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      Long QT Syndrome Type 2: Emerging Strategies for Correcting Class 2 KCNH2 ( hERG) Mutations and Identifying New Patients

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          Abstract

          Significant advances in our understanding of the molecular mechanisms that cause congenital long QT syndrome (LQTS) have been made. A wide variety of experimental approaches, including heterologous expression of mutant ion channel proteins and the use of inducible pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) from LQTS patients offer insights into etiology and new therapeutic strategies. This review briefly discusses the major molecular mechanisms underlying LQTS type 2 (LQT2), which is caused by loss-of-function (LOF) mutations in the KCNH2 gene (also known as the human ether-à-go-go-related gene or hERG). Almost half of suspected LQT2-causing mutations are missense mutations, and functional studies suggest that about 90% of these mutations disrupt the intracellular transport, or trafficking, of the KCNH2-encoded Kv11.1 channel protein to the cell surface membrane. In this review, we discuss emerging strategies that improve the trafficking and functional expression of trafficking-deficient LQT2 Kv11.1 channel proteins to the cell surface membrane and how new insights into the structure of the Kv11.1 channel protein will lead to computational approaches that identify which KCNH2 missense variants confer a high-risk for LQT2.

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          Benefits and limitations of genome-wide association studies

          Genome-wide association studies (GWAS) involve testing genetic variants across the genomes of many individuals to identify genotype-phenotype associations. GWAS have revolutionized the field of complex disease genetics over the past decade, providing numerous compelling associations for human complex traits and diseases. Despite clear successes in identifying novel disease susceptibility genes and biological pathways and in translating these findings into clinical care, GWAS have not been without controversy. Prominent criticisms include concerns that GWAS will eventually implicate the entire genome in disease predisposition and that most association signals reflect variants and genes with no direct biological relevance to disease. In this Review, we comprehensively assess the benefits and limitations of GWAS in human populations and discuss the relevance of performing more GWAS.
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            Machine Learning and Data Mining Methods in Diabetes Research

            The remarkable advances in biotechnology and health sciences have led to a significant production of data, such as high throughput genetic data and clinical information, generated from large Electronic Health Records (EHRs). To this end, application of machine learning and data mining methods in biosciences is presently, more than ever before, vital and indispensable in efforts to transform intelligently all available information into valuable knowledge. Diabetes mellitus (DM) is defined as a group of metabolic disorders exerting significant pressure on human health worldwide. Extensive research in all aspects of diabetes (diagnosis, etiopathophysiology, therapy, etc.) has led to the generation of huge amounts of data. The aim of the present study is to conduct a systematic review of the applications of machine learning, data mining techniques and tools in the field of diabetes research with respect to a) Prediction and Diagnosis, b) Diabetic Complications, c) Genetic Background and Environment, and e) Health Care and Management with the first category appearing to be the most popular. A wide range of machine learning algorithms were employed. In general, 85% of those used were characterized by supervised learning approaches and 15% by unsupervised ones, and more specifically, association rules. Support vector machines (SVM) arise as the most successful and widely used algorithm. Concerning the type of data, clinical datasets were mainly used. The title applications in the selected articles project the usefulness of extracting valuable knowledge leading to new hypotheses targeting deeper understanding and further investigation in DM.
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              Advances in protein structure prediction and design

              The prediction of protein three-dimensional structure from amino acid sequence has been a grand challenge problem in computational biophysics for decades, owing to its intrinsic scientific interest and also to the many potential applications for robust protein structure prediction algorithms, from genome interpretation to protein function prediction. More recently, the inverse problem —designing an amino acid sequence that will fold into a specified three-dimensional structure — has attracted growing attention as a potential route to the rational engineering of proteins with functions useful in biotechnology and medicine. Methods for the prediction and design of protein structures have advanced dramatically in the past decade. Increases in computing power and rapid growth in the protein sequence and structure databases have fuelled the development of new data-intensive and computationally-demanding approaches for structure prediction. New algorithms for designing protein folds and protein–protein interfaces have been used to engineer novel high-order assemblies and to design from scratch fluorescent proteins with novel or enhanced properties as well as signalling proteins with therapeutic potential. In this Review, we describe current approaches for protein structure prediction and design and highlight a selection of the successful applications they have enabled. Predicting how proteins fold enables to infer their function. Conversely, rational protein design allows engineering novel protein functionalities. Recent improvements in computational algorithms and technological advancements dramatically increased the accuracy and speed of protein structure modelling, providing novel opportunities for controlling protein function with potential applications in biomedicine, industry and research.
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                Author and article information

                Journal
                Biomolecules
                Biomolecules
                biomolecules
                Biomolecules
                MDPI
                2218-273X
                04 August 2020
                August 2020
                : 10
                : 8
                : 1144
                Affiliations
                [1 ]Department of Physiology, Cardiovascular Research Center, Center for Muscle Biology, University of Kentucky, Lexington, KY 40536, USA; makoto.ono@ 123456uky.edu (M.O.); deburgess@ 123456uky.edu (D.E.B.); eschr0@ 123456uky.edu (E.A.S.)
                [2 ]CHI Saint Joseph Hospital, Lexington, KY 40504, USA; samyelayi@ 123456sjhlex.org
                [3 ]Cellular and Molecular Arrhythmia Research Program, University of Wisconsin, Madison, WI 53706, USA; clanders@ 123456medicine.wisc.edu (C.L.A.); ctj@ 123456medicine.wisc.edu (C.T.J.)
                [4 ]Department of Cellular & Molecular Physiology, Loyola University Chicago, Chicago, IL 60153, USA; bsun@ 123456luc.edu (B.S.); kimmadisetty@ 123456luc.edu (K.I.); pkekeneshuskey@ 123456luc.edu (P.M.K.-H.)
                Author notes
                [* ]Correspondence: brian.delisle@ 123456uky.edu
                Article
                biomolecules-10-01144
                10.3390/biom10081144
                7464307
                32759882
                09022884-7ad8-4795-bf55-824a05616883
                © 2020 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 17 June 2020
                : 27 July 2020
                Categories
                Review

                long qt syndrome,ion channel,trafficking,kcnh2,herg
                long qt syndrome, ion channel, trafficking, kcnh2, herg

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