Modeling the flexibility of alpha helices in protein interfaces : structure based design and prediction of helix-mediated protein-protein interactions

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Chemistry, 2008.Vita.Includes bibliographical references.Protein-protein interactions play an essential role in many biological functions. Prediction and design of these interactions using computational methods requires models that can be used to efficiently sample structural variation. This thesis identifies methods that can be used to sample an important sub-space of protein structure: alpha helices that participate in protein interfaces. Helices, the global structural properties of which can be described with only a few variables, are particularly well suited for efficient sampling. Two methods for sampling helical backbones are presented: Crick parameterization for coiled coils and normal-mode analysis for all helices. These are shown to capture most of the variation seen in the PDB. In addition, these methods are applied to problems in protein structure prediction and design. Normal-mode analysis is used to design novel nanomolar peptide inhibitors of the apoptosis-related Bcl-2 family member, Bcl-xL, and a modification of Crick Parameterization is used to predict the binding orientation of dimeric coiled coils with greater than 80% accuracy. Finally, this study addresses the increase in computational time required by flexible-backbone methods and the use of cluster expansion to quickly map structural energies to sequence-based functions for increased efficiency.by James R. Apgar.Ph.D

Is there an optimal preoperative management strategy for phaeochromocytoma/paraganglioma?

Phaeochromocytomas and paragangliomas (PPGLs) are catecholamine secreting neuroendocrine tumours that predispose to haemodynamic instability. Currently, surgery is the only available curative treatment, but carries potential risks including hypertensive and hypotensive crises, cardiac arrhythmias, myocardial infarction and stroke, due to tumoral release of catecholamines during anaesthetic induction and tumour manipulation. The mortality associated with surgical resection of PPGL has significantly improved from 20-45% in the early 20th century (Apgar & Papper, AMA Archives of Surgery, 1951, 62, 634) to 0-2·9% in the early 21st century (Kinney et al. Journal of Cardiothoracic and Vascular Anesthesia, 2002, 16, 359), largely due to availability of effective pharmacological agents and advances in surgical and anaesthetic practice. However, surgical resection of PPGL still poses significant clinical management challenges. Preoperatively, alpha-adrenoceptor blockade is the mainstay of management, although various pharmacological strategies have been proposed, based largely on reports derived from retrospective data sets. To date, no consensus has been reached regarding the 'ideal' preoperative strategy due, in part, to a paucity of data from high-quality evidence-based studies comparing different treatment regimens. Here, based on the available literature, we address the Clinical Question: Is there an optimal preoperative management strategy for PPGL?National Institute for Health Research Cambridge Biomedical Research Centr

A Predictive Model of Intein Insertion Site for Use in the Engineering of Molecular Switches

Inteins are intervening protein domains with self-splicing ability that can be used as molecular switches to control activity of their host protein. Successfully engineering an intein into a host protein requires identifying an insertion site that permits intein insertion and splicing while allowing for proper folding of the mature protein post-splicing. By analyzing sequence and structure based properties of native intein insertion sites we have identified four features that showed significant correlation with the location of the intein insertion sites, and therefore may be useful in predicting insertion sites in other proteins that provide native-like intein function. Three of these properties, the distance to the active site and dimer interface site, the SVM score of the splice site cassette, and the sequence conservation of the site showed statistically significant correlation and strong predictive power, with area under the curve (AUC) values of 0.79, 0.76, and 0.73 respectively, while the distance to secondary structure/loop junction showed significance but with less predictive power (AUC of 0.54). In a case study of 20 insertion sites in the XynB xylanase, two features of native insertion sites showed correlation with the splice sites and demonstrated predictive value in selecting non-native splice sites. Structural modeling of intein insertions at two sites highlighted the role that the insertion site location could play on the ability of the intein to modulate activity of the host protein. These findings can be used to enrich the selection of insertion sites capable of supporting intein splicing and hosting an intein switch

Group B streptococcus serotype prevalence in reproductive-age women at a tertiary care military medical center relative to global serotype distribution

<p>Abstract</p> <p>Background</p> <p>Group B <it>Streptococcus </it>(GBS) serotype (Ia, Ib, II-IX) correlates with pathogen virulence and clinical prognosis. Epidemiological studies of seroprevalence are an important metric for determining the proportion of serotypes in a given population. The purpose of this study was to evaluate the prevalence of individual GBS serotypes at Madigan Healthcare System (Madigan), the largest military tertiary healthcare facility in the Pacific Northwestern United States, and to compare seroprevalences with international locations.</p> <p>Methods</p> <p>To determine serotype distribution at Madigan, we obtained GBS isolates from standard-of-care anogenital swabs from 207 women of indeterminate gravidity between ages 18-40 during a five month interval. Serotype was determined using a recently described molecular method of polymerase chain reaction by capsular polysaccharide synthesis (cps) genes associated with pathogen virulence.</p> <p>Results</p> <p>Serotypes Ia, III, and V were the most prevalent (28%, 27%, and 17%, respectively). A systematic review of global GBS seroprevalence, meta-analysis, and statistical comparison revealed strikingly similar serodistibution at Madigan relative to civilian-sector populations in Canada and the United States. Serotype Ia was the only serotype consistently higher in North American populations relative to other geographic regions (p < 0.005). The number of non-typeable isolates was significantly lower in the study (p < 0.005).</p> <p>Conclusion</p> <p>This study establishes PCR-based serotyping as a viable strategy for GBS epidemiological surveillance. Our results suggest that GBS seroprevalence remains stable in North America over the past two decades.</p

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Author Correction: Federated learning enables big data for rare cancer boundary detection.

10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14

Emerging concepts in biomarker discovery; The US-Japan workshop on immunological molecular markers in oncology

Supported by the Office of International Affairs, National Cancer Institute (NCI), the "US-Japan Workshop on Immunological Biomarkers in Oncology" was held in March 2009. The workshop was related to a task force launched by the International Society for the Biological Therapy of Cancer (iSBTc) and the United States Food and Drug Administration (FDA) to identify strategies for biomarker discovery and validation in the field of biotherapy. The effort will culminate on October 28th 2009 in the "iSBTc-FDA-NCI Workshop on Prognostic and Predictive Immunologic Biomarkers in Cancer", which will be held in Washington DC in association with the Annual Meeting. The purposes of the US-Japan workshop were a) to discuss novel approaches to enhance the discovery of predictive and/or prognostic markers in cancer immunotherapy; b) to define the state of the science in biomarker discovery and validation. The participation of Japanese and US scientists provided the opportunity to identify shared or discordant themes across the distinct immune genetic background and the diverse prevalence of disease between the two Nations

Federated Learning Enables Big Data for Rare Cancer Boundary Detection

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing