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Title: Mechanism of glucocerebrosidase activation and dysfunction in Gaucher disease unraveled by molecular dynamics and deep learning

Abstract

The lysosomal enzyme glucocerebrosidase-1 (GCase) catalyzes the cleavage of a major glycolipid glucosylceramide into glucose and ceramide. The absence of fully functional GCase leads to the accumulation of its lipid substrates in lysosomes, causing Gaucher disease, an autosomal recessive disorder that displays profound genotype–phenotype nonconcordance. More than 250 disease-causing mutations in GBA1, the gene encoding GCase, have been discovered, although only one of these, N370S, causes 70% of disease. In this work, we have used a knowledge-based docking protocol that considers experimental data of protein–protein binding to generate a complex between GCase and its known facilitator protein saposin C (SAPC). Multiscale molecular-dynamics simulations were used to study lipid self-assembly, membrane insertion, and the dynamics of the interactions between different components of the complex. Deep learning was applied to propose a model that explains the mechanism of GCase activation, which requires SAPC. Notably, we find that conformational changes in the loops at the entrance of the substrate-binding site are stabilized by direct interactions with SAPC and that the loss of such interactions induced by N370S and another common mutation, L444P, result in destabilization of the complex and reduced GCase activation. Our results provide an atomistic-level explanation for GCase activation and themore » precise mechanism through which N370S and L444P cause Gaucher disease.« less

Authors:
 [1]; ORCiD logo [2];  [3]; ORCiD logo [2];  [3];  [3];  [3];  [3];  [3];  [3];  [3];  [3];  [3];  [3];  [3]; ORCiD logo [1];  [3]
  1. Univ. College London, London (United Kingdom)
  2. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
  3. Icahn School of Medicine at Mount Sinai, New York, NY (United States)
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
National Institutes of Health (NIH); USDOE
OSTI Identifier:
1511949
Grant/Contract Number:  
AC05-00OR22725
Resource Type:
Accepted Manuscript
Journal Name:
Proceedings of the National Academy of Sciences of the United States of America
Additional Journal Information:
Journal Volume: 116; Journal Issue: 11; Journal ID: ISSN 0027-8424
Publisher:
National Academy of Sciences, Washington, DC (United States)
Country of Publication:
United States
Language:
English
Subject:
60 APPLIED LIFE SCIENCES; multiscale simulations; gene mutations; lysosomal storage disease; rare disease

Citation Formats

Romero, Raquel, Ramanathan, Arvind, Yuen, Tony, Bhowmik, Debsindhu, Mathew, Mehr, Munshi, Lubna Bashir, Javaid, Seher, Bloch, Madison, Lizneva, Daria, Rahimova, Alina, Khan, Ayesha, Taneja, Charit, Kim, Se-Min, Sun, Li, New, Maria I., Haider, Shozeb, and Zaidi, Mone. Mechanism of glucocerebrosidase activation and dysfunction in Gaucher disease unraveled by molecular dynamics and deep learning. United States: N. p., 2019. Web. doi:10.1073/pnas.1818411116.
Romero, Raquel, Ramanathan, Arvind, Yuen, Tony, Bhowmik, Debsindhu, Mathew, Mehr, Munshi, Lubna Bashir, Javaid, Seher, Bloch, Madison, Lizneva, Daria, Rahimova, Alina, Khan, Ayesha, Taneja, Charit, Kim, Se-Min, Sun, Li, New, Maria I., Haider, Shozeb, & Zaidi, Mone. Mechanism of glucocerebrosidase activation and dysfunction in Gaucher disease unraveled by molecular dynamics and deep learning. United States. doi:10.1073/pnas.1818411116.
Romero, Raquel, Ramanathan, Arvind, Yuen, Tony, Bhowmik, Debsindhu, Mathew, Mehr, Munshi, Lubna Bashir, Javaid, Seher, Bloch, Madison, Lizneva, Daria, Rahimova, Alina, Khan, Ayesha, Taneja, Charit, Kim, Se-Min, Sun, Li, New, Maria I., Haider, Shozeb, and Zaidi, Mone. Tue . "Mechanism of glucocerebrosidase activation and dysfunction in Gaucher disease unraveled by molecular dynamics and deep learning". United States. doi:10.1073/pnas.1818411116.
@article{osti_1511949,
title = {Mechanism of glucocerebrosidase activation and dysfunction in Gaucher disease unraveled by molecular dynamics and deep learning},
author = {Romero, Raquel and Ramanathan, Arvind and Yuen, Tony and Bhowmik, Debsindhu and Mathew, Mehr and Munshi, Lubna Bashir and Javaid, Seher and Bloch, Madison and Lizneva, Daria and Rahimova, Alina and Khan, Ayesha and Taneja, Charit and Kim, Se-Min and Sun, Li and New, Maria I. and Haider, Shozeb and Zaidi, Mone},
abstractNote = {The lysosomal enzyme glucocerebrosidase-1 (GCase) catalyzes the cleavage of a major glycolipid glucosylceramide into glucose and ceramide. The absence of fully functional GCase leads to the accumulation of its lipid substrates in lysosomes, causing Gaucher disease, an autosomal recessive disorder that displays profound genotype–phenotype nonconcordance. More than 250 disease-causing mutations inGBA1, the gene encoding GCase, have been discovered, although only one of these, N370S, causes 70% of disease. In this work, we have used a knowledge-based docking protocol that considers experimental data of protein–protein binding to generate a complex between GCase and its known facilitator protein saposin C (SAPC). Multiscale molecular-dynamics simulations were used to study lipid self-assembly, membrane insertion, and the dynamics of the interactions between different components of the complex. Deep learning was applied to propose a model that explains the mechanism of GCase activation, which requires SAPC. Notably, we find that conformational changes in the loops at the entrance of the substrate-binding site are stabilized by direct interactions with SAPC and that the loss of such interactions induced by N370S and another common mutation, L444P, result in destabilization of the complex and reduced GCase activation. Our results provide an atomistic-level explanation for GCase activation and the precise mechanism through which N370S and L444P cause Gaucher disease.},
doi = {10.1073/pnas.1818411116},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
number = 11,
volume = 116,
place = {United States},
year = {2019},
month = {2}
}

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This content will become publicly available on February 26, 2020
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