1
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Hong X, Tong X, Xie J, Liu P, Liu X, Song Q, Liu S, Liu S. An updated dataset and a structure-based prediction model for protein-RNA binding affinity. Proteins 2023; 91:1245-1253. [PMID: 37186412 DOI: 10.1002/prot.26503] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 03/08/2023] [Accepted: 04/12/2023] [Indexed: 05/17/2023]
Abstract
Understanding the process of protein-RNA interaction is essential for structural biology. The thermodynamic process is an important part to uncover the protein-RNA interaction mechanism. The regulatory networks between protein and RNA in organisms are dominated by the binding or dissociation in the cells. Therefore, determining the binding affinity for protein-RNA complexes can help us to understand the regulation mechanism of protein-RNA interaction. Since it is time-consuming and labor-intensive to determine the binding affinity for protein-RNA complexes by experimental methods, it is necessary and urgent to develop computational methods to predict that. To develop a binding affinity prediction model, first we update the dataset of protein-RNA binding affinity benchmark (PRBAB), which includes 145 complexes now. Second, we extract the structural features based on complex structure, and then we analyze and select the representative structural features to train the regression model. Third, we random select the subset from the PRBAB2.0 to fit the protein-RNA binding affinity determined by experiment. In the end, we tested our model on the nonredundant PDBbind dataset, and the results showed that Pearson correlation coefficient r = .57 and RMSE = 2.51 kcal/mol. The Pearson correlation coefficient achieves 0.7 while removing 5 complex structures with modified residues/nucleotides and metal ions. While testing on ProNAB, the results showed that 71.60% of the prediction achieves Pearson correlation coefficient r = .61 and RMSE = 1.56 kcal/mol with experiment values.
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Affiliation(s)
- Xu Hong
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Xiaoxue Tong
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Juan Xie
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Pinyu Liu
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Xudong Liu
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Qi Song
- Key Laboratory of Fermentation Engineering (Ministry of Education), Hubei University of Technology, Wuhan, China
| | - Sen Liu
- Key Laboratory of Fermentation Engineering (Ministry of Education), Hubei University of Technology, Wuhan, China
| | - Shiyong Liu
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, China
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2
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Imiołek M, Winssinger N. Two-Helix Supramolecular Proteomimetic Binders Assembled via PNA-Assisted Disulfide Crosslinking. Chembiochem 2023; 24:e202200561. [PMID: 36349499 DOI: 10.1002/cbic.202200561] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2022] [Revised: 11/05/2022] [Indexed: 11/10/2022]
Abstract
Peptidic motifs folded in a defined conformation are able to inhibit protein-protein interactions (PPIs) covering large interfaces and as such they are biomedical molecules of interest. Mimicry of such natural structures with synthetically tractable constructs often requires complex scaffolding and extensive optimization to preserve the fidelity of binding to the target. Here, we present a novel proteomimetic strategy based on a 2-helix binding motif that is brought together by hybridization of peptide nucleic acids (PNA) and stabilized by a rationally positioned intermolecular disulfide crosslink. Using a solid phase synthesis approach (SPPS), the building blocks are easily accessible and such supramolecular peptide-PNA helical hybrids could be further coiled using precise templated chemistry. The elaboration of the structural design afforded high affinity SARS CoV-2 RBD (receptor binding domain) binders without interference with the underlying peptide sequence, creating a basis for a new architecture of supramolecular proteomimetics.
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Affiliation(s)
- Mateusz Imiołek
- Department of Organic Chemistry, Faculty of Science, NCCR Chemical Biology, University of Geneva, 1211, Geneva, Switzerland
| | - Nicolas Winssinger
- Department of Organic Chemistry, Faculty of Science, NCCR Chemical Biology, University of Geneva, 1211, Geneva, Switzerland
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3
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Yang YX, Wang P, Zhu BT. Relative importance of interface and surface areas in protein-protein binding affinity prediction: A machine learning analysis based on linear regression and artificial neural network. Biophys Chem 2022; 283:106762. [DOI: 10.1016/j.bpc.2022.106762] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Revised: 01/11/2022] [Accepted: 01/14/2022] [Indexed: 11/02/2022]
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4
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Dhusia K, Wu Y. Classification of protein-protein association rates based on biophysical informatics. BMC Bioinformatics 2021; 22:408. [PMID: 34404340 PMCID: PMC8371850 DOI: 10.1186/s12859-021-04323-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2020] [Accepted: 08/10/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Proteins form various complexes to carry out their versatile functions in cells. The dynamic properties of protein complex formation are mainly characterized by the association rates which measures how fast these complexes can be formed. It was experimentally observed that the association rates span an extremely wide range with over ten orders of magnitudes. Identification of association rates within this spectrum for specific protein complexes is therefore essential for us to understand their functional roles. RESULTS To tackle this problem, we integrate physics-based coarse-grained simulations into a neural-network-based classification model to estimate the range of association rates for protein complexes in a large-scale benchmark set. The cross-validation results show that, when an optimal threshold was selected, we can reach the best performance with specificity, precision, sensitivity and overall accuracy all higher than 70%. The quality of our cross-validation data has also been testified by further statistical analysis. Additionally, given an independent testing set, we can successfully predict the group of association rates for eight protein complexes out of ten. Finally, the analysis of failed cases suggests the future implementation of conformational dynamics into simulation can further improve model. CONCLUSIONS In summary, this study demonstrated that a new modeling framework that combines biophysical simulations with bioinformatics approaches is able to identify protein-protein interactions with low association rates from those with higher association rates. This method thereby can serve as a useful addition to a collection of existing experimental approaches that measure biomolecular recognition.
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Affiliation(s)
- Kalyani Dhusia
- Department of Systems and Computational Biology, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, NY, 10461, USA
| | - Yinghao Wu
- Department of Systems and Computational Biology, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, NY, 10461, USA.
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5
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Dhusia K, Su Z, Wu Y. Understanding the Impacts of Conformational Dynamics on the Regulation of Protein-Protein Association by a Multiscale Simulation Method. J Chem Theory Comput 2020; 16:5323-5333. [PMID: 32667783 PMCID: PMC10829009 DOI: 10.1021/acs.jctc.0c00439] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Complexes formed among diverse proteins carry out versatile functions in nearly all physiological processes. Association rates which measure how fast proteins form various complexes are of fundamental importance to characterize their functions. The association rates are not only determined by the energetic features at binding interfaces of a protein complex but also influenced by the intrinsic conformational dynamics of each protein in the complex. Unfortunately, how this conformational effect regulates protein association has never been calibrated on a systematic level. To tackle this problem, we developed a multiscale strategy to incorporate the information on protein conformational variations from Langevin dynamic simulations into a kinetic Monte Carlo algorithm of protein-protein association. By systematically testing this approach against a large-scale benchmark set, we found the association of a protein complex with a relatively rigid structure tends to be reduced by its conformational fluctuations. With specific examples, we further show that higher degrees of structural flexibility in various protein complexes can facilitate the searching and formation of intermolecular interactions and thereby accelerate their associations. In general, the integration of conformational dynamics can improve the correlation between experimentally measured association rates and computationally derived association probabilities. Finally, we analyzed the statistical distributions of different secondary structural types on protein-protein binding interfaces and their preference to the change of association rates. Our study, to the best of our knowledge, is the first computational method that systematically estimates the impacts of protein conformational dynamics on protein-protein association. It throws lights on the molecular mechanisms of how protein-protein recognition is kinetically modulated.
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Affiliation(s)
- Kalyani Dhusia
- Department of Systems and Computational Biology, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, NY, 10461
| | - Zhaoqian Su
- Department of Systems and Computational Biology, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, NY, 10461
| | - Yinghao Wu
- Department of Systems and Computational Biology, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, NY, 10461
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6
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Using Coarse-Grained Simulations to Characterize the Mechanisms of Protein-Protein Association. Biomolecules 2020; 10:biom10071056. [PMID: 32679892 PMCID: PMC7407674 DOI: 10.3390/biom10071056] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2020] [Revised: 07/10/2020] [Accepted: 07/13/2020] [Indexed: 12/22/2022] Open
Abstract
The formation of functionally versatile protein complexes underlies almost every biological process. The estimation of how fast these complexes can be formed has broad implications for unravelling the mechanism of biomolecular recognition. This kinetic property is traditionally quantified by association rates, which can be measured through various experimental techniques. To complement these time-consuming and labor-intensive approaches, we developed a coarse-grained simulation approach to study the physical processes of protein–protein association. We systematically calibrated our simulation method against a large-scale benchmark set. By combining a physics-based force field with a statistically-derived potential in the simulation, we found that the association rates of more than 80% of protein complexes can be correctly predicted within one order of magnitude relative to their experimental measurements. We further showed that a mixture of force fields derived from complementary sources was able to describe the process of protein–protein association with mechanistic details. For instance, we show that association of a protein complex contains multiple steps in which proteins continuously search their local binding orientations and form non-native-like intermediates through repeated dissociation and re-association. Moreover, with an ensemble of loosely bound encounter complexes observed around their native conformation, we suggest that the transition states of protein–protein association could be highly diverse on the structural level. Our study also supports the idea in which the association of a protein complex is driven by a “funnel-like” energy landscape. In summary, these results shed light on our understanding of how protein–protein recognition is kinetically modulated, and our coarse-grained simulation approach can serve as a useful addition to the existing experimental approaches that measure protein–protein association rates.
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7
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Norval LW, Krämer SD, Gao M, Herz T, Li J, Rath C, Wöhrle J, Günther S, Roth G. KOFFI and Anabel 2.0-a new binding kinetics database and its integration in an open-source binding analysis software. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2020; 2019:5585575. [PMID: 31608948 PMCID: PMC6790968 DOI: 10.1093/database/baz101] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/25/2019] [Revised: 07/12/2019] [Accepted: 07/23/2019] [Indexed: 12/31/2022]
Abstract
The kinetics of featured interactions (KOFFI) database is a novel tool and resource for binding kinetics data from biomolecular interactions. While binding kinetics data are abundant in literature, finding valuable information is a laborious task. We used text extraction methods to store binding rates (association, dissociation) as well as corresponding meta-information (e.g. methods, devices) in a novel database. To date, over 270 articles were manually curated and binding data on over 1705 interactions was collected and stored in the (KOFFI) database. Moreover, the KOFFI database application programming interface was implemented in Anabel (open-source software for the analysis of binding interactions), enabling users to directly compare their own binding data analyses with related experiments described in the database.
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Affiliation(s)
- Leo William Norval
- ZBSA Center for Biological Systems Analysis, Albert-Ludwigs-University Freiburg, Habsburgerstrasse 49, D-79104 Freiburg, Germany.,Institute of Pharmaceutical Sciences, Pharmaceutical Bioinformatics, Albert-Ludwigs-University Freiburg, Hermann-Herder-Straße 9, D-79104 Freiburg, Germany
| | - Stefan Daniel Krämer
- ZBSA Center for Biological Systems Analysis, Albert-Ludwigs-University Freiburg, Habsburgerstrasse 49, D-79104 Freiburg, Germany.,Faculty for Biology, Albert-Ludwigs-University Freiburg, Schaenzlestrasse 1, D-79104 Freiburg, Germany
| | - Mingjie Gao
- Institute of Pharmaceutical Sciences, Pharmaceutical Bioinformatics, Albert-Ludwigs-University Freiburg, Hermann-Herder-Straße 9, D-79104 Freiburg, Germany
| | - Tobias Herz
- ZBSA Center for Biological Systems Analysis, Albert-Ludwigs-University Freiburg, Habsburgerstrasse 49, D-79104 Freiburg, Germany.,Faculty for Biology, Albert-Ludwigs-University Freiburg, Schaenzlestrasse 1, D-79104 Freiburg, Germany
| | - Jianyu Li
- Institute of Pharmaceutical Sciences, Pharmaceutical Bioinformatics, Albert-Ludwigs-University Freiburg, Hermann-Herder-Straße 9, D-79104 Freiburg, Germany
| | - Christin Rath
- ZBSA Center for Biological Systems Analysis, Albert-Ludwigs-University Freiburg, Habsburgerstrasse 49, D-79104 Freiburg, Germany.,Faculty for Biology, Albert-Ludwigs-University Freiburg, Schaenzlestrasse 1, D-79104 Freiburg, Germany.,BioCopy GmbH, Spechtweg 25, D-79110 Freiburg, Germany.,BIOSS Center for Biological Signalling Studies, Albert-Ludwigs-University Freiburg, Schänzlestrasse 18, D-79104 Freiburg, Germany
| | - Johannes Wöhrle
- ZBSA Center for Biological Systems Analysis, Albert-Ludwigs-University Freiburg, Habsburgerstrasse 49, D-79104 Freiburg, Germany.,IMTEK Department of Microsystems Engineering, Albert-Ludwigs-University Freiburg, Georges-Köhler-Allee 103, D-79110 Freiburg, Germany
| | - Stefan Günther
- Institute of Pharmaceutical Sciences, Pharmaceutical Bioinformatics, Albert-Ludwigs-University Freiburg, Hermann-Herder-Straße 9, D-79104 Freiburg, Germany
| | - Günter Roth
- ZBSA Center for Biological Systems Analysis, Albert-Ludwigs-University Freiburg, Habsburgerstrasse 49, D-79104 Freiburg, Germany.,Faculty for Biology, Albert-Ludwigs-University Freiburg, Schaenzlestrasse 1, D-79104 Freiburg, Germany.,BioCopy GmbH, Spechtweg 25, D-79110 Freiburg, Germany.,BIOSS Center for Biological Signalling Studies, Albert-Ludwigs-University Freiburg, Schänzlestrasse 18, D-79104 Freiburg, Germany
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8
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Raucci R, Laine E, Carbone A. Local Interaction Signal Analysis Predicts Protein-Protein Binding Affinity. Structure 2018; 26:905-915.e4. [PMID: 29779789 DOI: 10.1016/j.str.2018.04.006] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2017] [Revised: 02/06/2018] [Accepted: 04/10/2018] [Indexed: 12/27/2022]
Abstract
Several models estimating the strength of the interaction between proteins in a complex have been proposed. By exploring the geometry of contact distribution at protein-protein interfaces, we provide an improved model of binding energy. Local interaction signal analysis (LISA) is a radial function based on terms describing favorable and non-favorable contacts obtained by density functional theory, the support-core-rim interface residue distribution, non-interacting charged residues and secondary structures contribution. The three-dimensional organization of the contacts and their contribution on localized hot-sites over the entire interaction surface were numerically evaluated. LISA achieves a correlation of 0.81 (and a root-mean-square error of 2.35 ± 0.38 kcal/mol) when tested on 125 complexes for which experimental measurements were realized. LISA's performance is stable for subsets defined by functional composition and extent of conformational changes upon complex formation. A large-scale comparison with 17 other functions demonstrated the power of the geometrical model in the understanding of complex binding.
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Affiliation(s)
- Raffaele Raucci
- Sorbonne Université, CNRS, IBPS, Laboratoire de Biologie Computationnelle et Quantitative (LCQB), 4 Place Jussieu, 75005 Paris, France; Sorbonne Université, Institut des Sciences du Calcul et des Données (ISCD), 75005 Paris, France
| | - Elodie Laine
- Sorbonne Université, CNRS, IBPS, Laboratoire de Biologie Computationnelle et Quantitative (LCQB), 4 Place Jussieu, 75005 Paris, France
| | - Alessandra Carbone
- Sorbonne Université, CNRS, IBPS, Laboratoire de Biologie Computationnelle et Quantitative (LCQB), 4 Place Jussieu, 75005 Paris, France; Institut Universitaire de France, 75005 Paris, France.
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9
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Škrbić T, Zamuner S, Hong R, Seno F, Laio A, Trovato A. Vibrational entropy estimation can improve binding affinity prediction for non-obligatory protein complexes. Proteins 2018; 86:393-404. [DOI: 10.1002/prot.25454] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2017] [Revised: 12/22/2017] [Accepted: 01/05/2018] [Indexed: 01/10/2023]
Affiliation(s)
- Tatjana Škrbić
- Faculty of Physics; International School for Advanced Studies (SISSA/ISAS); Trieste Italy
- Department of Physics and Astronomy “Galileo Galilei”; University of Padova; Padova Italy
| | - Stefano Zamuner
- Department of Physics and Astronomy “Galileo Galilei”; University of Padova; Padova Italy
| | - Rolando Hong
- Faculty of Physics; International School for Advanced Studies (SISSA/ISAS); Trieste Italy
| | - Flavio Seno
- Department of Physics and Astronomy “Galileo Galilei”; University of Padova; Padova Italy
- Padova Section, National Institute of Nuclear Physics (INFN); Padova Italy
| | - Alessandro Laio
- Faculty of Physics; International School for Advanced Studies (SISSA/ISAS); Trieste Italy
| | - Antonio Trovato
- Department of Physics and Astronomy “Galileo Galilei”; University of Padova; Padova Italy
- Padova Section, National Institute of Nuclear Physics (INFN); Padova Italy
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10
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Zhou HX, Pang X. Electrostatic Interactions in Protein Structure, Folding, Binding, and Condensation. Chem Rev 2018; 118:1691-1741. [PMID: 29319301 DOI: 10.1021/acs.chemrev.7b00305] [Citation(s) in RCA: 454] [Impact Index Per Article: 75.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Charged and polar groups, through forming ion pairs, hydrogen bonds, and other less specific electrostatic interactions, impart important properties to proteins. Modulation of the charges on the amino acids, e.g., by pH and by phosphorylation and dephosphorylation, have significant effects such as protein denaturation and switch-like response of signal transduction networks. This review aims to present a unifying theme among the various effects of protein charges and polar groups. Simple models will be used to illustrate basic ideas about electrostatic interactions in proteins, and these ideas in turn will be used to elucidate the roles of electrostatic interactions in protein structure, folding, binding, condensation, and related biological functions. In particular, we will examine how charged side chains are spatially distributed in various types of proteins and how electrostatic interactions affect thermodynamic and kinetic properties of proteins. Our hope is to capture both important historical developments and recent experimental and theoretical advances in quantifying electrostatic contributions of proteins.
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Affiliation(s)
- Huan-Xiang Zhou
- Department of Chemistry and Department of Physics, University of Illinois at Chicago , Chicago, Illinois 60607, United States.,Department of Physics and Institute of Molecular Biophysics, Florida State University , Tallahassee, Florida 32306, United States
| | - Xiaodong Pang
- Department of Physics and Institute of Molecular Biophysics, Florida State University , Tallahassee, Florida 32306, United States
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11
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Jaruszewicz-Błońska J, Lipniacki T. Genetic toggle switch controlled by bacterial growth rate. BMC SYSTEMS BIOLOGY 2017; 11:117. [PMID: 29197392 PMCID: PMC5712128 DOI: 10.1186/s12918-017-0483-4] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/20/2017] [Accepted: 11/09/2017] [Indexed: 01/27/2023]
Abstract
Background In favorable conditions bacterial doubling time is less than 20 min, shorter than DNA replication time. In E. coli a single round of genome replication lasts about 40 min and it must be accomplished about 20 min before cell division. To achieve such fast growth rates bacteria perform multiple replication rounds simultaneously. As a result, when the division time is as short as 20 min E. coli has about 8 copies of origin of replication (ori) and the average copy number of the genes situated close to ori can be 4 times larger than those near the terminus of replication (ter). It implies that shortening of cell cycle may influence dynamics of regulatory pathways involving genes placed at distant loci. Results We analyze this effect in a model of a genetic toggle switch, i.e. a system of two mutually repressing genes, one localized in the vicinity of ori and the other localized in the vicinity of ter. Using a stochastic model that accounts for cell growth and divisions we demonstrate that shortening of the cell cycle can induce switching of the toggle to the state in which expression of the gene placed near ter is suppressed. The toggle bistability causes that the ratio of expression of the competing genes changes more than two orders of magnitude for a two-fold change of the doubling time. The increasing stability of the two toggle states enhances system sensitivity but also its reaction time. Conclusions By fusing the competing genes with fluorescent tags this mechanism could be tested and employed to create an indicator of the doubling time. By manipulating copy numbers of the competing genes and locus of the gene situated near ter, one can obtain equal average expression of both genes for any doubling time T between 20 and 120 min. Such a toggle would accurately report departures of the doubling time from T. Electronic supplementary material The online version of this article (doi:10.1186/s12918-017-0483-4) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Joanna Jaruszewicz-Błońska
- Institute of Fundamental Technological Research, Polish Academy of Sciences, Pawińskiego 5B, Warsaw, 02-106, Poland.
| | - Tomasz Lipniacki
- Institute of Fundamental Technological Research, Polish Academy of Sciences, Pawińskiego 5B, Warsaw, 02-106, Poland
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12
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Xie ZR, Chen J, Wu Y. Predicting Protein-protein Association Rates using Coarse-grained Simulation and Machine Learning. Sci Rep 2017; 7:46622. [PMID: 28418043 PMCID: PMC5394550 DOI: 10.1038/srep46622] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2016] [Accepted: 03/21/2017] [Indexed: 12/20/2022] Open
Abstract
Protein–protein interactions dominate all major biological processes in living cells. We have developed a new Monte Carlo-based simulation algorithm to study the kinetic process of protein association. We tested our method on a previously used large benchmark set of 49 protein complexes. The predicted rate was overestimated in the benchmark test compared to the experimental results for a group of protein complexes. We hypothesized that this resulted from molecular flexibility at the interface regions of the interacting proteins. After applying a machine learning algorithm with input variables that accounted for both the conformational flexibility and the energetic factor of binding, we successfully identified most of the protein complexes with overestimated association rates and improved our final prediction by using a cross-validation test. This method was then applied to a new independent test set and resulted in a similar prediction accuracy to that obtained using the training set. It has been thought that diffusion-limited protein association is dominated by long-range interactions. Our results provide strong evidence that the conformational flexibility also plays an important role in regulating protein association. Our studies provide new insights into the mechanism of protein association and offer a computationally efficient tool for predicting its rate.
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Affiliation(s)
- Zhong-Ru Xie
- Department of Systems and Computational Biology, Albert Einstein College of Medicine, Yeshiva University, 1300 Morris Park Avenue, Bronx, NY, 10461, USA
| | - Jiawen Chen
- Department of Systems and Computational Biology, Albert Einstein College of Medicine, Yeshiva University, 1300 Morris Park Avenue, Bronx, NY, 10461, USA
| | - Yinghao Wu
- Department of Systems and Computational Biology, Albert Einstein College of Medicine, Yeshiva University, 1300 Morris Park Avenue, Bronx, NY, 10461, USA
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13
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Chiu SH, Xie L. Toward High-Throughput Predictive Modeling of Protein Binding/Unbinding Kinetics. J Chem Inf Model 2016; 56:1164-74. [PMID: 27159844 DOI: 10.1021/acs.jcim.5b00632] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
One of the unaddressed challenges in drug discovery is that drug potency determined in vitro is not a reliable indicator of drug activity in vivo. Accumulated evidence suggests that in vivo activity is more strongly correlated with the binding/unbinding kinetics than the equilibrium thermodynamics of protein-ligand interactions (PLIs). However, existing experimental and computational techniques are insufficient in studying the molecular details of kinetics processes of PLIs on a large scale. Consequently, we not only have limited mechanistic understanding of the kinetic processes but also lack a practical platform for high-throughput screening and optimization of drug leads on the basis of their kinetic properties. For the first time, we address this unmet need by integrating coarse-grained normal mode analysis with multitarget machine learning (MTML). To test our method, HIV-1 protease is used as a model system. We find that computational models based on the residue normal mode directionality displacement of PLIs can not only recapitulate the results from all-atom molecular dynamics simulations but also predict protein-ligand binding/unbinding kinetics accurately. When this is combined with energetic features, the accuracy of combined kon and koff prediction reaches 74.35%. Furthermore, our integrated model provides us with new insights into the molecular determinants of the kinetics of PLIs. We propose that the coherent coupling of conformational dynamics and thermodynamic interactions between the receptor and the ligand may play a critical role in determining the kinetic rate constants of PLIs. In conclusion, we demonstrate that residue normal mode directionality displacement can serve as a kinetic fingerprint to capture long-time-scale conformational dynamics of the binding/unbinding kinetics. When this is coupled with MTML, it is possible to screen and optimize compounds on the basis of their binding/unbinding kinetics in a high-throughput fashion. The further development of such computational tools will bridge one of the critical missing links between in vitro compound screening and in vivo drug activity.
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Affiliation(s)
- See Hong Chiu
- Department of Computer Science, The Graduate Center, The City University of New York , 365 Fifth Avenue, New York, New York 10016, United States
| | - Lei Xie
- Department of Computer Science, The Graduate Center, The City University of New York , 365 Fifth Avenue, New York, New York 10016, United States.,Department of Computer Science, Hunter College, The City University of New York , 695 Park Avenue, New York, New York 10065, United States
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14
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Cao H, Huang Y, Liu Z. Interplay between binding affinity and kinetics in protein-protein interactions. Proteins 2016; 84:920-33. [PMID: 27018856 DOI: 10.1002/prot.25041] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2015] [Revised: 01/24/2016] [Accepted: 03/17/2016] [Indexed: 12/18/2022]
Abstract
To clarify the interplay between the binding affinity and kinetics of protein-protein interactions, and the possible role of intrinsically disordered proteins in such interactions, molecular simulations were carried out on 20 protein complexes. With bias potential and reweighting techniques, the free energy profiles were obtained under physiological affinities, which showed that the bound-state valley is deep with a barrier height of 12 - 33 RT. From the dependence of the affinity on interface interactions, the entropic contribution to the binding affinity is approximated to be proportional to the interface area. The extracted dissociation rates based on the Arrhenius law correlate reasonably well with the experimental values (Pearson correlation coefficient R = 0.79). For each protein complex, a linear free energy relationship between binding affinity and the dissociation rate was confirmed, but the distribution of the slopes for intrinsically disordered proteins showed no essential difference with that observed for ordered proteins. A comparison with protein folding was also performed. Proteins 2016; 84:920-933. © 2016 Wiley Periodicals, Inc.
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Affiliation(s)
- Huaiqing Cao
- College of Chemistry and Molecular Engineering, Peking University, Beijing, 100871, China.,Center for Quantitative Biology, and Beijing National Laboratory for Molecular Sciences (BNLMS), Peking University, Beijing, 100871, China
| | - Yongqi Huang
- Institute of Theoretical Chemistry, School of Chemistry and Chemical Engineering, Shandong University, Jinan, 250100, China
| | - Zhirong Liu
- College of Chemistry and Molecular Engineering, Peking University, Beijing, 100871, China.,Center for Quantitative Biology, and Beijing National Laboratory for Molecular Sciences (BNLMS), Peking University, Beijing, 100871, China
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15
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Das M, Basu G. Protein-protein association rates captured in a single geometric parameter. Proteins 2015; 83:1557-62. [DOI: 10.1002/prot.24860] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2015] [Revised: 06/04/2015] [Accepted: 07/02/2015] [Indexed: 02/05/2023]
Affiliation(s)
- Madhurima Das
- Department of Biophysics; Bose Institute; P-1/12 CIT Scheme VIIM Kolkata 700054 India
| | - Gautam Basu
- Department of Biophysics; Bose Institute; P-1/12 CIT Scheme VIIM Kolkata 700054 India
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16
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Vangone A, Bonvin AM. Contacts-based prediction of binding affinity in protein-protein complexes. eLife 2015. [PMID: 26193119 DOI: 10.7554/elife07454] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/12/2023] Open
Abstract
Almost all critical functions in cells rely on specific protein-protein interactions. Understanding these is therefore crucial in the investigation of biological systems. Despite all past efforts, we still lack a thorough understanding of the energetics of association of proteins. Here, we introduce a new and simple approach to predict binding affinity based on functional and structural features of the biological system, namely the network of interfacial contacts. We assess its performance against a protein-protein binding affinity benchmark and show that both experimental methods used for affinity measurements and conformational changes have a strong impact on prediction accuracy. Using a subset of complexes with reliable experimental binding affinities and combining our contacts and contact-types-based model with recent observations on the role of the non-interacting surface in protein-protein interactions, we reach a high prediction accuracy for such a diverse dataset outperforming all other tested methods.
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Affiliation(s)
- Anna Vangone
- Computational Structural Biology Group, Bijvoet Center for Biomolecular Research, Faculty of Science-Chemistry, Utrecht University, Utrecht, Netherlands
| | - Alexandre Mjj Bonvin
- Computational Structural Biology Group, Bijvoet Center for Biomolecular Research, Faculty of Science-Chemistry, Utrecht University, Utrecht, Netherlands
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17
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Vangone A, Bonvin AMJJ. Contacts-based prediction of binding affinity in protein-protein complexes. eLife 2015; 4:e07454. [PMID: 26193119 PMCID: PMC4523921 DOI: 10.7554/elife.07454] [Citation(s) in RCA: 313] [Impact Index Per Article: 34.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2015] [Accepted: 07/08/2015] [Indexed: 12/13/2022] Open
Abstract
Almost all critical functions in cells rely on specific protein-protein interactions. Understanding these is therefore crucial in the investigation of biological systems. Despite all past efforts, we still lack a thorough understanding of the energetics of association of proteins. Here, we introduce a new and simple approach to predict binding affinity based on functional and structural features of the biological system, namely the network of interfacial contacts. We assess its performance against a protein-protein binding affinity benchmark and show that both experimental methods used for affinity measurements and conformational changes have a strong impact on prediction accuracy. Using a subset of complexes with reliable experimental binding affinities and combining our contacts and contact-types-based model with recent observations on the role of the non-interacting surface in protein-protein interactions, we reach a high prediction accuracy for such a diverse dataset outperforming all other tested methods.
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Affiliation(s)
- Anna Vangone
- Computational Structural Biology Group, Bijvoet Center for Biomolecular Research, Faculty of Science—Chemistry, Utrecht University, Utrecht, Netherlands
| | - Alexandre MJJ Bonvin
- Computational Structural Biology Group, Bijvoet Center for Biomolecular Research, Faculty of Science—Chemistry, Utrecht University, Utrecht, Netherlands
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18
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Pei J, Yin N, Ma X, Lai L. Systems Biology Brings New Dimensions for Structure-Based Drug Design. J Am Chem Soc 2014; 136:11556-65. [DOI: 10.1021/ja504810z] [Citation(s) in RCA: 64] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Affiliation(s)
- Jianfeng Pei
- Center
for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
| | - Ning Yin
- Center
for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
| | - Xiaomin Ma
- Center
for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
| | - Luhua Lai
- Center
for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
- Beijing
National Laboratory for Molecular Science, State Key Laboratory for
Structural Chemistry of Unstable and Stable Species, College of Chemistry
and Molecular Engineering, Peking University, Beijing 100871, China
- Peking-Tsinghua
Center for Life Sciences, Peking University, Beijing 100871, China
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19
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Computational and experimental approaches to reveal the effects of single nucleotide polymorphisms with respect to disease diagnostics. Int J Mol Sci 2014; 15:9670-717. [PMID: 24886813 PMCID: PMC4100115 DOI: 10.3390/ijms15069670] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2014] [Revised: 05/15/2014] [Accepted: 05/16/2014] [Indexed: 12/25/2022] Open
Abstract
DNA mutations are the cause of many human diseases and they are the reason for natural differences among individuals by affecting the structure, function, interactions, and other properties of DNA and expressed proteins. The ability to predict whether a given mutation is disease-causing or harmless is of great importance for the early detection of patients with a high risk of developing a particular disease and would pave the way for personalized medicine and diagnostics. Here we review existing methods and techniques to study and predict the effects of DNA mutations from three different perspectives: in silico, in vitro and in vivo. It is emphasized that the problem is complicated and successful detection of a pathogenic mutation frequently requires a combination of several methods and a knowledge of the biological phenomena associated with the corresponding macromolecules.
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20
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Xie L, Ge X, Tan H, Xie L, Zhang Y, Hart T, Yang X, Bourne PE. Towards structural systems pharmacology to study complex diseases and personalized medicine. PLoS Comput Biol 2014; 10:e1003554. [PMID: 24830652 PMCID: PMC4022462 DOI: 10.1371/journal.pcbi.1003554] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
Genome-Wide Association Studies (GWAS), whole genome sequencing, and high-throughput omics techniques have generated vast amounts of genotypic and molecular phenotypic data. However, these data have not yet been fully explored to improve the effectiveness and efficiency of drug discovery, which continues along a one-drug-one-target-one-disease paradigm. As a partial consequence, both the cost to launch a new drug and the attrition rate are increasing. Systems pharmacology and pharmacogenomics are emerging to exploit the available data and potentially reverse this trend, but, as we argue here, more is needed. To understand the impact of genetic, epigenetic, and environmental factors on drug action, we must study the structural energetics and dynamics of molecular interactions in the context of the whole human genome and interactome. Such an approach requires an integrative modeling framework for drug action that leverages advances in data-driven statistical modeling and mechanism-based multiscale modeling and transforms heterogeneous data from GWAS, high-throughput sequencing, structural genomics, functional genomics, and chemical genomics into unified knowledge. This is not a small task, but, as reviewed here, progress is being made towards the final goal of personalized medicines for the treatment of complex diseases.
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Affiliation(s)
- Lei Xie
- Department of Computer Science, Hunter College, The City University of New York, New York, New York, United States of America
- Ph.D. Program in Computer Science, Biology, and Biochemistry, The Graduate Center, The City University of New York, New York, New York, United States of America
- * E-mail:
| | - Xiaoxia Ge
- Department of Computer Science, Hunter College, The City University of New York, New York, New York, United States of America
| | - Hepan Tan
- Department of Computer Science, Hunter College, The City University of New York, New York, New York, United States of America
| | - Li Xie
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, California, United States of America
| | - Yinliang Zhang
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, California, United States of America
| | - Thomas Hart
- Department of Biological Sciences, Hunter College, The City University of New York, New York, New York, United States of America
| | - Xiaowei Yang
- School of Public Health, Hunter College, The City University of New York, New York, New York, United States of America
| | - Philip E. Bourne
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, California, United States of America
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21
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Jaruszewicz J, Kimmel M, Lipniacki T. Stability of bacterial toggle switches is enhanced by cell-cycle lengthening by several orders of magnitude. PHYSICAL REVIEW. E, STATISTICAL, NONLINEAR, AND SOFT MATTER PHYSICS 2014; 89:022710. [PMID: 25353512 DOI: 10.1103/physreve.89.022710] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/27/2013] [Indexed: 06/04/2023]
Abstract
Bistable regulatory elements are important for nongenetic inheritance, increase of cell-to-cell heterogeneity allowing adaptation, and robust responses at the population level. Here, we study computationally the bistable genetic toggle switch-a small regulatory network consisting of a pair of mutual repressors-in growing and dividing bacteria. We show that as cells with an inhibited growth exhibit high stability of toggle states, cell growth and divisions lead to a dramatic increase of toggling rates. The toggling rates were found to increase with rate of cell growth, and can be up to six orders of magnitude larger for fast growing cells than for cells with the inhibited growth. The effect is caused mainly by the increase of protein and mRNA burst sizes associated with the faster growth. The observation that fast growth dramatically destabilizes toggle states implies that rapidly growing cells may vigorously explore the epigenetic landscape enabling nongenetic evolution, while cells with inhibited growth adhere to the local optima. This can be a clever population strategy that allows the slow growing (but stress resistant) cells to survive long periods of unfavorable conditions. Simultaneously, at favorable conditions, this stress resistant (but slowly growing-or not growing) subpopulation may be replenished due to a high switching rate from the fast growing population.
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Affiliation(s)
- Joanna Jaruszewicz
- Institute of Fundamental Technological Research, Polish Academy of Sciences, 02-106 Warsaw, Poland
| | - Marek Kimmel
- Departments of Statistics and Bioengineering, Rice University, Houston, Texas 77005, USA and Systems Engineering Group, Silesian University of Technology, 44-100 Gliwice, Poland
| | - Tomasz Lipniacki
- Institute of Fundamental Technological Research, Polish Academy of Sciences, 02-106 Warsaw, Poland and Department of Statistics, Rice University, Houston, Texas 77005, USA
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22
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Zhou HX, Bates PA. Modeling protein association mechanisms and kinetics. Curr Opin Struct Biol 2013; 23:887-93. [PMID: 23850142 PMCID: PMC3844007 DOI: 10.1016/j.sbi.2013.06.014] [Citation(s) in RCA: 70] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2013] [Revised: 06/19/2013] [Accepted: 06/19/2013] [Indexed: 11/21/2022]
Abstract
Substantial advances have been made in modeling protein association mechanisms and in calculating association rate constants (ka). We now have a clear understanding of the physical factors underlying the wide range of experimental ka values. Half of the association problem, where ka is limited by diffusion, is perhaps solved, and for the other half, where conformational changes become rate-limiting, a number of promising methods are being developed for ka calculations. Notably, the binding kinetics of disordered proteins are receiving growing attention, with 'dock-and-coalesce' emerging as a general mechanism. Progress too has been made in the modeling of protein association kinetics under conditions mimicking the heterogeneous, crowded environments of cells, an endeavor that should ultimately lead to a better understanding of cellular functions.
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Affiliation(s)
- Huan-Xiang Zhou
- Department of Physics and Institute of Molecular Biophysics, Florida State University, Tallahassee, FL 32306, USA.
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23
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Agius R, Torchala M, Moal IH, Fernández-Recio J, Bates PA. Characterizing changes in the rate of protein-protein dissociation upon interface mutation using hotspot energy and organization. PLoS Comput Biol 2013; 9:e1003216. [PMID: 24039569 PMCID: PMC3764008 DOI: 10.1371/journal.pcbi.1003216] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2013] [Accepted: 07/25/2013] [Indexed: 12/21/2022] Open
Abstract
Predicting the effects of mutations on the kinetic rate constants of protein-protein interactions is central to both the modeling of complex diseases and the design of effective peptide drug inhibitors. However, while most studies have concentrated on the determination of association rate constants, dissociation rates have received less attention. In this work we take a novel approach by relating the changes in dissociation rates upon mutation to the energetics and architecture of hotspots and hotregions, by performing alanine scans pre- and post-mutation. From these scans, we design a set of descriptors that capture the change in hotspot energy and distribution. The method is benchmarked on 713 kinetically characterized mutations from the SKEMPI database. Our investigations show that, with the use of hotspot descriptors, energies from single-point alanine mutations may be used for the estimation of off-rate mutations to any residue type and also multi-point mutations. A number of machine learning models are built from a combination of molecular and hotspot descriptors, with the best models achieving a Pearson's Correlation Coefficient of 0.79 with experimental off-rates and a Matthew's Correlation Coefficient of 0.6 in the detection of rare stabilizing mutations. Using specialized feature selection models we identify descriptors that are highly specific and, conversely, broadly important to predicting the effects of different classes of mutations, interface regions and complexes. Our results also indicate that the distribution of the critical stability regions across protein-protein interfaces is a function of complex size more strongly than interface area. In addition, mutations at the rim are critical for the stability of small complexes, but consistently harder to characterize. The relationship between hotregion size and the dissociation rate is also investigated and, using hotspot descriptors which model cooperative effects within hotregions, we show how the contribution of hotregions of different sizes, changes under different cooperative effects.
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Affiliation(s)
- Rudi Agius
- Biomolecular Modelling Laboratory, Cancer Research UK London Research Institute, London, United Kingdom
| | - Mieczyslaw Torchala
- Biomolecular Modelling Laboratory, Cancer Research UK London Research Institute, London, United Kingdom
| | - Iain H. Moal
- Joint BSC-IRB Research Program in Computational Biology, Life Science Department, Barcelona Supercomputing Center, Barcelona, Spain
| | - Juan Fernández-Recio
- Joint BSC-IRB Research Program in Computational Biology, Life Science Department, Barcelona Supercomputing Center, Barcelona, Spain
| | - Paul A. Bates
- Biomolecular Modelling Laboratory, Cancer Research UK London Research Institute, London, United Kingdom
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24
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Inoue H, Suganami A, Ishida I, Tamura Y, Maeda Y. Affinity maturation of a CDR3-grafted VHH using in silico analysis and surface plasmon resonance. J Biochem 2013; 154:325-32. [PMID: 23902829 DOI: 10.1093/jb/mvt058] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023] Open
Abstract
A requirement for advancing antibody-based medicine is the development of proteins that can bind with high affinity to a specific epitope related to a critical protein activity site. As a part of generating such proteins, we have succeeded in creating a binding protein without changing epitope by complementarity-determining region 3 (CDR3) grafting (Inoue et al., Affinity transfer to a human protein by CDR3 grafting of camelid VHH. Protein Sci. 20, 1971-1981). However, the affinity of the target-binding protein was low. In this manuscript, the affinity maturation of a target-binding protein was examined using CDR3-grafted camelid single domain antibody (VHH) as a model protein. Several amino acids in the CDR1 and CDR2 regions of VHH were mutated to tyrosines and/or serines and screened for affinity-matured proteins by using in silico analysis. The mutation of two amino acids in the CDR2 region to arginine and/or aspartic acid increased the affinity by decreasing the dissociation rate. The affinity of designed mutant increased by ∼20-fold over that of the original protein. In the present study, candidate mutants were narrowed down using in silico screening and computational modelling, thus avoiding much in vitro analytical effort. Therefore, the method used in this study is expected to be one of the useful for promoting affinity maturation of antibodies.
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Affiliation(s)
- Hidetoshi Inoue
- Kyowa Hakko Kirin Co., Ltd., 3-6-6 Asahi-machi, Machida, Tokyo 194-8533; and Department of Bioinformatics, Graduate School of Medicine, Chiba University, Chiba 260-8670, Japan
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25
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Abstract
Bistable regulatory elements enhance heterogeneity in cell populations and, in multicellular organisms, allow cells to specialize and specify their fate. Our study demonstrates that in a system of bistable genetic switch, the noise characteristics control in which of the two epigenetic attractors the cell population will settle. We focus on two types of noise: the gene switching noise and protein dimerization noise. We found that the change of magnitudes of these noise components for one of the two competing genes introduces a large asymmetry of the protein stationary probability distribution and changes the relative probability of individual gene activation. Interestingly, an increase of noise associated with a given gene can either promote or suppress the activation of the gene, depending on the type of noise. Namely, each gene is repressed by an increase of its gene switching noise and activated by an increase of its protein-product dimerization noise. The observed effect was found robust to the large, up to fivefold deviations of the model parameters. In summary, we demonstrated that noise itself may determine the relative strength of the epigenetic attractors, which may provide a unique mode of control of cell fate decisions.
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Affiliation(s)
- Joanna Jaruszewicz
- Institute of Fundamental Technological Research, Polish Academy of Sciences, 02-106 Warsaw, Poland
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26
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Kastritis PL, Bonvin AMJJ. On the binding affinity of macromolecular interactions: daring to ask why proteins interact. J R Soc Interface 2012; 10:20120835. [PMID: 23235262 PMCID: PMC3565702 DOI: 10.1098/rsif.2012.0835] [Citation(s) in RCA: 276] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023] Open
Abstract
Interactions between proteins are orchestrated in a precise and time-dependent manner, underlying cellular function. The binding affinity, defined as the strength of these interactions, is translated into physico-chemical terms in the dissociation constant (Kd), the latter being an experimental measure that determines whether an interaction will be formed in solution or not. Predicting binding affinity from structural models has been a matter of active research for more than 40 years because of its fundamental role in drug development. However, all available approaches are incapable of predicting the binding affinity of protein–protein complexes from coordinates alone. Here, we examine both theoretical and experimental limitations that complicate the derivation of structure–affinity relationships. Most work so far has concentrated on binary interactions. Systems of increased complexity are far from being understood. The main physico-chemical measure that relates to binding affinity is the buried surface area, but it does not hold for flexible complexes. For the latter, there must be a significant entropic contribution that will have to be approximated in the future. We foresee that any theoretical modelling of these interactions will have to follow an integrative approach considering the biology, chemistry and physics that underlie protein–protein recognition.
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Affiliation(s)
- Panagiotis L Kastritis
- Bijvoet Center for Biomolecular Research, Faculty of Science, Chemistry, Utrecht University, , Padualaan 8, Utrecht, The Netherlands
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27
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Barua D, Hlavacek WS, Lipniacki T. A computational model for early events in B cell antigen receptor signaling: analysis of the roles of Lyn and Fyn. THE JOURNAL OF IMMUNOLOGY 2012; 189:646-58. [PMID: 22711887 DOI: 10.4049/jimmunol.1102003] [Citation(s) in RCA: 38] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
BCR signaling regulates the activities and fates of B cells. BCR signaling encompasses two feedback loops emanating from Lyn and Fyn, which are Src family protein tyrosine kinases (SFKs). Positive feedback arises from SFK-mediated trans phosphorylation of BCR and receptor-bound Lyn and Fyn, which increases the kinase activities of Lyn and Fyn. Negative feedback arises from SFK-mediated cis phosphorylation of the transmembrane adapter protein PAG1, which recruits the cytosolic protein tyrosine kinase Csk to the plasma membrane, where it acts to decrease the kinase activities of Lyn and Fyn. To study the effects of the positive and negative feedback loops on the dynamical stability of BCR signaling and the relative contributions of Lyn and Fyn to BCR signaling, we consider in this study a rule-based model for early events in BCR signaling that encompasses membrane-proximal interactions of six proteins, as follows: BCR, Lyn, Fyn, Csk, PAG1, and Syk, a cytosolic protein tyrosine kinase that is activated as a result of SFK-mediated phosphorylation of BCR. The model is consistent with known effects of Lyn and Fyn deletions. We find that BCR signaling can generate a single pulse or oscillations of Syk activation depending on the strength of Ag signal and the relative levels of Lyn and Fyn. We also show that bistability can arise in Lyn- or Csk-deficient cells.
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Affiliation(s)
- Dipak Barua
- Los Alamos National Laboratory, Los Alamos, NM 87545, USA
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28
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Vreven T, Hwang H, Pierce BG, Weng Z. Prediction of protein-protein binding free energies. Protein Sci 2012; 21:396-404. [PMID: 22238219 DOI: 10.1002/pro.2027] [Citation(s) in RCA: 65] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2011] [Revised: 12/23/2011] [Accepted: 01/04/2012] [Indexed: 11/09/2022]
Abstract
We present an energy function for predicting binding free energies of protein-protein complexes, using the three-dimensional structures of the complex and unbound proteins as input. Our function is a linear combination of nine terms and achieves a correlation coefficient of 0.63 with experimental measurements when tested on a benchmark of 144 complexes using leave-one-out cross validation. Although we systematically tested both atomic and residue-based scoring functions, the selected function is dominated by residue-based terms. Our function is stable for subsets of the benchmark stratified by experimental pH and extent of conformational change upon complex formation, with correlation coefficients ranging from 0.61 to 0.66.
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Affiliation(s)
- Thom Vreven
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, Massachusetts 01605, USA
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29
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Prakash A, Luthra PM. Insilico study of the A2AR–D2R kinetics and interfacial contact surface for heteromerization. Amino Acids 2012; 43:1451-64. [DOI: 10.1007/s00726-012-1218-x] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2011] [Accepted: 01/04/2012] [Indexed: 12/28/2022]
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30
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Moal IH, Bates PA. Kinetic rate constant prediction supports the conformational selection mechanism of protein binding. PLoS Comput Biol 2012; 8:e1002351. [PMID: 22253587 PMCID: PMC3257286 DOI: 10.1371/journal.pcbi.1002351] [Citation(s) in RCA: 45] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2011] [Accepted: 11/29/2011] [Indexed: 12/24/2022] Open
Abstract
The prediction of protein-protein kinetic rate constants provides a fundamental test of our understanding of molecular recognition, and will play an important role in the modeling of complex biological systems. In this paper, a feature selection and regression algorithm is applied to mine a large set of molecular descriptors and construct simple models for association and dissociation rate constants using empirical data. Using separate test data for validation, the predicted rate constants can be combined to calculate binding affinity with accuracy matching that of state of the art empirical free energy functions. The models show that the rate of association is linearly related to the proportion of unbound proteins in the bound conformational ensemble relative to the unbound conformational ensemble, indicating that the binding partners must adopt a geometry near to that of the bound prior to binding. Mirroring the conformational selection and population shift mechanism of protein binding, the models provide a strong separate line of evidence for the preponderance of this mechanism in protein-protein binding, complementing structural and theoretical studies.
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Affiliation(s)
- Iain H. Moal
- Protein Interactions and Docking Laboratory, Life Sciences Department, Barcelona Supercomputing Center, Barcelona, Spain
| | - Paul A. Bates
- Biomolecular Modelling Laboratory, Cancer Research UK London Research Institute, London, United Kingdom
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31
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Moal IH, Agius R, Bates PA. Protein-protein binding affinity prediction on a diverse set of structures. Bioinformatics 2011; 27:3002-9. [PMID: 21903632 DOI: 10.1093/bioinformatics/btr513] [Citation(s) in RCA: 87] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/11/2024] Open
Abstract
MOTIVATION Accurate binding free energy functions for protein-protein interactions are imperative for a wide range of purposes. Their construction is predicated upon ascertaining the factors that influence binding and their relative importance. A recent benchmark of binding affinities has allowed, for the first time, the evaluation and construction of binding free energy models using a diverse set of complexes, and a systematic assessment of our ability to model the energetics of conformational changes. RESULTS We construct a large set of molecular descriptors using commonly available tools, introducing the use of energetic factors associated with conformational changes and disorder to order transitions, as well as features calculated on structural ensembles. The descriptors are used to train and test a binding free energy model using a consensus of four machine learning algorithms, whose performance constitutes a significant improvement over the other state of the art empirical free energy functions tested. The internal workings of the learners show how the descriptors are used, illuminating the determinants of protein-protein binding. AVAILABILITY The molecular descriptor set and descriptor values for all complexes are available in the Supplementary Material. A web server for the learners and coordinates for the bound and unbound structures can be accessed from the website: http://bmm.cancerresearchuk.org/~Affinity. CONTACT paul.bates@cancer.org.uk. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Iain H Moal
- Biomolecular Modelling Laboratory, Cancer Research UK London Research Institute, London WC2A 3LY, UK
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32
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Abstract
Rapid advances in our collective understanding of biomolecular structure and, in concert, of biochemical systems, coupled with developments in computational methods, have massively impacted the field of medicinal chemistry over the past two decades, with even greater changes appearing on the horizon. In this perspective, we endeavor to profile some of the most prominent determinants of change and speculate as to further evolution that may consequently occur during the next decade. The five main angles to be addressed are: protein-protein interactions; peptides and peptidomimetics; molecular diversity and pharmacological space; molecular pharmacodynamics (significance, potential and challenges); and early-stage clinical efficacy and safety. We then consider, in light of these, the future of medicinal chemistry and the educational preparation that will be required for future medicinal chemists.
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Affiliation(s)
- Seetharama D Satyanarayanajois
- Basic Pharmaceutical Sciences, College of Pharmacy, University of Louisiana at Monroe, 1800 Bienville Drive, Monroe LA 71201, USA.
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33
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Brown NG, Chow DC, Sankaran B, Zwart P, Prasad BVV, Palzkill T. Analysis of the binding forces driving the tight interactions between beta-lactamase inhibitory protein-II (BLIP-II) and class A beta-lactamases. J Biol Chem 2011; 286:32723-35. [PMID: 21775426 PMCID: PMC3173220 DOI: 10.1074/jbc.m111.265058] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2011] [Revised: 06/25/2011] [Indexed: 12/19/2022] Open
Abstract
β-Lactamases hydrolyze β-lactam antibiotics to provide drug resistance to bacteria. β-Lactamase inhibitory protein-II (BLIP-II) is a potent proteinaceous inhibitor that exhibits low picomolar affinity for class A β-lactamases. This study examines the driving forces for binding between BLIP-II and β-lactamases using a combination of presteady state kinetics, isothermal titration calorimetry, and x-ray crystallography. The measured dissociation rate constants for BLIP-II and various β-lactamases ranged from 10(-4) to 10(-7) s(-1) and are comparable with those found in some of the tightest known protein-protein interactions. The crystal structures of BLIP-II alone and in complex with Bacillus anthracis Bla1 β-lactamase revealed no significant side-chain movement in BLIP-II in the complex versus the monomer. The structural rigidity of BLIP-II minimizes the loss of the entropy upon complex formation and, as indicated by thermodynamics experiments, may be a key determinant of the observed potent inhibition of β-lactamases.
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Affiliation(s)
- Nicholas G. Brown
- From the Departments of Pharmacology
- Biochemistry and Molecular Biology, and
| | | | - Banumathi Sankaran
- The Berkeley Center for Structural Biology, Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - Peter Zwart
- The Berkeley Center for Structural Biology, Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - B. V. Venkataram Prasad
- Biochemistry and Molecular Biology, and
- Molecular Virology and Microbiology, Baylor College of Medicine, Houston, Texas 77030 and
| | - Timothy Palzkill
- From the Departments of Pharmacology
- Biochemistry and Molecular Biology, and
- Molecular Virology and Microbiology, Baylor College of Medicine, Houston, Texas 77030 and
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