Showing papers by "Philip E. Bourne published in 2022"

Real‐world evidence from over one million COVID‐19 vaccinations is consistent with reactivation of the varicella‐zoster virus

Abstract: Reactivation of the varicella‐zoster virus (VZV), which causes herpes zoster (HZ, synonym: shingles) in humans, can be a rare adverse reaction to vaccines. Recently, reports of cases after COVID‐19 vaccination have arisen.

A Comparative Analysis of COVID-19 Vaccines Based on over 580,000 Cases from the Vaccination Adverse Event Reporting System

Abstract: Background: The COVID-19 pandemic is being battled via the largest vaccination campaign in history, with more than eight billion doses administered thus far. Therefore, discussions about potentially adverse reactions, and broader safety concerns, are critical. The U.S. Vaccination Adverse Event Reporting System (VAERS) has recorded vaccination side effects for over 30 years. About 580,000 events have been filed for COVID-19 thus far, primarily for the Johnson & Johnson (New Jersey, USA), Pfizer/BioNTech (Mainz, Germany), and Moderna (Cambridge, USA) vaccines. Methods: Using available databases, we evaluated these three vaccines in terms of the occurrence of four generally-noticed adverse reactions—namely, cerebral venous sinus thrombosis, Guillain–Barré syndrome (a severe paralytic neuropathy), myocarditis, and pericarditis. Our statistical analysis also included a calculation of odds ratios (ORs) based on total vaccination numbers, accounting for incidence rates in the general population. Results: ORs for a number of adverse events and patient groups were (largely) increased, most notably for the occurrence of cerebral venous sinus thrombosis after vaccination with the Johnson & Johnson vaccine. The overall population OR of 10 increases to 12.5 when limited to women, and further yet (to 14.4) among women below age 50 yrs. In addition, elevated risks were found (i) for Guillain–Barré syndrome (OR of 11.6) and (ii) for myocarditis/pericarditis (ORs of 5.3/4.1, respectively) among young men (<25 yrs) vaccinated with the Pfizer/BioNTech vaccine. Conclusions: Any conclusions from such a retrospective, real-world data analysis must be drawn cautiously, and should be confirmed by prospective double-blinded clinical trials. In addition, we emphasize that the adverse events reported here are not specific side effects of COVID vaccines, and the significant, well-established benefits of COVID-19 vaccination outweigh the potential complications surveyed here.

Playing catch-up in building an open research commons

Abstract: Description As efforts advance around the globe, the US falls behind Many aspects of the research enterprise are rapidly changing to be more open, accessible, and supportive of rapid-response investigations (e.g., understanding COVID-19) and large cross-national research that addresses complex challenges (e.g., supply chain issues). Around the globe, there have been aggressive responses to the need for a unified open research commons (ORC)—an interoperable collection of data and compute resources within both the public and private sectors that is easy to use and accessible to all. Many nations are positioning themselves to be scientifically competitive in the years to come. But the US is falling behind in the accessibility and connectedness of its research computing and data infrastructure (1), compromising competitiveness and leadership and limiting global science that could benefit from US contributions. The challenge is more cultural and institutional than technical and demands immediate and sustained leadership and support, starting with policy-makers and research funders.

From biomedical cloud platforms to microservices: next steps in FAIR data and analysis

Abstract: The biomedical research community is investing heavily in biomedical cloud platforms. Cloud computing holds great promise for addressing challenges with big data and ensuring reproducibility in biology. However, despite their advantages, cloud platforms in and of themselves do not automatically support FAIRness. The global push to develop biomedical cloud platforms has led to new challenges, including platform lock-in, difficulty integrating across platforms, and duplicated effort for both users and developers. Here, we argue that these difficulties are systemic and emerge from incentives that encourage development effort on self-sufficient platforms and data repositories instead of interoperable microservices. We argue that many of these issues would be alleviated by prioritizing microservices and access to modular data in smaller chunks or summarized form. We propose that emphasizing modularity and interoperability would lead to a more powerful Unix-like ecosystem of web services for biomedical analysis and data retrieval. We challenge funders, developers, and researchers to support a vision to improve interoperability through microservices as the next generation of cloud-based bioinformatics.

From biomedical cloud platforms to microservices: next steps in FAIR data and analysis

Abstract: The biomedical research community is investing heavily in biomedical cloud platforms. Cloud computing holds great promise for addressing challenges with big data and ensuring reproducibility in biology. However, despite their advantages, cloud platforms in and of themselves do not automatically support FAIRness. The global push to develop biomedical cloud platforms has led to new challenges, including platform lock-in, difficulty integrating across platforms, and duplicated effort for both users and developers. Here, we argue that these difficulties are systemic and emerge from incentives that encourage development effort on self-sufficient platforms and data repositories instead of interoperable microservices. We argue that many of these issues would be alleviated by prioritizing microservices and access to modular data in smaller chunks or summarized form. We propose that emphasizing modularity and interoperability would lead to a more powerful Unix-like ecosystem of web services for biomedical analysis and data retrieval. We challenge funders, developers, and researchers to support a vision to improve interoperability through microservices as the next generation of cloud-based bioinformatics.

Title: The Most Difference in Means: A Statistic for Null and Near-Zero Results

Abstract: : Two-sample p-values test for statistical significance. Yet p-values cannot determine if a result has a negligible (near-zero) effect size, nor compare evidence for negligibility among independent studies. We propose the most difference in means (δ M ) statistic to assess the practical insignificance of results by measuring the evidence for a negligible effect size. Both δ M and the relative form of δ M allow hypothesis testing for negligibility and outperform other candidate statistics in identifying results with stronger evidence of negligible effect. We compile results from broadly related experiments and use the relative δ M to compare practical insignificance across different measurement methods and experiment models. Reporting the relative δ M builds consensus for negligible effect size by making near-zero results more quantitative and publishable. One-Sentence Summary: A two-sample statistic that compares the evidence for near-zero effect size among broadly related experiments.

The Most Difference in Means: A Statistic for the Strength of Null and Near-Zero Results

Abstract: : Statistical insignificance does not suggest the absence of effect, yet scientists must often use null results as evidence of negligible (near-zero) effect size to falsify scientific hypotheses. Doing so must assess a result’s null strength, defined as the evidence for a negligible effect size. Such an assessment would differentiate high null strength results that suggest a negligible effect size from low null strength results that suggest a broad range of potential effect sizes. We propose the most difference in means (δ M ) as a two-sample statistic that can both quantify null strength and perform a hypothesis test for determining negligible effect size. Scientists will likely disagree on the value of the threshold for negligible effect. To facilitate consensus when interpreting results, our statistic allows scientists to conclude that a result has negligible effect size using different thresholds with no recalculation of the statistic required. This is accomplished with a post-hoc hypothesis test for negligibility. To assist with selecting a threshold, δ M can also compare null strength between related results. Both δ M and the relative form of δ M outperform other candidate statistics in identifying results with higher null strength. We compile results from broadly related experiments and use the relative δ M to compare null strength across different treatments, measurement methods, and experiment models. Reporting the relative δ M may provide a technical solution to the file drawer problem by encouraging the publication of null and near-zero results and allowing scientists with different perspectives to reach consensus for which results have negligible effect. highlight results with exceptionally strong null strength. All of this can be done without recalculation of the statistic. To test our statistics against previously developed candidates, we characterize the multidimensional problem of assessing null strength with various functions of population parameters. These functions serve as ground truth for simulation testing. We use an integrated risk assessment to test δ M and the relative form of δ M (rδ M ) against several candidate statistics by evaluating their error rates in comparing the null strength between simulated experiment results. Our statistics were the only candidates that demonstrated better than random error rates across all investigations. We illustrate with real data how rδ M can be used to test for negligibility and compare the null strength of results from broadly related experiments that have a combination of different experiment models, conditions, populations, species, timepoints, treatments, and measurement techniques. We propose that reporting rδ M of null and near-zero results will provide a more useful interpretation than alternative analysis techniques.

Contra-Analysis: Prioritizing Meaningful Effect Size in Scientific Research

Abstract: At every phase of scientific research, scientists must decide how to allocate limited resources to pursue the research inquiries with the greatest potential. This prioritization dictates which controlled interventions are studied, awarded funding, published, reproduced with repeated experiments, investigated in related contexts, and translated for societal use. There are many factors that influence this decision-making, but interventions with larger effect size are often favored because they exert the greatest influence on the system studied . To inform these decisions, scientists must compare effect size across studies with dissimilar experiment designs to identify the interventions with the largest effect. These studies are often only loosely related in nature, using experiments with a combination of different populations, conditions, timepoints, measurement techniques, and experiment models that measure the same phenomenon with a continuous variable. We name this assessment contra-analysis and propose to use credible intervals of the relative difference in means to compare effect size across studies in a meritocracy between competing interventions. We propose a data visualization, the contra plot, that allows scientists to score and rank effect size between studies that measure the same phenomenon, aid in determining an appropriate threshold for meaningful effect, and perform hypothesis tests to determine which interventions have meaningful effect size. We illustrate the use of contra plots with real biomedical research data. Contra-analysis promotes a practical interpretation of effect size and facilitates the prioritization of scientific research.

The curse of the protein ribbon diagram

Abstract: Does reductionism, in the era of machine learning and now interpretable AI, facilitate or hinder scientific insight? The protein ribbon diagram, as a means of visual reductionism, is a case in point.

Systematic Exploration of Privileged Warheads for Covalent Kinase Drug Discovery

Abstract: Kinase-targeted drug discovery for cancer therapy has advanced significantly in the last three decades. Currently, diverse kinase inhibitors or degraders have been reported, such as allosteric inhibitors, covalent inhibitors, macrocyclic inhibitors, and PROTAC degraders. Out of these, covalent kinase inhibitors (CKIs) have been attracting attention due to their enhanced selectivity and exceptionally strong affinity. Eight covalent kinase drugs have been FDA-approved thus far. Here, we review current developments in CKIs. We explore the characteristics of the CKIs: the features of nucleophilic amino acids and the preferences of electrophilic warheads. We provide systematic insights into privileged warheads for repurposing to other kinase targets. Finally, we discuss trends in CKI development across the whole proteome.

Ten simple rules for improving communication among scientists

Abstract: Communication is a fundamental part of scientific development and methodology. With the advancement of the internet and social networks, communication has become rapid and sometimes overwhelming, especially in science. It is important to provide scientists with useful, effective, and dynamic tools to establish and build a fluid communication framework that allows for scientific advancement. Therefore, in this article, we present advice and recommendations that can help promote and improve science communication while respecting an adequate balance in the degree of commitment toward collaborative work. We have developed 10 rules shown in increasing order of commitment that are grouped into 3 key categories: (1) speak (based on active participation); (2) join (based on joining scientific groups); and (3) assess (based on the analysis and retrospective consideration of the weaknesses and strengths). We include examples and resources that provide actionable strategies for involvement and engagement with science communication, from basic steps to more advanced, introspective, and long-term commitments. Overall, we aim to help spread science from within and encourage and engage scientists to become involved in science communication effectively and dynamically.

Ten simple rules for using entrepreneurship skills to improve research careers and culture

Abstract: The academic path is not an easy one. The acknowledgment of the problems of the research precariat [1], the lack of stability and certainty in our careers, is not a new thing. The number of permanent positions in academic research has always limited the career prospects of many postdocs, but there are concerns the increasing supply of graduates is exacerbating this issue [2], and for scientists, job satisfaction is at an all-time low [3]. Indeed, the current situation is forcing many of us to reevaluate our options [4]. For many, this may mean leaving the academic world and moving to industry or enterprise, but this often can feel like failure [5]. We argue that the establishment of a scientific career can be thought of as an entrepreneurial enterprise. We contend that an acknowledgment of this viewpoint and reevaluation of what constitutes value and impact in this system can contribute towards an improved research culture. With this in mind, we present some rules informed by the world of enterprise and business to help the early career researcher develop and navigate their careers. Entrepreneurship can be defined as tAU : PleasenotethatasperPLOSstyle; italicsshouldnotbeusedforemphasis: he means by which new organisa ions are formed with their resultant job and wealth creation [6]. We can refocus this definition on the individual and define career entrepreneurship as the process of developing a career that results in increased value for the individual. Some common traits have been identified in entrepreneurship: risk taking, achievement, autonomy, self-efficacy (the ability to complete tasks), and locus of control (the control we believe we have on the outcomes of our lives [7]) [8], and these traits are also important markers and indicators for career progression in scientific research. Druker describes innovation as the ability to exploit change and argues that looking for change and exploiting it as an opportunity defines an entrepreneur [9]. Change is a commodity that is not in short supply in the world of scientific research, with changing political and funding landscapes as well as new discoveries and knowledge. Through the definitions and perspectives offered above, we can prepare ourselves to be able to navigate and exploit changes for the benefit of our careers using traits and techniques also found in entrepreneurship and in innovators.

Binding Site-enhanced Sequence Pretraining and Out-of-cluster Meta-learning Predict Genome-Wide Chemical-Protein Interactions for Dark Proteins

Abstract: Discovering chemical-protein interactions for millions of chemicals across the entire human and pathogen genomes is instrumental for chemical genomics, protein function prediction, drug discovery, and other applications. However, more than 90% of gene families remain dark, i.e., their small molecular ligands are undiscovered due to experimental limitations and human biases. Existing computational approaches typically fail when the unlabeled dark protein of interest differs from those with known ligands or structures. To address this challenge, we developed a deep learning framework PortalCG. PortalCG consists of four novel components: (i) a 3-dimensional ligand binding site enhanced sequence pre-training strategy to represent the whole universe of protein sequences in recognition of evolutionary linkage of ligand binding sites across gene families, (ii) an end-to-end pretraining-fine-tuning strategy to simulate the folding process of protein-ligand interactions and reduce the impact of inaccuracy of predicted structures on function predictions under a sequence-structure-function paradigm, (iii) a new out-of-cluster meta-learning algorithm that extracts and accumulates information learned from predicting ligands of distinct gene families (meta-data) and applies the meta-data to a dark gene family, and (iv) stress model selection that uses different gene families in the test data from those in the training and development data sets to facilitate model deployment in a real-world scenario. In extensive and rigorous benchmark experiments, PortalCG considerably outperformed state-of-the-art techniques of machine learning and protein-ligand docking when applied to dark gene families, and demonstrated its generalization power for off-target predictions and compound screenings under out-of-distribution (OOD) scenarios. Furthermore, in an external validation for the multi-target compound screening, the performance of PortalCG surpassed the human design. Our results also suggested that a differentiable sequence-structure-function deep learning framework where protein structure information serve as an intermediate layer could be superior to conventional methodology where the use of predicted protein structures for predicting protein functions from sequences. We applied PortalCG to two case studies to exemplify its potential in drug discovery: designing selective dual-antagonists of Dopamine receptors for the treatment of Opioid Use Disorder, and illuminating the undruggable human genome for targeting diseases that do not have effective and safe therapeutics. Our results suggested that PortalCG is a viable solution to the OOD problem in exploring the understudied protein functional space. Author Summary Many complex diseases such as Alzheimer’s disease, mental disorders, and substance use disorders do not have effective and safe therapeutics due to the polygenic nature of diseases and the lack of thoroughly validate drug targets and their ligands. Identifying small molecule ligands for all proteins encoded in the human genome will provide new opportunity for drug discovery of currently untreatable diseases. However, the small molecule ligand of more than 90% gene families is completely unknown. Existing protein-ligand docking and machine learning methods often fail when the protein of interest is dissimilar to those with known functions or structures. We develop a new deep learning framework PortalCG for efficiently and accurately predicting ligands of understudied proteins which are out of reach of existing methods. Our method achieves unprecedented accuracy over state-of-the-arts by incorporating ligand binding site information and sequence-to-structure-to-function paradigm into a novel deep meta-learning algorithms. In a case study, the performance of PortalCG surpassed the human design. The proposed computational framework will shed new light into how chemicals modulate biological system as demonstrated by applications to drug repurposing and designing polypharmacology. It will open a new door to developing effective and safe therapeutics for currently incurable diseases. PortalCG can be extended to other scientific inquiries such as predicting protein-protein interactions and protein-nucleic acid recognition.

Explainable Deep Generative Models, Ancestral Fragments, and Murky Regions of the Protein Structure Universe

Abstract: Modern proteins did not arise abruptly, as singular events, but rather over the course of at least 3.5 billion years of evolution. Can machine learning teach us how this occurred? The molecular evolutionary processes that yielded the intricate three-dimensional (3D) structures of proteins involve duplication, recombination and mutation of genetic elements, corresponding to short peptide fragments. Identifying and elucidating these ancestral fragments is crucial to deciphering the interrelationships amongst proteins, as well as how evolution acts upon protein sequences, structures & functions. Traditionally, structural fragments have been found using sequence-based and 3D structural alignment approaches, but that becomes challenging when proteins have undergone extensive permutations—allowing two proteins to share a common architecture, though their topologies may drastically differ (a phenomenon termed the Urfold). We have designed a new framework to identify compact, potentially-discontinuous peptide fragments by combining (i) deep generative models of protein superfamilies with (ii) layerwise relevance propagation (LRP) to identify atoms of great relevance in creating an embedding during an allsuperfamilies × alldomains analysis. Our approach recapitulates known relationships amongst the evolutionarily ancient small β-barrels (e.g. SH3 and OB folds) and amongst P-loop–containing proteins (e.g. Rossmann and P-loop NTPases), previously established via manual analysis. Because of the generality of our deep model’s approach, we anticipate that it can enable the discovery of new ancestral peptides. In a sense, our framework uses LRP as an ‘explainable AI’ approach, in conjunction with a recent deep generative model of protein structure (termed DeepUrfold), in order to leverage decades worth of structural biology knowledge to decipher the underlying molecular bases for protein structural relationships—including those which are exceedingly remote, yet discoverable via deep learning.

The Least Difference in Means: A Statistic for Effect Size Strength and Practical Significance

Abstract: : With limited resources, scientific inquiries must be prioritized for further study, funding, and translation based on their practical significance: whether the effect size is large enough to be meaningful in the real world. Doing so must evaluate a result’s effect strength, defined as a conservative assessment of practical significance. We propose the least difference in means (δ L ) as a two-sample statistic that can quantify effect strength and perform a hypothesis test to determine if a result has a meaningful effect size. To facilitate consensus, δ L allows scientists to compare effect strength between related results and choose different thresholds for hypothesis testing without recalculation. Both δ L and the relative δ L outperform other candidate statistics in identifying results with higher effect strength. We use real data to demonstrate how the relative δ L compares effect strength across broadly related experiments. The relative δ L can prioritize research based on the strength of their results.

Ten simple rules for organizing a special session at a scientific conference

Abstract: Special sessions are important parts of scientific meetings and conferences: They gather together researchers and students interested in a specific topic and can strongly contribute to the success of the conference itself. Moreover, they can be the first step for trainees and students to the organization of a scientific event. Organizing a special session, however, can be uneasy for beginners and students. Here, we provide ten simple rules to follow to organize a special session at a scientific conference.