1
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Zhu Y, Yu M, Aisikaer M, Zhang C, He Y, Chen Z, Yang Y, Han R, Li Z, Zhang F, Ding J, Lu X. Contriving a novel of CHB therapeutic vaccine based on IgV_CTLA-4 and L protein via immunoinformatics approach. J Biomol Struct Dyn 2024; 42:6323-6341. [PMID: 37424209 DOI: 10.1080/07391102.2023.2234043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2023] [Accepted: 07/01/2023] [Indexed: 07/11/2023]
Abstract
Chronic infection induced by immune tolerance to hepatitis B virus (HBV) is one of the most common causes of hepatic cirrhosis and hepatoma. Fortunately, the application of therapeutic vaccine can not only reverse HBV-tolerance, but also serve a potentially effective therapeutic strategy for treating chronic hepatitis B (CHB). However, the clinical effect of the currently developed CHB therapeutic vaccine is not optimistic due to the weak immunogenicity. Given that the human leukocyte antigen CTLA-4 owns strong binding ability to the surface B7 molecules (CD80 and CD86) of antigen presenting cell (APCs), the immunoglobulin variable region of CTLA-4 (IgV_CTLA-4) was fused with the L protein of HBV to contrive a novel therapeutic vaccine (V_C4HBL) for CHB in this study. We found that the addition of IgV_CTLA-4 did not interfere with the formation of L protein T cell and B cell epitopes after analysis by means of immunoinformatics approaches. Meanwhile, we also found that the IgV_CTLA-4 had strong binding force to B7 molecules through molecular docking and molecular dynamics (MD) simulation. Notably, our vaccine V_C4HBL showed good immunogenicity and antigenicity by in vitro and in vivo experiments. Therefore, the V_C4HBL is promising to again effectively activate the cellular and humoral immunity of CHB patients, and provides a potentially effective therapeutic strategy for the treatment of CHB in the future.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Yuejie Zhu
- Reproductive Medicine Center, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, China
- Infectious Disease Center, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, China
| | - Mingkai Yu
- Department of Immunology, School of Basic Medical Sciences, Xinjiang Medical University, Urumqi, China
- Xinjiang Key Molecular Biology Laboratory of Endemic Disease, Xinjiang Medical University, Urumqi, China
| | - Maierhaba Aisikaer
- Department of Immunology, School of Basic Medical Sciences, Xinjiang Medical University, Urumqi, China
- Xinjiang Key Molecular Biology Laboratory of Endemic Disease, Xinjiang Medical University, Urumqi, China
| | - Chuntao Zhang
- Department of Microbiology, School of Basic Medical Sciences, Xinjiang Medical University, Urumqi, China
| | - Yueyue He
- Department of Immunology, School of Basic Medical Sciences, Xinjiang Medical University, Urumqi, China
- Xinjiang Key Molecular Biology Laboratory of Endemic Disease, Xinjiang Medical University, Urumqi, China
| | - Zhiqiang Chen
- Department of Immunology, School of Basic Medical Sciences, Xinjiang Medical University, Urumqi, China
- Xinjiang Key Molecular Biology Laboratory of Endemic Disease, Xinjiang Medical University, Urumqi, China
| | - Yinyin Yang
- Xinjiang Key Molecular Biology Laboratory of Endemic Disease, Xinjiang Medical University, Urumqi, China
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Xinjiang Medical University, Urumqi, China
| | - Rui Han
- Reproductive Medicine Center, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, China
| | - Zhiwei Li
- Clinical Laboratory Center, Xinjiang Uygur Autonomous Region People's Hospital, Urumqi, China
| | - Fengbo Zhang
- Department of Clinical Laboratory, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, China
| | - Jianbing Ding
- Department of Immunology, School of Basic Medical Sciences, Xinjiang Medical University, Urumqi, China
- Xinjiang Key Molecular Biology Laboratory of Endemic Disease, Xinjiang Medical University, Urumqi, China
| | - Xiaobo Lu
- Infectious Disease Center, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, China
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Darawshi O, Yassin O, Shmuel M, Wek RC, Mahdizadeh SJ, Eriksson LA, Hatzoglou M, Tirosh B. Phosphorylation of GCN2 by mTOR confers adaptation to conditions of hyper-mTOR activation under stress. J Biol Chem 2024:107575. [PMID: 39013537 DOI: 10.1016/j.jbc.2024.107575] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2024] [Revised: 07/02/2024] [Accepted: 07/05/2024] [Indexed: 07/18/2024] Open
Abstract
Adaptation to shortage in free amino acids (AA) is mediated by two pathways, the integrated stress response (ISR) and the mechanistic target of rapamycin (mTOR). In response to reduced levels, primarily of leucine or arginine, mTOR in its complex 1 configuration (mTORC1) is suppressed leading to a decrease in translation initiation and elongation. The eIF2α kinase general control nonderepressible 2 (GCN2) is activated by uncharged tRNAs, leading to induction of the ISR in response to a broader range of AA shortage. ISR confers a reduced translation initiation, while promoting the selective synthesis of stress proteins, such as ATF4. To efficiently adapt to AA starvation, the two pathways are cross-regulated at multiple levels. Here we identified a new mechanism of ISR/mTORC1 crosstalk that optimizes survival under AA starvation, when mTORC1 is forced to remain active. mTORC1 activation during acute AA shortage, augmented ATF4 expression in a GCN2-dependent manner. Under these conditions, enhanced GCN2 activity was not dependent on tRNA sensing, inferring a different activation mechanism. We identified a labile physical interaction between GCN2 and mTOR that results in a phosphorylation of GCN2 on serine 230 by mTOR, which promotes GCN2 activity. When examined under prolonged AA starvation, GCN2 phosphorylation by mTOR promoted survival. Our data unveils an adaptive mechanism to AA starvation, when mTORC1 evades inhibition.
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Affiliation(s)
- Odai Darawshi
- The School of Pharmacy, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Olaya Yassin
- The School of Pharmacy, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Miri Shmuel
- The School of Pharmacy, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Ronald C Wek
- Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, ID, USA
| | - S Jalil Mahdizadeh
- Department of Chemistry and Molecular Biology, University of Gothenburg, Sweden
| | - Leif A Eriksson
- Department of Chemistry and Molecular Biology, University of Gothenburg, Sweden
| | - Maria Hatzoglou
- Department of Genetics and Genome Sciences, Case Western Reserve University, Cleveland, OH, USA
| | - Boaz Tirosh
- Department of Biochemistry, Case Western Reserve University, Cleveland, OH, USA.
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Ambalavanan A, Mallikarjuna MG, Bansal S, Bashyal BM, Subramanian S, Kumar A, Prakash G. Genome-wide characterization of the NBLRR gene family provides evolutionary and functional insights into blast resistance in pearl millet (Cenchrus americanus (L.) Morrone). PLANTA 2024; 259:143. [PMID: 38704489 DOI: 10.1007/s00425-024-04413-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Accepted: 04/14/2024] [Indexed: 05/06/2024]
Abstract
MAIN CONCLUSION The investigation is the first report on genome-wide identification and characterization of NBLRR genes in pearl millet. We have shown the role of gene loss and purifying selection in the divergence of NBLRRs in Poaceae lineage and candidate CaNBLRR genes for resistance to Magnaporthe grisea infection. Plants have evolved multiple integral mechanisms to counteract the pathogens' infection, among which plant immunity through NBLRR (nucleotide-binding site, leucine-rich repeat) genes is at the forefront. The genome-wide mining in pearl millet (Cenchrus americanus (L.) Morrone) revealed 146 CaNBLRRs. The variation in the branch length of NBLRRs showed the dynamic nature of NBLRRs in response to evolving pathogen races. The orthology of NBLRRs showed a predominance of many-to-one orthologs, indicating the divergence of NBLRRs in the pearl millet lineage mainly through gene loss events followed by gene gain through single-copy duplications. Further, the purifying selection (Ka/Ks < 1) shaped the expansion of NBLRRs within the lineage of pear millet and other members of Poaceae. Presence of cis-acting elements, viz. TCA element, G-box, MYB, SARE, ABRE and conserved motifs annotated with P-loop, kinase 2, RNBS-A, RNBS-D, GLPL, MHD, Rx-CC and LRR suggests their putative role in disease resistance and stress regulation. The qRT-PCR analysis in pearl millet lines showing contrasting responses to Magnaporthe grisea infection identified CaNBLRR20, CaNBLRR33, CaNBLRR46 CaNBLRR51, CaNBLRR78 and CaNBLRR146 as putative candidates. Molecular docking showed the involvement of three and two amino acid residues of LRR domains forming hydrogen bonds (histidine, arginine and threonine) and salt bridges (arginine and lysine) with effectors. Whereas 14 and 20 amino acid residues of CaNBLRR78 and CaNBLRR20 showed hydrophobic interactions with 11 and 9 amino acid residues of effectors, Mg.00g064570.m01 and Mg.00g006570.m01, respectively. The present investigation gives a comprehensive overview of CaNBLRRs and paves the foundation for their utility in pearl millet resistance breeding through understanding of host-pathogen interactions.
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Affiliation(s)
- Aruljothi Ambalavanan
- Division of Plant Pathology, ICAR Indian Agricultural Research Institute, New Delhi, 110012, India
| | | | - Shilpi Bansal
- Division of Plant Pathology, ICAR Indian Agricultural Research Institute, New Delhi, 110012, India
- Department of Science and Humanities, SRM Institute of Science and Technology, Modinagar, Uttar Pradesh, 201204, India
| | - Bishnu Maya Bashyal
- Division of Plant Pathology, ICAR Indian Agricultural Research Institute, New Delhi, 110012, India
| | - Sabtharishi Subramanian
- Division of Entomology, ICAR Indian Agricultural Research Institute, New Delhi, 110012, India
| | - Aundy Kumar
- Division of Plant Pathology, ICAR Indian Agricultural Research Institute, New Delhi, 110012, India
| | - Ganesan Prakash
- Division of Plant Pathology, ICAR Indian Agricultural Research Institute, New Delhi, 110012, India.
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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] [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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5
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Amormino C, Russo E, Tedeschi V, Fiorillo MT, Paiardini A, Spallotta F, Rosanò L, Tuosto L, Kunkl M. Targeting staphylococcal enterotoxin B binding to CD28 as a new strategy for dampening superantigen-mediated intestinal epithelial barrier dysfunctions. Front Immunol 2024; 15:1365074. [PMID: 38510259 PMCID: PMC10951378 DOI: 10.3389/fimmu.2024.1365074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Accepted: 02/26/2024] [Indexed: 03/22/2024] Open
Abstract
Staphylococcus aureus is a gram-positive bacterium that may cause intestinal inflammation by secreting enterotoxins, which commonly cause food-poisoning and gastrointestinal injuries. Staphylococcal enterotoxin B (SEB) acts as a superantigen (SAg) by binding in a bivalent manner the T-cell receptor (TCR) and the costimulatory receptor CD28, thus stimulating T cells to produce large amounts of inflammatory cytokines, which may affect intestinal epithelial barrier integrity and functions. However, the role of T cell-mediated SEB inflammatory activity remains unknown. Here we show that inflammatory cytokines produced by T cells following SEB stimulation induce dysfunctions in Caco-2 intestinal epithelial cells by promoting actin cytoskeleton remodelling and epithelial cell-cell junction down-regulation. We also found that SEB-activated inflammatory T cells promote the up-regulation of epithelial-mesenchymal transition transcription factors (EMT-TFs) in a nuclear factor-κB (NF-κB)- and STAT3-dependent manner. Finally, by using a structure-based design approach, we identified a SEB mimetic peptide (pSEB116-132) that, by blocking the binding of SEB to CD28, dampens inflammatory-mediated dysregulation of intestinal epithelial barrier.
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Affiliation(s)
- Carola Amormino
- Department of Biology and Biotechnologies “Charles Darwin”, Sapienza University of Rome, Rome, Italy
| | - Emanuela Russo
- Department of Biology and Biotechnologies “Charles Darwin”, Sapienza University of Rome, Rome, Italy
| | - Valentina Tedeschi
- Department of Biology and Biotechnologies “Charles Darwin”, Sapienza University of Rome, Rome, Italy
| | - Maria Teresa Fiorillo
- Department of Biology and Biotechnologies “Charles Darwin”, Sapienza University of Rome, Rome, Italy
| | - Alessandro Paiardini
- Department of Biochemical Sciences “A. Rossi Fanelli”, Sapienza University of Rome, Rome, Italy
| | - Francesco Spallotta
- Department of Biology and Biotechnologies “Charles Darwin”, Sapienza University of Rome, Rome, Italy
- Laboratory affiliated to Instituto Pasteur Italia-Fondazione Cenci Bolognetti, Rome, Italy
| | - Laura Rosanò
- Institute of Molecular Biology and Pathology, CNR, Rome, Italy
| | - Loretta Tuosto
- Department of Biology and Biotechnologies “Charles Darwin”, Sapienza University of Rome, Rome, Italy
| | - Martina Kunkl
- Department of Biology and Biotechnologies “Charles Darwin”, Sapienza University of Rome, Rome, Italy
- Neuroimmunology Unit, IRCCS Santa Lucia Foundation, Rome, Italy
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6
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Gentile A, Fulgione A, Auzino B, Iovane V, Gallo D, Garramone R, Iaccarino N, Randazzo A, Iovane G, Cuomo P, Capparelli R, Iannelli D. In vivo biological validation of in silico analysis: A novel approach for predicting the effects of TLR4 exon 3 polymorphisms on brucellosis. INFECTION, GENETICS AND EVOLUTION : JOURNAL OF MOLECULAR EPIDEMIOLOGY AND EVOLUTIONARY GENETICS IN INFECTIOUS DISEASES 2024; 118:105552. [PMID: 38218390 DOI: 10.1016/j.meegid.2024.105552] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 01/09/2024] [Accepted: 01/10/2024] [Indexed: 01/15/2024]
Abstract
The role of the Toll-like receptor 4 (TLR4) is of recognising intracellular and extracellular pathogens and of activating the immune response. This process can be compromised by single nucleotide polymorphisms (SNPs) which might affect the activity of several TLRs. The aim of this study is of ascertaining whether SNPs in the TLR4 of Bubalus bubalis infected by Brucella abortus, compromise the protein functionality. For this purpose, a computational analysis was performed. Next, computational predictions were confirmed by performing genotyping analysis. Finally, NMR-based metabolomics analysis was performed to identify potential biomarkers for brucellosis. The results indicate two SNPs (c. 672 A > C and c. 902 G > C) as risk factor for brucellosis in Bubalus bubalis, and three metabolites (lactate, 3-hydroxybutyrate and acetate) as biological markers for predicting the risk of developing the disease. These metabolites, together with TLR4 structural modifications in the MD2 interaction domain, are a clear signature of the immune system alteration during diverse Gram-negative bacterial infections. This suggests the possibility to extend this study to other pathogens, including Mycobacterium tuberculosis. In conclusion, this study combines multidisciplinary approaches to evaluate the biological and structural effects of SNPs on protein function.
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Affiliation(s)
- Antonio Gentile
- Department of Agricultural Sciences, University of Naples Federico II, Portici, Naples 80055, Italy
| | - Andrea Fulgione
- Department of Agricultural Sciences, University of Naples Federico II, Portici, Naples 80055, Italy
| | - Barbara Auzino
- Department of Agricultural Sciences, University of Naples Federico II, Portici, Naples 80055, Italy
| | - Valentina Iovane
- Department of Agricultural Sciences, University of Naples Federico II, Portici, Naples 80055, Italy
| | - Daniela Gallo
- Department of Agricultural Sciences, University of Naples Federico II, Portici, Naples 80055, Italy
| | - Raffaele Garramone
- Department of Agricultural Sciences, University of Naples Federico II, Portici, Naples 80055, Italy
| | - Nunzia Iaccarino
- Department of Pharmacy, University of Naples Federico II, Naples 80131, Italy
| | - Antonio Randazzo
- Department of Pharmacy, University of Naples Federico II, Naples 80131, Italy
| | - Giuseppe Iovane
- Department of Veterinary Medicine and Animal Productions, University of Naples Federico II, Naples 80137, Italy
| | - Paola Cuomo
- Department of Agricultural Sciences, University of Naples Federico II, Portici, Naples 80055, Italy
| | - Rosanna Capparelli
- Department of Agricultural Sciences, University of Naples Federico II, Portici, Naples 80055, Italy.
| | - Domenico Iannelli
- Department of Agricultural Sciences, University of Naples Federico II, Portici, Naples 80055, Italy
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Zhang Y, Wang X, Zhang Z, Huang Y, Kihara D. Assessment of Protein-Protein Docking Models Using Deep Learning. Methods Mol Biol 2024; 2780:149-162. [PMID: 38987469 DOI: 10.1007/978-1-0716-3985-6_10] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/12/2024]
Abstract
Protein-protein interactions are involved in almost all processes in a living cell and determine the biological functions of proteins. To obtain mechanistic understandings of protein-protein interactions, the tertiary structures of protein complexes have been determined by biophysical experimental methods, such as X-ray crystallography and cryogenic electron microscopy. However, as experimental methods are costly in resources, many computational methods have been developed that model protein complex structures. One of the difficulties in computational protein complex modeling (protein docking) is to select the most accurate models among many models that are usually generated by a docking method. This article reviews advances in protein docking model assessment methods, focusing on recent developments that apply deep learning to several network architectures.
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Affiliation(s)
- Yuanyuan Zhang
- Department of Computer Science, Purdue University, West Lafayette, IN, USA
| | - Xiao Wang
- Department of Computer Science, Purdue University, West Lafayette, IN, USA
| | - Zicong Zhang
- Department of Computer Science, Purdue University, West Lafayette, IN, USA
| | - Yunhan Huang
- Department of Computer Science, Purdue University, West Lafayette, IN, USA
| | - Daisuke Kihara
- Department of Computer Science, Purdue University, West Lafayette, IN, USA.
- Department of Biological Sciences, Purdue University, West Lafayette, IN, USA.
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8
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Kuder KJ. Docking Foundations: From Rigid to Flexible Docking. Methods Mol Biol 2024; 2780:3-14. [PMID: 38987460 DOI: 10.1007/978-1-0716-3985-6_1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/12/2024]
Abstract
Despite the development of methods for the experimental determination of protein structures, the dissonance between the number of known sequences and their solved structures is still enormous. This is particularly evident in protein-protein complexes. To fill this gap, diverse technologies have been developed to study protein-protein interactions (PPIs) in a cellular context including a range of biological and computational methods. The latter derive from techniques originally published and applied almost half a century ago and are based on interdisciplinary knowledge from the nexus of the fields of biology, chemistry, and physics about protein sequences, structures, and their folding. Protein-protein docking, the main protagonist of this chapter, is routinely treated as an integral part of protein research. Herein, we describe the basic foundations of the whole process in general terms, but step by step from protein representations through docking methods and evaluation of complexes to their final validation.
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Affiliation(s)
- Kamil J Kuder
- Department of Technology and Biotechnology of Drugs, Faculty of Pharmacy, Jagiellonian University Medical College, Kraków, Poland.
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9
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Zięba A, Matosiuk D. Sampling and Scoring in Protein-Protein Docking. Methods Mol Biol 2024; 2780:15-26. [PMID: 38987461 DOI: 10.1007/978-1-0716-3985-6_2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/12/2024]
Abstract
Protein-protein docking is considered one of the most important techniques supporting experimental proteomics. Recent developments in the field of computer science helped to improve this computational technique so that it better handles the complexity of protein nature. Sampling algorithms are responsible for the generation of numerous protein-protein ensembles. Unfortunately, a primary docking output comprises a set of both near-native poses and decoys. Application of the efficient scoring function helps to differentiate poses with the most favorable properties from those that are very unlikely to represent a natural state of the complex. This chapter explains the importance of sampling and scoring in the process of protein-protein docking. Moreover, it summarizes advances in the field.
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Affiliation(s)
- Agata Zięba
- Department of Synthesis and Chemical Technology of Pharmaceutical Substances with Computer Modeling Laboratory, Faculty of Pharmacy, Medical University of Lublin, Lublin, Poland.
| | - Dariusz Matosiuk
- Department of Synthesis and Chemical Technology of Pharmaceutical Substances with Computer Modeling Laboratory, Faculty of Pharmacy, Medical University of Lublin, Lublin, Poland
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10
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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 : THE PREPRINT SERVER FOR BIOLOGY 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] [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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11
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Abdul Kadir FFN, Che Nordin MA, S M N Mydin RB, Choong YS, Che Omar MT. Molecular interaction analysis of anti-IL-8 scFv-10F8-6His against IL-8 monomer through molecular docking and molecular dynamic simulations. J Biomol Struct Dyn 2023:1-11. [PMID: 37837430 DOI: 10.1080/07391102.2023.2269254] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Accepted: 10/04/2023] [Indexed: 10/16/2023]
Abstract
Elevated interleukin 8 (IL-8) expression has been linked to unfavorable outcomes in a range of inflammatory conditions, such as rheumatoid arthritis, psoriasis, and cancer. The human monoclonal antibody (HuMab) 10F8 and the hybridoma 35B11-B bind to an epitope on human IL-8, respectively. 10F8 inhibited interaction between IL-8 and neutrophils in eczema and pustulosis palmoplantaris patients while 35B11-B decreased size lesion in rat model. The binding interaction of monoclonal antibodies and IL-8, especially how complementarity-determining region (CDR) loops could bind the N-terminal of IL-8, has not been fully deliberated at molecular-level. Here, we used a combination of molecular docking, heated and long coarse-grained molecular dynamics simulations to identify key residues of established interaction. Based on heated MD simulation, docked pose of complexes generated by ClusPro showed good binding stability throughout of 70 ns simulation. Based on long molecular dynamic simulations, key residues for the binding were identified throughout of 1000 ns simulation. TYR-53, ASP-99, and ARG-100 of heavy chain CDR together with TYR-33 of light chain CDR are among the highest contributing energy residues within the binding interaction. Meanwhile, LYS11 and TYR13 of IL-8 are important for the determination of overall binding energy. Furthermore, the result of decomposition residues analysis is in good agreement with the interaction analysis data. Current study provides a list of important interacting residues and further scrutiny on these residues is essential for future development and design of a new and stable recombinant antibody against IL-8.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
| | - Muhamad Alif Che Nordin
- Biological Section, School of Distance Education, Universiti Sains Malaysia, Penang, Malaysia
| | - Rabiatul Basria S M N Mydin
- Biomedical Sciences Department, Advanced Medical and Dental Institute, Universiti Sains Malaysia, Penang, Malaysia
| | - Yee Siew Choong
- Institute for Research in Molecular Medicine, Universiti Sains Malaysia, Penang, Malaysia
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12
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Guo X, Pan X, Sun Q, Hu Y, Shi J. Design of a novel multiepitope vaccine against Chlamydia pneumoniae using the extracellular protein as a target. Sci Rep 2023; 13:15070. [PMID: 37700027 PMCID: PMC10497608 DOI: 10.1038/s41598-023-42222-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Accepted: 09/06/2023] [Indexed: 09/14/2023] Open
Abstract
Chlamydia pneumoniae (C. pneumoniae) infection in humans is universal and causes various respiratory infectious diseases, making a safe and effective preventive vaccine essential. In this study, a multi-epitope vaccine with CTLA-4 extracellular structure was constructed by an immunoinformatics approach. Since MOMP protein is the major extracellular protein in C. pneumoniae and has good immunogenicity and high conservation, we selected the MOMP protein of C. pneumoniae as the antigen target, predicted the T and B cell epitopes of the MOMP protein and then connected the CTLA-4 extracellular structure with the predicted dominant epitopes by various linkers to construct a multi-epitope vaccine. The biochemical characterization of the multi-epitope vaccine showed its immunogenicity and anti-allergic properties. The tertiary structure of this vaccine, along with molecular docking, molecular dynamics simulation, and principal component analysis, showed that the multi-epitope vaccine structure interacted with B7 (B7-1, B7-2) and toll-like receptors (TLR-2, TLR-4). Ultimately, the vaccine was cloned and effectively expressed in silico on an insect baculovirus expression vector (pFastBac1). These analyses showed that the designed vaccine could potentially target antigen-presenting cells and was immune to C. pneumoniae, which provided novel strategies for developing the vaccine.
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Affiliation(s)
- Xiaomei Guo
- Yunnan Provincial Key Laboratory of Vector-Borne Diseases Control and Research, Institute of Medical Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, 935 Jiaoling Road, Kunming, 650118, Yunnan, China
- Kunming Medical University, Kunming, Yunnan, China
| | - Xiaohong Pan
- Yunnan Provincial Key Laboratory of Vector-Borne Diseases Control and Research, Institute of Medical Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, 935 Jiaoling Road, Kunming, 650118, Yunnan, China
| | - Qiangming Sun
- Yunnan Provincial Key Laboratory of Vector-Borne Diseases Control and Research, Institute of Medical Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, 935 Jiaoling Road, Kunming, 650118, Yunnan, China.
- National Kunming High-Level Biosafety Primate Research Center, Institute of Medical Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, Kunming, Yunnan, China.
| | - Yunzhang Hu
- Yunnan Provincial Key Laboratory of Vector-Borne Diseases Control and Research, Institute of Medical Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, 935 Jiaoling Road, Kunming, 650118, Yunnan, China.
- Kunming Medical University, Kunming, Yunnan, China.
| | - Jiandong Shi
- Yunnan Provincial Key Laboratory of Vector-Borne Diseases Control and Research, Institute of Medical Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, 935 Jiaoling Road, Kunming, 650118, Yunnan, China.
- National Kunming High-Level Biosafety Primate Research Center, Institute of Medical Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, Kunming, Yunnan, China.
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13
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Christoffer C, Kihara D. Modeling protein-nucleic acid complexes with extremely large conformational changes using Flex-LZerD. Proteomics 2023; 23:e2200322. [PMID: 36529945 PMCID: PMC10448949 DOI: 10.1002/pmic.202200322] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Revised: 12/08/2022] [Accepted: 12/13/2022] [Indexed: 12/23/2022]
Abstract
Proteins and nucleic acids are key components in many processes in living cells, and interactions between proteins and nucleic acids are often crucial pathway components. In many cases, large flexibility of proteins as they interact with nucleic acids is key to their function. To understand the mechanisms of these processes, it is necessary to consider the 3D atomic structures of such protein-nucleic acid complexes. When such structures are not yet experimentally determined, protein docking can be used to computationally generate useful structure models. However, such docking has long had the limitation that the consideration of flexibility is usually limited to small movements or to small structures. We previously developed a method of flexible protein docking which could model ordered proteins which undergo large-scale conformational changes, which we also showed was compatible with nucleic acids. Here, we elaborate on the ability of that pipeline, Flex-LZerD, to model specifically interactions between proteins and nucleic acids, and demonstrate that Flex-LZerD can model more interactions and types of conformational change than previously shown.
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Affiliation(s)
- Charles Christoffer
- Department of Computer Science, Purdue University, West Lafayette, Indiana, USA
| | - Daisuke Kihara
- Department of Computer Science, Purdue University, West Lafayette, Indiana, USA
- Department of Biological Sciences, Purdue University, West Lafayette, Indiana, USA
- Purdue University Center for Cancer Research, Purdue University, West Lafayette, Indiana, USA
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14
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Hammerstad M, Rugtveit AK, Dahlen S, Andersen HK, Hersleth HP. Functional Diversity of Homologous Oxidoreductases-Tuning of Substrate Specificity by a FAD-Stacking Residue for Iron Acquisition and Flavodoxin Reduction. Antioxidants (Basel) 2023; 12:1224. [PMID: 37371954 DOI: 10.3390/antiox12061224] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Revised: 05/30/2023] [Accepted: 06/01/2023] [Indexed: 06/29/2023] Open
Abstract
Although bacterial thioredoxin reductase-like ferredoxin/flavodoxin NAD(P)+ oxidoreductases (FNRs) are similar in terms of primary sequences and structures, they participate in diverse biological processes by catalyzing a range of different redox reactions. Many of the reactions are critical for the growth, survival of, and infection by pathogens, and insight into the structural basis for substrate preference, specificity, and reaction kinetics is crucial for the detailed understanding of these redox pathways. Bacillus cereus (Bc) encodes three FNR paralogs, two of which have assigned distinct biological functions in bacillithiol disulfide reduction and flavodoxin (Fld) reduction. Bc FNR2, the endogenous reductase of the Fld-like protein NrdI, belongs to a distinct phylogenetic cluster of homologous oxidoreductases containing a conserved His residue stacking the FAD cofactor. In this study, we have assigned a function to FNR1, in which the His residue is replaced by a conserved Val, in the reduction of the heme-degrading monooxygenase IsdG, ultimately facilitating the release of iron in an important iron acquisition pathway. The Bc IsdG structure was solved, and IsdG-FNR1 interactions were proposed through protein-protein docking. Mutational studies and bioinformatics analyses confirmed the importance of the conserved FAD-stacking residues on the respective reaction rates, proposing a division of FNRs into four functionally unique sequence similarity clusters likely related to the nature of this residue.
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Affiliation(s)
- Marta Hammerstad
- Department of Biosciences, Section for Biochemistry and Molecular Biology, University of Oslo, P.O. Box 1066, Blindern, NO-0316 Oslo, Norway
| | - Anne Kristine Rugtveit
- Department of Biosciences, Section for Biochemistry and Molecular Biology, University of Oslo, P.O. Box 1066, Blindern, NO-0316 Oslo, Norway
| | - Sondov Dahlen
- Department of Biosciences, Section for Biochemistry and Molecular Biology, University of Oslo, P.O. Box 1066, Blindern, NO-0316 Oslo, Norway
| | - Hilde Kristin Andersen
- Department of Biosciences, Section for Biochemistry and Molecular Biology, University of Oslo, P.O. Box 1066, Blindern, NO-0316 Oslo, Norway
| | - Hans-Petter Hersleth
- Department of Biosciences, Section for Biochemistry and Molecular Biology, University of Oslo, P.O. Box 1066, Blindern, NO-0316 Oslo, Norway
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15
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Jiménez-García B, Roel-Touris J, Barradas-Bautista D. The LightDock Server: Artificial Intelligence-powered modeling of macromolecular interactions. Nucleic Acids Res 2023:7151343. [PMID: 37140054 DOI: 10.1093/nar/gkad327] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Revised: 04/05/2023] [Accepted: 04/17/2023] [Indexed: 05/05/2023] Open
Abstract
Computational docking is an instrumental method of the structural biology toolbox. Specifically, integrative modeling software, such as LightDock, arise as complementary and synergetic methods to experimental structural biology techniques. Ubiquitousness and accessibility are fundamental features to promote ease of use and to improve user experience. With this goal in mind, we have developed the LightDock Server, a web server for the integrative modeling of macromolecular interactions, along with several dedicated usage modes. The server builds upon the LightDock macromolecular docking framework, which has proved useful for modeling medium-to-high flexible complexes, antibody-antigen interactions, or membrane-associated protein assemblies. We believe that this free-to-use resource will be a valuable addition to the structural biology community and can be accessed online at: https://server.lightdock.org/.
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Affiliation(s)
| | - Jorge Roel-Touris
- Protein Design and Modeling Lab, Department of Structural Biology, Molecular Biology Institute of Barcelona (IBMB-CSIC), Baldiri Reixac 15, 08028Barcelona, Spain
| | - Didier Barradas-Bautista
- Kaust Visualization Lab, Core lab Division, King Abdullah University of Science and Technology (KAUST), 23955-6900, Thuwal, Saudi Arabia
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16
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Kunkl M, Amormino C, Spallotta F, Caristi S, Fiorillo MT, Paiardini A, Kaempfer R, Tuosto L. Bivalent binding of staphylococcal superantigens to the TCR and CD28 triggers inflammatory signals independently of antigen presenting cells. Front Immunol 2023; 14:1170821. [PMID: 37207220 PMCID: PMC10189049 DOI: 10.3389/fimmu.2023.1170821] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Accepted: 04/21/2023] [Indexed: 05/21/2023] Open
Abstract
Staphylococcus aureus superantigens (SAgs) such as staphylococcal enterotoxin A (SEA) and B (SEB) are potent toxins stimulating T cells to produce high levels of inflammatory cytokines, thus causing toxic shock and sepsis. Here we used a recently released artificial intelligence-based algorithm to better elucidate the interaction between staphylococcal SAgs and their ligands on T cells, the TCR and CD28. The obtained computational models together with functional data show that SEB and SEA are able to bind to the TCR and CD28 stimulating T cells to activate inflammatory signals independently of MHC class II- and B7-expressing antigen presenting cells. These data reveal a novel mode of action of staphylococcal SAgs. By binding to the TCR and CD28 in a bivalent way, staphylococcal SAgs trigger both the early and late signalling events, which lead to massive inflammatory cytokine secretion.
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Affiliation(s)
- Martina Kunkl
- Department of Biology and Biotechnologies “Charles Darwin”, Sapienza University, Rome, Italy
| | - Carola Amormino
- Department of Biology and Biotechnologies “Charles Darwin”, Sapienza University, Rome, Italy
| | - Francesco Spallotta
- Department of Biology and Biotechnologies “Charles Darwin”, Sapienza University, Rome, Italy
- Laboratory affiliated to Istituto Pasteur Italia-Fondazione Cenci Bolognetti, Sapienza University, Rome, Italy
| | - Silvana Caristi
- Department of Biology and Biotechnologies “Charles Darwin”, Sapienza University, Rome, Italy
| | - Maria Teresa Fiorillo
- Department of Biology and Biotechnologies “Charles Darwin”, Sapienza University, Rome, Italy
| | - Alessandro Paiardini
- Department of Biochemical Sciences “A. Rossi Fanelli”, Sapienza University of Rome, Rome, Italy
| | - Raymond Kaempfer
- Department of Biochemistry and Molecular Biology, The Institute for Medical Research Israel-Canada, The Hebrew University-Hadassah Medical School, Jerusalem, Israel
| | - Loretta Tuosto
- Department of Biology and Biotechnologies “Charles Darwin”, Sapienza University, Rome, Italy
- Laboratory affiliated to Istituto Pasteur Italia-Fondazione Cenci Bolognetti, Sapienza University, Rome, Italy
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17
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Dey D, Tanaka R, Ito H. Structural Characterization of the Chlorophyllide a Oxygenase (CAO) Enzyme Through an In Silico Approach. J Mol Evol 2023; 91:225-235. [PMID: 36869271 DOI: 10.1007/s00239-023-10100-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Accepted: 02/16/2023] [Indexed: 03/05/2023]
Abstract
Chlorophyllide a oxygenase (CAO) is responsible for converting chlorophyll a to chlorophyll b in a two-step oxygenation reaction. CAO belongs to the family of Rieske-mononuclear iron oxygenases. Although the structure and reaction mechanism of other Rieske monooxygenases have been described, a member of plant Rieske non-heme iron-dependent monooxygenase has not been structurally characterized. The enzymes in this family usually form a trimeric structure and electrons are transferred between the non-heme iron site and the Rieske center of the adjoining subunits. CAO is supposed to form a similar structural arrangement. However, in Mamiellales such as Micromonas and Ostreococcus, CAO is encoded by two genes where non-heme iron site and Rieske cluster localize on the distinct polypeptides. It is not clear if they can form a similar structural organization to achieve the enzymatic activity. In this study, the tertiary structures of CAO from the model plant Arabidopsis thaliana and the Prasinophyte Micromonas pusilla were predicted by deep learning-based methods, followed by energy minimization and subsequent stereochemical quality assessment of the predicted models. Furthermore, the chlorophyll a binding cavity and the interaction of ferredoxin, which is the electron donor, on the surface of Micromonas CAO were predicted. The electron transfer pathway was predicted in Micromonas CAO and the overall structure of the CAO active site was conserved even though it forms a heterodimeric complex. The structures presented in this study will serve as a basis for understanding the reaction mechanism and regulation of the plant monooxygenase family to which CAO belongs.
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Affiliation(s)
- Debayan Dey
- Graduate School of Life Science, Hokkaido University, N10 W8, Sapporo, 060-0810, Japan
- Institute of Low Temperature Science, Hokkaido University, N19 W8, Sapporo, 060-0819, Japan
| | - Ryouichi Tanaka
- Institute of Low Temperature Science, Hokkaido University, N19 W8, Sapporo, 060-0819, Japan
| | - Hisashi Ito
- Institute of Low Temperature Science, Hokkaido University, N19 W8, Sapporo, 060-0819, Japan.
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18
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Cerutti C, Shi JR, Vanacker JM. Multifaceted Transcriptional Network of Estrogen-Related Receptor Alpha in Health and Disease. Int J Mol Sci 2023; 24:ijms24054265. [PMID: 36901694 PMCID: PMC10002233 DOI: 10.3390/ijms24054265] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Revised: 02/15/2023] [Accepted: 02/18/2023] [Indexed: 02/23/2023] Open
Abstract
Estrogen-related receptors (ERRα, β and γ in mammals) are orphan members of the nuclear receptor superfamily acting as transcription factors. ERRs are expressed in several cell types and they display various functions in normal and pathological contexts. Amongst others, they are notably involved in bone homeostasis, energy metabolism and cancer progression. In contrast to other nuclear receptors, the activities of the ERRs are apparently not controlled by a natural ligand but they rely on other means such as the availability of transcriptional co-regulators. Here we focus on ERRα and review the variety of co-regulators that have been identified by various means for this receptor and their reported target genes. ERRα cooperates with distinct co-regulators to control the expression of distinct sets of target genes. This exemplifies the combinatorial specificity of transcriptional regulation that induces discrete cellular phenotypes depending on the selected coregulator. We finally propose an integrated view of the ERRα transcriptional network.
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19
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Comparative genomics and interactomics of polyadenylation factors for the prediction of new parasite targets: Entamoeba histolytica as a working model. Biosci Rep 2023; 43:232462. [PMID: 36651565 PMCID: PMC9912109 DOI: 10.1042/bsr20221911] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Revised: 01/05/2023] [Accepted: 01/13/2023] [Indexed: 01/19/2023] Open
Abstract
Protein-protein interactions (PPI) play a key role in predicting the function of a target protein and drug ability to affect an entire biological system. Prediction of PPI networks greatly contributes to determine a target protein and signal pathways related to its function. Polyadenylation of mRNA 3'-end is essential for gene expression regulation and several polyadenylation factors have been shown as valuable targets for controlling protozoan parasites that affect human health. Here, by using a computational strategy based on sequence-based prediction approaches, phylogenetic analyses, and computational prediction of PPI networks, we compared interactomes of polyadenylation factors in relevant protozoan parasites and the human host, to identify key proteins and define potential targets for pathogen control. Then, we used Entamoeba histolytica as a working model to validate our computational results. RT-qPCR assays confirmed the coordinated modulation of connected proteins in the PPI network and evidenced that silencing of the bottleneck protein EhCFIm25 affects the expression of interacting proteins. In addition, molecular dynamics simulations and docking approaches allowed to characterize the relationships between EhCFIm25 and Ehnopp34, two connected bottleneck proteins. Interestingly, the experimental identification of EhCFIm25 interactome confirmed the close relationships among proteins involved in gene expression regulation and evidenced new links with moonlight proteins in E. histolytica, suggesting a connection between RNA biology and metabolism as described in other organisms. Altogether, our results strengthened the relevance of comparative genomics and interactomics of polyadenylation factors for the prediction of new targets for the control of these human pathogens.
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20
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Shin WH, Kumazawa K, Imai K, Hirokawa T, Kihara D. Quantitative comparison of protein-protein interaction interface using physicochemical feature-based descriptors of surface patches. Front Mol Biosci 2023; 10:1110567. [PMID: 36814641 PMCID: PMC9939524 DOI: 10.3389/fmolb.2023.1110567] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Accepted: 01/24/2023] [Indexed: 02/09/2023] Open
Abstract
Driving mechanisms of many biological functions in a cell include physical interactions of proteins. As protein-protein interactions (PPIs) are also important in disease development, protein-protein interactions are highlighted in the pharmaceutical industry as possible therapeutic targets in recent years. To understand the variety of protein-protein interactions in a proteome, it is essential to establish a method that can identify similarity and dissimilarity between protein-protein interactions for inferring the binding of similar molecules, including drugs and other proteins. In this study, we developed a novel method, protein-protein interaction-Surfer, which compares and quantifies similarity of local surface regions of protein-protein interactions. protein-protein interaction-Surfer represents a protein-protein interaction surface with overlapping surface patches, each of which is described with a three-dimensional Zernike descriptor (3DZD), a compact mathematical representation of 3D function. 3DZD captures both the 3D shape and physicochemical properties of the protein surface. The performance of protein-protein interaction-Surfer was benchmarked on datasets of protein-protein interactions, where we were able to show that protein-protein interaction-Surfer finds similar potential drug binding regions that do not share sequence and structure similarity. protein-protein interaction-Surfer is available at https://kiharalab.org/ppi-surfer.
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Affiliation(s)
- Woong-Hee Shin
- Department of Chemistry Education, Sunchon National University, Suncheon, South Korea,Department of Advanced Components and Materials Engineering, Sunchon National University, Suncheon, South Korea
| | - Keiko Kumazawa
- Pharmaceutical Discovery Research Laboratories, Teijin Pharma Limited, Tokyo, Japan
| | - Kenichiro Imai
- Cellular and Molecular Biotechnology Research Institute, National Institute of Advanced Industrial Science and Technology, Tokyo, Japan
| | - Takatsugu Hirokawa
- Division of Biomedical Science, Faculty of Medicine, University of Tsukuba, Tsukuba, Japan,Transborder Medical Research Center, University of Tsukuba, Tsukuba, Japan
| | - Daisuke Kihara
- Department of Biological Sciences, Purdue University, West Lafayette, IN, United States,Department of Computer Science, Purdue University, West Lafayette, IN, United States,Center for Cancer Research, Purdue University, West Lafayette, IN, United States,*Correspondence: Daisuke Kihara,
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21
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Fatouros PR, Roy U, Sur S. Implications of SARS-CoV-2 spike protein interactions with Zn-bound form of ACE2: a computational structural study. Biometals 2023:10.1007/s10534-023-00491-z. [PMID: 36725769 PMCID: PMC9891659 DOI: 10.1007/s10534-023-00491-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Accepted: 01/13/2023] [Indexed: 02/03/2023]
Abstract
The COVID-19 pandemic has generated a major interest in designing inhibitors to prevent SARS-CoV-2 binding on host cells to protect against infection. One promising approach to such research utilizes molecular dynamics simulation to identify potential inhibitors that can prevent the interaction between spike (S) protein on the virus and angiotensin converting enzyme 2 (ACE2) receptor on the host cells. In these studies, many groups have chosen to exclude the ACE2-bound zinc (Zn) ion, which is critical for its enzymatic activity. While the relatively distant location of Zn ion from the S protein binding site (S1 domain), combined with the difficulties in modeling this ion has motivated the decision of exclusion, Zn can potentially contribute to the structural stability of the entire protein, and thus, may have implications on S protein-ACE2 interaction. In this study, the authors model both the ACE2-S1 and ACE2-inhibitor (mAb) system to investigate if there are variations in structure and the readouts due to the presence of Zn ion. Although distant from the S1 or inhibitor binding region, inclusion/exclusion of Zn has statistically significant effects on the structural stability and binding free energy in these systems. In particular, the binding free energy of the ACE2-S1 and ACE2-inhibitor structures is - 3.26 and - 14.8 kcal/mol stronger, respectively, in the Zn-bound structure than in the Zn-free structures. This finding suggests that including Zn may be important in screening potentially inhibitors and may be particularly important in modeling monoclonal antibodies, which may be more sensitive to changes in antigen structure.
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Affiliation(s)
- Peter R. Fatouros
- Department of Chemical and Biomolecular Engineering, Clarkson University, 8 Clarkson Avenue, Potsdam, NY 13699 USA
| | - Urmi Roy
- Department of Chemistry and Biomolecular Science, Clarkson University, 8 Clarkson Avenue, Potsdam, NY 13699 USA
| | - Shantanu Sur
- Department of Biology, Clarkson University, 8 Clarkson Avenue, Potsdam, NY 13699 USA
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22
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Lanjanian H, Hosseini S, Narimani Z, Meknatkhah S, Riazi GH. A knowledge-based protein-protein interaction inhibition (KPI) pipeline: an insight from drug repositioning for COVID-19 inhibition. J Biomol Struct Dyn 2023; 41:11700-11713. [PMID: 36622367 DOI: 10.1080/07391102.2022.2163425] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Accepted: 12/22/2022] [Indexed: 01/10/2023]
Abstract
The inhibition of protein-protein interactions (PPIs) by small molecules is an exciting drug discovery strategy. Here, we aimed to develop a pipeline to identify candidate small molecules to inhibit PPIs. Therefore, KPI, a Knowledge-based Protein-Protein Interaction Inhibition pipeline, was introduced to improve the discovery of PPI inhibitors. Then, phytochemicals from a collection of known Middle Eastern antiviral herbs were screened to identify potential inhibitors of key PPIs involved in COVID-19. Here, the following investigations were sequenced: 1) Finding the binding partner and the interface of the proteins in PPIs, 2) Performing the blind ligand-protein inhibition (LPI) simulations, 3) Performing the local LPI simulations, 4) Simulating the interactions of the proteins and their binding partner in the presence and absence of the ligands, and 5) Performing the molecular dynamics simulations. The pharmacophore groups involved in the LPI were also characterized. Aloin, Genistein, Neoglucobrassicin, and Rutin are our new pipeline candidates for inhibiting PPIs involved in COVID-19. We also propose KPI for drug repositioning studies.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Hossein Lanjanian
- Cellular and Molecular Endocrine Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Shadi Hosseini
- Department of Medicinal Chemistry, Faculty of Pharmacy, Tehran University of Medical Science, Tehran, Iran
| | - Zahra Narimani
- Department of Computer Science and Information Technology, Institute for Advanced Studies in Basic Sciences (IASBS), Zanjan, Iran
| | - Sogol Meknatkhah
- Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
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23
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Launay R, Teppa E, Esque J, André I. Modeling Protein Complexes and Molecular Assemblies Using Computational Methods. Methods Mol Biol 2023; 2553:57-77. [PMID: 36227539 DOI: 10.1007/978-1-0716-2617-7_4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Many biological molecules are assembled into supramolecular complexes that are necessary to perform functions in the cell. Better understanding and characterization of these molecular assemblies are thus essential to further elucidate molecular mechanisms and key protein-protein interactions that could be targeted to modulate the protein binding affinity or develop new binders. Experimental access to structural information on these supramolecular assemblies is often hampered by the size of these systems that make their recombinant production and characterization rather difficult. Computational methods combining both structural data, molecular modeling techniques, and sequence coevolution information can thus offer a good alternative to gain access to the structural organization of protein complexes and assemblies. Herein, we present some computational methods to predict structural models of the protein partners, to search for interacting regions using coevolution information, and to build molecular assemblies. The approach is exemplified using a case study to model the succinate-quinone oxidoreductase heterocomplex.
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Affiliation(s)
- Romain Launay
- Toulouse Biotechnology Institute, TBI, Université de Toulouse, CNRS, INRAE, INSA, Toulouse Cedex 04, France
| | - Elin Teppa
- Toulouse Biotechnology Institute, TBI, Université de Toulouse, CNRS, INRAE, INSA, Toulouse Cedex 04, France
| | - Jérémy Esque
- Toulouse Biotechnology Institute, TBI, Université de Toulouse, CNRS, INRAE, INSA, Toulouse Cedex 04, France.
| | - Isabelle André
- Toulouse Biotechnology Institute, TBI, Université de Toulouse, CNRS, INRAE, INSA, Toulouse Cedex 04, France.
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24
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Harini K, Christoffer C, Gromiha MM, Kihara D. Pairwise and Multi-chain Protein Docking Enhanced Using LZerD Web Server. Methods Mol Biol 2023; 2690:355-373. [PMID: 37450159 PMCID: PMC10561630 DOI: 10.1007/978-1-0716-3327-4_28] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/18/2023]
Abstract
Interactions of proteins with other macromolecules have important structural and functional roles in the basic processes of living cells. To understand and elucidate the mechanisms of interactions, it is important to know the 3D structures of the complexes. Proteomes contain numerous protein-protein complexes, for which experimentally determined structures often do not exist. Computational techniques can be a practical alternative to obtain useful complex structure models. Here, we present a web server that provides access to the LZerD and Multi-LZerD protein docking tools, which can perform both pairwise and multi-chain docking. The web server is user-friendly, with options to visualize the distribution and structures of binding poses of top-scoring models. The LZerD web server is available at https://lzerd.kiharalab.org . This chapter dictates the algorithm and step-by-step procedure to model the monomeric structures with AttentiveDist, and also provides the detail of pairwise LZerD docking, and multi-LZerD. This also provided case studies for each of the three modules.
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Affiliation(s)
- Kannan Harini
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, India
- Department of Biological Sciences, Purdue University, West Lafayette, IN, USA
| | | | - M Michael Gromiha
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, India
- Department of Biological Sciences, Purdue University, West Lafayette, IN, USA
| | - Daisuke Kihara
- Department of Biological Sciences, Purdue University, West Lafayette, IN, USA.
- Department of Computer Science, Purdue University, West Lafayette, IN, USA.
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25
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Christoffer C, Kihara D. Domain-Based Protein Docking with Extremely Large Conformational Changes. J Mol Biol 2022; 434:167820. [PMID: 36089054 PMCID: PMC9992458 DOI: 10.1016/j.jmb.2022.167820] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Revised: 08/31/2022] [Accepted: 09/03/2022] [Indexed: 11/17/2022]
Abstract
Proteins are key components in many processes in living cells, and physical interactions with other proteins and nucleic acids often form key parts of their functions. In many cases, large flexibility of proteins as they interact is key to their function. To understand the mechanisms of these processes, it is necessary to consider the 3D structures of such protein complexes. When such structures are not yet experimentally determined, protein docking has long been present to computationally generate useful structure models. However, protein docking has long had the limitation that the consideration of flexibility is usually limited to very small movements or very small structures. Methods have been developed which handle minor flexibility via normal mode or other structure sampling, but new methods are required to model ordered proteins which undergo large-scale conformational changes to elucidate their function at the molecular level. Here, we present Flex-LZerD, a framework for docking such complexes. Via partial assembly multidomain docking and an iterative normal mode analysis admitting curvilinear motions, we demonstrate the ability to model the assembly of a variety of protein-protein and protein-nucleic acid complexes.
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Affiliation(s)
- Charles Christoffer
- Department of Computer Science, Purdue University, West Lafayette, IN 47907, USA
| | - 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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26
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Bryant P, Pozzati G, Zhu W, Shenoy A, Kundrotas P, Elofsson A. Predicting the structure of large protein complexes using AlphaFold and Monte Carlo tree search. Nat Commun 2022; 13:6028. [PMID: 36224222 PMCID: PMC9556563 DOI: 10.1038/s41467-022-33729-4] [Citation(s) in RCA: 69] [Impact Index Per Article: 34.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Accepted: 09/29/2022] [Indexed: 11/30/2022] Open
Abstract
AlphaFold can predict the structure of single- and multiple-chain proteins with very high accuracy. However, the accuracy decreases with the number of chains, and the available GPU memory limits the size of protein complexes which can be predicted. Here we show that one can predict the structure of large complexes starting from predictions of subcomponents. We assemble 91 out of 175 complexes with 10–30 chains from predicted subcomponents using Monte Carlo tree search, with a median TM-score of 0.51. There are 30 highly accurate complexes (TM-score ≥0.8, 33% of complete assemblies). We create a scoring function, mpDockQ, that can distinguish if assemblies are complete and predict their accuracy. We find that complexes containing symmetry are accurately assembled, while asymmetrical complexes remain challenging. The method is freely available and accesible as a Colab notebook https://colab.research.google.com/github/patrickbryant1/MoLPC/blob/master/MoLPC.ipynb. The accuracy of AlphaFold decreases with the number of protein chains and the available GPU memory limits the size of protein complexes that can be predicted. Here, the authors show that complexes with 10–30 chains can be assembled from predicted subcomponents using Monte Carlo tree search.
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Affiliation(s)
- Patrick Bryant
- Science for Life Laboratory, 172 21, Solna, Sweden. .,Department of Biochemistry and Biophysics, Stockholm University, 106 91, Stockholm, Sweden.
| | - Gabriele Pozzati
- Science for Life Laboratory, 172 21, Solna, Sweden.,Department of Biochemistry and Biophysics, Stockholm University, 106 91, Stockholm, Sweden
| | - Wensi Zhu
- Science for Life Laboratory, 172 21, Solna, Sweden.,Department of Biochemistry and Biophysics, Stockholm University, 106 91, Stockholm, Sweden
| | - Aditi Shenoy
- Science for Life Laboratory, 172 21, Solna, Sweden.,Department of Biochemistry and Biophysics, Stockholm University, 106 91, Stockholm, Sweden
| | - Petras Kundrotas
- Science for Life Laboratory, 172 21, Solna, Sweden.,Center for Computational Biology, The University of Kansas, Lawrence, KS, 66047, USA
| | - Arne Elofsson
- Science for Life Laboratory, 172 21, Solna, Sweden.,Department of Biochemistry and Biophysics, Stockholm University, 106 91, Stockholm, Sweden
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27
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Verburgt J, Zhang Z, Kihara D. Multi-level analysis of intrinsically disordered protein docking methods. Methods 2022; 204:55-63. [PMID: 35609776 PMCID: PMC9701586 DOI: 10.1016/j.ymeth.2022.05.006] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 05/17/2022] [Accepted: 05/19/2022] [Indexed: 12/29/2022] Open
Abstract
Intrinsically Disordered Proteins (IDPs) are a class of proteins in which at least some region of the protein does not possess any stable structure in solution in the physiological condition but may adopt an ordered structure upon binding to a globular receptor. These IDP-receptor complexes are thus subject to protein complex modeling in which computational techniques are applied to accurately reproduce the IDP ligand-receptor interactions. This often exists in the form of protein docking, in which the 3D structures of both the subunits are known, but the position of the ligand relative to the receptor is not. Here, we evaluate the performance of three IDP-receptor modeling tools with metrics that characterize the IDP-receptor interface at various resolutions. We show that all three methods are able to properly identify the general binding site, as identified by lower resolution metrics, but begin to struggle with higher resolution metrics that capture biophysical interactions.
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Affiliation(s)
- Jacob Verburgt
- Department of Biological Sciences, Purdue University, West Lafayette, IN, 47907, USA
| | - Zicong Zhang
- Department of Computer Science, Purdue University, West Lafayette, IN, 47907, USA
| | - Daisuke Kihara
- Department of Biological Sciences, Purdue University, West Lafayette, IN, 47907, USA,Department of Computer Science, Purdue University, West Lafayette, IN, 47907, USA,Purdue University Center for Cancer Research, Purdue University, West Lafayette, IN, 47907, USA,Corresponding Author
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28
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Furman O, Zaporozhets A, Tobi D, Bazylevich A, Firer MA, Patsenker L, Gellerman G, Lubin BCR. Novel Cyclic Peptides for Targeting EGFR and EGRvIII Mutation for Drug Delivery. Pharmaceutics 2022; 14:pharmaceutics14071505. [PMID: 35890400 PMCID: PMC9318536 DOI: 10.3390/pharmaceutics14071505] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 07/17/2022] [Accepted: 07/18/2022] [Indexed: 02/05/2023] Open
Abstract
The epidermal growth factor–epidermal growth factor receptor (EGF-EGFR) pathway has become the main focus of selective chemotherapeutic intervention. As a result, two classes of EGFR inhibitors have been clinically approved, namely monoclonal antibodies and small molecule kinase inhibitors. Despite an initial good response rate to these drugs, most patients develop drug resistance. Therefore, new treatment approaches are needed. In this work, we aimed to find a new EGFR-specific, short cyclic peptide, which could be used for targeted drug delivery. Phage display peptide technology and biopanning were applied to three EGFR expressing cells, including cells expressing the EGFRvIII mutation. DNA from the internalized phage was extracted and the peptide inserts were sequenced using next-generation sequencing (NGS). Eleven peptides were selected for further investigation using binding, internalization, and competition assays, and the results were confirmed by confocal microscopy and peptide docking. Among these eleven peptides, seven showed specific and selective binding and internalization into EGFR positive (EGFR+ve) cells, with two of them—P6 and P9—also demonstrating high specificity for non-small cell lung cancer (NSCLC) and glioblastoma cells, respectively. These peptides were chemically conjugated to camptothecin (CPT). The conjugates were more cytotoxic to EGFR+ve cells than free CPT. Our results describe a novel cyclic peptide, which can be used for targeted drug delivery to cells overexpressing the EGFR and EGFRvIII mutation.
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Affiliation(s)
- Olga Furman
- Department of Chemical Engineering, Biotechnology and Materials, Ariel University, Ariel 40700, Israel; (O.F.); (M.A.F.)
- Agriculture and Oenology Department, Eastern Regional R&D Center, Ariel 40700, Israel
| | - Alisa Zaporozhets
- Department of Chemical Sciences, Ariel University, Ariel 40700, Israel; (A.Z.); (A.B.); (L.P.); (G.G.)
| | - Dror Tobi
- Adelson School of Medicine, Ariel University, Ariel 40700, Israel;
- Department of Molecular Biology, Ariel University, Ariel 40700, Israel
| | - Andrii Bazylevich
- Department of Chemical Sciences, Ariel University, Ariel 40700, Israel; (A.Z.); (A.B.); (L.P.); (G.G.)
| | - Michael A. Firer
- Department of Chemical Engineering, Biotechnology and Materials, Ariel University, Ariel 40700, Israel; (O.F.); (M.A.F.)
- Adelson School of Medicine, Ariel University, Ariel 40700, Israel;
- Ariel Center for Applied Cancer Research, Ariel 40700, Israel
| | - Leonid Patsenker
- Department of Chemical Sciences, Ariel University, Ariel 40700, Israel; (A.Z.); (A.B.); (L.P.); (G.G.)
| | - Gary Gellerman
- Department of Chemical Sciences, Ariel University, Ariel 40700, Israel; (A.Z.); (A.B.); (L.P.); (G.G.)
- Ariel Center for Applied Cancer Research, Ariel 40700, Israel
| | - Bat Chen R. Lubin
- Department of Chemical Engineering, Biotechnology and Materials, Ariel University, Ariel 40700, Israel; (O.F.); (M.A.F.)
- Agriculture and Oenology Department, Eastern Regional R&D Center, Ariel 40700, Israel
- Correspondence: ; Tel.: +972-50-6554655
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29
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Pozzati G, Kundrotas P, Elofsson A. Scoring of protein–protein docking models utilizing predicted interface residues. Proteins 2022; 90:1493-1505. [PMID: 35246997 PMCID: PMC9314140 DOI: 10.1002/prot.26330] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2021] [Revised: 02/23/2022] [Accepted: 02/28/2022] [Indexed: 11/08/2022]
Abstract
Scoring docking solutions is a difficult task, and many methods have been developed for this purpose. In docking, only a handful of the hundreds of thousands of models generated by docking algorithms are acceptable, causing difficulties when developing scoring functions. Today's best scoring functions can significantly increase the number of top‐ranked models but still fail for most targets. Here, we examine the possibility of utilizing predicted interface residues to score docking models generated during the scan stage of a docking algorithm. Many methods have been developed to infer the regions of a protein surface that interact with another protein, but most have not been benchmarked using docking algorithms. This study systematically tests different interface prediction methods for scoring >300.000 low‐resolution rigid‐body template free docking decoys. Overall we find that contact‐based interface prediction by BIPSPI is the best method to score docking solutions, with >12% of first ranked docking models being acceptable. Additional experiments indicated precision as a high‐importance metric when estimating interface prediction quality, focusing on docking constraints production. Finally, we discussed several limitations for adopting interface predictions as constraints in a docking protocol.
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Affiliation(s)
- Gabriele Pozzati
- Department of Biochemistry and Biophysics and Science for Life Laboratory Stockholm University Solna Sweden
| | - Petras Kundrotas
- Department of Biochemistry and Biophysics and Science for Life Laboratory Stockholm University Solna Sweden
- Center for Bioinformatics and Department of Molecular Biosciences University of Kansas Lawrence Kansas USA
| | - Arne Elofsson
- Department of Biochemistry and Biophysics and Science for Life Laboratory Stockholm University Solna Sweden
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30
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Castrosanto MA, Clemente AJN, Whitfield AE, Alviar KB. In silico analysis of the predicted protein-protein interaction of syntaxin-18, a putative receptor of Peregrinus maidis Ashmead (Hemiptera: Delphacidae) with Maize mosaic virus glycoprotein. J Biomol Struct Dyn 2022; 41:3956-3963. [PMID: 35377265 DOI: 10.1080/07391102.2022.2059569] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
Abstract
The corn planthopper, Peregrinus maidis Ashmead (Hemiptera:Delphacidae), is a widely distributed insect pest which serves as a vector of two phytopathogenic viruses, Maize mosaic virus (MMV) and Maize stripe virus (MStV). It transmits the viruses in a persistent and propagative manner. MMV is an alphanucleorhabdovirus with a negative-sense, single-stranded RNA unsegmented genome. One identified insect vector protein that may serve as receptor to MMV is Syntaxin-18 (PmStx18) which belongs to the SNAREs (soluble N-ethylmaleimide-sensitive factor attachment protein receptors) proteins. SNAREs play major roles in the final stage of docking and subsequent fusion of diverse vesicle-mediated transport events. In this work, in silico analysis of the interaction of MMV glycoprotein (MMV G) and PmStx18 was performed. Various freely available protein-protein docking web servers were used to predict the 3 D complex of MMV G and PmStx18. Analysis and protein-protein interaction (PPI) count showed that the complex predicted by the ZDOCK server has the highest number of interaction and highest affinity, as suggested by the calculated solvation free energy gain upon formation of the interface (ΔiG = -31 kcal/mol). Molecular dynamics simulation of the complex revealed important interactions at the interface over the course of 25 ns. This is the first in silico analysis performed for the interaction on a putative receptor of P. maidis and MMV G. The results of the PPI prediction provide novel information for studying the role of Stx18 in the transport, docking and fusion events involved in virus particle transport in the insect vector cells and its release.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Melvin A Castrosanto
- Institute of Chemistry, College of Arts and Sciences, University of the Philippines Los Baños, Los Baños, Laguna Philippines
| | - Apel Jae N Clemente
- Institute of Weed Science, Entomology and Plant Pathology, College of Agriculture and Food Science, University of the Philippines Los Baños, Los Baños, Laguna Philippines
| | - Anna E Whitfield
- Department of Entomology and Plant Pathology, North Carolina State University, NC, USA
| | - Karen B Alviar
- Institute of Weed Science, Entomology and Plant Pathology, College of Agriculture and Food Science, University of the Philippines Los Baños, Los Baños, Laguna Philippines
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31
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Ru Z, Yu M, Zhu Y, Chen Z, Zhang F, Zhang Z, Ding J. Immmunoinformatics-based design of a multi-epitope vaccine with CTLA-4 extracellular domain to combat Helicobacter pylori. FASEB J 2022; 36:e22252. [PMID: 35294065 DOI: 10.1096/fj.202101538rr] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Revised: 02/17/2022] [Accepted: 03/01/2022] [Indexed: 11/11/2022]
Abstract
In view of the high infection rate of Helicobacter pylori, a safe and effective vaccine is urgently needed. Recent trends in vaccine design have shifted toward safe and specific epitope-based vaccines. In this study, by using different immunoinformatics approaches, a total of eight linear B cell epitopes, four HTL and three CTL epitopes of FlaA and UreB proteins of H. pylori G27 strain were screened out, we also predicted the conformational epitopes of the two proteins. Then, the dominant epitopes were sequentially linked by appropriate linkers, and the cytotoxic T lymphocyte-associated antigen 4 extracellular domain was attached to the N-terminal of the epitope sequence. Meanwhile, molecular docking, molecular dynamics simulations and principal component analysis were performed to show that the multi-epitope vaccine structure had strong interactions with B7 (B7-1, B7-2) and Toll-like receptors (TLR-2, -4). Eventually, the effectiveness of the vaccine was validated using in silico cloning. These analyses suggested that the designed vaccine could target antigen-presenting cells and had high potency against H. pylori, which could provide a reference for the future development of efficient H. pylori vaccines.
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Affiliation(s)
- Zhenyu Ru
- Department of Gastroenterology, the First Affiliated Hospital of Xinjiang Medical University, Urumqi, China
| | - Mingkai Yu
- Department of Immunology, School of Basic Medical Sciences, Xinjiang Medical University, Urumqi, China
| | - Yuejie Zhu
- Center of Reproductive Medicine, the First Affiliated Hospital of Xinjiang Medical University, Urumqi, China
| | - Zhiqiang Chen
- Department of Immunology, School of Basic Medical Sciences, Xinjiang Medical University, Urumqi, China
| | - Fengbo Zhang
- Department of Clinical Laboratory, the First Affiliated Hospital of Xinjiang Medical University, Urumqi, China.,State Key Laboratory of Pathogenesis, Prevention, Treatment of Central Asian High Incidence Diseases, the First Affiliated Hospital of Xinjiang Medical University, Urumqi, China
| | - Zhiqiang Zhang
- Department of Gastroenterology, the First Affiliated Hospital of Xinjiang Medical University, Urumqi, China.,State Key Laboratory of Pathogenesis, Prevention, Treatment of Central Asian High Incidence Diseases, the First Affiliated Hospital of Xinjiang Medical University, Urumqi, China
| | - Jianbing Ding
- Department of Immunology, School of Basic Medical Sciences, Xinjiang Medical University, Urumqi, China
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32
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Karaca E, Prévost C, Sacquin-Mora S. Modeling the Dynamics of Protein–Protein Interfaces, How and Why? Molecules 2022; 27:molecules27061841. [PMID: 35335203 PMCID: PMC8950966 DOI: 10.3390/molecules27061841] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2022] [Revised: 03/06/2022] [Accepted: 03/08/2022] [Indexed: 12/07/2022] Open
Abstract
Protein–protein assemblies act as a key component in numerous cellular processes. Their accurate modeling at the atomic level remains a challenge for structural biology. To address this challenge, several docking and a handful of deep learning methodologies focus on modeling protein–protein interfaces. Although the outcome of these methods has been assessed using static reference structures, more and more data point to the fact that the interaction stability and specificity is encoded in the dynamics of these interfaces. Therefore, this dynamics information must be taken into account when modeling and assessing protein interactions at the atomistic scale. Expanding on this, our review initially focuses on the recent computational strategies aiming at investigating protein–protein interfaces in a dynamic fashion using enhanced sampling, multi-scale modeling, and experimental data integration. Then, we discuss how interface dynamics report on the function of protein assemblies in globular complexes, in fuzzy complexes containing intrinsically disordered proteins, as well as in active complexes, where chemical reactions take place across the protein–protein interface.
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Affiliation(s)
- Ezgi Karaca
- Izmir Biomedicine and Genome Center, Izmir 35340, Turkey;
- Izmir International Biomedicine and Genome Institute, Dokuz Eylul University, Izmir 35340, Turkey
| | - Chantal Prévost
- CNRS, Laboratoire de Biochimie Théorique, UPR9080, Université de Paris, 13 rue Pierre et Marie Curie, 75005 Paris, France;
- Institut de Biologie Physico-Chimique, Fondation Edmond de Rothschild, PSL Research University, 75006 Paris, France
| | - Sophie Sacquin-Mora
- CNRS, Laboratoire de Biochimie Théorique, UPR9080, Université de Paris, 13 rue Pierre et Marie Curie, 75005 Paris, France;
- Institut de Biologie Physico-Chimique, Fondation Edmond de Rothschild, PSL Research University, 75006 Paris, France
- Correspondence:
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33
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Elhabashy H, Merino F, Alva V, Kohlbacher O, Lupas AN. Exploring protein-protein interactions at the proteome level. Structure 2022; 30:462-475. [DOI: 10.1016/j.str.2022.02.004] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Revised: 10/26/2021] [Accepted: 02/02/2022] [Indexed: 02/08/2023]
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34
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Verburgt J, Kihara D. Benchmarking of structure refinement methods for protein complex models. Proteins 2022; 90:83-95. [PMID: 34309909 PMCID: PMC8671191 DOI: 10.1002/prot.26188] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2021] [Revised: 06/24/2021] [Accepted: 07/22/2021] [Indexed: 01/03/2023]
Abstract
Protein structure docking is the process in which the quaternary structure of a protein complex is predicted from individual tertiary structures of the protein subunits. Protein docking is typically performed in two main steps. The subunits are first docked while keeping them rigid to form the complex, which is then followed by structure refinement. Structure refinement is crucial for a practical use of computational protein docking models, as it is aimed for correcting conformations of interacting residues and atoms at the interface. Here, we benchmarked the performance of eight existing protein structure refinement methods in refinement of protein complex models. We show that the fraction of native contacts between subunits is by far the most straightforward metric to improve. However, backbone dependent metrics, based on the Root Mean Square Deviation proved more difficult to improve via refinement.
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Affiliation(s)
- Jacob Verburgt
- Department of Biological Sciences, Purdue University, West Lafayette, IN, 47907, USA
| | - Daisuke Kihara
- Department of Biological Sciences, Purdue University, West Lafayette, IN, 47907, USA
- Department of Computer Science, Purdue University, West Lafayette, IN, 47907, USA
- Purdue University Center for Cancer Research, Purdue University, West Lafayette, IN, 47907, USA
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35
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Lensink MF, Brysbaert G, Mauri T, Nadzirin N, Velankar S, Chaleil RAG, Clarence T, Bates PA, Kong R, Liu B, Yang G, Liu M, Shi H, Lu X, Chang S, Roy RS, Quadir F, Liu J, Cheng J, Antoniak A, Czaplewski C, Giełdoń A, Kogut M, Lipska AG, Liwo A, Lubecka EA, Maszota-Zieleniak M, Sieradzan AK, Ślusarz R, Wesołowski PA, Zięba K, Del Carpio Muñoz CA, Ichiishi E, Harmalkar A, Gray JJ, Bonvin AMJJ, Ambrosetti F, Vargas Honorato R, Jandova Z, Jiménez-García B, Koukos PI, Van Keulen S, Van Noort CW, Réau M, Roel-Touris J, Kotelnikov S, Padhorny D, Porter KA, Alekseenko A, Ignatov M, Desta I, Ashizawa R, Sun Z, Ghani U, Hashemi N, Vajda S, Kozakov D, Rosell M, Rodríguez-Lumbreras LA, Fernandez-Recio J, Karczynska A, Grudinin S, Yan Y, Li H, Lin P, Huang SY, Christoffer C, Terashi G, Verburgt J, Sarkar D, Aderinwale T, Wang X, Kihara D, Nakamura T, Hanazono Y, Gowthaman R, Guest JD, Yin R, Taherzadeh G, Pierce BG, Barradas-Bautista D, Cao Z, Cavallo L, Oliva R, Sun Y, Zhu S, Shen Y, Park T, Woo H, Yang J, Kwon S, Won J, Seok C, Kiyota Y, Kobayashi S, Harada Y, Takeda-Shitaka M, Kundrotas PJ, Singh A, Vakser IA, Dapkūnas J, Olechnovič K, Venclovas Č, Duan R, Qiu L, Xu X, Zhang S, Zou X, Wodak SJ. Prediction of protein assemblies, the next frontier: The CASP14-CAPRI experiment. Proteins 2021; 89:1800-1823. [PMID: 34453465 PMCID: PMC8616814 DOI: 10.1002/prot.26222] [Citation(s) in RCA: 60] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Revised: 07/24/2021] [Accepted: 08/05/2021] [Indexed: 12/19/2022]
Abstract
We present the results for CAPRI Round 50, the fourth joint CASP-CAPRI protein assembly prediction challenge. The Round comprised a total of twelve targets, including six dimers, three trimers, and three higher-order oligomers. Four of these were easy targets, for which good structural templates were available either for the full assembly, or for the main interfaces (of the higher-order oligomers). Eight were difficult targets for which only distantly related templates were found for the individual subunits. Twenty-five CAPRI groups including eight automatic servers submitted ~1250 models per target. Twenty groups including six servers participated in the CAPRI scoring challenge submitted ~190 models per target. The accuracy of the predicted models was evaluated using the classical CAPRI criteria. The prediction performance was measured by a weighted scoring scheme that takes into account the number of models of acceptable quality or higher submitted by each group as part of their five top-ranking models. Compared to the previous CASP-CAPRI challenge, top performing groups submitted such models for a larger fraction (70-75%) of the targets in this Round, but fewer of these models were of high accuracy. Scorer groups achieved stronger performance with more groups submitting correct models for 70-80% of the targets or achieving high accuracy predictions. Servers performed less well in general, except for the MDOCKPP and LZERD servers, who performed on par with human groups. In addition to these results, major advances in methodology are discussed, providing an informative overview of where the prediction of protein assemblies currently stands.
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Affiliation(s)
- Marc F Lensink
- CNRS UMR8576 UGSF, Institute for Structural and Functional Glycobiology, University of Lille, Lille, France
| | - Guillaume Brysbaert
- CNRS UMR8576 UGSF, Institute for Structural and Functional Glycobiology, University of Lille, Lille, France
| | - Théo Mauri
- CNRS UMR8576 UGSF, Institute for Structural and Functional Glycobiology, University of Lille, Lille, France
| | - Nurul Nadzirin
- Protein Data Bank in Europe (PDBe), European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, UK
| | - Sameer Velankar
- Protein Data Bank in Europe (PDBe), European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, UK
| | | | - Tereza Clarence
- Biomolecular Modelling Laboratory, The Francis Crick Institute, London, UK
| | - Paul A Bates
- Biomolecular Modelling Laboratory, The Francis Crick Institute, London, UK
| | - Ren Kong
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou, China
| | - Bin Liu
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou, China
| | - Guangbo Yang
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou, China
| | - Ming Liu
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou, China
| | - Hang Shi
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou, China
| | - Xufeng Lu
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou, China
| | - Shan Chang
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou, China
| | - Raj S Roy
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, Missouri, USA
| | - Farhan Quadir
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, Missouri, USA
| | - Jian Liu
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, Missouri, USA
| | - Jianlin Cheng
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, Missouri, USA
- Institute for Data Science and Informatics, University of Missouri, Columbia, Missouri, USA
| | - Anna Antoniak
- Faculty of Chemistry, University of Gdansk, Gdansk, Poland
| | | | - Artur Giełdoń
- Faculty of Chemistry, University of Gdansk, Gdansk, Poland
| | - Mateusz Kogut
- Faculty of Chemistry, University of Gdansk, Gdansk, Poland
| | | | - Adam Liwo
- Faculty of Chemistry, University of Gdansk, Gdansk, Poland
| | - Emilia A Lubecka
- Faculty of Electronics, Telecommunications and Informatics, Gdansk University of Technology, Gdansk, Poland
| | | | | | - Rafał Ślusarz
- Faculty of Chemistry, University of Gdansk, Gdansk, Poland
| | - Patryk A Wesołowski
- Faculty of Chemistry, University of Gdansk, Gdansk, Poland
- Intercollegiate Faculty of Biotechnology, University of Gdansk and Medical University of Gdansk, Gdansk, Poland
| | - Karolina Zięba
- Faculty of Chemistry, University of Gdansk, Gdansk, Poland
| | | | - Eiichiro Ichiishi
- International University of Health and Welfare Hospital (IUHW Hospital), Nasushiobara City, Japan
| | - Ameya Harmalkar
- Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, USA
| | - Jeffrey J Gray
- Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, USA
| | - Alexandre M J J Bonvin
- Computational Structural Biology Group, Bijvoet Centre for Biomolecular Research, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Francesco Ambrosetti
- Computational Structural Biology Group, Bijvoet Centre for Biomolecular Research, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Rodrigo Vargas Honorato
- Computational Structural Biology Group, Bijvoet Centre for Biomolecular Research, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Zuzana Jandova
- Computational Structural Biology Group, Bijvoet Centre for Biomolecular Research, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Brian Jiménez-García
- Computational Structural Biology Group, Bijvoet Centre for Biomolecular Research, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Panagiotis I Koukos
- Computational Structural Biology Group, Bijvoet Centre for Biomolecular Research, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Siri Van Keulen
- Computational Structural Biology Group, Bijvoet Centre for Biomolecular Research, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Charlotte W Van Noort
- Computational Structural Biology Group, Bijvoet Centre for Biomolecular Research, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Manon Réau
- Computational Structural Biology Group, Bijvoet Centre for Biomolecular Research, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Jorge Roel-Touris
- Computational Structural Biology Group, Bijvoet Centre for Biomolecular Research, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Sergei Kotelnikov
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York, USA
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York, USA
- Innopolis University, Russia
| | - Dzmitry Padhorny
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York, USA
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York, USA
| | - Kathryn A Porter
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, USA
| | - Andrey Alekseenko
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York, USA
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York, USA
- Institute of Computer-Aided Design of the Russian Academy of Sciences, Moscow, Russia
| | - Mikhail Ignatov
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York, USA
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York, USA
| | - Israel Desta
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, USA
| | - Ryota Ashizawa
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York, USA
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York, USA
| | - Zhuyezi Sun
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, USA
| | - Usman Ghani
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, USA
| | - Nasser Hashemi
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, USA
| | - Sandor Vajda
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, USA
- Department of Chemistry, Boston University, Boston, Massachusetts, USA
| | - Dima Kozakov
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York, USA
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York, USA
| | - Mireia Rosell
- Instituto de Ciencias de la Vid y del Vino (ICVV), CSIC - Universidad de la Rioja - Gobierno de La Rioja, Logrono, Spain
- Barcelona Supercomputing Center (BSC), Barcelona, Spain
| | - Luis A Rodríguez-Lumbreras
- Instituto de Ciencias de la Vid y del Vino (ICVV), CSIC - Universidad de la Rioja - Gobierno de La Rioja, Logrono, Spain
- Barcelona Supercomputing Center (BSC), Barcelona, Spain
| | - Juan Fernandez-Recio
- Instituto de Ciencias de la Vid y del Vino (ICVV), CSIC - Universidad de la Rioja - Gobierno de La Rioja, Logrono, Spain
- Barcelona Supercomputing Center (BSC), Barcelona, Spain
| | | | - Sergei Grudinin
- Université Grenoble Alpes, Inria, CNRS, Grenoble INP, LJK, Grenoble, France
| | - Yumeng Yan
- School of Physics, Huazhong University of Science and Technology, Wuhan, China
| | - Hao Li
- School of Physics, Huazhong University of Science and Technology, Wuhan, China
| | - Peicong Lin
- School of Physics, Huazhong University of Science and Technology, Wuhan, China
| | - Sheng-You Huang
- School of Physics, Huazhong University of Science and Technology, Wuhan, China
| | - Charles Christoffer
- Department of Computer Science, Purdue University, West Lafayette, Indiana, USA
| | - Genki Terashi
- Department of Biological Sciences, Purdue University, West Lafayette, Indiana, USA
| | - Jacob Verburgt
- Department of Biological Sciences, Purdue University, West Lafayette, Indiana, USA
| | - Daipayan Sarkar
- Department of Biological Sciences, Purdue University, West Lafayette, Indiana, USA
| | - Tunde Aderinwale
- Department of Computer Science, Purdue University, West Lafayette, Indiana, USA
| | - Xiao Wang
- Department of Computer Science, Purdue University, West Lafayette, Indiana, USA
| | - Daisuke Kihara
- Department of Computer Science, Purdue University, West Lafayette, Indiana, USA
- Department of Biological Sciences, Purdue University, West Lafayette, Indiana, USA
| | - Tsukasa Nakamura
- Graduate School of Information Sciences, Tohoku University, Sendai, Miyagi, Japan
| | - Yuya Hanazono
- Institute for Quantum Life Science, National Institutes for Quantum and Radiological Science and Technology, Tokai, Ibaraki, Japan
| | - Ragul Gowthaman
- University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, Maryland, USA
- Department of Cell Biology and Molecular Genetics, University of Maryland, Maryland, USA
| | - Johnathan D Guest
- University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, Maryland, USA
- Department of Cell Biology and Molecular Genetics, University of Maryland, Maryland, USA
| | - Rui Yin
- University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, Maryland, USA
- Department of Cell Biology and Molecular Genetics, University of Maryland, Maryland, USA
| | - Ghazaleh Taherzadeh
- University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, Maryland, USA
- Department of Cell Biology and Molecular Genetics, University of Maryland, Maryland, USA
| | - Brian G Pierce
- University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, Maryland, USA
- Department of Cell Biology and Molecular Genetics, University of Maryland, Maryland, USA
| | | | - Zhen Cao
- King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
| | - Luigi Cavallo
- King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
| | - Romina Oliva
- University of Naples "Parthenope", Napoli, Italy
| | - Yuanfei Sun
- Department of Electrical and Computer Engineering, Texas A&M University, Texas, USA
| | - Shaowen Zhu
- Department of Electrical and Computer Engineering, Texas A&M University, Texas, USA
| | - Yang Shen
- Department of Electrical and Computer Engineering, Texas A&M University, Texas, USA
| | - Taeyong Park
- Department of Chemistry, Seoul National University, Seoul, Republic of Korea
| | - Hyeonuk Woo
- Department of Chemistry, Seoul National University, Seoul, Republic of Korea
| | - Jinsol Yang
- Department of Chemistry, Seoul National University, Seoul, Republic of Korea
| | - Sohee Kwon
- Department of Chemistry, Seoul National University, Seoul, Republic of Korea
| | - Jonghun Won
- Department of Chemistry, Seoul National University, Seoul, Republic of Korea
| | - Chaok Seok
- Department of Chemistry, Seoul National University, Seoul, Republic of Korea
| | - Yasuomi Kiyota
- School of Pharmacy, Kitasato University, Minato-ku, Tokyo, Japan
| | | | - Yoshiki Harada
- School of Pharmacy, Kitasato University, Minato-ku, Tokyo, Japan
| | | | - Petras J Kundrotas
- Computational Biology Program and Department of Molecular Biosciences, University of Kansas, Lawrence, Kansas, USA
| | - Amar Singh
- Computational Biology Program and Department of Molecular Biosciences, University of Kansas, Lawrence, Kansas, USA
| | - Ilya A Vakser
- Computational Biology Program and Department of Molecular Biosciences, University of Kansas, Lawrence, Kansas, USA
| | - Justas Dapkūnas
- Institute of Biotechnology, Life Sciences Center, Vilnius University, Vilnius, Lithuania
| | - Kliment Olechnovič
- Institute of Biotechnology, Life Sciences Center, Vilnius University, Vilnius, Lithuania
| | - Česlovas Venclovas
- Institute of Biotechnology, Life Sciences Center, Vilnius University, Vilnius, Lithuania
| | - Rui Duan
- Dalton Cardiovascular Research Center, University of Missouri, Columbia, Missouri, USA
| | - Liming Qiu
- Dalton Cardiovascular Research Center, University of Missouri, Columbia, Missouri, USA
| | - Xianjin Xu
- Dalton Cardiovascular Research Center, University of Missouri, Columbia, Missouri, USA
| | - Shuang Zhang
- Dalton Cardiovascular Research Center, University of Missouri, Columbia, Missouri, USA
| | - Xiaoqin Zou
- Institute for Data Science and Informatics, University of Missouri, Columbia, Missouri, USA
- Dalton Cardiovascular Research Center, University of Missouri, Columbia, Missouri, USA
- Department of Physics and Astronomy, University of Missouri, Columbia, Missouri, USA
- Department of Biochemistry, University of Missouri, Columbia, Missouri, USA
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36
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Abstract
The biological significance of proteins attracted the scientific community in exploring their characteristics. The studies shed light on the interaction patterns and functions of proteins in a living body. Due to their practical difficulties, reliable experimental techniques pave the way for introducing computational methods in the interaction prediction. Automated methods reduced the difficulties but could not yet replace experimental studies as the field is still evolving. Interaction prediction problem being critical needs highly accurate results, but none of the existing methods could offer reliable performance that can parallel with experimental results yet. This article aims to assess the existing computational docking algorithms, their challenges, and future scope. Blind docking techniques are quite helpful when no information other than the individual structures are available. As more and more complex structures are being added to different databases, information-driven approaches can be a good alternative. Artificial intelligence, ruling over the major fields, is expected to take over this domain very shortly.
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Christoffer C, Bharadwaj V, Luu R, Kihara D. LZerD Protein-Protein Docking Webserver Enhanced With de novo Structure Prediction. Front Mol Biosci 2021; 8:724947. [PMID: 34466411 PMCID: PMC8403062 DOI: 10.3389/fmolb.2021.724947] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2021] [Accepted: 07/21/2021] [Indexed: 01/25/2023] Open
Abstract
Protein-protein docking is a useful tool for modeling the structures of protein complexes that have yet to be experimentally determined. Understanding the structures of protein complexes is a key component for formulating hypotheses in biophysics regarding the functional mechanisms of complexes. Protein-protein docking is an established technique for cases where the structures of the subunits have been determined. While the number of known structures deposited in the Protein Data Bank is increasing, there are still many cases where the structures of individual proteins that users want to dock are not determined yet. Here, we have integrated the AttentiveDist method for protein structure prediction into our LZerD webserver for protein-protein docking, which enables users to simply submit protein sequences and obtain full-complex atomic models, without having to supply any structure themselves. We have further extended the LZerD docking interface with a symmetrical homodimer mode. The LZerD server is available at https://lzerd.kiharalab.org/.
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Affiliation(s)
- Charles Christoffer
- Department of Computer Science, Purdue University, West Lafayette, IN, United States
| | - Vijay Bharadwaj
- Department of Computer Science, Purdue University, West Lafayette, IN, United States
| | - Ryan Luu
- Department of Computer Science, Purdue University, West Lafayette, IN, United States
| | - Daisuke Kihara
- Department of Computer Science, Purdue University, West Lafayette, IN, United States.,Department of Biological Sciences, Purdue University, West Lafayette, IN, United States
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38
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Gaber A, Pavšič M. Modeling and Structure Determination of Homo-Oligomeric Proteins: An Overview of Challenges and Current Approaches. Int J Mol Sci 2021; 22:9081. [PMID: 34445785 PMCID: PMC8396596 DOI: 10.3390/ijms22169081] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Revised: 08/20/2021] [Accepted: 08/20/2021] [Indexed: 12/12/2022] Open
Abstract
Protein homo-oligomerization is a very common phenomenon, and approximately half of proteins form homo-oligomeric assemblies composed of identical subunits. The vast majority of such assemblies possess internal symmetry which can be either exploited to help or poses challenges during structure determination. Moreover, aspects of symmetry are critical in the modeling of protein homo-oligomers either by docking or by homology-based approaches. Here, we first provide a brief overview of the nature of protein homo-oligomerization. Next, we describe how the symmetry of homo-oligomers is addressed by crystallographic and non-crystallographic symmetry operations, and how biologically relevant intermolecular interactions can be deciphered from the ordered array of molecules within protein crystals. Additionally, we describe the most important aspects of protein homo-oligomerization in structure determination by NMR. Finally, we give an overview of approaches aimed at modeling homo-oligomers using computational methods that specifically address their internal symmetry and allow the incorporation of other experimental data as spatial restraints to achieve higher model reliability.
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