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Sinclair M, Stein RA, Sheehan JH, Hawes EM, O’Brien RM, Tajkhorshid E, Claxton DP. Integrative analysis of pathogenic variants in glucose-6-phosphatase based on an AlphaFold2 model. PNAS NEXUS 2024; 3:pgae036. [PMID: 38328777 PMCID: PMC10849595 DOI: 10.1093/pnasnexus/pgae036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Accepted: 01/09/2024] [Indexed: 02/09/2024]
Abstract
Mediating the terminal reaction of gluconeogenesis and glycogenolysis, the integral membrane protein glucose-6-phosphate catalytic subunit 1 (G6PC1) regulates hepatic glucose production by catalyzing hydrolysis of glucose-6-phosphate (G6P) within the lumen of the endoplasmic reticulum. Consistent with its vital contribution to glucose homeostasis, inactivating mutations in G6PC1 causes glycogen storage disease (GSD) type 1a characterized by hepatomegaly and severe hypoglycemia. Despite its physiological importance, the structural basis of G6P binding to G6PC1 and the molecular disruptions induced by missense mutations within the active site that give rise to GSD type 1a are unknown. In this study, we determine the atomic interactions governing G6P binding as well as explore the perturbations imposed by disease-linked missense variants by subjecting an AlphaFold2 G6PC1 structural model to molecular dynamics simulations and in silico predictions of thermodynamic stability validated with robust in vitro and in situ biochemical assays. We identify a collection of side chains, including conserved residues from the signature phosphatidic acid phosphatase motif, that contribute to a hydrogen bonding and van der Waals network stabilizing G6P in the active site. The introduction of GSD type 1a mutations modified the thermodynamic landscape, altered side chain packing and substrate-binding interactions, and induced trapping of catalytic intermediates. Our results, which corroborate the high quality of the AF2 model as a guide for experimental design and to interpret outcomes, not only confirm the active-site structural organization but also identify previously unobserved mechanistic contributions of catalytic and noncatalytic side chains.
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Affiliation(s)
- Matt Sinclair
- Theoretical and Computational Biophysics Group, NIH Center for Macromolecular Modeling and Visualization, Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
- Department of Biochemistry, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Richard A Stein
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN 37232, USA
- Center for Applied Artificial Intelligence in Protein Dynamics, Vanderbilt University, Nashville, TN 37240, USA
| | - Jonathan H Sheehan
- Center for Structural Biology, Vanderbilt University, Nashville, TN 37240, USA
- Division of Infectious Diseases, Department of Internal Medicine, Washington University School of Medicine, St Louis, MO 63110, USA
| | - Emily M Hawes
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN 37232, USA
| | - Richard M O’Brien
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN 37232, USA
| | - Emad Tajkhorshid
- Theoretical and Computational Biophysics Group, NIH Center for Macromolecular Modeling and Visualization, Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
- Department of Biochemistry, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
- Center for Biophysics and Quantitative Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Derek P Claxton
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN 37232, USA
- Center for Applied Artificial Intelligence in Protein Dynamics, Vanderbilt University, Nashville, TN 37240, USA
- Center for Structural Biology, Vanderbilt University, Nashville, TN 37240, USA
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Nourbakhsh M, Degn K, Saksager A, Tiberti M, Papaleo E. Prediction of cancer driver genes and mutations: the potential of integrative computational frameworks. Brief Bioinform 2024; 25:bbad519. [PMID: 38261338 PMCID: PMC10805075 DOI: 10.1093/bib/bbad519] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Revised: 11/27/2023] [Accepted: 12/11/2023] [Indexed: 01/24/2024] Open
Abstract
The vast amount of available sequencing data allows the scientific community to explore different genetic alterations that may drive cancer or favor cancer progression. Software developers have proposed a myriad of predictive tools, allowing researchers and clinicians to compare and prioritize driver genes and mutations and their relative pathogenicity. However, there is little consensus on the computational approach or a golden standard for comparison. Hence, benchmarking the different tools depends highly on the input data, indicating that overfitting is still a massive problem. One of the solutions is to limit the scope and usage of specific tools. However, such limitations force researchers to walk on a tightrope between creating and using high-quality tools for a specific purpose and describing the complex alterations driving cancer. While the knowledge of cancer development increases daily, many bioinformatic pipelines rely on single nucleotide variants or alterations in a vacuum without accounting for cellular compartments, mutational burden or disease progression. Even within bioinformatics and computational cancer biology, the research fields work in silos, risking overlooking potential synergies or breakthroughs. Here, we provide an overview of databases and datasets for building or testing predictive cancer driver tools. Furthermore, we introduce predictive tools for driver genes, driver mutations, and the impact of these based on structural analysis. Additionally, we suggest and recommend directions in the field to avoid silo-research, moving towards integrative frameworks.
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Affiliation(s)
- Mona Nourbakhsh
- Cancer Systems Biology, Section for Bioinformatics, Department of Health Technology, Technical University of Denmark, 2800 Lyngby, Denmark
| | - Kristine Degn
- Cancer Systems Biology, Section for Bioinformatics, Department of Health Technology, Technical University of Denmark, 2800 Lyngby, Denmark
| | - Astrid Saksager
- Cancer Systems Biology, Section for Bioinformatics, Department of Health Technology, Technical University of Denmark, 2800 Lyngby, Denmark
| | - Matteo Tiberti
- Cancer Structural Biology, Danish Cancer Institute, 2100 Copenhagen, Denmark
| | - Elena Papaleo
- Cancer Systems Biology, Section for Bioinformatics, Department of Health Technology, Technical University of Denmark, 2800 Lyngby, Denmark
- Cancer Structural Biology, Danish Cancer Institute, 2100 Copenhagen, Denmark
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Alderson TR, Pritišanac I, Kolarić Đ, Moses AM, Forman-Kay JD. Systematic identification of conditionally folded intrinsically disordered regions by AlphaFold2. Proc Natl Acad Sci U S A 2023; 120:e2304302120. [PMID: 37878721 PMCID: PMC10622901 DOI: 10.1073/pnas.2304302120] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Accepted: 08/30/2023] [Indexed: 10/27/2023] Open
Abstract
The AlphaFold Protein Structure Database contains predicted structures for millions of proteins. For the majority of human proteins that contain intrinsically disordered regions (IDRs), which do not adopt a stable structure, it is generally assumed that these regions have low AlphaFold2 confidence scores that reflect low-confidence structural predictions. Here, we show that AlphaFold2 assigns confident structures to nearly 15% of human IDRs. By comparison to experimental NMR data for a subset of IDRs that are known to conditionally fold (i.e., upon binding or under other specific conditions), we find that AlphaFold2 often predicts the structure of the conditionally folded state. Based on databases of IDRs that are known to conditionally fold, we estimate that AlphaFold2 can identify conditionally folding IDRs at a precision as high as 88% at a 10% false positive rate, which is remarkable considering that conditionally folded IDR structures were minimally represented in its training data. We find that human disease mutations are nearly fivefold enriched in conditionally folded IDRs over IDRs in general and that up to 80% of IDRs in prokaryotes are predicted to conditionally fold, compared to less than 20% of eukaryotic IDRs. These results indicate that a large majority of IDRs in the proteomes of human and other eukaryotes function in the absence of conditional folding, but the regions that do acquire folds are more sensitive to mutations. We emphasize that the AlphaFold2 predictions do not reveal functionally relevant structural plasticity within IDRs and cannot offer realistic ensemble representations of conditionally folded IDRs.
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Affiliation(s)
- T. Reid Alderson
- Department of Biochemistry, University of Toronto, Toronto, ONM5S 1A8, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, ONM5S 1A8, Canada
| | - Iva Pritišanac
- Department of Cell and Systems Biology, University of Toronto, Toronto, ONM5S 35G, Canada
- Molecular Medicine Program, The Hospital for Sick Children, Toronto, ONM5G 0A4, Canada
- Department of Molecular Biology and Biochemistry, Gottfried Schatz Research Center for Cell Signaling, Metabolism and Aging, Medical University of Graz, Graz8010, Austria
| | - Đesika Kolarić
- Department of Molecular Biology and Biochemistry, Gottfried Schatz Research Center for Cell Signaling, Metabolism and Aging, Medical University of Graz, Graz8010, Austria
| | - Alan M. Moses
- Department of Cell and Systems Biology, University of Toronto, Toronto, ONM5S 35G, Canada
| | - Julie D. Forman-Kay
- Department of Biochemistry, University of Toronto, Toronto, ONM5S 1A8, Canada
- Molecular Medicine Program, The Hospital for Sick Children, Toronto, ONM5G 0A4, Canada
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Sinclair M, Stein RA, Sheehan JH, Hawes EM, O'Brien RM, Tajkhorshid E, Claxton DP. Molecular mechanisms of catalytic inhibition for active site mutations in glucose-6-phosphatase catalytic subunit 1 linked to glycogen storage disease. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.13.532485. [PMID: 36993754 PMCID: PMC10054992 DOI: 10.1101/2023.03.13.532485] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Mediating the terminal reaction of gluconeogenesis and glycogenolysis, the integral membrane protein G6PC1 regulates hepatic glucose production by catalyzing hydrolysis of glucose-6-phosphate (G6P) within the lumen of the endoplasmic reticulum. Consistent with its vital contribution to glucose homeostasis, inactivating mutations in G6PC1 cause glycogen storage disease (GSD) type 1a characterized by hepatomegaly and severe hypoglycemia. Despite its physiological importance, the structural basis of G6P binding to G6PC1 and the molecular disruptions induced by missense mutations within the active site that give rise to GSD type 1a are unknown. Exploiting a computational model of G6PC1 derived from the groundbreaking structure prediction algorithm AlphaFold2 (AF2), we combine molecular dynamics (MD) simulations and computational predictions of thermodynamic stability with a robust in vitro screening platform to define the atomic interactions governing G6P binding as well as explore the energetic perturbations imposed by disease-linked variants. We identify a collection of side chains, including conserved residues from the signature phosphatidic acid phosphatase motif, that contribute to a hydrogen bonding and van der Waals network stabilizing G6P in the active site. Introduction of GSD type 1a mutations into the G6PC1 sequence elicits changes in G6P binding energy, thermostability and structural properties, suggesting multiple pathways of catalytic impairment. Our results, which corroborate the high quality of the AF2 model as a guide for experimental design and to interpret outcomes, not only confirm active site structural organization but also suggest novel mechanistic contributions of catalytic and non-catalytic side chains.
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