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Roterman I, Stapor K, Konieczny L. Transmembrane proteins-Different anchoring systems. Proteins 2024; 92:593-609. [PMID: 38062872 DOI: 10.1002/prot.26646] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Revised: 11/03/2023] [Accepted: 11/17/2023] [Indexed: 04/13/2024]
Abstract
Transmembrane proteins are active in amphipathic environments. To stabilize the protein in such surrounding the exposure of hydrophobic residues on the protein surface is required. Transmembrane proteins are responsible for the transport of various molecules. Therefore, they often represent structures in the form of channels. This analysis focused on the stability and local flexibility of transmembrane proteins, particularly those related to their biological activity. Different forms of anchorage were identified using the fuzzy oil-drop model (FOD) and its modified form, FOD-M. The mainly helical as well as β-barrel structural forms are compared with respect to the mechanism of stabilization in the cell membrane. The different anchoring system was found to stabilize protein molecules with possible local fluctuation.
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Affiliation(s)
- Irena Roterman
- Department of Bioinformatics and Telemedicine, Jagiellonian University-Medical College, Krakow, Poland
| | - Katarzyna Stapor
- Faculty of Automatic, Electronics and Computer Science, Department of Applied Informatics, Silesian University of Technology, Gliwice, Poland
| | - Leszek Konieczny
- Chair of Medical Biochemistry, Jagiellonian University-Medical College, Krakow, Poland
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2
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Christoffer C, Harini K, Archit G, Kihara D. Assembly of Protein Complexes in and on the Membrane with Predicted Spatial Arrangement Constraints. J Mol Biol 2024; 436:168486. [PMID: 38336197 PMCID: PMC10942765 DOI: 10.1016/j.jmb.2024.168486] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Revised: 01/17/2024] [Accepted: 02/05/2024] [Indexed: 02/12/2024]
Abstract
Membrane proteins play crucial roles in various cellular processes, and their interactions with other proteins in and on the membrane are essential for their proper functioning. While an increasing number of structures of more membrane proteins are being determined, the available structure data is still sparse. To gain insights into the mechanisms of membrane protein complexes, computational docking methods are necessary due to the challenge of experimental determination. Here, we introduce Mem-LZerD, a rigid-body membrane docking algorithm designed to take advantage of modern membrane modeling and protein docking techniques to facilitate the docking of membrane protein complexes. Mem-LZerD is based on the LZerD protein docking algorithm, which has been constantly among the top servers in many rounds of CAPRI protein docking assessment. By employing a combination of geometric hashing, newly constrained by the predicted membrane height and tilt angle, and model scoring accounting for the energy of membrane insertion, we demonstrate the capability of Mem-LZerD to model diverse membrane protein-protein complexes. Mem-LZerD successfully performed unbound docking on 13 of 21 (61.9%) transmembrane complexes in an established benchmark, more than shown by previous approaches. It was additionally tested on new datasets of 44 transmembrane complexes and 92 peripheral membrane protein complexes, of which it successfully modeled 35 (79.5%) and 15 (16.3%) complexes respectively. When non-blind orientations of peripheral targets were included, the number of successes increased to 54 (58.7%). We further demonstrate that Mem-LZerD produces complex models which are suitable for molecular dynamics simulation. Mem-LZerD is made available at https://lzerd.kiharalab.org.
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Affiliation(s)
- Charles Christoffer
- Department of Computer Science, Purdue University, West Lafayette, IN 47907, USA
| | - Kannan Harini
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600036, India; Department of Biological Sciences, Purdue University, West Lafayette, IN 47907, USA
| | - Gupta Archit
- Department of Biological Sciences, Purdue University, West Lafayette, IN 47907, USA; Department of Genetic Engineering, SRM Institute of Science and Technology, Kattankulathur 603203, India
| | - Daisuke Kihara
- Department of Computer Science, Purdue University, West Lafayette, IN 47907, USA; Department of Biological Sciences, Purdue University, West Lafayette, IN 47907, USA; Purdue University Center for Cancer Research, Purdue University, West Lafayette, IN 47907, USA.
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3
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Woods H, Leman JK, Meiler J. Modeling membrane geometries implicitly in Rosetta. Protein Sci 2024; 33:e4908. [PMID: 38358133 PMCID: PMC10868433 DOI: 10.1002/pro.4908] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Revised: 01/05/2024] [Accepted: 01/08/2024] [Indexed: 02/16/2024]
Abstract
Interactions between membrane proteins (MPs) and lipid bilayers are critical for many cellular functions. In the Rosetta molecular modeling suite, the implicit membrane energy function is based on a "slab" model, which represent the membrane as a flat bilayer. However, in nature membranes often have a curvature that is important for function and/or stability. Even more prevalent, in structural biology research MPs are reconstituted in model membrane systems such as micelles, bicelles, nanodiscs, or liposomes. Thus, we have modified the existing membrane energy potentials within the RosettaMP framework to allow users to model MPs in different membrane geometries. We show that these modifications can be utilized in core applications within Rosetta such as structure refinement, protein-protein docking, and protein design. For MP structures found in curved membranes, refining these structures in curved, implicit membranes produces higher quality models with structures closer to experimentally determined structures. For MP systems embedded in multiple membranes, representing both membranes results in more favorable scores compared to only representing one of the membranes. Modeling MPs in geometries mimicking the membrane model system used in structure determination can improve model quality and model discrimination.
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Affiliation(s)
- Hope Woods
- Center of Structural Biology, Vanderbilt UniversityNashvilleTennesseeUSA
- Chemical and Physical Biology ProgramVanderbilt UniversityNashvilleTennesseeUSA
| | | | - Jens Meiler
- Center of Structural Biology, Vanderbilt UniversityNashvilleTennesseeUSA
- Department of ChemistryVanderbilt UniversityNashvilleTennesseeUSA
- Institute for Drug Discovery, Leipzig University Medical SchoolLeipzigGermany
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4
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Christoffer C, Harini K, Archit G, Kihara D. Assembly of Protein Complexes In and On the Membrane with Predicted Spatial Arrangement Constraints. bioRxiv 2023:2023.10.20.563303. [PMID: 37961264 PMCID: PMC10634698 DOI: 10.1101/2023.10.20.563303] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Membrane proteins play crucial roles in various cellular processes, and their interactions with other proteins in and on the membrane are essential for their proper functioning. While an increasing number of structures of more membrane proteins are being determined, the available structure data is still sparse. To gain insights into the mechanisms of membrane protein complexes, computational docking methods are necessary due to the challenge of experimental determination. Here, we introduce Mem-LZerD, a rigid-body membrane docking algorithm designed to take advantage of modern membrane modeling and protein docking techniques to facilitate the docking of membrane protein complexes. Mem-LZerD is based on the LZerD protein docking algorithm, which has been constantly among the top servers in many rounds of CAPRI protein docking assessment. By employing a combination of geometric hashing, newly constrained by the predicted membrane height and tilt angle, and model scoring accounting for the energy of membrane insertion, we demonstrate the capability of Mem-LZerD to model diverse membrane protein-protein complexes. Mem-LZerD successfully performed unbound docking on 13 of 21 (61.9%) transmembrane complexes in an established benchmark, more than shown by previous approaches. It was additionally tested on new datasets of 44 transmembrane complexes and 92 peripheral membrane protein complexes, of which it successfully modeled 35 (79.5%) and 15 (16.3%) complexes respectively. When non-blind orientations of peripheral targets were included, the number of successes increased to 54 (58.7%). We further demonstrate that Mem-LZerD produces complex models which are suitable for molecular dynamics simulation. Mem-LZerD is made available at https://lzerd.kiharalab.org.
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Affiliation(s)
- Charles Christoffer
- Department of Computer Science, Purdue University, West Lafayette, IN, 47907, USA
| | - Kannan Harini
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600036, India
- Department of Biological Sciences, Purdue University, West Lafayette, IN, 47907, USA
| | - Gupta Archit
- Department of Biological Sciences, Purdue University, West Lafayette, IN, 47907, USA
- Department of Genetic Engineering, SRM Institute of Science and Technology, Kattankulathur 603203, India
| | - Daisuke Kihara
- Department of Computer Science, Purdue University, West Lafayette, IN, 47907, USA
- Department of Biological Sciences, Purdue University, West Lafayette, IN, 47907, USA
- Purdue University Center for Cancer Research, Purdue University, West Lafayette, IN, 47907, USA
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5
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Miller B, Kim SJ, Mehta HH, Cao K, Kumagai H, Thumaty N, Leelaprachakul N, Braniff RG, Jiao H, Vaughan J, Diedrich J, Saghatelian A, Arpawong TE, Crimmins EM, Ertekin-Taner N, Tubi MA, Hare ET, Braskie MN, Décarie-Spain L, Kanoski SE, Grodstein F, Bennett DA, Zhao L, Toga AW, Wan J, Yen K, Cohen P. Mitochondrial DNA variation in Alzheimer's disease reveals a unique microprotein called SHMOOSE. Mol Psychiatry 2023; 28:1813-1826. [PMID: 36127429 PMCID: PMC10027624 DOI: 10.1038/s41380-022-01769-3] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/05/2021] [Revised: 08/15/2022] [Accepted: 08/26/2022] [Indexed: 01/22/2023]
Abstract
Mitochondrial DNA variants have previously associated with disease, but the underlying mechanisms have been largely elusive. Here, we report that mitochondrial SNP rs2853499 associated with Alzheimer's disease (AD), neuroimaging, and transcriptomics. We mapped rs2853499 to a novel mitochondrial small open reading frame called SHMOOSE with microprotein encoding potential. Indeed, we detected two unique SHMOOSE-derived peptide fragments in mitochondria by using mass spectrometry-the first unique mass spectrometry-based detection of a mitochondrial-encoded microprotein to date. Furthermore, cerebrospinal fluid (CSF) SHMOOSE levels in humans correlated with age, CSF tau, and brain white matter volume. We followed up on these genetic and biochemical findings by carrying out a series of functional experiments. SHMOOSE acted on the brain following intracerebroventricular administration, differentiated mitochondrial gene expression in multiple models, localized to mitochondria, bound the inner mitochondrial membrane protein mitofilin, and boosted mitochondrial oxygen consumption. Altogether, SHMOOSE has vast implications for the fields of neurobiology, Alzheimer's disease, and microproteins.
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Affiliation(s)
- Brendan Miller
- Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, USA
- Neuroscience Graduate Program, University of Southern California, Los Angeles, CA, USA
| | - Su-Jeong Kim
- Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, USA
| | - Hemal H Mehta
- Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, USA
| | - Kevin Cao
- Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, USA
| | - Hiroshi Kumagai
- Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, USA
| | - Neehar Thumaty
- Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, USA
| | - Naphada Leelaprachakul
- Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, USA
| | - Regina Gonzalez Braniff
- Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, USA
| | - Henry Jiao
- Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, USA
| | - Joan Vaughan
- Clayton Foundation Laboratories for Peptide Biology, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Jolene Diedrich
- Clayton Foundation Laboratories for Peptide Biology, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Alan Saghatelian
- Clayton Foundation Laboratories for Peptide Biology, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Thalida E Arpawong
- Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, USA
| | - Eileen M Crimmins
- Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, USA
| | | | - Meral A Tubi
- Imaging Genetics Center, Institute for Neuroimaging and Informatics, University of Southern California, Los Angeles, CA, USA
- Department of Neurology, University of Southern California, Los Angeles, CA, USA
| | - Evan T Hare
- Imaging Genetics Center, Institute for Neuroimaging and Informatics, University of Southern California, Los Angeles, CA, USA
- Department of Neurology, University of Southern California, Los Angeles, CA, USA
| | - Meredith N Braskie
- Imaging Genetics Center, Institute for Neuroimaging and Informatics, University of Southern California, Los Angeles, CA, USA
- Department of Neurology, University of Southern California, Los Angeles, CA, USA
| | - Léa Décarie-Spain
- Human and Evolutionary Biology Section, Department of Biological Sciences, Dornsife College of Letters, Arts and Sciences, University of Southern California, Los Angeles, CA, USA
| | - Scott E Kanoski
- Neuroscience Graduate Program, University of Southern California, Los Angeles, CA, USA
- Human and Evolutionary Biology Section, Department of Biological Sciences, Dornsife College of Letters, Arts and Sciences, University of Southern California, Los Angeles, CA, USA
| | - Francine Grodstein
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA
| | - David A Bennett
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA
| | - Lu Zhao
- Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of University of Southern California, Los Angeles, CA, USA
| | - Arthur W Toga
- Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of University of Southern California, Los Angeles, CA, USA
| | - Junxiang Wan
- Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, USA
| | - Kelvin Yen
- Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, USA
| | - Pinchas Cohen
- Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, USA.
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Jiang V, Khare SD, Banta S. Computational structure prediction provides a plausible mechanism for electron transfer by the outer membrane protein Cyc2 from Acidithiobacillus ferrooxidans. Protein Sci 2021; 30:1640-1652. [PMID: 33969560 DOI: 10.1002/pro.4106] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Revised: 04/30/2021] [Accepted: 05/03/2021] [Indexed: 12/14/2022]
Abstract
Cyc2 is the key protein in the outer membrane of Acidithiobacillus ferrooxidans that mediates electron transfer between extracellular inorganic iron and the intracellular central metabolism. This cytochrome c is specific for iron and interacts with periplasmic proteins to complete a reversible electron transport chain. A structure of Cyc2 has not yet been characterized experimentally. Here we describe a structural model of Cyc2, and associated proteins, to highlight a plausible mechanism for the ferrous iron electron transfer chain. A comparative modeling protocol specific for trans membrane beta barrel (TMBB) proteins in acidophilic conditions (pH ~ 2) was applied to the primary sequence of Cyc2. The proposed structure has three main regimes: Extracellular loops exposed to low-pH conditions, a TMBB, and an N-terminal cytochrome-like region within the periplasmic space. The Cyc2 model was further refined by identifying likely iron and heme docking sites. This represents the first computational model of Cyc2 that accounts for the membrane microenvironment and the acidity in the extracellular matrix. This approach can be used to model other TMBBs which can be critical for chemolithotrophic microbial growth.
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Affiliation(s)
- Virginia Jiang
- Department of Chemical Engineering, Columbia University in the City of New York, New York, New York, USA
| | - Sagar D Khare
- Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, New Jersey, USA
| | - Scott Banta
- Department of Chemical Engineering, Columbia University in the City of New York, New York, New York, USA
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