1
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Siebenmorgen T, Saremi Nanji Y, Zacharias M. Refinement of Docked Protein-Protein Complexes Using Repulsive Scaling Replica Exchange Simulations. Methods Mol Biol 2024; 2780:289-302. [PMID: 38987474 DOI: 10.1007/978-1-0716-3985-6_15] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/12/2024]
Abstract
Accurate prediction and evaluation of protein-protein complex structures is of major importance to understand the cellular interactome. Predicted complex structures based on deep learning approaches or traditional docking methods require often structural refinement and rescoring for realistic evaluation. Standard molecular dynamics (MD) simulations are time-consuming and often do not structurally improve docking solutions. Better refinement can be achieved with our recently developed replica-exchange-based scheme employing different levels of repulsive biasing between proteins in each replica simulation (RS-REMD). The bias acts specifically on the intermolecular interactions based on an increase in effective pairwise van der Waals radii without changing interactions within each protein or with the solvent. It allows for an improvement of the predicted protein-protein complex structure and simultaneous realistic free energy scoring of protein-protein complexes. The setup of RS-REMD simulations is described in detail including the application on two examples (all necessary scripts and input files can be obtained from https://gitlab.com/TillCyrill/mmib ).
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Affiliation(s)
- Till Siebenmorgen
- Technical University of Munich, Physics Department and Center of Functional Protein Assemblies, Garching, Germany
| | - Yasmin Saremi Nanji
- Technical University of Munich, Physics Department and Center of Functional Protein Assemblies, Garching, Germany
| | - Martin Zacharias
- Technical University of Munich, Physics Department and Center of Functional Protein Assemblies, Garching, Germany.
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2
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Aschenbrenner I, Siebenmorgen T, Lopez A, Parr M, Ruckgaber P, Kerle A, Rührnößl F, Catici D, Haslbeck M, Frishman D, Sattler M, Zacharias M, Feige MJ. Assembly-dependent Structure Formation Shapes Human Interleukin-23 versus Interleukin-12 Secretion. J Mol Biol 2023; 435:168300. [PMID: 37805067 DOI: 10.1016/j.jmb.2023.168300] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Revised: 09/29/2023] [Accepted: 10/01/2023] [Indexed: 10/09/2023]
Abstract
Interleukin 12 (IL-12) family cytokines connect the innate and adaptive branches of the immune system and regulate immune responses. A unique characteristic of this family is that each member is anα:βheterodimer. For human αsubunits it has been shown that they depend on theirβsubunit for structure formation and secretion from cells. Since subunits are shared within the family and IL-12 as well as IL-23 use the same βsubunit, subunit competition may influence cytokine secretion and thus downstream immunological functions. Here, we rationally design a folding-competent human IL-23α subunit that does not depend on itsβsubunit for structure formation. This engineered variant still forms a functional heterodimeric cytokine but shows less chaperone dependency and stronger affinity in assembly with its βsubunit. It forms IL-23 more efficiently than its natural counterpart, skewing the balance of IL-12 and IL-23 towards more IL-23 formation. Together, our study shows that folding-competent human IL-12 familyαsubunits are obtainable by only few mutations and compatible with assembly and function of the cytokine. These findings might suggest that human α subunits have evolved for assembly-dependent folding to maintain and regulate correct IL-12 family member ratios in the light of subunit competition.
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Affiliation(s)
- Isabel Aschenbrenner
- Technical University of Munich, TUM School of Natural Sciences, Department of Bioscience, Center for Functional Protein Assemblies (CPA), Garching, Germany
| | - Till Siebenmorgen
- Technical University of Munich, TUM School of Natural Sciences, Department of Bioscience, Center for Functional Protein Assemblies (CPA), Garching, Germany; Helmholtz Munich, Molecular Targets & Therapeutics Center, Institute of Structural Biology, Neuherberg, Germany
| | - Abraham Lopez
- Technical University of Munich, TUM School of Natural Sciences, Department of Bioscience, Bavarian NMR Center, Garching, Germany; Helmholtz Munich, Molecular Targets & Therapeutics Center, Institute of Structural Biology, Neuherberg, Germany
| | - Marina Parr
- Technical University of Munich, TUM School of Life Sciences, Department of Bioinformatics, Freising, Germany
| | - Philipp Ruckgaber
- Technical University of Munich, TUM School of Natural Sciences, Department of Bioscience, Center for Functional Protein Assemblies (CPA), Garching, Germany
| | - Anna Kerle
- Technical University of Munich, TUM School of Natural Sciences, Department of Bioscience, Center for Functional Protein Assemblies (CPA), Garching, Germany
| | - Florian Rührnößl
- Technical University of Munich, TUM School of Natural Sciences, Department of Bioscience, Center for Functional Protein Assemblies (CPA), Garching, Germany
| | - Dragana Catici
- Technical University of Munich, TUM School of Natural Sciences, Department of Bioscience, Center for Functional Protein Assemblies (CPA), Garching, Germany
| | - Martin Haslbeck
- Technical University of Munich, TUM School of Natural Sciences, Department of Bioscience, Center for Functional Protein Assemblies (CPA), Garching, Germany
| | - Dmitrij Frishman
- Technical University of Munich, TUM School of Life Sciences, Department of Bioinformatics, Freising, Germany
| | - Michael Sattler
- Technical University of Munich, TUM School of Natural Sciences, Department of Bioscience, Bavarian NMR Center, Garching, Germany; Helmholtz Munich, Molecular Targets & Therapeutics Center, Institute of Structural Biology, Neuherberg, Germany
| | - Martin Zacharias
- Technical University of Munich, TUM School of Natural Sciences, Department of Bioscience, Center for Functional Protein Assemblies (CPA), Garching, Germany
| | - Matthias J Feige
- Technical University of Munich, TUM School of Natural Sciences, Department of Bioscience, Center for Functional Protein Assemblies (CPA), Garching, Germany.
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3
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Goncalves BG, Heise RM, Banerjee IA. Development of Self-Assembled Biomimetic Nanoscale Collagen-like Peptide-Based Scaffolds for Tissue Engineering: An In Silico and Laboratory Study. Biomimetics (Basel) 2023; 8:548. [PMID: 37999189 PMCID: PMC10669358 DOI: 10.3390/biomimetics8070548] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Revised: 11/01/2023] [Accepted: 11/02/2023] [Indexed: 11/25/2023] Open
Abstract
Development of biocomposite scaffolds has gained tremendous attention due to their potential for tissue regeneration. However, most scaffolds often contain animal-derived collagen that may elicit an immunological response, necessitating the development of new biomaterials. Herein, we developed a new collagen-like peptide,(Pro-Ala-His)10 (PAH)10, and explored its ability to be utilized as a functional biomaterial by incorporating it with a newly synthesized peptide-based self-assembled gel. The gel was prepared by conjugating a pectin derivative, galataric acid, with a pro-angiogenic peptide (LHYQDLLQLQY) and further functionalized with a cortistatin-derived peptide, (Phe-Trp-Lys-Thr)4 (FWKT)4, and the bio-ionic liquid choline acetate. The self-assembly of (PAH)10 and its interactions with the galactarate-peptide conjugates were examined using replica exchange molecular dynamics (REMD) simulations. Results revealed the formation of a multi-layered scaffold, with enhanced stability at higher temperatures. We then synthesized the scaffold and examined its physicochemical properties and its ability to integrate with aortic smooth muscle cells. The scaffold was further utilized as a bioink for bioprinting to form three-dimensional cell-scaffold matrices. Furthermore, the formation of actin filaments and elongated cell morphology was observed. These results indicate that the (PAH)10 hybrid scaffold provides a suitable environment for cell adhesion, proliferation and growth, making it a potentially valuable biomaterial for tissue engineering.
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Affiliation(s)
| | | | - Ipsita A. Banerjee
- Department of Chemistry, Fordham University, 441 East Fordham Road, Bronx, New York, NY 10458, USA; (B.G.G.); (R.M.H.)
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4
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Parui S, Robertson JC, Somani S, Tresadern G, Liu C, Dill KA. MELD-Bracket Ranks Binding Affinities of Diverse Sets of Ligands. J Chem Inf Model 2023; 63:2857-2865. [PMID: 37093848 DOI: 10.1021/acs.jcim.3c00243] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/25/2023]
Abstract
Affinity ranking of structurally diverse small-molecule ligands is a challenging problem with important applications in structure-based drug discovery. Absolute binding free energy methods can model diverse ligands, but the high computational cost of the current methods limits application to data sets with few ligands. We recently developed MELD-Bracket, a Molecular Dynamics method for efficient affinity ranking of ligands [ JCTC 2022, 18 (1), 374-379]. It utilizes a Bayesian framework to guide sampling to relevant regions of phase space, and it couples this with a bracket-like competition on a pool of ligands. Here we find that 6-competitor MELD-Bracket can rank dozens of diverse ligands that have low structural similarity and different net charges. We benchmark it on four protein systems─PTB1B, Tyk2, BACE, and JAK3─having varied modes of interactions. We also validated 8-competitor and 12-competitor protocols. The MELD-Bracket protocols presented here may have the appropriate balance of accuracy and computational efficiency to be suitable for ranking diverse ligands from typical drug discovery campaigns.
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Affiliation(s)
- Sridip Parui
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York 11794, United States
| | - James C Robertson
- Janssen Research and Development, Spring House, Pennsylvania 19477, United States
| | - Sandeep Somani
- Janssen Research and Development, Spring House, Pennsylvania 19477, United States
| | - Gary Tresadern
- Janssen Research and Development, Turnhoutseweg 30, Beerse B-2340, Belgium
| | - Cong Liu
- Center for the Development of Therapeutics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, United States
| | - Ken A Dill
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York 11794, United States
- Department of Chemistry, Stony Brook University, Stony Brook, New York 11794, United States
- Department of Physics and Astronomy, Stony Brook University, Stony Brook, New York 11794, United States
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5
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Marcisz M, Maszota-Zieleniak M, Samsonov SA. Repulsive Scaling Replica Exchange Molecular Dynamics in Modeling Protein-Glycosaminoglycan Complexes. Methods Mol Biol 2023; 2619:153-167. [PMID: 36662469 DOI: 10.1007/978-1-0716-2946-8_12] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
Glycosaminoglycans are long linear periodic anionic polysaccharides consisting of disaccharide units exhibiting different sulfation patterns forming a highly heterogeneous group of molecules. Due to their flexibility, length, high charge, and periodicity, they are challenging for computational approaches. Despite their biological significance in terms of the important role in various diseases (e.g., Alzheimer, cancer, SARS-CoV-2) and proper cell functioning (e.g., proliferation, maturation), there is a lack of effective molecular docking tools designed specifically for glycosaminoglycans due to their challenging physical-chemical nature. In this chapter we present protocols for the Repulsive Scaling Replica Exchange Molecular Dynamics (RS-REMD) methods to dock glycosaminoglycans with both implicit and explicit solvent models implemented. This novel molecular dynamics-based replica exchange technique should help to elevate our current knowledge on the complexes and interactions between glycosaminoglycans and their protein receptors.
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Affiliation(s)
- Mateusz Marcisz
- Faculty of Chemistry, University of Gdańsk, Gdańsk, Poland
- Intercollegiate Faculty of Biotechnology of UG and MUG, Gdańsk, Poland
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6
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Tunjic TM, Weber N, Brunsteiner M. Computer aided drug design in the development of proteolysis targeting chimeras. Comput Struct Biotechnol J 2023; 21:2058-2067. [PMID: 36968015 PMCID: PMC10030821 DOI: 10.1016/j.csbj.2023.02.042] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 02/21/2023] [Accepted: 02/22/2023] [Indexed: 03/18/2023] Open
Abstract
Proteolysis targeting chimeras represent a class of drug molecules with a number of attractive properties, most notably a potential to work for targets that, so far, have been in-accessible for conventional small molecule inhibitors. Due to their different mechanism of action, and physico-chemical properties, many of the methods that have been designed and applied for computer aided design of traditional small molecule drugs are not applicable for proteolysis targeting chimeras. Here we review recent developments in this field focusing on three aspects: de-novo linker-design, estimation of absorption for beyond-rule-of-5 compounds, and the generation and ranking of ternary complex structures. In spite of this field still being young, we find that a good number of models and algorithms are available, with the potential to assist the design of such compounds in-silico, and accelerate applied pharmaceutical research.
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7
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Marcisz M, Gaardløs M, Bojarski KK, Siebenmorgen T, Zacharias M, Samsonov SA. Explicit solvent repulsive scaling replica exchange molecular dynamics (RS-REMD) in molecular modeling of protein-glycosaminoglycan complexes. J Comput Chem 2022; 43:1633-1640. [PMID: 35796487 DOI: 10.1002/jcc.26965] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Revised: 06/20/2022] [Accepted: 06/22/2022] [Indexed: 11/10/2022]
Abstract
Glycosaminoglcyans (GAGs), linear anionic periodic polysaccharides, are crucial for many biologically relevant functions in the extracellular matrix. By interacting with proteins GAGs mediate processes such as cancer development, cell proliferation and the onset of neurodegenerative diseases. Despite this eminent importance of GAGs, they still represent a limited focus for the computational community in comparison to other classes of biomolecules. Therefore, there is a lack of modeling tools designed specifically for docking GAGs. One has to rely on existing docking software developed mostly for small drug molecules substantially differing from GAGs in their basic physico-chemical properties. In this study, we present an updated protocol for docking GAGs based on the Repulsive Scaling Replica Exchange Molecular Dynamics (RS-REMD) that includes explicit solvent description. The use of this water model improved docking performance both in terms of its accuracy and speed. This method represents a significant computational progress in GAG-related research.
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Affiliation(s)
- Mateusz Marcisz
- Faculty of Chemistry, University of Gdańsk, Gdańsk, Poland.,Intercollegiate Faculty of Biotechnology, Universuty of Gdańsk and Medical University of Gdańsk, Gdańsk, Poland
| | | | - Krzysztof K Bojarski
- Faculty of Chemistry, University of Gdańsk, Gdańsk, Poland.,Department of Physical Chemistry, Gdańsk University of Technology, Gdańsk, Poland
| | - Till Siebenmorgen
- Institute of Structural Biology, Helmholtz Zentrum München, Neuherberg, Germany
| | - Martin Zacharias
- Physics Department, Technical University of Munich, Garching, Germany
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8
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Harmalkar A, Mahajan SP, Gray JJ. 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] [MESH Headings] [Grants] [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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Affiliation(s)
- Ameya Harmalkar
- Department of Chemical and Biomolecular Engineering, The Johns Hopkins University, Baltimore, Maryland, United States of America
| | - Sai Pooja Mahajan
- Department of Chemical and Biomolecular Engineering, The Johns Hopkins University, Baltimore, Maryland, United States of America
| | - Jeffrey J. Gray
- Department of Chemical and Biomolecular Engineering, The Johns Hopkins University, Baltimore, Maryland, United States of America
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9
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Modeling glycosaminoglycan–protein complexes. Curr Opin Struct Biol 2022; 73:102332. [DOI: 10.1016/j.sbi.2022.102332] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Revised: 12/20/2021] [Accepted: 01/06/2022] [Indexed: 12/23/2022]
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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; 27:1841. [PMID: 35335203 PMCID: PMC8950966 DOI: 10.3390/molecules27061841] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [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
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11
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Wang J, Miao Y. Protein-Protein Interaction-Gaussian Accelerated Molecular Dynamics (PPI-GaMD): Characterization of Protein Binding Thermodynamics and Kinetics. J Chem Theory Comput 2022; 18:1275-1285. [PMID: 35099970 DOI: 10.1021/acs.jctc.1c00974] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
Protein-protein interactions (PPIs) play key roles in many fundamental biological processes such as cellular signaling and immune responses. However, it has proven challenging to simulate repetitive protein association and dissociation in order to calculate binding free energies and kinetics of PPIs due to long biological timescales and complex protein dynamics. To address this challenge, we have developed a new computational approach to all-atom simulations of PPIs based on a robust Gaussian accelerated molecular dynamics (GaMD) technique. The method, termed "PPI-GaMD", selectively boosts interaction potential energy between protein partners to facilitate their slow dissociation. Meanwhile, another boost potential is applied to the remaining potential energy of the entire system to effectively model the protein's flexibility and rebinding. PPI-GaMD has been demonstrated on a model system of the ribonuclease barnase interactions with its inhibitor barstar. Six independent 2 μs PPI-GaMD simulations have captured repetitive barstar dissociation and rebinding events, which enable calculations of the protein binding thermodynamics and kinetics simultaneously. The calculated binding free energies and kinetic rate constants agree well with the experimental data. Furthermore, PPI-GaMD simulations have provided mechanistic insights into barstar binding to barnase, which involves long-range electrostatic interactions and multiple binding pathways, being consistent with previous experimental and computational findings of this model system. In summary, PPI-GaMD provides a highly efficient and easy-to-use approach for binding free energy and kinetics calculations of PPIs.
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Affiliation(s)
- Jinan Wang
- Center for Computational Biology and Department of Molecular Biosciences, University of Kansas, Lawrence, Kansas 66047, United States
| | - Yinglong Miao
- Center for Computational Biology and Department of Molecular Biosciences, University of Kansas, Lawrence, Kansas 66047, United States
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12
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Abstract
The biological significance of proteins attracted the scientific community in exploring their characteristics. The studies shed light on the interaction patterns and functions of proteins in a living body. Due to their practical difficulties, reliable experimental techniques pave the way for introducing computational methods in the interaction prediction. Automated methods reduced the difficulties but could not yet replace experimental studies as the field is still evolving. Interaction prediction problem being critical needs highly accurate results, but none of the existing methods could offer reliable performance that can parallel with experimental results yet. This article aims to assess the existing computational docking algorithms, their challenges, and future scope. Blind docking techniques are quite helpful when no information other than the individual structures are available. As more and more complex structures are being added to different databases, information-driven approaches can be a good alternative. Artificial intelligence, ruling over the major fields, is expected to take over this domain very shortly.
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13
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Marcisz M, Zacharias M, Samsonov SA. Modeling Protein-Glycosaminoglycan Complexes: Does the Size Matter? J Chem Inf Model 2021; 61:4475-4485. [PMID: 34494837 PMCID: PMC8479808 DOI: 10.1021/acs.jcim.1c00664] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
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Docking glycosaminoglycans (GAGs) has been challenging because
of the complex nature of these long periodic linear and negatively
charged polysaccharides. Although standard docking tools like Autodock3
are successful when docking GAGs up to hexameric length, they experience
challenges to properly dock longer GAGs. Similar limitations concern
other docking approaches typically developed for docking ligands of
limited size to proteins. At the same time, most of more advanced
docking approaches are challenging for a user who is inexperienced
with complex in silico methodologies. In this work,
we evaluate the binding energies of complexes with different lengths
of GAGs using all-atom molecular dynamics simulations. Based on this
analysis, we propose a new docking protocol for long GAGs that consists
of conventional docking of short GAGs and further elongation with
the use of a coarse-grained representation of the GAG parts not being
in direct contact with its protein receptor. This method automated
by a simple script is straightforward to use within the Autodock3
framework but also useful in combination with other standard docking
tools. We believe that this method with some minor case-specific modifications
could also be used for docking other linear charged polymers.
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Affiliation(s)
- Mateusz Marcisz
- Faculty of Chemistry, University of Gdańsk, ul. Wita Stwosza 63, 80-308 Gdańsk, Poland.,Intercollegiate Faculty of Biotechnology of UG and MUG, ul. Abrahama 58, 80-307 Gdańsk, Poland
| | - Martin Zacharias
- Center of Functional Protein Assemblies, Technical University of Munich, Ernst-Otto-Fischer-Str. 8, 85748 Garching, Germany
| | - Sergey A Samsonov
- Faculty of Chemistry, University of Gdańsk, ul. Wita Stwosza 63, 80-308 Gdańsk, Poland
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14
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Marcisz M, Maszota-Zieleniak M, Huard B, Samsonov SA. Advanced Molecular Dynamics Approaches to Model a Tertiary Complex APRIL/TACI with Long Glycosaminoglycans. Biomolecules 2021; 11:biom11091349. [PMID: 34572563 PMCID: PMC8465899 DOI: 10.3390/biom11091349] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2021] [Revised: 09/08/2021] [Accepted: 09/10/2021] [Indexed: 02/05/2023] Open
Abstract
Glycosaminoglycans (GAGs) are linear anionic periodic polysaccharides participating in a number of biologically relevant processes in the extracellular matrix via interactions with their protein targets. Due to their periodicity, conformational flexibility, pseudo-symmetry of the sulfation pattern, and the key role of electrostatics, these molecules are challenging for both experimental and theoretical approaches. In particular, conventional molecular docking applied for GAGs longer than 10-mer experiences severe difficulties. In this work, for the first time, 24- and 48-meric GAGs were docked using all-atomic repulsive-scaling Hamiltonian replica exchange molecular dynamics (RS-REMD), a novel methodology based on replicas with van der Waals radii of interacting molecules being scaled. This approach performed well for proteins complexed with oligomeric GAGs and is independent of their length, which distinguishes it from other molecular docking approaches. We built a model of long GAGs in complex with a proliferation-inducing ligand (APRIL) prebound to its receptors, the B cell maturation antigen and the transmembrane activator and calcium modulator and cyclophilin ligand interactor (TACI). Furthermore, the prediction power of the RS-REMD for this tertiary complex was evaluated. We conclude that the TACI–GAG interaction could be potentially amplified by TACI’s binding to APRIL. RS-REMD outperformed Autodock3, the docking program previously proven the best for short GAGs.
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Affiliation(s)
- Mateusz Marcisz
- Faculty of Chemistry, University of Gdańsk, ul. Wita Stwosza 63, 80-308 Gdańsk, Poland; (M.M.); (M.M.-Z.)
- Intercollegiate Faculty of Biotechnology of UG and MUG, ul. Abrahama 58, 80-307 Gdańsk, Poland
| | - Martyna Maszota-Zieleniak
- Faculty of Chemistry, University of Gdańsk, ul. Wita Stwosza 63, 80-308 Gdańsk, Poland; (M.M.); (M.M.-Z.)
| | - Bertrand Huard
- Laboratory TIMC-IMAG, University Grenoble-Alpes, CNRS UMR 5525, 38700 La Tronche, France;
| | - Sergey A. Samsonov
- Faculty of Chemistry, University of Gdańsk, ul. Wita Stwosza 63, 80-308 Gdańsk, Poland; (M.M.); (M.M.-Z.)
- Correspondence: ; Tel.: +48-58-523-51-66
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15
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Agamennone M, Nicoli A, Bayer S, Weber V, Borro L, Gupta S, Fantacuzzi M, Di Pizio A. Protein-protein interactions at a glance: Protocols for the visualization of biomolecular interactions. Methods Cell Biol 2021; 166:271-307. [PMID: 34752337 DOI: 10.1016/bs.mcb.2021.06.012] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
Protein-protein interactions (PPIs) play a key role in many biological processes and are intriguing targets for drug discovery campaigns. Advancements in experimental and computational techniques are leading to a growth of data accessibility, and, with it, an increased need for the analysis of PPIs. In this respect, visualization tools are essential instruments to represent and analyze biomolecular interactions. In this chapter, we reviewed some of the available tools, highlighting their features, and describing their functions with practical information on their usage.
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Affiliation(s)
| | - Alessandro Nicoli
- Leibniz-Institute for Food Systems Biology at the Technical University of Munich, Freising, Germany
| | - Sebastian Bayer
- Leibniz-Institute for Food Systems Biology at the Technical University of Munich, Freising, Germany
| | - Verena Weber
- Leibniz-Institute for Food Systems Biology at the Technical University of Munich, Freising, Germany
| | - Luca Borro
- Department of Imaging, Advanced Cardiovascular Imaging Unit, Bambino Gesù Children's Hospital, IRCCS, Rome, Italy
| | - Shailendra Gupta
- Department of Systems Biology and Bioinformatics, University of Rostock, Rostock, Germany
| | | | - Antonella Di Pizio
- Leibniz-Institute for Food Systems Biology at the Technical University of Munich, Freising, Germany.
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16
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Maszota-Zieleniak M, Danielsson A, Samsonov SA. The potential role of glycosaminoglycans in serum amyloid A fibril formation by in silico approaches. Matrix Biol Plus 2021; 12:100080. [PMID: 34401710 PMCID: PMC8350538 DOI: 10.1016/j.mbplus.2021.100080] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Revised: 06/10/2021] [Accepted: 07/09/2021] [Indexed: 12/14/2022] Open
Abstract
SAA dimer binds GAGs stronger than its monomer. Due to its net negative charge SAA prefers to bind short GAG sequences. GAG binding by SAA is electrostatics-driven and rather unspecific. GAG binding site is constituted predominantly by the N-terminal helix residues. GAG binding could potenitally attenuate unfolding of the N-terminal helix.
Serum amyloid A (SAA) is actively involved in such pathological processes as atherosclerosis, rheumatoid arthritis, cancer and Alzheimer's disease by its aggregation. One of the factors that can attenuate its aggregation and so affects its physiological role is its interactions with glycosminoglycans (GAGs), linear anionic periodic polysaccharides. These molecules located in the extracellular matrix of the cell are highly variable in their chemical composition and sulfation patterns. Despite the available experimental evidence of SAA-GAG interactions, no mechanistic details at atomic level have been reported for these systems so far. In our work we aimed to apply diverse computational tools to characterize SAA-GAG complexes formation and to answer questions about their potential specificity, energetic patterns, particular SAA residues involved in these interactions, favourable oligomeric state of the protein and the potential influence of GAGs on SAA aggregation. Molecular docking, conventional and replica exchange molecular dynamics approaches were applied to corroborate the experimental knowledge and to propose the corresponding molecular models. SAA-GAG complex formation was found to be electrostatics-driven and rather unspecific of a GAG sulfation pattern, more favorable for the dimer than for the monomer when binding to a short GAG oligosaccharide through its N-terminal helix, potentially contributing to the unfolding of this helix, which could lead to the promotion of the protein aggregation. The data obtained add to the specific knowledge on SAA-GAG systems and deepen the general understanding of protein-GAG interactions that is of a considerable value for the development of GAG-based approaches in a broad theurapeutic context.
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Affiliation(s)
| | - Annemarie Danielsson
- Faculty of Chemistry, University of Gdańsk, ul. Wita Stwosza 63, 80-308 Gdańsk, Poland
| | - Sergey A Samsonov
- Faculty of Chemistry, University of Gdańsk, ul. Wita Stwosza 63, 80-308 Gdańsk, Poland
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17
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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: 5] [Impact Index Per Article: 1.7] [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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18
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Künze G, Huster D, Samsonov SA. Investigation of the structure of regulatory proteins interacting with glycosaminoglycans by combining NMR spectroscopy and molecular modeling - the beginning of a wonderful friendship. Biol Chem 2021; 402:1337-1355. [PMID: 33882203 DOI: 10.1515/hsz-2021-0119] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2021] [Accepted: 04/09/2021] [Indexed: 11/15/2022]
Abstract
The interaction of regulatory proteins with extracellular matrix or cell surface-anchored glycosaminoglycans (GAGs) plays important roles in molecular recognition, wound healing, growth, inflammation and many other processes. In spite of their high biological relevance, protein-GAG complexes are significantly underrepresented in structural databases because standard tools for structure determination experience difficulties in studying these complexes. Co-crystallization with subsequent X-ray analysis is hampered by the high flexibility of GAGs. NMR spectroscopy experiences difficulties related to the periodic nature of the GAGs and the sparse proton network between protein and GAG with distances that typically exceed the detection limit of nuclear Overhauser enhancement spectroscopy. In contrast, computer modeling tools have advanced over the last years delivering specific protein-GAG docking approaches successfully complemented with molecular dynamics (MD)-based analysis. Especially the combination of NMR spectroscopy in solution providing sparse structural constraints with molecular docking and MD simulations represents a useful synergy of forces to describe the structure of protein-GAG complexes. Here we review recent methodological progress in this field and bring up examples where the combination of new NMR methods along with cutting-edge modeling has yielded detailed structural information on complexes of highly relevant cytokines with GAGs.
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Affiliation(s)
- Georg Künze
- Center for Structural Biology, Vanderbilt University, 465 21st Ave S, 5140 MRB3, Nashville, TN37240, USA.,Department of Chemistry, Vanderbilt University, 7330 Stevenson Center, Station B 351822, Nashville, TN37235, USA.,Institute for Drug Discovery, University of Leipzig, Brüderstr. 34, D-04103Leipzig, Germany
| | - Daniel Huster
- Institute for Medical Physics and Biophysics, University of Leipzig, Härtelstr. 16-18, D-04107Leipzig, Germany
| | - Sergey A Samsonov
- Faculty of Chemistry, University of Gdańsk, Ul. Wita Stwosza 63, 80-308Gdańsk, Poland
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19
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Maszota-Zieleniak M, Marcisz M, Kogut MM, Siebenmorgen T, Zacharias M, Samsonov SA. Evaluation of replica exchange with repulsive scaling approach for docking glycosaminoglycans. J Comput Chem 2021; 42:1040-1053. [PMID: 33768554 DOI: 10.1002/jcc.26496] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2020] [Revised: 01/19/2021] [Accepted: 01/27/2021] [Indexed: 12/14/2022]
Abstract
Glycosaminoglycans (GAGs), long linear periodic anionic polysaccharides, are key molecules in the extracellular matrix (ECM). Therefore, deciphering their role in the biologically relevant context is important for fundamental understanding of the processes ongoing in ECM and for establishing new strategies in the regenerative medicine. Although GAGs represent a number of computational challenges, molecular docking is a powerful tool for analysis of their interactions. Despite the recent development of GAG-specific docking approaches, there is plenty of room for improvement. Here, replica exchange molecular dynamics with repulsive scaling (REMD-RS) recently proved to be a successful approach for protein-protein complexes, was applied to dock GAGs. In this method, effective pairwise radii are increased in different Hamiltonian replicas. REMD-RS is shown to be an attractive alternative to classical docking approaches for GAGs. This work contributes to setting up of GAG-specific computational protocols and provides new insights into the nature of these biological systems.
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Affiliation(s)
| | | | | | - Till Siebenmorgen
- Physics Department, Technical University of Munich, Garching, Germany
| | - Martin Zacharias
- Physics Department, Technical University of Munich, Garching, Germany
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20
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Kogut MM, Maszota-Zieleniak M, Marcisz M, Samsonov SA. Computational insights into the role of calcium ions in protein–glycosaminoglycan systems. Phys Chem Chem Phys 2021; 23:3519-3530. [DOI: 10.1039/d0cp05438k] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
The prediction power of computational methodologies for studying the role of ions in protein–glycosaminoglycan interactions was critically assessed.
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21
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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: 25] [Impact Index Per Article: 6.3] [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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22
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Siebenmorgen T, Zacharias M. Efficient Refinement and Free Energy Scoring of Predicted Protein-Protein Complexes Using Replica Exchange with Repulsive Scaling. J Chem Inf Model 2020; 60:5552-5562. [PMID: 33075222 DOI: 10.1021/acs.jcim.0c00853] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Accurate prediction and evaluation of protein-protein complex structures are of major importance to understand the cellular interactome. Typically, putative complexes are predicted based on docking methods, and simple force field or knowledge-based scoring functions are applied to evaluate single complex structures. We have extended a replica-exchange-based scheme employing different levels of a repulsive biasing between partners in each replica simulation (RS-REMD) to simultaneously refine and score protein-protein complexes. The bias acts specifically on the 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. The method provides a free energy score that correlates quite well with experimental binding free energies on a set of 36 complexes with correlation coefficients of 0.77 and 0.55 in explicit and implicit solvent simulations, respectively. For a large set of docked decoy complexes, significant improvement of docked complexes was found in many cases with the starting structure in the vicinity (within 20 Å) of the native complex. In the majority of cases (14 out of 20 in explicit solvent), near native docking solutions were identified as the best scoring complexes. The approach is computational demanding but may offer a route for refinement and realistic ranking of predicted protein-protein docking geometries.
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Affiliation(s)
- Till Siebenmorgen
- Physik-Department T38, Technische Universität München, James-Franck-Str. 1, 85748 Garching, Germany
| | - Martin Zacharias
- Physik-Department T38, Technische Universität München, James-Franck-Str. 1, 85748 Garching, Germany
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23
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Transient Unfolding and Long-Range Interactions in Viral BCL2 M11 Enable Binding to the BECN1 BH3 Domain. Biomolecules 2020; 10:biom10091308. [PMID: 32932757 PMCID: PMC7564285 DOI: 10.3390/biom10091308] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2020] [Revised: 09/03/2020] [Accepted: 09/04/2020] [Indexed: 01/07/2023] Open
Abstract
Viral BCL2 proteins (vBCL2s) help to sustain chronic infection of host proteins to inhibit apoptosis and autophagy. However, details of conformational changes in vBCL2s that enable binding to BH3Ds remain unknown. Using all-atom, multiple microsecond-long molecular dynamic simulations (totaling 17 μs) of the murine γ-herpesvirus 68 vBCL2 (M11), and statistical inference techniques, we show that regions of M11 transiently unfold and refold upon binding of the BH3D. Further, we show that this partial unfolding/refolding within M11 is mediated by a network of hydrophobic interactions, which includes residues that are 10 Å away from the BH3D binding cleft. We experimentally validate the role of these hydrophobic interactions by quantifying the impact of mutating these residues on binding to the Beclin1/BECN1 BH3D, demonstrating that these mutations adversely affect both protein stability and binding. To our knowledge, this is the first study detailing the binding-associated conformational changes and presence of long-range interactions within vBCL2s.
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24
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Jost Lopez A, Quoika PK, Linke M, Hummer G, Köfinger J. Quantifying Protein-Protein Interactions in Molecular Simulations. J Phys Chem B 2020; 124:4673-4685. [PMID: 32379446 PMCID: PMC7294537 DOI: 10.1021/acs.jpcb.9b11802] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
![]()
Interactions
among proteins, nucleic acids, and other macromolecules
are essential for their biological functions and shape the physicochemcial
properties of the crowded environments inside living cells. Binding
interactions are commonly quantified by dissociation constants Kd, and both binding and nonbinding interactions
are quantified by second osmotic virial coefficients B2. As a measure of nonspecific binding and stickiness, B2 is receiving renewed attention in the context
of so-called liquid–liquid phase separation in protein and
nucleic acid solutions. We show that Kd is fully determined by B2 and the fraction
of the dimer observed in molecular simulations of two proteins in
a box. We derive two methods to calculate B2. From molecular dynamics or Monte Carlo simulations using implicit
solvents, we can determine B2 from insertion
and removal energies by applying Bennett’s acceptance ratio
(BAR) method or the (binless) weighted histogram analysis method (WHAM).
From simulations using implicit or explicit solvents, one can estimate B2 from the probability that the two molecules
are within a volume large enough to cover their range of interactions.
We validate these methods for coarse-grained Monte Carlo simulations
of three weakly binding proteins. Our estimates for Kd and B2 allow us to separate
out the contributions of nonbinding interactions to B2. Comparison of calculated and measured values of Kd and B2 can be
used to (re-)parameterize and improve molecular force fields by calibrating
specific affinities, overall stickiness, and nonbinding interactions.
The accuracy and efficiency of Kd and B2 calculations make them well suited for high-throughput
studies of large interactomes.
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Affiliation(s)
- Alfredo Jost Lopez
- Department of Theoretical Biophysics, Max Planck Institute of Biophysics, Max-von-Laue-Straße 3, 60438 Frankfurt am Main, Germany
| | - Patrick K Quoika
- Department of Theoretical Biophysics, Max Planck Institute of Biophysics, Max-von-Laue-Straße 3, 60438 Frankfurt am Main, Germany
| | - Max Linke
- Department of Theoretical Biophysics, Max Planck Institute of Biophysics, Max-von-Laue-Straße 3, 60438 Frankfurt am Main, Germany
| | - Gerhard Hummer
- Department of Theoretical Biophysics, Max Planck Institute of Biophysics, Max-von-Laue-Straße 3, 60438 Frankfurt am Main, Germany.,Institute for Biophysics, Goethe University, Max-von-Laue-Straße 9, 60438 Frankfurt am Main, Germany
| | - Jürgen Köfinger
- Department of Theoretical Biophysics, Max Planck Institute of Biophysics, Max-von-Laue-Straße 3, 60438 Frankfurt am Main, Germany
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