Bi-Allelic Novel Variants in CLIC5 Identified in a Cameroonian
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An Effective Computational Method Incorporating Multiple Secondary
Old Dominion University ODU Digital Commons Computer Science Faculty Publications Computer Science 2017 An Effective Computational Method Incorporating Multiple Secondary Structure Predictions in Topology Determination for Cryo-EM Images Abhishek Biswas Old Dominion University Desh Ranjan Old Dominion University Mohammad Zubair Old Dominion University Stephanie Zeil Old Dominion University Kamal Al Nasr See next page for additional authors Follow this and additional works at: https://digitalcommons.odu.edu/computerscience_fac_pubs Part of the Biochemistry Commons, Computer Sciences Commons, Molecular Biology Commons, and the Statistics and Probability Commons Repository Citation Biswas, Abhishek; Ranjan, Desh; Zubair, Mohammad; Zeil, Stephanie; Al Nasr, Kamal; and He, Jing, "An Effective Computational Method Incorporating Multiple Secondary Structure Predictions in Topology Determination for Cryo-EM Images" (2017). Computer Science Faculty Publications. 81. https://digitalcommons.odu.edu/computerscience_fac_pubs/81 Original Publication Citation Biswas, A., Ranjan, D., Zubair, M., Zeil, S., Al Nasr, K., & He, J. (2017). An effective computational method incorporating multiple secondary structure predictions in topology determination for cryo-em images. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 14(3), 578-586. doi:10.1109/tcbb.2016.2543721 This Article is brought to you for free and open access by the Computer Science at ODU Digital Commons. It has been accepted for inclusion in Computer Science Faculty Publications by an authorized administrator of ODU Digital Commons. For more information, please contact [email protected]. Authors Abhishek Biswas, Desh Ranjan, Mohammad Zubair, Stephanie Zeil, Kamal Al Nasr, and Jing He This article is available at ODU Digital Commons: https://digitalcommons.odu.edu/computerscience_fac_pubs/81 HHS Public Access Author manuscript Author ManuscriptAuthor Manuscript Author IEEE/ACM Manuscript Author Trans Comput Manuscript Author Biol Bioinform. -
Screening and Identification of Key Biomarkers in Clear Cell Renal Cell Carcinoma Based on Bioinformatics Analysis
bioRxiv preprint doi: https://doi.org/10.1101/2020.12.21.423889; this version posted December 23, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Screening and identification of key biomarkers in clear cell renal cell carcinoma based on bioinformatics analysis Basavaraj Vastrad1, Chanabasayya Vastrad*2 , Iranna Kotturshetti 1. Department of Biochemistry, Basaveshwar College of Pharmacy, Gadag, Karnataka 582103, India. 2. Biostatistics and Bioinformatics, Chanabasava Nilaya, Bharthinagar, Dharwad 580001, Karanataka, India. 3. Department of Ayurveda, Rajiv Gandhi Education Society`s Ayurvedic Medical College, Ron, Karnataka 562209, India. * Chanabasayya Vastrad [email protected] Ph: +919480073398 Chanabasava Nilaya, Bharthinagar, Dharwad 580001 , Karanataka, India bioRxiv preprint doi: https://doi.org/10.1101/2020.12.21.423889; this version posted December 23, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Abstract Clear cell renal cell carcinoma (ccRCC) is one of the most common types of malignancy of the urinary system. The pathogenesis and effective diagnosis of ccRCC have become popular topics for research in the previous decade. In the current study, an integrated bioinformatics analysis was performed to identify core genes associated in ccRCC. An expression dataset (GSE105261) was downloaded from the Gene Expression Omnibus database, and included 26 ccRCC and 9 normal kideny samples. Assessment of the microarray dataset led to the recognition of differentially expressed genes (DEGs), which was subsequently used for pathway and gene ontology (GO) enrichment analysis. -
A Computational Approach for Defining a Signature of Β-Cell Golgi Stress in Diabetes Mellitus
Page 1 of 781 Diabetes A Computational Approach for Defining a Signature of β-Cell Golgi Stress in Diabetes Mellitus Robert N. Bone1,6,7, Olufunmilola Oyebamiji2, Sayali Talware2, Sharmila Selvaraj2, Preethi Krishnan3,6, Farooq Syed1,6,7, Huanmei Wu2, Carmella Evans-Molina 1,3,4,5,6,7,8* Departments of 1Pediatrics, 3Medicine, 4Anatomy, Cell Biology & Physiology, 5Biochemistry & Molecular Biology, the 6Center for Diabetes & Metabolic Diseases, and the 7Herman B. Wells Center for Pediatric Research, Indiana University School of Medicine, Indianapolis, IN 46202; 2Department of BioHealth Informatics, Indiana University-Purdue University Indianapolis, Indianapolis, IN, 46202; 8Roudebush VA Medical Center, Indianapolis, IN 46202. *Corresponding Author(s): Carmella Evans-Molina, MD, PhD ([email protected]) Indiana University School of Medicine, 635 Barnhill Drive, MS 2031A, Indianapolis, IN 46202, Telephone: (317) 274-4145, Fax (317) 274-4107 Running Title: Golgi Stress Response in Diabetes Word Count: 4358 Number of Figures: 6 Keywords: Golgi apparatus stress, Islets, β cell, Type 1 diabetes, Type 2 diabetes 1 Diabetes Publish Ahead of Print, published online August 20, 2020 Diabetes Page 2 of 781 ABSTRACT The Golgi apparatus (GA) is an important site of insulin processing and granule maturation, but whether GA organelle dysfunction and GA stress are present in the diabetic β-cell has not been tested. We utilized an informatics-based approach to develop a transcriptional signature of β-cell GA stress using existing RNA sequencing and microarray datasets generated using human islets from donors with diabetes and islets where type 1(T1D) and type 2 diabetes (T2D) had been modeled ex vivo. To narrow our results to GA-specific genes, we applied a filter set of 1,030 genes accepted as GA associated. -
Chapter 13 Protein Structure Learning Objectives
Chapter 13 Protein structure Learning objectives Upon completing this material you should be able to: ■ understand the principles of protein primary, secondary, tertiary, and quaternary structure; ■use the NCBI tool CN3D to view a protein structure; ■use the NCBI tool VAST to align two structures; ■explain the role of PDB including its purpose, contents, and tools; ■explain the role of structure annotation databases such as SCOP and CATH; and ■describe approaches to modeling the three-dimensional structure of proteins. Outline Overview of protein structure Principles of protein structure Protein Data Bank Protein structure prediction Intrinsically disordered proteins Protein structure and disease Overview: protein structure The three-dimensional structure of a protein determines its capacity to function. Christian Anfinsen and others denatured ribonuclease, observed rapid refolding, and demonstrated that the primary amino acid sequence determines its three-dimensional structure. We can study protein structure to understand problems such as the consequence of disease-causing mutations; the properties of ligand-binding sites; and the functions of homologs. Outline Overview of protein structure Principles of protein structure Protein Data Bank Protein structure prediction Intrinsically disordered proteins Protein structure and disease Protein primary and secondary structure Results from three secondary structure programs are shown, with their consensus. h: alpha helix; c: random coil; e: extended strand Protein tertiary and quaternary structure Quarternary structure: the four subunits of hemoglobin are shown (with an α 2β2 composition and one beta globin chain high- lighted) as well as four noncovalently attached heme groups. The peptide bond; phi and psi angles The peptide bond; phi and psi angles in DeepView Protein secondary structure Protein secondary structure is determined by the amino acid side chains. -
Increasing the Accuracy of Single Sequence Prediction Methods Using a Deep Semi-Supervised Learning Framework Lewis Moffat 1,∗ and David T
Structural Bioinformatics Downloaded from https://academic.oup.com/bioinformatics/advance-article/doi/10.1093/bioinformatics/btab491/6313164 by guest on 07 July 2021 Increasing the Accuracy of Single Sequence Prediction Methods Using a Deep Semi-Supervised Learning Framework Lewis Moffat 1,∗ and David T. Jones 1,∗ 1Department of Computer Science, University College London, Gower Street, London WC1E 6BT, UK; and Francis Crick Institute, 1 Midland Road, London NW1 1AT, UK ∗To whom correspondence should be addressed. Associate Editor: XXXXXXX Received on XXXXX; revised on XXXXX; accepted on XXXXX Abstract Motivation: Over the past 50 years, our ability to model protein sequences with evolutionary information has progressed in leaps and bounds. However, even with the latest deep learning methods, the modelling of a critically important class of proteins, single orphan sequences, remains unsolved. Results: By taking a bioinformatics approach to semi-supervised machine learning, we develop Profile Augmentation of Single Sequences (PASS), a simple but powerful framework for building accurate single- sequence methods. To demonstrate the effectiveness of PASS we apply it to the mature field of secondary structure prediction. In doing so we develop S4PRED, the successor to the open-source PSIPRED-Single method, which achieves an unprecedented Q3 score of 75.3% on the standard CB513 test. PASS provides a blueprint for the development of a new generation of predictive methods, advancing our ability to model individual protein sequences. Availability: The S4PRED model is available as open source software on the PSIPRED GitHub repository (https://github.com/psipred/s4pred), along with documentation. It will also be provided as a part of the PSIPRED web service (http://bioinf.cs.ucl.ac.uk/psipred/) Contact: [email protected], [email protected] Supplementary information: Supplementary data are available at Bioinformatics online. -
Transcriptomic and Proteomic Profiling Provides Insight Into
BASIC RESEARCH www.jasn.org Transcriptomic and Proteomic Profiling Provides Insight into Mesangial Cell Function in IgA Nephropathy † † ‡ Peidi Liu,* Emelie Lassén,* Viji Nair, Celine C. Berthier, Miyuki Suguro, Carina Sihlbom,§ † | † Matthias Kretzler, Christer Betsholtz, ¶ Börje Haraldsson,* Wenjun Ju, Kerstin Ebefors,* and Jenny Nyström* *Department of Physiology, Institute of Neuroscience and Physiology, §Proteomics Core Facility at University of Gothenburg, University of Gothenburg, Gothenburg, Sweden; †Division of Nephrology, Department of Internal Medicine and Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan; ‡Division of Molecular Medicine, Aichi Cancer Center Research Institute, Nagoya, Japan; |Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden; and ¶Integrated Cardio Metabolic Centre, Karolinska Institutet Novum, Huddinge, Sweden ABSTRACT IgA nephropathy (IgAN), the most common GN worldwide, is characterized by circulating galactose-deficient IgA (gd-IgA) that forms immune complexes. The immune complexes are deposited in the glomerular mesangium, leading to inflammation and loss of renal function, but the complete pathophysiology of the disease is not understood. Using an integrated global transcriptomic and proteomic profiling approach, we investigated the role of the mesangium in the onset and progression of IgAN. Global gene expression was investigated by microarray analysis of the glomerular compartment of renal biopsy specimens from patients with IgAN (n=19) and controls (n=22). Using curated glomerular cell type–specific genes from the published literature, we found differential expression of a much higher percentage of mesangial cell–positive standard genes than podocyte-positive standard genes in IgAN. Principal coordinate analysis of expression data revealed clear separation of patient and control samples on the basis of mesangial but not podocyte cell–positive standard genes. -
Further Confirmation of the Association of SLC12A2 with Non-Syndromic
www.nature.com/jhg ARTICLE OPEN Further confirmation of the association of SLC12A2 with non-syndromic autosomal-dominant hearing impairment Samuel M. Adadey1,2,9, Isabelle Schrauwen3,9, Elvis Twumasi Aboagye1, Thashi Bharadwaj3, Kevin K. Esoh 2, Sulman Basit 4, 3 3 5 2 6 1 Anushree Acharya , Liz M. Nouel-Saied ,✉ Khurram Liaqat , Edmond✉ Wonkam-Tingang , Shaheen Mowla , Gordon A. Awandare , Wasim Ahmad 7, Suzanne M. Leal 3,8 and Ambroise Wonkam 2 © The Author(s) 2021 Congenital hearing impairment (HI) is genetically heterogeneous making its genetic diagnosis challenging. Investigation of novel HI genes and variants will enhance our understanding of the molecular mechanisms and to aid genetic diagnosis. We performed exome sequencing and analysis using DNA samples from affected members of two large families from Ghana and Pakistan, segregating autosomal-dominant (AD) non-syndromic HI (NSHI). Using in silico approaches, we modeled and evaluated the effect of the likely pathogenic variants on protein structure and function. We identified two likely pathogenic variants in SLC12A2, c.2935G>A:p.(E979K) and c.2939A>T:p.(E980V), which segregate with NSHI in a Ghanaian and Pakistani family, respectively. SLC12A2 encodes an ion transporter crucial in the homeostasis of the inner ear endolymph and has recently been reported to be implicated in syndromic and non-syndromic HI. Both variants were mapped to alternatively spliced exon 21 of the SLC12A2 gene. Exon 21 encodes for 17 residues in the cytoplasmatic tail of SLC12A2, is highly conserved between species, and preferentially expressed in cochlear tissues. A review of previous studies and our current data showed that out of ten families with either AD non-syndromic or syndromic HI, eight (80%) had variants within the 17 amino acid residue region of exon 21 (48 bp), suggesting that this alternate domain is critical to the transporter activity in the inner ear. -
Bayesian Model of Protein Primary Sequence for Secondary Structure Prediction
Bayesian Model of Protein Primary Sequence for Secondary Structure Prediction Qiwei Li1, David B. Dahl2*, Marina Vannucci1, Hyun Joo3, Jerry W. Tsai3 1 Department of Statistics, Rice University, Houston, Texas, United States of America, 2 Department of Statistics, Brigham Young University, Provo, Utah, United States of America, 3 Department of Chemistry, University of the Pacific, Stockton, California, United States of America Abstract Determining the primary structure (i.e., amino acid sequence) of a protein has become cheaper, faster, and more accurate. Higher order protein structure provides insight into a protein’s function in the cell. Understanding a protein’s secondary structure is a first step towards this goal. Therefore, a number of computational prediction methods have been developed to predict secondary structure from just the primary amino acid sequence. The most successful methods use machine learning approaches that are quite accurate, but do not directly incorporate structural information. As a step towards improving secondary structure reduction given the primary structure, we propose a Bayesian model based on the knob- socket model of protein packing in secondary structure. The method considers the packing influence of residues on the secondary structure determination, including those packed close in space but distant in sequence. By performing an assessment of our method on 2 test sets we show how incorporation of multiple sequence alignment data, similarly to PSIPRED, provides balance and improves the accuracy of the predictions. Software implementing the methods is provided as a web application and a stand-alone implementation. Citation: Li Q, Dahl DB, Vannucci M, Joo H, Tsai JW (2014) Bayesian Model of Protein Primary Sequence for Secondary Structure Prediction. -
Transcriptomic Uniqueness and Commonality of the Ion Channels and Transporters in the Four Heart Chambers Sanda Iacobas1, Bogdan Amuzescu2 & Dumitru A
www.nature.com/scientificreports OPEN Transcriptomic uniqueness and commonality of the ion channels and transporters in the four heart chambers Sanda Iacobas1, Bogdan Amuzescu2 & Dumitru A. Iacobas3,4* Myocardium transcriptomes of left and right atria and ventricles from four adult male C57Bl/6j mice were profled with Agilent microarrays to identify the diferences responsible for the distinct functional roles of the four heart chambers. Female mice were not investigated owing to their transcriptome dependence on the estrous cycle phase. Out of the quantifed 16,886 unigenes, 15.76% on the left side and 16.5% on the right side exhibited diferential expression between the atrium and the ventricle, while 5.8% of genes were diferently expressed between the two atria and only 1.2% between the two ventricles. The study revealed also chamber diferences in gene expression control and coordination. We analyzed ion channels and transporters, and genes within the cardiac muscle contraction, oxidative phosphorylation, glycolysis/gluconeogenesis, calcium and adrenergic signaling pathways. Interestingly, while expression of Ank2 oscillates in phase with all 27 quantifed binding partners in the left ventricle, the percentage of in-phase oscillating partners of Ank2 is 15% and 37% in the left and right atria and 74% in the right ventricle. The analysis indicated high interventricular synchrony of the ion channels expressions and the substantially lower synchrony between the two atria and between the atrium and the ventricle from the same side. Starting with crocodilians, the heart pumps the blood through the pulmonary circulation and the systemic cir- culation by the coordinated rhythmic contractions of its upper lef and right atria (LA, RA) and lower lef and right ventricles (LV, RV). -
Data Supporting Characterization of CLIC1, CLIC4, CLIC5 and Dmclic Antibodies and Localization of Clics in Endoplasmic Reticulum of Cardiomyocytes
Data in Brief 7 (2016) 1038–1044 Contents lists available at ScienceDirect Data in Brief journal homepage: www.elsevier.com/locate/dib Data Article Data supporting characterization of CLIC1, CLIC4, CLIC5 and DmCLIC antibodies and localization of CLICs in endoplasmic reticulum of cardiomyocytes Devasena Ponnalagu a, Shubha Gururaja Rao a, Jason Farber a, Wenyu Xin a, Ahmed Tafsirul Hussain a, Kajol Shah a, Soichi Tanda b, Mark A. Berryman c, John C. Edwards d, Harpreet Singh a,n a Department of Pharmacology and Physiology, Drexel University College of Medicine, Philadelphia, PA 19102, United States b Department of Biological Sciences, Ohio University, Athens, OH 45701, United States c Department of Biomedical Sciences, Ohio University, Athens, OH 45701, United States d Division of Nephrology, St. Louis University, St. Louis, MO 63110, United States article info abstract Article history: Chloride intracellular channel (CLICs) proteins show 60–70% Received 22 January 2016 sequence identity to each other, and exclusively localize to the Received in revised form intracellular organelle membranes and cytosol. In support of 22 February 2016 our recent publication, “Molecular identity of cardiac mito- Accepted 16 March 2016 chondrial chloride intracellular channel proteins” (Ponnalagu Available online 26 March 2016 et al., 2016) [1], it was important to characterize the specificity Keywords: of different CLIC paralogs/ortholog (CLIC1, CLIC4, CLIC5 and Chloride intracellular channels DmCLIC) antibodies used to decipher their localization in car- Endoplasmic reticulum diac cells. In addition, localization of CLICs in the other orga- Mitochondria nelles such as endoplasmic reticulum (ER) of cardiomyocytes Cardiomyocytes was established. This article also provides data on the different primers used to show the relative abundance of CLIC paralogs in cardiac tissue and the specificity of the various CLIC antibodies used. -
The Structure and Dynamics of Bmr1 Protein from Brugia Malayi: in Silico Approaches
Int. J. Mol. Sci. 2014, 15, 11082-11099; doi:10.3390/ijms150611082 OPEN ACCESS International Journal of Molecular Sciences ISSN 1422-0067 www.mdpi.com/journal/ijms Article The Structure and Dynamics of BmR1 Protein from Brugia malayi: In Silico Approaches Bee Yin Khor, Gee Jun Tye, Theam Soon Lim, Rahmah Noordin and Yee Siew Choong * Institute for Research in Molecular Medicine, Universiti Sains Malaysia, Minden, Penang 11800, Malaysia; E-Mails: [email protected] (B.Y.K.); [email protected] (G.J.T.); [email protected] (T.S.L.); [email protected] (R.N.) * Author to whom correspondence should be addressed; E-Mail: [email protected]; Tel.: +604-653-4837; Fax: +604-653-4803. Received: 28 January 2014; in revised form: 25 March 2014 / Accepted: 4 June 2014 / Published: 19 June 2014 Abstract: Brugia malayi is a filarial nematode, which causes lymphatic filariasis in humans. In 1995, the disease has been identified by the World Health Organization (WHO) as one of the second leading causes of permanent and long-term disability and thus it is targeted for elimination by year 2020. Therefore, accurate filariasis diagnosis is important for management and elimination programs. A recombinant antigen (BmR1) from the Bm17DIII gene product was used for antibody-based filariasis diagnosis in “Brugia Rapid”. However, the structure and dynamics of BmR1 protein is yet to be elucidated. Here we study the three dimensional structure and dynamics of BmR1 protein using comparative modeling, threading and ab initio protein structure prediction. The best predicted structure obtained via an ab initio method (Rosetta) was further refined and minimized. -
The PSIPRED Protein Structure Prediction Server Liam J
Vol. 16 no. 4 2000 BIOINFORMATICS APPLICATIONS NOTE Pages 404–405 The PSIPRED protein structure prediction server Liam J. McGuffin 1, 2, Kevin Bryson 1 and David T. Jones 1, 2 1Protein Bioinformatics Group, Department of Biological Sciences, University of Warwick, Coventry CV4 7AL, UK and 2Department of Biological Sciences, Brunel University, Uxbridge, UB8 3PH, UK Received on November 8, 1999; accepted on December 14, 1999 Abstract Prediction of secondary structure (PSIPRED) Summary: The PSIPRED protein structure prediction A new highly accurate secondary structure prediction server allows users to submit a protein sequence, perform method, PSIPRED incorporates two feed-forward neural a prediction of their choice and receive the results of the networks which perform an analysis on output obtained prediction both textually via e-mail and graphically via the from PSI-BLAST (Position Specific Iterated BLAST) web. The user may select one of three prediction methods (Altschul et al., 1997). to apply to their sequence: PSIPRED, a highly accurate Using a stringent cross validation procedure to evaluate secondary structure prediction method; MEMSAT 2, a performance, PSIPRED has been shown to be capable new version of a widely used transmembrane topology of achieving an average Q3 score of 76.5%. This is prediction method; or GenTHREADER, a sequence profile the highest level of accuracy published for any method based fold recognition method. to date. Predictions produced by PSIPRED were also Availability: Freely available to non-commercial users at submitted to the CASP3 (Third Critical Assessment http://globin.bio.warwick.ac.uk/psipred/ of Structure Prediction) server and assessed during the Contact: [email protected] CASP3 meeting, which took place in December 1998 at Asilomar.