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Manavalan, P.

Publications and source records attributed to Manavalan, P..

2 recordsLinked to original sources

The ΔF508 CFTR defect: molecular mechanism of suppressor mutation V510D and the contribution of transmembrane helix unraveling

Cystic fibrosis (CF) results from mutations within the gene encoding the Cystic Fibrosis Transmembrane Conductance Regulator (CFTR), a transmembrane chloride channel found on the apical surface of epithelial cells. The most common CF-causing mutation results in a deletion of phenylalanine 508 ({Delta}F508-CFTR), a residue normally found within the NBD1 domain. Loss of F508 causes NBD1 to be less thermodynamically stable and prevents proper tertiary folding of CFTR. As a result, CFTR is not properly trafficked to the cell surface. Recently, progress has been made towards the development of small molecule "correctors" that can restore CFTR tertiary structure and stabilize the channel to overcome the instability inherent in {Delta}F508-CFTR. However, the resultant improvement in channel activity has been modest, and the need for potent correctors remains. To fully inform such efforts, a better understanding of the molecular pathology associated with {Delta}F508-CFTR is required. Here we present a comprehensive study of the impact of F508 deletion on both purified NBD1 and full-length CFTR. Through the use of homology modeling, molecular dynamics simulations, mutational analysis, biochemical, biophysical and functional characterization studies, we obtained insight into how the {Delta}F508 mutation may lead to helical unraveling of transmembrane domains 10 and 11 (TM10, TM11), and how the known suppressor mutations V510D and R1070W, as well as novel second site suppressor mutations (SSSMs) identified in this work, may act to rescue {Delta}F508-CFTR maturation and trafficking.

biochemistry↗

The Block Object Storage Service (bossDB): A Cloud-Native Approach for Petascale Neuroscience Discovery

Large volumetric neuroimaging datasets have grown in size over the past ten years from gigabytes to terabytes, with petascale data becoming available and more common over the next few years. Current approaches to store and analyze these emerging datasets are insufficient in their ability to scale in both cost-effectiveness and performance. Additionally, enabling large-scale processing and annotation is critical as these data grow too large for manual inspection. We provide a new cloud-native managed service for large and multi-modal experiments, with support for data ingest, storage, visualization, and sharing through a RESTful Application Programming Interface (API) and web-based user interface. Our project is open source and can be easily and cost-effectively used for a variety of modalities and applications.

bioinformatics↗