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Pathira Kankanamge LS, Ruffner LA, Touch MM, Pina M, Beuning PJ, Ondrechen MJ. Functional annotation of haloacid dehalogenase superfamily structural genomics proteins. Biochem J 2023; 480:1553-1569. [PMID: 37747786 DOI: 10.1042/bcj20230057] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Revised: 09/20/2023] [Accepted: 09/25/2023] [Indexed: 09/26/2023]
Abstract
Haloacid dehalogenases (HAD) are members of a large superfamily that includes many Structural Genomics proteins with poorly characterized functionality. This superfamily consists of multiple types of enzymes that can act as sugar phosphatases, haloacid dehalogenases, phosphonoacetaldehyde hydrolases, ATPases, or phosphate monoesterases. Here, we report on predicted functional annotations and experimental testing by direct biochemical assay for Structural Genomics proteins from the HAD superfamily. To characterize the functions of HAD superfamily members, nine representative HAD proteins and 21 structural genomics proteins are analyzed. Using techniques based on computed chemical and electrostatic properties of individual amino acids, the functions of five structural genomics proteins from the HAD superfamily are predicted and validated by biochemical assays. A dehalogenase-like hydrolase, RSc1362 (Uniprot Q8XZN3, PDB 3UMB) is predicted to be a dehalogenase and dehalogenase activity is confirmed experimentally. Four proteins predicted to be sugar phosphatases are characterized as follows: a sugar phosphatase from Thermophilus volcanium (Uniprot Q978Y6) with trehalose-6-phosphate phosphatase and fructose-6-phosphate phosphatase activity; haloacid dehalogenase-like hydrolase from Bacteroides thetaiotaomicron (Uniprot Q8A2F3; PDB 3NIW) with fructose-6-phosphate phosphatase and sucrose-6-phosphate phosphatase activity; putative phosphatase from Eubacterium rectale (Uniprot D0VWU2; PDB 3DAO) as a sucrose-6-phosphate phosphatase; and hypothetical protein from Geobacillus kaustophilus (Uniprot Q5L139; PDB 2PQ0) as a fructose-6-phosphate phosphatase. Most of these sugar phosphatases showed some substrate promiscuity.
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Affiliation(s)
| | - Lydia A Ruffner
- Department of Chemistry and Chemical Biology, Northeastern University, Boston, MA 02115, U.S.A
| | - Mong Mary Touch
- Department of Chemistry and Chemical Biology, Northeastern University, Boston, MA 02115, U.S.A
| | - Manuel Pina
- Department of Chemistry and Chemical Biology, Northeastern University, Boston, MA 02115, U.S.A
| | - Penny J Beuning
- Department of Chemistry and Chemical Biology, Northeastern University, Boston, MA 02115, U.S.A
| | - Mary Jo Ondrechen
- Department of Chemistry and Chemical Biology, Northeastern University, Boston, MA 02115, U.S.A
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2
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Rappoport D, Jinich A. Enzyme Substrate Prediction from Three-Dimensional Feature Representations Using Space-Filling Curves. J Chem Inf Model 2023; 63:1637-1648. [PMID: 36802628 DOI: 10.1021/acs.jcim.3c00005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/22/2023]
Abstract
Compact and interpretable structural feature representations are required for accurately predicting properties and function of proteins. In this work, we construct and evaluate three-dimensional feature representations of protein structures based on space-filling curves (SFCs). We focus on the problem of enzyme substrate prediction, using two ubiquitous enzyme families as case studies: the short-chain dehydrogenase/reductases (SDRs) and the S-adenosylmethionine-dependent methyltransferases (SAM-MTases). Space-filling curves such as the Hilbert curve and the Morton curve generate a reversible mapping from discretized three-dimensional to one-dimensional representations and thus help to encode three-dimensional molecular structures in a system-independent way and with only a few adjustable parameters. Using three-dimensional structures of SDRs and SAM-MTases generated using AlphaFold2, we assess the performance of the SFC-based feature representations in predictions on a new benchmark database of enzyme classification tasks including their cofactor and substrate selectivity. Gradient-boosted tree classifiers yield binary prediction accuracy of 0.77-0.91 and area under curve (AUC) characteristics of 0.83-0.92 for the classification tasks. We investigate the effects of amino acid encoding, spatial orientation, and (the few) parameters of SFC-based encodings on the accuracy of the predictions. Our results suggest that geometry-based approaches such as SFCs are promising for generating protein structural representations and are complementary to the existing protein feature representations such as evolutionary scale modeling (ESM) sequence embeddings.
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Affiliation(s)
- Dmitrij Rappoport
- Department of Chemistry, University of California, Irvine, 1102 Natural Sciences 2, Irvine, California 92697, United States
| | - Adrian Jinich
- Weill Cornell Medicine, 1300 York Avenue, Box 65, New York, New York 10065, United States
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5
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Guterres H, Park SJ, Zhang H, Im W. CHARMM-GUI LBS Finder & Refiner for Ligand Binding Site Prediction and Refinement. J Chem Inf Model 2021; 61:3744-3751. [PMID: 34296608 DOI: 10.1021/acs.jcim.1c00561] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
A protein performs its task by binding a variety of ligands in its local region that is also known as the ligand-binding-site (LBS). Therefore, accurate prediction, characterization, and refinement of LBS can facilitate protein functional annotations and structure-based drug design. In this work, we present CHARMM-GUI LBS Finder & Refiner (https://www.charmm-gui.org/input/lbsfinder) that predicts potential LBS, offers interactive features for local LBS structure analysis, and prepares various molecular dynamics (MD) systems and inputs by setting up distance restraint potentials for LBS structure refinement. LBS Finder & Refiner supports 5 different commonly used simulation programs, such as NAMD, AMBER, GROMACS, GENESIS, and OpenMM, for LBS structure refinement together with hydrogen mass repartitioning. The capability of LBS Finder & Refiner is illustrated through LBS structure predictions and refinements of 48 modeled and 20 apo benchmark target proteins. Overall, successful LBS structure predictions and refinements are seen in our benchmark tests. We hope that LBS Finder & Refiner is useful to predict, characterize, and refine potential LBS on any given protein of interest.
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Affiliation(s)
- Hugo Guterres
- Departments of Biological Sciences, Chemistry, Bioengineering, and Computer Science and Engineering, Lehigh University, Bethlehem, Pennsylvania 18015, United States
| | - Sang-Jun Park
- Departments of Biological Sciences, Chemistry, Bioengineering, and Computer Science and Engineering, Lehigh University, Bethlehem, Pennsylvania 18015, United States
| | - Han Zhang
- Departments of Biological Sciences, Chemistry, Bioengineering, and Computer Science and Engineering, Lehigh University, Bethlehem, Pennsylvania 18015, United States
| | - Wonpil Im
- Departments of Biological Sciences, Chemistry, Bioengineering, and Computer Science and Engineering, Lehigh University, Bethlehem, Pennsylvania 18015, United States
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6
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Bowles B, Ferrer A, Nishimura CJ, Pinto E Vairo F, Rey T, Leheup B, Sullivan J, Schoch K, Stong N, Agolini E, Cocciadiferro D, Williams A, Cummings A, Loddo S, Genovese S, Roadhouse C, McWalter K, Wentzensen IM, Li C, Babovic-Vuksanovic D, Lanpher BC, Dentici ML, Ankala A, Hamm JA, Dallapiccola B, Radio FC, Shashi V, Gérard B, Bloch-Zupan A, Smith RJ, Klee EW. TSPEAR variants are primarily associated with ectodermal dysplasia and tooth agenesis but not hearing loss: A novel cohort study. Am J Med Genet A 2021; 185:2417-2433. [PMID: 34042254 PMCID: PMC8361973 DOI: 10.1002/ajmg.a.62347] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2020] [Revised: 04/12/2021] [Accepted: 04/22/2021] [Indexed: 12/30/2022]
Abstract
Biallelic loss‐of‐function variants in the thrombospondin‐type laminin G domain and epilepsy‐associated repeats (TSPEAR) gene have recently been associated with ectodermal dysplasia and hearing loss. The first reports describing a TSPEAR disease association identified this gene is a cause of nonsyndromic hearing loss, but subsequent reports involving additional affected families have questioned this evidence and suggested a stronger association with ectodermal dysplasia. To clarify genotype–phenotype associations for TSPEAR variants, we characterized 13 individuals with biallelic TSPEAR variants. Individuals underwent either exome sequencing or panel‐based genetic testing. Nearly all of these newly reported individuals (11/13) have phenotypes that include tooth agenesis or ectodermal dysplasia, while three newly reported individuals have hearing loss. Of the individuals displaying hearing loss, all have additional variants in other hearing‐loss‐associated genes, specifically TMPRSS3, GJB2, and GJB6, that present competing candidates for their hearing loss phenotype. When presented alongside previous reports, the overall evidence supports the association of TSPEAR variants with ectodermal dysplasia and tooth agenesis features but creates significant doubt as to whether TSPEAR variants are a monogenic cause of hearing loss. Further functional evidence is needed to evaluate this phenotypic association.
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Affiliation(s)
- Bradley Bowles
- Center for Individualized Medicine, Mayo Clinic, Rochester, Minnesota, USA
| | - Alejandro Ferrer
- Center for Individualized Medicine, Mayo Clinic, Rochester, Minnesota, USA
| | - Carla J Nishimura
- Molecular Otolaryngology and Renal Research Laboratories, University of Iowa Carver College of Medicine, Iowa City, Iowa, USA
| | - Filippo Pinto E Vairo
- Center for Individualized Medicine, Mayo Clinic, Rochester, Minnesota, USA.,Department of Clinical Genomics, Mayo Clinic, Rochester, Minnesota, USA
| | - Tristan Rey
- Faculté de Chirurgie Dentaire, Université de Strasbourg, Strasbourg, France.,Laboratoires de Diagnostic génétique, Pôle de Biologie, Hôpitaux Universitaires de Strasbourg, Institut de Génétique Médicale d'Alsace, Strasbourg, France.,Institut de Génétique et de Biologie Moléculaire et Cellulaire (IGBMC), INSERM U1258, CNRS-UMR7104, Université de Strasbourg, Illkirch, France
| | - Bruno Leheup
- Département de Médecine Infantile, CHRU de Nancy, Nancy, France
| | - Jennifer Sullivan
- Department of Pediatrics, Duke University, Durham, North Carolina, USA
| | - Kelly Schoch
- Department of Pediatrics, Duke University, Durham, North Carolina, USA
| | - Nicholas Stong
- Institute for Genomic Medicine, Columbia University, New York, New York, USA.,Brystol Myers Squibb, New York, New York, USA
| | - Emanuele Agolini
- Laboratory of Medical Genetics, Bambino Gesù Children's Hospital, IRCCS, Rome, Italy
| | - Dario Cocciadiferro
- Laboratory of Medical Genetics, Bambino Gesù Children's Hospital, IRCCS, Rome, Italy
| | - Abigail Williams
- Department of Pediatrics, East Tennessee Children's Hospital, Knoxville, Tennessee, USA
| | - Alex Cummings
- Department of Pediatrics, East Tennessee Children's Hospital, Knoxville, Tennessee, USA.,University of Wisconsin Hospitals and Clinics, Madison, Wisconsin, USA
| | - Sara Loddo
- Laboratory of Medical Genetics, Bambino Gesù Children's Hospital, IRCCS, Rome, Italy
| | - Silvia Genovese
- Laboratory of Medical Genetics, Bambino Gesù Children's Hospital, IRCCS, Rome, Italy
| | - Chelsea Roadhouse
- Department of Pediatrics, McMaster University, Hamilton, Ontario, Canada
| | | | | | | | - Chumei Li
- Department of Pediatrics, McMaster University, Hamilton, Ontario, Canada
| | - Dusica Babovic-Vuksanovic
- Center for Individualized Medicine, Mayo Clinic, Rochester, Minnesota, USA.,Department of Clinical Genomics, Mayo Clinic, Rochester, Minnesota, USA
| | - Brendan C Lanpher
- Center for Individualized Medicine, Mayo Clinic, Rochester, Minnesota, USA.,Department of Clinical Genomics, Mayo Clinic, Rochester, Minnesota, USA
| | - Maria Lisa Dentici
- Genetics and Rare Diseases Research Division, Molecular Genetics and Functional Genomics Research Unit, Bambino Gesù Children's Hospital, IRCCS, Rome, Italy
| | - Arun Ankala
- EGL Genetics LLC, Tucker, Georgia, USA.,Emory University School of Medicine, Atlanta, Georgia, USA
| | - J Austin Hamm
- Department of Pediatrics, East Tennessee Children's Hospital, Knoxville, Tennessee, USA
| | - Bruno Dallapiccola
- Genetics and Rare Diseases Research Division, Molecular Genetics and Functional Genomics Research Unit, Bambino Gesù Children's Hospital, IRCCS, Rome, Italy
| | - Francesca Clementina Radio
- Genetics and Rare Diseases Research Division, Molecular Genetics and Functional Genomics Research Unit, Bambino Gesù Children's Hospital, IRCCS, Rome, Italy
| | - Vandana Shashi
- Department of Pediatrics, Duke University, Durham, North Carolina, USA
| | - Benedicte Gérard
- Laboratoires de Diagnostic génétique, Pôle de Biologie, Hôpitaux Universitaires de Strasbourg, Institut de Génétique Médicale d'Alsace, Strasbourg, France
| | - Agnes Bloch-Zupan
- Faculté de Chirurgie Dentaire, Université de Strasbourg, Strasbourg, France.,Centre de référence des maladies rares orales et dentaires O-Rares, Filière Santé Maladies rares TETE COU, European Reference Network CRANIO, Pôle de Médecine et Chirurgie Bucco-dentaires, Hôpital Civil, Hôpitaux Universitaires de Strasbourg (HUS), Strasbourg, France.,Institut de Génétique et de Biologie Moléculaire et Cellulaire (IGBMC), INSERM U1258, CNRS-UMR7104, Université de Strasbourg, Illkirch, France
| | - Richard J Smith
- Molecular Otolaryngology and Renal Research Laboratories, University of Iowa Carver College of Medicine, Iowa City, Iowa, USA
| | - Eric W Klee
- Center for Individualized Medicine, Mayo Clinic, Rochester, Minnesota, USA
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