Bibliographic record
Abstract
The 2020 volume of Protein Engineering, Design and Selection (PEDS) marks the beginning of my appointment as Editor-in-Chief of the journal. Since its foundation in 1986, PEDS has built a strong reputation as a respected publishing destination for our research community. I am thrilled to be given the opportunity to build on the journal’s legacy of rigorous and constructive manuscript review by leading experts that has been part of the journal’s fabric since day one. My esteemed predecessors Alan Fersht and Valerie Daggett, Senior Editors for 15 years, did an exceptional job in preserving the high publication standards of PEDS; please join me in thanking them for their dedicated service to the journal. As the new Editor-in-Chief, I aim to build upon the journal’s strengths, whilst expanding in new directions to ensure PEDS remains the default destination for high-quality protein engineering, design, and evolution papers that are of high interest to our research community. We will continue to publish innovative original research and review articles, relevant to the engineering, design, and selection of proteins for use in biotechnology and therapy, and essential to our understanding of the fundamental links between protein sequence, structure, dynamics, function and evolution. PEDS will also remain dedicated to a rigorous manuscript review process that is fair, but demanding, to ensure authors receive constructive, expert feedback to improve their work. More than ever, I want to increase PEDS’ connection to the research community, with a focus on enhancing author experience. In pursuit of these aims, we are introducing several new initiatives, some of which I highlight below. As recent authors will have noticed, manuscript review is now handled by five leading experts appointed as Associate Editors, who will endeavor to provide a fast, transparent and rigorous yet fair review process. The Associate Editors are making every effort to identify quality expert reviewers who put genuine time into review and provide constructive criticism. To help with manuscript review, Associate Editors are also drawing from the rejuvenated Editorial Advisory Board, comprised of a diverse group of established leaders and rising stars. These individuals have been selected to reflect the diversity of our field, with scientists of different genders, ethnicities, geographic locations, research expertise and career stages represented. This team will continue to advise me on how best to develop the journal for the benefit of our whole research community. Starting with Volume 33, the journal will no longer be available in print and all articles will be published online only. This change ensures that all articles are released as soon as possible following acceptance, prioritizing speed of publication. With the exception of an optional Open Access fee, authors publishing in PEDS will incur no charges whatsoever. We are continually exploring new ways to enhance the visibility of published papers, in collaboration with the Oxford University Press marketing team. The @ProtEngDesSel Twitter feed has recently been established to announce newly published papers and publicize our other activities, such as our Webinar Series. The option to provide a Graphical Abstract has been introduced, and I would encourage authors to take advantage of the additional promotional opportunities this provides, not least the possibility of featuring on the cover of PEDS in the future. Moving forward, we will also be highlighting papers as ‘Editor’s Choice’ selected by the Editorial Board for their significance to the field, and made freely available for maximum dissemination. I will also be initiating an annual award for the best paper published in the journal, to be given to the junior scientist responsible for the main body of work. My hope is that this award will assist in the career development of the next generation of protein engineers. Recent developments in high-throughput screening and next-generation sequencing have increased the pace at which large mutational data sets are generated. Given the importance of such data sets in protein engineering and design research, in consultation with key members of the relevant scientific communities, PEDS aims to establish an article format that will facilitate more effective communication of these types of results to the broader community. PEDS will publish a recurring series of review and methodology articles from leading experts, all of which will be freely available upon publication. The first collection will be comprised of short reviews on topical themes and is expected online in 2021. PEDS is a community-run journal that was founded by and for research scientists; anyone with suggestions on how we can make the journal the best it can be for our community is encouraged to get in touch. I very much look forward to hearing from you and anticipate reading plenty of exciting new protein engineering and design results in the journal in the years to come!
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.013 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".