Redundancy-aware learning of protein structure-function relationships

The protein kinases are a large family of enzymes that play a fundamental role in propagating signals within the cell. Because of the high degree of binding site similarity shared among protein kinases, designing drug compounds with high specificity among the kinases has proven difficult. However, computational approaches to comparing the 3-dimensional geometry and physicochemical properties of key binding site residues, referred to here as substructures, have been shown to be informative of inhibitor selectivity. This thesis introduces two fundamental approaches for the comparative analysis of substructure similarity and demonstrates the importance of each method on a variety of large protein structure datasets for multiple biological applications. The Family-wise Alignment of SubStructural Templates Framework (The FASST Framework) provides an unsupervised learning approach for identifying substructure clusterings. The substructure clusterings identified by FASST allow for the automatic evaluation of substructure variability, the identification of distinct structural conformations and the selection of anomalous outlier structures within large structure datasets. These clusterings are shown to be capable of identifying biologically meaningful structure trends among a diverse number of protein families. The FASST Live visualization and analysis platform provides multiple comparative analysis pipelines and allows the user to interactively explore the substructure clusterings computed by FASST. The Combinatorial Clustering Of Residue Position Subsets (CCORPS) method provides a supervised learning approach for identifying structural features that are correlated with a given set of annotation labels. The ability of CCORPS to identify structural features predictive of functional divergence among families of homologous enzymes is demonstrated across 48 distinct protein families. The CCORPS method is further demonstrated to generalize to the very difficult problem of predicting protein kinase inhibitor affinity. CCORPS is demonstrated to make perfect or near-perfect predictions for the binding ability of 12 of the 38 kinase inhibitors studied, while only having overall poor predictive ability for 1 of the 38 compounds. Additionally, CCORPS is shown to identify shared structural features across phylogenetically diverse groups of kinases that are correlated with binding affinity for particular inhibitors; such instances of structural similarity among phylogenetically diverse kinases are also shown to not be rare among kinases. Finally, these function-specific structural features may serve as potential starting points for the development of highly specific kinase inhibitors. Importantly, both The FASST Framework and CCORPS implement a redundancy-aware approach to dealing with structure overrepresentation that allows for the incorporation of all available structure data. As shown in this thesis, surprising structural variability exists even among structure datasets consisting of a single protein sequence. By incorporating the full variety of structural conformations within the analysis, the methods presented here provide a richer view of the variability of large protein structure datasets

The TLR4 D299G and T399I SNPs Are Constitutively Active to Up-Regulate Expression of Trif-Dependent Genes

Peer reviewedPublisher PD

https://doi.org/10.3389/fnins.2017.00219 Decreasing Sedentary Behavior: Effects on Academic Performance, Meta-Cognition, and Sleep

There is growing interest in using activity workstations as a method of increasing light physical activity in normally sedentary environments. The current study (N = 117) compared the effects of studying in college students while slowly pedaling a stationary bike with a desktop with studying at traditional desks across 10 weeks in an academic semester. The students were assigned to study either on the stationary bike or at a traditional desk located in the campus library for a minimum of 2 h a week. During the 10 weeks, the students studied for tests or worked on other required academic activities while working at their assigned desk. In addition, the participants completed a pre survey, weekly surveys, and a post survey. We found that although students studying at the traditional desks reported more ease of studying and more effective studying than those using the stationary bikes, the two groups performed equally well on tests in an introductory psychology course. Moreover, the students using the traditional desks reported a decrease in sleep quality later in the semester while those using the activity workstation reported stable levels of sleep quality. The current results indicate that activity workstations could be implemented in university settings to encourage light physical activity without negatively affecting academic performance while providing possible long-term health and well-being benefits. Furthermore, the results suggests that activity workstations could be a means of combating sedentary behavior in environments where individuals are expected to sit either while waiting (e.g., doctor\u27s waiting rooms, airports) or when completing a necessary task (e.g., the workplace, educational settings)

Critiquing Protein Family Classification Models Using Sufficient Input Subsets.

In many application domains, neural networks are highly accurate and have been deployed at large scale. However, users often do not have good tools for understanding how these models arrive at their predictions. This has hindered adoption in fields such as the life and medical sciences, where researchers require that models base their decisions on underlying biological phenomena rather than peculiarities of the dataset. We propose a set of methods for critiquing deep learning models and demonstrate their application for protein family classification, a task for which high-accuracy models have considerable potential impact. Our methods extend the Sufficient Input Subsets (SIS) technique, which we use to identify subsets of features in each protein sequence that are alone sufficient for classification. Our suite of tools analyzes these subsets to shed light on the decision-making criteria employed by models trained on this task. These tools show that while deep models may perform classification for biologically relevant reasons, their behavior varies considerably across the choice of network architecture and parameter initialization. While the techniques that we develop are specific to the protein sequence classification task, the approach taken generalizes to a broad set of scientific contexts in which model interpretability is essential

The UKIDSS Galactic Plane Survey

'The definitive version is available at www.blackwell-synergy.com .' Copyright Blackwell Publishing DOI: 10.1111/j.1365-2966.2008.13924.xThe UKIDSS Galactic Plane Survey (GPS) is one of the five near-infrared Public Legacy Surveys that are being undertaken by the UKIDSS consortium, using the Wide Field Camera on the United Kingdom Infrared TelescopePeer reviewe

Phase II double-blind placebo-controlled randomized study of armodafinil for brain radiation-induced fatigue

BACKGROUND: Common acute-term side effects of brain radiotherapy (RT) include fatigue, drowsiness, decreased physical functioning, and decreased quality of life (QOL). We hypothesized that armodafinil (a wakefulness-promoting drug known to reduce fatigue and increase cognitive function in breast cancer patients receiving chemotherapy) would result in reduced fatigue and sleepiness for patients receiving brain RT. METHODS: A phase II, multi-institutional, placebo-controlled randomized trial assessed feasibility of armodafinil 150 mg/day in participants receiving brain RT, from whom we obtained estimates of variability for fatigue, sleepiness, QOL, cognitive function, and treatment effect. RESULTS: From September 20, 2010, to October 20, 2012, 54 participants enrolled with 80% retention and 94% self-reported compliance. There were no grade 4-5 toxicities, and the incidence of grade 2-3 toxicities was similar between treatment arms, the most common of which were anxiety and nausea (15%), headaches (19%), and insomnia (20%). There were no statistically significant differences in end-RT or 4 week post-RT outcomes between armodafinil and placebo in any outcomes (Functional Assessment of Chronic Illness Therapy [FACIT]-Fatigue, Brief Fatigue Inventory, Epworth Sleepiness Scale, FACT-Brain, and FACIT-cognitive function). However, in participants with more baseline fatigue, those treated with armodafinil did better than those who received the placebo on the end-RT assessments for several outcomes. CONCLUSION: Armodafinil 150 mg/day was well tolerated in primary brain tumor patients undergoing RT with good compliance. While there was no overall significant effect on fatigue, those with greater baseline fatigue experienced improved QOL and reduced fatigue when using armodafinil. These data suggest that a prospective, phase III randomized trial is warranted for patients with greater baseline fatigue

Combinatorial Clustering of Residue Position Subsets Predicts Inhibitor Affinity across the Human Kinome

The protein kinases are a large family of enzymes that play fundamental roles in propagating signals within the cell. Because of the high degree of binding site similarity shared among protein kinases, designing drug compounds with high specificity among the kinases has proven difficult. However, computational approaches to comparing the 3-dimensional geometry and physicochemical properties of key binding site residue positions have been shown to be informative of inhibitor selectivity. The Combinatorial Clustering Of Residue Position Subsets (CCORPS) method, introduced here, provides a semi-supervised learning approach for identifying structural features that are correlated with a given set of annotation labels. Here, CCORPS is applied to the problem of identifying structural features of the kinase ATP binding site that are informative of inhibitor binding. CCORPS is demonstrated to make perfect or near-perfect predictions for the binding affinity profile of 8 of the 38 kinase inhibitors studied, while only having overall poor predictive ability for 1 of the 38 compounds. Additionally, CCORPS is shown to identify shared structural features across phylogenetically diverse groups of kinases that are correlated with binding affinity for particular inhibitors; such instances of structural similarity among phylogenetically diverse kinases are also shown to not be rare among kinases. Finally, these function-specific structural features may serve as potential starting points for the development of highly specific kinase inhibitors

High Density Lipoprotein (HDL) Promotes Glucose Uptake in Adipocytes and Glycogen Synthesis in Muscle Cells

Background: High density lipoprotein (HDL) was reported to decrease plasma glucose and promote insulin secretion in type 2 diabetes patients. This investigation was designed to determine the effects and mechanisms of HDL on glucose uptake in adipocytes and glycogen synthesis in muscle cells. Methods and Results: Actions of HDL on glucose uptake and GLUT4 translocation were assessed with 1- [ 3 H]-2deoxyglucose and plasma membrane lawn, respectively, in 3T3-L1 adipocytes. Glycogen analysis was performed with amyloglucosidase and glucose oxidase-peroxidase methods in normal and palmitate-treated L6 cells. Small interfering RNA was used to observe role of scavenger receptor type I (SR-BI) in glucose uptake of HDL. Corresponding signaling molecules were detected by immunoblotting. HDL stimulated glucose uptake in a time- and concentration-dependent manner in 3T3-L1 adipocytes. GLUT4 translocation was significantly increased by HDL. Glycogen deposition got enhanced in L6 muscle cells paralleling with elevated glycogen synthase kinase3 (GSK3) phosphorylation. Meanwhile, increased phosphorylations of Akt-Ser473 and AMP activated protein kinase (AMPK) a were detected in 3T3-L1 adipocytes. Glucose uptake and Akt-Ser473 activation but not AMPK-a were diminished in SR-BI knock-down 3T3-L1 cells. Conclusions: HDL stimulates glucose uptake in 3T3-L1 adipocytes through enhancing GLUT4 translocation by mechanisms involving PI3K/Akt via SR-BI and AMPK signaling pathways, and increases glycogen deposition in L6 muscle cells throug

Cellular and Genetic Analysis of Wound Healing in Drosophila Larvae

To establish a genetic system to study postembryonic wound healing, we characterized epidermal wound healing in Drosophila larvae. Following puncture wounding, larvae begin to bleed but within an hour a plug forms in the wound gap. Over the next couple of hours the outer part of the plug melanizes to form a scab, and epidermal cells surrounding the plug orient toward it and then fuse to form a syncytium. Subsequently, more-peripheral cells orient toward and fuse with the central syncytium. During this time, the Jun N-terminal kinase (JNK) pathway is activated in a gradient emanating out from the wound, and the epidermal cells spread along or through the wound plug to reestablish a continuous epithelium and its basal lamina and apical cuticle lining. Inactivation of the JNK pathway inhibits epidermal spreading and reepithelialization but does not affect scab formation or other wound healing responses. Conversely, mutations that block scab formation, and a scabless wounding procedure, provide evidence that the scab stabilizes the wound site but is not required to initiate other wound responses. However, in the absence of a scab, the JNK pathway is hyperinduced, reepithelialization initiates but is not always completed, and a chronic wound ensues. The results demonstrate that the cellular responses of wound healing are under separate genetic control, and that the responses are coordinated by multiple signals emanating from the wound site, including a negative feedback signal between scab formation and the JNK pathway. Cell biological and molecular parallels to vertebrate wound healing lead us to speculate that wound healing is an ancient response that has diversified during evolution

BRCA1 loss activates cathepsin L–mediated degradation of 53BP1 in breast cancer cells

Loss of 53BP1 rescues BRCA1 deficiency and is associated with BRCA1-deficient and triple-negative breast cancers (TNBC) and with resistance to genotoxic drugs. The mechanisms responsible for decreased 53BP1 transcript and protein levels in tumors remain unknown. Here, we demonstrate that BRCA1 loss activates cathepsin L (CTSL)–mediated degradation of 53BP1. Activation of this pathway rescued homologous recombination repair and allowed BRCA1-deficient cells to bypass growth arrest. Importantly, depletion or inhibition of CTSL with vitamin D or specific inhibitors stabilized 53BP1 and increased genomic instability in response to radiation and poly(adenosine diphosphate–ribose) polymerase inhibitors, compromising proliferation. Analysis of human breast tumors identified nuclear CTSL as a positive biomarker for TNBC, which correlated inversely with 53BP1. Importantly, nuclear levels of CTSL, vitamin D receptor, and 53BP1 emerged as a novel triple biomarker signature for stratification of patients with BRCA1-mutated tumors and TNBC, with potential predictive value for drug response. We identify here a novel pathway with prospective relevance for diagnosis and customization of breast cancer therapy