51
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Gavrilov Y, Kümmerer F, Orioli S, Prestel A, Lindorff-Larsen K, Teilum K. Double Mutant of Chymotrypsin Inhibitor 2 Stabilized through Increased Conformational Entropy. Biochemistry 2022; 61:160-170. [PMID: 35019273 DOI: 10.1021/acs.biochem.1c00749] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
The conformational heterogeneity of a folded protein can affect not only its function but also stability and folding. We recently discovered and characterized a stabilized double mutant (L49I/I57V) of the protein CI2 and showed that state-of-the-art prediction methods could not predict the increased stability relative to the wild-type protein. Here, we have examined whether changed native-state dynamics, and resulting entropy changes, can explain the stability changes in the double mutant protein, as well as the two single mutant forms. We have combined NMR relaxation measurements of the ps-ns dynamics of amide groups in the backbone and the methyl groups in the side chains with molecular dynamics simulations to quantify the native-state dynamics. The NMR experiments reveal that the mutations have different effects on the conformational flexibility of CI2: a reduction in conformational dynamics (and entropy estimated from this) of the native state of the L49I variant correlates with its decreased stability, while increased dynamics of the I57V and L49I/I57V variants correlates with their increased stability. These findings suggest that explicitly accounting for changes in native-state entropy might be needed to improve the predictions of the effect of mutations on protein stability.
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Affiliation(s)
- Yulian Gavrilov
- Structural Biology and NMR Laboratory and the Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Ole Maaløes Vej 5, 2200 Copenhagen N, Denmark
| | - Felix Kümmerer
- Structural Biology and NMR Laboratory and the Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Ole Maaløes Vej 5, 2200 Copenhagen N, Denmark
| | - Simone Orioli
- Structural Biology and NMR Laboratory and the Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Ole Maaløes Vej 5, 2200 Copenhagen N, Denmark.,Structural Biophysics, Niels Bohr Institute, University of Copenhagen, Blegdamsvej 17, 2100 Copenhagen Ø, Denmark
| | - Andreas Prestel
- Structural Biology and NMR Laboratory and the Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Ole Maaløes Vej 5, 2200 Copenhagen N, Denmark
| | - Kresten Lindorff-Larsen
- Structural Biology and NMR Laboratory and the Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Ole Maaløes Vej 5, 2200 Copenhagen N, Denmark
| | - Kaare Teilum
- Structural Biology and NMR Laboratory and the Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Ole Maaløes Vej 5, 2200 Copenhagen N, Denmark
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52
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Høie MH, Cagiada M, Beck Frederiksen AH, Stein A, Lindorff-Larsen K. Predicting and interpreting large-scale mutagenesis data using analyses of protein stability and conservation. Cell Rep 2022; 38:110207. [PMID: 35021073 DOI: 10.1016/j.celrep.2021.110207] [Citation(s) in RCA: 43] [Impact Index Per Article: 21.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2021] [Revised: 10/01/2021] [Accepted: 12/13/2021] [Indexed: 01/23/2023] Open
Abstract
Understanding and predicting the functional consequences of single amino acid changes is central in many areas of protein science. Here, we collect and analyze experimental measurements of effects of >150,000 variants in 29 proteins. We use biophysical calculations to predict changes in stability for each variant and assess them in light of sequence conservation. We find that the sequence analyses give more accurate prediction of variant effects than predictions of stability and that about half of the variants that show loss of function do so due to stability effects. We construct a machine learning model to predict variant effects from protein structure and sequence alignments and show how the two sources of information support one another and enable mechanistic interpretations. Together, our results show how one can leverage large-scale experimental assessments of variant effects to gain deeper and general insights into the mechanisms that cause loss of function.
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Affiliation(s)
- Magnus Haraldson Høie
- Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, DK-2200 Copenhagen N, Denmark
| | - Matteo Cagiada
- Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, DK-2200 Copenhagen N, Denmark
| | - Anders Haagen Beck Frederiksen
- Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, DK-2200 Copenhagen N, Denmark
| | - Amelie Stein
- Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, DK-2200 Copenhagen N, Denmark.
| | - Kresten Lindorff-Larsen
- Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, DK-2200 Copenhagen N, Denmark.
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53
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Fas BA, Maiani E, Sora V, Kumar M, Mashkoor M, Lambrughi M, Tiberti M, Papaleo E. The conformational and mutational landscape of the ubiquitin-like marker for autophagosome formation in cancer. Autophagy 2021; 17:2818-2841. [PMID: 33302793 PMCID: PMC8525936 DOI: 10.1080/15548627.2020.1847443] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2019] [Revised: 10/28/2020] [Accepted: 11/03/2020] [Indexed: 02/06/2023] Open
Abstract
Macroautophagy/autophagy is a cellular process to recycle damaged cellular components, and its modulation can be exploited for disease treatments. A key autophagy player is the ubiquitin-like protein MAP1LC3B/LC3B. Mutations and changes in MAP1LC3B expression occur in cancer samples. However, the investigation of the effects of these mutations on MAP1LC3B protein structure is still missing. Despite many LC3B structures that have been solved, a comprehensive study, including dynamics, has not yet been undertaken. To address this knowledge gap, we assessed nine physical models for biomolecular simulations for their capabilities to describe the structural ensemble of MAP1LC3B. With the resulting MAP1LC3B structural ensembles, we characterized the impact of 26 missense mutations from pan-cancer studies with different approaches, and we experimentally validated our prediction for six variants using cellular assays. Our findings shed light on damaging or neutral mutations in MAP1LC3B, providing an atlas of its modifications in cancer. In particular, P32Q mutation was found detrimental for protein stability with a propensity to aggregation. In a broader context, our framework can be applied to assess the pathogenicity of protein mutations or to prioritize variants for experimental studies, allowing to comprehensively account for different aspects that mutational events alter in terms of protein structure and function.Abbreviations: ATG: autophagy-related; Cα: alpha carbon; CG: coarse-grained; CHARMM: Chemistry at Harvard macromolecular mechanics; CONAN: contact analysis; FUNDC1: FUN14 domain containing 1; FYCO1: FYVE and coiled-coil domain containing 1; GABARAP: GABA type A receptor-associated protein; GROMACS: Groningen machine for chemical simulations; HP: hydrophobic pocket; LIR: LC3 interacting region; MAP1LC3B/LC3B microtubule associated protein 1 light chain 3 B; MD: molecular dynamics; OPTN: optineurin; OSF: open software foundation; PE: phosphatidylethanolamine, PLEKHM1: pleckstrin homology domain-containing family M 1; PSN: protein structure network; PTM: post-translational modification; SA: structural alphabet; SLiM: short linear motif; SQSTM1/p62: sequestosome 1; WT: wild-type.
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Affiliation(s)
- Burcu Aykac Fas
- Computational Biology Laboratory, Danish Cancer Society Research Center, Copenhagen, Denmark
| | - Emiliano Maiani
- Computational Biology Laboratory, Danish Cancer Society Research Center, Copenhagen, Denmark
| | - Valentina Sora
- Computational Biology Laboratory, Danish Cancer Society Research Center, Copenhagen, Denmark
| | - Mukesh Kumar
- Computational Biology Laboratory, Danish Cancer Society Research Center, Copenhagen, Denmark
| | - Maliha Mashkoor
- Computational Biology Laboratory, Danish Cancer Society Research Center, Copenhagen, Denmark
| | - Matteo Lambrughi
- Computational Biology Laboratory, Danish Cancer Society Research Center, Copenhagen, Denmark
| | - Matteo Tiberti
- Computational Biology Laboratory, Danish Cancer Society Research Center, Copenhagen, Denmark
| | - Elena Papaleo
- Computational Biology Laboratory, Danish Cancer Society Research Center, Copenhagen, Denmark
- Translational Disease Systems Biology, Faculty of Health and Medical Sciences, Novo Nordisk Foundation Center for Protein Research University of Copenhagen, Copenhagen, Denmark
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54
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Huttinger ZM, Haynes LM, Yee A, Kretz CA, Holding ML, Siemieniak DR, Lawrence DA, Ginsburg D. Deep mutational scanning of the plasminogen activator inhibitor-1 functional landscape. Sci Rep 2021; 11:18827. [PMID: 34552126 PMCID: PMC8458277 DOI: 10.1038/s41598-021-97871-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2021] [Accepted: 08/31/2021] [Indexed: 11/09/2022] Open
Abstract
The serine protease inhibitor (SERPIN) plasminogen activator inhibitor-1 (PAI-1) is a key regulator of the fibrinolytic system, inhibiting the serine proteases tissue- and urokinase-type plasminogen activator (tPA and uPA, respectively). Missense variants render PAI-1 non-functional through misfolding, leading to its turnover as a protease substrate, or to a more rapid transition to the latent/inactive state. Deep mutational scanning was performed to evaluate the impact of amino acid sequence variation on PAI-1 inhibition of uPA using an M13 filamentous phage display system. Error prone PCR was used to construct a mutagenized PAI-1 library encompassing ~ 70% of potential single amino acid substitutions. The relative effects of 27% of all possible missense variants on PAI-1 inhibition of uPA were determined using high-throughput DNA sequencing. 826 missense variants demonstrated conserved inhibitory activity while 1137 resulted in loss of PAI-1 inhibitory function. The least evolutionarily conserved regions of PAI-1 were also identified as being the most tolerant of missense mutations. The results of this screen confirm previous low-throughput mutational studies, including those of the reactive center loop. These data provide a powerful resource for explaining structure-function relationships for PAI-1 and for the interpretation of human genomic sequence variants.
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Affiliation(s)
- Zachary M Huttinger
- Life Sciences Institute, University of Michigan, Ann Arbor, MI, USA.,Cellular and Molecular Biology Program, University of Michigan Medical School, Ann Arbor, MI, USA.,Department of Otolaryngology, The Ohio State University College of Medicine, Columbus, OH, USA
| | - Laura M Haynes
- Life Sciences Institute, University of Michigan, Ann Arbor, MI, USA
| | - Andrew Yee
- Department of Pediatrics, Baylor College of Medicine, Houston, TX, USA
| | - Colin A Kretz
- Department of Medicine, McMaster University and the Thrombosis and Atherosclerosis Research Institute, Hamilton, ON, Canada
| | | | - David R Siemieniak
- Life Sciences Institute, University of Michigan, Ann Arbor, MI, USA.,Howard Hughes Medical Institute, Ann Arbor, MI, USA
| | - Daniel A Lawrence
- Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, MI, USA.,Department of Molecular and Integrative Physiology, University of Michigan Medical School, Ann Arbor, MI, USA
| | - David Ginsburg
- Life Sciences Institute, University of Michigan, Ann Arbor, MI, USA. .,Cellular and Molecular Biology Program, University of Michigan Medical School, Ann Arbor, MI, USA. .,Howard Hughes Medical Institute, Ann Arbor, MI, USA. .,Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, MI, USA. .,Departments of Human Genetics and Pediatrics, University of Michigan, Ann Arbor, MI, USA.
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55
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Woodard J, Zheng W, Zhang Y. Protein structural features predict responsiveness to pharmacological chaperone treatment for three lysosomal storage disorders. PLoS Comput Biol 2021; 17:e1009370. [PMID: 34529671 PMCID: PMC8478239 DOI: 10.1371/journal.pcbi.1009370] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Revised: 09/28/2021] [Accepted: 08/21/2021] [Indexed: 12/15/2022] Open
Abstract
Three-dimensional structures of proteins can provide important clues into the efficacy of personalized treatment. We perform a structural analysis of variants within three inherited lysosomal storage disorders, comparing variants responsive to pharmacological chaperone treatment to those unresponsive to such treatment. We find that predicted ΔΔG of mutation is higher on average for variants unresponsive to treatment, in the case of datasets for both Fabry disease and Pompe disease, in line with previous findings. Using both a single decision tree and an advanced machine learning approach based on the larger Fabry dataset, we correctly predict responsiveness of three Gaucher disease variants, and we provide predictions for untested variants. Many variants are predicted to be responsive to treatment, suggesting that drug-based treatments may be effective for a number of variants in Gaucher disease. In our analysis, we observe dependence on a topological feature reporting on contact arrangements which is likely connected to the order of folding of protein residues, and we provide a potential justification for this observation based on steady-state cellular kinetics. Pharmacological chaperones are small molecule drugs that bind to proteins to help stabilize the folded state. One set of diseases for which this treatment has been effective is the lysosomal storage disorders, which are caused by defective lysosomal enzymes. However, not all genotypes are equally responsive to treatment. For instance, missense mutants that are particularly destabilized relative to WT are less likely to respond. The availability of datasets containing responsiveness data for large numbers of mutants, along with crystal structures of the protein involved in each disease, make machine learning methods incorporating sequence-based and structural data feasible. We hypothesize that data from two diseases, Fabry and Pompe disease, may be useful for predicting responsiveness of variants in the related Gaucher disease. Results suggest that many rare variants in Gaucher disease could be amenable to existing drugs. Results also suggest that drug responsiveness depends on protein topology in such a way that mutations in early-to-fold residues are more likely to be non-responsive to pharmacological chaperone treatment, which is consistent with a simple kinetic model of stability rescue. This study provides an example of how machine learning can be used to inform further studies towards personalized treatment in medicine.
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Affiliation(s)
- Jaie Woodard
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Wei Zheng
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Yang Zhang
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, United States of America
- Department of Biological Chemistry, University of Michigan, Ann Arbor, Michigan, United States of America
- * E-mail:
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56
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Hamborg L, Granata D, Olsen JG, Roche JV, Pedersen LE, Nielsen AT, Lindorff-Larsen K, Teilum K. Synergistic stabilization of a double mutant in chymotrypsin inhibitor 2 from a library screen in E. coli. Commun Biol 2021; 4:980. [PMID: 34408246 PMCID: PMC8373930 DOI: 10.1038/s42003-021-02490-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Accepted: 07/26/2021] [Indexed: 12/12/2022] Open
Abstract
Most single point mutations destabilize folded proteins. Mutations that stabilize a protein typically only have a small effect and multiple mutations are often needed to substantially increase the stability. Multiple point mutations may act synergistically on the stability, and it is often not straightforward to predict their combined effect from the individual contributions. Here, we have applied an efficient in-cell assay in E. coli to select variants of the barley chymotrypsin inhibitor 2 with increased stability. We find two variants that are more than 3.8 kJ mol-1 more stable than the wild-type. In one case, the increased stability is the effect of the single substitution D55G. The other case is a double mutant, L49I/I57V, which is 5.1 kJ mol-1 more stable than the sum of the effects of the individual mutations. In addition to demonstrating the strength of our selection system for finding stabilizing mutations, our work also demonstrate how subtle conformational effects may modulate stability.
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Affiliation(s)
- Louise Hamborg
- Structural Biology and NMR Laboratory and the Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen N, Denmark
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kemitorvet, Lyngby, Denmark
| | - Daniele Granata
- Structural Biology and NMR Laboratory and the Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen N, Denmark
| | - Johan G Olsen
- Structural Biology and NMR Laboratory and the Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen N, Denmark
| | - Jennifer Virginia Roche
- Structural Biology and NMR Laboratory and the Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen N, Denmark
| | - Lasse Ebdrup Pedersen
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kemitorvet, Lyngby, Denmark
| | - Alex Toftgaard Nielsen
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kemitorvet, Lyngby, Denmark
| | - Kresten Lindorff-Larsen
- Structural Biology and NMR Laboratory and the Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen N, Denmark
| | - Kaare Teilum
- Structural Biology and NMR Laboratory and the Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen N, Denmark.
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57
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Lindorff-Larsen K, Kragelund BB. On the potential of machine learning to examine the relationship between sequence, structure, dynamics and function of intrinsically disordered proteins. J Mol Biol 2021; 433:167196. [PMID: 34390736 DOI: 10.1016/j.jmb.2021.167196] [Citation(s) in RCA: 38] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Revised: 08/03/2021] [Accepted: 08/04/2021] [Indexed: 11/29/2022]
Abstract
Intrinsically disordered proteins (IDPs) constitute a broad set of proteins with few uniting and many diverging properties. IDPs-and intrinsically disordered regions (IDRs) interspersed between folded domains-are generally characterized as having no persistent tertiary structure; instead they interconvert between a large number of different and often expanded structures. IDPs and IDRs are involved in an enormously wide range of biological functions and reveal novel mechanisms of interactions, and while they defy the common structure-function paradigm of folded proteins, their structural preferences and dynamics are important for their function. We here discuss open questions in the field of IDPs and IDRs, focusing on areas where machine learning and other computational methods play a role. We discuss computational methods aimed to predict transiently formed local and long-range structure, including methods for integrative structural biology. We discuss the many different ways in which IDPs and IDRs can bind to other molecules, both via short linear motifs, as well as in the formation of larger dynamic complexes such as biomolecular condensates. We discuss how experiments are providing insight into such complexes and may enable more accurate predictions. Finally, we discuss the role of IDPs in disease and how new methods are needed to interpret the mechanistic effects of genomic variants in IDPs.
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Affiliation(s)
- Kresten Lindorff-Larsen
- Structural Biology and NMR Laboratory & Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen. Ole Maaløes Vej 5, DK-2200 Copenhagen N, Denmark.
| | - Birthe B Kragelund
- Structural Biology and NMR Laboratory & Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen. Ole Maaløes Vej 5, DK-2200 Copenhagen N, Denmark.
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58
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Nagati JS, Kobeissy PH, Nguyen MQ, Xu M, Garcia T, Comerford SA, Hammer RE, Garcia JA. Mammalian acetate-dependent acetyl CoA synthetase 2 contains multiple protein destabilization and masking elements. J Biol Chem 2021; 297:101037. [PMID: 34343565 PMCID: PMC8405932 DOI: 10.1016/j.jbc.2021.101037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Revised: 07/13/2021] [Accepted: 07/30/2021] [Indexed: 11/05/2022] Open
Abstract
Besides contributing to anabolism, cellular metabolites serve as substrates or cofactors for enzymes and may also have signaling functions. Given these roles, multiple control mechanisms likely ensure fidelity of metabolite-generating enzymes. Acetate-dependent acetyl CoA synthetases (ACS) are de novo sources of acetyl CoA, a building block for fatty acids and a substrate for acetyltransferases. Eukaryotic acetate-dependent acetyl CoA synthetase 2 (Acss2) is predominantly cytosolic, but is also found in the nucleus following oxygen or glucose deprivation, or upon acetate exposure. Acss2-generated acetyl CoA is used in acetylation of Hypoxia-Inducible Factor 2 (HIF-2), a stress-responsive transcription factor. Mutation of a putative nuclear localization signal in endogenous Acss2 abrogates HIF-2 acetylation and signaling, but surprisingly also results in reduced Acss2 protein levels due to unmasking of two protein destabilization elements (PDE) in the Acss2 hinge region. In the current study, we identify up to four additional PDE in the Acss2 hinge region and determine that a previously identified PDE, the ABC domain, consists of two functional PDE. We show that the ABC domain and other PDE are likely masked by intramolecular interactions with other domains in the Acss2 hinge region. We also characterize mice with a prematurely truncated Acss2 that exposes a putative ABC domain PDE, which exhibits reduced Acss2 protein stability and impaired HIF-2 signaling. Finally, using primary mouse embryonic fibroblasts, we demonstrate that the reduced stability of select Acss2 mutant proteins is due to a shortened half-life, which is a result of enhanced degradation via a nonproteasome, nonautophagy pathway.
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Affiliation(s)
- Jason S Nagati
- Department of Medicine, Columbia University Medical Center, New York, New York, USA
| | - Philippe H Kobeissy
- Department of Medicine, Columbia University Medical Center, New York, New York, USA
| | - Minh Q Nguyen
- Department of Medicine, Columbia University Medical Center, New York, New York, USA
| | - Min Xu
- Department of Medicine, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Trent Garcia
- Department of Medicine, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Sarah A Comerford
- Department of Molecular Genetics, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Robert E Hammer
- Department of Biochemistry, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Joseph A Garcia
- Department of Medicine, Columbia University Medical Center, New York, New York, USA; Department of Research, James J. Peters VA Medical Center, New York, New York, USA.
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59
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Markin CJ, Mokhtari DA, Sunden F, Appel MJ, Akiva E, Longwell SA, Sabatti C, Herschlag D, Fordyce PM. Revealing enzyme functional architecture via high-throughput microfluidic enzyme kinetics. Science 2021; 373:373/6553/eabf8761. [PMID: 34437092 DOI: 10.1126/science.abf8761] [Citation(s) in RCA: 73] [Impact Index Per Article: 24.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2020] [Accepted: 05/24/2021] [Indexed: 12/21/2022]
Abstract
Systematic and extensive investigation of enzymes is needed to understand their extraordinary efficiency and meet current challenges in medicine and engineering. We present HT-MEK (High-Throughput Microfluidic Enzyme Kinetics), a microfluidic platform for high-throughput expression, purification, and characterization of more than 1500 enzyme variants per experiment. For 1036 mutants of the alkaline phosphatase PafA (phosphate-irrepressible alkaline phosphatase of Flavobacterium), we performed more than 670,000 reactions and determined more than 5000 kinetic and physical constants for multiple substrates and inhibitors. We uncovered extensive kinetic partitioning to a misfolded state and isolated catalytic effects, revealing spatially contiguous regions of residues linked to particular aspects of function. Regions included active-site proximal residues but extended to the enzyme surface, providing a map of underlying architecture not possible to derive from existing approaches. HT-MEK has applications that range from understanding molecular mechanisms to medicine, engineering, and design.
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Affiliation(s)
- C J Markin
- Department of Biochemistry, Stanford University, Stanford, CA 94305, USA
| | - D A Mokhtari
- Department of Biochemistry, Stanford University, Stanford, CA 94305, USA
| | - F Sunden
- Department of Biochemistry, Stanford University, Stanford, CA 94305, USA
| | - M J Appel
- Department of Biochemistry, Stanford University, Stanford, CA 94305, USA
| | - E Akiva
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA 94158, USA
| | - S A Longwell
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
| | - C Sabatti
- Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA.,Department of Statistics, Stanford University, Stanford, CA 94305, USA
| | - D Herschlag
- Department of Biochemistry, Stanford University, Stanford, CA 94305, USA. .,Department of Chemical Engineering, Stanford University, Stanford, CA 94305, USA.,ChEM-H Institute, Stanford University, Stanford, CA 94305, USA
| | - P M Fordyce
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA. .,ChEM-H Institute, Stanford University, Stanford, CA 94305, USA.,Department of Genetics, Stanford University, Stanford, CA 94305, USA.,Chan Zuckerberg Biohub; San Francisco, CA 94110, USA
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60
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Ollodart AR, Yeh CLC, Miller AW, Shirts BH, Gordon AS, Dunham MJ. Multiplexing mutation rate assessment: determining pathogenicity of Msh2 variants in Saccharomyces cerevisiae. Genetics 2021; 218:iyab058. [PMID: 33848333 PMCID: PMC8225350 DOI: 10.1093/genetics/iyab058] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Accepted: 04/02/2021] [Indexed: 01/01/2023] Open
Abstract
Despite the fundamental importance of mutation rate as a driving force in evolution and disease risk, common methods to assay mutation rate are time-consuming and tedious. Established methods such as fluctuation tests and mutation accumulation experiments are low-throughput and often require significant optimization to ensure accuracy. We established a new method to determine the mutation rate of many strains simultaneously by tracking mutation events in a chemostat continuous culture device and applying deep sequencing to link mutations to alleles of a DNA-repair gene. We applied this method to assay the mutation rate of hundreds of Saccharomyces cerevisiae strains carrying mutations in the gene encoding Msh2, a DNA repair enzyme in the mismatch repair pathway. Loss-of-function mutations in MSH2 are associated with hereditary nonpolyposis colorectal cancer, an inherited disorder that increases risk for many different cancers. However, the vast majority of MSH2 variants found in human populations have insufficient evidence to be classified as either pathogenic or benign. We first benchmarked our method against Luria-Delbrück fluctuation tests using a collection of published MSH2 missense variants. Our pooled screen successfully identified previously characterized nonfunctional alleles as high mutators. We then created an additional 185 human missense variants in the yeast ortholog, including both characterized and uncharacterized alleles curated from ClinVar and other clinical testing data. In a set of alleles of known pathogenicity, our assay recapitulated ClinVar's classification; we then estimated pathogenicity for 157 variants classified as uncertain or conflicting reports of significance. This method is capable of studying the mutation rate of many microbial species and can be applied to problems ranging from the generation of high-fidelity polymerases to measuring the frequency of antibiotic resistance emergence.
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Affiliation(s)
- Anja R Ollodart
- Molecular Cellular Biology Program, University of Washington, Seattle, WA 98195, USA
- Genome Sciences Department, University of Washington, Seattle, WA 98195, USA
| | - Chiann-Ling C Yeh
- Genome Sciences Department, University of Washington, Seattle, WA 98195, USA
| | - Aaron W Miller
- Genome Sciences Department, University of Washington, Seattle, WA 98195, USA
| | - Brian H Shirts
- Department of Laboratory Medicine, University of Washington, Seattle, WA 98195, USA
| | - Adam S Gordon
- Department of Pharmacology, Northwestern University, Chicago, IL 60208, USA
| | - Maitreya J Dunham
- Genome Sciences Department, University of Washington, Seattle, WA 98195, USA
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61
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Shen C, Ge Z, Dong C, Wang C, Shao J, Cai W, Huang P, Fan H, Li J, Zhang Y, Yue M. Genetic Variants in KIR/HLA-C Genes Are Associated With the Susceptibility to HCV Infection in a High-Risk Chinese Population. Front Immunol 2021; 12:632353. [PMID: 34220799 PMCID: PMC8253047 DOI: 10.3389/fimmu.2021.632353] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2020] [Accepted: 05/31/2021] [Indexed: 12/24/2022] Open
Abstract
Background KIR/HLA-C signaling pathway influences the innate immune response which is the first defense to hepatitis C virus (HCV) infection. The aim of this study was to determine the association between the genetic polymorphisms of KIR/HLA-C genes and the outcomes of HCV infection in a high-risk Chinese population. Methods In this case-control study, four single nucleotide polymorphisms (SNPs) of KIR/HLA-C genes (KIR2DS4/KIR2DS1/KIR2DL1 rs35440472, HLA-C rs2308557, HLA-C rs1130838, and HLA-C rs2524094) were genotyped by TaqMan assay among drug users and hemodialysis (HD) patients including 1,378 uninfected control cases, 307 subjects with spontaneous viral clearance, and 217 patients with persistent HCV infection. Bioinformatics analysis was used to functionally annotate the SNPs. Results After logistic regression analysis, the rs35440472-A and rs1130838-A alleles were found to be associated with a significantly elevated risk of HCV infection (OR = 1.562, 95% CI: 1.229–1.987, P < 0.001; OR = 2.134, 95% CI: 1.180–3.858, P = 0.012, respectively), which remained significant after Bonferroni correction (0.05/4). The combined effect of their risk alleles and risk genotypes (rs35440472-AA and rs1130838-AA) were linked to the increased risk of HCV infection in a locus-dosage manner (all Ptrend < 0.001). Based on the SNPinfo web server, rs35440472 was predicted to be a transcription factor binding site (TFBS) while rs1130838 was predicted to have a splicing (ESE or ESS) function. Conclusion KIR2DS4/KIR2DS1/KIR2DL1 rs35440472-A and HLA-C rs1130838-A variants are associated with increased susceptibility to HCV infection in a high-risk Chinese population.
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Affiliation(s)
- Chao Shen
- Department of Epidemiology and Biostatistics, Key Laboratory of Infectious Diseases, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Zhijun Ge
- Department of Critical Care Medicine, The Affiliated Yixing Hospital of Jiangsu University, Yixing, China
| | - Chen Dong
- Department of Epidemiology and Statistics, School of Public Health, Medical College of Soochow University, Suzhou, China
| | - Chunhui Wang
- Institute of Epidemiology and Microbiology, Eastern Theater Command Centers for Disease Control and Prevention, Nanjing, China.,Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Jianguo Shao
- Department of Digestive Medicine, Third Affiliated Hospital of Nantong University, Nantong, China
| | - Weihua Cai
- Department of General Surgery, Third Affiliated Hospital of Nantong University, Nantong, China
| | - Peng Huang
- Department of Epidemiology and Biostatistics, Key Laboratory of Infectious Diseases, School of Public Health, Nanjing Medical University, Nanjing, China.,Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Haozhi Fan
- Department of Information, First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Jun Li
- Department of Infectious Diseases, First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Yun Zhang
- Institute of Epidemiology and Microbiology, Eastern Theater Command Centers for Disease Control and Prevention, Nanjing, China.,Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Ming Yue
- Department of Infectious Diseases, First Affiliated Hospital of Nanjing Medical University, Nanjing, China
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62
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Petrosino M, Novak L, Pasquo A, Chiaraluce R, Turina P, Capriotti E, Consalvi V. Analysis and Interpretation of the Impact of Missense Variants in Cancer. Int J Mol Sci 2021; 22:ijms22115416. [PMID: 34063805 PMCID: PMC8196604 DOI: 10.3390/ijms22115416] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Revised: 05/03/2021] [Accepted: 05/17/2021] [Indexed: 01/10/2023] Open
Abstract
Large scale genome sequencing allowed the identification of a massive number of genetic variations, whose impact on human health is still unknown. In this review we analyze, by an in silico-based strategy, the impact of missense variants on cancer-related genes, whose effect on protein stability and function was experimentally determined. We collected a set of 164 variants from 11 proteins to analyze the impact of missense mutations at structural and functional levels, and to assess the performance of state-of-the-art methods (FoldX and Meta-SNP) for predicting protein stability change and pathogenicity. The result of our analysis shows that a combination of experimental data on protein stability and in silico pathogenicity predictions allowed the identification of a subset of variants with a high probability of having a deleterious phenotypic effect, as confirmed by the significant enrichment of the subset in variants annotated in the COSMIC database as putative cancer-driving variants. Our analysis suggests that the integration of experimental and computational approaches may contribute to evaluate the risk for complex disorders and develop more effective treatment strategies.
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Affiliation(s)
- Maria Petrosino
- Dipartimento Scienze Biochimiche “A. Rossi Fanelli”, Sapienza University of Rome, 00185 Roma, Italy; (M.P.); (L.N.); (R.C.)
| | - Leonore Novak
- Dipartimento Scienze Biochimiche “A. Rossi Fanelli”, Sapienza University of Rome, 00185 Roma, Italy; (M.P.); (L.N.); (R.C.)
| | - Alessandra Pasquo
- ENEA CR Frascati, Diagnostics and Metrology Laboratory FSN-TECFIS-DIM, 00044 Frascati, Italy;
| | - Roberta Chiaraluce
- Dipartimento Scienze Biochimiche “A. Rossi Fanelli”, Sapienza University of Rome, 00185 Roma, Italy; (M.P.); (L.N.); (R.C.)
| | - Paola Turina
- Dipartimento di Farmacia e Biotecnologie (FaBiT), University of Bologna, 40126 Bologna, Italy;
| | - Emidio Capriotti
- Dipartimento di Farmacia e Biotecnologie (FaBiT), University of Bologna, 40126 Bologna, Italy;
- Correspondence: (E.C.); (V.C.)
| | - Valerio Consalvi
- Dipartimento Scienze Biochimiche “A. Rossi Fanelli”, Sapienza University of Rome, 00185 Roma, Italy; (M.P.); (L.N.); (R.C.)
- Correspondence: (E.C.); (V.C.)
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63
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64
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Caldararu O, Blundell TL, Kepp KP. Three Simple Properties Explain Protein Stability Change upon Mutation. J Chem Inf Model 2021; 61:1981-1988. [PMID: 33848149 DOI: 10.1021/acs.jcim.1c00201] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Accurate prediction of protein stability upon mutation enables rational engineering of new proteins and insights into protein evolution and monogenetic diseases caused by single-point amino acid substitutions. Many tools have been developed to this aim, ranging from energy-based models to machine-learning methods that use large amounts of experimental data. However, as the methods become more complex, the interpretation of the chemistry underlying the protein stability effects becomes obscure. It is thus of interest to identify the simplest prediction model that retains complete amino acid specific interpretation; for a given number of input descriptors, we expect such a model to be almost universal. In this study, we identify such a limiting model, SimBa, a simple multilinear regression model trained on a substitution-type-balanced experimental data set. The model accounts only for the solvent accessibility of the site, volume difference, and polarity difference caused by mutation. Our results show that this very simple and directly applicable model performs comparably to other much more complex, widely used protein stability prediction methods. This suggests that a hard limit of ∼1 kcal/mol numerical accuracy and an R ∼ 0.5 trend accuracy exists and that new features, such as account of unfolded states, water colocalization, and amino acid correlations, are required to improve accuracy to, e.g., 1/2 kcal/mol.
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Affiliation(s)
- Octav Caldararu
- DTU Chemistry, Technical University of Denmark, Building 206, 2800 Kgs. Lyngby, Denmark
| | - Tom L Blundell
- Department of Biochemistry, University of Cambridge, Cambridge, CB2 1GA, United Kingdom
| | - Kasper P Kepp
- DTU Chemistry, Technical University of Denmark, Building 206, 2800 Kgs. Lyngby, Denmark
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65
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Narayanan KK, Procko E. Deep Mutational Scanning of Viral Glycoproteins and Their Host Receptors. Front Mol Biosci 2021; 8:636660. [PMID: 33898517 PMCID: PMC8062978 DOI: 10.3389/fmolb.2021.636660] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Accepted: 03/18/2021] [Indexed: 11/17/2022] Open
Abstract
Deep mutational scanning or deep mutagenesis is a powerful tool for understanding the sequence diversity available to viruses for adaptation in a laboratory setting. It generally involves tracking an in vitro selection of protein sequence variants with deep sequencing to map mutational effects based on changes in sequence abundance. Coupled with any of a number of selection strategies, deep mutagenesis can explore the mutational diversity available to viral glycoproteins, which mediate critical roles in cell entry and are exposed to the humoral arm of the host immune response. Mutational landscapes of viral glycoproteins for host cell attachment and membrane fusion reveal extensive epistasis and potential escape mutations to neutralizing antibodies or other therapeutics, as well as aiding in the design of optimized immunogens for eliciting broadly protective immunity. While less explored, deep mutational scans of host receptors further assist in understanding virus-host protein interactions. Critical residues on the host receptors for engaging with viral spikes are readily identified and may help with structural modeling. Furthermore, mutations may be found for engineering soluble decoy receptors as neutralizing agents that specifically bind viral targets with tight affinity and limited potential for viral escape. By untangling the complexities of how sequence contributes to viral glycoprotein and host receptor interactions, deep mutational scanning is impacting ideas and strategies at multiple levels for combatting circulating and emergent virus strains.
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Affiliation(s)
| | - Erik Procko
- Department of Biochemistry and Cancer Center at Illinois, University of Illinois, Urbana, IL, United States
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66
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Gersing SK, Wang Y, Grønbæk-Thygesen M, Kampmeyer C, Clausen L, Willemoës M, Andréasson C, Stein A, Lindorff-Larsen K, Hartmann-Petersen R. Mapping the degradation pathway of a disease-linked aspartoacylase variant. PLoS Genet 2021; 17:e1009539. [PMID: 33914734 PMCID: PMC8084241 DOI: 10.1371/journal.pgen.1009539] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2020] [Accepted: 04/06/2021] [Indexed: 11/19/2022] Open
Abstract
Canavan disease is a severe progressive neurodegenerative disorder that is characterized by swelling and spongy degeneration of brain white matter. The disease is genetically linked to polymorphisms in the aspartoacylase (ASPA) gene, including the substitution C152W. ASPA C152W is associated with greatly reduced protein levels in cells, yet biophysical experiments suggest a wild-type like thermal stability. Here, we use ASPA C152W as a model to investigate the degradation pathway of a disease-causing protein variant. When we expressed ASPA C152W in Saccharomyces cerevisiae, we found a decreased steady state compared to wild-type ASPA as a result of increased proteasomal degradation. However, molecular dynamics simulations of ASPA C152W did not substantially deviate from wild-type ASPA, indicating that the native state is structurally preserved. Instead, we suggest that the C152W substitution interferes with the de novo folding pathway resulting in increased proteasomal degradation before reaching its stable conformation. Systematic mapping of the protein quality control components acting on misfolded and aggregation-prone species of C152W, revealed that the degradation is highly dependent on the molecular chaperone Hsp70, its co-chaperone Hsp110 as well as several quality control E3 ubiquitin-protein ligases, including Ubr1. In addition, the disaggregase Hsp104 facilitated refolding of aggregated ASPA C152W, while Cdc48 mediated degradation of insoluble ASPA protein. In human cells, ASPA C152W displayed increased proteasomal turnover that was similarly dependent on Hsp70 and Hsp110. Our findings underscore the use of yeast to determine the protein quality control components involved in the degradation of human pathogenic variants in order to identify potential therapeutic targets.
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Affiliation(s)
- Sarah K. Gersing
- The Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Yong Wang
- The Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Martin Grønbæk-Thygesen
- The Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Caroline Kampmeyer
- The Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Lene Clausen
- The Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Martin Willemoës
- The Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Claes Andréasson
- Department of Molecular Biosciences, The Wenner-Gren Institute, Stockholm University, Stockholm, Sweden
| | - Amelie Stein
- The Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Kresten Lindorff-Larsen
- The Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Rasmus Hartmann-Petersen
- The Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark
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67
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Cagiada M, Johansson KE, Valanciute A, Nielsen SV, Hartmann-Petersen R, Yang JJ, Fowler DM, Stein A, Lindorff-Larsen K. Understanding the Origins of Loss of Protein Function by Analyzing the Effects of Thousands of Variants on Activity and Abundance. Mol Biol Evol 2021; 38:3235-3246. [PMID: 33779753 PMCID: PMC8321532 DOI: 10.1093/molbev/msab095] [Citation(s) in RCA: 47] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
Understanding and predicting how amino acid substitutions affect proteins are keys to our basic understanding of protein function and evolution. Amino acid changes may affect protein function in a number of ways including direct perturbations of activity or indirect effects on protein folding and stability. We have analyzed 6,749 experimentally determined variant effects from multiplexed assays on abundance and activity in two proteins (NUDT15 and PTEN) to quantify these effects and find that a third of the variants cause loss of function, and about half of loss-of-function variants also have low cellular abundance. We analyze the structural and mechanistic origins of loss of function and use the experimental data to find residues important for enzymatic activity. We performed computational analyses of protein stability and evolutionary conservation and show how we may predict positions where variants cause loss of activity or abundance. In this way, our results link thermodynamic stability and evolutionary conservation to experimental studies of different properties of protein fitness landscapes.
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Affiliation(s)
- Matteo Cagiada
- Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Kristoffer E Johansson
- Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Audrone Valanciute
- Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Sofie V Nielsen
- Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Rasmus Hartmann-Petersen
- Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Jun J Yang
- Department of Pharmaceutical Sciences, St. Jude Children's Research Hospital, Memphis, TN, USA.,Department of Oncology, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Douglas M Fowler
- Department of Genome Sciences, University of Washington, Seattle, WA, USA.,Department of Bioengineering, University of Washington, Seattle, WA, USA
| | - Amelie Stein
- Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Kresten Lindorff-Larsen
- Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark
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68
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Modeling mutational effects on biochemical phenotypes using convolutional neural networks: application to SARS-CoV-2. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2021. [PMID: 33532766 PMCID: PMC7852230 DOI: 10.1101/2021.01.28.428521] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Biochemical phenotypes are major indexes for protein structure and function characterization. They are determined, at least in part, by the intrinsic physicochemical properties of amino acids and may be reflected in the protein three-dimensional structure. Modeling mutational effects on biochemical phenotypes is a critical step for understanding protein function and disease mechanism as well as enabling drug discovery. Deep Mutational Scanning (DMS) experiments have been performed on SARS-CoV-2’s spike receptor binding domain and the human ACE2 zinc-binding peptidase domain - both central players in viral infection and evolution and antibody evasion - quantifying how mutations impact binding affinity and protein expression. Here, we modeled biochemical phenotypes from massively parallel assays, using convolutional neural networks trained on protein sequence mutations in the virus and human host. We found that neural networks are significantly predictive of binding affinity, protein expression, and antibody escape, learning complex interactions and higher-order features that are difficult to capture with conventional methods from structural biology. Integrating the intrinsic physicochemical properties of amino acids, including hydrophobicity, solvent-accessible surface area, and long-range non-bonded energy per atom, significantly improved prediction (empirical p<0.01) though there was such a strong dependence on the sequence data alone to yield reasonably good prediction. We observed concordance of the DMS data and our neural network predictions with an independent study on intermolecular interactions from molecular dynamics (multiple 500 ns or 1 μs all-atom) simulations of the spike protein-ACE2 interface, with critical implications for the use of deep learning to dissect molecular mechanisms. The mutation- or genetically-determined component of a biochemical phenotype estimated from the neural networks has improved causal inference properties relative to the original phenotype and can facilitate crucial insights into disease pathophysiology and therapeutic design.
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69
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Banford S, McCorvie TJ, Pey AL, Timson DJ. Galactosemia: Towards Pharmacological Chaperones. J Pers Med 2021; 11:jpm11020106. [PMID: 33562227 PMCID: PMC7914515 DOI: 10.3390/jpm11020106] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2020] [Revised: 02/02/2021] [Accepted: 02/04/2021] [Indexed: 02/07/2023] Open
Abstract
Galactosemia is a rare inherited metabolic disease resulting from mutations in the four genes which encode enzymes involved in the metabolism of galactose. The current therapy, the removal of galactose from the diet, is inadequate. Consequently, many patients suffer lifelong physical and cognitive disability. The phenotype varies from almost asymptomatic to life-threatening disability. The fundamental biochemical cause of the disease is a decrease in enzymatic activity due to failure of the affected protein to fold and/or function correctly. Many novel therapies have been proposed for the treatment of galactosemia. Often, these are designed to treat the symptoms and not the fundamental cause. Pharmacological chaperones (PC) (small molecules which correct the folding of misfolded proteins) represent an exciting potential therapy for galactosemia. In theory, they would restore enzyme function, thus preventing downstream pathological consequences. In practice, no PCs have been identified for potential application in galactosemia. Here, we review the biochemical basis of the disease, identify opportunities for the application of PCs and describe how these might be discovered. We will conclude by considering some of the clinical issues which will affect the future use of PCs in the treatment of galactosemia.
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Affiliation(s)
- Samantha Banford
- South Eastern Health and Social Care Trust, Downpatrick BT30 6RL, UK;
| | - Thomas J. McCorvie
- Structural Genomics Consortium, University of Oxford, Oxford OX3 7DQ, UK;
| | - Angel L. Pey
- Departamento de Química Física, Unidad de Excelencia de Química aplicada a Biomedicina y Medioambiente e Instituto de Biotecnología, Facultad de Ciencias, Universidad de Granada, 18071 Granada, Spain;
| | - David J. Timson
- School of Pharmacy and Biomolecular Sciences, The University of Brighton, Brighton BN2 4GJ, UK
- Correspondence:
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70
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Li Y, Burgman B, Khatri IS, Pentaparthi SR, Su Z, McGrail DJ, Li Y, Wu E, Eckhardt SG, Sahni N, Yi SS. e-MutPath: computational modeling reveals the functional landscape of genetic mutations rewiring interactome networks. Nucleic Acids Res 2021; 49:e2. [PMID: 33211847 PMCID: PMC7797045 DOI: 10.1093/nar/gkaa1015] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2020] [Revised: 10/07/2020] [Accepted: 10/20/2020] [Indexed: 02/06/2023] Open
Abstract
Understanding the functional impact of cancer somatic mutations represents a critical knowledge gap for implementing precision oncology. It has been increasingly appreciated that the interaction profile mediated by a genomic mutation provides a fundamental link between genotype and phenotype. However, specific effects on biological signaling networks for the majority of mutations are largely unknown by experimental approaches. To resolve this challenge, we developed e-MutPath (edgetic Mutation-mediated Pathway perturbations), a network-based computational method to identify candidate ‘edgetic’ mutations that perturb functional pathways. e-MutPath identifies informative paths that could be used to distinguish disease risk factors from neutral elements and to stratify disease subtypes with clinical relevance. The predicted targets are enriched in cancer vulnerability genes, known drug targets but depleted for proteins associated with side effects, demonstrating the power of network-based strategies to investigate the functional impact and perturbation profiles of genomic mutations. Together, e-MutPath represents a robust computational tool to systematically assign functions to genetic mutations, especially in the context of their specific pathway perturbation effect.
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Affiliation(s)
- Yongsheng Li
- Department of Oncology, Livestrong Cancer Institutes, Dell Medical School, The University of Texas at Austin, Austin, TX 78712, USA.,Oden Institute for Computational Engineering and Sciences (ICES), The University of Texas at Austin, Austin, TX 78712, USA
| | - Brandon Burgman
- Department of Oncology, Livestrong Cancer Institutes, Dell Medical School, The University of Texas at Austin, Austin, TX 78712, USA.,Interdisciplinary Life Sciences Graduate Programs (ILSGP), College of Natural Sciences, The University of Texas at Austin, Austin, TX 78712, USA
| | - Ishaani S Khatri
- Department of Oncology, Livestrong Cancer Institutes, Dell Medical School, The University of Texas at Austin, Austin, TX 78712, USA.,Oden Institute for Computational Engineering and Sciences (ICES), The University of Texas at Austin, Austin, TX 78712, USA
| | - Sairahul R Pentaparthi
- Department of Oncology, Livestrong Cancer Institutes, Dell Medical School, The University of Texas at Austin, Austin, TX 78712, USA
| | - Zhe Su
- Department of Oncology, Livestrong Cancer Institutes, Dell Medical School, The University of Texas at Austin, Austin, TX 78712, USA.,Oden Institute for Computational Engineering and Sciences (ICES), The University of Texas at Austin, Austin, TX 78712, USA
| | - Daniel J McGrail
- Department of Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Yang Li
- Department of Epigenetics and Molecular Carcinogenesis, The University of Texas MD Anderson Science Park, Smithville, TX 78957, USA
| | - Erxi Wu
- Department of Oncology, Livestrong Cancer Institutes, Dell Medical School, The University of Texas at Austin, Austin, TX 78712, USA.,Neuroscience Institute and Department of Neurosurgery, Baylor Scott & White Health, Temple, TX 76502, USA.,Department of Surgery, Texas A & M University Health Science Center, College of Medicine, Temple, TX 76508, USA.,Department of Pharmaceutical Sciences, Texas A & M University Health Science Center, College of Pharmacy, College Station, TX 77843, USA
| | - S Gail Eckhardt
- Department of Oncology, Livestrong Cancer Institutes, Dell Medical School, The University of Texas at Austin, Austin, TX 78712, USA.,Interdisciplinary Life Sciences Graduate Programs (ILSGP), College of Natural Sciences, The University of Texas at Austin, Austin, TX 78712, USA
| | - Nidhi Sahni
- Department of Epigenetics and Molecular Carcinogenesis, The University of Texas MD Anderson Science Park, Smithville, TX 78957, USA.,Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.,Program in Quantitative and Computational Biosciences (QCB), Baylor College of Medicine, Houston, TX 77030, USA
| | - S Stephen Yi
- Department of Oncology, Livestrong Cancer Institutes, Dell Medical School, The University of Texas at Austin, Austin, TX 78712, USA.,Oden Institute for Computational Engineering and Sciences (ICES), The University of Texas at Austin, Austin, TX 78712, USA.,Interdisciplinary Life Sciences Graduate Programs (ILSGP), College of Natural Sciences, The University of Texas at Austin, Austin, TX 78712, USA.,Department of Biomedical Engineering, Cockrell School of Engineering, The University of Texas at Austin, Austin, TX 78712, USA
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71
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Birolo G, Benevenuta S, Fariselli P, Capriotti E, Giorgio E, Sanavia T. Protein Stability Perturbation Contributes to the Loss of Function in Haploinsufficient Genes. Front Mol Biosci 2021; 8:620793. [PMID: 33598480 PMCID: PMC7882701 DOI: 10.3389/fmolb.2021.620793] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2020] [Accepted: 01/07/2021] [Indexed: 11/29/2022] Open
Abstract
Missense variants are among the most studied genome modifications as disease biomarkers. It has been shown that the “perturbation” of the protein stability upon a missense variant (in terms of absolute ΔΔG value, i.e., |ΔΔG|) has a significant, but not predictive, correlation with the pathogenicity of that variant. However, here we show that this correlation becomes significantly amplified in haploinsufficient genes. Moreover, the enrichment of pathogenic variants increases at the increasing protein stability perturbation value. These findings suggest that protein stability perturbation might be considered as a potential cofactor in diseases associated with haploinsufficient genes reporting missense variants.
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Affiliation(s)
| | | | | | - Emidio Capriotti
- Department of Pharmacy and Biotechnology (FaBiT), University of Bologna, Italy
| | - Elisa Giorgio
- Department of Molecular Medicine, University of Pavia, Italy
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72
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SAAFEC-SEQ: A Sequence-Based Method for Predicting the Effect of Single Point Mutations on Protein Thermodynamic Stability. Int J Mol Sci 2021; 22:ijms22020606. [PMID: 33435356 PMCID: PMC7827184 DOI: 10.3390/ijms22020606] [Citation(s) in RCA: 58] [Impact Index Per Article: 19.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2020] [Revised: 12/23/2020] [Accepted: 01/06/2021] [Indexed: 01/04/2023] Open
Abstract
Modeling the effect of mutations on protein thermodynamics stability is useful for protein engineering and understanding molecular mechanisms of disease-causing variants. Here, we report a new development of the SAAFEC method, the SAAFEC-SEQ, which is a gradient boosting decision tree machine learning method to predict the change of the folding free energy caused by amino acid substitutions. The method does not require the 3D structure of the corresponding protein, but only its sequence and, thus, can be applied on genome-scale investigations where structural information is very sparse. SAAFEC-SEQ uses physicochemical properties, sequence features, and evolutionary information features to make the predictions. It is shown to consistently outperform all existing state-of-the-art sequence-based methods in both the Pearson correlation coefficient and root-mean-squared-error parameters as benchmarked on several independent datasets. The SAAFEC-SEQ has been implemented into a web server and is available as stand-alone code that can be downloaded and embedded into other researchers’ code.
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73
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Sruthi C, Balaram H, Prakash MK. Toward Developing Intuitive Rules for Protein Variant Effect Prediction Using Deep Mutational Scanning Data. ACS OMEGA 2020; 5:29667-29677. [PMID: 33251402 PMCID: PMC7689672 DOI: 10.1021/acsomega.0c02402] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/22/2020] [Accepted: 07/28/2020] [Indexed: 05/30/2023]
Abstract
Protein structure and function can be severely altered by even a single amino acid mutation. Predictions of mutational effects using extensive artificial intelligence (AI)-based models, although accurate, remain as enigmatic as the experimental observations in terms of improving intuitions about the contributions of various factors. Inspired by Lipinski's rules for drug-likeness, we devise simple thresholding criteria on five different descriptors such as conservation, which have so far been limited to qualitative interpretations such as high conservation implies high mutational effect. We analyze systematic deep mutational scanning data of all possible single amino acid substitutions on seven proteins (25153 mutations) to first define these thresholds and then to evaluate the scope and limits of the predictions. At this stage, the approach allows us to comment easily and with a low error rate on the subset of mutations classified as neutral or deleterious by all of the descriptors. We hope that complementary to the accurate AI predictions, these thresholding rules or their subsequent modifications will serve the purpose of codifying the knowledge about the effects of mutations.
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Affiliation(s)
- Cheloor
Kovilakam Sruthi
- Theoretical
Sciences Unit, Jawaharlal Nehru Centre for
Advanced Scientific Research, Bangalore 560064, India
| | - Hemalatha Balaram
- Molecular
Biology and Genetics Unit, Jawaharlal Nehru
Centre for Advanced Scientific Research, Bangalore 560064, India
| | - Meher K. Prakash
- Theoretical
Sciences Unit, Jawaharlal Nehru Centre for
Advanced Scientific Research, Bangalore 560064, India
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74
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Pacheco-García JL, Cano-Muñoz M, Sánchez-Ramos I, Salido E, Pey AL. Naturally-Occurring Rare Mutations Cause Mild to Catastrophic Effects in the Multifunctional and Cancer-Associated NQO1 Protein. J Pers Med 2020; 10:E207. [PMID: 33153185 PMCID: PMC7711955 DOI: 10.3390/jpm10040207] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2020] [Revised: 10/27/2020] [Accepted: 11/02/2020] [Indexed: 12/13/2022] Open
Abstract
The functional and pathological implications of the enormous genetic diversity of the human genome are mostly unknown, primarily due to our unability to predict pathogenicity in a high-throughput manner. In this work, we characterized the phenotypic consequences of eight naturally-occurring missense variants on the multifunctional and disease-associated NQO1 protein using biophysical and structural analyses on several protein traits. Mutations found in both exome-sequencing initiatives and in cancer cell lines cause mild to catastrophic effects on NQO1 stability and function. Importantly, some mutations perturb functional features located structurally far from the mutated site. These effects are well rationalized by considering the nature of the mutation, its location in protein structure and the local stability of its environment. Using a set of 22 experimentally characterized mutations in NQO1, we generated experimental scores for pathogenicity that correlate reasonably well with bioinformatic scores derived from a set of commonly used algorithms, although the latter fail to semiquantitatively predict the phenotypic alterations caused by a significant fraction of mutations individually. These results provide insight into the propagation of mutational effects on multifunctional proteins, the implementation of in silico approaches for establishing genotype-phenotype correlations and the molecular determinants underlying loss-of-function in genetic diseases.
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Affiliation(s)
- Juan Luis Pacheco-García
- Departamento de Química Física, Facultad de Ciencias, Universidad de Granada, 18071 Granada, Spain; (J.L.P.-G.); (M.C.-M.); (I.S.-R.)
| | - Mario Cano-Muñoz
- Departamento de Química Física, Facultad de Ciencias, Universidad de Granada, 18071 Granada, Spain; (J.L.P.-G.); (M.C.-M.); (I.S.-R.)
| | - Isabel Sánchez-Ramos
- Departamento de Química Física, Facultad de Ciencias, Universidad de Granada, 18071 Granada, Spain; (J.L.P.-G.); (M.C.-M.); (I.S.-R.)
| | - Eduardo Salido
- Centre for Biomedical Research on Rare Diseases (CIBERER), Hospital Universitario de Canarias, 38320 Tenerife, Spain;
| | - Angel L. Pey
- Departamento de Química Física y Unidad de Excelencia de Química Aplicada a Biomedicina y Medioambiente (UEQ), Facultad de Ciencias, Universidad de Granada, 18071 Granada, Spain
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75
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Li B, Yang YT, Capra JA, Gerstein MB. Predicting changes in protein thermodynamic stability upon point mutation with deep 3D convolutional neural networks. PLoS Comput Biol 2020; 16:e1008291. [PMID: 33253214 PMCID: PMC7728386 DOI: 10.1371/journal.pcbi.1008291] [Citation(s) in RCA: 48] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2020] [Revised: 12/10/2020] [Accepted: 08/26/2020] [Indexed: 12/22/2022] Open
Abstract
Predicting mutation-induced changes in protein thermodynamic stability (ΔΔG) is of great interest in protein engineering, variant interpretation, and protein biophysics. We introduce ThermoNet, a deep, 3D-convolutional neural network (3D-CNN) designed for structure-based prediction of ΔΔGs upon point mutation. To leverage the image-processing power inherent in CNNs, we treat protein structures as if they were multi-channel 3D images. In particular, the inputs to ThermoNet are uniformly constructed as multi-channel voxel grids based on biophysical properties derived from raw atom coordinates. We train and evaluate ThermoNet with a curated data set that accounts for protein homology and is balanced with direct and reverse mutations; this provides a framework for addressing biases that have likely influenced many previous ΔΔG prediction methods. ThermoNet demonstrates performance comparable to the best available methods on the widely used Ssym test set. In addition, ThermoNet accurately predicts the effects of both stabilizing and destabilizing mutations, while most other methods exhibit a strong bias towards predicting destabilization. We further show that homology between Ssym and widely used training sets like S2648 and VariBench has likely led to overestimated performance in previous studies. Finally, we demonstrate the practical utility of ThermoNet in predicting the ΔΔGs for two clinically relevant proteins, p53 and myoglobin, and for pathogenic and benign missense variants from ClinVar. Overall, our results suggest that 3D-CNNs can model the complex, non-linear interactions perturbed by mutations, directly from biophysical properties of atoms.
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Affiliation(s)
- Bian Li
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, Connecticut, United States of America
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut, United States of America
- Department of Biological Sciences and Vanderbilt Genetics Institute, Vanderbilt University, Nashville, Tennessee, United States of America
| | - Yucheng T. Yang
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, Connecticut, United States of America
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut, United States of America
| | - John A. Capra
- Department of Biological Sciences and Vanderbilt Genetics Institute, Vanderbilt University, Nashville, Tennessee, United States of America
| | - Mark B. Gerstein
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, Connecticut, United States of America
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut, United States of America
- Department of Computer Science, Yale University, New Haven, Connecticut, United States of America
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76
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Clausen L, Stein A, Grønbæk-Thygesen M, Nygaard L, Søltoft CL, Nielsen SV, Lisby M, Ravid T, Lindorff-Larsen K, Hartmann-Petersen R. Folliculin variants linked to Birt-Hogg-Dubé syndrome are targeted for proteasomal degradation. PLoS Genet 2020; 16:e1009187. [PMID: 33137092 PMCID: PMC7660926 DOI: 10.1371/journal.pgen.1009187] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2020] [Revised: 11/12/2020] [Accepted: 10/10/2020] [Indexed: 01/24/2023] Open
Abstract
Germline mutations in the folliculin (FLCN) tumor suppressor gene are linked to Birt-Hogg-Dubé (BHD) syndrome, a dominantly inherited genetic disease characterized by predisposition to fibrofolliculomas, lung cysts, and renal cancer. Most BHD-linked FLCN variants include large deletions and splice site aberrations predicted to cause loss of function. The mechanisms by which missense variants and short in-frame deletions in FLCN trigger disease are unknown. Here, we present an integrated computational and experimental study that reveals that the majority of such disease-causing FLCN variants cause loss of function due to proteasomal degradation of the encoded FLCN protein, rather than directly ablating FLCN function. Accordingly, several different single-site FLCN variants are present at strongly reduced levels in cells. In line with our finding that FLCN variants are protein quality control targets, several are also highly insoluble and fail to associate with the FLCN-binding partners FNIP1 and FNIP2. The lack of FLCN binding leads to rapid proteasomal degradation of FNIP1 and FNIP2. Half of the tested FLCN variants are mislocalized in cells, and one variant (ΔE510) forms perinuclear protein aggregates. A yeast-based stability screen revealed that the deubiquitylating enzyme Ubp15/USP7 and molecular chaperones regulate the turnover of the FLCN variants. Lowering the temperature led to a stabilization of two FLCN missense proteins, and for one (R362C), function was re-established at low temperature. In conclusion, we propose that most BHD-linked FLCN missense variants and small in-frame deletions operate by causing misfolding and degradation of the FLCN protein, and that stabilization and resulting restoration of function may hold therapeutic potential of certain disease-linked variants. Our computational saturation scan encompassing both missense variants and single site deletions in FLCN may allow classification of rare FLCN variants of uncertain clinical significance.
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Affiliation(s)
- Lene Clausen
- The Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Amelie Stein
- The Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Martin Grønbæk-Thygesen
- The Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Lasse Nygaard
- The Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Cecilie L. Søltoft
- The Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Sofie V. Nielsen
- The Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Michael Lisby
- The Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Tommer Ravid
- Department of Biological Chemistry, The Alexander Silberman Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Kresten Lindorff-Larsen
- The Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Rasmus Hartmann-Petersen
- The Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark
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77
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Pey AL. Towards Accurate Genotype-Phenotype Correlations in the CYP2D6 Gene. J Pers Med 2020; 10:jpm10040158. [PMID: 33049937 PMCID: PMC7711719 DOI: 10.3390/jpm10040158] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2020] [Accepted: 10/01/2020] [Indexed: 12/17/2022] Open
Abstract
Establishing accurate and large-scale genotype-phenotype correlations and predictions of individual response to pharmacological treatments are two of the holy grails of Personalized Medicine. These tasks are challenging and require an integrated knowledge of the complex processes that regulate gene expression and, ultimately, protein functionality in vivo, the effects of mutations/polymorphisms and the different sources of interindividual phenotypic variability. A remarkable example of our advances in these challenging tasks is the highly polymorphic CYP2D6 gene, which encodes a cytochrome P450 enzyme involved in the metabolization of many of the most marketed drugs (including SARS-Cov-2 therapies such as hydroxychloroquine). Since the introduction of simple activity scores (AS) over 10 years ago, its ability to establish genotype-phenotype correlations on the drug metabolizing capacity of this enzyme in human population has provided lessons that will help to improve this type of score for this, and likely many other human genes and proteins. Multidisciplinary research emerges as the best approach to incorporate additional concepts to refine and improve such functional/activity scores for the CYP2D6 gene, as well as for many other human genes associated with simple and complex genetic diseases.
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Affiliation(s)
- Angel L Pey
- Departamento de Química Física, Unidad de Excelencia de Química aplicada a Biomedicina y Medioambiente, Facultad de Ciencias, Universidad de Granada, 18071 Granada, Spain
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78
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Li X, Lehner B. Biophysical ambiguities prevent accurate genetic prediction. Nat Commun 2020; 11:4923. [PMID: 33004824 PMCID: PMC7529754 DOI: 10.1038/s41467-020-18694-0] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2020] [Accepted: 09/04/2020] [Indexed: 12/27/2022] Open
Abstract
A goal of biology is to predict how mutations combine to alter phenotypes, fitness and disease. It is often assumed that mutations combine additively or with interactions that can be predicted. Here, we show using simulations that, even for the simple example of the lambda phage transcription factor CI repressing a gene, this assumption is incorrect and that perfect measurements of the effects of mutations on a trait and mechanistic understanding can be insufficient to predict what happens when two mutations are combined. This apparent paradox arises because mutations can have different biophysical effects to cause the same change in a phenotype and the outcome in a double mutant depends upon what these hidden biophysical changes actually are. Pleiotropy and non-monotonic functions further confound prediction of how mutations interact. Accurate prediction of phenotypes and disease will sometimes not be possible unless these biophysical ambiguities can be resolved using additional measurements.
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Affiliation(s)
- Xianghua Li
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Dr. Aiguader 88, Barcelona, 08003, Spain
| | - Ben Lehner
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Dr. Aiguader 88, Barcelona, 08003, Spain. .,Universitat Pompeu Fabra (UPF), Barcelona, Spain. .,ICREA, Pg. Luis Companys 23, Barcelona, 08010, Spain.
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79
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Gerasimavicius L, Liu X, Marsh JA. Identification of pathogenic missense mutations using protein stability predictors. Sci Rep 2020; 10:15387. [PMID: 32958805 PMCID: PMC7506547 DOI: 10.1038/s41598-020-72404-w] [Citation(s) in RCA: 59] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2020] [Accepted: 08/31/2020] [Indexed: 12/17/2022] Open
Abstract
Attempts at using protein structures to identify disease-causing mutations have been dominated by the idea that most pathogenic mutations are disruptive at a structural level. Therefore, computational stability predictors, which assess whether a mutation is likely to be stabilising or destabilising to protein structure, have been commonly used when evaluating new candidate disease variants, despite not having been developed specifically for this purpose. We therefore tested 13 different stability predictors for their ability to discriminate between pathogenic and putatively benign missense variants. We find that one method, FoldX, significantly outperforms all other predictors in the identification of disease variants. Moreover, we demonstrate that employing predicted absolute energy change scores improves performance of nearly all predictors in distinguishing pathogenic from benign variants. Importantly, however, we observe that the utility of computational stability predictors is highly heterogeneous across different proteins, and that they are all inferior to the best performing variant effect predictors for identifying pathogenic mutations. We suggest that this is largely due to alternate molecular mechanisms other than protein destabilisation underlying many pathogenic mutations. Thus, better ways of incorporating protein structural information and molecular mechanisms into computational variant effect predictors will be required for improved disease variant prioritisation.
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Affiliation(s)
- Lukas Gerasimavicius
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, EH4 2XU, UK
| | - Xin Liu
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, EH4 2XU, UK
| | - Joseph A Marsh
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, EH4 2XU, UK.
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80
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Kumar M, Papaleo E. A pan-cancer assessment of alterations of the kinase domain of ULK1, an upstream regulator of autophagy. Sci Rep 2020; 10:14874. [PMID: 32913252 PMCID: PMC7483646 DOI: 10.1038/s41598-020-71527-4] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2019] [Accepted: 06/22/2020] [Indexed: 02/06/2023] Open
Abstract
Autophagy is a key clearance process to recycle damaged cellular components. One important upstream regulator of autophagy is ULK1 kinase. Several three-dimensional structures of the ULK1 catalytic domain are available, but a comprehensive study, including molecular dynamics, is missing. Also, an exhaustive description of ULK1 alterations found in cancer samples is presently lacking. We here applied a framework which links -omics data to structural protein ensembles to study ULK1 alterations from genomics data available for more than 30 cancer types. We predicted the effects of mutations on ULK1 function and structural stability, accounting for protein dynamics, and the different layers of changes that a mutation can induce in a protein at the functional and structural level. ULK1 is down-regulated in gynecological tumors. In other cancer types, ULK2 could compensate for ULK1 downregulation and, in the majority of the cases, no marked changes in expression have been found. 36 missense mutations of ULK1, not limited to the catalytic domain, are co-occurring with mutations in a large number of ULK1 interactors or substrates, suggesting a pronounced effect of the upstream steps of autophagy in many cancer types. Moreover, our results pinpoint that more than 50% of the mutations in the kinase domain of ULK1, here investigated, are predicted to affect protein stability. Three mutations (S184F, D102N, and A28V) are predicted with only impact on kinase activity, either modifying the functional dynamics or the capability to exert effects from distal sites to the functional and catalytic regions. The framework here applied could be extended to other protein targets to aid the classification of missense mutations from cancer genomics studies, as well as to prioritize variants for experimental validation, or to select the appropriate biological readouts for experiments.
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Affiliation(s)
- Mukesh Kumar
- Computational Biology Laboratory, Center for Autophagy, Recycling and Disease (CARD), Danish Cancer Society Research Center, Strandboulevarden 49, 2100, Copenhagen, Denmark
| | - Elena Papaleo
- Computational Biology Laboratory, Center for Autophagy, Recycling and Disease (CARD), Danish Cancer Society Research Center, Strandboulevarden 49, 2100, Copenhagen, Denmark.
- Translational Disease System Biology, Faculty of Health and Medical Sciences, Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Denmark.
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81
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Cohen M, Pignatti E, Dines M, Mory A, Ekhilevitch N, Kolodny R, Flück CE, Tiosano D. In Silico Structural and Biochemical Functional Analysis of a Novel CYP21A2 Pathogenic Variant. Int J Mol Sci 2020; 21:ijms21165857. [PMID: 32824094 PMCID: PMC7461554 DOI: 10.3390/ijms21165857] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2020] [Revised: 08/06/2020] [Accepted: 08/07/2020] [Indexed: 12/12/2022] Open
Abstract
Classical congenital adrenal hyperplasia (CAH) caused by pathogenic variants in the steroid 21-hydroxylase gene (CYP21A2) is a severe life-threatening condition. We present a detailed investigation of the molecular and functional characteristics of a novel pathogenic variant in this gene. The patient, 46 XX newborn, was diagnosed with classical salt wasting CAH in the neonatal period after initially presenting with ambiguous genitalia. Multiplex ligation-dependent probe analysis demonstrated a full deletion of the paternal CYP21A2 gene, and Sanger sequencing revealed a novel de novo CYP21A2 variant c.694–696del (E232del) in the other allele. This variant resulted in the deletion of a non-conserved single amino acid, and its functional relevance was initially undetermined. We used both in silico and in vitro methods to determine the mechanistic significance of this mutation. Computational analysis relied on the solved structure of the protein (Protein-data-bank ID 4Y8W), structure prediction of the mutated protein, evolutionary analysis, and manual inspection. We predicted impaired stability and functionality of the protein due to a rotatory disposition of amino acids in positions downstream of the deletion. In vitro biochemical evaluation of enzymatic activity supported these predictions, demonstrating reduced protein levels to 22% compared to the wild-type form and decreased hydroxylase activity to 1–4%. This case demonstrates the potential of combining in-silico analysis based on evolutionary information and structure prediction with biochemical studies. This approach can be used to investigate other genetic variants to understand their potential effects.
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Affiliation(s)
- Michal Cohen
- Pediatric Endocrinology Unit, Ruth Rappaport Children’s Hospital, Rambam Healthcare Campus, Haifa 352540, Israel;
- The Ruth and Bruce Rappaport Faculty of Medicine, Technion, Israel Institute of Technology, Haifa 352540, Israel
- Correspondence:
| | - Emanuele Pignatti
- Pediatric Endocrinology, Diabetology and Metabolism, Department of Pediatrics, University Hospital Inselspital, University of Bern, 3010 Bern, Switzerland; (E.P.); (C.E.F.)
- Department of BioMedical Research, University Hospital Inselspital, University of Bern, 3010 Bern, Switzerland
| | - Monica Dines
- Sagol Department of Neurobiology, University of Haifa, Mount Carmel, Haifa 31905, Israel;
| | - Adi Mory
- Genetics Institute, Rambam Health Care Campus, Haifa 3525408, Israel; (A.M.); (N.E.)
| | - Nina Ekhilevitch
- Genetics Institute, Rambam Health Care Campus, Haifa 3525408, Israel; (A.M.); (N.E.)
| | - Rachel Kolodny
- Department of Computer Science, University of Haifa, Mount Carmel, Haifa 3498838, Israel;
| | - Christa E. Flück
- Pediatric Endocrinology, Diabetology and Metabolism, Department of Pediatrics, University Hospital Inselspital, University of Bern, 3010 Bern, Switzerland; (E.P.); (C.E.F.)
- Department of BioMedical Research, University Hospital Inselspital, University of Bern, 3010 Bern, Switzerland
| | - Dov Tiosano
- Pediatric Endocrinology Unit, Ruth Rappaport Children’s Hospital, Rambam Healthcare Campus, Haifa 352540, Israel;
- The Ruth and Bruce Rappaport Faculty of Medicine, Technion, Israel Institute of Technology, Haifa 352540, Israel
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82
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Glazer AM, Wada Y, Li B, Muhammad A, Kalash OR, O'Neill MJ, Shields T, Hall L, Short L, Blair MA, Kroncke BM, Capra JA, Roden DM. High-Throughput Reclassification of SCN5A Variants. Am J Hum Genet 2020; 107:111-123. [PMID: 32533946 PMCID: PMC7332654 DOI: 10.1016/j.ajhg.2020.05.015] [Citation(s) in RCA: 85] [Impact Index Per Article: 21.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2019] [Accepted: 05/19/2020] [Indexed: 12/19/2022] Open
Abstract
Partial or complete loss-of-function variants in SCN5A are the most common genetic cause of the arrhythmia disorder Brugada syndrome (BrS1). However, the pathogenicity of SCN5A variants is often unknown or disputed; 80% of the 1,390 SCN5A missense variants observed in at least one individual to date are variants of uncertain significance (VUSs). The designation of VUS is a barrier to the use of sequence data in clinical care. We selected 83 variants: 10 previously studied control variants, 10 suspected benign variants, and 63 suspected Brugada syndrome-associated variants, selected on the basis of their frequency in the general population and in individuals with Brugada syndrome. We used high-throughput automated patch clamping to study the function of the 83 variants, with the goal of reclassifying variants with functional data. The ten previously studied controls had functional properties concordant with published manual patch clamp data. All 10 suspected benign variants had wild-type-like function. 22 suspected BrS variants had loss of channel function (<10% normalized peak current) and 22 variants had partial loss of function (10%-50% normalized peak current). The previously unstudied variants were initially classified as likely benign (n = 2), likely pathogenic (n = 10), or VUSs (n = 61). After the patch clamp studies, 16 variants were benign/likely benign, 45 were pathogenic/likely pathogenic, and only 12 were still VUSs. Structural modeling identified likely mechanisms for loss of function including altered thermostability and disruptions to alpha helices, disulfide bonds, or the permeation pore. High-throughput patch clamping enabled reclassification of the majority of tested VUSs in SCN5A.
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Affiliation(s)
- Andrew M Glazer
- Vanderbilt Center for Arrhythmia Research and Therapeutics, Division of Clinical Pharmacology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Yuko Wada
- Vanderbilt Center for Arrhythmia Research and Therapeutics, Division of Clinical Pharmacology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Bian Li
- Department of Biological Sciences, Center for Structural Biology, and Vanderbilt Genetics Institute, Vanderbilt University, Nashville, TN 37235, USA
| | - Ayesha Muhammad
- Vanderbilt Center for Arrhythmia Research and Therapeutics, Division of Clinical Pharmacology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Olivia R Kalash
- Vanderbilt Center for Arrhythmia Research and Therapeutics, Division of Clinical Pharmacology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Matthew J O'Neill
- Vanderbilt Center for Arrhythmia Research and Therapeutics, Division of Clinical Pharmacology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Tiffany Shields
- Vanderbilt Center for Arrhythmia Research and Therapeutics, Division of Clinical Pharmacology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Lynn Hall
- Vanderbilt Center for Arrhythmia Research and Therapeutics, Division of Clinical Pharmacology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Laura Short
- Vanderbilt Center for Arrhythmia Research and Therapeutics, Division of Clinical Pharmacology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Marcia A Blair
- Vanderbilt Center for Arrhythmia Research and Therapeutics, Division of Clinical Pharmacology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Brett M Kroncke
- Vanderbilt Center for Arrhythmia Research and Therapeutics, Division of Clinical Pharmacology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, USA; Department of Pharmacology, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - John A Capra
- Department of Biological Sciences, Center for Structural Biology, and Vanderbilt Genetics Institute, Vanderbilt University, Nashville, TN 37235, USA
| | - Dan M Roden
- Vanderbilt Center for Arrhythmia Research and Therapeutics, Division of Clinical Pharmacology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, USA; Department of Pharmacology, Vanderbilt University Medical Center, Nashville, TN 37232, USA; Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37232, USA.
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83
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Rogers JM. Peptide Folding and Binding Probed by Systematic Non-canonical Mutagenesis. Front Mol Biosci 2020; 7:100. [PMID: 32671094 PMCID: PMC7326784 DOI: 10.3389/fmolb.2020.00100] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2020] [Accepted: 05/04/2020] [Indexed: 12/20/2022] Open
Abstract
Many proteins and peptides fold upon binding another protein. Mutagenesis has proved an essential tool in the study of these multi-step molecular recognition processes. By comparing the biophysical behavior of carefully selected mutants, the concert of interactions and conformational changes that occur during folding and binding can be separated and assessed. Recently, this mutagenesis approach has been radically expanded by deep mutational scanning methods, which allow for many thousands of mutations to be examined in parallel. Furthermore, these high-throughput mutagenesis methods have been expanded to include mutations to non-canonical amino acids, returning peptide structure-activity relationships with unprecedented depth and detail. These developments are timely, as the insights they provide can guide the optimization of de novo cyclic peptides, a promising new modality for chemical probes and therapeutic agents.
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Affiliation(s)
- Joseph M Rogers
- Department of Drug Design and Pharmacology, University of Copenhagen, Copenhagen, Denmark
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84
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Broom A, Trainor K, Jacobi Z, Meiering EM. Computational Modeling of Protein Stability: Quantitative Analysis Reveals Solutions to Pervasive Problems. Structure 2020; 28:717-726.e3. [DOI: 10.1016/j.str.2020.04.003] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2019] [Revised: 03/26/2020] [Accepted: 04/06/2020] [Indexed: 12/20/2022]
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85
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Shamsi Z, Chan M, Shukla D. TLmutation: Predicting the Effects of Mutations Using Transfer Learning. J Phys Chem B 2020; 124:3845-3854. [PMID: 32308006 DOI: 10.1021/acs.jpcb.0c00197] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
A reccurring challenge in bioinformatics is predicting the phenotypic consequence of amino acid variation in proteins. With the recent advancements in sequencing techniques, sufficient genomic data has become available to train models that predict the evolutionary statistical energies, but there is still inadequate experimental data to directly predict functional effects. One approach to overcome this data scarcity is to apply transfer learning and train more models with available data sets. In this study, we propose a set of transfer learning algorithms we call TLmutation, which implements a supervised transfer learning algorithm that transfers knowledge from survival data of a protein to a particular function of that protein. This is followed by an unsupervised transfer learning algorithm that extends the knowledge to a homologous protein. We explore the application of our algorithms in three cases. First, we test the supervised transfer on 17 previously published deep mutagenesis data sets to complete and refine missing data points. We further investigate these data sets to identify which mutations build better predictors of variant functions. In the second case, we apply the algorithm to predict higher-order mutations solely from single point mutagenesis data. Finally, we perform the unsupervised transfer learning algorithm to predict mutational effects of homologous proteins from experimental data sets. These algorithms are generalized to transfer knowledge between Markov random field models. We show the benefit of our transfer learning algorithms to utilize informative deep mutational data and provide new insights into protein variant functions. As these algorithms are generalized to transfer knowledge between Markov random field models, we expect these algorithms to be applicable to other disciplines.
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Affiliation(s)
- Zahra Shamsi
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Matthew Chan
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Diwakar Shukla
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Center for Biophysics and Quantitative Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Cancer Center at Illinois, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,NIH Center for Macromolecular Modeling and Bioinformatics, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
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86
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Reeb J, Wirth T, Rost B. Variant effect predictions capture some aspects of deep mutational scanning experiments. BMC Bioinformatics 2020; 21:107. [PMID: 32183714 PMCID: PMC7077003 DOI: 10.1186/s12859-020-3439-4] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2019] [Accepted: 03/03/2020] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Deep mutational scanning (DMS) studies exploit the mutational landscape of sequence variation by systematically and comprehensively assaying the effect of single amino acid variants (SAVs; also referred to as missense mutations, or non-synonymous Single Nucleotide Variants - missense SNVs or nsSNVs) for particular proteins. We assembled SAV annotations from 22 different DMS experiments and normalized the effect scores to evaluate variant effect prediction methods. Three trained on traditional variant effect data (PolyPhen-2, SIFT, SNAP2), a regression method optimized on DMS data (Envision), and a naïve prediction using conservation information from homologs. RESULTS On a set of 32,981 SAVs, all methods captured some aspects of the experimental effect scores, albeit not the same. Traditional methods such as SNAP2 correlated slightly more with measurements and better classified binary states (effect or neutral). Envision appeared to better estimate the precise degree of effect. Most surprising was that the simple naïve conservation approach using PSI-BLAST in many cases outperformed other methods. All methods captured beneficial effects (gain-of-function) significantly worse than deleterious (loss-of-function). For the few proteins with multiple independent experimental measurements, experiments differed substantially, but agreed more with each other than with predictions. CONCLUSIONS DMS provides a new powerful experimental means of understanding the dynamics of the protein sequence space. As always, promising new beginnings have to overcome challenges. While our results demonstrated that DMS will be crucial to improve variant effect prediction methods, data diversity hindered simplification and generalization.
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Affiliation(s)
- Jonas Reeb
- Department of Informatics, Bioinformatics & Computational Biology - i12, TUM (Technical University of Munich), Boltzmannstr 3, 85748, Garching/Munich, Germany.
| | - Theresa Wirth
- Department of Informatics, Bioinformatics & Computational Biology - i12, TUM (Technical University of Munich), Boltzmannstr 3, 85748, Garching/Munich, Germany
| | - Burkhard Rost
- Department of Informatics, Bioinformatics & Computational Biology - i12, TUM (Technical University of Munich), Boltzmannstr 3, 85748, Garching/Munich, Germany
- Institute for Advanced Study (TUM-IAS), Lichtenbergstr 2a, 85748, Garching/Munich, Germany
- TUM School of Life Sciences Weihenstephan (WZW), Alte Akademie 8, Freising, Germany
- Department of Biochemistry and Molecular Biophysics, Columbia University, 701 West, 168th Street, New York, NY, 10032, USA
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87
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Penn WD, McKee AG, Kuntz CP, Woods H, Nash V, Gruenhagen TC, Roushar FJ, Chandak M, Hemmerich C, Rusch DB, Meiler J, Schlebach JP. Probing biophysical sequence constraints within the transmembrane domains of rhodopsin by deep mutational scanning. SCIENCE ADVANCES 2020; 6:eaay7505. [PMID: 32181350 PMCID: PMC7056298 DOI: 10.1126/sciadv.aay7505] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/15/2019] [Accepted: 12/11/2019] [Indexed: 05/15/2023]
Abstract
Membrane proteins must balance the sequence constraints associated with folding and function against the hydrophobicity required for solvation within the bilayer. We recently found the expression and maturation of rhodopsin are limited by the hydrophobicity of its seventh transmembrane domain (TM7), which contains polar residues that are essential for function. On the basis of these observations, we hypothesized that rhodopsin's expression should be less tolerant of mutations in TM7 relative to those within hydrophobic TM domains. To test this hypothesis, we used deep mutational scanning to compare the effects of 808 missense mutations on the plasma membrane expression of rhodopsin in HEK293T cells. Our results confirm that a higher proportion of mutations within TM7 (37%) decrease rhodopsin's plasma membrane expression relative to those within a hydrophobic TM domain (TM2, 25%). These results in conjunction with an evolutionary analysis suggest solvation energetics likely restricts the evolutionary sequence space of polar TM domains.
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Affiliation(s)
- Wesley D. Penn
- Department of Chemistry, Indiana University, Bloomington, IN 47405, USA
| | - Andrew G. McKee
- Department of Chemistry, Indiana University, Bloomington, IN 47405, USA
| | - Charles P. Kuntz
- Department of Chemistry, Indiana University, Bloomington, IN 47405, USA
| | - Hope Woods
- Department of Chemistry, Vanderbilt University, Nashville, TN 37235, USA
- Chemical and Physical Biology Program, Vanderbilt University, Nashville, TN 37235, USA
| | - Veronica Nash
- Department of Chemistry, Indiana University, Bloomington, IN 47405, USA
| | | | | | - Mahesh Chandak
- Department of Chemistry, Indiana University, Bloomington, IN 47405, USA
| | - Chris Hemmerich
- Center for Genomics and Bioinformatics, Indiana University, Bloomington, IN 47405, USA
| | - Douglas B. Rusch
- Center for Genomics and Bioinformatics, Indiana University, Bloomington, IN 47405, USA
| | - Jens Meiler
- Department of Chemistry, Vanderbilt University, Nashville, TN 37235, USA
| | - Jonathan P. Schlebach
- Department of Chemistry, Indiana University, Bloomington, IN 47405, USA
- Corresponding author.
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88
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Alderson TR, Kay LE. Unveiling invisible protein states with NMR spectroscopy. Curr Opin Struct Biol 2020; 60:39-49. [DOI: 10.1016/j.sbi.2019.10.008] [Citation(s) in RCA: 51] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2019] [Accepted: 10/28/2019] [Indexed: 12/24/2022]
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89
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Gopi S, Devanshu D, Rajasekaran N, Anantakrishnan S, Naganathan AN. pPerturb: A Server for Predicting Long-Distance Energetic Couplings and Mutation-Induced Stability Changes in Proteins via Perturbations. ACS OMEGA 2020; 5:1142-1146. [PMID: 31984271 PMCID: PMC6977024 DOI: 10.1021/acsomega.9b03371] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/11/2019] [Accepted: 12/20/2019] [Indexed: 05/02/2023]
Abstract
The strength of intraprotein interactions or contact network is one of the dominant factors determining the thermodynamic stabilities of proteins. The nature and the extent of connectivity of this network also play a role in allosteric signal propagation characteristics upon ligand binding to a protein domain. Here, we develop a server for rapid quantification of the strength of an interaction network by employing an experimentally consistent perturbation approach previously validated against a large data set of 375 mutations in 19 different proteins. The web server can be employed to predict the extent of destabilization of proteins arising from mutations in the protein interior in experimentally relevant units. Moreover, coupling distances-a measure of the extent of percolation on perturbation-and overall perturbation magnitudes are predicted in a residue-specific manner, enabling a first look at the distribution of energetic couplings in a protein or its changes upon ligand binding. We show specific examples of how the server can be employed to probe for the distribution of local stabilities in a protein, to examine changes in side chain orientations or packing before and after ligand binding, and to predict changes in stabilities of proteins upon mutations of buried residues. The web server is freely available at http://pbl.biotech.iitm.ac.in/pPerturb and supports recent versions of all major browsers.
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Affiliation(s)
- Soundhararajan Gopi
- Department
of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras (IITM), Chennai 600036, India
| | - Devanshu Devanshu
- Department
of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras (IITM), Chennai 600036, India
| | - Nandakumar Rajasekaran
- Department
of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras (IITM), Chennai 600036, India
- Department
of Biology, Johns Hopkins University, Baltimore, Maryland 21218, United States
| | - Sathvik Anantakrishnan
- Department
of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras (IITM), Chennai 600036, India
| | - Athi N. Naganathan
- Department
of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras (IITM), Chennai 600036, India
- E-mail:
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90
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Galano-Frutos JJ, García-Cebollada H, Sancho J. Molecular dynamics simulations for genetic interpretation in protein coding regions: where we are, where to go and when. Brief Bioinform 2019; 22:3-19. [PMID: 31813950 DOI: 10.1093/bib/bbz146] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2019] [Revised: 09/22/2019] [Accepted: 10/25/2019] [Indexed: 12/18/2022] Open
Abstract
The increasing ease with which massive genetic information can be obtained from patients or healthy individuals has stimulated the development of interpretive bioinformatics tools as aids in clinical practice. Most such tools analyze evolutionary information and simple physical-chemical properties to predict whether replacement of one amino acid residue with another will be tolerated or cause disease. Those approaches achieve up to 80-85% accuracy as binary classifiers (neutral/pathogenic). As such accuracy is insufficient for medical decision to be based on, and it does not appear to be increasing, more precise methods, such as full-atom molecular dynamics (MD) simulations in explicit solvent, are also discussed. Then, to describe the goal of interpreting human genetic variations at large scale through MD simulations, we restrictively refer to all possible protein variants carrying single-amino-acid substitutions arising from single-nucleotide variations as the human variome. We calculate its size and develop a simple model that allows calculating the simulation time needed to have a 0.99 probability of observing unfolding events of any unstable variant. The knowledge of that time enables performing a binary classification of the variants (stable-potentially neutral/unstable-pathogenic). Our model indicates that the human variome cannot be simulated with present computing capabilities. However, if they continue to increase as per Moore's law, it could be simulated (at 65°C) spending only 3 years in the task if we started in 2031. The simulation of individual protein variomes is achievable in short times starting at present. International coordination seems appropriate to embark upon massive MD simulations of protein variants.
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Affiliation(s)
- Juan J Galano-Frutos
- Protein Folding and Molecular Design (ProtMol)' group at BIFI, University of Zaragoza
| | | | - Javier Sancho
- Protein Folding and Molecular Design (ProtMol)' group at BIFI, University of Zaragoza
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91
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Tang N, Sandahl TD, Ott P, Kepp KP. Computing the Pathogenicity of Wilson's Disease ATP7B Mutations: Implications for Disease Prevalence. J Chem Inf Model 2019; 59:5230-5243. [PMID: 31751128 DOI: 10.1021/acs.jcim.9b00852] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Genetic variations in the gene encoding the copper-transport protein ATP7B are the primary cause of Wilson's disease. Controversially, clinical prevalence seems much smaller than the prevalence estimated by genetic screening tools, causing fear that many people are undiagnosed, although early diagnosis and treatment is essential. To address this issue, we benchmarked 16 state-of-the-art computational disease-prediction methods against established data of missense ATP7B mutations. Our results show that the quality of the methods varies widely. We show the importance of optimizing the threshold of the methods used to distinguish pathogenic from nonpathogenic mutations against data of clinically confirmed pathogenic and nonpathogenic mutations. We find that most methods use thresholds that predict too many ATP7B mutations to be pathogenic. Thus, our findings explain the current controversy on Wilson's disease prevalence because meta-analysis and text search methods include many computational estimates that lead to higher disease prevalence than clinically observed. As proteins and diseases differ widely, a one-size-fits-all threshold cannot distinguish pathogenic and nonpathogenic mutations efficiently, as shown here. We also show that amino acid changes with small evolutionary substitution probability, mainly due to amino acid volume, are more associated with the disease, implying a pathological effect on the conformational state of the protein, which could affect copper transport or adenosine triphosphate recognition and hydrolysis. These findings may be a first step toward a more quantitative genotype-phenotype relationship of Wilson's disease.
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Affiliation(s)
- Ning Tang
- DTU Chemistry , Technical University of Denmark , Kemitorvet 206 , 2800 Kongens Lyngby , Denmark
| | - Thomas D Sandahl
- Department of Hepatology and Gastroenterology , Aarhus University Hospital , 8200 Aarhus , Denmark
| | - Peter Ott
- Department of Hepatology and Gastroenterology , Aarhus University Hospital , 8200 Aarhus , Denmark
| | - Kasper P Kepp
- DTU Chemistry , Technical University of Denmark , Kemitorvet 206 , 2800 Kongens Lyngby , Denmark
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92
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Caswell RC, Owens MM, Gunning AC, Ellard S, Wright CF. Using Structural Analysis In Silico to Assess the Impact of Missense Variants in MEN1. J Endocr Soc 2019; 3:2258-2275. [PMID: 31737856 PMCID: PMC6846327 DOI: 10.1210/js.2019-00260] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/11/2019] [Accepted: 09/20/2019] [Indexed: 12/11/2022] Open
Abstract
Despite the rapid expansion in recent years of databases reporting either benign or pathogenic genetic variations, the interpretation of novel missense variants remains challenging, particularly for clinical or genetic testing laboratories where functional analysis is often unfeasible. Previous studies have shown that thermodynamic analysis of protein structure in silico can discriminate between groups of benign and pathogenic missense variants. However, although structures exist for many human disease‒associated proteins, such analysis remains largely unexploited in clinical laboratories. Here, we analyzed the predicted effect of 338 known missense variants on the structure of menin, the MEN1 gene product. Results provided strong discrimination between pathogenic and benign variants, with a threshold of >4 kcal/mol for the predicted change in stability, providing a strong indicator of pathogenicity. Subsequent analysis of seven novel missense variants identified during clinical testing of patients with MEN1 showed that all seven were predicted to destabilize menin by >4 kcal/mol. We conclude that structural analysis provides a useful tool in understanding the effect of missense variants in MEN1 and that integration of proteomic with genomic data could potentially contribute to the classification of novel variants in this disease.
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Affiliation(s)
- Richard C Caswell
- Institute of Biomedical and Clinical Science, University of Exeter School of Medicine, Exeter, United Kingdom
| | - Martina M Owens
- Department of Molecular Genetics, Royal Devon and Exeter NHS Foundation Trust, Exeter, United Kingdom
| | - Adam C Gunning
- Institute of Biomedical and Clinical Science, University of Exeter School of Medicine, Exeter, United Kingdom
| | - Sian Ellard
- Department of Molecular Genetics, Royal Devon and Exeter NHS Foundation Trust, Exeter, United Kingdom
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93
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Abildgaard AB, Stein A, Nielsen SV, Schultz-Knudsen K, Papaleo E, Shrikhande A, Hoffmann ER, Bernstein I, Gerdes AM, Takahashi M, Ishioka C, Lindorff-Larsen K, Hartmann-Petersen R. Computational and cellular studies reveal structural destabilization and degradation of MLH1 variants in Lynch syndrome. eLife 2019; 8:e49138. [PMID: 31697235 PMCID: PMC6837844 DOI: 10.7554/elife.49138] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2019] [Accepted: 10/23/2019] [Indexed: 12/13/2022] Open
Abstract
Defective mismatch repair leads to increased mutation rates, and germline loss-of-function variants in the repair component MLH1 cause the hereditary cancer predisposition disorder known as Lynch syndrome. Early diagnosis is important, but complicated by many variants being of unknown significance. Here we show that a majority of the disease-linked MLH1 variants we studied are present at reduced cellular levels. We show that destabilized MLH1 variants are targeted for chaperone-assisted proteasomal degradation, resulting also in degradation of co-factors PMS1 and PMS2. In silico saturation mutagenesis and computational predictions of thermodynamic stability of MLH1 missense variants revealed a correlation between structural destabilization, reduced steady-state levels and loss-of-function. Thus, we suggest that loss of stability and cellular degradation is an important mechanism underlying many MLH1 variants in Lynch syndrome. Combined with analyses of conservation, the thermodynamic stability predictions separate disease-linked from benign MLH1 variants, and therefore hold potential for Lynch syndrome diagnostics.
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Affiliation(s)
- Amanda B Abildgaard
- Department of Biology, The Linderstrøm-Lang Centre for Protein ScienceUniversity of CopenhagenCopenhagenDenmark
| | - Amelie Stein
- Department of Biology, The Linderstrøm-Lang Centre for Protein ScienceUniversity of CopenhagenCopenhagenDenmark
| | - Sofie V Nielsen
- Department of Biology, The Linderstrøm-Lang Centre for Protein ScienceUniversity of CopenhagenCopenhagenDenmark
| | - Katrine Schultz-Knudsen
- Department of Biology, The Linderstrøm-Lang Centre for Protein ScienceUniversity of CopenhagenCopenhagenDenmark
| | - Elena Papaleo
- Department of Biology, The Linderstrøm-Lang Centre for Protein ScienceUniversity of CopenhagenCopenhagenDenmark
| | - Amruta Shrikhande
- DNRF Center for Chromosome Stability, Department of Cellular and Molecular MedicineUniversity of CopenhagenCopenhagenDenmark
| | - Eva R Hoffmann
- DNRF Center for Chromosome Stability, Department of Cellular and Molecular MedicineUniversity of CopenhagenCopenhagenDenmark
| | - Inge Bernstein
- Department of Surgical GastroenterologyAalborg University HospitalAalborgDenmark
| | | | - Masanobu Takahashi
- Department of Medical OncologyTohoku University Hospital, Tohoku UniversitySendaiJapan
| | - Chikashi Ishioka
- Department of Medical OncologyTohoku University Hospital, Tohoku UniversitySendaiJapan
| | - Kresten Lindorff-Larsen
- Department of Biology, The Linderstrøm-Lang Centre for Protein ScienceUniversity of CopenhagenCopenhagenDenmark
| | - Rasmus Hartmann-Petersen
- Department of Biology, The Linderstrøm-Lang Centre for Protein ScienceUniversity of CopenhagenCopenhagenDenmark
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94
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Srivastava Y, Tan DS, Malik V, Weng M, Javed A, Cojocaru V, Wu G, Veerapandian V, Cheung LWT, Jauch R. Cancer-associated missense mutations enhance the pluripotency reprogramming activity of OCT4 and SOX17. FEBS J 2019; 287:122-144. [PMID: 31569299 DOI: 10.1111/febs.15076] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2019] [Revised: 07/26/2019] [Accepted: 09/29/2019] [Indexed: 12/21/2022]
Abstract
The functional consequences of cancer-associated missense mutations are unclear for the majority of proteins. We have previously demonstrated that the activity of SOX and Pit-Oct-Unc (POU) family factors during pluripotency reprogramming can be switched and enhanced with rationally placed point mutations. Here, we interrogated cancer mutation databases and identified recurrently mutated positions at critical structural interfaces of the DNA-binding domains of paralogous SOX and POU family transcription factors. Using the conversion of mouse embryonic fibroblasts to induced pluripotent stem cells as functional readout, we identified several gain-of-function mutations that enhance pluripotency reprogramming by SOX2 and OCT4. Wild-type SOX17 cannot support reprogramming but the recurrent missense mutation SOX17-V118M is capable of inducing pluripotency. Furthermore, SOX17-V118M promotes oncogenic transformation, enhances thermostability and elevates cellular protein levels of SOX17. We conclude that the mutational profile of SOX and POU family factors in cancer can guide the design of high-performance reprogramming factors. Furthermore, we propose cellular reprogramming as a suitable assay to study the functional impact of cancer-associated mutations.
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Affiliation(s)
- Yogesh Srivastava
- CAS Key Laboratory of Regenerative Biology, Joint School of Life Sciences, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China.,Guangzhou Medical University, China.,Genome Regulation Laboratory, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Daisylyn Senna Tan
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Vikas Malik
- CAS Key Laboratory of Regenerative Biology, Joint School of Life Sciences, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China.,Guangzhou Medical University, China.,Genome Regulation Laboratory, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Mingxi Weng
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Asif Javed
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Vlad Cojocaru
- Department of Cell and Developmental Biology, Max Planck Institute for Molecular Biomedicine, Münster, Germany
| | - Guangming Wu
- Department of Cell and Developmental Biology, Max Planck Institute for Molecular Biomedicine, Münster, Germany
| | - Veeramohan Veerapandian
- CAS Key Laboratory of Regenerative Biology, Joint School of Life Sciences, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China.,Guangzhou Medical University, China.,Genome Regulation Laboratory, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China.,University of Chinese Academy of Sciences, Beijing, China.,Department of Developmental Biology, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Lydia W T Cheung
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Ralf Jauch
- CAS Key Laboratory of Regenerative Biology, Joint School of Life Sciences, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China.,Guangzhou Medical University, China.,Genome Regulation Laboratory, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China.,School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
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95
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Karstensen HG, Rendtorff ND, Hindbæk LS, Colombo R, Stein A, Birkebæk NH, Hartmann-Petersen R, Lindorff-Larsen K, Højland AT, Petersen MB, Tranebjærg L. Novel HARS2 missense variants identified in individuals with sensorineural hearing impairment and Perrault syndrome. Eur J Med Genet 2019; 63:103733. [PMID: 31449985 DOI: 10.1016/j.ejmg.2019.103733] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2019] [Revised: 07/16/2019] [Accepted: 07/22/2019] [Indexed: 11/17/2022]
Abstract
Biallelic variants in HARS2 have been associated with Perrault syndrome, characterized by sensorineural hearing impairment and premature ovarian insufficiency. Here we report three novel families, compound heterozygous for missense variants in HARS2 identified by next-generation sequencing, namely c.172A > G (p.Lys58Glu) and c.448C > T (p.Arg150Cys) identified in two sisters aged 13 and 16 years and their older brother, c.448C > T (p.Arg150Cys) and c.980G > A (p.Arg327Gln) identified in a seven year old girl, and finally c.137T > A (p.Leu46Gln) and c.259C > T (p.Arg87Cys) identified in a 32 year old woman. Clinically, all five individuals presented with early onset, rapidly progressive hearing impairment. Whereas the oldest female fulfilled the criteria of Perrault syndrome, the three younger females, aged 7, 13 and 16, all had apparently normal ovarian function, apart from irregular menstrual periods in the oldest female at age 16. The present report expands the list of HARS2 variants and helps gain further knowledge to the phenotype.
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Affiliation(s)
- Helena Gásdal Karstensen
- The Kennedy Center, Department of Clinical Genetics, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Nanna Dahl Rendtorff
- The Kennedy Center, Department of Clinical Genetics, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark.
| | - Lone Sandbjerg Hindbæk
- The Kennedy Center, Department of Clinical Genetics, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Roberto Colombo
- Institute of Clinical Biochemistry, Faculty of Medicine, Catholic University, IRCCS Policlinico Agostino Gemelli, Rome, Italy; Centro per Lo Studio Delle Malattie Ereditarie Rare, Niguarda Ca' Granda Metropolitan Hospital, Milan, Italy
| | - Amelie Stein
- The Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Denmark
| | | | - Rasmus Hartmann-Petersen
- The Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Denmark
| | - Kresten Lindorff-Larsen
- The Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Denmark
| | - Allan Thomas Højland
- Department of Clinical Genetics, Aalborg University Hospital, Denmark; Department of Clinical Medicine, Aalborg University Hospital, Denmark
| | - Michael Bjørn Petersen
- Department of Clinical Genetics, Aalborg University Hospital, Denmark; Department of Clinical Medicine, Aalborg University Hospital, Denmark
| | - Lisbeth Tranebjærg
- The Kennedy Center, Department of Clinical Genetics, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark; Institute of Clinical Medicine, The Panum Institute, University of Copenhagen, Denmark
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Beaver SK, Mesa-Torres N, Pey AL, Timson DJ. NQO1: A target for the treatment of cancer and neurological diseases, and a model to understand loss of function disease mechanisms. BIOCHIMICA ET BIOPHYSICA ACTA-PROTEINS AND PROTEOMICS 2019; 1867:663-676. [PMID: 31091472 DOI: 10.1016/j.bbapap.2019.05.002] [Citation(s) in RCA: 56] [Impact Index Per Article: 11.2] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Received: 04/15/2019] [Revised: 05/07/2019] [Accepted: 05/09/2019] [Indexed: 01/08/2023]
Abstract
NAD(P)H quinone oxidoreductase 1 (NQO1) is a multi-functional protein that catalyses the reduction of quinones (and other molecules), thus playing roles in xenobiotic detoxification and redox balance, and also has roles in stabilising apoptosis regulators such as p53. The structure and enzymology of NQO1 is well-characterised, showing a substituted enzyme mechanism in which NAD(P)H binds first and reduces an FAD cofactor in the active site, assisted by a charge relay system involving Tyr-155 and His-161. Protein dynamics play important role in physio-pathological aspects of this protein. NQO1 is a good target to treat cancer due to its overexpression in cancer cells. A polymorphic form of NQO1 (p.P187S) is associated with increased cancer risk and certain neurological disorders (such as multiple sclerosis and Alzheimer´s disease), possibly due to its roles in the antioxidant defence. p.P187S has greatly reduced FAD affinity and stability, due to destabilization of the flavin binding site and the C-terminal domain, which leading to reduced activity and enhanced degradation. Suppressor mutations partially restore the activity of p.P187S by local stabilization of these regions, and showing long-range allosteric communication within the protein. Consequently, the correction of NQO1 misfolding by pharmacological chaperones is a viable strategy, which may be useful to treat cancer and some neurological conditions, targeting structural spots linked to specific disease-mechanisms. Thus, NQO1 emerges as a good model to investigate loss of function mechanisms in genetic diseases as well as to improve strategies to discriminate between neutral and pathogenic variants in genome-wide sequencing studies.
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Affiliation(s)
- Sarah K Beaver
- School of Pharmacy and Biomolecular Sciences, University of Brighton, Huxley Building, Lewes Road, Brighton BN2 4GJ, UK
| | - Noel Mesa-Torres
- Department of Physical Chemistry, Faculty of Sciences, University of Granada, Av. Fuentenueva s/n, 18071, Spain
| | - Angel L Pey
- Department of Physical Chemistry, Faculty of Sciences, University of Granada, Av. Fuentenueva s/n, 18071, Spain.
| | - David J Timson
- School of Pharmacy and Biomolecular Sciences, University of Brighton, Huxley Building, Lewes Road, Brighton BN2 4GJ, UK.
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