1
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Nebe M, Kehr S, Schmitz S, Breitfeld J, Lorenz J, Le Duc D, Stadler PF, Meiler J, Kiess W, Garten A, Kirstein AS. Small integral membrane protein 10 like 1 downregulation enhances differentiation of adipose progenitor cells. Biochem Biophys Res Commun 2022; 604:57-62. [PMID: 35290761 DOI: 10.1016/j.bbrc.2022.03.014] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2022] [Accepted: 03/02/2022] [Indexed: 11/02/2022]
Abstract
Small integral membrane protein 10 like 1 (SMIM10L1) was identified by RNA sequencing as the most significantly downregulated gene in Phosphatase and Tensin Homologue (PTEN) knockdown adipose progenitor cells (APCs). PTEN is a tumor suppressor that antagonizes the growth promoting Phosphoinositide 3-kinase (PI3K)/AKT/mechanistic Target of Rapamycin (mTOR) cascade. Diseases caused by germline pathogenic variants in PTEN are summarized as PTEN Hamartoma Tumor Syndrome (PHTS). This overgrowth syndrome is associated with lipoma formation, especially in pediatric patients. The mechanisms underlying this adipose tissue dysfunction remain elusive. We observed that SMIM10L1 downregulation in APCs led to an enhanced adipocyte differentiation in two- and three-dimensional cell culture and increased expression of adipogenesis markers. Furthermore, SMIM10L1 knockdown cells showed a decreased expression of PTEN, pointing to a mutual crosstalk between PTEN and SMIM10L1. In line with these observations, SMIM10L1 knockdown cells showed increased activation of PI3K/AKT/mTOR signaling and concomitantly increased expression of the adipogenic transcription factor SREBP1. We computationally predicted an α-helical structure and membrane association of SMIM10L1. These results support a specific role for SMIM10L1 in regulating adipogenesis, potentially by increasing PI3K/AKT/mTOR signaling, which might be conducive to lipoma formation in pediatric patients with PHTS.
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Affiliation(s)
- Michèle Nebe
- University Hospital for Children & Adolescents, Center for Pediatric Research, Leipzig University, Leipzig, Germany
| | - Stephanie Kehr
- Bioinformatics Group, Department of Computer Science and Interdisciplinary Center for Bioinformatics, Leipzig University, Leipzig, Germany
| | - Samuel Schmitz
- Department of Chemistry, Vanderbilt University, Nashville, TN, USA
| | - Jana Breitfeld
- Medical Department III-Endocrinology, Nephrology, Rheumatology, Leipzig University Medical Center, Leipzig, Germany
| | - Judith Lorenz
- University Hospital for Children & Adolescents, Center for Pediatric Research, Leipzig University, Leipzig, Germany
| | - Diana Le Duc
- Institute of Human Genetics, Leipzig University Medical Center, Leipzig, Germany; Department of Evolutionary Genetics, Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany
| | - Peter F Stadler
- Bioinformatics Group, Department of Computer Science and Interdisciplinary Center for Bioinformatics, Leipzig University, Leipzig, Germany; Max Planck Institute for Mathematics in the Sciences, Leipzig, Germany
| | - Jens Meiler
- Department of Chemistry, Vanderbilt University, Nashville, TN, USA; Institute of Drug Discovery, Faculty of Medicine, Leipzig University, Leipzig, Germany
| | - Wieland Kiess
- University Hospital for Children & Adolescents, Center for Pediatric Research, Leipzig University, Leipzig, Germany
| | - Antje Garten
- University Hospital for Children & Adolescents, Center for Pediatric Research, Leipzig University, Leipzig, Germany
| | - Anna S Kirstein
- University Hospital for Children & Adolescents, Center for Pediatric Research, Leipzig University, Leipzig, Germany.
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2
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Kukimoto-Niino M, Katsura K, Kaushik R, Ehara H, Yokoyama T, Uchikubo-Kamo T, Nakagawa R, Mishima-Tsumagari C, Yonemochi M, Ikeda M, Hanada K, Zhang KYJ, Shirouzu M. Cryo-EM structure of the human ELMO1-DOCK5-Rac1 complex. SCIENCE ADVANCES 2021; 7:7/30/eabg3147. [PMID: 34290093 PMCID: PMC8294757 DOI: 10.1126/sciadv.abg3147] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/28/2020] [Accepted: 06/03/2021] [Indexed: 05/28/2023]
Abstract
The dedicator of cytokinesis (DOCK) family of guanine nucleotide exchange factors (GEFs) promotes cell motility, phagocytosis, and cancer metastasis through activation of Rho guanosine triphosphatases. Engulfment and cell motility (ELMO) proteins are binding partners of DOCK and regulate Rac activation. Here, we report the cryo-electron microscopy structure of the active ELMO1-DOCK5 complex bound to Rac1 at 3.8-Å resolution. The C-terminal region of ELMO1, including the pleckstrin homology (PH) domain, aids in the binding of the catalytic DOCK homology region 2 (DHR-2) domain of DOCK5 to Rac1 in its nucleotide-free state. A complex α-helical scaffold between ELMO1 and DOCK5 stabilizes the binding of Rac1. Mutagenesis studies revealed that the PH domain of ELMO1 enhances the GEF activity of DOCK5 through specific interactions with Rac1. The structure provides insights into how ELMO modulates the biochemical activity of DOCK and how Rac selectivity is achieved by ELMO.
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Affiliation(s)
- Mutsuko Kukimoto-Niino
- RIKEN Center for Biosystems Dynamics Research, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan
| | - Kazushige Katsura
- RIKEN Center for Biosystems Dynamics Research, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan
| | - Rahul Kaushik
- RIKEN Center for Biosystems Dynamics Research, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan
| | - Haruhiko Ehara
- RIKEN Center for Biosystems Dynamics Research, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan
| | - Takeshi Yokoyama
- RIKEN Center for Biosystems Dynamics Research, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan
- Graduate School of Life Sciences, Tohoku University, 2-1-1 Katahira, Aoba-ku, Sendai, Miyagi 980-8577, Japan
| | - Tomomi Uchikubo-Kamo
- RIKEN Center for Biosystems Dynamics Research, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan
| | - Reiko Nakagawa
- RIKEN Center for Biosystems Dynamics Research, 2-2-3 Minatojima-minamimachi, Chuo-ku, Kobe, Hyogo 650-0047, Japan
| | - Chiemi Mishima-Tsumagari
- RIKEN Center for Biosystems Dynamics Research, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan
| | - Mayumi Yonemochi
- RIKEN Center for Biosystems Dynamics Research, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan
| | - Mariko Ikeda
- RIKEN Center for Biosystems Dynamics Research, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan
| | - Kazuharu Hanada
- RIKEN Center for Biosystems Dynamics Research, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan
| | - Kam Y J Zhang
- RIKEN Center for Biosystems Dynamics Research, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan
| | - Mikako Shirouzu
- RIKEN Center for Biosystems Dynamics Research, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan.
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3
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Del Alamo D, Tessmer MH, Stein RA, Feix JB, Mchaourab HS, Meiler J. Rapid Simulation of Unprocessed DEER Decay Data for Protein Fold Prediction. Biophys J 2020; 118:366-375. [PMID: 31892409 PMCID: PMC6976798 DOI: 10.1016/j.bpj.2019.12.011] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2019] [Revised: 11/13/2019] [Accepted: 12/04/2019] [Indexed: 01/02/2023] Open
Abstract
Despite advances in sampling and scoring strategies, Monte Carlo modeling methods still struggle to accurately predict de novo the structures of large proteins, membrane proteins, or proteins of complex topologies. Previous approaches have addressed these shortcomings by leveraging sparse distance data gathered using site-directed spin labeling and electron paramagnetic resonance spectroscopy to improve protein structure prediction and refinement outcomes. However, existing computational implementations entail compromises between coarse-grained models of the spin label that lower the resolution and explicit models that lead to resource-intense simulations. These methods are further limited by their reliance on distance distributions, which are calculated from a primary refocused echo decay signal and contain uncertainties that may require manual refinement. Here, we addressed these challenges by developing RosettaDEER, a scoring method within the Rosetta software suite capable of simulating double electron-electron resonance spectroscopy decay traces and distance distributions between spin labels fast enough to fold proteins de novo. We demonstrate that the accuracy of resulting distance distributions match or exceed those generated by more computationally intensive methods. Moreover, decay traces generated from these distributions recapitulate intermolecular background coupling parameters even when the time window of data collection is truncated. As a result, RosettaDEER can discriminate between poorly folded and native-like models by using decay traces that cannot be accurately converted into distance distributions using regularized fitting approaches. Finally, using two challenging test cases, we demonstrate that RosettaDEER leverages these experimental data for protein fold prediction more effectively than previous methods. These benchmarking results confirm that RosettaDEER can effectively leverage sparse experimental data for a wide array of modeling applications built into the Rosetta software suite.
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Affiliation(s)
- Diego Del Alamo
- Department of Chemistry and Center for Structural Biology; Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, Tennessee
| | | | - Richard A Stein
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, Tennessee
| | - Jimmy B Feix
- Department of Biophysics, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Hassane S Mchaourab
- Department of Chemistry and Center for Structural Biology; Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, Tennessee
| | - Jens Meiler
- Department of Chemistry and Center for Structural Biology; Institut for Drug Discovery, Leipzig University, Leipzig, Germany.
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4
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Vucinic J, Simoncini D, Ruffini M, Barbe S, Schiex T. Positive multistate protein design. Bioinformatics 2019; 36:122-130. [DOI: 10.1093/bioinformatics/btz497] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2019] [Revised: 05/20/2019] [Accepted: 06/11/2019] [Indexed: 11/12/2022] Open
Abstract
Abstract
Motivation
Structure-based computational protein design (CPD) plays a critical role in advancing the field of protein engineering. Using an all-atom energy function, CPD tries to identify amino acid sequences that fold into a target structure and ultimately perform a desired function. The usual approach considers a single rigid backbone as a target, which ignores backbone flexibility. Multistate design (MSD) allows instead to consider several backbone states simultaneously, defining challenging computational problems.
Results
We introduce efficient reductions of positive MSD problems to Cost Function Networks with two different fitness definitions and implement them in the Pompd (Positive Multistate Protein design) software. Pompd is able to identify guaranteed optimal sequences of positive multistate full protein redesign problems and exhaustively enumerate suboptimal sequences close to the MSD optimum. Applied to nuclear magnetic resonance and back-rubbed X-ray structures, we observe that the average energy fitness provides the best sequence recovery. Our method outperforms state-of-the-art guaranteed computational design approaches by orders of magnitudes and can solve MSD problems with sizes previously unreachable with guaranteed algorithms.
Availability and implementation
https://forgemia.inra.fr/thomas.schiex/pompd as documented Open Source.
Supplementary information
Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Jelena Vucinic
- LISBP, Université de Toulouse, CNRS, INRA, INSA, 31400 Toulouse, France
- MIAT, Université de Toulouse, INRA, 31326 Castanet-Tolosan Cedex, France
| | - David Simoncini
- LISBP, Université de Toulouse, CNRS, INRA, INSA, 31400 Toulouse, France
- IRIT UMR 5505-CNRS, Université de Toulouse, 31042 Cedex 9, France
| | - Manon Ruffini
- LISBP, Université de Toulouse, CNRS, INRA, INSA, 31400 Toulouse, France
- MIAT, Université de Toulouse, INRA, 31326 Castanet-Tolosan Cedex, France
| | - Sophie Barbe
- LISBP, Université de Toulouse, CNRS, INRA, INSA, 31400 Toulouse, France
| | - Thomas Schiex
- MIAT, Université de Toulouse, INRA, 31326 Castanet-Tolosan Cedex, France
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5
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Simoncini D, Zhang KYJ, Schiex T, Barbe S. A structural homology approach for computational protein design with flexible backbone. Bioinformatics 2018; 35:2418-2426. [DOI: 10.1093/bioinformatics/bty975] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2018] [Revised: 11/01/2018] [Accepted: 11/28/2018] [Indexed: 01/09/2023] Open
Abstract
Abstract
Motivation
Structure-based Computational Protein design (CPD) plays a critical role in advancing the field of protein engineering. Using an all-atom energy function, CPD tries to identify amino acid sequences that fold into a target structure and ultimately perform a desired function. Energy functions remain however imperfect and injecting relevant information from known structures in the design process should lead to improved designs.
Results
We introduce Shades, a data-driven CPD method that exploits local structural environments in known protein structures together with energy to guide sequence design, while sampling side-chain and backbone conformations to accommodate mutations. Shades (Structural Homology Algorithm for protein DESign), is based on customized libraries of non-contiguous in-contact amino acid residue motifs. We have tested Shades on a public benchmark of 40 proteins selected from different protein families. When excluding homologous proteins, Shades achieved a protein sequence recovery of 30% and a protein sequence similarity of 46% on average, compared with the PFAM protein family of the target protein. When homologous structures were added, the wild-type sequence recovery rate achieved 93%.
Availability and implementation
Shades source code is available at https://bitbucket.org/satsumaimo/shades as a patch for Rosetta 3.8 with a curated protein structure database and ITEM library creation software.
Supplementary information
Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- David Simoncini
- Laboratoire d'Ingénierie des Systèmes Biologiques et des Procédés, LISBP, Université de Toulouse, CNRS, INRA, INSA, F Toulouse cedex 04, France
- Institut de recherche en informatique de Toulouse, IRIT, UMR 5505-CNRS, Université de Toulouse, Cedex 9, France
| | - Kam Y J Zhang
- Laboratory for Structural Bioinformatics, Center for Biosystems Dynamics Research, RIKEN, Yokohama, Kanagawa, Japan
| | - Thomas Schiex
- Institut de recherche en informatique de Toulouse, UMR 5505-CNRS, Université de Toulouse, Cedex 9, France
| | - Sophie Barbe
- Laboratoire d'Ingénierie des Systèmes Biologiques et des Procédés, LISBP, Université de Toulouse, CNRS, INRA, INSA, F Toulouse cedex 04, France
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6
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Berenger F, Simoncini D, Voet A, Shrestha R, Zhang KYJ. Fragger: a protein fragment picker for structural queries. F1000Res 2017; 6:1722. [PMID: 29399321 PMCID: PMC5773926 DOI: 10.12688/f1000research.12486.2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 04/05/2018] [Indexed: 12/02/2022] Open
Abstract
Protein modeling and design activities often require querying the Protein Data Bank (PDB) with a structural fragment, possibly containing gaps. For some applications, it is preferable to work on a specific subset of the PDB or with unpublished structures. These requirements, along with specific user needs, motivated the creation of a new software to manage and query 3D protein fragments. Fragger is a protein fragment picker that allows protein fragment databases to be created and queried. All fragment lengths are supported and any set of PDB files can be used to create a database. Fragger can efficiently search a fragment database with a query fragment and a distance threshold. Matching fragments are ranked by distance to the query. The query fragment can have structural gaps and the allowed amino acid sequences matching a query can be constrained via a regular expression of one-letter amino acid codes. Fragger also incorporates a tool to compute the backbone RMSD of one versus many fragments in high throughput. Fragger should be useful for protein design, loop grafting and related structural bioinformatics tasks.
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Affiliation(s)
- Francois Berenger
- System Cohort Division, Medical Institute of Bioregulation, Kyushu University, Fukuoka, Japan
| | | | - Arnout Voet
- Laboratory of Biomolecular Modelling and Design, KU Leuven, Heverlee, Belgium
| | - Rojan Shrestha
- Department of Systems and Computational Biology, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Kam Y J Zhang
- Structural Bioinformatics Team, Division of Structural and Synthetic Biology, Center for Life Science Technologies, RIKEN, Yokohama, Kanagawa, Japan
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7
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Löffler P, Schmitz S, Hupfeld E, Sterner R, Merkl R. Rosetta:MSF: a modular framework for multi-state computational protein design. PLoS Comput Biol 2017; 13:e1005600. [PMID: 28604768 PMCID: PMC5484525 DOI: 10.1371/journal.pcbi.1005600] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2017] [Revised: 06/26/2017] [Accepted: 05/27/2017] [Indexed: 12/20/2022] Open
Abstract
Computational protein design (CPD) is a powerful technique to engineer existing proteins or to design novel ones that display desired properties. Rosetta is a software suite including algorithms for computational modeling and analysis of protein structures and offers many elaborate protocols created to solve highly specific tasks of protein engineering. Most of Rosetta’s protocols optimize sequences based on a single conformation (i. e. design state). However, challenging CPD objectives like multi-specificity design or the concurrent consideration of positive and negative design goals demand the simultaneous assessment of multiple states. This is why we have developed the multi-state framework MSF that facilitates the implementation of Rosetta’s single-state protocols in a multi-state environment and made available two frequently used protocols. Utilizing MSF, we demonstrated for one of these protocols that multi-state design yields a 15% higher performance than single-state design on a ligand-binding benchmark consisting of structural conformations. With this protocol, we designed de novo nine retro-aldolases on a conformational ensemble deduced from a (βα)8-barrel protein. All variants displayed measurable catalytic activity, testifying to a high success rate for this concept of multi-state enzyme design. Protein engineering, i. e. the targeted modification or design of proteins has tremendous potential for medical and industrial applications. One generally applicable strategy for protein engineering is rational protein design: based on detailed knowledge of structure and function, computer programs like Rosetta propose the sequence of a protein possessing the desired properties. So far, most computer protocols have used rigid structures for design, which is a simplification because a protein’s structure is more accurately specified by a conformational ensemble. We have now implemented a framework for computational protein design that allows certain design protocols of Rosetta to make use of multiple design states like structural ensembles. An in silico assessment simulating ligand-binding design showed that this new approach generates more reliably native-like sequences than a single-state approach. As a proof-of-concept, we introduced de novo retro-aldolase activity into a scaffold protein and characterized nine variants experimentally, all of which were catalytically active.
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Affiliation(s)
- Patrick Löffler
- Institute of Biophysics and Physical Biochemistry, University of Regensburg, Regensburg, Germany
| | - Samuel Schmitz
- Institute of Biophysics and Physical Biochemistry, University of Regensburg, Regensburg, Germany
| | - Enrico Hupfeld
- Institute of Biophysics and Physical Biochemistry, University of Regensburg, Regensburg, Germany
| | - Reinhard Sterner
- Institute of Biophysics and Physical Biochemistry, University of Regensburg, Regensburg, Germany
| | - Rainer Merkl
- Institute of Biophysics and Physical Biochemistry, University of Regensburg, Regensburg, Germany
- * E-mail:
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8
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Identify High-Quality Protein Structural Models by Enhanced K-Means. BIOMED RESEARCH INTERNATIONAL 2017; 2017:7294519. [PMID: 28421198 PMCID: PMC5381204 DOI: 10.1155/2017/7294519] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/23/2016] [Revised: 02/09/2017] [Accepted: 02/19/2017] [Indexed: 01/01/2023]
Abstract
Background. One critical issue in protein three-dimensional structure prediction using either ab initio or comparative modeling involves identification of high-quality protein structural models from generated decoys. Currently, clustering algorithms are widely used to identify near-native models; however, their performance is dependent upon different conformational decoys, and, for some algorithms, the accuracy declines when the decoy population increases. Results. Here, we proposed two enhanced K-means clustering algorithms capable of robustly identifying high-quality protein structural models. The first one employs the clustering algorithm SPICKER to determine the initial centroids for basic K-means clustering (SK-means), whereas the other employs squared distance to optimize the initial centroids (K-means++). Our results showed that SK-means and K-means++ were more robust as compared with SPICKER alone, detecting 33 (59%) and 42 (75%) of 56 targets, respectively, with template modeling scores better than or equal to those of SPICKER. Conclusions. We observed that the classic K-means algorithm showed a similar performance to that of SPICKER, which is a widely used algorithm for protein-structure identification. Both SK-means and K-means++ demonstrated substantial improvements relative to results from SPICKER and classical K-means.
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9
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Simoncini D, Schiex T, Zhang KYJ. Balancing exploration and exploitation in population-based sampling improves fragment-based de novo protein structure prediction. Proteins 2017; 85:852-858. [PMID: 28066917 DOI: 10.1002/prot.25244] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2016] [Revised: 11/29/2016] [Accepted: 12/18/2016] [Indexed: 01/17/2023]
Abstract
Conformational search space exploration remains a major bottleneck for protein structure prediction methods. Population-based meta-heuristics typically enable the possibility to control the search dynamics and to tune the balance between local energy minimization and search space exploration. EdaFold is a fragment-based approach that can guide search by periodically updating the probability distribution over the fragment libraries used during model assembly. We implement the EdaFold algorithm as a Rosetta protocol and provide two different probability update policies: a cluster-based variation (EdaRosec ) and an energy-based one (EdaRoseen ). We analyze the search dynamics of our new Rosetta protocols and show that EdaRosec is able to provide predictions with lower C αRMSD to the native structure than EdaRoseen and Rosetta AbInitio Relax protocol. Our software is freely available as a C++ patch for the Rosetta suite and can be downloaded from http://www.riken.jp/zhangiru/software/. Our protocols can easily be extended in order to create alternative probability update policies and generate new search dynamics. Proteins 2017; 85:852-858. © 2016 Wiley Periodicals, Inc.
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Affiliation(s)
- David Simoncini
- INRA MIAT, UR 875, Castanet-Tolosan Cedex, 31326, France.,Structural Bioinformatics Team, Division of Structural and Synthetic Biology, Center for Life Science Technologies, RIKEN, 1-7-22 Suehiro, Yokohama, Kanagawa, 230-0045, Japan
| | - Thomas Schiex
- INRA MIAT, UR 875, Castanet-Tolosan Cedex, 31326, France
| | - Kam Y J Zhang
- Structural Bioinformatics Team, Division of Structural and Synthetic Biology, Center for Life Science Technologies, RIKEN, 1-7-22 Suehiro, Yokohama, Kanagawa, 230-0045, Japan
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10
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Belsom A, Schneider M, Fischer L, Brock O, Rappsilber J. Serum Albumin Domain Structures in Human Blood Serum by Mass Spectrometry and Computational Biology. Mol Cell Proteomics 2016; 15:1105-16. [PMID: 26385339 PMCID: PMC4813692 DOI: 10.1074/mcp.m115.048504] [Citation(s) in RCA: 73] [Impact Index Per Article: 9.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2015] [Revised: 09/16/2015] [Indexed: 01/12/2023] Open
Abstract
Chemical cross-linking combined with mass spectrometry has proven useful for studying protein-protein interactions and protein structure, however the low density of cross-link data has so far precluded its use in determining structures de novo. Cross-linking density has been typically limited by the chemical selectivity of the standard cross-linking reagents that are commonly used for protein cross-linking. We have implemented the use of a heterobifunctional cross-linking reagent, sulfosuccinimidyl 4,4'-azipentanoate (sulfo-SDA), combining a traditional sulfo-N-hydroxysuccinimide (sulfo-NHS) ester and a UV photoactivatable diazirine group. This diazirine yields a highly reactive and promiscuous carbene species, the net result being a greatly increased number of cross-links compared with homobifunctional, NHS-based cross-linkers. We present a novel methodology that combines the use of this high density photo-cross-linking data with conformational space search to investigate the structure of human serum albumin domains, from purified samples, and in its native environment, human blood serum. Our approach is able to determine human serum albumin domain structures with good accuracy: root-mean-square deviation to crystal structure are 2.8/5.6/2.9 Å (purified samples) and 4.5/5.9/4.8Å (serum samples) for domains A/B/C for the first selected structure; 2.5/4.9/2.9 Å (purified samples) and 3.5/5.2/3.8 Å (serum samples) for the best out of top five selected structures. Our proof-of-concept study on human serum albumin demonstrates initial potential of our approach for determining the structures of more proteins in the complex biological contexts in which they function and which they may require for correct folding. Data are available via ProteomeXchange with identifier PXD001692.
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Affiliation(s)
- Adam Belsom
- From the ‡Wellcome Trust Centre for Cell Biology, University of Edinburgh, Edinburgh EH9 3BF, United Kingdom
| | - Michael Schneider
- §Robotics and Biology Laboratory, Technische Universität Berlin, 10587 Berlin, Germany
| | - Lutz Fischer
- From the ‡Wellcome Trust Centre for Cell Biology, University of Edinburgh, Edinburgh EH9 3BF, United Kingdom
| | - Oliver Brock
- §Robotics and Biology Laboratory, Technische Universität Berlin, 10587 Berlin, Germany
| | - Juri Rappsilber
- From the ‡Wellcome Trust Centre for Cell Biology, University of Edinburgh, Edinburgh EH9 3BF, United Kingdom; ¶Department of Bioanalytics, Institute of Biotechnology, Technische Universität Berlin, 13355 Berlin, Germany.
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11
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Mabrouk M, Werner T, Schneider M, Putz I, Brock O. Analysis of free modeling predictions by RBO aleph in CASP11. Proteins 2015; 84 Suppl 1:87-104. [PMID: 26492194 DOI: 10.1002/prot.24950] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2015] [Revised: 09/28/2015] [Accepted: 10/19/2015] [Indexed: 12/15/2022]
Abstract
The CASP experiment is a biannual benchmark for assessing protein structure prediction methods. In CASP11, RBO Aleph ranked as one of the top-performing automated servers in the free modeling category. This category consists of targets for which structural templates are not easily retrievable. We analyze the performance of RBO Aleph and show that its success in CASP was a result of its ab initio structure prediction protocol. A detailed analysis of this protocol demonstrates that two components unique to our method greatly contributed to prediction quality: residue-residue contact prediction by EPC-map and contact-guided conformational space search by model-based search (MBS). Interestingly, our analysis also points to a possible fundamental problem in evaluating the performance of protein structure prediction methods: Improvements in components of the method do not necessarily lead to improvements of the entire method. This points to the fact that these components interact in ways that are poorly understood. This problem, if indeed true, represents a significant obstacle to community-wide progress. Proteins 2016; 84(Suppl 1):87-104. © 2015 Wiley Periodicals, Inc.
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Affiliation(s)
- Mahmoud Mabrouk
- Department of Electrical Engineering and Computer Science, Robotics and Biology Laboratory, Technische Universität Berlin, Berlin, 10587, Germany
| | - Tim Werner
- Department of Electrical Engineering and Computer Science, Robotics and Biology Laboratory, Technische Universität Berlin, Berlin, 10587, Germany
| | - Michael Schneider
- Department of Electrical Engineering and Computer Science, Robotics and Biology Laboratory, Technische Universität Berlin, Berlin, 10587, Germany
| | - Ines Putz
- Department of Electrical Engineering and Computer Science, Robotics and Biology Laboratory, Technische Universität Berlin, Berlin, 10587, Germany
| | - Oliver Brock
- Department of Electrical Engineering and Computer Science, Robotics and Biology Laboratory, Technische Universität Berlin, Berlin, 10587, Germany.
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Simoncini D, Nakata H, Ogata K, Nakamura S, Zhang KY. Quality Assessment of Predicted Protein Models Using Energies Calculated by the Fragment Molecular Orbital Method. Mol Inform 2015; 34:97-104. [PMID: 27490032 DOI: 10.1002/minf.201400108] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2014] [Accepted: 10/13/2014] [Indexed: 12/12/2022]
Abstract
Protein structure prediction directly from sequences is a very challenging problem in computational biology. One of the most successful approaches employs stochastic conformational sampling to search an empirically derived energy function landscape for the global energy minimum state. Due to the errors in the empirically derived energy function, the lowest energy conformation may not be the best model. We have evaluated the use of energy calculated by the fragment molecular orbital method (FMO energy) to assess the quality of predicted models and its ability to identify the best model among an ensemble of predicted models. The fragment molecular orbital method implemented in GAMESS was used to calculate the FMO energy of predicted models. When tested on eight protein targets, we found that the model ranking based on FMO energies is better than that based on empirically derived energies when there is sufficient diversity among these models. This model diversity can be estimated prior to the FMO energy calculations. Our result demonstrates that the FMO energy calculated by the fragment molecular orbital method is a practical and promising measure for the assessment of protein model quality and the selection of the best protein model among many generated.
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Affiliation(s)
- David Simoncini
- Structural Bioinformatics Team, Division of Structural and Synthetic Biology, Center for Life Science Technologies, RIKEN, 1-7-22 Suehiro, Yokohama, Kanagawa 230-0045, Japan phone: +81(0)45-503-9560/fax: +81(0)45-503-9559.,Present address: Mathématiques et Informatique Appliquées de Toulouse, Unité de Recherche 875, Institut National de la Recherche Agronomique, F-31320 Castanet-Tolosan, France
| | - Hiroya Nakata
- RIKEN Research Cluster for Innovation, 2-1 Hirosawa, Wako, Saitama 351-0198 Japan phone/fax: +81(0)48-467-9477/+81(0)48-467-8503.,Department of Biomolecular Engineering, Tokyo Institute of Technology, 4259 Nagatsutacho, Midori-ku, Yokohama, Kanagawa 226-8501, Japan.,Japan Society for the Promotion of Science, Kojimachi Business Center Building, 5-3-1 Kojimachi, Chiyoda-ku, Tokyo 102-0083, Japan
| | - Koji Ogata
- RIKEN Research Cluster for Innovation, 2-1 Hirosawa, Wako, Saitama 351-0198 Japan phone/fax: +81(0)48-467-9477/+81(0)48-467-8503
| | - Shinichiro Nakamura
- RIKEN Research Cluster for Innovation, 2-1 Hirosawa, Wako, Saitama 351-0198 Japan phone/fax: +81(0)48-467-9477/+81(0)48-467-8503.
| | - Kam Yj Zhang
- Structural Bioinformatics Team, Division of Structural and Synthetic Biology, Center for Life Science Technologies, RIKEN, 1-7-22 Suehiro, Yokohama, Kanagawa 230-0045, Japan phone: +81(0)45-503-9560/fax: +81(0)45-503-9559.
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13
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Sun HP, Huang Y, Wang XF, Zhang Y, Shen HB. Improving accuracy of protein contact prediction using balanced network deconvolution. Proteins 2015; 83:485-96. [PMID: 25524593 DOI: 10.1002/prot.24744] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2014] [Revised: 11/20/2014] [Accepted: 12/02/2014] [Indexed: 12/28/2022]
Abstract
Residue contact map is essential for protein three-dimensional structure determination. But most of the current contact prediction methods based on residue co-evolution suffer from high false-positives as introduced by indirect and transitive contacts (i.e., residues A-B and B-C are in contact, but A-C are not). Built on the work by Feizi et al. (Nat Biotechnol 2013; 31:726-733), which demonstrated a general network model to distinguish direct dependencies by network deconvolution, this study presents a new balanced network deconvolution (BND) algorithm to identify optimized dependency matrix without limit on the eigenvalue range in the applied network systems. The algorithm was used to filter contact predictions of five widely used co-evolution methods. On the test of proteins from three benchmark datasets of the 9th critical assessment of protein structure prediction (CASP9), CASP10, and PSICOV (precise structural contact prediction using sparse inverse covariance estimation) database experiments, the BND can improve the medium- and long-range contact predictions at the L/5 cutoff by 55.59% and 47.68%, respectively, without additional central processing unit cost. The improvement is statistically significant, with a P-value < 5.93 × 10(-3) in the Student's t-test. A further comparison with the ab initio structure predictions in CASPs showed that the usefulness of the current co-evolution-based contact prediction to the three-dimensional structure modeling relies on the number of homologous sequences existing in the sequence databases. BND can be used as a general contact refinement method, which is freely available at: http://www.csbio.sjtu.edu.cn/bioinf/BND/.
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Affiliation(s)
- Hai-Ping Sun
- Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai, 200240, China; Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai, 200240, China
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14
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Slabaugh E, Sethaphong L, Xiao C, Amick J, Anderson CT, Haigler CH, Yingling YG. Computational and genetic evidence that different structural conformations of a non-catalytic region affect the function of plant cellulose synthase. JOURNAL OF EXPERIMENTAL BOTANY 2014; 65:6645-53. [PMID: 25262226 PMCID: PMC4246192 DOI: 10.1093/jxb/eru383] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/18/2023]
Abstract
The β-1,4-glucan chains comprising cellulose are synthesized by cellulose synthases in the plasma membranes of diverse organisms including bacteria and plants. Understanding structure-function relationships in the plant enzymes involved in cellulose synthesis (CESAs) is important because cellulose is the most abundant component in the plant cell wall, a key renewable biomaterial. Here, we explored the structure and function of the region encompassing transmembrane helices (TMHs) 5 and 6 in CESA using computational and genetic tools. Ab initio computational structure prediction revealed novel bi-modal structural conformations of the region between TMH5 and 6 that may affect CESA function. Here we present our computational findings on this region in three CESAs of Arabidopsis thaliana (AtCESA1, 3, and 6), the Atcesa3(ixr1-2) mutant, and a novel missense mutation in AtCESA1. A newly engineered point mutation in AtCESA1 (Atcesa1(F954L) ) that altered the structural conformation in silico resulted in a protein that was not fully functional in the temperature-sensitive Atcesa1(rsw1-1) mutant at the restrictive temperature. The combination of computational and genetic results provides evidence that the ability of the TMH5-6 region to adopt specific structural conformations is important for CESA function.
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Affiliation(s)
- Erin Slabaugh
- Department of Crop Science and Department of Plant and Microbial Biology, North Carolina State University, Raleigh, NC 27695, USA
| | - Latsavongsakda Sethaphong
- Department of Materials Science and Engineering, North Carolina State University, Raleigh, NC 27695, USA
| | - Chaowen Xiao
- Department of Biology, the Pennsylvania State University, University Park, PA 16802, USA
| | - Joshua Amick
- Department of Materials Science and Engineering, North Carolina State University, Raleigh, NC 27695, USA
| | - Charles T Anderson
- Department of Biology, the Pennsylvania State University, University Park, PA 16802, USA
| | - Candace H Haigler
- Department of Crop Science and Department of Plant and Microbial Biology, North Carolina State University, Raleigh, NC 27695, USA
| | - Yaroslava G Yingling
- Department of Materials Science and Engineering, North Carolina State University, Raleigh, NC 27695, USA
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Jamroz M, Kolinski A. ClusCo: clustering and comparison of protein models. BMC Bioinformatics 2013; 14:62. [PMID: 23433004 PMCID: PMC3645956 DOI: 10.1186/1471-2105-14-62] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2012] [Accepted: 02/17/2013] [Indexed: 12/21/2022] Open
Abstract
BACKGROUND The development, optimization and validation of protein modeling methods require efficient tools for structural comparison. Frequently, a large number of models need to be compared with the target native structure. The main reason for the development of Clusco software was to create a high-throughput tool for all-versus-all comparison, because calculating similarity matrix is the one of the bottlenecks in the protein modeling pipeline. RESULTS Clusco is fast and easy-to-use software for high-throughput comparison of protein models with different similarity measures (cRMSD, dRMSD, GDT_TS, TM-Score, MaxSub, Contact Map Overlap) and clustering of the comparison results with standard methods: K-means Clustering or Hierarchical Agglomerative Clustering. CONCLUSIONS The application was highly optimized and written in C/C++, including the code for parallel execution on CPU and GPU, which resulted in a significant speedup over similar clustering and scoring computation programs.
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Affiliation(s)
- Michal Jamroz
- Laboratory of Theory of Biopolymers, Faculty of Chemistry, University of Warsaw, Pasteura 1, 02-093 Warsaw, Poland
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