Brown University | Computational Biology

Background: Human geneticists are now capable of measuring more than one million DNA sequence variations from across the human genome. The new challenge is to develop computationally feasible methods capable of analyzing these data for... more

Advances in the video gaming industry have led to the production of low-cost, high-performance graphics processing units (GPUs) that possess more memory bandwidth and computational capability than central processing units (CPUs), the... more

Motivation: Epistasis, the presence of gene-gene interactions, has been hypothesized to be at the root of many common human diseases, but current genome-wide association studies largely ignore its role. Multifactor dimensionality... more

This object-specific case study focuses on cupreous artifacts excavated from the Great Temple complex of Petra, Jordan to demonstrate how the use of compositional X-ray analyses alongside two experimental applications (ImageJ software and... more

The intracellular (IC) face of the G-protein coupled receptors (GPCR), bradykinin (BK) B2 and angiotensin (AT) 1a, is similar in sequence homology and in size. Both receptors are known to link to Gai and Gaq but differ markedly in a... more

The intracellular (IC) face of the G-protein coupled receptors (GPCR), bradykinin (BK) B2 and angiotensin (AT) 1a, is similar in sequence homology and in size. Both receptors are known to link to Gai and Gaq but differ markedly in a number of physiologic actions, particularly with respect to their hemodynamic action. We made single as well as multiple, global replacements within the IC of BKB2R with the corresponding regions of the AT1aR. When stably transfected into Rat-1 cells, these hybrid receptors all bound BK with high affinity. Single replacement of the intracellular loop 2 (IC2) or the distal 34 residues of the C-terminus (dCt) with the corresponding regions of AT1aR resulted in chimera, which turned over phosphotidylinositol (PI) and released arachidonic acid (ARA) as WT BKB2R. In contrast, incorporation of the AT1aR IC3 in a single replacement abolished signal transduction. However, the simultaneous exchange of IC2 and IC3 of BKB2R with AT1aR resulted in a receptor responding to BK with PI turnover and ARA release approximately 4-fold greater than WT BKB2R. Likewise, the simultaneous replacement of IC2 and dCt resulted in a 2.8-and 1.6-fold increase in PI turnover and ARA release, respectively. In contrast, the dual replacement of IC3 and dCt could not overcome the deleterious effects of the IC3 replacement, resulting in very low PI activation and ARA release. Replacement of all three IC domains (IC2, IC3, and dCt) resulted in PI closer to that of AT1aR than BKB2R. The uptake of the receptor chimeras was similar to that of WT BKB2R with the exception of the IC3/dCt dual mutant, which exhibited very poor internalization (18% at 60 0 ). When transfected into Rat-1 cells, the AT1aR markedly increased the expression of connective tissue growth factor (CTGF) mRNA, while BK slightly decreased it. The dual IC2/dCt and triple IC2/IC3/dCt hybrids both upregulated CTGF mRNA in response to BK. These results show that the IC face of the BKB2R can be exchanged with that of AT1aR, producing hybrid receptors, which take on the functional characteristics of AT1aR. The characterization of the chimera with stepwise replacement of the IC domains should allow for assignment of specific roles to the individual loops and C-terminus in the signaling and internalization of the BKB2R and facilitate the generation of a receptor with BKB2R binding and AT1aR function.

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- by Dale Mierke and +1
  Abhas Gupta
- •
- 11
  Gene expression, Signal Transduction, Cell line, Rats

Disulfide bonds are conserved strongly among proteins of related structure and function. Despite the explosive growth of protein sequence databases and the vast numbers of sequence search tools, no tool exists to draw relations between... more

Disulfide bonds are conserved strongly among proteins of related structure and function. Despite the explosive growth of protein sequence databases and the vast numbers of sequence search tools, no tool exists to draw relations between the disulfide patterns of homologous proteins. We present a comprehensive database of disulfide bonding patterns and a search method to find proteins with similar disulfide patterns. The disulfide database was constructed using disulfide annotations extracted from SwissProt, and was expanded significantly from 16,736 to 94,499 disulfide-containing domains by an inference method that combines SwissProt annotations with Pfam multiple alignments. To search the database, we define a disulfide description, called the disulfide signature, which encodes both spacings between cysteine residues and cysteine connectivity. A web tool was developed that allows users to search for related disulfide patterns and for subpatterns resulting from the removal of one or more disulfides from the pattern. We explore the possibility of using disulfide pattern conservation to identify protein homologs that are undetectable by PSI-BLAST. Examples include the homology between a sea anemone antihypertensive/antiviral protein and a sea anemone neurotoxin, and the homology between tick anticoagulant peptide and bovine trypsin inhibitor. In both examples, there is a clear structural similarity and a functional relationship. We used the database to find structural homologs for the Cripto CFC domain. The identification of a von Willebrand Factor C (VWFC)-like domain agrees with its functional role and explains mutation data. We believe that the rapid increase in structure determinations arising from structural genomics efforts and advances in mass spectrometry techniques will greatly increase the number of disulfide annotations. This information will become a valuable resource for structural and functional annotations of proteins. The availability of a searchable disulfide pattern database will thus provide a powerful new addition to existing homolog discovery methods.

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- by Abhas Gupta
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- 29
  Bioinformatics, Genetics, Molecular Biology, Genomics

We report a detailed classification of disulfide patterns to further understand the role of disulfides in protein structure and function. The classification is applied to a unique searchable database of disulfide patterns derived from the... more

◥ Purpose: With increasing incidence of renal mass, it is important to make a pretreatment differentiation between benign renal mass and malignant tumor. We aimed to develop a deep learning model that distinguishes benign renal tumors... more

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- by Subhanik Purkayastha
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- 3
  Machine Learning, Cancer, Radiomics

Objective: To develop a machine learning (ML) pipeline based on radiomics to predict Coronavirus Disease 2019 (COVID-19) severity and the future deterioration to critical illness using CT and clinical variables. Materials and Methods:... more

Objective: To develop a machine learning (ML) pipeline based on radiomics to predict Coronavirus Disease 2019 (COVID-19) severity and the future deterioration to critical illness using CT and clinical variables. Materials and Methods: Clinical data were collected from 981 patients from a multi-institutional international cohort with real-time polymerase chain reaction-confirmed COVID-19. Radiomics features were extracted from chest CT of the patients. The data of the cohort were randomly divided into training, validation, and test sets using a 7:1:2 ratio. A ML pipeline consisting of a model to predict severity and time-to-event model to predict progression to critical illness were trained on radiomics features and clinical variables. The receiver operating characteristic area under the curve (ROC-AUC), concordance index (C-index), and time-dependent ROC-AUC were calculated to determine model performance, which was compared with consensus CT severity scores obtained by visual interpretation by radiologists. Results: Among 981 patients with confirmed COVID-19, 274 patients developed critical illness. Radiomics features and clinical variables resulted in the best performance for the prediction of disease severity with a highest test ROC-AUC of 0.76 compared with 0.70 (0.76 vs. 0.70, p = 0.023) for visual CT severity score and clinical variables. The progression prediction model achieved a test C-index of 0.868 when it was based on the combination of CT radiomics and clinical variables compared with 0.767 when based on CT radiomics features alone (p < 0.001), 0.847 when based on clinical variables alone (p = 0.110), and 0.860 when based on the combination of visual CT severity scores and clinical variables (p = 0.549). Furthermore, the model based on the combination of CT radiomics and clinical variables achieved time-dependent ROC-AUCs of 0.897, 0.933, and 0.927 for the prediction of progression risks at 3, 5 and 7 days, respectively. Conclusion: CT radiomics features combined with clinical variables were predictive of COVID-19 severity and progression to critical illness with fairly high accuracy.

Computational Biology

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