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Delaunay M, Ha-Duong T. Computational Tools and Strategies to Develop Peptide-Based Inhibitors of Protein-Protein Interactions. METHODS IN MOLECULAR BIOLOGY (CLIFTON, N.J.) 2022; 2405:205-230. [PMID: 35298816 DOI: 10.1007/978-1-0716-1855-4_11] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Protein-protein interactions play crucial and subtle roles in many biological processes and modifications of their fine mechanisms generally result in severe diseases. Peptide derivatives are very promising therapeutic agents for modulating protein-protein associations with sizes and specificities between those of small compounds and antibodies. For the same reasons, rational design of peptide-based inhibitors naturally borrows and combines computational methods from both protein-ligand and protein-protein research fields. In this chapter, we aim to provide an overview of computational tools and approaches used for identifying and optimizing peptides that target protein-protein interfaces with high affinity and specificity. We hope that this review will help to implement appropriate in silico strategies for peptide-based drug design that builds on available information for the systems of interest.
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Affiliation(s)
| | - Tâp Ha-Duong
- Université Paris-Saclay, CNRS, BioCIS, Châtenay-Malabry, France.
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2
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Mardt A, Noé F. Progress in deep Markov state modeling: Coarse graining and experimental data restraints. J Chem Phys 2021; 155:214106. [PMID: 34879670 DOI: 10.1063/5.0064668] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Recent advances in deep learning frameworks have established valuable tools for analyzing the long-timescale behavior of complex systems, such as proteins. In particular, the inclusion of physical constraints, e.g., time-reversibility, was a crucial step to make the methods applicable to biophysical systems. Furthermore, we advance the method by incorporating experimental observables into the model estimation showing that biases in simulation data can be compensated for. We further develop a new neural network layer in order to build a hierarchical model allowing for different levels of details to be studied. Finally, we propose an attention mechanism, which highlights important residues for the classification into different states. We demonstrate the new methodology on an ultralong molecular dynamics simulation of the Villin headpiece miniprotein.
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Affiliation(s)
- Andreas Mardt
- Department of Mathematics and Computer Science, Freie Universität Berlin, Berlin, Germany
| | - Frank Noé
- Department of Mathematics and Computer Science, Freie Universität Berlin, Berlin, Germany
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3
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Vu QN, Young R, Sudhakar HK, Gao T, Huang T, Tan YS, Lau YH. Cyclisation strategies for stabilising peptides with irregular conformations. RSC Med Chem 2021; 12:887-901. [PMID: 34263169 PMCID: PMC8230030 DOI: 10.1039/d1md00098e] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Accepted: 04/12/2021] [Indexed: 11/21/2022] Open
Abstract
Cyclisation is a common synthetic strategy for enhancing the therapeutic potential of peptide-based molecules. While there are extensive studies on peptide cyclisation for reinforcing regular secondary structures such as α-helices and β-sheets, there are remarkably few reports of cyclising peptides which adopt irregular conformations in their bioactive target-bound state. In this review, we highlight examples where cyclisation techniques have been successful in stabilising irregular conformations, then discuss how the design of cyclic constraints for irregularly structured peptides can be informed by existing β-strand stabilisation approaches, new computational design techniques, and structural principles extracted from cyclic peptide library screening hits. Through this analysis, we demonstrate how existing peptide cyclisation techniques can be adapted to address the synthetic design challenge of stabilising irregularly structured binding motifs.
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Affiliation(s)
- Quynh Ngoc Vu
- School of Chemistry, Eastern Ave, The University of Sydney NSW 2006 Australia
| | - Reginald Young
- School of Chemistry, Eastern Ave, The University of Sydney NSW 2006 Australia
| | | | - Tianyi Gao
- School of Chemistry, Eastern Ave, The University of Sydney NSW 2006 Australia
| | - Tiancheng Huang
- School of Chemistry, Eastern Ave, The University of Sydney NSW 2006 Australia
| | - Yaw Sing Tan
- Bioinformatics Institute, Agency for Science, Technology and Research (ASTAR) 30 Biopolis Street, #07-01, Matrix Singapore 138671 Singapore
| | - Yu Heng Lau
- School of Chemistry, Eastern Ave, The University of Sydney NSW 2006 Australia
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4
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Ge Y, Zhang S, Erdelyi M, Voelz VA. Solution-State Preorganization of Cyclic β-Hairpin Ligands Determines Binding Mechanism and Affinities for MDM2. J Chem Inf Model 2021; 61:2353-2367. [PMID: 33905247 PMCID: PMC9960209 DOI: 10.1021/acs.jcim.1c00029] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
Understanding mechanisms of protein folding and binding is crucial to designing their molecular function. Molecular dynamics (MD) simulations and Markov state model (MSM) approaches provide a powerful way to understand complex conformational change that occurs over long time scales. Such dynamics are important for the design of therapeutic peptidomimetic ligands, whose affinity and binding mechanism are dictated by a combination of folding and binding. To examine the role of preorganization in peptide binding to protein targets, we performed massively parallel explicit-solvent MD simulations of cyclic β-hairpin ligands designed to mimic the p53 transactivation domain and competitively bind mouse double minute 2 homologue (MDM2). Disrupting the MDM2-p53 interaction is a therapeutic strategy to prevent degradation of the p53 tumor suppressor in cancer cells. MSM analysis of over 3 ms of aggregate trajectory data enabled us to build a detailed mechanistic model of coupled folding and binding of four cyclic peptides which we compare to experimental binding affinities and rates. The results show a striking relationship between the relative preorganization of each ligand in solution and its affinity for MDM2. Specifically, changes in peptide conformational populations predicted by the MSMs suggest that entropy loss upon binding is the main factor influencing affinity. The MSMs also enable detailed examination of non-native interactions which lead to misfolded states and comparison of structural ensembles with experimental NMR measurements. In contrast to an MSM study of p53 transactivation domain (TAD) binding to MDM2, MSMs of cyclic β-hairpin binding show a conformational selection mechanism. Finally, we make progress toward predicting accurate off rates of cyclic peptides using multiensemble Markov models (MEMMs) constructed from unbiased and biased simulated trajectories.
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Affiliation(s)
- Yunui Ge
- Department of Chemistry, Temple University, Philadelphia, PA 19122, USA
| | - Si Zhang
- Department of Chemistry, Temple University, Philadelphia, PA 19122, USA
| | - Mate Erdelyi
- Department of Chemistry - BMC, Uppsala University, SE-75123 Uppsala, Sweden
| | - Vincent A. Voelz
- Department of Chemistry, Temple University, Philadelphia, PA 19122, USA
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5
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Damjanovic J, Miao J, Huang H, Lin YS. Elucidating Solution Structures of Cyclic Peptides Using Molecular Dynamics Simulations. Chem Rev 2021; 121:2292-2324. [PMID: 33426882 DOI: 10.1021/acs.chemrev.0c01087] [Citation(s) in RCA: 40] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Protein-protein interactions are vital to biological processes, but the shape and size of their interfaces make them hard to target using small molecules. Cyclic peptides have shown promise as protein-protein interaction modulators, as they can bind protein surfaces with high affinity and specificity. Dozens of cyclic peptides are already FDA approved, and many more are in various stages of development as immunosuppressants, antibiotics, antivirals, or anticancer drugs. However, most cyclic peptide drugs so far have been natural products or derivatives thereof, with de novo design having proven challenging. A key obstacle is structural characterization: cyclic peptides frequently adopt multiple conformations in solution, which are difficult to resolve using techniques like NMR spectroscopy. The lack of solution structural information prevents a thorough understanding of cyclic peptides' sequence-structure-function relationship. Here we review recent development and application of molecular dynamics simulations with enhanced sampling to studying the solution structures of cyclic peptides. We describe novel computational methods capable of sampling cyclic peptides' conformational space and provide examples of computational studies that relate peptides' sequence and structure to biological activity. We demonstrate that molecular dynamics simulations have grown from an explanatory technique to a full-fledged tool for systematic studies at the forefront of cyclic peptide therapeutic design.
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Affiliation(s)
- Jovan Damjanovic
- Department of Chemistry, Tufts University, Medford, Massachusetts 02155, United States
| | - Jiayuan Miao
- Department of Chemistry, Tufts University, Medford, Massachusetts 02155, United States
| | - He Huang
- Department of Chemistry, Tufts University, Medford, Massachusetts 02155, United States
| | - Yu-Shan Lin
- Department of Chemistry, Tufts University, Medford, Massachusetts 02155, United States
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6
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Abstract
Molecular dynamics (MD) simulations play more and more important roles in studying conformations of cyclic peptides in solution. Here we describe how to use replica-exchange molecular dynamics (REMD) implemented in Gromacs software package to simulate peptides with backbone cyclization and stapled peptides with side-chain linkages. Some of our methods for trajectory analyses and our residue-specific force fields are also described.
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7
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Geng H, Chen F, Ye J, Jiang F. Applications of Molecular Dynamics Simulation in Structure Prediction of Peptides and Proteins. Comput Struct Biotechnol J 2019; 17:1162-1170. [PMID: 31462972 PMCID: PMC6709365 DOI: 10.1016/j.csbj.2019.07.010] [Citation(s) in RCA: 43] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2019] [Revised: 07/07/2019] [Accepted: 07/23/2019] [Indexed: 12/21/2022] Open
Abstract
Compared with rapid accumulation of protein sequences from high-throughput DNA sequencing, obtaining experimental 3D structures of proteins is still much more difficult, making protein structure prediction (PSP) potentially very useful. Currently, a vast majority of PSP efforts are based on data mining of known sequences, structures and their relationships (informatics-based). However, if closely related template is not available, these methods are usually much less reliable than experiments. They may also be problematic in predicting the structures of naturally occurring or designed peptides. On the other hand, physics-based methods including molecular dynamics (MD) can utilize our understanding of detailed atomic interactions determining biomolecular structures. In this mini-review, we show that all-atom MD can predict structures of cyclic peptides and other peptide foldamers with accuracy similar to experiments. Then, some notable successes in reproducing experimental 3D structures of small proteins through MD simulations (some with replica-exchange) of the folding were summarized. We also describe advancements of MD-based refinement of structure models, and the integration of limited experimental or bioinformatics data into MD-based structure modeling.
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Affiliation(s)
- Hao Geng
- Lab of Computational Chemistry and Drug Design, State Key Laboratory of Chemical Oncogenomics, Peking University Shenzhen Graduate School, Shenzhen 518055, China
| | - Fangfang Chen
- Guangdong and Shenzhen Key Laboratory of Male Reproductive Medicine and Genetics, Peking University Shenzhen Hospital, Shenzhen PKU-HKUST Medical Center, Shenzhen 518036, China
| | - Jing Ye
- Guangdong and Shenzhen Key Laboratory of Male Reproductive Medicine and Genetics, Peking University Shenzhen Hospital, Shenzhen PKU-HKUST Medical Center, Shenzhen 518036, China
| | - Fan Jiang
- Lab of Computational Chemistry and Drug Design, State Key Laboratory of Chemical Oncogenomics, Peking University Shenzhen Graduate School, Shenzhen 518055, China
- NanoAI Biotech Co.,Ltd., Silicon Valley Compound, Longhua District, Shenzhen 518109, China
- Corresponding author at: Lab of Computational Chemistry and Drug Design, State Key Laboratory of Chemical Oncogenomics, Peking University Shenzhen Graduate School, Shenzhen 518055, China.
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8
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Acharyya A, Ge Y, Wu H, DeGrado WF, Voelz VA, Gai F. Exposing the Nucleation Site in α-Helix Folding: A Joint Experimental and Simulation Study. J Phys Chem B 2019; 123:1797-1807. [PMID: 30694671 DOI: 10.1021/acs.jpcb.8b12220] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
One of the fundamental events in protein folding is α-helix formation, which involves sequential development of a series of helical hydrogen bonds between the backbone C═O group of residues i and the -NH group of residues i + 4. While we now know a great deal about α-helix folding dynamics, a key question that remains to be answered is where the productive helical nucleation event occurs. Statistically, a helical nucleus (or the first helical hydrogen-bond) can form anywhere within the peptide sequence in question; however, the one that leads to productive folding may only form at a preferred location. This consideration is based on the fact that the α-helical structure is inherently asymmetric, due to the specific alignment of the helical hydrogen bonds. While this hypothesis is plausible, validating it is challenging because there is not an experimental observable that can be used to directly pinpoint the location of the productive nucleation process. Therefore, in this study we combine several techniques, including peptide cross-linking, laser-induced temperature-jump infrared spectroscopy, and molecular dynamics simulations, to tackle this challenge. Taken together, our experimental and simulation results support an α-helix folding mechanism wherein the productive nucleus is formed at the N-terminus, which propagates toward the C-terminal end of the peptide to yield the folded structure. In addition, our results show that incorporation of a cross-linker can lead to formation of differently folded conformations, underscoring the need for all-atom simulations to quantitatively assess the proposed cross-linking design.
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Affiliation(s)
- Arusha Acharyya
- Department of Chemistry , University of Pennsylvania , Philadelphia , Pennsylvania 19104 , United States
| | - Yunhui Ge
- Department of Chemistry , Temple University , Philadelphia , Pennsylvania 19122 , United States
| | - Haifan Wu
- Department of Pharmaceutical Chemistry , University of California , San Francisco , California 94158 , United States
| | - William F DeGrado
- Department of Pharmaceutical Chemistry , University of California , San Francisco , California 94158 , United States
| | - Vincent A Voelz
- Department of Chemistry , Temple University , Philadelphia , Pennsylvania 19122 , United States
| | - Feng Gai
- Department of Chemistry , University of Pennsylvania , Philadelphia , Pennsylvania 19104 , United States
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9
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Slough DP, McHugh SM, Lin YS. Understanding and designing head-to-tail cyclic peptides. Biopolymers 2018; 109:e23113. [PMID: 29528114 PMCID: PMC6135719 DOI: 10.1002/bip.23113] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2017] [Revised: 02/23/2018] [Accepted: 02/26/2018] [Indexed: 01/30/2023]
Abstract
Cyclic peptides (CPs) are an exciting class of molecules with a variety of applications. However, design strategies for CP therapeutics, for example, are generally limited by a poor understanding of their sequence-structure relationships. This knowledge gap often leads to a trial-and-error approach for designing CPs for a specific purpose, which is both costly and time-consuming. Herein, we describe the current experimental and computational efforts in understanding and designing head-to-tail CPs along with their respective challenges. In addition, we provide several future directions in the field of computational CP design to improve its accuracy, efficiency and applicability. These advances, combined with experimental techniques, shall ultimately provide a better understanding of these interesting molecules and a reliable working platform to rationally design CPs with desired characteristics.
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Affiliation(s)
| | | | - Yu-Shan Lin
- Department of Chemistry, Tufts University, Medford, Massachusetts, 02155, United States
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10
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Slough DP, McHugh SM, Cummings AE, Dai P, Pentelute BL, Kritzer JA, Lin YS. Designing Well-Structured Cyclic Pentapeptides Based on Sequence-Structure Relationships. J Phys Chem B 2018; 122:3908-3919. [PMID: 29589926 PMCID: PMC6071411 DOI: 10.1021/acs.jpcb.8b01747] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Cyclic peptides are a promising class of molecules for unique applications. Unfortunately, cyclic peptide design is severely limited by the difficulty in predicting the conformations they will adopt in solution. In this work, we use explicit-solvent molecular dynamics simulations to design well-structured cyclic peptides by studying their sequence-structure relationships. Critical to our approach is an enhanced sampling method that exploits the essential transitional motions of cyclic peptides to efficiently sample their conformational space. We simulated a range of cyclic pentapeptides from all-glycine to a library of cyclo-(X1X2AAA) peptides to map their conformational space and determine cooperative effects of neighboring residues. By combining the results from all cyclo-(X1X2AAA) peptides, we developed a scoring function to predict the structural preferences for X1-X2 residues within cyclic pentapeptides. Using this scoring function, we designed a cyclic pentapeptide, cyclo-(GNSRV), predicted to be well structured in aqueous solution. Subsequent circular dichroism and NMR spectroscopy revealed that this cyclic pentapeptide is indeed well structured in water, with a nuclear Overhauser effect and J-coupling values consistent with the predicted structure.
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Affiliation(s)
- Diana P. Slough
- Department of Chemistry, Tufts University, Medford, Massachusetts 02155, USA
| | - Sean M. McHugh
- Department of Chemistry, Tufts University, Medford, Massachusetts 02155, USA
| | | | - Peng Dai
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
| | - Bradley L. Pentelute
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
| | - Joshua A. Kritzer
- Department of Chemistry, Tufts University, Medford, Massachusetts 02155, USA
| | - Yu -Shan Lin
- Department of Chemistry, Tufts University, Medford, Massachusetts 02155, USA
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11
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Ge Y, Voelz VA. Model Selection Using BICePs: A Bayesian Approach for Force Field Validation and Parameterization. J Phys Chem B 2018. [PMID: 29518328 DOI: 10.1021/acs.jpcb.7b11871] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
The Bayesian Inference of Conformational Populations (BICePs) algorithm reconciles theoretical predictions of conformational state populations with sparse and/or noisy experimental measurements. Among its key advantages is its ability to perform objective model selection through a quantity we call the BICePs score, which reflects the integrated posterior evidence in favor of a given model, computed through free energy estimation methods. Here, we explore how the BICePs score can be used for force field validation and parametrization. Using a 2D lattice protein as a toy model, we demonstrate that BICePs is able to select the correct value of an interaction energy parameter given ensemble-averaged experimental distance measurements. We show that if conformational states are sufficiently fine-grained, the results are robust to experimental noise and measurement sparsity. Using these insights, we apply BICePs to perform force field evaluations for all-atom simulations of designed β-hairpin peptides against experimental NMR chemical shift measurements. These tests suggest that BICePs scores can be used for model selection in the context of all-atom simulations. We expect this approach to be particularly useful for the computational foldamer design as a tool for improving general-purpose force fields given sparse experimental measurements.
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Affiliation(s)
- Yunhui Ge
- Department of Chemistry , Temple University , Philadelphia , Pennsylvania 19122 , United States
| | - Vincent A Voelz
- Department of Chemistry , Temple University , Philadelphia , Pennsylvania 19122 , United States
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12
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McHugh SM, Yu H, Slough DP, Lin YS. Mapping the sequence-structure relationships of simple cyclic hexapeptides. Phys Chem Chem Phys 2018; 19:3315-3324. [PMID: 28091629 DOI: 10.1039/c6cp06192c] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
Cyclic peptides are promising protein-protein interaction modulators with high binding affinities and specificities, as well as enhanced stabilities and oral availabilities over linear analogs. Despite their relatively small size and cyclic architecture, it is currently difficult to predict the favored conformation(s) of most classes of cyclic peptides. An improved understanding of the sequence-structure relationships for cyclic peptides will offer an avenue for the rational design of cyclic peptides as possible therapeutics. In this work, we systematically explored the sequence-structure relationships for two cyclic hexapeptide systems using molecular dynamics simulation techniques. Starting with an all-glycine cyclic hexapeptide, cyclo-G6, we systematically replaced glycine residues with alanines and characterized the structural ensembles of different variants. The same process was repeated with valines to investigate the effects of larger side chains. An analysis of the origin of structure preferences was performed using thermodynamics decomposition and several general observations are reported.
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Affiliation(s)
- Sean M McHugh
- Department of Chemistry, Tufts University, Medford, Massachusetts 02155, USA.
| | - Hongtao Yu
- Department of Chemistry, Tufts University, Medford, Massachusetts 02155, USA.
| | - Diana P Slough
- Department of Chemistry, Tufts University, Medford, Massachusetts 02155, USA.
| | - Yu-Shan Lin
- Department of Chemistry, Tufts University, Medford, Massachusetts 02155, USA.
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13
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Slough DP, Yu H, McHugh SM, Lin YS. Toward accurately modeling N-methylated cyclic peptides. Phys Chem Chem Phys 2018; 19:5377-5388. [PMID: 28155950 DOI: 10.1039/c6cp07700e] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Cyclic peptides have unique properties and can target protein surfaces specifically and potently. N-Methylation provides a promising way to further optimize the pharmacokinetic and structural profiles of cyclic peptides. The capability to accurately model structures adopted by N-methylated cyclic peptides would facilitate rational design of this interesting and useful class of molecules. We apply molecular dynamics simulations with advanced enhanced sampling methods to efficiently characterize the structural ensembles of N-methylated cyclic peptides, while simultaneously evaluating the overall performance of several simulation force fields. We find that one of the residue-specific force fields, RSFF2, is able to recapitulate experimental structures of the N-methylated cyclic peptide benchmarks tested here when the correct amide isomers are used as initial configurations and enforced during the simulations. Thus, using our simulation approach, it is possible to accurately and efficiently predict the structures of N-methylated cyclic peptides if sufficient information is available to determine the correct amide cis/trans configuration. Moreover, our results suggest that, upon further optimization of RSFF2 to more reliably predict cis/trans isomers, molecular dynamics simulations will be able to de novo predict N-methylated cyclic peptides in the near future, strongly motivating such continued optimization.
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Affiliation(s)
- Diana P Slough
- Department of Chemistry, Tufts University, Medford, Massachusetts 02155, USA.
| | - Hongtao Yu
- Department of Chemistry, Tufts University, Medford, Massachusetts 02155, USA.
| | - Sean M McHugh
- Department of Chemistry, Tufts University, Medford, Massachusetts 02155, USA.
| | - Yu-Shan Lin
- Department of Chemistry, Tufts University, Medford, Massachusetts 02155, USA.
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14
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Rodríguez-Espigares I, Kaczor AA, Stepniewski TM, Selent J. Challenges and Opportunities in Drug Discovery of Biased Ligands. Methods Mol Biol 2018; 1705:321-334. [PMID: 29188569 DOI: 10.1007/978-1-4939-7465-8_14] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
The observation of biased agonism in G protein-coupled receptors (GPCRs) has provided new approaches for the development of more efficacious and safer drugs. However, in order to rationally design biased drugs, one must understand the molecular basis of this phenomenon. Computational approaches can help in exploring the conformational universe of GPCRs and detecting conformational states with relevance for distinct functional outcomes. This information is extremely valuable for the development of new therapeutic agents that promote desired conformational receptor states and responses while avoiding the ones leading to undesired side-effects.This book chapter intends to introduce the reader to powerful computational approaches for sampling the conformational space of these receptors, focusing first on molecular dynamics and the analysis of the produced data through methods such as dimensionality reduction, Markov State Models and adaptive sampling. Then, we show how to seek for compounds that target distinct conformational states via docking and virtual screening. In addition, we describe how to detect receptor-ligand interactions that drive signaling bias and comment current challenges and opportunities of presented methods.
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Affiliation(s)
- Ismael Rodríguez-Espigares
- Department of Experimental and Health Sciences, Research Programme on Biomedical Informatics (GRIB), Hospital del Mar Medical Research Institute (IMIM), Pompeu Fabra University (UPF), Dr. Aiguader 88, E-08003, Barcelona, Spain
| | - Agnieszka A Kaczor
- Department of Synthesis and Chemical Technology of Pharmaceutical Substances with Computer Modelling Lab, Faculty of Pharmacy with Division of Medical Analytics, Medical University of Lublin, 4A Chodzki St., PL-20093, Lublin, Poland.,Department of Pharmaceutical Chemistry, School of Pharmacy, University of Eastern Finland, Yliopistonranta 1, P.O. Box 1627, FI-70211, Kuopio, Finland
| | - Tomasz Maciej Stepniewski
- Department of Experimental and Health Sciences, Research Programme on Biomedical Informatics (GRIB), Hospital del Mar Medical Research Institute (IMIM), Pompeu Fabra University (UPF), Dr. Aiguader 88, E-08003, Barcelona, Spain
| | - Jana Selent
- Department of Experimental and Health Sciences, Research Programme on Biomedical Informatics (GRIB), Hospital del Mar Medical Research Institute (IMIM), Pompeu Fabra University (UPF), Dr. Aiguader 88, E-08003, Barcelona, Spain.
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15
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Mechanisms of Lipid Scrambling by the G Protein-Coupled Receptor Opsin. Structure 2017; 26:356-367.e3. [PMID: 29290486 DOI: 10.1016/j.str.2017.11.020] [Citation(s) in RCA: 43] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2017] [Revised: 10/29/2017] [Accepted: 11/27/2017] [Indexed: 01/05/2023]
Abstract
Several class-A G protein-coupled receptor (GPCR) proteins act as constitutive phospholipid scramblases catalyzing the transbilayer translocation of >10,000 phospholipids per second when reconstituted into synthetic vesicles. To address the molecular mechanism by which these proteins facilitate rapid lipid scrambling, we carried out large-scale ensemble atomistic molecular dynamics simulations of the opsin GPCR. We report that, in the process of scrambling, lipid head groups traverse a dynamically revealed hydrophilic pathway in the region between transmembrane helices 6 and 7 of the protein while their hydrophobic tails remain in the bilayer environment. We present quantitative kinetic models of the translocation process based on Markov State Model analysis. As key residues on the lipid translocation pathway are conserved within the class-A GPCR family, our results illuminate unique aspects of GPCR structure and dynamics while providing a rigorous basis for the design of variants of these proteins with defined scramblase activity.
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16
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Ge Y, Kier BL, Andersen NH, Voelz VA. Computational and Experimental Evaluation of Designed β-Cap Hairpins Using Molecular Simulations and Kinetic Network Models. J Chem Inf Model 2017; 57:1609-1620. [PMID: 28614661 DOI: 10.1021/acs.jcim.7b00132] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Molecular simulation has been used to model the detailed folding properties of peptides, yet prospective computational peptide design by such approaches remains challenging and nontrivial. To test the accuracy of simulation-based hairpin design, we characterized the folding properties of a series of so-called β-cap hairpin peptides designed to mimic a conserved hairpin of LapD, a bacterial intracellular signaling protein, both experimentally by NMR spectroscopy and computationally by implicit-solvent replica-exchange molecular dynamics using three different AMBER force fields (ff96, ff99sb-ildn, and ff99sb-ildn-NMR). A unique challenge presented by these designs is the presence of both a terminal Trp-Trp capping motif and a conserved GWxQ motif in the hairpin turn required for binding to LapG. Consistent with previous studies, we found AMBER ff96 to be the most accurate when used with the OBC GBSA implicit solvent model, despite its known bias toward β-sheet conformations when used in explicit-solvent simulations. To gain microscopic insight into the folding landscape of the hairpin designs, we additionally performed parallel simulations on the Folding@home distributed computing platform using AMBER ff99sb-ildn-NMR with TIP3P explicit solvent. Markov state models (MSMs) built from trajectory data reveal a number of non-native interactions between Trp and other amino acid side chains, creating potential problems in achieving well-folded hairpin structures in solution.
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Affiliation(s)
- Yunhui Ge
- Department of Chemistry, Temple University , Philadelphia, Pennsylvania 19122, United States
| | - Brandon L Kier
- Department of Chemistry, University of Washington , Seattle, Washington 98195, United States
| | - Niels H Andersen
- Department of Chemistry, University of Washington , Seattle, Washington 98195, United States
| | - Vincent A Voelz
- Department of Chemistry, Temple University , Philadelphia, Pennsylvania 19122, United States
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17
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Razavi AM, Khelashvili G, Weinstein H. A Markov State-based Quantitative Kinetic Model of Sodium Release from the Dopamine Transporter. Sci Rep 2017; 7:40076. [PMID: 28059145 PMCID: PMC5216462 DOI: 10.1038/srep40076] [Citation(s) in RCA: 58] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2016] [Accepted: 11/30/2016] [Indexed: 12/24/2022] Open
Abstract
The dopamine transporter (DAT) belongs to the neurotransmitter:sodium symporter (NSS) family of membrane proteins that are responsible for reuptake of neurotransmitters from the synaptic cleft to terminate a neuronal signal and enable subsequent neurotransmitter release from the presynaptic neuron. The release of one sodium ion from the crystallographically determined sodium binding site Na2 had been identified as an initial step in the transport cycle which prepares the transporter for substrate translocation by stabilizing an inward-open conformation. We have constructed Markov State Models (MSMs) from extensive molecular dynamics simulations of human DAT (hDAT) to explore the mechanism of this sodium release. Our results quantify the release process triggered by hydration of the Na2 site that occurs concomitantly with a conformational transition from an outward-facing to an inward-facing state of the transporter. The kinetics of the release process are computed from the MSM, and transition path theory is used to identify the most probable sodium release pathways. An intermediate state is discovered on the sodium release pathway, and the results reveal the importance of various modes of interaction of the N-terminus of hDAT in controlling the pathways of release.
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Affiliation(s)
- Asghar M Razavi
- Department of Physiology and Biophysics, Weill Cornell Medical College of Cornell University, New York, NY 10065, USA
| | - George Khelashvili
- Department of Physiology and Biophysics, Weill Cornell Medical College of Cornell University, New York, NY 10065, USA
| | - Harel Weinstein
- Department of Physiology and Biophysics, Weill Cornell Medical College of Cornell University, New York, NY 10065, USA.,Institute for Computational Biomedicine, Weill Medical College of Cornell University, New York, NY 10065, USA
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18
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Elucidating Mechanisms of Molecular Recognition Between Human Argonaute and miRNA Using Computational Approaches. Methods Mol Biol 2016. [PMID: 27924488 DOI: 10.1007/978-1-4939-6563-2_18] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/05/2024]
Abstract
MicroRNA (miRNA) and Argonaute (AGO) protein together form the RNA-induced silencing complex (RISC) that plays an essential role in the regulation of gene expression. Elucidating the underlying mechanism of AGO-miRNA recognition is thus of great importance not only for the in-depth understanding of miRNA function but also for inspiring new drugs targeting miRNAs. In this chapter we introduce a combined computational approach of molecular dynamics (MD) simulations, Markov state models (MSMs), and protein-RNA docking to investigate AGO-miRNA recognition. Constructed from MD simulations, MSMs can elucidate the conformational dynamics of AGO at biologically relevant timescales. Protein-RNA docking can then efficiently identify the AGO conformations that are geometrically accessible to miRNA. Using our recent work on human AGO2 as an example, we explain the rationale and the workflow of our method in details. This combined approach holds great promise to complement experiments in unraveling the mechanisms of molecular recognition between large, flexible, and complex biomolecules.
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19
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McHugh SM, Rogers JR, Solomon SA, Yu H, Lin YS. Computational methods to design cyclic peptides. Curr Opin Chem Biol 2016; 34:95-102. [PMID: 27592259 DOI: 10.1016/j.cbpa.2016.08.004] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2016] [Revised: 08/08/2016] [Accepted: 08/10/2016] [Indexed: 10/21/2022]
Abstract
Cyclic peptides (CPs) are promising modulators of protein-protein interactions (PPIs), but their application remains challenging. It is currently difficult to predict the structures and bioavailability of CPs. The ability to design CPs using computer modeling would greatly facilitate the development of CPs as potent PPI modulators for fundamental studies and as potential therapeutics. Herein, we describe computational methods to generate CP libraries for virtual screening, as well as current efforts to accurately predict the conformations adopted by CPs. These advances are making it possible to envision robust computational design of active CPs. However, unique properties of CPs pose significant challenges associated with sampling CP conformational space and accurately describing CP energetics. These major obstacles to structure prediction likely must be solved before robust design of active CPs can be reliably achieved.
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Affiliation(s)
- Sean M McHugh
- Department of Chemistry, Tufts University, Medford, MA 02155, United States
| | - Julia R Rogers
- Department of Chemistry, Tufts University, Medford, MA 02155, United States
| | - Sarah A Solomon
- Department of Chemistry, Tufts University, Medford, MA 02155, United States
| | - Hongtao Yu
- Department of Chemistry, Tufts University, Medford, MA 02155, United States
| | - Yu-Shan Lin
- Department of Chemistry, Tufts University, Medford, MA 02155, United States.
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20
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Witek J, Keller BG, Blatter M, Meissner A, Wagner T, Riniker S. Kinetic Models of Cyclosporin A in Polar and Apolar Environments Reveal Multiple Congruent Conformational States. J Chem Inf Model 2016; 56:1547-62. [DOI: 10.1021/acs.jcim.6b00251] [Citation(s) in RCA: 73] [Impact Index Per Article: 9.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Jagna Witek
- Laboratory
of Physical Chemistry, ETH Zürich, Vladimir-Prelog-Weg 2, 8093 Zürich, Switzerland
| | - Bettina G. Keller
- Department
of Biology, Chemistry, Pharmacy, Freie Universität Berlin, Takustrasse 3, 14195 Berlin, Germany
| | - Markus Blatter
- Novartis
Institutes for BioMedical Research, Novartis Pharma AG, Novartis Campus, 4002 Basel, Switzerland
| | - Axel Meissner
- Novartis
Institutes for BioMedical Research, Novartis Pharma AG, Novartis Campus, 4002 Basel, Switzerland
| | - Trixie Wagner
- Novartis
Institutes for BioMedical Research, Novartis Pharma AG, Novartis Campus, 4002 Basel, Switzerland
| | - Sereina Riniker
- Laboratory
of Physical Chemistry, ETH Zürich, Vladimir-Prelog-Weg 2, 8093 Zürich, Switzerland
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21
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Nemmara VV, Nicholas RA, Pratt RF. Synthesis and Kinetic Analysis of Two Conformationally Restricted Peptide Substrates of Escherichia coli Penicillin-Binding Protein 5. Biochemistry 2016; 55:4065-76. [PMID: 27420403 DOI: 10.1021/acs.biochem.6b00576] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Escherichia coli PBP5 (penicillin-binding protein 5) is a dd-carboxypeptidase involved in bacterial cell wall maturation. Beyond the C-terminal d-alanyl-d-alanine moiety, PBP5, like the essential high-molecular mass PBPs, has little specificity for other elements of peptidoglycan structure, at least as elicited in vitro by small peptidoglycan fragments. On the basis of the crystal structure of a stem pentapeptide derivative noncovalently bound to E. coli PBP6 (Protein Data Bank entry 3ITB ), closely similar in structure to PBP5, we have modeled a pentapeptide structure at the active site of PBP5. Because the two termini of the pentapeptide are directed into solution in the PBP6 crystal structure, we then modeled a 19-membered cyclic peptide analogue by cross-linking the terminal amines by succinylation. An analogous smaller, 17-membered cyclic peptide, in which the l-lysine of the original was replaced by l-diaminobutyric acid, could also be modeled into the active site. We anticipated that, just as the reactivity of stem peptide fragments of peptidoglycan with PBPs in vivo may be entropically enhanced by immobilization in the polymer, so too would that of our cyclic peptides with respect to their acyclic analogues in vitro. This paper describes the synthesis of the peptides described above that were required to examine this hypothesis and presents an analysis of their structures and reaction kinetics with PBP5.
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Affiliation(s)
- Venkatesh V Nemmara
- Department of Chemistry, Wesleyan University , Lawn Avenue, Middletown, Connecticut 06459, United States
| | - Robert A Nicholas
- Department of Pharmacology, University of North Carolina at Chapel Hill , Chapel Hill, North Carolina 27599-7365, United States
| | - R F Pratt
- Department of Chemistry, Wesleyan University , Lawn Avenue, Middletown, Connecticut 06459, United States
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22
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Peraro L, Siegert TR, Kritzer JA. Conformational Restriction of Peptides Using Dithiol Bis-Alkylation. Methods Enzymol 2016; 580:303-32. [PMID: 27586339 DOI: 10.1016/bs.mie.2016.05.035] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
Macrocyclic peptides are highly promising as inhibitors of protein-protein interactions. While many bond-forming reactions can be used to make cyclic peptides, most have limitations that make this chemical space challenging to access. Recently, a variety of cysteine alkylation reactions have been used in rational design and library approaches for cyclic peptide discovery and development. We and others have found that this chemistry is versatile and robust enough to produce a large variety of conformationally constrained cyclic peptides. In this chapter, we describe applications, methods, mechanistic insights, and troubleshooting for dithiol bis-alkylation reactions for the production of cyclic peptides. This method for efficient solution-phase macrocyclization is highly useful for the rapid production and screening of loop-based inhibitors of protein-protein interactions.
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Affiliation(s)
- L Peraro
- Tufts University, Medford, MA, United States
| | - T R Siegert
- Tufts University, Medford, MA, United States
| | - J A Kritzer
- Tufts University, Medford, MA, United States.
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23
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Geng L, Wang Z, Jia X, Han Q, Xiang Z, Li D, Yang X, Zhang D, Bu X, Wang W, Hu Z, Fang Q. HER2 Targeting Peptides Screening and Applications in Tumor Imaging and Drug Delivery. Am J Cancer Res 2016; 6:1261-73. [PMID: 27279916 PMCID: PMC4893650 DOI: 10.7150/thno.14302] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2015] [Accepted: 04/14/2016] [Indexed: 01/22/2023] Open
Abstract
Herein, computational-aided one-bead-one-compound (OBOC) peptide library design combined with in situ single-bead sequencing microarray methods were successfully applied in screening peptides targeting at human epidermal growth factor receptor-2 (HER2), a biomarker of human breast cancer. As a result, 72 novel peptides clustered into three sequence motifs which are PYL***NP, YYL***NP and PPL***NP were acquired. Particularly one of the peptides, P51, has nanomolar affinity and high specificity for HER2 in ex vivo and in vivo tests. Moreover, doxorubicin (DOX)-loaded liposome nanoparticles were modified with peptide P51 or P25 and demonstrated to improve the targeted delivery against HER2 positive cells. Our study provides an efficient peptide screening method with a combination of techniques and the novel screened peptides with a clear binding site on HER2 can be used as probes for tumor imaging and targeted drug delivery.
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24
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Rodríguez-Espigares I, Kaczor AA, Selent J. In silico Exploration of the Conformational Universe of GPCRs. Mol Inform 2016; 35:227-37. [PMID: 27492237 DOI: 10.1002/minf.201600012] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2016] [Accepted: 04/14/2016] [Indexed: 12/17/2022]
Abstract
The structural plasticity of G protein coupled receptors (GPCRs) leads to a conformational universe going from inactive to active receptor states with several intermediate states. Many of them have not been captured yet and their role for GPCR activation is not well understood. The study of this conformational space and the transition dynamics between different receptor populations is a major challenge in molecular biophysics. The rational design of effector molecules that target such receptor populations allows fine-tuning receptor signalling with higher specificity to produce drugs with safer therapeutic profiles. In this minireview, we outline highly conserved receptor regions which are considered determinant for the establishment of distinct receptor states. We then discuss in-silico approaches such as dimensionality reduction methods and Markov State Models to explore the GPCR conformational universe and exploit the obtained conformations through structure-based drug design.
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Affiliation(s)
- Ismael Rodríguez-Espigares
- Pharmacoinformatics group, Research Programme on Biomedical Informatics (GRIB), Universitat Pompeu Fabra (UPF)-Hospital del Mar Medical Research Institute (IMIM), Parc de Recerca Biomèdica de Barcelona (PRBB), Dr. Aiguader, 88, 08003, Barcelona, Spain
| | - Agnieszka A Kaczor
- Department of Synthesis and Chemical Technology of Pharmaceutical Substances with Computer Modeling Lab, Faculty of Pharmacy with Division for Medical Analytics, Medical University of Lublin, 4A Chodźki St., PL-20059, Lublin, Poland.,School of Pharmacy, University of Eastern Finland, Yliopistonranta 1, P.O. Box 1627, FI-70211, Kuopio, Finland
| | - Jana Selent
- Pharmacoinformatics group, Research Programme on Biomedical Informatics (GRIB), Universitat Pompeu Fabra (UPF)-Hospital del Mar Medical Research Institute (IMIM), Parc de Recerca Biomèdica de Barcelona (PRBB), Dr. Aiguader, 88, 08003, Barcelona, Spain.
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25
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Geng H, Jiang F, Wu YD. Accurate Structure Prediction and Conformational Analysis of Cyclic Peptides with Residue-Specific Force Fields. J Phys Chem Lett 2016; 7:1805-10. [PMID: 27128113 DOI: 10.1021/acs.jpclett.6b00452] [Citation(s) in RCA: 42] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
Cyclic peptides (CPs) are promising candidates for drugs, chemical biology tools, and self-assembling nanomaterials. However, the development of reliable and accurate computational methods for their structure prediction has been challenging. Here, 20 all-trans CPs of 5-12 residues selected from Cambridge Structure Database have been simulated using replica-exchange molecular dynamics with four different force fields. Our recently developed residue-specific force fields RSFF1 and RSFF2 can correctly identify the crystal-like conformations of more than half CPs as the most populated conformation. The RSFF2 performs the best, which consistently predicts the crystal structures of 17 out of 20 CPs with rmsd < 1.1 Å. We also compared the backbone (ϕ, ψ) sampling of residues in CPs with those in short linear peptides and in globular proteins. In general, unlike linear peptides, CPs have local conformational free energies and entropies quite similar to globular proteins.
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Affiliation(s)
- Hao Geng
- Laboratory of Computational Chemistry and Drug Design, Laboratory of Chemical Genomics, Peking University Shenzhen Graduate School , Shenzhen 518055, China
| | - Fan Jiang
- Laboratory of Computational Chemistry and Drug Design, Laboratory of Chemical Genomics, Peking University Shenzhen Graduate School , Shenzhen 518055, China
| | - Yun-Dong Wu
- Laboratory of Computational Chemistry and Drug Design, Laboratory of Chemical Genomics, Peking University Shenzhen Graduate School , Shenzhen 518055, China
- College of Chemistry and Molecular Engineering, Peking University , Beijing 100871, China
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26
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McHugh SM, Rogers JR, Yu H, Lin YS. Insights into How Cyclic Peptides Switch Conformations. J Chem Theory Comput 2016; 12:2480-8. [DOI: 10.1021/acs.jctc.6b00193] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Sean M. McHugh
- Department
of Chemistry, Tufts University, Medford, Massachusetts 02155, United States
| | - Julia R. Rogers
- Department
of Chemistry, Tufts University, Medford, Massachusetts 02155, United States
| | - Hongtao Yu
- Department
of Chemistry, Tufts University, Medford, Massachusetts 02155, United States
| | - Yu-Shan Lin
- Department
of Chemistry, Tufts University, Medford, Massachusetts 02155, United States
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27
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Zhu L, Jiang H, Sheong FK, Cui X, Gao X, Wang Y, Huang X. A Flexible Domain-Domain Hinge Promotes an Induced-fit Dominant Mechanism for the Loading of Guide-DNA into Argonaute Protein in Thermus thermophilus. J Phys Chem B 2016; 120:2709-20. [PMID: 26908081 DOI: 10.1021/acs.jpcb.5b12426] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
Argonaute proteins (Ago) are core components of the RNA Induced Silencing Complex (RISC) that load and utilize small guide nucleic acids to silence mRNAs or cleave foreign DNAs. Despite the essential role of Ago in gene regulation and defense against virus, the molecular mechanism of guide-strand loading into Ago remains unclear. We explore such a mechanism in the bacterium Thermus thermophilus Ago (TtAgo), via a computational approach combining molecular dynamics, bias-exchange metadynamics, and protein-DNA docking. We show that apo TtAgo adopts multiple closed states that are unable to accommodate guide-DNA. Conformations able to accommodate the guide are beyond the reach of thermal fluctuations from the closed states. These results suggest an induced-fit dominant mechanism for guide-strand loading in TtAgo, drastically different from the two-step mechanism for human Ago 2 (hAgo2) identified in our previous study. Such a difference between TtAgo and hAgo2 is found to mainly originate from the distinct rigidity of their L1-PAZ hinge. Further comparison among known Ago structures from various species indicates that the L1-PAZ hinge may be flexible in general for prokaryotic Ago's but rigid for eukaryotic Ago's.
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Affiliation(s)
| | | | | | - Xuefeng Cui
- Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology , Thuwal 23955-6900, Saudi Arabia
| | - Xin Gao
- Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology , Thuwal 23955-6900, Saudi Arabia
| | - Yanli Wang
- Laboratory of Non-Coding RNA, Institute of Biophysics, Chinese Academy of Sciences , Beijing 100101, China
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28
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Zhou G, Voelz VA. Using Kinetic Network Models To Probe Non-Native Salt-Bridge Effects on α-Helix Folding. J Phys Chem B 2016; 120:926-35. [PMID: 26769494 DOI: 10.1021/acs.jpcb.5b11767] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Salt-bridge interactions play an important role in stabilizing many protein structures, and have been shown to be designable features for protein design. In this work, we study the effects of non-native salt bridges on the folding of a soluble alanine-based peptide (Fs peptide) using extensive all-atom molecular dynamics simulations performed on the Folding@home distributed computing platform. Using Markov State Models, we show how non-native salt-bridges affect the folding kinetics of Fs peptide by perturbing specific conformational states. Furthermore, we present methods for the automatic detection and analysis of such states. These results provide insight into helix folding mechanisms and useful information to guide simulation-based computational protein design.
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Affiliation(s)
- Guangfeng Zhou
- Department of Chemistry, Temple University , 1901 North 13th Street, Beury Hall, Philadelphia, Pennsylvania 19122, United States
| | - Vincent A Voelz
- Department of Chemistry, Temple University , 1901 North 13th Street, Beury Hall, Philadelphia, Pennsylvania 19122, United States
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29
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Abstract
Cyclic peptides are a promising class of molecules that can be used to target specific protein-protein interactions. A computational method to accurately predict their structures would substantially advance the development of cyclic peptides as modulators of protein-protein interactions. Here, we develop a computational method that integrates bias-exchange metadynamics simulations, a Boltzmann reweighting scheme, dihedral principal component analysis and a modified density peak-based cluster analysis to provide a converged structural description for cyclic peptides. Using this method, we evaluate the performance of a number of popular protein force fields on a model cyclic peptide. All the tested force fields seem to over-stabilize the α-helix and PPII/β regions in the Ramachandran plot, commonly populated by linear peptides and proteins. Our findings suggest that re-parameterization of a force field that well describes the full Ramachandran plot is necessary to accurately model cyclic peptides.
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Affiliation(s)
- Hongtao Yu
- Department of Chemistry, Tufts University, Medford, Massachusetts 02155, USA.
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30
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Jiang H, Sheong FK, Zhu L, Gao X, Bernauer J, Huang X. Markov State Models Reveal a Two-Step Mechanism of miRNA Loading into the Human Argonaute Protein: Selective Binding followed by Structural Re-arrangement. PLoS Comput Biol 2015; 11:e1004404. [PMID: 26181723 PMCID: PMC4504477 DOI: 10.1371/journal.pcbi.1004404] [Citation(s) in RCA: 48] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2015] [Accepted: 06/16/2015] [Indexed: 01/17/2023] Open
Abstract
Argonaute (Ago) proteins and microRNAs (miRNAs) are central components in RNA interference, which is a key cellular mechanism for sequence-specific gene silencing. Despite intensive studies, molecular mechanisms of how Ago recognizes miRNA remain largely elusive. In this study, we propose a two-step mechanism for this molecular recognition: selective binding followed by structural re-arrangement. Our model is based on the results of a combination of Markov State Models (MSMs), large-scale protein-RNA docking, and molecular dynamics (MD) simulations. Using MSMs, we identify an open state of apo human Ago-2 in fast equilibrium with partially open and closed states. Conformations in this open state are distinguished by their largely exposed binding grooves that can geometrically accommodate miRNA as indicated in our protein-RNA docking studies. miRNA may then selectively bind to these open conformations. Upon the initial binding, the complex may perform further structural re-arrangement as shown in our MD simulations and eventually reach the stable binary complex structure. Our results provide novel insights in Ago-miRNA recognition mechanisms and our methodology holds great potential to be widely applied in the studies of other important molecular recognition systems. In RNA interference, Argonaute proteins and microRNAs together form the functional core that regulates the gene expression with high sequence specificity. Elucidating the detailed mechanism of molecular recognition between Argonaute proteins and microRNAs is thus important not only for the fundamental understanding of RNA interference, but also for the further development of microRNA-based therapeutic application. In this work, we propose a two-step model to understand the mechanism of microRNA loading into human Argonaute-2: selective binding followed by structural re-arrangement. Our model is based on the results from a combined approach of molecular dynamics simulations, Markov State Models and protein-RNA docking. In particular, we identify a metastable open state of apo hAgo2 in rapid equilibrium with other states. Some of conformations in this open state have largely exposed RNA binding groove that can accommodate microRNA. We further show that the initial Argonaute-microRNA binding complex undergoes structural re-arrangement to reach stable binary crystal structure. These results provide novel insights into the underlying mechanism of Argonaute-microRNA recognition. In addition, our method is readily applicable to the investigation of other complex molecular recognition events such as protein-protein interactions and protein-ligand binding.
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Affiliation(s)
- Hanlun Jiang
- Bioengineering Graduate Program, Division of Biomedical Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong
- The HKUST Shenzhen Research Institute, Shenzhen, China
| | - Fu Kit Sheong
- Department of Chemistry, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong
| | - Lizhe Zhu
- The HKUST Shenzhen Research Institute, Shenzhen, China
- Department of Chemistry, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong
- Center of Systems Biology and Human Health, School of Science and Institute for Advance Study, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong
| | - Xin Gao
- Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
| | - Julie Bernauer
- Inria Saclay-Île de France, Bâtiment Alan Turing, Campus de l’École Polytechnique, Palaiseau, France
- Laboratoire d’Informatique de l’École Polytechnique (LIX), CNRS UMR 7161, École Polytechnique, Palaiseau, France
- * E-mail: (JB); (XH)
| | - Xuhui Huang
- Bioengineering Graduate Program, Division of Biomedical Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong
- The HKUST Shenzhen Research Institute, Shenzhen, China
- Department of Chemistry, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong
- Center of Systems Biology and Human Health, School of Science and Institute for Advance Study, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong
- * E-mail: (JB); (XH)
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31
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Razavi AM, Voelz VA. Kinetic Network Models of Tryptophan Mutations in β-Hairpins Reveal the Importance of Non-Native Interactions. J Chem Theory Comput 2015; 11:2801-12. [DOI: 10.1021/acs.jctc.5b00088] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Affiliation(s)
- Asghar M. Razavi
- Department
of Chemistry, Temple University, Philadelphia, Pennsylvania 19122, United States
| | - Vincent A. Voelz
- Department
of Chemistry, Temple University, Philadelphia, Pennsylvania 19122, United States
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32
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Wakefield AE, Wuest WM, Voelz VA. Molecular Simulation of Conformational Pre-Organization in Cyclic RGD Peptides. J Chem Inf Model 2015; 55:806-13. [PMID: 25741627 DOI: 10.1021/ci500768u] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
To test the ability of molecular simulations to accurately predict the solution-state conformational properties of peptidomimetics, we examined a test set of 18 cyclic RGD peptides selected from the literature, including the anticancer drug candidate cilengitide, whose favorable binding affinity to integrin has been ascribed to its pre-organization in solution. For each design, we performed all-atom replica-exchange molecular dynamics simulations over several microseconds and compared the results to extensive published NMR data. We find excellent agreement with experimental NOE distance restraints, suggesting that molecular simulation can be a useful tool for the computational design of pre-organized solution-state structure. Moreover, our analysis of conformational populations estimates that, despite the potential for increased flexibility due to backbone amide isomerizaton, N-methylation provides about 0.5 kcal/mol of reduced conformational entropy to cyclic RGD peptides. The combination of pre-organization and binding-site compatibility explains the strong binding affinity of cilengitide to integrin.
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Affiliation(s)
- Amanda E Wakefield
- Department of Chemistry, Temple University, Philadelphia, Pennsylvania 19122, United States
| | - William M Wuest
- Department of Chemistry, Temple University, Philadelphia, Pennsylvania 19122, United States
| | - Vincent A Voelz
- Department of Chemistry, Temple University, Philadelphia, Pennsylvania 19122, United States
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33
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Shukla D, Hernández CX, Weber JK, Pande VS. Markov state models provide insights into dynamic modulation of protein function. Acc Chem Res 2015; 48:414-22. [PMID: 25625937 PMCID: PMC4333613 DOI: 10.1021/ar5002999] [Citation(s) in RCA: 169] [Impact Index Per Article: 18.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
![]()
Protein
function is inextricably linked to protein dynamics. As we move from
a static structural picture to a dynamic ensemble view of protein
structure and function, novel computational paradigms are required
for observing and understanding conformational dynamics of proteins
and its functional implications. In principle, molecular dynamics
simulations can provide the time evolution of atomistic models of
proteins, but the long time scales associated with functional dynamics
make it difficult to observe rare dynamical transitions. The issue
of extracting essential functional components of protein dynamics
from noisy simulation data presents another set of challenges in obtaining
an unbiased understanding of protein motions. Therefore, a methodology
that provides a statistical framework for efficient sampling and a
human-readable view of the key aspects of functional dynamics from
data analysis is required. The Markov state model (MSM), which has
recently become popular worldwide for studying protein dynamics, is
an example of such a framework. In this Account, we review the
use of Markov state models for efficient sampling of the hierarchy
of time scales associated with protein dynamics, automatic identification
of key conformational states, and the degrees of freedom associated
with slow dynamical processes. Applications of MSMs for studying long
time scale phenomena such as activation mechanisms of cellular signaling
proteins has yielded novel insights into protein function. In particular,
from MSMs built using large-scale simulations of GPCRs and kinases,
we have shown that complex conformational changes in proteins can
be described in terms of structural changes in key structural motifs
or “molecular switches” within the protein, the transitions
between functionally active and inactive states of proteins proceed
via multiple pathways, and ligand or substrate binding modulates the
flux through these pathways. Finally, MSMs also provide a theoretical
toolbox for studying the effect of nonequilibrium perturbations on
conformational dynamics. Considering that protein dynamics in vivo
occur under nonequilibrium conditions, MSMs coupled with nonequilibrium
statistical mechanics provide a way to connect cellular components
to their functional environments. Nonequilibrium perturbations of
protein folding MSMs reveal the presence of dynamically frozen glass-like
states in their conformational landscape. These frozen states are
also observed to be rich in β-sheets, which indicates their
possible role in the nucleation of β-sheet rich aggregates such
as those observed in amyloid-fibril formation. Finally, we describe
how MSMs have been used to understand the dynamical behavior of intrinsically
disordered proteins such as amyloid-β, human islet amyloid polypeptide,
and p53. While certainly not a panacea for studying functional dynamics,
MSMs provide a rigorous theoretical foundation for understanding complex
entropically dominated processes and a convenient lens for viewing
protein motions.
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Affiliation(s)
- Diwakar Shukla
- Department of Chemistry, ‡Biophysics Program, and §SIMBIOS, NIH Center
for Biomedical Computation, Stanford University, Stanford, California 94305, United States
| | - Carlos X. Hernández
- Department of Chemistry, ‡Biophysics Program, and §SIMBIOS, NIH Center
for Biomedical Computation, Stanford University, Stanford, California 94305, United States
| | - Jeffrey K. Weber
- Department of Chemistry, ‡Biophysics Program, and §SIMBIOS, NIH Center
for Biomedical Computation, Stanford University, Stanford, California 94305, United States
| | - Vijay S. Pande
- Department of Chemistry, ‡Biophysics Program, and §SIMBIOS, NIH Center
for Biomedical Computation, Stanford University, Stanford, California 94305, United States
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34
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Schwantes C, Pande VS. Modeling molecular kinetics with tICA and the kernel trick. J Chem Theory Comput 2015; 11:600-8. [PMID: 26528090 PMCID: PMC4610300 DOI: 10.1021/ct5007357] [Citation(s) in RCA: 79] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2014] [Indexed: 11/28/2022]
Abstract
The allure of a molecular dynamics simulation is that, given a sufficiently accurate force field, it can provide an atomic-level view of many interesting phenomena in biology. However, the result of a simulation is a large, high-dimensional time series that is difficult to interpret. Recent work has introduced the time-structure based Independent Components Analysis (tICA) method for analyzing MD, which attempts to find the slowest decorrelating linear functions of the molecular coordinates. This method has been used in conjunction with Markov State Models (MSMs) to provide estimates of the characteristic eigenprocesses contained in a simulation (e.g., protein folding, ligand binding). Here, we extend the tICA method using the kernel trick to arrive at nonlinear solutions. This is a substantial improvement as it allows for kernel-tICA (ktICA) to provide estimates of the characteristic eigenprocesses directly without building an MSM.
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Affiliation(s)
- Christian
R. Schwantes
- Department of Chemistry, Department of Computer Science, Department of Structural Biology, and Program in Biophysics, Stanford University, Stanford, California 94305, United States
| | - Vijay S. Pande
- Department of Chemistry, Department of Computer Science, Department of Structural Biology, and Program in Biophysics, Stanford University, Stanford, California 94305, United States
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35
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Sheong FK, Silva DA, Meng L, Zhao Y, Huang X. Automatic state partitioning for multibody systems (APM): an efficient algorithm for constructing Markov state models to elucidate conformational dynamics of multibody systems. J Chem Theory Comput 2014; 11:17-27. [PMID: 26574199 DOI: 10.1021/ct5007168] [Citation(s) in RCA: 32] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
The conformational dynamics of multibody systems plays crucial roles in many important problems. Markov state models (MSMs) are powerful kinetic network models that can predict long-time-scale dynamics using many short molecular dynamics simulations. Although MSMs have been successfully applied to conformational changes of individual proteins, the analysis of multibody systems is still a challenge because of the complexity of the dynamics that occur on a mixture of drastically different time scales. In this work, we have developed a new algorithm, automatic state partitioning for multibody systems (APM), for constructing MSMs to elucidate the conformational dynamics of multibody systems. The APM algorithm effectively addresses different time scales in the multibody systems by directly incorporating dynamics into geometric clustering when identifying the metastable conformational states. We have applied the APM algorithm to a 2D potential that can mimic a protein-ligand binding system and the aggregation of two hydrophobic particles in water and have shown that it can yield tremendous enhancements in the computational efficiency of MSM construction and the accuracy of the models.
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Affiliation(s)
- Fu Kit Sheong
- HKUST Shenzhen Research Institute , Nanshan, Shenzhen 518057, China
| | | | - Luming Meng
- HKUST Shenzhen Research Institute , Nanshan, Shenzhen 518057, China
| | | | - Xuhui Huang
- HKUST Shenzhen Research Institute , Nanshan, Shenzhen 518057, China
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36
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Voelz VA, Elman B, Razavi AM, Zhou G. Surprisal Metrics for Quantifying Perturbed Conformational Dynamics in Markov State Models. J Chem Theory Comput 2014; 10:5716-28. [DOI: 10.1021/ct500827g] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Affiliation(s)
- Vincent A. Voelz
- Department of Chemistry, Temple University, Philadelphia, Pennsylvania 19122, United States
| | - Brandon Elman
- Department of Chemistry, Temple University, Philadelphia, Pennsylvania 19122, United States
| | - Asghar M. Razavi
- Department of Chemistry, Temple University, Philadelphia, Pennsylvania 19122, United States
| | - Guangfeng Zhou
- Department of Chemistry, Temple University, Philadelphia, Pennsylvania 19122, United States
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37
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Chatterjee D, Cooley RB, Boyd CD, Mehl RA, O'Toole GA, Sondermann H. Mechanistic insight into the conserved allosteric regulation of periplasmic proteolysis by the signaling molecule cyclic-di-GMP. eLife 2014; 3:e03650. [PMID: 25182848 PMCID: PMC4359373 DOI: 10.7554/elife.03650] [Citation(s) in RCA: 36] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] Open
Abstract
Stable surface adhesion of cells is one of the early pivotal steps in bacterial biofilm formation, a prevalent adaptation strategy in response to changing environments. In Pseudomonas fluorescens, this process is regulated by the Lap system and the second messenger cyclic-di-GMP. High cytoplasmic levels of cyclic-di-GMP activate the transmembrane receptor LapD that in turn recruits the periplasmic protease LapG, preventing it from cleaving a cell surface-bound adhesin, thereby promoting cell adhesion. In this study, we elucidate the molecular basis of LapG regulation by LapD and reveal a remarkably sensitive switching mechanism that is controlled by LapD's HAMP domain. LapD appears to act as a coincidence detector, whereby a weak interaction of LapG with LapD transmits a transient outside-in signal that is reinforced only when cyclic-di-GMP levels increase. Given the conservation of key elements of this receptor system in many bacterial species, the results are broadly relevant for cyclic-di-GMP- and HAMP domain-regulated transmembrane signaling. DOI:http://dx.doi.org/10.7554/eLife.03650.001 While bacteria often live as unicellular microorganisms, many bacteria are capable of sticking together on a surface and forming a multicellular structure called a biofilm. Bacterial biofilms occur frequently in nature; for example, on the roots of plants and submerged rocks. While these biofilms are generally innocuous, others pose significant health threats to humans, causing tooth decay, gum disease, and—when they occur on implanted devices such as prosthetic heart valves—potentially serious infections. When in biofilms, many bacteria are tolerant to antibiotics; therefore, working out how to disrupt these films is crucial for developing new treatments. The microorganism Pseudomonas fluorescens is an example of a bacterium that can be found living in a complex biofilm. In response to certain environmental cues, free-swimming P. fluorescens cells adhere to a surface and produce a slime that encases them in a robust biofilm. The decision to shift between a free-swimming and a biofilm life-style is orchestrated by a signaling molecule found inside the bacteria called cyclic-di-GMP. In P. fluorescens, the availability of nutrients—in particular, phosphate—controls how much cyclic-di-GMP is produced inside the cell. If not enough phosphate is available, the level of cyclic-di-GMP falls and the biofilm disperses. Cyclic-di-GMP affects the stability of the biofilm via a group of proteins called the Lap system. When levels of cyclic-di-GMP are high, cyclic-di-GMP binds to a protein called LapD, which can then in turn bind to an enzyme known as LapG. When bound to LapD, LapG is unable to break apart the molecules that help P. fluorescens cells bind to a surface, and so a biofilm can form. If cyclic-di-GMP levels drop, fewer LapD molecules can bind to cyclic-di-GMP. As cyclic-di-GMP-unbound LapD proteins interact poorly with LapG, this leaves some LapG molecules able to destabilize the attachments between the cells and the surface, which disperses the biofilm. Here, Chatterjee et al. reveal the molecular mechanism by which LapD and LapG interact in P. fluorescens. When cyclic-di-GMP is bound to LapD, the shape of LapD changes to produce features that fit into the surface of LapG. It is this shape compatibility, more so than an increase in the number or quality of interactions between the chemical groups that make up the proteins, that enables LapD to bind to LapG. Chatterjee et al. also provide evidence that the LapD–LapG interaction can be disrupted, thereby raising the possibility that biofilm formation could be manipulated by targeting this system. Given that systems similar to the P. fluorescens Lap system exist in numerous other bacterial species, including important pathogens, the findings of Chatterjee et al. could assist efforts to develop medicines and products that eradicate bacterial biofilms. LapD also shares many structural elements with a large number of other signaling proteins; therefore, these findings could also improve the understanding of how other cell signaling systems work. DOI:http://dx.doi.org/10.7554/eLife.03650.002
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Affiliation(s)
- Debashree Chatterjee
- Department of Molecular Medicine, College of Veterinary Medicine, Cornell University, Ithaca, United States
| | - Richard B Cooley
- Department of Molecular Medicine, College of Veterinary Medicine, Cornell University, Ithaca, United States
| | - Chelsea D Boyd
- Department of Microbiology and Immunology, Geisel School of Medicine at Dartmouth, Hanover, United States
| | - Ryan A Mehl
- Department of Biochemistry and Biophysics, Oregon State University, Corvallis, United States
| | - George A O'Toole
- Department of Microbiology and Immunology, Geisel School of Medicine at Dartmouth, Hanover, United States
| | - Holger Sondermann
- Department of Molecular Medicine, College of Veterinary Medicine, Cornell University, Ithaca, United States
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