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Bayarsaikhan B, Zsidó BZ, Börzsei R, Hetényi C. Efficient Refinement of Complex Structures of Flexible Histone Peptides Using Post-Docking Molecular Dynamics Protocols. Int J Mol Sci 2024; 25:5945. [PMID: 38892133 PMCID: PMC11172440 DOI: 10.3390/ijms25115945] [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: 04/24/2024] [Revised: 05/26/2024] [Accepted: 05/27/2024] [Indexed: 06/21/2024] Open
Abstract
Histones are keys to many epigenetic events and their complexes have therapeutic and diagnostic importance. The determination of the structures of histone complexes is fundamental in the design of new drugs. Computational molecular docking is widely used for the prediction of target-ligand complexes. Large, linear peptides like the tail regions of histones are challenging ligands for docking due to their large conformational flexibility, extensive hydration, and weak interactions with the shallow binding pockets of their reader proteins. Thus, fast docking methods often fail to produce complex structures of such peptide ligands at a level appropriate for drug design. To address this challenge, and improve the structural quality of the docked complexes, post-docking refinement has been applied using various molecular dynamics (MD) approaches. However, a final consensus has not been reached on the desired MD refinement protocol. In this present study, MD refinement strategies were systematically explored on a set of problematic complexes of histone peptide ligands with relatively large errors in their docked geometries. Six protocols were compared that differ in their MD simulation parameters. In all cases, pre-MD hydration of the complex interface regions was applied to avoid the unwanted presence of empty cavities. The best-performing protocol achieved a median of 32% improvement over the docked structures in terms of the change in root mean squared deviations from the experimental references. The influence of structural factors and explicit hydration on the performance of post-docking MD refinements are also discussed to help with their implementation in future methods and applications.
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Affiliation(s)
- Bayartsetseg Bayarsaikhan
- Pharmacoinformatics Unit, Department of Pharmacology and Pharmacotherapy, Medical School, University of Pécs, Szigeti út 12, H-7624 Pécs, Hungary; (B.B.); (B.Z.Z.); (R.B.)
| | - Balázs Zoltán Zsidó
- Pharmacoinformatics Unit, Department of Pharmacology and Pharmacotherapy, Medical School, University of Pécs, Szigeti út 12, H-7624 Pécs, Hungary; (B.B.); (B.Z.Z.); (R.B.)
| | - Rita Börzsei
- Pharmacoinformatics Unit, Department of Pharmacology and Pharmacotherapy, Medical School, University of Pécs, Szigeti út 12, H-7624 Pécs, Hungary; (B.B.); (B.Z.Z.); (R.B.)
| | - Csaba Hetényi
- Pharmacoinformatics Unit, Department of Pharmacology and Pharmacotherapy, Medical School, University of Pécs, Szigeti út 12, H-7624 Pécs, Hungary; (B.B.); (B.Z.Z.); (R.B.)
- National Laboratory for Drug Research and Development, Magyar tudósok krt. 2, H-1117 Budapest, Hungary
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2
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Tan C, Reilly B, Ma G, Murao A, Jha A, Aziz M, Wang P. Neutrophils disrupt B-1a cell homeostasis by targeting Siglec-G to exacerbate sepsis. Cell Mol Immunol 2024:10.1038/s41423-024-01165-7. [PMID: 38789529 DOI: 10.1038/s41423-024-01165-7] [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: 12/12/2023] [Accepted: 04/11/2024] [Indexed: 05/26/2024] Open
Abstract
B-1a cells, an innate-like cell population, are crucial for pathogen defense and the regulation of inflammation through their release of natural IgM and IL-10. In sepsis, B-1a cell numbers are decreased in the peritoneal cavity as they robustly migrate to the spleen. Within the spleen, migrating B-1a cells differentiate into plasma cells, leading to alterations in their original phenotype and functionality. We discovered a key player, sialic acid-binding immunoglobulin-like lectin-G (Siglec-G), which is expressed predominantly on B-1a cells and negatively regulates B-1a cell migration to maintain homeostasis. Siglec-G interacts with CXCR4/CXCL12 to modulate B-1a cell migration. Neutrophils aid B-1a cell migration via neutrophil elastase (NE)-mediated Siglec-G cleavage. Human studies revealed increased NE expression in septic patients. We identified an NE cleavage sequence in silico, leading to the discovery of a decoy peptide that protects Siglec-G, preserves peritoneal B-1a cells, reduces inflammation, and enhances sepsis survival. The role of Siglec-G in inhibiting B-1a cell migration to maintain their inherent phenotype and function is compromised by NE in sepsis, offering valuable insights into B-1a cell homeostasis. Employing a small decoy peptide to prevent NE-mediated Siglec-G cleavage has emerged as a promising strategy to sustain peritoneal B-1a cell homeostasis, alleviate inflammation, and ultimately improve outcomes in sepsis patients.
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Affiliation(s)
- Chuyi Tan
- Center for Immunology and Inflammation, the Feinstein Institutes for Medical Research, Manhasset, New York, USA
- Department of Pathophysiology, Xiangya School of Medicine, Central South University, Changsha, Hunan, China
| | - Bridgette Reilly
- Center for Immunology and Inflammation, the Feinstein Institutes for Medical Research, Manhasset, New York, USA
| | - Gaifeng Ma
- Center for Immunology and Inflammation, the Feinstein Institutes for Medical Research, Manhasset, New York, USA
| | - Atsushi Murao
- Center for Immunology and Inflammation, the Feinstein Institutes for Medical Research, Manhasset, New York, USA
| | - Alok Jha
- Center for Immunology and Inflammation, the Feinstein Institutes for Medical Research, Manhasset, New York, USA
| | - Monowar Aziz
- Center for Immunology and Inflammation, the Feinstein Institutes for Medical Research, Manhasset, New York, USA.
- Departments of Surgery and Molecular Medicine, Zucker School of Medicine at Hofstra/Northwell, Manhasset, New York, USA.
| | - Ping Wang
- Center for Immunology and Inflammation, the Feinstein Institutes for Medical Research, Manhasset, New York, USA.
- Departments of Surgery and Molecular Medicine, Zucker School of Medicine at Hofstra/Northwell, Manhasset, New York, USA.
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Grassmann G, Miotto M, Desantis F, Di Rienzo L, Tartaglia GG, Pastore A, Ruocco G, Monti M, Milanetti E. Computational Approaches to Predict Protein-Protein Interactions in Crowded Cellular Environments. Chem Rev 2024; 124:3932-3977. [PMID: 38535831 PMCID: PMC11009965 DOI: 10.1021/acs.chemrev.3c00550] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 02/20/2024] [Accepted: 02/21/2024] [Indexed: 04/11/2024]
Abstract
Investigating protein-protein interactions is crucial for understanding cellular biological processes because proteins often function within molecular complexes rather than in isolation. While experimental and computational methods have provided valuable insights into these interactions, they often overlook a critical factor: the crowded cellular environment. This environment significantly impacts protein behavior, including structural stability, diffusion, and ultimately the nature of binding. In this review, we discuss theoretical and computational approaches that allow the modeling of biological systems to guide and complement experiments and can thus significantly advance the investigation, and possibly the predictions, of protein-protein interactions in the crowded environment of cell cytoplasm. We explore topics such as statistical mechanics for lattice simulations, hydrodynamic interactions, diffusion processes in high-viscosity environments, and several methods based on molecular dynamics simulations. By synergistically leveraging methods from biophysics and computational biology, we review the state of the art of computational methods to study the impact of molecular crowding on protein-protein interactions and discuss its potential revolutionizing effects on the characterization of the human interactome.
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Affiliation(s)
- Greta Grassmann
- Department
of Biochemical Sciences “Alessandro Rossi Fanelli”, Sapienza University of Rome, Rome 00185, Italy
- Center
for Life Nano & Neuro Science, Istituto
Italiano di Tecnologia, Rome 00161, Italy
| | - Mattia Miotto
- Center
for Life Nano & Neuro Science, Istituto
Italiano di Tecnologia, Rome 00161, Italy
| | - Fausta Desantis
- Center
for Life Nano & Neuro Science, Istituto
Italiano di Tecnologia, Rome 00161, Italy
- The
Open University Affiliated Research Centre at Istituto Italiano di
Tecnologia, Genoa 16163, Italy
| | - Lorenzo Di Rienzo
- Center
for Life Nano & Neuro Science, Istituto
Italiano di Tecnologia, Rome 00161, Italy
| | - Gian Gaetano Tartaglia
- Center
for Life Nano & Neuro Science, Istituto
Italiano di Tecnologia, Rome 00161, Italy
- Department
of Neuroscience and Brain Technologies, Istituto Italiano di Tecnologia, Genoa 16163, Italy
- Center
for Human Technologies, Genoa 16152, Italy
| | - Annalisa Pastore
- Experiment
Division, European Synchrotron Radiation
Facility, Grenoble 38043, France
| | - Giancarlo Ruocco
- Center
for Life Nano & Neuro Science, Istituto
Italiano di Tecnologia, Rome 00161, Italy
- Department
of Physics, Sapienza University, Rome 00185, Italy
| | - Michele Monti
- RNA
System Biology Lab, Department of Neuroscience and Brain Technologies, Istituto Italiano di Tecnologia, Genoa 16163, Italy
| | - Edoardo Milanetti
- Center
for Life Nano & Neuro Science, Istituto
Italiano di Tecnologia, Rome 00161, Italy
- Department
of Physics, Sapienza University, Rome 00185, Italy
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Murao A, Jha A, Ma G, Chaung W, Aziz M, Wang P. A Synthetic Poly(A) Tail Targeting Extracellular CIRP Inhibits Sepsis. JOURNAL OF IMMUNOLOGY (BALTIMORE, MD. : 1950) 2023; 211:1144-1153. [PMID: 37585248 PMCID: PMC10528014 DOI: 10.4049/jimmunol.2300228] [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: 03/29/2023] [Accepted: 07/26/2023] [Indexed: 08/17/2023]
Abstract
Sepsis is an infectious inflammatory disease that often results in acute lung injury (ALI). Cold-inducible RNA-binding protein (CIRP) is an intracellular RNA chaperon that binds to mRNA's poly(A) tail. However, CIRP can be released in sepsis, and extracellular CIRP (eCIRP) is a damage-associated molecular pattern, exaggerating inflammation, ALI, and mortality. In this study, we developed an engineered poly(A) mRNA mimic, AAAAAAAAAAAA, named A12, with 2'-O-methyl ribose modification and terminal phosphorothioate linkages to protect it from RNase degradation, exhibiting an increased half-life. A12 selectively and strongly interacted with the RNA-binding motif of eCIRP, thereby preventing eCIRP's binding to its receptor, TLR4. In vitro treatment with A12 significantly decreased eCIRP-induced macrophage MAPK and NF-κB activation and inflammatory transcription factor upregulation. A12 also attenuated proinflammatory cytokine production induced by eCIRP in vitro and in vivo in macrophages and mice, respectively. We revealed that treating cecal ligation and puncture-induced sepsis with A12 significantly reduced serum organ injury markers and cytokine levels and ALI, and it decreased bacterial loads in the blood and peritoneal fluid, ultimately improving their survival. Thus, A12's ability to attenuate the clinical models of sepsis sheds lights on inflammatory disease pathophysiology and prevention of the disease progress.
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Affiliation(s)
- Atsushi Murao
- Center for Immunology and Inflammation, The Feinstein Institutes for Medical Research, Manhasset, New York
| | - Alok Jha
- Center for Immunology and Inflammation, The Feinstein Institutes for Medical Research, Manhasset, New York
| | - Gaifeng Ma
- Center for Immunology and Inflammation, The Feinstein Institutes for Medical Research, Manhasset, New York
| | - Wayne Chaung
- Center for Immunology and Inflammation, The Feinstein Institutes for Medical Research, Manhasset, New York
| | - Monowar Aziz
- Center for Immunology and Inflammation, The Feinstein Institutes for Medical Research, Manhasset, New York
- Departments of Surgery and Molecular Medicine, Zucker School of Medicine at Hofstra/Northwell, Manhasset, New York
| | - Ping Wang
- Center for Immunology and Inflammation, The Feinstein Institutes for Medical Research, Manhasset, New York
- Departments of Surgery and Molecular Medicine, Zucker School of Medicine at Hofstra/Northwell, Manhasset, New York
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Adiyaman R, Edmunds NS, Genc AG, Alharbi SMA, McGuffin LJ. Improvement of protein tertiary and quaternary structure predictions using the ReFOLD refinement method and the AlphaFold2 recycling process. BIOINFORMATICS ADVANCES 2023; 3:vbad078. [PMID: 37359722 PMCID: PMC10290552 DOI: 10.1093/bioadv/vbad078] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Revised: 05/09/2023] [Accepted: 06/13/2023] [Indexed: 06/28/2023]
Abstract
Motivation The accuracy gap between predicted and experimental structures has been significantly reduced following the development of AlphaFold2 (AF2). However, for many targets, AF2 models still have room for improvement. In previous CASP experiments, highly computationally intensive MD simulation-based methods have been widely used to improve the accuracy of single 3D models. Here, our ReFOLD pipeline was adapted to refine AF2 predictions while maintaining high model accuracy at a modest computational cost. Furthermore, the AF2 recycling process was utilized to improve 3D models by using them as custom template inputs for tertiary and quaternary structure predictions. Results According to the Molprobity score, 94% of the generated 3D models by ReFOLD were improved. AF2 recycling showed an improvement rate of 87.5% (using MSAs) and 81.25% (using single sequences) for monomeric AF2 models and 100% (MSA) and 97.8% (single sequence) for monomeric non-AF2 models, as measured by the average change in lDDT. By the same measure, the recycling of multimeric models showed an improvement rate of as much as 80% for AF2-Multimer (AF2M) models and 94% for non-AF2M models. Availability and implementation Refinement using AlphaFold2-Multimer recycling is available as part of the MultiFOLD docker package (https://hub.docker.com/r/mcguffin/multifold). The ReFOLD server is available at https://www.reading.ac.uk/bioinf/ReFOLD/ and the modified scripts can be downloaded from https://www.reading.ac.uk/bioinf/downloads/. Supplementary information Supplementary data are available at Bioinformatics Advances online.
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Affiliation(s)
- Recep Adiyaman
- School of Biological Sciences, University of Reading, Reading RG6 6EX, UK
| | - Nicholas S Edmunds
- School of Biological Sciences, University of Reading, Reading RG6 6EX, UK
| | - Ahmet G Genc
- School of Biological Sciences, University of Reading, Reading RG6 6EX, UK
| | - Shuaa M A Alharbi
- School of Biological Sciences, University of Reading, Reading RG6 6EX, UK
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Extracellular CIRP dysregulates macrophage bacterial phagocytosis in sepsis. Cell Mol Immunol 2023; 20:80-93. [PMID: 36471113 PMCID: PMC9794804 DOI: 10.1038/s41423-022-00961-3] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Accepted: 11/12/2022] [Indexed: 12/12/2022] Open
Abstract
In sepsis, macrophage bacterial phagocytosis is impaired, but the mechanism is not well elucidated. Extracellular cold-inducible RNA-binding protein (eCIRP) is a damage-associated molecular pattern that causes inflammation. However, whether eCIRP regulates macrophage bacterial phagocytosis is unknown. Here, we reported that the bacterial loads in the blood and peritoneal fluid were decreased in CIRP-/- mice and anti-eCIRP Ab-treated mice after sepsis. Increased eCIRP levels were correlated with decreased bacterial clearance in septic mice. CIRP-/- mice showed a marked increase in survival after sepsis. Recombinant murine CIRP (rmCIRP) significantly decreased the phagocytosis of bacteria by macrophages in vivo and in vitro. rmCIRP decreased the protein expression of actin-binding proteins, ARP2, and p-cofilin in macrophages. rmCIRP significantly downregulated the protein expression of βPIX, a Rac1 activator. We further demonstrated that STAT3 and βPIX formed a complex following rmCIRP treatment, preventing βPIX from activating Rac1. We also found that eCIRP-induced STAT3 phosphorylation was required for eCIRP's action in actin remodeling. Inhibition of STAT3 phosphorylation prevented the formation of the STAT3-βPIX complex, restoring ARP2 and p-cofilin expression and membrane protrusion in rmCIRP-treated macrophages. The STAT3 inhibitor stattic rescued the macrophage phagocytic dysfunction induced by rmCIRP. Thus, we identified a novel mechanism of macrophage phagocytic dysfunction caused by eCIRP, which provides a new therapeutic target to ameliorate sepsis.
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7
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Reilly B, Tan C, Murao A, Nofi C, Jha A, Aziz M, Wang P. Necroptosis-Mediated eCIRP Release in Sepsis. J Inflamm Res 2022; 15:4047-4059. [PMID: 35873387 PMCID: PMC9304637 DOI: 10.2147/jir.s370615] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Accepted: 07/07/2022] [Indexed: 11/25/2022] Open
Abstract
Introduction Extracellular cold-inducible RNA-binding protein (eCIRP) is an endogenous pro-inflammatory mediator that exacerbates injury in inflammation and sepsis. The mechanisms in which eCIRP is released have yet to be fully explored. Necroptosis is a programmed cell death that is dependent on the activation of mixed lineage kinase domain-like pseudo kinase (MLKL) which causes the release of damage-associated molecular patterns. We hypothesize that eCIRP is released through necroptosis and intensifies inflammation in sepsis. Methods RAW264.7 cells were treated with pan-caspase inhibitor z-VAD (15 μM) 1 h before stimulation with LPS (1 μg/mL). Necroptosis inhibitor, Necrostatin-1 (Nec-1) (10 μM) was added to the cells with LPS simultaneously. After 24 h of LPS stimulation, cytotoxicity was determined by LDH assay. eCIRP levels in the culture supernatants and phospho-MLKL (p-MLKL) from cell lysates were assessed by Western blot. p-MLKL interaction with the cell membrane was visualized by immunofluorescence. Sepsis was induced in C57BL/6 mice by cecal ligation and puncture (CLP). Mice were treated with Nec-1 (1 mg/kg) or DMSO. 20 h post-surgery, serum and peritoneal fluid levels of eCIRP, TNF-α and IL-6 were determined by ELISA. H&E staining of lung tissue sections was performed. Results We found that in RAW264.7 cells, LPS+z-VAD induces necroptosis as evidenced by an increase in p-MLKL levels and causes eCIRP release. Nec-1 reduces both p-MLKL activation and eCIRP release in LPS+z-VAD-treated RAW264.7 cells. Nec-1 also inhibits the release of eCIRP, TNF-α and IL-6 in the serum and peritoneal fluid in CLP-induced septic mice. We predicted a transient interaction between eCIRP and MLKL using a computational model, suggesting that eCIRP may exit the cell via the pores formed by p-MLKL. Conclusion Necroptosis is a novel mechanism of eCIRP release in sepsis. Targeting necroptosis may ameliorate inflammation and injury in sepsis by inhibiting eCIRP release.
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Affiliation(s)
- Bridgette Reilly
- Center for Immunology and Inflammation, The Feinstein Institutes for Medical Research, Manhasset, NY, USA
| | - Chuyi Tan
- Center for Immunology and Inflammation, The Feinstein Institutes for Medical Research, Manhasset, NY, USA
| | - Atsushi Murao
- Center for Immunology and Inflammation, The Feinstein Institutes for Medical Research, Manhasset, NY, USA
| | - Colleen Nofi
- Center for Immunology and Inflammation, The Feinstein Institutes for Medical Research, Manhasset, NY, USA.,Department of Surgery, Zucker School of Medicine at Hofstra/Northwell, Manhasset, NY, USA
| | - Alok Jha
- Center for Immunology and Inflammation, The Feinstein Institutes for Medical Research, Manhasset, NY, USA
| | - Monowar Aziz
- Center for Immunology and Inflammation, The Feinstein Institutes for Medical Research, Manhasset, NY, USA.,Department of Surgery, Zucker School of Medicine at Hofstra/Northwell, Manhasset, NY, USA.,Department of Molecular Medicine, Zucker School of Medicine at Hofstra/Northwell, Manhasset, NY, USA
| | - Ping Wang
- Center for Immunology and Inflammation, The Feinstein Institutes for Medical Research, Manhasset, NY, USA.,Department of Surgery, Zucker School of Medicine at Hofstra/Northwell, Manhasset, NY, USA.,Department of Molecular Medicine, Zucker School of Medicine at Hofstra/Northwell, Manhasset, NY, USA
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8
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Induced fit with replica exchange improves protein complex structure prediction. PLoS Comput Biol 2022; 18:e1010124. [PMID: 35658008 PMCID: PMC9200320 DOI: 10.1371/journal.pcbi.1010124] [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: 12/22/2021] [Revised: 06/15/2022] [Accepted: 04/20/2022] [Indexed: 11/19/2022] Open
Abstract
Despite the progress in prediction of protein complexes over the last decade, recent blind protein complex structure prediction challenges revealed limited success rates (less than 20% models with DockQ score > 0.4) on targets that exhibit significant conformational change upon binding. To overcome limitations in capturing backbone motions, we developed a new, aggressive sampling method that incorporates temperature replica exchange Monte Carlo (T-REMC) and conformational sampling techniques within docking protocols in Rosetta. Our method, ReplicaDock 2.0, mimics induced-fit mechanism of protein binding to sample backbone motions across putative interface residues on-the-fly, thereby recapitulating binding-partner induced conformational changes. Furthermore, ReplicaDock 2.0 clocks in at 150-500 CPU hours per target (protein-size dependent); a runtime that is significantly faster than Molecular Dynamics based approaches. For a benchmark set of 88 proteins with moderate to high flexibility (unbound-to-bound iRMSD over 1.2 Å), ReplicaDock 2.0 successfully docks 61% of moderately flexible complexes and 35% of highly flexible complexes. Additionally, we demonstrate that by biasing backbone sampling particularly towards residues comprising flexible loops or hinge domains, highly flexible targets can be predicted to under 2 Å accuracy. This indicates that additional gains are possible when mobile protein segments are known.
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Tan C, Reilly B, Jha A, Murao A, Lee Y, Brenner M, Aziz M, Wang P. Active Release of eCIRP via Gasdermin D Channels to Induce Inflammation in Sepsis. JOURNAL OF IMMUNOLOGY (BALTIMORE, MD. : 1950) 2022; 208:2184-2195. [PMID: 35418465 PMCID: PMC9050887 DOI: 10.4049/jimmunol.2101004] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Accepted: 02/23/2022] [Indexed: 12/12/2022]
Abstract
Extracellular cold-inducible RNA binding protein (eCIRP) is an inflammatory mediator that causes inflammation and tissue injury in sepsis. Gasdermin D (GSDMD) is a protein that, when cleaved, forms pores in the cell membrane, releasing intracellular contents into the extracellular milieu to exacerbate inflammation. We hypothesize that eCIRP is released actively from viable macrophages via GSDMD pores. We found that LPS induced eCIRP secretion from macrophages into the extracellular space. LPS significantly increased the expression of caspase-11 and cleavage of the GSDMD, as evidenced by increased N-terminal GSDMD expression in RAW 264.7 cells and mouse primary peritoneal macrophages. GSDMD inhibitor disulfiram decreased eCIRP release in vitro. Treatment with glycine to prevent pyroptosis-induced cell lysis did not significantly decrease eCIRP release from LPS-treated macrophages, indicating that eCIRP was actively released and was independent of pyroptosis. Downregulation of GSDMD gene expression by siRNA transfection suppressed eCIRP release in vitro after LPS stimulation. Moreover, GSDMD-/- peritoneal macrophages and mice had decreased levels of eCIRP in the culture supernatants and in blood treated with LPS in vitro and in vivo, respectively. GSDMD inhibitor disulfiram inhibited serum levels of eCIRP in endotoxemia and cecal ligation and puncture-induced sepsis. We conclude that eCIRP release from living macrophages is mediated through GSDMD pores, suggesting that targeting GSDMD could be a novel and potential therapeutic approach to inhibit eCIRP-mediated inflammation in sepsis.
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Affiliation(s)
- Chuyi Tan
- Center for Immunology and Inflammation, The Feinstein Institutes for Medical Research, Manhasset, NY
| | - Bridgette Reilly
- Center for Immunology and Inflammation, The Feinstein Institutes for Medical Research, Manhasset, NY
| | - Alok Jha
- Center for Immunology and Inflammation, The Feinstein Institutes for Medical Research, Manhasset, NY
| | - Atsushi Murao
- Center for Immunology and Inflammation, The Feinstein Institutes for Medical Research, Manhasset, NY
| | - Yongchan Lee
- Center for Immunology and Inflammation, The Feinstein Institutes for Medical Research, Manhasset, NY
| | - Max Brenner
- Center for Immunology and Inflammation, The Feinstein Institutes for Medical Research, Manhasset, NY.,Department of Molecular Medicine, Zucker School of Medicine at Hofstra/Northwell, Manhasset, NY; and
| | - Monowar Aziz
- Center for Immunology and Inflammation, The Feinstein Institutes for Medical Research, Manhasset, NY; .,Department of Molecular Medicine, Zucker School of Medicine at Hofstra/Northwell, Manhasset, NY; and.,Department of Surgery, Zucker School of Medicine at Hofstra/Northwell, Manhasset, NY
| | - Ping Wang
- Center for Immunology and Inflammation, The Feinstein Institutes for Medical Research, Manhasset, NY; .,Department of Molecular Medicine, Zucker School of Medicine at Hofstra/Northwell, Manhasset, NY; and.,Department of Surgery, Zucker School of Medicine at Hofstra/Northwell, Manhasset, NY
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10
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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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11
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From complete cross-docking to partners identification and binding sites predictions. PLoS Comput Biol 2022; 18:e1009825. [PMID: 35089918 PMCID: PMC8827487 DOI: 10.1371/journal.pcbi.1009825] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2021] [Revised: 02/09/2022] [Accepted: 01/11/2022] [Indexed: 11/19/2022] Open
Abstract
Proteins ensure their biological functions by interacting with each other. Hence, characterising protein interactions is fundamental for our understanding of the cellular machinery, and for improving medicine and bioengineering. Over the past years, a large body of experimental data has been accumulated on who interacts with whom and in what manner. However, these data are highly heterogeneous and sometimes contradictory, noisy, and biased. Ab initio methods provide a means to a "blind" protein-protein interaction network reconstruction. Here, we report on a molecular cross-docking-based approach for the identification of protein partners. The docking algorithm uses a coarse-grained representation of the protein structures and treats them as rigid bodies. We applied the approach to a few hundred of proteins, in the unbound conformations, and we systematically investigated the influence of several key ingredients, such as the size and quality of the interfaces, and the scoring function. We achieved some significant improvement compared to previous works, and a very high discriminative power on some specific functional classes. We provide a readout of the contributions of shape and physico-chemical complementarity, interface matching, and specificity, in the predictions. In addition, we assessed the ability of the approach to account for protein surface multiple usages, and we compared it with a sequence-based deep learning method. This work may contribute to guiding the exploitation of the large amounts of protein structural models now available toward the discovery of unexpected partners and their complex structure characterisation.
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12
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Murao A, Tan C, Jha A, Wang P, Aziz M. Exosome-Mediated eCIRP Release From Macrophages to Induce Inflammation in Sepsis. Front Pharmacol 2021; 12:791648. [PMID: 34938194 PMCID: PMC8687456 DOI: 10.3389/fphar.2021.791648] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Accepted: 11/15/2021] [Indexed: 12/12/2022] Open
Abstract
Extracellular cold-inducible RNA-binding protein (eCIRP) is an important damage-associated molecular pattern (DAMP). Despite our understanding of the potentially harmful effects of eCIRP in sepsis, how eCIRP is released from cells remains elusive. Exosomes are endosome-derived extracellular vesicles, which carry proteins, lipids, and nucleic acids to facilitate intercellular communication and several extracellular functions. We hypothesized that eCIRP is released via exosomes to induce inflammation in sepsis. Exosomes isolated from the supernatants of LPS-treated macrophage culture and serum of endotoxemia and polymicrobial sepsis mice showed high purity, as revealed by their unique median sizes ranging between 70 and 126 nm in diameter. eCIRP levels of the exosomes were significantly increased after LPS treatment in the supernatants of macrophage culture, mouse serum, and cecal ligation and puncture (CLP)-induced sepsis mouse serum. Protease protection assay demonstrated the majority of eCIRP was present on the surface of exosomes. Treatment of WT macrophages and mice with exosomes isolated from LPS-treated WT mice serum increased TNFα and IL-6 production. However, treatment with CIRP-/- mice serum exosomes significantly decreased these levels compared with WT exosome-treated conditions. CIRP-/- mice serum exosomes significantly decreased neutrophil migration in vitro compared with WT exosomes. Treatment of mice with serum exosomes isolated from CIRP-/- mice significantly reduced neutrophil infiltration into the peritoneal cavity. Our data suggest that eCIRP can be released via exosomes to induce cytokine production and neutrophil migration. Thus, exosomal eCIRP could be a potential target to inhibit inflammation.
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Affiliation(s)
- Atsushi Murao
- Center for Immunology and Inflammation, The Feinstein Institutes for Medical Research, Manhasset, NY, United States
| | - Chuyi Tan
- Center for Immunology and Inflammation, The Feinstein Institutes for Medical Research, Manhasset, NY, United States
| | - Alok Jha
- Center for Immunology and Inflammation, The Feinstein Institutes for Medical Research, Manhasset, NY, United States
| | - Ping Wang
- Center for Immunology and Inflammation, The Feinstein Institutes for Medical Research, Manhasset, NY, United States
- Departments of Surgery and Molecular Medicine, Zucker School of Medicine at Hofstra/Northwell, Manhasset, NY, United States
| | - Monowar Aziz
- Center for Immunology and Inflammation, The Feinstein Institutes for Medical Research, Manhasset, NY, United States
- Departments of Surgery and Molecular Medicine, Zucker School of Medicine at Hofstra/Northwell, Manhasset, NY, United States
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13
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Kurcinski M, Kmiecik S, Zalewski M, Kolinski A. Protein-Protein Docking with Large-Scale Backbone Flexibility Using Coarse-Grained Monte-Carlo Simulations. Int J Mol Sci 2021; 22:ijms22147341. [PMID: 34298961 PMCID: PMC8306105 DOI: 10.3390/ijms22147341] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2021] [Revised: 07/03/2021] [Accepted: 07/04/2021] [Indexed: 12/21/2022] Open
Abstract
Most of the protein–protein docking methods treat proteins as almost rigid objects. Only the side-chains flexibility is usually taken into account. The few approaches enabling docking with a flexible backbone typically work in two steps, in which the search for protein–protein orientations and structure flexibility are simulated separately. In this work, we propose a new straightforward approach for docking sampling. It consists of a single simulation step during which a protein undergoes large-scale backbone rearrangements, rotations, and translations. Simultaneously, the other protein exhibits small backbone fluctuations. Such extensive sampling was possible using the CABS coarse-grained protein model and Replica Exchange Monte Carlo dynamics at a reasonable computational cost. In our proof-of-concept simulations of 62 protein–protein complexes, we obtained acceptable quality models for a significant number of cases.
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14
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Abstract
Molecular dynamics (MD) simulations have become increasingly useful in the modern drug development process. In this review, we give a broad overview of the current application possibilities of MD in drug discovery and pharmaceutical development. Starting from the target validation step of the drug development process, we give several examples of how MD studies can give important insights into the dynamics and function of identified drug targets such as sirtuins, RAS proteins, or intrinsically disordered proteins. The role of MD in antibody design is also reviewed. In the lead discovery and lead optimization phases, MD facilitates the evaluation of the binding energetics and kinetics of the ligand-receptor interactions, therefore guiding the choice of the best candidate molecules for further development. The importance of considering the biological lipid bilayer environment in the MD simulations of membrane proteins is also discussed, using G-protein coupled receptors and ion channels as well as the drug-metabolizing cytochrome P450 enzymes as relevant examples. Lastly, we discuss the emerging role of MD simulations in facilitating the pharmaceutical formulation development of drugs and candidate drugs. Specifically, we look at how MD can be used in studying the crystalline and amorphous solids, the stability of amorphous drug or drug-polymer formulations, and drug solubility. Moreover, since nanoparticle drug formulations are of great interest in the field of drug delivery research, different applications of nano-particle simulations are also briefly summarized using multiple recent studies as examples. In the future, the role of MD simulations in facilitating the drug development process is likely to grow substantially with the increasing computer power and advancements in the development of force fields and enhanced MD methodologies.
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15
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Harmalkar A, Gray JJ. Advances to tackle backbone flexibility in protein docking. Curr Opin Struct Biol 2020; 67:178-186. [PMID: 33360497 DOI: 10.1016/j.sbi.2020.11.011] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2020] [Revised: 11/18/2020] [Accepted: 11/25/2020] [Indexed: 12/11/2022]
Abstract
Computational docking methods can provide structural models of protein-protein complexes, but protein backbone flexibility upon association often thwarts accurate predictions. In recent blind challenges, medium or high accuracy models were submitted in less than 20% of the 'difficult' targets (with significant backbone change or uncertainty). Here, we describe recent developments in protein-protein docking and highlight advances that tackle backbone flexibility. In molecular dynamics and Monte Carlo approaches, enhanced sampling techniques have reduced time-scale limitations. Internal coordinate formulations can now capture realistic motions of monomers and complexes using harmonic dynamics. And machine learning approaches adaptively guide docking trajectories or generate novel binding site predictions from deep neural networks trained on protein interfaces. These tools poise the field to break through the longstanding challenge of correctly predicting complex structures with significant conformational change.
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Affiliation(s)
- Ameya Harmalkar
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Jeffrey J Gray
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA; Program in Molecular Biophysics, Institute for Nanobiotechnology, and Center for Computational Biology, Johns Hopkins University, Baltimore, MD, USA.
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16
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Tanemura KA, Pei J, Merz KM. Refinement of pairwise potentials via logistic regression to score protein-protein interactions. Proteins 2020; 88:1559-1568. [PMID: 32729132 DOI: 10.1002/prot.25973] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2020] [Revised: 05/17/2020] [Accepted: 06/14/2020] [Indexed: 12/20/2022]
Abstract
Protein-protein interactions (PPIs) are ubiquitous and functionally of great importance in biological systems. Hence, the accurate prediction of PPIs by protein-protein docking and scoring tools is highly desirable in order to characterize their structure and biological function. Ab initio docking protocols are divided into the sampling of docking poses to produce at least one near-native structure, and then to evaluate the vast candidate structures by scoring. Concurrent development in both sampling and scoring is crucial for the deployment of protein-protein docking software. In the present work, we apply a machine learning model on pairwise potentials to refine the task of protein quaternary structure native structure detection among decoys. A decoy set was featurized using the Knowledge and Empirical Combined Scoring Algorithm 2 (KECSA2) pairwise potential. The highly unbalanced decoy set was then balanced using a comparison concept between native and decoy structures. The resultant comparison descriptors were used to train a logistic regression (LR) classifier. The LR model yielded the optimal performance for native detection among decoys compared with conventional scoring functions, while exhibiting lesser performance for the detection of low root mean square deviation decoy structures. Its deployment on an independent benchmark set confirms that the scoring function performs competitively relative to other scoring functions. The scripts used are available at https://github.com/TanemuraKiyoto/PPI-native-detection-via-LR.
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Affiliation(s)
- Kiyoto A Tanemura
- Department of Chemistry, Michigan State University, East Lansing, Michigan, USA
| | - Jun Pei
- Department of Chemistry, Michigan State University, East Lansing, Michigan, USA
| | - Kenneth M Merz
- Department of Chemistry, Michigan State University, East Lansing, Michigan, USA
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17
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Zsidó BZ, Hetényi C. Molecular Structure, Binding Affinity, and Biological Activity in the Epigenome. Int J Mol Sci 2020; 21:ijms21114134. [PMID: 32531926 PMCID: PMC7311975 DOI: 10.3390/ijms21114134] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2020] [Revised: 06/07/2020] [Accepted: 06/08/2020] [Indexed: 02/07/2023] Open
Abstract
Development of valid structure–activity relationships (SARs) is a key to the elucidation of pathomechanisms of epigenetic diseases and the development of efficient, new drugs. The present review is based on selected methodologies and applications supplying molecular structure, binding affinity and biological activity data for the development of new SARs. An emphasis is placed on emerging trends and permanent challenges of new discoveries of SARs in the context of proteins as epigenetic drug targets. The review gives a brief overview and classification of the molecular background of epigenetic changes, and surveys both experimental and theoretical approaches in the field. Besides the results of sophisticated, cutting edge techniques such as cryo-electron microscopy, protein crystallography, and isothermal titration calorimetry, examples of frequently used assays and fast screening techniques are also selected. The review features how different experimental methods and theoretical approaches complement each other and result in valid SARs of the epigenome.
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18
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Singh A, Dauzhenka T, Kundrotas PJ, Sternberg MJE, Vakser IA. Application of docking methodologies to modeled proteins. Proteins 2020; 88:1180-1188. [PMID: 32170770 DOI: 10.1002/prot.25889] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2019] [Revised: 02/15/2020] [Accepted: 03/07/2020] [Indexed: 12/12/2022]
Abstract
Protein docking is essential for structural characterization of protein interactions. Besides providing the structure of protein complexes, modeling of proteins and their complexes is important for understanding the fundamental principles and specific aspects of protein interactions. The accuracy of protein modeling, in general, is still less than that of the experimental approaches. Thus, it is important to investigate the applicability of docking techniques to modeled proteins. We present new comprehensive benchmark sets of protein models for the development and validation of protein docking, as well as a systematic assessment of free and template-based docking techniques on these sets. As opposed to previous studies, the benchmark sets reflect the real case modeling/docking scenario where the accuracy of the models is assessed by the modeling procedure, without reference to the native structure (which would be unknown in practical applications). We also expanded the analysis to include docking of protein pairs where proteins have different structural accuracy. The results show that, in general, the template-based docking is less sensitive to the structural inaccuracies of the models than the free docking. The near-native docking poses generated by the template-based approach, typically, also have higher ranks than those produces by the free docking (although the free docking is indispensable in modeling the multiplicity of protein interactions in a crowded cellular environment). The results show that docking techniques are applicable to protein models in a broad range of modeling accuracy. The study provides clear guidelines for practical applications of docking to protein models.
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Affiliation(s)
- Amar Singh
- Computational Biology Program, The University of Kansas, Lawrence, Kansas, USA
| | - Taras Dauzhenka
- Computational Biology Program, The University of Kansas, Lawrence, Kansas, USA
| | - Petras J Kundrotas
- Computational Biology Program, The University of Kansas, Lawrence, Kansas, USA
| | - Michael J E Sternberg
- Centre for Integrative Systems Biology and Bioinformatics, Department of Life Sciences, Imperial College London, South Kensington, London, UK
| | - Ilya A Vakser
- Computational Biology Program, The University of Kansas, Lawrence, Kansas, USA.,Department of Molecular Biosciences, The University of Kansas, Lawrence, Kansas, USA
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19
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Siebenmorgen T, Engelhard M, Zacharias M. Prediction of protein-protein complexes using replica exchange with repulsive scaling. J Comput Chem 2020; 41:1436-1447. [PMID: 32149420 DOI: 10.1002/jcc.26187] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2019] [Revised: 02/04/2020] [Accepted: 02/22/2020] [Indexed: 12/14/2022]
Abstract
The realistic prediction of protein-protein complex structures is import to ultimately model the interaction of all proteins in a cell and for the design of new protein-protein interactions. In principle, molecular dynamics (MD) simulations allow one to follow the association process under realistic conditions including full partner flexibility and surrounding solvent. However, due to the many local binding energy minima at the surface of protein partners, MD simulations are frequently trapped for long times in transient association states. We have designed a replica-exchange based scheme employing different levels of a repulsive biasing between partners in each replica simulation. The bias acts only on intermolecular interactions based on an increase in effective pairwise van der Waals radii (repulsive scaling (RS)-REMD) without affecting interactions within each protein or with the solvent. For a set of five protein test cases (out of six) the RS-REMD technique allowed the sampling of near-native complex structures even when starting from the opposide site with respect to the native binding site for one partner. Using the same start structures and same computational demand regular MD simulations sampled near native complex structures only for one case. The method showed also improved results for the refinement of docked structures in the vicinity of the native binding geometry compared to regular MD refinement.
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Affiliation(s)
- Till Siebenmorgen
- Physik-Department T38, Technische Universität München, Garching, Germany
| | - Michael Engelhard
- Physik-Department T38, Technische Universität München, Garching, Germany
| | - Martin Zacharias
- Physik-Department T38, Technische Universität München, Garching, Germany
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20
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Glashagen G, de Vries S, Uciechowska-Kaczmarzyk U, Samsonov SA, Murail S, Tuffery P, Zacharias M. Coarse-grained and atomic resolution biomolecular docking with the ATTRACT approach. Proteins 2019; 88:1018-1028. [PMID: 31785163 DOI: 10.1002/prot.25860] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2019] [Revised: 11/20/2019] [Accepted: 11/27/2019] [Indexed: 01/17/2023]
Abstract
The ATTRACT protein-protein docking program has been employed to predict protein-protein complex structures in CAPRI rounds 38-45. For 11 out of 16 targets acceptable or better quality solutions have been submitted (~70%). It includes also several cases of peptide-protein docking and the successful prediction of the geometry of carbohydrate-protein interactions. The option of combining rigid body minimization and simultaneous optimization in collective degrees of freedom based on elastic network modes was employed and systematically evaluated. Application to a large benchmark set indicates a modest improvement in docking performance compared to rigid docking. Possible further improvements of the docking approach in particular at the scoring and the flexible refinement steps are discussed.
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Affiliation(s)
- Glenn Glashagen
- Physik-Department T38, Technische Universität München, Garching, Germany
| | - Sjoerd de Vries
- Université de Paris, CNRS UMR 8251, INSERM ERL U1133, Paris, France.,Ressource Parisienne en Bioinformatique Structurale (RPBS), Paris, France
| | | | | | - Samuel Murail
- Université de Paris, CNRS UMR 8251, INSERM ERL U1133, Paris, France
| | - Pierre Tuffery
- Université de Paris, CNRS UMR 8251, INSERM ERL U1133, Paris, France.,Ressource Parisienne en Bioinformatique Structurale (RPBS), Paris, France
| | - Martin Zacharias
- Physik-Department T38, Technische Universität München, Garching, Germany
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21
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Siebenmorgen T, Zacharias M. Computational prediction of protein–protein binding affinities. WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL MOLECULAR SCIENCE 2019. [DOI: 10.1002/wcms.1448] [Citation(s) in RCA: 50] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Affiliation(s)
- Till Siebenmorgen
- Physics Department T38 Technical University of Munich Garching Germany
| | - Martin Zacharias
- Physics Department T38 Technical University of Munich Garching Germany
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22
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Marze NA, Roy Burman SS, Sheffler W, Gray JJ. Efficient flexible backbone protein-protein docking for challenging targets. Bioinformatics 2019; 34:3461-3469. [PMID: 29718115 DOI: 10.1093/bioinformatics/bty355] [Citation(s) in RCA: 94] [Impact Index Per Article: 18.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2017] [Accepted: 04/27/2018] [Indexed: 11/15/2022] Open
Abstract
Motivation Binding-induced conformational changes challenge current computational docking algorithms by exponentially increasing the conformational space to be explored. To restrict this search to relevant space, some computational docking algorithms exploit the inherent flexibility of the protein monomers to simulate conformational selection from pre-generated ensembles. As the ensemble size expands with increased flexibility, these methods struggle with efficiency and high false positive rates. Results Here, we develop and benchmark RosettaDock 4.0, which efficiently samples large conformational ensembles of flexible proteins and docks them using a novel, six-dimensional, coarse-grained score function. A strong discriminative ability allows an eight-fold higher enrichment of near-native candidate structures in the coarse-grained phase compared to RosettaDock 3.2. It adaptively samples 100 conformations each of the ligand and the receptor backbone while increasing computational time by only 20-80%. In local docking of a benchmark set of 88 proteins of varying degrees of flexibility, the expected success rate (defined as cases with ≥50% chance of achieving 3 near-native structures in the 5 top-ranked ones) for blind predictions after resampling is 77% for rigid complexes, 49% for moderately flexible complexes and 31% for highly flexible complexes. These success rates on flexible complexes are a substantial step forward from all existing methods. Additionally, for highly flexible proteins, we demonstrate that when a suitable conformer generation method exists, the method successfully docks the complex. Availability and implementation As a part of the Rosetta software suite, RosettaDock 4.0 is available at https://www.rosettacommons.org to all non-commercial users for free and to commercial users for a fee. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Nicholas A Marze
- Department of Chemical & Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Shourya S Roy Burman
- Department of Chemical & Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - William Sheffler
- Department of Biochemistry, University of Washington, Seattle, WA, USA.,Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Jeffrey J Gray
- Department of Chemical & Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA.,Program in Molecular Biophysics, Johns Hopkins University, Baltimore, MD, USA.,Institute for NanoBioTechnology, Johns Hopkins University, Baltimore, MD, USA.,Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD, USA
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23
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Popov P, Grudinin S, Kurdiuk A, Buslaev P, Redon S. Controlled-advancement rigid-body optimization of nanosystems. J Comput Chem 2019; 40:2391-2399. [PMID: 31254466 DOI: 10.1002/jcc.26016] [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: 03/29/2019] [Revised: 05/23/2019] [Accepted: 06/06/2019] [Indexed: 11/11/2022]
Abstract
In this study, we propose a novel optimization algorithm, with application to the refinement of molecular complexes. Particularly, we consider optimization problem as the calculation of quasi-static trajectories of rigid bodies influenced by the inverse-inertia-weighted energy gradient and introduce the concept of advancement region that guarantees displacement of a molecule strictly within a relevant region of conformational space. The advancement region helps to avoid typical energy minimization pitfalls, thus, the algorithm is suitable to work with arbitrary energy functions and arbitrary types of molecular complexes without necessary tuning of its hyper-parameters. Our method, called controlled-advancement rigid-body optimization of nanosystems (Carbon), is particularly useful for the large-scale molecular refinement, as for example, the putative binding candidates obtained with protein-protein docking pipelines. Implementation of Carbon with user-friendly interface is available in the SAMSON platform for molecular modeling at https://www.samson-connect.net. © 2019 Wiley Periodicals, Inc.
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Affiliation(s)
- Petr Popov
- Skolkovo Institute of Science and Technology, Moscow, Russia
| | - Sergei Grudinin
- CNRS, Grenoble INP, LJK, University Grenoble Alpes, Inria, 38000, Grenoble, France
| | - Andrii Kurdiuk
- Moscow Institute of Physics and Technology, Dolgoprudny, Russia
| | - Pavel Buslaev
- Moscow Institute of Physics and Technology, Dolgoprudny, Russia
| | - Stephane Redon
- CNRS, Grenoble INP, LJK, University Grenoble Alpes, Inria, 38000, Grenoble, France
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24
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de Vries SJ, Rey J, Schindler CEM, Zacharias M, Tuffery P. The pepATTRACT web server for blind, large-scale peptide-protein docking. Nucleic Acids Res 2019; 45:W361-W364. [PMID: 28460116 PMCID: PMC5570166 DOI: 10.1093/nar/gkx335] [Citation(s) in RCA: 66] [Impact Index Per Article: 13.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2017] [Accepted: 04/18/2017] [Indexed: 12/20/2022] Open
Abstract
Peptide–protein interactions are ubiquitous in the cell and form an important part of the interactome. Computational docking methods can complement experimental characterization of these complexes, but current protocols are not applicable on the proteome scale. pepATTRACT is a novel docking protocol that is fully blind, i.e. it does not require any information about the binding site. In various stages of its development, pepATTRACT has participated in CAPRI, making successful predictions for five out of seven protein–peptide targets. Its performance is similar or better than state-of-the-art local docking protocols that do require binding site information. Here we present a novel web server that carries out the rigid-body stage of pepATTRACT. On the peptiDB benchmark, the web server generates a correct model in the top 50 in 34% of the cases. Compared to the full pepATTRACT protocol, this leads to some loss of performance, but the computation time is reduced from ∼18 h to ∼10 min. Combined with the fact that it is fully blind, this makes the web server well-suited for large-scale in silico protein–peptide docking experiments. The rigid-body pepATTRACT server is freely available at http://bioserv.rpbs.univ-paris-diderot.fr/services/pepATTRACT.
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Affiliation(s)
- Sjoerd J de Vries
- INSERM UMR-S 973/Université Paris Diderot/Sorbonne Paris Cité/RPBS, Paris 75205, France
| | - Julien Rey
- INSERM UMR-S 973/Université Paris Diderot/Sorbonne Paris Cité/RPBS, Paris 75205, France
| | | | - Martin Zacharias
- Physik T38, Technische Universität München, Garching 85748, Germany
| | - Pierre Tuffery
- INSERM UMR-S 973/Université Paris Diderot/Sorbonne Paris Cité/RPBS, Paris 75205, France
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25
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Roy Burman SS, Yovanno RA, Gray JJ. Flexible Backbone Assembly and Refinement of Symmetrical Homomeric Complexes. Structure 2019; 27:1041-1051.e8. [PMID: 31006588 PMCID: PMC6719319 DOI: 10.1016/j.str.2019.03.014] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2018] [Revised: 01/24/2019] [Accepted: 03/15/2019] [Indexed: 01/18/2023]
Abstract
Symmetrical homomeric proteins are ubiquitous in every domain of life, and information about their structure is essential to decipher function. The size of these complexes often makes them intractable to high-resolution structure determination experiments. Computational docking algorithms offer a promising alternative for modeling large complexes with arbitrary symmetry. Accuracy of existing algorithms, however, is limited by backbone inaccuracies when using homology-modeled monomers. Here, we present Rosetta SymDock2 with a broad search of symmetrical conformational space using a six-dimensional coarse-grained score function followed by an all-atom flexible-backbone refinement, which we demonstrate to be essential for physically realistic modeling of tightly packed complexes. In global docking of a benchmark set of complexes of different point symmetries-starting from homology-modeled monomers-we successfully dock (defined as predicting three near-native structures in the five top-scoring models) 17 out of 31 cyclic complexes and 3 out of 12 dihedral complexes.
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Affiliation(s)
- Shourya S Roy Burman
- Department of Chemical & Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Remy A Yovanno
- Program in Molecular Biophysics, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Jeffrey J Gray
- Department of Chemical & Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21218, USA; Program in Molecular Biophysics, Johns Hopkins University, Baltimore, MD 21218, USA; Institute for NanoBioTechnology, Johns Hopkins University, Baltimore, MD 21218, USA; Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD 21218, USA.
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26
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Degiacomi MT. Coupling Molecular Dynamics and Deep Learning to Mine Protein Conformational Space. Structure 2019; 27:1034-1040.e3. [PMID: 31031199 DOI: 10.1016/j.str.2019.03.018] [Citation(s) in RCA: 54] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2018] [Revised: 01/25/2019] [Accepted: 03/25/2019] [Indexed: 01/09/2023]
Abstract
Flexibility is often a key determinant of protein function. To elucidate the link between their molecular structure and role in an organism, computational techniques such as molecular dynamics can be leveraged to characterize their conformational space. Extensive sampling is, however, required to obtain reliable results, useful to rationalize experimental data or predict outcomes before experiments are carried out. We demonstrate that a generative neural network trained on protein structures produced by molecular simulation can be used to obtain new, plausible conformations complementing pre-existing ones. To demonstrate this, we show that a trained neural network can be exploited in a protein-protein docking scenario to account for broad hinge motions taking place upon binding. Overall, this work shows that neural networks can be used as an exploratory tool for the study of molecular conformational space.
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Affiliation(s)
- Matteo T Degiacomi
- Department of Chemistry, Durham University, South Road, Durham DH1 3LE, UK.
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27
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Siebenmorgen T, Zacharias M. Evaluation of Predicted Protein-Protein Complexes by Binding Free Energy Simulations. J Chem Theory Comput 2019; 15:2071-2086. [PMID: 30698954 DOI: 10.1021/acs.jctc.8b01022] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
The accurate prediction of protein-protein complex geometries is of major importance to ultimately model the complete interactome of interacting proteins in a cell. A major bottleneck is the realistic free energy evaluation of predicted docked structures. Typically, simple scoring functions applied to single-complex structures are employed that neglect conformational entropy and often solvent effects completely. The binding free energy of a predicted protein-protein complex can, however, be calculated using umbrella sampling (US) along a predefined dissociation/association coordinate of a complex. We employed atomistic US-molecular dynamics simulations including appropriate conformational and axial restraints and an implicit generalized Born solvent model to calculate binding free energies of a large set of docked decoys for 20 different complexes. Free energies associated with the restraints were calculated separately. In principle, the approach includes all energetic and entropic contributions to the binding process. The evaluation of docked complexes based on binding free energy calculation was in better agreement with experiment compared to a simple scoring based on energy minimization or MD refinement using exactly the same force field description. Even calculated absolute binding free energies of structures close to the native binding geometry showed a reasonable correlation to experiment. However, still for a number of complexes docked decoys of lower free energy than near-native geometries were found indicating inaccuracies in the force field or the implicit solvent model. Although time consuming the approach may open up a new route for realistic ranking of predicted geometries based on calculated free energy of binding.
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Affiliation(s)
- Till Siebenmorgen
- Physik-Department T38 , Technische Universität München , James-Franck-Strasse 1 , 85748 Garching , Germany
| | - Martin Zacharias
- Physik-Department T38 , Technische Universität München , James-Franck-Strasse 1 , 85748 Garching , Germany
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Agrawal P, Singh H, Srivastava HK, Singh S, Kishore G, Raghava GPS. Benchmarking of different molecular docking methods for protein-peptide docking. BMC Bioinformatics 2019; 19:426. [PMID: 30717654 PMCID: PMC7394329 DOI: 10.1186/s12859-018-2449-y] [Citation(s) in RCA: 72] [Impact Index Per Article: 14.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2018] [Accepted: 10/29/2018] [Indexed: 11/10/2022] Open
Abstract
Background Molecular docking studies on protein-peptide interactions are a challenging and time-consuming task because peptides are generally more flexible than proteins and tend to adopt numerous conformations. There are several benchmarking studies on protein-protein, protein-ligand and nucleic acid-ligand docking interactions. However, a series of docking methods is not rigorously validated for protein-peptide complexes in the literature. Considering the importance and wide application of peptide docking, we describe benchmarking of 6 docking methods on 133 protein-peptide complexes having peptide length between 9 to 15 residues. The performance of docking methods was evaluated using CAPRI parameters like FNAT, I-RMSD, L-RMSD. Result Firstly, we performed blind docking and evaluate the performance of the top docking pose of each method. It was observed that FRODOCK performed better than other methods with average L-RMSD of 12.46 Å. The performance of all methods improved significantly for their best docking pose and achieved highest average L-RMSD of 3.72 Å in case of FRODOCK. Similarly, we performed re-docking and evaluated the performance of the top and best docking pose of each method. We achieved the best performance in case of ZDOCK with average L-RMSD 8.60 Å and 2.88 Å for the top and best docking pose respectively. Methods were also evaluated on 40 protein-peptide complexes used in the previous benchmarking study, where peptide have length up to 5 residues. In case of best docking pose, we achieved the highest average L-RMSD of 4.45 Å and 2.09 Å for the blind docking using FRODOCK and re-docking using AutoDock Vina respectively. Conclusion The study shows that FRODOCK performed best in case of blind docking and ZDOCK in case of re-docking. There is a need to improve the ranking of docking pose generated by different methods, as the present ranking scheme is not satisfactory. To facilitate the scientific community for calculating CAPRI parameters between native and docked complexes, we developed a web-based service named PPDbench (http://webs.iiitd.edu.in/raghava/ppdbench/). Electronic supplementary material The online version of this article (10.1186/s12859-018-2449-y) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Piyush Agrawal
- Center for Computation Biology, Indraprastha Institute of Information Technology, Okhla Phase III, New Delhi, 110020, India.,CSIR-Institute of Microbial Technology, Sector 39A, Chandigarh, India
| | - Harinder Singh
- CSIR-Institute of Microbial Technology, Sector 39A, Chandigarh, India
| | | | - Sandeep Singh
- CSIR-Institute of Microbial Technology, Sector 39A, Chandigarh, India
| | - Gaurav Kishore
- CSIR-Institute of Microbial Technology, Sector 39A, Chandigarh, India
| | - Gajendra P S Raghava
- Center for Computation Biology, Indraprastha Institute of Information Technology, Okhla Phase III, New Delhi, 110020, India. .,CSIR-Institute of Microbial Technology, Sector 39A, Chandigarh, India.
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29
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Blaszczyk M, Gront D, Kmiecik S, Kurcinski M, Kolinski M, Ciemny MP, Ziolkowska K, Panek M, Kolinski A. Protein Structure Prediction Using Coarse-Grained Models. SPRINGER SERIES ON BIO- AND NEUROSYSTEMS 2019. [DOI: 10.1007/978-3-319-95843-9_2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
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30
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Modeling of Protein Structural Flexibility and Large-Scale Dynamics: Coarse-Grained Simulations and Elastic Network Models. Int J Mol Sci 2018; 19:ijms19113496. [PMID: 30404229 PMCID: PMC6274762 DOI: 10.3390/ijms19113496] [Citation(s) in RCA: 42] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2018] [Revised: 10/29/2018] [Accepted: 10/31/2018] [Indexed: 12/13/2022] Open
Abstract
Fluctuations of protein three-dimensional structures and large-scale conformational transitions are crucial for the biological function of proteins and their complexes. Experimental studies of such phenomena remain very challenging and therefore molecular modeling can be a good alternative or a valuable supporting tool for the investigation of large molecular systems and long-time events. In this minireview, we present two alternative approaches to the coarse-grained (CG) modeling of dynamic properties of protein systems. We discuss two CG representations of polypeptide chains used for Monte Carlo dynamics simulations of protein local dynamics and conformational transitions, and highly simplified structure-based elastic network models of protein flexibility. In contrast to classical all-atom molecular dynamics, the modeling strategies discussed here allow the quite accurate modeling of much larger systems and longer-time dynamic phenomena. We briefly describe the main features of these models and outline some of their applications, including modeling of near-native structure fluctuations, sampling of large regions of the protein conformational space, or possible support for the structure prediction of large proteins and their complexes.
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31
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Harrer N, Schindler CEM, Bruetzel LK, Forné I, Ludwigsen J, Imhof A, Zacharias M, Lipfert J, Mueller-Planitz F. Structural Architecture of the Nucleosome Remodeler ISWI Determined from Cross-Linking, Mass Spectrometry, SAXS, and Modeling. Structure 2018; 26:282-294.e6. [PMID: 29395785 DOI: 10.1016/j.str.2017.12.015] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2017] [Revised: 10/25/2017] [Accepted: 12/27/2017] [Indexed: 11/17/2022]
Abstract
Chromatin remodeling factors assume critical roles by regulating access to nucleosomal DNA. To determine the architecture of the Drosophila ISWI remodeling enzyme, we developed an integrative structural approach that combines protein cross-linking, mass spectrometry, small-angle X-ray scattering, and computational modeling. The resulting structural model shows the ATPase module in a resting state with both ATPase lobes twisted against each other, providing support for a conformation that was recently trapped by crystallography. The autoinhibiting NegC region does not protrude from the ATPase module as suggested previously. The regulatory NTR domain is located near both ATPase lobes. The full-length enzyme is flexible and can adopt a compact structure in solution with the C-terminal HSS domain packing against the ATPase module. Our data imply a series of conformational changes upon activation of the enzyme and illustrate how the NTR, NegC, and HSS domains contribute to regulation of the ATPase module.
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Affiliation(s)
- Nadine Harrer
- Molecular Biology, Biomedical Center, Faculty of Medicine, LMU Munich, 82152 Martinsried, Germany
| | - Christina E M Schindler
- Physics Department (T38), Technical University of Munich, 85748 Garching, Germany; Center for Integrated Protein Science Munich, 81377 Munich, Germany
| | - Linda K Bruetzel
- Department of Physics, Nanosystems Initiative Munich, and Center for Nanoscience, LMU Munich, 80799 Munich, Germany
| | - Ignasi Forné
- Molecular Biology, Biomedical Center, Faculty of Medicine, LMU Munich, 82152 Martinsried, Germany
| | - Johanna Ludwigsen
- Molecular Biology, Biomedical Center, Faculty of Medicine, LMU Munich, 82152 Martinsried, Germany
| | - Axel Imhof
- Molecular Biology, Biomedical Center, Faculty of Medicine, LMU Munich, 82152 Martinsried, Germany
| | - Martin Zacharias
- Physics Department (T38), Technical University of Munich, 85748 Garching, Germany; Center for Integrated Protein Science Munich, 81377 Munich, Germany
| | - Jan Lipfert
- Department of Physics, Nanosystems Initiative Munich, and Center for Nanoscience, LMU Munich, 80799 Munich, Germany.
| | - Felix Mueller-Planitz
- Molecular Biology, Biomedical Center, Faculty of Medicine, LMU Munich, 82152 Martinsried, Germany.
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32
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de Vries SJ, Zacharias M. Fast and accurate grid representations for atom-based docking with partner flexibility. J Comput Chem 2017; 38:1538-1546. [DOI: 10.1002/jcc.24795] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2016] [Revised: 01/18/2017] [Accepted: 01/19/2017] [Indexed: 12/12/2022]
Affiliation(s)
- Sjoerd J. de Vries
- MTi, UMR-S 973, Physics Department T38; Technische Universität München; James-Franck-Strasse 1 85748 Garching Germany
| | - Martin Zacharias
- MTi, UMR-S 973, Physics Department T38; Technische Universität München; James-Franck-Strasse 1 85748 Garching Germany
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33
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Application of the ATTRACT Coarse-Grained Docking and Atomistic Refinement for Predicting Peptide-Protein Interactions. Methods Mol Biol 2017. [PMID: 28236233 DOI: 10.1007/978-1-4939-6798-8_5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register]
Abstract
Peptide-protein interactions are abundant in the cell and form an important part of the interactome. Large-scale modeling of peptide-protein complexes requires a fully blind approach; i.e., simultaneously predicting the peptide-binding site and the peptide conformation to high accuracy. Here, we present one of the first fully blind peptide-protein docking protocols, pepATTRACT. It combines a coarse-grained ensemble docking search of the entire protein surface with two stages of atomistic flexible refinement. pepATTRACT yields high-quality predictions for 70 % of the cases when tested on a large benchmark of peptide-protein complexes. This performance in fully blind mode is similar to state-of-the-art local docking approaches that use information on the location of the binding site. Limiting the search to the peptide-binding region, the resulting pepATTRACT-local approach further improves the performance. Docking scripts for pepATTRACT and pepATTRACT-local can be generated via a web interface at www.attract.ph.tum.de/peptide.html . Here, we explain how to set up a docking run with the pepATTRACT web interface and demonstrate its usage by an application on binding of disordered regions from tumor suppressor p53 to a partner protein.
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34
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Zhang Z, Ehmann U, Zacharias M. Monte Carlo replica-exchange based ensemble docking of protein conformations. Proteins 2017; 85:924-937. [DOI: 10.1002/prot.25262] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2016] [Revised: 01/11/2017] [Accepted: 01/19/2017] [Indexed: 12/14/2022]
Affiliation(s)
- Zhe Zhang
- Physik-Department T38; Technische Universität München; Garching 85748 Germany
- Department Chemie; Technische Universität München, Biomolecular NMR and Munich Center for Integrated Protein Science; Garching 85747 Germany
- College of Life and Health Sciences; Northeast University; Shenyang P.R. China
| | - Uwe Ehmann
- Physik-Department T38; Technische Universität München; Garching 85748 Germany
| | - Martin Zacharias
- Physik-Department T38; Technische Universität München; Garching 85748 Germany
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35
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Ostermeir K, Zacharias M. Accelerated flexible protein-ligand docking using Hamiltonian replica exchange with a repulsive biasing potential. PLoS One 2017; 12:e0172072. [PMID: 28207811 PMCID: PMC5313199 DOI: 10.1371/journal.pone.0172072] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2016] [Accepted: 01/30/2017] [Indexed: 02/04/2023] Open
Abstract
A molecular dynamics replica exchange based method has been developed that allows rapid identification of putative ligand binding sites on the surface of biomolecules. The approach employs a set of ambiguity restraints in replica simulations between receptor and ligand that allow close contacts in the reference replica but promotes transient dissociation in higher replicas. This avoids long-lived trapping of the ligand or partner proteins at nonspecific, sticky, sites on the receptor molecule and results in accelerated exploration of the possible binding regions. In contrast to common docking methods that require knowledge of the binding site, exclude solvent and often keep parts of receptor and ligand rigid the approach allows for full flexibility of binding partners. Application to peptide-protein, protein-protein and a drug-receptor system indicate rapid sampling of near-native binding regions even in case of starting far away from the native binding site outperforming continuous MD simulations. An application on a DNA minor groove binding ligand in complex with DNA demonstrates that it can also be used in explicit solvent simulations.
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Affiliation(s)
- Katja Ostermeir
- Physik-Department T38, Technische Universität München, Garching, Germany
| | - Martin Zacharias
- Physik-Department T38, Technische Universität München, Garching, Germany
- * E-mail:
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36
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Sasse A, de Vries SJ, Schindler CEM, de Beauchêne IC, Zacharias M. Rapid Design of Knowledge-Based Scoring Potentials for Enrichment of Near-Native Geometries in Protein-Protein Docking. PLoS One 2017; 12:e0170625. [PMID: 28118389 PMCID: PMC5261736 DOI: 10.1371/journal.pone.0170625] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2016] [Accepted: 01/07/2017] [Indexed: 01/15/2023] Open
Abstract
Protein-protein docking protocols aim to predict the structures of protein-protein complexes based on the structure of individual partners. Docking protocols usually include several steps of sampling, clustering, refinement and re-scoring. The scoring step is one of the bottlenecks in the performance of many state-of-the-art protocols. The performance of scoring functions depends on the quality of the generated structures and its coupling to the sampling algorithm. A tool kit, GRADSCOPT (GRid Accelerated Directly SCoring OPTimizing), was designed to allow rapid development and optimization of different knowledge-based scoring potentials for specific objectives in protein-protein docking. Different atomistic and coarse-grained potentials can be created by a grid-accelerated directly scoring dependent Monte-Carlo annealing or by a linear regression optimization. We demonstrate that the scoring functions generated by our approach are similar to or even outperform state-of-the-art scoring functions for predicting near-native solutions. Of additional importance, we find that potentials specifically trained to identify the native bound complex perform rather poorly on identifying acceptable or medium quality (near-native) solutions. In contrast, atomistic long-range contact potentials can increase the average fraction of near-native poses by up to a factor 2.5 in the best scored 1% decoys (compared to existing scoring), emphasizing the need of specific docking potentials for different steps in the docking protocol.
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Affiliation(s)
- Alexander Sasse
- Physik Department T38, Technische Universität München, James-Franck-Straße, Garching, Germany
| | - Sjoerd J. de Vries
- Physik Department T38, Technische Universität München, James-Franck-Straße, Garching, Germany
| | | | | | - Martin Zacharias
- Physik Department T38, Technische Universität München, James-Franck-Straße, Garching, Germany
- * E-mail:
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37
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Ludwigsen J, Pfennig S, Singh AK, Schindler C, Harrer N, Forné I, Zacharias M, Mueller-Planitz F. Concerted regulation of ISWI by an autoinhibitory domain and the H4 N-terminal tail. eLife 2017; 6. [PMID: 28109157 PMCID: PMC5305211 DOI: 10.7554/elife.21477] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2016] [Accepted: 01/20/2017] [Indexed: 01/08/2023] Open
Abstract
ISWI-family nucleosome remodeling enzymes need the histone H4 N-terminal tail to mobilize nucleosomes. Here we mapped the H4-tail binding pocket of ISWI. Surprisingly the binding site was adjacent to but not overlapping with the docking site of an auto-regulatory motif, AutoN, in the N-terminal region (NTR) of ISWI, indicating that AutoN does not act as a simple pseudosubstrate as suggested previously. Rather, AutoN cooperated with a hitherto uncharacterized motif, termed AcidicN, to confer H4-tail sensitivity and discriminate between DNA and nucleosomes. A third motif in the NTR, ppHSA, was functionally required in vivo and provided structural stability by clamping the NTR to Lobe 2 of the ATPase domain. This configuration is reminiscent of Chd1 even though Chd1 contains an unrelated NTR. Our results shed light on the intricate structural and functional regulation of ISWI by the NTR and uncover surprising parallels with Chd1. DOI:http://dx.doi.org/10.7554/eLife.21477.001 In the cells of animals, plants and other eukaryotes, DNA wraps tightly around proteins called histones to form structures known as nucleosomes that resemble beads on a string. When nucleosomes are sufficiently close to each other they interact and clump together, which compacts the DNA and prevents the genes in that stretch of DNA being activated. But how do cells mobilize their nucleosomes? A nucleosome remodeling enzyme called ISWI can slide nucleosomes along DNA. ISWI becomes active when it interacts with a ‘tail’ region of a histone protein called H4. However, the H4 tail prefers to interact with neighboring nucleosomes instead of with ISWI. Therefore when ISWI slides a nucleosome close to another one, the H4 tail of the nucleosome binds instead to its new neighbor so that ISWI cannot continue to slide. By this mechanism, ISWI is proposed to pile up nucleosomes, which subsequently compact, leading to the inactivation of this part of the genome. To investigate how ISWI recognizes the H4 tail, Ludwigsen et al. mapped where the H4 tail binds to ISWI by combining the biochemical methods of cross-linking and mass spectrometry. In addition, mutagenesis experiments identified a new motif in the enzyme that is essential for recognizing the H4 tail. In the absence of the nucleosome, this motif – called AcidicN – works with a neighboring motif called AutoN to keep ISWI in an inactive state. The two motifs also work together to enable ISWI to distinguish between nucleosomes and DNA. Further evidence suggests that other remodeling enzymes have similar regulation mechanisms; therefore this method of controlling nucleosome remodeling may have been conserved throughout evolution. Further studies are now needed to detect the shape changes that occur in ISWI as it recognizes the histone tail and work out how this leads to nucleosome remodeling. Inside cells, ISWI is usually found within large complexes that consist of many proteins. It therefore also remains to be discovered whether the proteins in these complexes impose additional layers of regulation and complexity on the activity of ISWI. DOI:http://dx.doi.org/10.7554/eLife.21477.002
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Affiliation(s)
- Johanna Ludwigsen
- Biomedical Center, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Sabrina Pfennig
- Biomedical Center, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Ashish K Singh
- Biomedical Center, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Christina Schindler
- Physics Department (T38), Technische Universität München, Munich, Germany.,Center for Integrated Protein Science Munich, Munich, Germany
| | - Nadine Harrer
- Biomedical Center, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Ignasi Forné
- Biomedical Center, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Martin Zacharias
- Physics Department (T38), Technische Universität München, Munich, Germany.,Center for Integrated Protein Science Munich, Munich, Germany
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38
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Srivastav VK, Singh V, Tiwari M. Recent Advancements in Docking Methodologies. Oncology 2017. [DOI: 10.4018/978-1-5225-0549-5.ch033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Nowadays molecular docking has become an important methodology in CADD (Computer-Aided Drug Design)-assisted drug discovery process. It is an important computational tool widely used to predict binding mode, binding affinity and binding free energy of a protein-ligand complex. The important factors responsible for accurate results in docking studies are correct binding site prediction, use of suitable small-molecule databases, consistent docking pose, high dock score with good MD (Molecular Dynamics), clarity whether the compound is an inhibitor or agonist, etc. However, still there are several limitations which make it difficult to obtain accurate results from docking studies. In this chapter, the main focus is on recent advancements in various aspects of molecular docking such as ligand sampling, protein flexibility, scoring functions, fragment docking, post-processing, docking into homology models and protein-protein docking.
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Affiliation(s)
| | - Vineet Singh
- Shri Govindram Seksaria Institute of Technology and Science, India
| | - Meena Tiwari
- Shri Govindram Seksaria Institute of Technology and Science, India
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39
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Schindler CEM, Chauvot de Beauchêne I, de Vries SJ, Zacharias M. Protein-protein and peptide-protein docking and refinement using ATTRACT in CAPRI. Proteins 2016; 85:391-398. [PMID: 27785830 DOI: 10.1002/prot.25196] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2016] [Revised: 09/13/2016] [Accepted: 09/27/2016] [Indexed: 11/06/2022]
Abstract
The ATTRACT coarse-grained docking approach in combination with various types of atomistic, flexible refinement methods has been applied to predict protein-protein and peptide-protein complexes in CAPRI rounds 28-36. For a large fraction of CAPRI targets (12 out of 18), at least one model of acceptable or better quality was generated, corresponding to a success rate of 67%. In particular, for several peptide-protein complexes excellent predictions were achieved. In several cases, a combination of template-based modeling and extensive molecular dynamics-based refinement yielded medium and even high quality solutions. In one particularly challenging case, the structure of an ubiquitylation enzyme bound to the nucleosome was correctly predicted as a set of acceptable quality solutions. Based on the experience with the CAPRI targets, new interface refinement approaches and methods for ab-initio peptide-protein docking have been developed. Failures and possible improvements of the docking method with respect to scoring and protein flexibility will also be discussed. Proteins 2017; 85:391-398. © 2016 Wiley Periodicals, Inc.
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Affiliation(s)
- Christina E M Schindler
- Physics Department T38, Technische Universität München, Garching, 85748, Germany.,Center for Integrated Protein Science Munich, München, 81377, Germany
| | | | - Sjoerd J de Vries
- Physics Department T38, Technische Universität München, Garching, 85748, Germany
| | - Martin Zacharias
- Physics Department T38, Technische Universität München, Garching, 85748, Germany.,Center for Integrated Protein Science Munich, München, 81377, Germany
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40
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Gromiha MM, Yugandhar K, Jemimah S. Protein-protein interactions: scoring schemes and binding affinity. Curr Opin Struct Biol 2016; 44:31-38. [PMID: 27866112 DOI: 10.1016/j.sbi.2016.10.016] [Citation(s) in RCA: 80] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2016] [Revised: 09/30/2016] [Accepted: 10/25/2016] [Indexed: 01/16/2023]
Abstract
Protein-protein interactions mediate several cellular functions, which can be understood from the information obtained using the three-dimensional structures of protein-protein complexes and binding affinity data. This review focuses on computational aspects of predicting the best native-like complex structure and binding affinities. The first part covers the prediction of protein-protein complex structures and the advantages of conformational searching and scoring functions in protein-protein docking. The second part is devoted to various aspects of protein-protein interaction thermodynamics, such as databases for binding affinities and other thermodynamic parameters, computational methods to predict the binding affinity using either the three-dimensional structures of complexes or amino acid sequences, and change in binding affinities of the complexes upon mutations. We provide the latest developments on protein-protein docking and binding affinity studies along with a list of available computational resources for understanding protein-protein interactions.
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Affiliation(s)
- M Michael Gromiha
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600036, Tamil Nadu, India.
| | - K Yugandhar
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600036, Tamil Nadu, India
| | - Sherlyn Jemimah
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600036, Tamil Nadu, India
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41
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Kuroda D, Gray JJ. Pushing the Backbone in Protein-Protein Docking. Structure 2016; 24:1821-1829. [PMID: 27568930 DOI: 10.1016/j.str.2016.06.025] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2015] [Revised: 05/17/2016] [Accepted: 06/19/2016] [Indexed: 02/01/2023]
Abstract
Conformational changes of proteins that occur upon binding typically confound computational docking algorithms. In this study, we test computational methods to capture protein backbone conformational change related to binding. To address how well existing algorithms can sample bound-like backbones, we query seven techniques including Monte Carlo-based sampling, molecular dynamics, and normal mode analysis. All methods tested rarely sample near-bound states from the unbound conformation. Nevertheless, the direction of the predicted motions overlap with the actual conformational change. We next forced the backbone from the unbound toward the bound conformation to create a family of docking energy landscapes. Seventy percent of docking targets succeed when the unbound backbones is pushed to within 0.6 Å of the bound. Current methods can capture an average of 22% of unbound-bound transitions through conformer selection methods and another 57% through induced-fit methodologies, delineating a stubborn gap (21%) in backbone motion not covered by any current approach.
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Affiliation(s)
- Daisuke Kuroda
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21218, USA; Department of Analytical and Physical Chemistry, Showa University School of Pharmacy, Tokyo 142-8555, Japan
| | - Jeffrey J Gray
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21218, USA; Program in Molecular Biophysics, Johns Hopkins University, Baltimore, MD 21218, USA.
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42
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Heo L, Lee H, Seok C. GalaxyRefineComplex: Refinement of protein-protein complex model structures driven by interface repacking. Sci Rep 2016; 6:32153. [PMID: 27535582 PMCID: PMC4989233 DOI: 10.1038/srep32153] [Citation(s) in RCA: 76] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2016] [Accepted: 08/03/2016] [Indexed: 12/13/2022] Open
Abstract
Protein-protein docking methods have been widely used to gain an atomic-level understanding of protein interactions. However, docking methods that employ low-resolution energy functions are popular because of computational efficiency. Low-resolution docking tends to generate protein complex structures that are not fully optimized. GalaxyRefineComplex takes such low-resolution docking structures and refines them to improve model accuracy in terms of both interface contact and inter-protein orientation. This refinement method allows flexibility at the protein interface and in the overall docking structure to capture conformational changes that occur upon binding. Symmetric refinement is also provided for symmetric homo-complexes. This method was validated by refining models produced by available docking programs, including ZDOCK and M-ZDOCK, and was successfully applied to CAPRI targets in a blind fashion. An example of using the refinement method with an existing docking method for ligand binding mode prediction of a drug target is also presented. A web server that implements the method is freely available at http://galaxy.seoklab.org/refinecomplex.
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Affiliation(s)
- Lim Heo
- Department of Chemistry, Seoul National University, Seoul 08826, Republic of Korea
| | - Hasup Lee
- Department of Chemistry, Seoul National University, Seoul 08826, Republic of Korea
| | - Chaok Seok
- Department of Chemistry, Seoul National University, Seoul 08826, Republic of Korea
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43
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Schindler C, de Vries S, Sasse A, Zacharias M. SAXS Data Alone can Generate High-Quality Models of Protein-Protein Complexes. Structure 2016; 24:1387-1397. [DOI: 10.1016/j.str.2016.06.007] [Citation(s) in RCA: 35] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2016] [Revised: 06/08/2016] [Accepted: 06/08/2016] [Indexed: 11/29/2022]
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44
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Agostino M, Mancera RL, Ramsland PA, Fernández-Recio J. Optimization of protein-protein docking for predicting Fc-protein interactions. J Mol Recognit 2016; 29:555-568. [PMID: 27445195 DOI: 10.1002/jmr.2555] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2016] [Revised: 06/12/2016] [Accepted: 06/14/2016] [Indexed: 01/08/2023]
Abstract
The antibody crystallizable fragment (Fc) is recognized by effector proteins as part of the immune system. Pathogens produce proteins that bind Fc in order to subvert or evade the immune response. The structural characterization of the determinants of Fc-protein association is essential to improve our understanding of the immune system at the molecular level and to develop new therapeutic agents. Furthermore, Fc-binding peptides and proteins are frequently used to purify therapeutic antibodies. Although several structures of Fc-protein complexes are available, numerous others have not yet been determined. Protein-protein docking could be used to investigate Fc-protein complexes; however, improved approaches are necessary to efficiently model such cases. In this study, a docking-based structural bioinformatics approach is developed for predicting the structures of Fc-protein complexes. Based on the available set of X-ray structures of Fc-protein complexes, three regions of the Fc, loosely corresponding to three turns within the structure, were defined as containing the essential features for protein recognition and used as restraints to filter the initial docking search. Rescoring the filtered poses with an optimal scoring strategy provided a success rate of approximately 80% of the test cases examined within the top ranked 20 poses, compared to approximately 20% by the initial unrestrained docking. The developed docking protocol provides a significant improvement over the initial unrestrained docking and will be valuable for predicting the structures of currently undetermined Fc-protein complexes, as well as in the design of peptides and proteins that target Fc.
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Affiliation(s)
- Mark Agostino
- School of Biomedical Sciences, Curtin Health Innovation Research Institute and Curtin Institute for Computation, Curtin University, Perth, Australia.,Joint BSC-CRG-IRB Research Program in Computational Biology, Life Sciences Department, Barcelona Supercomputing Center, Barcelona, Spain.,Centre for Biomedical Research, Burnet Institute, Melbourne, Australia
| | - Ricardo L Mancera
- School of Biomedical Sciences, Curtin Health Innovation Research Institute and Curtin Institute for Computation, Curtin University, Perth, Australia
| | - Paul A Ramsland
- Centre for Biomedical Research, Burnet Institute, Melbourne, Australia. .,School of Science, RMIT University, Bundoora, Australia. .,Department of Surgery Austin Health, University of Melbourne, Heidelberg, Australia. .,Department of Immunology, Monash University, Alfred Medical Research and Education Precinct, Melbourne, Australia.
| | - Juan Fernández-Recio
- Joint BSC-CRG-IRB Research Program in Computational Biology, Life Sciences Department, Barcelona Supercomputing Center, Barcelona, Spain.
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Kmiecik S, Gront D, Kolinski M, Wieteska L, Dawid AE, Kolinski A. Coarse-Grained Protein Models and Their Applications. Chem Rev 2016; 116:7898-936. [DOI: 10.1021/acs.chemrev.6b00163] [Citation(s) in RCA: 555] [Impact Index Per Article: 69.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Affiliation(s)
- Sebastian Kmiecik
- Faculty
of Chemistry, University of Warsaw, Pasteura 1, 02-093 Warsaw, Poland
| | - Dominik Gront
- Faculty
of Chemistry, University of Warsaw, Pasteura 1, 02-093 Warsaw, Poland
| | - Michal Kolinski
- Bioinformatics
Laboratory, Mossakowski Medical Research Center of the Polish Academy of Sciences, Pawinskiego 5, 02-106 Warsaw, Poland
| | - Lukasz Wieteska
- Faculty
of Chemistry, University of Warsaw, Pasteura 1, 02-093 Warsaw, Poland
- Department
of Medical Biochemistry, Medical University of Lodz, Mazowiecka 6/8, 92-215 Lodz, Poland
| | | | - Andrzej Kolinski
- Faculty
of Chemistry, University of Warsaw, Pasteura 1, 02-093 Warsaw, Poland
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Sacquin-Mora S, Prévost C. Docking Peptides on Proteins: How to Open a Lock, in the Dark, with a Flexible Key. Structure 2016; 23:1373-1374. [PMID: 26244840 DOI: 10.1016/j.str.2015.07.004] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Abstract
In this issue of Structure, Schindler et al. (2015b) present us with pepATTRACT, a protocol embedded in the ATTRACT docking engine for fully blind flexible peptide docking on proteins that yields high quality models of complexes.
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Affiliation(s)
- Sophie Sacquin-Mora
- Laboratoire de Biochimie Théorique, CNRS UPR 9080, Univ Paris Diderot, Sorbonne Paris Cité, Institut de Biologie Physico-Chimique, 13 rue Pierre et Marie Curie, F-75005 Paris, France
| | - Chantal Prévost
- Laboratoire de Biochimie Théorique, CNRS UPR 9080, Univ Paris Diderot, Sorbonne Paris Cité, Institut de Biologie Physico-Chimique, 13 rue Pierre et Marie Curie, F-75005 Paris, France.
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47
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de Vries SJ, Chauvot de Beauchêne I, Schindler CEM, Zacharias M. Cryo-EM Data Are Superior to Contact and Interface Information in Integrative Modeling. Biophys J 2016; 110:785-97. [PMID: 26846888 DOI: 10.1016/j.bpj.2015.12.038] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2015] [Revised: 11/18/2015] [Accepted: 12/14/2015] [Indexed: 12/29/2022] Open
Abstract
Protein-protein interactions carry out a large variety of essential cellular processes. Cryo-electron microscopy (cryo-EM) is a powerful technique for the modeling of protein-protein interactions at a wide range of resolutions, and recent developments have caused a revolution in the field. At low resolution, cryo-EM maps can drive integrative modeling of the interaction, assembling existing structures into the map. Other experimental techniques can provide information on the interface or on the contacts between the monomers in the complex. This inevitably raises the question regarding which type of data is best suited to drive integrative modeling approaches. Systematic comparison of the prediction accuracy and specificity of the different integrative modeling paradigms is unavailable to date. Here, we compare EM-driven, interface-driven, and contact-driven integrative modeling paradigms. Models were generated for the protein docking benchmark using the ATTRACT docking engine and evaluated using the CAPRI two-star criterion. At 20 Å resolution, EM-driven modeling achieved a success rate of 100%, outperforming the other paradigms even with perfect interface and contact information. Therefore, even very low resolution cryo-EM data is superior in predicting heterodimeric and heterotrimeric protein assemblies. Our study demonstrates that a force field is not necessary, cryo-EM data alone is sufficient to accurately guide the monomers into place. The resulting rigid models successfully identify regions of conformational change, opening up perspectives for targeted flexible remodeling.
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Affiliation(s)
- Sjoerd J de Vries
- Physik-Department T38, Technische Universität München, Garching, Germany.
| | | | - Christina E M Schindler
- Physik-Department T38, Technische Universität München, Garching, Germany; Center for Integrated Protein Science Munich (CIPSM) at the Physics Department, Technische Universität München, Garching, Germany
| | - Martin Zacharias
- Physik-Department T38, Technische Universität München, Garching, Germany; Center for Integrated Protein Science Munich (CIPSM) at the Physics Department, Technische Universität München, Garching, Germany
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48
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Schindler C, de Vries S, Zacharias M. Development and Application of a Fully Blind Flexible Peptide-protein Docking Protocol, pepATTRACT. Bio Protoc 2016. [DOI: 10.21769/bioprotoc.1831] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022] Open
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49
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de Vries SJ, Schindler CEM, Chauvot de Beauchêne I, Zacharias M. A web interface for easy flexible protein-protein docking with ATTRACT. Biophys J 2015; 108:462-5. [PMID: 25650913 DOI: 10.1016/j.bpj.2014.12.015] [Citation(s) in RCA: 67] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2014] [Revised: 12/06/2014] [Accepted: 12/10/2014] [Indexed: 01/03/2023] Open
Abstract
Protein-protein docking programs can give valuable insights into the structure of protein complexes in the absence of an experimental complex structure. Web interfaces can facilitate the use of docking programs by structural biologists. Here, we present an easy web interface for protein-protein docking with the ATTRACT program. While aimed at nonexpert users, the web interface still covers a considerable range of docking applications. The web interface supports systematic rigid-body protein docking with the ATTRACT coarse-grained force field, as well as various kinds of protein flexibility. The execution of a docking protocol takes up to a few hours on a standard desktop computer.
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Affiliation(s)
- Sjoerd J de Vries
- Physics Department, Technische Universität München, Garching, Germany.
| | | | | | - Martin Zacharias
- Physics Department, Technische Universität München, Garching, Germany
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50
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Schindler CEM, de Vries SJ, Zacharias M. Fully Blind Peptide-Protein Docking with pepATTRACT. Structure 2015; 23:1507-1515. [PMID: 26146186 DOI: 10.1016/j.str.2015.05.021] [Citation(s) in RCA: 87] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2015] [Revised: 05/21/2015] [Accepted: 05/25/2015] [Indexed: 02/02/2023]
Abstract
Peptide-protein interactions are ubiquitous in the cell and form an important part of the interactome. Computational docking methods can complement experimental characterization of these complexes, but current protocols are not applicable on the proteome scale. Here, we present a new fully blind flexible peptide-protein docking protocol, pepATTRACT, which combines a rapid coarse-grained global peptide docking search of the entire protein surface with a two-stage atomistic flexible refinement. Global unbound-unbound docking yielded near-native models for 70% of the docking cases when testing the protocol on the largest benchmark of peptide-protein complexes available to date. This performance is similar to that of state-of-the-art local docking protocols that rely on information about the binding site. Upon restricting the search to the peptide binding region, the resulting pepATTRACT-local approach outperformed existing methods. Docking scripts for pepATTRACT and pepATTRACT-local can be generated via a web interface at www.attract.ph.tum.de/peptide.html.
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Affiliation(s)
- Christina E M Schindler
- Physics Department T38, Technische Universität München, James-Franck-Straße 1, 85748 Garching, Germany
| | - Sjoerd J de Vries
- Physics Department T38, Technische Universität München, James-Franck-Straße 1, 85748 Garching, Germany
| | - Martin Zacharias
- Physics Department T38, Technische Universität München, James-Franck-Straße 1, 85748 Garching, Germany.
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