1
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Petris PC, Sweere AJM. Buffer Screening of Protein Formulations Using a Coarse-Grained Protocol Based on Medicinal Chemistry Interactions. J Phys Chem B 2024; 128:9353-9362. [PMID: 39318336 DOI: 10.1021/acs.jpcb.4c04105] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/26/2024]
Abstract
In drug and vaccine development, the designed protein formulation should be highly stable against the temperature, pH, buffer, excipients, and other environmental settings. Similarly, in a sensing unit, one needs to know how strongly two biomolecules bind to guide the design of the biorecognition unit accordingly. Typically, the community performs a series of experiments to thoroughly examine the parameter space, the so-called design-of-experiment (DoE) method, to identify the optimal formulation conditions. Unfortunately, extensive physical testing entails high costs, repeatability issues, and a lack of in-depth knowledge of the underlying mechanisms that affect the final outcome. To address these challenges, we developed a physics-based simulation protocol for buffer screening of protein formulations. We are introducing a coarse-grained molecular simulation protocol that consists of six different interactions. The so-called medicinal chemistry interactions (electrostatics, hydrophobicity, hydrogen bonding propensity, disulfide bonding, and water-water) are based on the physical nature of the protein's amino acid and the partitioning/polarity of any other chemical constituent. The protocol is applied in immunoglobulin-based monoclonal antibodies. We have analyzed the protein behavior as a function of acidity (pH) to discover the isoelectric point by solving the Poisson-Boltzmann equation in a mesoscale grid. To identify the conditions under which the protein oligomerizes in a given buffer, pH, temperature, and ionic strength, we are performing dissipative particle dynamics (DPD) simulations. The protocol allows researchers to reach the high time/space scales required to study protein formulations in their full complexity. Combined with the disruptive protein folding artificial intelligence (AI) algorithms that have been recently developed, the protocol creates a powerful digital framework for cultivating advanced pharmaceutical and biological applications.
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Affiliation(s)
- Panagiotis C Petris
- Siemens Industry Software Netherlands B.V., The Hague 2595 BN, The Netherlands
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2
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Karmakar T, Finney AR, Salvalaglio M, Yazaydin AO, Perego C. Non-Equilibrium Modeling of Concentration-Driven processes with Constant Chemical Potential Molecular Dynamics Simulations. Acc Chem Res 2023; 56:1156-1167. [PMID: 37120847 PMCID: PMC10193523 DOI: 10.1021/acs.accounts.2c00811] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2022] [Indexed: 05/02/2023]
Abstract
ConspectusConcentration-driven processes in solution, i.e., phenomena that are sustained by persistent concentration gradients, such as crystallization and surface adsorption, are fundamental chemical processes. Understanding such phenomena is crucial for countless applications, from pharmaceuticals to biotechnology. Molecular dynamics (MD), both in- and out-of-equilibrium, plays an essential role in the current understanding of concentration-driven processes. Computational costs, however, impose drastic limitations on the accessible scale of simulated systems, hampering the effective study of such phenomena. In particular, due to these size limitations, closed system MD of concentration-driven processes is affected by solution depletion/enrichment that unavoidably impacts the dynamics of the chemical phenomena under study. As a notable example, in simulations of crystallization from solution, the transfer of monomers between the liquid and crystal phases results in a gradual depletion/enrichment of solution concentration, altering the driving force for phase transition. In contrast, this effect is negligible in experiments, given the macroscopic size of the solution volume. Because of these limitations, accurate MD characterization of concentration-driven phenomena has proven to be a long-standing simulation challenge. While disparate equilibrium and nonequilibrium simulation strategies have been proposed to address the study of such processes, the methodologies are in continuous development.In this context, a novel simulation technique named constant chemical potential molecular dynamics (CμMD) was recently proposed. CμMD employs properly designed, concentration-dependent external forces that regulate the flux of solute species between selected subregions of the simulation volume. This enables simulations of systems under a constant chemical drive in an efficient and straightforward way. The CμMD scheme was originally applied to the case of crystal growth from solution and then extended to the simulation of various physicochemical processes, resulting in new variants of the method. This Account illustrates the CμMD method and the key advances enabled by it in the framework of in silico chemistry. We review results obtained in crystallization studies, where CμMD allows growth rate calculations and equilibrium shape predictions, and in adsorption studies, where adsorption thermodynamics on porous or solid surfaces was correctly characterized via CμMD. Furthermore, we will discuss the application of CμMD variants to simulate permeation through porous materials, solution separation, and nucleation upon fixed concentration gradients. While presenting the numerous applications of the method, we provide an original and comprehensive assessment of concentration-driven simulations using CμMD. To this end, we also shed light on the theoretical and technical foundations of CμMD, underlining the novelty and specificity of the method with respect to existing techniques while stressing its current limitations. Overall, the application of CμMD to a diverse range of fields provides new insight into many physicochemical processes, the in silico study of which has been hitherto limited by finite-size effects. In this context, CμMD stands out as a general-purpose method that promises to be an invaluable simulation tool for studying molecular-scale concentration-driven phenomena.
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Affiliation(s)
- Tarak Karmakar
- Department
of Chemistry, Indian Institute of Technology,
Delhi, Hauz Khas, New Delhi 110016, India
| | - Aaron R. Finney
- Thomas
Young Centre and Department of Chemical Engineering, University College London, Torrington Place, London WC1E 7JE, United
Kingdom
| | - Matteo Salvalaglio
- Thomas
Young Centre and Department of Chemical Engineering, University College London, Torrington Place, London WC1E 7JE, United
Kingdom
| | - A. Ozgur Yazaydin
- Thomas
Young Centre and Department of Chemical Engineering, University College London, Torrington Place, London WC1E 7JE, United
Kingdom
| | - Claudio Perego
- Department
of Innovative Technologies, University of
Applied Sciences and Arts of Southern Switzerland, Polo Universitario
Lugano, via la Santa 1, 6962 Lugano-Viganello, Switzerland
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3
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Fiorentini R, Tarenzi T, Potestio R. Fast, Accurate, and System-Specific Variable-Resolution Modeling of Proteins. J Chem Inf Model 2023; 63:1260-1275. [PMID: 36735551 PMCID: PMC9976289 DOI: 10.1021/acs.jcim.2c01311] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Indexed: 02/04/2023]
Abstract
In recent years, a few multiple-resolution modeling strategies have been proposed, in which functionally relevant parts of a biomolecule are described with atomistic resolution, with the remainder of the system being concurrently treated using a coarse-grained model. In most cases, the parametrization of the latter requires lengthy reference all-atom simulations and/or the usage of off-shelf coarse-grained force fields, whose interactions have to be refined to fit the specific system under examination. Here, we overcome these limitations through a novel multiresolution modeling scheme for proteins, dubbed coarse-grained anisotropic network model for variable resolution simulations, or CANVAS. This scheme enables a user-defined modulation of the resolution level throughout the system structure; a fast parametrization of the potential without the necessity of reference simulations; and the straightforward usage of the model on the most commonly used molecular dynamics platforms. The method is presented and validated with two case studies, the enzyme adenylate kinase and the therapeutic antibody pembrolizumab, by comparing the results obtained with the CANVAS model against fully atomistic simulations. The modeling software, implemented in Python, is made freely available for the community on a collaborative github repository.
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Affiliation(s)
- Raffaele Fiorentini
- Department
of Physics, University of Trento, via Sommarive 14, I-38123 Trento, Italy
- INFN-TIFPA, Trento
Institute for Fundamental Physics and Applications, via Sommarive 14, I-38123 Trento, Italy
| | - Thomas Tarenzi
- Department
of Physics, University of Trento, via Sommarive 14, I-38123 Trento, Italy
- INFN-TIFPA, Trento
Institute for Fundamental Physics and Applications, via Sommarive 14, I-38123 Trento, Italy
| | - Raffaello Potestio
- Department
of Physics, University of Trento, via Sommarive 14, I-38123 Trento, Italy
- INFN-TIFPA, Trento
Institute for Fundamental Physics and Applications, via Sommarive 14, I-38123 Trento, Italy
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4
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In Search of a Dynamical Vocabulary: A Pipeline to Construct a Basis of Shared Traits in Large-Scale Motions of Proteins. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12147157] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
The paradigmatic sequence–structure–dynamics–function relation in proteins is currently well established in the scientific community; in particular, a large effort has been made to probe the first connection, indeed providing convincing evidence of its strength and rationalizing it in a quantitative and general framework. In contrast, however, the role of dynamics as a link between structure and function has eluded a similarly clear-cut verification and description. In this work, we propose a pipeline aimed at building a basis for the quantitative characterization of the large-scale dynamics of a set of proteins, starting from the sole knowledge of their native structures. The method hinges on a dynamics-based clusterization, which allows a straightforward comparison with structural and functional protein classifications. The resulting basis set, obtained through the application to a group of related proteins, is shown to reproduce the salient large-scale dynamical features of the dataset. Most interestingly, the basis set is shown to encode the fluctuation patterns of homologous proteins not belonging to the initial dataset, thus highlighting the general applicability of the pipeline used to build it.
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5
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Basciu A, Callea L, Motta S, Bonvin AM, Bonati L, Vargiu AV. No dance, no partner! A tale of receptor flexibility in docking and virtual screening. VIRTUAL SCREENING AND DRUG DOCKING 2022. [DOI: 10.1016/bs.armc.2022.08.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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6
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Giulini M, Rigoli M, Mattiotti G, Menichetti R, Tarenzi T, Fiorentini R, Potestio R. From System Modeling to System Analysis: The Impact of Resolution Level and Resolution Distribution in the Computer-Aided Investigation of Biomolecules. Front Mol Biosci 2021; 8:676976. [PMID: 34164432 PMCID: PMC8215203 DOI: 10.3389/fmolb.2021.676976] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2021] [Accepted: 05/06/2021] [Indexed: 12/18/2022] Open
Abstract
The ever increasing computer power, together with the improved accuracy of atomistic force fields, enables researchers to investigate biological systems at the molecular level with remarkable detail. However, the relevant length and time scales of many processes of interest are still hardly within reach even for state-of-the-art hardware, thus leaving important questions often unanswered. The computer-aided investigation of many biological physics problems thus largely benefits from the usage of coarse-grained models, that is, simplified representations of a molecule at a level of resolution that is lower than atomistic. A plethora of coarse-grained models have been developed, which differ most notably in their granularity; this latter aspect determines one of the crucial open issues in the field, i.e. the identification of an optimal degree of coarsening, which enables the greatest simplification at the expenses of the smallest information loss. In this review, we present the problem of coarse-grained modeling in biophysics from the viewpoint of system representation and information content. In particular, we discuss two distinct yet complementary aspects of protein modeling: on the one hand, the relationship between the resolution of a model and its capacity of accurately reproducing the properties of interest; on the other hand, the possibility of employing a lower resolution description of a detailed model to extract simple, useful, and intelligible information from the latter.
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Affiliation(s)
- Marco Giulini
- Physics Department, University of Trento, Trento, Italy.,INFN-TIFPA, Trento Institute for Fundamental Physics and Applications, Trento, Italy
| | - Marta Rigoli
- Physics Department, University of Trento, Trento, Italy.,INFN-TIFPA, Trento Institute for Fundamental Physics and Applications, Trento, Italy
| | - Giovanni Mattiotti
- Physics Department, University of Trento, Trento, Italy.,INFN-TIFPA, Trento Institute for Fundamental Physics and Applications, Trento, Italy
| | - Roberto Menichetti
- Physics Department, University of Trento, Trento, Italy.,INFN-TIFPA, Trento Institute for Fundamental Physics and Applications, Trento, Italy
| | - Thomas Tarenzi
- Physics Department, University of Trento, Trento, Italy.,INFN-TIFPA, Trento Institute for Fundamental Physics and Applications, Trento, Italy
| | - Raffaele Fiorentini
- Physics Department, University of Trento, Trento, Italy.,INFN-TIFPA, Trento Institute for Fundamental Physics and Applications, Trento, Italy
| | - Raffaello Potestio
- Physics Department, University of Trento, Trento, Italy.,INFN-TIFPA, Trento Institute for Fundamental Physics and Applications, Trento, Italy
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7
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Korshunova K, Carloni P. Ligand Affinities within the Open-Boundary Molecular Mechanics/Coarse-Grained Framework (I): Alchemical Transformations within the Hamiltonian Adaptive Resolution Scheme. J Phys Chem B 2021; 125:789-797. [PMID: 33443434 DOI: 10.1021/acs.jpcb.0c09805] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
Our recently developed Open-Boundary Molecular Mechanics/Coarse Grained (OB-MM/CG) framework predicts ligand poses in important pharmaceutical targets, such as G-protein Coupled Receptors, even when experimental structural information is lacking. The approach, which is based on GROMOS and AMBER force fields, allows for grand-canonical simulations of protein-ligand complexes by using the Hamiltonian Adaptive Resolution Scheme (H-AdResS) for the solvent. Here, we present a key step toward the estimation of ligand binding affinities for their targets within this approach. This is the implementation of the H-AdResS in the GROMACS code. The accuracy of our implementation is established by calculating hydration free energies of several molecules in water by means of alchemical transformations. The deviations of the GROMOS- and AMBER-based H-AdResS results from the reference fully atomistic simulations are smaller than the accuracy of the force field and/or they are in the range of the published results. Importantly, our predictions are in good agreement with experimental data. The current implementation paves the way to the use of the OB-MM/CG framework for the study of large biological systems.
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Affiliation(s)
- Ksenia Korshunova
- Department of Physics, RWTH Aachen University, 52074 Aachen, Germany.,Computational Biomedicine, Institute of Advanced Simulations IAS-5/Institute for Neuroscience and Medicine INM-9, Forschungszentrum Jülich GmbH, 52428 Jülich, Germany
| | - Paolo Carloni
- Department of Physics, RWTH Aachen University, 52074 Aachen, Germany.,Computational Biomedicine, Institute of Advanced Simulations IAS-5/Institute for Neuroscience and Medicine INM-9, Forschungszentrum Jülich GmbH, 52428 Jülich, Germany.,Molecular Neuroscience and Neuroimaging (INM-11), Forschungszentrum Jülich GmbH, 52428 Jülich, Germany
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8
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Schneider J, Ribeiro R, Alfonso-Prieto M, Carloni P, Giorgetti A. Hybrid MM/CG Webserver: Automatic Set Up of Molecular Mechanics/Coarse-Grained Simulations for Human G Protein-Coupled Receptor/Ligand Complexes. Front Mol Biosci 2020; 7:576689. [PMID: 33102525 PMCID: PMC7500467 DOI: 10.3389/fmolb.2020.576689] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2020] [Accepted: 08/13/2020] [Indexed: 12/12/2022] Open
Abstract
Hybrid Molecular Mechanics/Coarse-Grained (MM/CG) simulations help predict ligand poses in human G protein-coupled receptors (hGPCRs), the most important protein superfamily for pharmacological applications. This approach allows the description of the ligand, the binding cavity, and the surrounding water molecules at atomistic resolution, while coarse-graining the rest of the receptor. Here, we present the Hybrid MM/CG Webserver (mmcg.grs.kfa-juelich.de) that automatizes and speeds up the MM/CG simulation setup of hGPCR/ligand complexes. Initial structures for such complexes can be easily and efficiently generated with other webservers. The Hybrid MM/CG server also allows for equilibration of the systems, either fully automatically or interactively. The results are visualized online (using both interactive 3D visualizations and analysis plots), helping the user identify possible issues and modify the setup parameters accordingly. Furthermore, the prepared system can be downloaded and the simulation continued locally.
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Affiliation(s)
- Jakob Schneider
- Computational Biomedicine, Institute for Advanced Simulation IAS-5/Institute for Neuroscience and Medicine INM-9, Forschungszentrum Jülich GmbH, Jülich, Germany.,JARA-Institute: Molecular Neuroscience and Neuroimaging, Institute for Neuroscience and Medicine INM-11/JARA-BRAIN Institute JBI-2, Forschungszentrum Jülich GmbH, Jülich, Germany.,Department of Physics, RWTH Aachen University, Aachen, Germany
| | - Rui Ribeiro
- Department of Biotechnology, University of Verona, Verona, Italy
| | - Mercedes Alfonso-Prieto
- Computational Biomedicine, Institute for Advanced Simulation IAS-5/Institute for Neuroscience and Medicine INM-9, Forschungszentrum Jülich GmbH, Jülich, Germany.,Medical Faculty, Cécile and Oskar Vogt Institute for Brain Research, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Paolo Carloni
- Computational Biomedicine, Institute for Advanced Simulation IAS-5/Institute for Neuroscience and Medicine INM-9, Forschungszentrum Jülich GmbH, Jülich, Germany.,JARA-Institute: Molecular Neuroscience and Neuroimaging, Institute for Neuroscience and Medicine INM-11/JARA-BRAIN Institute JBI-2, Forschungszentrum Jülich GmbH, Jülich, Germany.,Department of Physics, RWTH Aachen University, Aachen, Germany
| | - Alejandro Giorgetti
- Computational Biomedicine, Institute for Advanced Simulation IAS-5/Institute for Neuroscience and Medicine INM-9, Forschungszentrum Jülich GmbH, Jülich, Germany.,Department of Biotechnology, University of Verona, Verona, Italy
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9
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Schneider J, Korshunova K, Si Chaib Z, Giorgetti A, Alfonso-Prieto M, Carloni P. Ligand Pose Predictions for Human G Protein-Coupled Receptors: Insights from the Amber-Based Hybrid Molecular Mechanics/Coarse-Grained Approach. J Chem Inf Model 2020; 60:5103-5116. [PMID: 32786708 DOI: 10.1021/acs.jcim.0c00661] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Human G protein-coupled receptors (hGPCRs) are the most frequent targets of Food and Drug Administration (FDA)-approved drugs. Structural bioinformatics, along with molecular simulation, can support structure-based drug design targeting hGPCRs. In this context, several years ago, we developed a hybrid molecular mechanics (MM)/coarse-grained (CG) approach to predict ligand poses in low-resolution hGPCR models. The approach was based on the GROMOS96 43A1 and PRODRG united-atom force fields for the MM part. Here, we present a new MM/CG implementation using, instead, the Amber 14SB and GAFF all-atom potentials for proteins and ligands, respectively. The new implementation outperforms the previous one, as shown by a variety of applications on models of hGPCR/ligand complexes at different resolutions, and it is also more user-friendly. Thus, it emerges as a useful tool to predict poses in low-resolution models and provides insights into ligand binding similarly to all-atom molecular dynamics, albeit at a lower computational cost.
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Affiliation(s)
- Jakob Schneider
- Computational Biomedicine, Institute for Advanced Simulations IAS-5/Institute for Neuroscience and Medicine INM-9, Forschungszentrum Jülich GmbH, 52428 Jülich, Germany.,Department of Physics, RWTH Aachen University, 52074 Aachen, Germany.,JARA-Institute: Molecular Neuroscience and Neuroimaging, Institute for Neuroscience and Medicine INM-11/JARA-BRAIN Institute JBI-2, Forschungszentrum Jülich GmbH, 52428 Jülich, Germany
| | - Ksenia Korshunova
- Computational Biomedicine, Institute for Advanced Simulations IAS-5/Institute for Neuroscience and Medicine INM-9, Forschungszentrum Jülich GmbH, 52428 Jülich, Germany.,Department of Physics, RWTH Aachen University, 52074 Aachen, Germany
| | - Zeineb Si Chaib
- Computational Biomedicine, Institute for Advanced Simulations IAS-5/Institute for Neuroscience and Medicine INM-9, Forschungszentrum Jülich GmbH, 52428 Jülich, Germany.,RWTH Aachen University, 52062 Aachen, Germany
| | - Alejandro Giorgetti
- Computational Biomedicine, Institute for Advanced Simulations IAS-5/Institute for Neuroscience and Medicine INM-9, Forschungszentrum Jülich GmbH, 52428 Jülich, Germany.,Department of Biotechnology, University of Verona, 37314 Verona, Italy.,JARA-HPC, IAS-5/INM-9 Computational Biomedicine, Forschungszentrum Jülich GmbH, 52428 Jülich, Germany
| | - Mercedes Alfonso-Prieto
- Computational Biomedicine, Institute for Advanced Simulations IAS-5/Institute for Neuroscience and Medicine INM-9, Forschungszentrum Jülich GmbH, 52428 Jülich, Germany.,JARA-HPC, IAS-5/INM-9 Computational Biomedicine, Forschungszentrum Jülich GmbH, 52428 Jülich, Germany.,Cecile and Oskar Vogt Institute for Brain Research, Medical Faculty, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany
| | - Paolo Carloni
- Computational Biomedicine, Institute for Advanced Simulations IAS-5/Institute for Neuroscience and Medicine INM-9, Forschungszentrum Jülich GmbH, 52428 Jülich, Germany.,Department of Physics, RWTH Aachen University, 52074 Aachen, Germany.,JARA-Institute: Molecular Neuroscience and Neuroimaging, Institute for Neuroscience and Medicine INM-11/JARA-BRAIN Institute JBI-2, Forschungszentrum Jülich GmbH, 52428 Jülich, Germany.,JARA-HPC, IAS-5/INM-9 Computational Biomedicine, Forschungszentrum Jülich GmbH, 52428 Jülich, Germany
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10
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Fiorentini R, Kremer K, Potestio R. Ligand-protein interactions in lysozyme investigated through a dual-resolution model. Proteins 2020; 88:1351-1360. [PMID: 32525263 PMCID: PMC7497117 DOI: 10.1002/prot.25954] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2020] [Revised: 05/04/2020] [Accepted: 05/16/2020] [Indexed: 12/12/2022]
Abstract
A fully atomistic (AT) modeling of biological macromolecules at relevant length- and time-scales is often cumbersome or not even desirable, both in terms of computational effort required and a posteriori analysis. This difficulty can be overcome with the use of multiresolution models, in which different regions of the same system are concurrently described at different levels of detail. In enzymes, computationally expensive AT detail is crucial in the modeling of the active site in order to capture, for example, the chemically subtle process of ligand binding. In contrast, important yet more collective properties of the remainder of the protein can be reproduced with a coarser description. In the present work, we demonstrate the effectiveness of this approach through the calculation of the binding free energy of hen egg white lysozyme with the inhibitor di-N-acetylchitotriose. Particular attention is payed to the impact of the mapping, that is, the selection of AT and coarse-grained residues, on the binding free energy. It is shown that, in spite of small variations of the binding free energy with respect to the active site resolution, the separate contributions coming from different energetic terms (such as electrostatic and van der Waals interactions) manifest a stronger dependence on the mapping, thus pointing to the existence of an optimal level of intermediate resolution.
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Affiliation(s)
| | - Kurt Kremer
- Max Planck Institute for Polymer Research, Mainz, Germany
| | - Raffaello Potestio
- Physics Department, University of Trento, Trento, Italy.,INFN-TIFPA, Trento Institute for Fundamental Physics and Applications, Trento, Italy
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11
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Barroso da Silva FL, Carloni P, Cheung D, Cottone G, Donnini S, Foegeding EA, Gulzar M, Jacquier JC, Lobaskin V, MacKernan D, Mohammad Hosseini Naveh Z, Radhakrishnan R, Santiso EE. Understanding and Controlling Food Protein Structure and Function in Foods: Perspectives from Experiments and Computer Simulations. Annu Rev Food Sci Technol 2020; 11:365-387. [PMID: 31951485 DOI: 10.1146/annurev-food-032519-051640] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
The structure and interactions of proteins play a critical role in determining the quality attributes of many foods, beverages, and pharmaceutical products. Incorporating a multiscale understanding of the structure-function relationships of proteins can provide greater insight into, and control of, the relevant processes at play. Combining data from experimental measurements, human sensory panels, and computer simulations through machine learning allows the construction of statistical models relating nanoscale properties of proteins to the physicochemical properties, physiological outcomes, and tastes of foods. This review highlights several examples of advanced computer simulations at molecular, mesoscale, and multiscale levels that shed light on the mechanisms at play in foods, thereby facilitating their control. It includes a practical simulation toolbox for those new to in silico modeling.
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Affiliation(s)
- Fernando Luís Barroso da Silva
- School of Pharmaceutical Sciences at Ribeirão Preto, University of São Paulo, BR-14040-903, Ribeirão Preto, São Paulo, Brazil
| | - Paolo Carloni
- Institute for Computational Biomedicine (IAS-5/INM-9), Forschungszentrum Jülich, 52425 Jülich, Germany.,Department of Physics, RWTH Aachen University, 52062 Aachen, Germany
| | - David Cheung
- School of Chemistry, National University of Ireland Galway, Galway, Ireland
| | - Grazia Cottone
- Department of Physics and Chemistry, University of Palermo, 90128 Palermo, Italy
| | - Serena Donnini
- Department of Biological and Environmental Science, University of Jyväskylä, Jyväskylä 40014, Finland
| | - E Allen Foegeding
- Department of Food, Bioprocessing, & Nutrition Sciences, North Carolina State University, Raleigh, North Carolina 27695, USA
| | - Muhammad Gulzar
- UCD School of Agriculture and Food Science, University College Dublin, Dublin 4, Ireland
| | | | | | - Donal MacKernan
- UCD School of Physics, University College Dublin, Dublin 4, Ireland
| | | | - Ravi Radhakrishnan
- Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania, 19104, USA
| | - Erik E Santiso
- Department of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, North Carolina 27695, USA
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12
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Recent Advances in Coarse-Grained Models for Biomolecules and Their Applications. Int J Mol Sci 2019; 20:ijms20153774. [PMID: 31375023 PMCID: PMC6696403 DOI: 10.3390/ijms20153774] [Citation(s) in RCA: 65] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2019] [Revised: 07/28/2019] [Accepted: 07/30/2019] [Indexed: 12/23/2022] Open
Abstract
Molecular dynamics simulations have emerged as a powerful tool to study biological systems at varied length and timescales. The conventional all-atom molecular dynamics simulations are being used by the wider scientific community in routine to capture the conformational dynamics and local motions. In addition, recent developments in coarse-grained models have opened the way to study the macromolecular complexes for time scales up to milliseconds. In this review, we have discussed the principle, applicability and recent development in coarse-grained models for biological systems. The potential of coarse-grained simulation has been reviewed through state-of-the-art examples of protein folding and structure prediction, self-assembly of complexes, membrane systems and carbohydrates fiber models. The multiscale simulation approaches have also been discussed in the context of their emerging role in unravelling hierarchical level information of biosystems. We conclude this review with the future scope of coarse-grained simulations as a constantly evolving tool to capture the dynamics of biosystems.
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13
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Alfonso-Prieto M, Navarini L, Carloni P. Understanding Ligand Binding to G-Protein Coupled Receptors Using Multiscale Simulations. Front Mol Biosci 2019; 6:29. [PMID: 31131282 PMCID: PMC6510167 DOI: 10.3389/fmolb.2019.00029] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2019] [Accepted: 04/11/2019] [Indexed: 12/18/2022] Open
Abstract
Human G-protein coupled receptors (GPCRs) convey a wide variety of extracellular signals inside the cell and they are one of the main targets for pharmaceutical intervention. Rational drug design requires structural information on these receptors; however, the number of experimental structures is scarce. This gap can be filled by computational models, based on homology modeling and docking techniques. Nonetheless, the low sequence identity across GPCRs and the chemical diversity of their ligands may limit the quality of these models and hence refinement using molecular dynamics simulations is recommended. This is the case for olfactory and bitter taste receptors, which constitute the first and third largest GPCR groups and show sequence identities with the available GPCR templates below 20%. We have developed a molecular dynamics approach, based on the combination of molecular mechanics and coarse grained (MM/CG), tailored to study ligand binding in GPCRs. This approach has been applied so far to bitter taste receptor complexes, showing significant predictive power. The protein/ligand interactions observed in the simulations were consistent with extensive mutagenesis and functional data. Moreover, the simulations predicted several binding residues not previously tested, which were subsequently verified by carrying out additional experiments. Comparison of the simulations of two bitter taste receptors with different ligand selectivity also provided some insights into the binding determinants of bitter taste receptors. Although the MM/CG approach has been applied so far to a limited number of GPCR/ligand complexes, the excellent agreement of the computational models with the mutagenesis and functional data supports the applicability of this method to other GPCRs for which experimental structures are missing. This is particularly important for the challenging case of GPCRs with low sequence identity with available templates, for which molecular docking shows limited predictive power.
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Affiliation(s)
- Mercedes Alfonso-Prieto
- Institute for Advanced Simulation IAS-5 and Institute of Neuroscience and Medicine INM-9, Computational Biomedicine, Forschungszentrum Jülich, Jülich, Germany.,Medical Faculty, Cécile and Oskar Vogt Institute for Brain Research, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | | | - Paolo Carloni
- Institute for Advanced Simulation IAS-5 and Institute of Neuroscience and Medicine INM-9, Computational Biomedicine, Forschungszentrum Jülich, Jülich, Germany.,Institute for Neuroscience and Medicine INM-11, Forschungszentrum Jülich, Jülich, Germany.,Department of Physics, Rheinisch-Westfälische Technische Hochschule (RWTH) Aachen University, Aachen, Germany.,VNU Key Laboratory "Multiscale Simulation of Complex Systems", VNU University of Science, Vietnam National University, Hanoi, Vietnam
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