 near-native structures in the 5 top-ranked ones) for blind predictions after resampling is 77% for rigid complexes, 49% for moderately flexible complexes and 31% for highly flexible complexes. These success rates on flexible complexes are a substantial step forward from all existing methods. Additionally, for highly flexible proteins, we demonstrate that when a suitable conformer generation method exists, the method successfully docks the complex.

     Availability and implementation

    As a part of the Rosetta software suite, RosettaDock 4.0 is available at https://www.rosettacommons.org to all non-commercial users for free and to commercial users for a fee.

     Supplementary information

    Supplementary data are available at Bioinformatics online.

     
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  10. ; ; ; ; ; ; ; ; ; ; et al ( , Proceedings of the National Academy of Sciences)