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Yoshino R, Yasuo N, Hagiwara Y, Ishida T, Inaoka DK, Amano Y, Tateishi Y, Ohno K, Namatame I, Niimi T, Orita M, Kita K, Akiyama Y, Sekijima M. Discovery of a Hidden Trypanosoma cruzi Spermidine Synthase Binding Site and Inhibitors through In Silico, In Vitro, and X-ray Crystallography. ACS OMEGA 2023; 8:25850-25860. [PMID: 37521650 PMCID: PMC10373461 DOI: 10.1021/acsomega.3c01314] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Accepted: 06/28/2023] [Indexed: 08/01/2023]
Abstract
In drug discovery research, the selection of promising binding sites and understanding the binding mode of compounds are crucial fundamental studies. The current understanding of the proteins-ligand binding model extends beyond the simple lock and key model to include the induced-fit model, which alters the conformation to match the shape of the ligand, and the pre-existing equilibrium model, selectively binding structures with high binding affinity from a diverse ensemble of proteins. Although methods for detecting target protein binding sites and virtual screening techniques using docking simulation are well-established, with numerous studies reported, they only consider a very limited number of structures in the diverse ensemble of proteins, as these methods are applied to a single structure. Molecular dynamics (MD) simulation is a method for predicting protein dynamics and can detect potential ensembles of protein binding sites and hidden sites unobservable in a single-point structure. In this study, to demonstrate the utility of virtual screening with protein dynamics, MD simulations were performed on Trypanosoma cruzi spermidine synthase to obtain an ensemble of dominant binding sites with a high probability of existence. The structure of the binding site obtained through MD simulation revealed pockets in addition to the active site that was present in the initial structure. Using the obtained binding site structures, virtual screening of 4.8 million compounds by docking simulation, in vitro assays, and X-ray analysis was conducted, successfully identifying two hit compounds.
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Affiliation(s)
- Ryunosuke Yoshino
- Transborder
Medical Research Center, University of Tsukuba, Tsukuba 305-8577, Japan
- Education
Academy of Computational Life Sciences (ACLS), Tokyo Institute of Technology, Yokohama 226-8501, Japan
| | - Nobuaki Yasuo
- Tokyo
Tech Academy for Convergence of Materials and Informatics (TAC-MI), Tokyo Institute of Technology, Meguro, Tokyo 152-8550, Japan
| | - Yohsuke Hagiwara
- Education
Academy of Computational Life Sciences (ACLS), Tokyo Institute of Technology, Yokohama 226-8501, Japan
- Medicinal
Chemistry Research Labs, Drug Discovery Research, Astellas Pharma Inc, Miyukigaoka, Tsukuba 305-8585, Japan
| | - Takashi Ishida
- School
of Computing, Tokyo Institute of Technology, Tokyo 152-8550, Japan
| | - Daniel Ken Inaoka
- School of
Tropical Medicine and Global Health, Nagasaki
University, Sakamoto, Nagasaki 852-8523, Japan
- Department
of Biomedical Chemistry, Graduate School of Medicine, The University of Tokyo, Tokyo 113-0033, Japan
| | - Yasushi Amano
- Medicinal
Chemistry Research Labs, Drug Discovery Research, Astellas Pharma Inc, Miyukigaoka, Tsukuba 305-8585, Japan
| | - Yukihiro Tateishi
- Medicinal
Chemistry Research Labs, Drug Discovery Research, Astellas Pharma Inc, Miyukigaoka, Tsukuba 305-8585, Japan
| | - Kazuki Ohno
- Education
Academy of Computational Life Sciences (ACLS), Tokyo Institute of Technology, Yokohama 226-8501, Japan
- Medicinal
Chemistry Research Labs, Drug Discovery Research, Astellas Pharma Inc, Miyukigaoka, Tsukuba 305-8585, Japan
| | - Ichiji Namatame
- Medicinal
Chemistry Research Labs, Drug Discovery Research, Astellas Pharma Inc, Miyukigaoka, Tsukuba 305-8585, Japan
| | - Tatsuya Niimi
- Medicinal
Chemistry Research Labs, Drug Discovery Research, Astellas Pharma Inc, Miyukigaoka, Tsukuba 305-8585, Japan
| | - Masaya Orita
- Medicinal
Chemistry Research Labs, Drug Discovery Research, Astellas Pharma Inc, Miyukigaoka, Tsukuba 305-8585, Japan
| | - Kiyoshi Kita
- School of
Tropical Medicine and Global Health, Nagasaki
University, Sakamoto, Nagasaki 852-8523, Japan
- Department
of Biomedical Chemistry, Graduate School of Medicine, The University of Tokyo, Tokyo 113-0033, Japan
| | - Yutaka Akiyama
- Education
Academy of Computational Life Sciences (ACLS), Tokyo Institute of Technology, Yokohama 226-8501, Japan
- School
of Computing, Tokyo Institute of Technology, Tokyo 152-8550, Japan
| | - Masakazu Sekijima
- Education
Academy of Computational Life Sciences (ACLS), Tokyo Institute of Technology, Yokohama 226-8501, Japan
- School
of Computing, Tokyo Institute of Technology, Tokyo 152-8550, Japan
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2
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Fabunmi B, Adegaye A, Ogunjo S. Identification and characterization of molecular entities differentially expressed in bacteria genome upon treatment with glyphosate shock. Heliyon 2023; 9:e13868. [PMID: 36950589 PMCID: PMC10025891 DOI: 10.1016/j.heliyon.2023.e13868] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Revised: 02/13/2023] [Accepted: 02/14/2023] [Indexed: 03/15/2023] Open
Abstract
Antimetabolites developed from enzymes in the shikimate pathway are appealing targets. There are, however, certain unidentified molecular entities that show bacterial sensitivity to glyphosate shock. This study aims to identify the expression pattern of such entities following treatment with glyphosate shock and to characterize them structurally and functionally. Understanding such entities' catalytic structure and modulatory role guides the design and development of novel antibiotics. This study's functional profiling of 16S rRNA sequencing data and transcriptome analysis of glyphosate-exposedE. coli revealed that two genes were upregulated and twenty-eight were downregulated after glyphosate shock. We discovered the differential expression of some processes based on functional gene analysis, such as global and overview maps (4.2195 on average), carbohydrate metabolism (0.6858 on average), amino acid metabolism (0.5032 on average), and co-factor and vitamin metabolism (0.5032 on average) (0.2876 on average). After examining the two data sets, we discovered that some unidentified proteins were strongly expressed after glyphosate treatment. After examining the two datasets, we discovered a protein with no unique features expressed when treated with glyphosate. The Ecs2020 model looks to be the most stable in structural modeling investigations, while the catalytic residues sought in drug development are anticipated. Furthermore, biological processes and cellular component enrichment analysis revealed that the differentially expressed genes were strongly related to the trehalose manufacturing process and represented the cell membrane's outer membrane component. To estimate the functional gene content of soil sample metagenomics based on 16S rRNA, predictive functional analysis was done with R using the Tax4Fun2 package. On the other hand, transcriptome analysis was carried out using the R tool GEO2R. The results could be a good starting point for making new antibiotic medicines.
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Affiliation(s)
- B.T. Fabunmi
- Department of Biological Sciences, Achievers University Owo, Ondo State, Nigeria
- Corresponding author.
| | - A.C. Adegaye
- Department of Crop, Soil and Pest Management, Federal University of Technology, Akure, Nigeria
| | - S.T. Ogunjo
- Department of Physics, Federal University of Technology, Akure, Nigeria
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3
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Leander M, Liu Z, Cui Q, Raman S. Deep mutational scanning and machine learning reveal structural and molecular rules governing allosteric hotspots in homologous proteins. eLife 2022; 11:e79932. [PMID: 36226916 PMCID: PMC9662819 DOI: 10.7554/elife.79932] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Accepted: 10/13/2022] [Indexed: 01/29/2023] Open
Abstract
A fundamental question in protein science is where allosteric hotspots - residues critical for allosteric signaling - are located, and what properties differentiate them. We carried out deep mutational scanning (DMS) of four homologous bacterial allosteric transcription factors (aTFs) to identify hotspots and built a machine learning model with this data to glean the structural and molecular properties of allosteric hotspots. We found hotspots to be distributed protein-wide rather than being restricted to 'pathways' linking allosteric and active sites as is commonly assumed. Despite structural homology, the location of hotspots was not superimposable across the aTFs. However, common signatures emerged when comparing hotspots coincident with long-range interactions, suggesting that the allosteric mechanism is conserved among the homologs despite differences in molecular details. Machine learning with our large DMS datasets revealed global structural and dynamic properties to be a strong predictor of whether a residue is a hotspot than local and physicochemical properties. Furthermore, a model trained on one protein can predict hotspots in a homolog. In summary, the overall allosteric mechanism is embedded in the structural fold of the aTF family, but the finer, molecular details are sequence-specific.
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Affiliation(s)
- Megan Leander
- Department of Biochemistry, University of Wisconsin-MadisonMadisonUnited States
| | - Zhuang Liu
- Department of Physics, Boston UniversityBostonUnited States
| | - Qiang Cui
- Department of Physics, Boston UniversityBostonUnited States
- Department of Chemistry, Boston UniversityBostonUnited States
| | - Srivatsan Raman
- Department of Biochemistry, University of Wisconsin-MadisonMadisonUnited States
- Department of Bacteriology, University of Wisconsin-MadisonMadisonUnited States
- Department of Chemical and Biological Engineering, University of Wisconsin-MadisonMadisonUnited States
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Wu N, Yaliraki SN, Barahona M. Prediction of Protein Allosteric Signalling Pathways and Functional Residues Through Paths of Optimised Propensity. J Mol Biol 2022; 434:167749. [PMID: 35841931 DOI: 10.1016/j.jmb.2022.167749] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Revised: 07/09/2022] [Accepted: 07/11/2022] [Indexed: 11/28/2022]
Abstract
Allostery commonly refers to the mechanism that regulates protein activity through the binding of a molecule at a different, usually distal, site from the orthosteric site. The omnipresence of allosteric regulation in nature and its potential for drug design and screening render the study of allostery invaluable. Nevertheless, challenges remain as few computational methods are available to effectively predict allosteric sites, identify signalling pathways involved in allostery, or to aid with the design of suitable molecules targeting such sites. Recently, bond-to-bond propensity analysis has been shown successful at identifying allosteric sites for a large and diverse group of proteins from knowledge of the orthosteric sites and its ligands alone by using network analysis applied to energy-weighted atomistic protein graphs. To address the identification of signalling pathways, we propose here a method to compute and score paths of optimised propensity that link the orthosteric site with the identified allosteric sites, and identifies crucial residues that contribute to those paths. We showcase the approach with three well-studied allosteric proteins: h-Ras, caspase-1, and 3-phosphoinositide-dependent kinase-1 (PDK1). Key residues in both orthosteric and allosteric sites were identified and showed agreement with experimental results, and pivotal signalling residues along the pathway were also revealed, thus providing alternative targets for drug design. By using the computed path scores, we were also able to differentiate the activity of different allosteric modulators.
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Affiliation(s)
- Nan Wu
- Department of Chemistry Imperial College London, United Kingdom
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Khairallah A, Ross CJ, Tastan Bishop Ö. GTP Cyclohydrolase I as a Potential Drug Target: New Insights into Its Allosteric Modulation via Normal Mode Analysis. J Chem Inf Model 2021; 61:4701-4719. [PMID: 34450011 DOI: 10.1021/acs.jcim.1c00898] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Guanosine triphosphate (GTP) cyclohydrolase I (GCH1) catalyzes the conversion of GTP into dihydroneopterin triphosphate (DHNP). DHNP is the first intermediate of the folate de novo biosynthesis pathway in prokaryotic and lower eukaryotic microorganisms and the tetrahydrobiopterin (BH4) biosynthesis pathway in higher eukaryotes. The de novo folate biosynthesis provides essential cofactors for DNA replication, cell division, and synthesis of key amino acids in rapidly replicating pathogen cells, such as Plasmodium falciparum (P. falciparum), a causative agent of malaria. In eukaryotes, the product of the BH4 biosynthesis pathway is essential for the production of nitric oxide and several neurotransmitter precursors. An increased copy number of the malaria parasite P. falciparum GCH1 gene has been reported to influence antimalarial antifolate drug resistance evolution, whereas mutations in the human GCH1 are associated with neuropathic and inflammatory pain disorders. Thus, GCH1 stands as an important and attractive drug target for developing therapeutics. The GCH1 intrinsic dynamics that modulate its activity remains unclear, and key sites that exert allosteric effects across the structure are yet to be elucidated. This study employed the anisotropic network model to analyze the intrinsic motions of the GCH1 structure alone and in complex with its regulatory partner protein. We showed that the GCH1 tunnel-gating mechanism is regulated by a global shear motion and an outward expansion of the central five-helix bundle. We further identified hotspot residues within sites of structural significance for the GCH1 intrinsic allosteric modulation. The obtained results can provide a solid starting point to design novel antineuropathic treatments for humans and novel antimalarial drugs against the malaria parasite P. falciparum GCH1 enzyme.
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Affiliation(s)
- Afrah Khairallah
- Research Unit in Bioinformatics (RUBi), Department of Biochemistry and Microbiology, Rhodes University, Grahamstown 6140, South Africa
| | - Caroline J Ross
- Research Unit in Bioinformatics (RUBi), Department of Biochemistry and Microbiology, Rhodes University, Grahamstown 6140, South Africa
| | - Özlem Tastan Bishop
- Research Unit in Bioinformatics (RUBi), Department of Biochemistry and Microbiology, Rhodes University, Grahamstown 6140, South Africa
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6
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Chatzigoulas A, Cournia Z. Rational design of allosteric modulators: Challenges and successes. WIRES COMPUTATIONAL MOLECULAR SCIENCE 2021. [DOI: 10.1002/wcms.1529] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Affiliation(s)
- Alexios Chatzigoulas
- Biomedical Research Foundation Academy of Athens Athens Greece
- Department of Informatics and Telecommunications National and Kapodistrian University of Athens Athens Greece
| | - Zoe Cournia
- Biomedical Research Foundation Academy of Athens Athens Greece
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7
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Abstract
Allosteric regulation in proteins is fundamental to many important biological processes. Allostery has been employed to control protein functions by regulating protein activity. Engineered allosteric regulation allows controlling protein activity in subsecond time scale and has a broad range of applications, from dissecting spatiotemporal dynamics in biochemical cascades to applications in biotechnology and medicine. Here, we review the concept of allostery in proteins and various approaches to identify allosteric sites and pathways. We then provide an overview of strategies and tools used in allosteric protein regulation and their utility in biological applications. We highlight various classes of proteins, where regulation is achieved through allostery. Finally, we analyze the current problems, critical challenges, and future prospective in achieving allosteric regulation in proteins.
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Affiliation(s)
| | - Jiaxing Chen
- Department of Pharmacology, Penn State College of Medicine, Hershey, Pennsylvania 17033-0850, United States
| | - Nikolay V Dokholyan
- Department of Pharmacology, Penn State College of Medicine, Hershey, Pennsylvania 17033-0850, United States
- Departments of Biochemistry & Molecular Biology, Penn State College of Medicine, Hershey, Pennsylvania 17033-0850, United States
- Department of Chemistry, Penn State University, University Park, Pennsylvania 16802, United States
- Department of Biomedical Engineering, Penn State University, University Park, Pennsylvania 16802, United States
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8
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Functional plasticity and evolutionary adaptation of allosteric regulation. Proc Natl Acad Sci U S A 2020; 117:25445-25454. [PMID: 32999067 DOI: 10.1073/pnas.2002613117] [Citation(s) in RCA: 56] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] Open
Abstract
Allostery is a fundamental regulatory mechanism of protein function. Despite notable advances, understanding the molecular determinants of allostery remains an elusive goal. Our current knowledge of allostery is principally shaped by a structure-centric view, which makes it difficult to understand the decentralized character of allostery. We present a function-centric approach using deep mutational scanning to elucidate the molecular basis and underlying functional landscape of allostery. We show that allosteric signaling exhibits a high degree of functional plasticity and redundancy through myriad mutational pathways. Residues critical for allosteric signaling are surprisingly poorly conserved while those required for structural integrity are highly conserved, suggesting evolutionary pressure to preserve fold over function. Our results suggest multiple solutions to the thermodynamic conditions of cooperativity, in contrast to the common view of a finely tuned allosteric residue network maintained under selection.
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9
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Mishra SK, Muthye V, Kandoi G. Computational Methods for Predicting Functions at the mRNA Isoform Level. Int J Mol Sci 2020; 21:ijms21165686. [PMID: 32784445 PMCID: PMC7460821 DOI: 10.3390/ijms21165686] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2020] [Revised: 08/05/2020] [Accepted: 08/06/2020] [Indexed: 11/16/2022] Open
Abstract
Multiple mRNA isoforms of the same gene are produced via alternative splicing, a biological mechanism that regulates protein diversity while maintaining genome size. Alternatively spliced mRNA isoforms of the same gene may sometimes have very similar sequence, but they can have significantly diverse effects on cellular function and regulation. The products of alternative splicing have important and diverse functional roles, such as response to environmental stress, regulation of gene expression, human heritable, and plant diseases. The mRNA isoforms of the same gene can have dramatically different functions. Despite the functional importance of mRNA isoforms, very little has been done to annotate their functions. The recent years have however seen the development of several computational methods aimed at predicting mRNA isoform level biological functions. These methods use a wide array of proteo-genomic data to develop machine learning-based mRNA isoform function prediction tools. In this review, we discuss the computational methods developed for predicting the biological function at the individual mRNA isoform level.
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10
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Wang J, Jain A, McDonald LR, Gambogi C, Lee AL, Dokholyan NV. Mapping allosteric communications within individual proteins. Nat Commun 2020; 11:3862. [PMID: 32737291 PMCID: PMC7395124 DOI: 10.1038/s41467-020-17618-2] [Citation(s) in RCA: 88] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2019] [Accepted: 06/30/2020] [Indexed: 02/05/2023] Open
Abstract
Allostery in proteins influences various biological processes such as regulation of gene transcription and activities of enzymes and cell signaling. Computational approaches for analysis of allosteric coupling provide inexpensive opportunities to predict mutations and to design small-molecule agents to control protein function and cellular activity. We develop a computationally efficient network-based method, Ohm, to identify and characterize allosteric communication networks within proteins. Unlike previously developed simulation-based approaches, Ohm relies solely on the structure of the protein of interest. We use Ohm to map allosteric networks in a dataset composed of 20 proteins experimentally identified to be allosterically regulated. Further, the Ohm allostery prediction for the protein CheY correlates well with NMR CHESCA studies. Our webserver, Ohm.dokhlab.org, automatically determines allosteric network architecture and identifies critical coupled residues within this network.
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Affiliation(s)
- Jian Wang
- Department of Pharmacology, Penn State College of Medicine, Hershey, PA, 17033-0850, USA
| | - Abha Jain
- Division of Chemical Biology and Medicinal Chemistry, Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599-7363, USA
| | - Leanna R McDonald
- Division of Chemical Biology and Medicinal Chemistry, Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599-7363, USA
| | - Craig Gambogi
- Division of Chemical Biology and Medicinal Chemistry, Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599-7363, USA
| | - Andrew L Lee
- Division of Chemical Biology and Medicinal Chemistry, Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599-7363, USA
| | - Nikolay V Dokholyan
- Department of Pharmacology, Penn State College of Medicine, Hershey, PA, 17033-0850, USA.
- Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA.
- Departments of Biochemistry & Molecular Biology, Penn State College of Medicine, Hershey, PA, 17033-0850, USA.
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Abstract
Recently, machine learning (ML) has established itself in various worldwide benchmarking competitions in computational biology, including Critical Assessment of Structure Prediction (CASP) and Drug Design Data Resource (D3R) Grand Challenges. However, the intricate structural complexity and high ML dimensionality of biomolecular datasets obstruct the efficient application of ML algorithms in the field. In addition to data and algorithm, an efficient ML machinery for biomolecular predictions must include structural representation as an indispensable component. Mathematical representations that simplify the biomolecular structural complexity and reduce ML dimensionality have emerged as a prime winner in D3R Grand Challenges. This review is devoted to the recent advances in developing low-dimensional and scalable mathematical representations of biomolecules in our laboratory. We discuss three classes of mathematical approaches, including algebraic topology, differential geometry, and graph theory. We elucidate how the physical and biological challenges have guided the evolution and development of these mathematical apparatuses for massive and diverse biomolecular data. We focus the performance analysis on protein-ligand binding predictions in this review although these methods have had tremendous success in many other applications, such as protein classification, virtual screening, and the predictions of solubility, solvation free energies, toxicity, partition coefficients, protein folding stability changes upon mutation, etc.
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Affiliation(s)
- Duc Duy Nguyen
- Department of Mathematics, Michigan State University, MI 48824, USA.
| | - Zixuan Cang
- Department of Mathematics, Michigan State University, MI 48824, USA.
| | - Guo-Wei Wei
- Department of Mathematics, Michigan State University, MI 48824, USA. and Department of Biochemistry and Molecular Biology, Michigan State University, MI 48824, USA and Department of Electrical and Computer Engineering, Michigan State University, MI 48824, USA
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12
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A topology-based network tree for the prediction of protein-protein binding affinity changes following mutation. NAT MACH INTELL 2020; 2:116-123. [PMID: 34170981 PMCID: PMC7223817 DOI: 10.1038/s42256-020-0149-6] [Citation(s) in RCA: 83] [Impact Index Per Article: 20.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2019] [Accepted: 01/10/2020] [Indexed: 12/14/2022]
Abstract
The ability to predict protein-protein interactions is crucial to our understanding of a wide range of biological activities and functions in the human body, and for guiding drug discovery. Despite considerable efforts to develop suitable computational methods, predicting protein-protein interaction binding affinity changes following mutation (ΔΔG) remains a severe challenge. Algebraic topology, a champion in recent worldwide competitions for protein-ligand binding affinity predictions, is a promising approach to simplifying the complexity of biological structures. Here we introduce element- and site-specific persistent homology (a new branch of algebraic topology) to simplify the structural complexity of protein-protein complexes and embed crucial biological information into topological invariants. We also propose a new deep learning algorithm called NetTree to take advantage of convolutional neural networks and gradient-boosting trees. A topology-based network tree is constructed by integrating the topological representation and NetTree for predicting protein-protein interaction ΔΔG. Tests on major benchmark datasets indicate that the proposed topology-based network tree is an important improvement over the current state of the art in predicting ΔΔG.
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13
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He X, Ni D, Lu S, Zhang J. Characteristics of Allosteric Proteins, Sites, and Modulators. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2019; 1163:107-139. [DOI: 10.1007/978-981-13-8719-7_6] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
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14
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Greener JG, Sternberg MJE. Structure-based prediction of protein allostery. Curr Opin Struct Biol 2018; 50:1-8. [DOI: 10.1016/j.sbi.2017.10.002] [Citation(s) in RCA: 66] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2017] [Accepted: 10/02/2017] [Indexed: 11/15/2022]
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15
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Cang Z, Wei GW. Integration of element specific persistent homology and machine learning for protein-ligand binding affinity prediction. INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING 2018; 34. [PMID: 28677268 DOI: 10.1002/cnm.2914] [Citation(s) in RCA: 93] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/14/2017] [Revised: 06/27/2017] [Accepted: 06/29/2017] [Indexed: 05/17/2023]
Abstract
Protein-ligand binding is a fundamental biological process that is paramount to many other biological processes, such as signal transduction, metabolic pathways, enzyme construction, cell secretion, and gene expression. Accurate prediction of protein-ligand binding affinities is vital to rational drug design and the understanding of protein-ligand binding and binding induced function. Existing binding affinity prediction methods are inundated with geometric detail and involve excessively high dimensions, which undermines their predictive power for massive binding data. Topology provides the ultimate level of abstraction and thus incurs too much reduction in geometric information. Persistent homology embeds geometric information into topological invariants and bridges the gap between complex geometry and abstract topology. However, it oversimplifies biological information. This work introduces element specific persistent homology (ESPH) or multicomponent persistent homology to retain crucial biological information during topological simplification. The combination of ESPH and machine learning gives rise to a powerful paradigm for macromolecular analysis. Tests on 2 large data sets indicate that the proposed topology-based machine-learning paradigm outperforms other existing methods in protein-ligand binding affinity predictions. ESPH reveals protein-ligand binding mechanism that can not be attained from other conventional techniques. The present approach reveals that protein-ligand hydrophobic interactions are extended to 40Å away from the binding site, which has a significant ramification to drug and protein design.
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Affiliation(s)
- Zixuan Cang
- Department of Mathematics, Michigan State University, MI 48824, USA
| | - Guo-Wei Wei
- Department of Mathematics, Michigan State University, MI 48824, USA
- Department of Biochemistry and Molecular Biology, Michigan State University, MI 48824, USA
- Department of Electrical and Computer Engineering, Michigan State University, MI 48824, USA
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16
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Affiliation(s)
- Srivatsan Raman
- Department of Biochemistry
and Department of Bacteriology, University of Wisconsin—Madison, Madison, Wisconsin 53706, United States
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17
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Cang Z, Mu L, Wei GW. Representability of algebraic topology for biomolecules in machine learning based scoring and virtual screening. PLoS Comput Biol 2018; 14:e1005929. [PMID: 29309403 PMCID: PMC5774846 DOI: 10.1371/journal.pcbi.1005929] [Citation(s) in RCA: 139] [Impact Index Per Article: 23.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2017] [Revised: 01/19/2018] [Accepted: 12/15/2017] [Indexed: 12/05/2022] Open
Abstract
This work introduces a number of algebraic topology approaches, including multi-component persistent homology, multi-level persistent homology, and electrostatic persistence for the representation, characterization, and description of small molecules and biomolecular complexes. In contrast to the conventional persistent homology, multi-component persistent homology retains critical chemical and biological information during the topological simplification of biomolecular geometric complexity. Multi-level persistent homology enables a tailored topological description of inter- and/or intra-molecular interactions of interest. Electrostatic persistence incorporates partial charge information into topological invariants. These topological methods are paired with Wasserstein distance to characterize similarities between molecules and are further integrated with a variety of machine learning algorithms, including k-nearest neighbors, ensemble of trees, and deep convolutional neural networks, to manifest their descriptive and predictive powers for protein-ligand binding analysis and virtual screening of small molecules. Extensive numerical experiments involving 4,414 protein-ligand complexes from the PDBBind database and 128,374 ligand-target and decoy-target pairs in the DUD database are performed to test respectively the scoring power and the discriminatory power of the proposed topological learning strategies. It is demonstrated that the present topological learning outperforms other existing methods in protein-ligand binding affinity prediction and ligand-decoy discrimination.
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Affiliation(s)
- Zixuan Cang
- Department of Mathematics, Michigan State University, East Lansing, Michigan, United States of America
| | - Lin Mu
- Computer Science and Mathematics Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee, United States of America
| | - Guo-Wei Wei
- Department of Mathematics, Michigan State University, East Lansing, Michigan, United States of America
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, Michigan, United States of America
- Department of Electrical and Computer Engineering, Michigan State University, East Lansing, Michigan, United States of America
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18
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Smith RD, Lu J, Carlson HA. Are there physicochemical differences between allosteric and competitive ligands? PLoS Comput Biol 2017; 13:e1005813. [PMID: 29125840 PMCID: PMC5699844 DOI: 10.1371/journal.pcbi.1005813] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2017] [Revised: 11/22/2017] [Accepted: 10/05/2017] [Indexed: 01/04/2023] Open
Abstract
Previous studies have compared the physicochemical properties of allosteric compounds to non-allosteric compounds. Those studies have found that allosteric compounds tend to be smaller, more rigid, more hydrophobic, and more drug-like than non-allosteric compounds. However, previous studies have not properly corrected for the fact that some protein targets have much more data than other systems. This generates concern regarding the possible skew that can be introduced by the inherent bias in the available data. Hence, this study aims to determine how robust the previous findings are to the addition of newer data. This study utilizes the Allosteric Database (ASD v3.0) and ChEMBL v20 to systematically obtain large datasets of both allosteric and competitive ligands. This dataset contains 70,219 and 9,511 unique ligands for the allosteric and competitive sets, respectively. Physically relevant compound descriptors were computed to examine the differences in their chemical properties. Particular attention was given to removing redundancy in the data and normalizing across ligand diversity and varied protein targets. The resulting distributions only show that allosteric ligands tend to be more aromatic and rigid and do not confirm the increase in hydrophobicity or difference in drug-likeness. These results are robust across different normalization schemes.
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Affiliation(s)
- Richard D. Smith
- Department of Medicinal Chemistry, University of Michigan, Ann Arbor, MI, United States of America
| | - Jing Lu
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, United States of America
| | - Heather A. Carlson
- Department of Medicinal Chemistry, University of Michigan, Ann Arbor, MI, United States of America
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, United States of America
- * E-mail:
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19
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Cang Z, Wei GW. TopologyNet: Topology based deep convolutional and multi-task neural networks for biomolecular property predictions. PLoS Comput Biol 2017; 13:e1005690. [PMID: 28749969 PMCID: PMC5549771 DOI: 10.1371/journal.pcbi.1005690] [Citation(s) in RCA: 155] [Impact Index Per Article: 22.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2017] [Revised: 08/08/2017] [Accepted: 07/18/2017] [Indexed: 11/18/2022] Open
Abstract
Although deep learning approaches have had tremendous success in image, video and audio processing, computer vision, and speech recognition, their applications to three-dimensional (3D) biomolecular structural data sets have been hindered by the geometric and biological complexity. To address this problem we introduce the element-specific persistent homology (ESPH) method. ESPH represents 3D complex geometry by one-dimensional (1D) topological invariants and retains important biological information via a multichannel image-like representation. This representation reveals hidden structure-function relationships in biomolecules. We further integrate ESPH and deep convolutional neural networks to construct a multichannel topological neural network (TopologyNet) for the predictions of protein-ligand binding affinities and protein stability changes upon mutation. To overcome the deep learning limitations from small and noisy training sets, we propose a multi-task multichannel topological convolutional neural network (MM-TCNN). We demonstrate that TopologyNet outperforms the latest methods in the prediction of protein-ligand binding affinities, mutation induced globular protein folding free energy changes, and mutation induced membrane protein folding free energy changes. AVAILABILITY weilab.math.msu.edu/TDL/.
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Affiliation(s)
- Zixuan Cang
- Department of Mathematics, Michigan State University, East Lansing, MI 48824, USA
| | - Guo-Wei Wei
- Department of Mathematics, Michigan State University, East Lansing, MI 48824, USA
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI 48824, USA
- Department of Electrical and Computer Engineering, Michigan State University, East Lansing, MI 48824, USA
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20
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Ma X, Meng H, Lai L. Motions of Allosteric and Orthosteric Ligand-Binding Sites in Proteins are Highly Correlated. J Chem Inf Model 2016; 56:1725-33. [DOI: 10.1021/acs.jcim.6b00039] [Citation(s) in RCA: 57] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Affiliation(s)
- Xiaomin Ma
- Center for Quantitative Biology, ‡BNLMS, State Key
Laboratory for Structural
Chemistry of Unstable and Stable Species, College of Chemistry and
Molecular Engineering, and §Peking-Tsinghua Center for Life Sciences, Peking University, Beijing 100871, China
| | - Hu Meng
- Center for Quantitative Biology, ‡BNLMS, State Key
Laboratory for Structural
Chemistry of Unstable and Stable Species, College of Chemistry and
Molecular Engineering, and §Peking-Tsinghua Center for Life Sciences, Peking University, Beijing 100871, China
| | - Luhua Lai
- Center for Quantitative Biology, ‡BNLMS, State Key
Laboratory for Structural
Chemistry of Unstable and Stable Species, College of Chemistry and
Molecular Engineering, and §Peking-Tsinghua Center for Life Sciences, Peking University, Beijing 100871, China
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21
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Amor BRC, Schaub MT, Yaliraki SN, Barahona M. Prediction of allosteric sites and mediating interactions through bond-to-bond propensities. Nat Commun 2016; 7:12477. [PMID: 27561351 PMCID: PMC5007447 DOI: 10.1038/ncomms12477] [Citation(s) in RCA: 55] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2015] [Accepted: 07/05/2016] [Indexed: 11/09/2022] Open
Abstract
Allostery is a fundamental mechanism of biological regulation, in which binding of a molecule at a distant location affects the active site of a protein. Allosteric sites provide targets to fine-tune protein activity, yet we lack computational methodologies to predict them. Here we present an efficient graph-theoretical framework to reveal allosteric interactions (atoms and communication pathways strongly coupled to the active site) without a priori information of their location. Using an atomistic graph with energy-weighted covalent and weak bonds, we define a bond-to-bond propensity quantifying the non-local effect of instantaneous bond fluctuations propagating through the protein. Significant interactions are then identified using quantile regression. We exemplify our method with three biologically important proteins: caspase-1, CheY, and h-Ras, correctly predicting key allosteric interactions, whose significance is additionally confirmed against a reference set of 100 proteins. The almost-linear scaling of our method renders it suitable for high-throughput searches for candidate allosteric sites.
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Affiliation(s)
- B R C Amor
- Department of Chemistry, Imperial College London, London SW7 2AZ, UK.,Institute of Chemical Biology, Imperial College London, London SW7 2AZ, UK
| | - M T Schaub
- Department of Mathematics, Imperial College London, London SW7 2AZ, UK
| | - S N Yaliraki
- Department of Chemistry, Imperial College London, London SW7 2AZ, UK.,Institute of Chemical Biology, Imperial College London, London SW7 2AZ, UK
| | - M Barahona
- Institute of Chemical Biology, Imperial College London, London SW7 2AZ, UK.,Department of Mathematics, Imperial College London, London SW7 2AZ, UK
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22
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Taguchi J, Kitao A. Dynamic profile analysis to characterize dynamics-driven allosteric sites in enzymes. Biophys Physicobiol 2016; 13:117-126. [PMID: 27924265 PMCID: PMC5042162 DOI: 10.2142/biophysico.13.0_117] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2015] [Accepted: 02/10/2016] [Indexed: 01/02/2023] Open
Abstract
We examine the dynamic features of non-trivial allosteric binding sites to elucidate potential drug binding sites. These allosteric sites were previously found to be allosteric after determination of the protein-drug co-crystal structure. After comprehensive search in the Protein Data Bank, we identify 10 complex structures with allosteric ligands whose structures are very similar to their functional forms. Then, possible pockets on the protein surface are searched as potential ligand binding sites. To mimic ligand binding to the pocket, complex models are generated to fill out each pocket with pseudo ligand blocks consisting of spheres. Normal mode analysis of the elastic network model is performed for the complex models and unbound structures to assess the change of protein dynamics induced by ligand binding. We examine nine profiles to describe the dynamic and positional characteristics of the pockets, and identify the change of fluctuation around the ligand, ΔMSFbs, as the best profile for distinguishing the allosteric sites from the other sites in 8 structures. These cases should be considered as examples of dynamics-driven allostery, which accompanies significant changes in protein dynamics. ΔMSFbs is suggested to be used for the search of potential dynamics-driven allosteric sites in proteins for drug discovery.
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Affiliation(s)
- Junko Taguchi
- Tsukuba Research Center, Taiho Pharmaceutical Co., Ltd, Tsukuba, Ibaraki, Japan
| | - Akio Kitao
- Institute of Molecular and Cellular Biosciences, The University of Tokyo, Tokyo 113-0032, Japan
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23
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Common Internal Allosteric Network Links Anesthetic Binding Sites in a Pentameric Ligand-Gated Ion Channel. PLoS One 2016; 11:e0158795. [PMID: 27403526 PMCID: PMC4942068 DOI: 10.1371/journal.pone.0158795] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2015] [Accepted: 06/22/2016] [Indexed: 01/10/2023] Open
Abstract
General anesthetics bind reversibly to ion channels, modifying their global conformational distributions, but the underlying atomic mechanisms are not completely known. We examine this issue by way of the model protein Gloeobacter violaceous ligand-gated ion channel (GLIC) using computational molecular dynamics, with a coarse-grained model to enhance sampling. We find that in flooding simulations, both propofol and a generic particle localize to the crystallographic transmembrane anesthetic binding region, and that propofol also localizes to an extracellular region shared with the crystallographic ketamine binding site. Subsequent simulations to probe these binding modes in greater detail demonstrate that ligand binding induces structural asymmetry in GLIC. Consequently, we employ residue interaction correlation analysis to describe the internal allosteric network underlying the coupling of ligand and distant effector sites necessary for conformational change. Overall, the results suggest that the same allosteric network may underlie the actions of various anesthetics, regardless of binding site.
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24
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Lisi GP, Manley GA, Hendrickson H, Rivalta I, Batista VS, Loria JP. Dissecting Dynamic Allosteric Pathways Using Chemically Related Small-Molecule Activators. Structure 2016; 24:1155-66. [PMID: 27238967 PMCID: PMC4938718 DOI: 10.1016/j.str.2016.04.010] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2016] [Revised: 04/20/2016] [Accepted: 04/22/2016] [Indexed: 01/26/2023]
Abstract
The allosteric mechanism of the heterodimeric enzyme imidazole glycerol phosphate synthase was studied in detail with solution nuclear magnetic resonance spectroscopy and molecular dynamics simulations. We studied IGPS in complex with a series of allosteric activators corresponding to a large range of catalytic rate enhancements (26- to 4,900-fold), in which ligand binding is entropically driven. Conformational flexibility on the millisecond timescale plays a crucial role in intersubunit communication. Carr-Purcell-Meiboom-Gill relaxation dispersion experiments probing Ile, Leu, and Val methyl groups reveal that the apo- and glutamine-mimicked complexes are static on the millisecond timescale. Domain-wide motions are stimulated in the presence of the allosteric activators. These studies, in conjunction with ligand titrations, demonstrate that the allosteric network is widely dispersed and varies with the identity of the effector. Furthermore, we find that stronger allosteric ligands create more conformational flexibility on the millisecond timescale throughout HisF. This domain-wide loosening leads to maximum catalytic activity.
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Affiliation(s)
- George P Lisi
- Department of Chemistry, Yale University, New Haven, CT 06520, USA
| | - Gregory A Manley
- Department of Chemistry, Yale University, New Haven, CT 06520, USA
| | | | - Ivan Rivalta
- École Normale Supérieure de Lyon, CNRS, Université Lyon 1, Laboratoire de Chimie UMR 5182, 46, Allée d'Italie, 69364 Lyon Cedex 07, France
| | - Victor S Batista
- Department of Chemistry, Yale University, New Haven, CT 06520, USA.
| | - J Patrick Loria
- Department of Chemistry, Yale University, New Haven, CT 06520, USA; Department of Molecular Biophysics & Biochemistry, Yale University, New Haven, CT 06520, USA.
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25
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Abstract
Allostery is a ubiquitous biological regulatory process in which distant binding sites within a protein or enzyme are functionally and thermodynamically coupled. Allosteric interactions play essential roles in many enzymological mechanisms, often facilitating formation of enzyme-substrate complexes and/or product release. Thus, elucidating the forces that drive allostery is critical to understanding the complex transformations of biomolecules. Currently, a number of models exist to describe allosteric behavior, taking into account energetics as well as conformational rearrangements and fluctuations. In the following Review, we discuss the use of solution NMR techniques designed to probe allosteric mechanisms in enzymes. NMR spectroscopy is unequaled in its ability to detect structural and dynamical changes in biomolecules, and the case studies presented herein demonstrate the range of insights to be gained from this valuable method. We also provide a detailed technical discussion of several specialized NMR experiments that are ideally suited for the study of enzymatic allostery.
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Affiliation(s)
- George P. Lisi
- Department of Chemistry, Yale University, New Haven, CT 06520
| | - J. Patrick Loria
- Department of Chemistry, Yale University, New Haven, CT 06520
- Department of Molecular Biophysics & Biochemistry, Yale University, New Haven, CT 06520
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26
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Ma X, Qi Y, Lai L. Allosteric sites can be identified based on the residue-residue interaction energy difference. Proteins 2016; 83:1375-84. [PMID: 25185787 DOI: 10.1002/prot.24681] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2014] [Revised: 08/20/2014] [Accepted: 08/22/2014] [Indexed: 11/08/2022]
Abstract
Allosteric drugs act at a distance to regulate protein functions. They have several advantages over conventional orthosteric drugs, including diverse regulation types and fewer side effects. However, the rational design of allosteric ligands remains a challenge, especially when it comes to the identification allosteric binding sites. As the binding of allosteric ligands may induce changes in the pattern of residue-residue interactions, we calculated the residue-residue interaction energies within the allosteric site based on the molecular mechanics generalized Born surface area energy decomposition scheme. Using a dataset of 17 allosteric proteins with structural data for both the apo and the ligand-bound state available, we used conformational ensembles generated by molecular dynamics simulations to compute the differences in the residue-residue interaction energies in known allosteric sites from both states. For all the known sites, distinct interaction energy differences (>25%) were observed. We then used CAVITY, a binding site detection program to identify novel putative allosteric sites in the same proteins. This yielded a total of 31 "druggable binding sites," of which 21 exhibited >25% difference in residue interaction energies, and were hence predicted as novel allosteric sites. Three of the predicted allosteric sites were supported by recent experimental studies. All the predicted sites may serve as novel allosteric sites for allosteric ligand design. Our study provides a computational method for identifying novel allosteric sites for allosteric drug design.
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Affiliation(s)
- Xiaomin Ma
- Center for Quantitative Biology, Peking University, Beijing, 100871, China
| | - Yifei Qi
- Center for Quantitative Biology, Peking University, Beijing, 100871, China
| | - Luhua Lai
- Center for Quantitative Biology, Peking University, Beijing, 100871, China.,BNLMS, State Key Laboratory for Structural Chemistry of Unstable and Stable Species, and Peking-Tsinghua Center for Life Sciences at College of Chemistry and Molecular Engineering, Peking University, Beijing, 100871, China
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27
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Kalescky R, Zhou H, Liu J, Tao P. Rigid Residue Scan Simulations Systematically Reveal Residue Entropic Roles in Protein Allostery. PLoS Comput Biol 2016; 12:e1004893. [PMID: 27115535 PMCID: PMC4846164 DOI: 10.1371/journal.pcbi.1004893] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2015] [Accepted: 04/01/2016] [Indexed: 12/22/2022] Open
Abstract
Intra-protein information is transmitted over distances via allosteric processes. This ubiquitous protein process allows for protein function changes due to ligand binding events. Understanding protein allostery is essential to understanding protein functions. In this study, allostery in the second PDZ domain (PDZ2) in the human PTP1E protein is examined as model system to advance a recently developed rigid residue scan method combining with configurational entropy calculation and principal component analysis. The contributions from individual residues to whole-protein dynamics and allostery were systematically assessed via rigid body simulations of both unbound and ligand-bound states of the protein. The entropic contributions of individual residues to whole-protein dynamics were evaluated based on covariance-based correlation analysis of all simulations. The changes of overall protein entropy when individual residues being held rigid support that the rigidity/flexibility equilibrium in protein structure is governed by the La Châtelier's principle of chemical equilibrium. Key residues of PDZ2 allostery were identified with good agreement with NMR studies of the same protein bound to the same peptide. On the other hand, the change of entropic contribution from each residue upon perturbation revealed intrinsic differences among all the residues. The quasi-harmonic and principal component analyses of simulations without rigid residue perturbation showed a coherent allosteric mode from unbound and bound states, respectively. The projection of simulations with rigid residue perturbation onto coherent allosteric modes demonstrated the intrinsic shifting of ensemble distributions supporting the population-shift theory of protein allostery. Overall, the study presented here provides a robust and systematic approach to estimate the contribution of individual residue internal motion to overall protein dynamics and allostery.
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Affiliation(s)
- Robert Kalescky
- Department of Chemistry, Center for Drug Discovery, Design, and Delivery (CD4), Center for Scientific Computation, Southern Methodist University, Dallas, Texas, United States of America
| | - Hongyu Zhou
- Department of Chemistry, Center for Drug Discovery, Design, and Delivery (CD4), Center for Scientific Computation, Southern Methodist University, Dallas, Texas, United States of America
| | - Jin Liu
- Department of Chemistry, Center for Drug Discovery, Design, and Delivery (CD4), Center for Scientific Computation, Southern Methodist University, Dallas, Texas, United States of America
- Department of Pharmaceutical Sciences, University of North Texas System College of Pharmacy, University of North Texas Health Science Center, Fort Worth, Texas, United States of America
- * E-mail: (JL); (PT)
| | - Peng Tao
- Department of Chemistry, Center for Drug Discovery, Design, and Delivery (CD4), Center for Scientific Computation, Southern Methodist University, Dallas, Texas, United States of America
- * E-mail: (JL); (PT)
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28
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Cimermancic P, Weinkam P, Rettenmaier TJ, Bichmann L, Keedy DA, Woldeyes RA, Schneidman-Duhovny D, Demerdash ON, Mitchell JC, Wells JA, Fraser JS, Sali A. CryptoSite: Expanding the Druggable Proteome by Characterization and Prediction of Cryptic Binding Sites. J Mol Biol 2016; 428:709-719. [PMID: 26854760 DOI: 10.1016/j.jmb.2016.01.029] [Citation(s) in RCA: 137] [Impact Index Per Article: 17.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2015] [Revised: 01/29/2016] [Accepted: 01/30/2016] [Indexed: 01/04/2023]
Abstract
Many proteins have small-molecule binding pockets that are not easily detectable in the ligand-free structures. These cryptic sites require a conformational change to become apparent; a cryptic site can therefore be defined as a site that forms a pocket in a holo structure, but not in the apo structure. Because many proteins appear to lack druggable pockets, understanding and accurately identifying cryptic sites could expand the set of drug targets. Previously, cryptic sites were identified experimentally by fragment-based ligand discovery and computationally by long molecular dynamics simulations and fragment docking. Here, we begin by constructing a set of structurally defined apo-holo pairs with cryptic sites. Next, we comprehensively characterize the cryptic sites in terms of their sequence, structure, and dynamics attributes. We find that cryptic sites tend to be as conserved in evolution as traditional binding pockets but are less hydrophobic and more flexible. Relying on this characterization, we use machine learning to predict cryptic sites with relatively high accuracy (for our benchmark, the true positive and false positive rates are 73% and 29%, respectively). We then predict cryptic sites in the entire structurally characterized human proteome (11,201 structures, covering 23% of all residues in the proteome). CryptoSite increases the size of the potentially "druggable" human proteome from ~40% to ~78% of disease-associated proteins. Finally, to demonstrate the utility of our approach in practice, we experimentally validate a cryptic site in protein tyrosine phosphatase 1B using a covalent ligand and NMR spectroscopy. The CryptoSite Web server is available at http://salilab.org/cryptosite.
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Affiliation(s)
- Peter Cimermancic
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA 94158, USA; Graduate Group in Biological and Medical Informatics,University of California, San Francisco, San Francisco, CA 94158, USA.
| | - Patrick Weinkam
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA 94158, USA
| | - T Justin Rettenmaier
- Graduate Group in Chemistry and Chemical Biology, University of California, San Francisco, San Francisco, CA 94158, USA; Pharmaceutical Chemistry and California Institute for Quantitative Biosciences, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Leon Bichmann
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Daniel A Keedy
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Rahel A Woldeyes
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA 94158, USA; Graduate Group in Chemistry and Chemical Biology, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Dina Schneidman-Duhovny
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Omar N Demerdash
- Department of Chemical and Biomolecular Engineering, University of California, Berkeley, Berkeley, CA 94720, USA
| | - Julie C Mitchell
- Departments of Biochemistry and Mathematics, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - James A Wells
- Pharmaceutical Chemistry and California Institute for Quantitative Biosciences, University of California, San Francisco, San Francisco, CA 94158, USA; Cellular and Molecular Pharmacology and California Institute for Quantitative Biosciences, University of California, San Francisco, San Francisco, CA 94158, USA
| | - James S Fraser
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Andrej Sali
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA 94158, USA; Pharmaceutical Chemistry and California Institute for Quantitative Biosciences, University of California, San Francisco, San Francisco, CA 94158, USA. http://salilab.org
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29
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Chen ASY, Westwood NJ, Brear P, Rogers GW, Mavridis L, Mitchell JBO. A Random Forest Model for Predicting Allosteric and Functional Sites on Proteins. Mol Inform 2016; 35:125-35. [DOI: 10.1002/minf.201500108] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2015] [Accepted: 12/28/2015] [Indexed: 01/17/2023]
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30
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Greener JG, Sternberg MJE. AlloPred: prediction of allosteric pockets on proteins using normal mode perturbation analysis. BMC Bioinformatics 2015; 16:335. [PMID: 26493317 PMCID: PMC4619270 DOI: 10.1186/s12859-015-0771-1] [Citation(s) in RCA: 67] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2015] [Accepted: 10/13/2015] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND Despite being hugely important in biological processes, allostery is poorly understood and no universal mechanism has been discovered. Allosteric drugs are a largely unexplored prospect with many potential advantages over orthosteric drugs. Computational methods to predict allosteric sites on proteins are needed to aid the discovery of allosteric drugs, as well as to advance our fundamental understanding of allostery. RESULTS AlloPred, a novel method to predict allosteric pockets on proteins, was developed. AlloPred uses perturbation of normal modes alongside pocket descriptors in a machine learning approach that ranks the pockets on a protein. AlloPred ranked an allosteric pocket top for 23 out of 40 known allosteric proteins, showing comparable and complementary performance to two existing methods. In 28 of 40 cases an allosteric pocket was ranked first or second. The AlloPred web server, freely available at http://www.sbg.bio.ic.ac.uk/allopred/home, allows visualisation and analysis of predictions. The source code and dataset information are also available from this site. CONCLUSIONS Perturbation of normal modes can enhance our ability to predict allosteric sites on proteins. Computational methods such as AlloPred assist drug discovery efforts by suggesting sites on proteins for further experimental study.
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Affiliation(s)
- Joe G Greener
- Centre for Integrative Systems Biology and Bioinformatics, Department of Life Sciences, Imperial College London, London, SW7 2AZ, UK.
| | - Michael J E Sternberg
- Centre for Integrative Systems Biology and Bioinformatics, Department of Life Sciences, Imperial College London, London, SW7 2AZ, UK
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31
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Aubailly S, Piazza F. Cutoff lensing: predicting catalytic sites in enzymes. Sci Rep 2015; 5:14874. [PMID: 26445900 PMCID: PMC4597221 DOI: 10.1038/srep14874] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2015] [Accepted: 09/10/2015] [Indexed: 01/12/2023] Open
Abstract
Predicting function-related amino acids in proteins with unknown function or unknown allosteric binding sites in drug-targeted proteins is a task of paramount importance in molecular biomedicine. In this paper we introduce a simple, light and computationally inexpensive structure-based method to identify catalytic sites in enzymes. Our method, termed cutoff lensing, is a general procedure consisting in letting the cutoff used to build an elastic network model increase to large values. A validation of our method against a large database of annotated enzymes shows that optimal values of the cutoff exist such that three different structure-based indicators allow one to recover a maximum of the known catalytic sites. Interestingly, we find that the larger the structures the greater the predictive power afforded by our method. Possible ways to combine the three indicators into a single figure of merit and into a specific sequential analysis are suggested and discussed with reference to the classic case of HIV-protease. Our method could be used as a complement to other sequence- and/or structure-based methods to narrow the results of large-scale screenings.
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Affiliation(s)
- Simon Aubailly
- Université d'Orléans, Centre de Biophysique Moléculaire, CNRS-UPR4301, Rue C. Sadron, 45071, Orléans, France
| | - Francesco Piazza
- Université d'Orléans, Centre de Biophysique Moléculaire, CNRS-UPR4301, Rue C. Sadron, 45071, Orléans, France
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32
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Laraia L, McKenzie G, Spring DR, Venkitaraman AR, Huggins DJ. Overcoming Chemical, Biological, and Computational Challenges in the Development of Inhibitors Targeting Protein-Protein Interactions. CHEMISTRY & BIOLOGY 2015; 22:689-703. [PMID: 26091166 PMCID: PMC4518475 DOI: 10.1016/j.chembiol.2015.04.019] [Citation(s) in RCA: 115] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/19/2014] [Revised: 04/01/2015] [Accepted: 04/08/2015] [Indexed: 01/19/2023]
Abstract
Protein-protein interactions (PPIs) underlie the majority of biological processes, signaling, and disease. Approaches to modulate PPIs with small molecules have therefore attracted increasing interest over the past decade. However, there are a number of challenges inherent in developing small-molecule PPI inhibitors that have prevented these approaches from reaching their full potential. From target validation to small-molecule screening and lead optimization, identifying therapeutically relevant PPIs that can be successfully modulated by small molecules is not a simple task. Following the recent review by Arkin et al., which summarized the lessons learnt from prior successes, we focus in this article on the specific challenges of developing PPI inhibitors and detail the recent advances in chemistry, biology, and computation that facilitate overcoming them. We conclude by providing a perspective on the field and outlining four innovations that we see as key enabling steps for successful development of small-molecule inhibitors targeting PPIs.
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Affiliation(s)
- Luca Laraia
- Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, UK; Medical Research Council Cancer Unit, University of Cambridge, Hutchison/MRC Research Centre, Hills Road, Cambridge CB2 0XZ, UK
| | - Grahame McKenzie
- Medical Research Council Cancer Unit, University of Cambridge, Hutchison/MRC Research Centre, Hills Road, Cambridge CB2 0XZ, UK
| | - David R Spring
- Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, UK
| | - Ashok R Venkitaraman
- Medical Research Council Cancer Unit, University of Cambridge, Hutchison/MRC Research Centre, Hills Road, Cambridge CB2 0XZ, UK
| | - David J Huggins
- Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, UK; Medical Research Council Cancer Unit, University of Cambridge, Hutchison/MRC Research Centre, Hills Road, Cambridge CB2 0XZ, UK; Theory of Condensed Matter Group, Cavendish Laboratory, University of Cambridge, 19 JJ Thomson Avenue, Cambridge CB3 0HE, UK.
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33
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Ribeiro AA, Ortiz V. Local elastic constants of LacI and implications for allostery. J Mol Graph Model 2015; 57:106-13. [DOI: 10.1016/j.jmgm.2015.01.013] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2014] [Revised: 01/28/2015] [Accepted: 01/29/2015] [Indexed: 11/28/2022]
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34
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Soltan Ghoraie L, Burkowski F, Zhu M. Sparse networks of directly coupled, polymorphic, and functional side chains in allosteric proteins. Proteins 2015; 83:497-516. [DOI: 10.1002/prot.24752] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2014] [Revised: 12/05/2014] [Accepted: 12/13/2014] [Indexed: 02/05/2023]
Affiliation(s)
| | - Forbes Burkowski
- School of Computer Science, University of Waterloo; Waterloo Ontario Canada
| | - Mu Zhu
- Department of Statistics and Actuarial Science; University of Waterloo; Waterloo Ontario Canada
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35
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Varma S, Botlani M, Leighty RE. Discerning intersecting fusion-activation pathways in the Nipah virus using machine learning. Proteins 2014; 82:3241-54. [DOI: 10.1002/prot.24541] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2014] [Revised: 02/10/2014] [Accepted: 02/14/2014] [Indexed: 12/19/2022]
Affiliation(s)
- Sameer Varma
- Department of Cell Biology; Microbiology and Molecular Biology, University of South Florida; Tampa Florida 33620
| | - Mohsen Botlani
- Department of Cell Biology; Microbiology and Molecular Biology, University of South Florida; Tampa Florida 33620
| | - Ralph E. Leighty
- Department of Cell Biology; Microbiology and Molecular Biology, University of South Florida; Tampa Florida 33620
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36
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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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37
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Xia Y, DiPrimio N, Keppel TR, Vo B, Fraser K, Battaile KP, Egan C, Bystroff C, Lovell S, Weis DD, Anderson JC, Karanicolas J. The designability of protein switches by chemical rescue of structure: mechanisms of inactivation and reactivation. J Am Chem Soc 2013; 135:18840-9. [PMID: 24313858 DOI: 10.1021/ja407644b] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
The ability to selectively activate function of particular proteins via pharmacological agents is a longstanding goal in chemical biology. Recently, we reported an approach for designing a de novo allosteric effector site directly into the catalytic domain of an enzyme. This approach is distinct from traditional chemical rescue of enzymes in that it relies on disruption and restoration of structure, rather than active site chemistry, as a means to achieve modulate function. However, rationally identifying analogous de novo binding sites in other enzymes represents a key challenge for extending this approach to introduce allosteric control into other enzymes. Here we show that mutation sites leading to protein inactivation via tryptophan-to-glycine substitution and allowing (partial) reactivation by the subsequent addition of indole are remarkably frequent. Through a suite of methods including a cell-based reporter assay, computational structure prediction and energetic analysis, fluorescence studies, enzymology, pulse proteolysis, X-ray crystallography, and hydrogen-deuterium mass spectrometry, we find that these switchable proteins are most commonly modulated indirectly, through control of protein stability. Addition of indole in these cases rescues activity not by reverting a discrete conformational change, as we had observed in the sole previously reported example, but rather rescues activity by restoring protein stability. This important finding will dramatically impact the design of future switches and sensors built by this approach, since evaluating stability differences associated with cavity-forming mutations is a far more tractable task than predicting allosteric conformational changes. By analogy to natural signaling systems, the insights from this study further raise the exciting prospect of modulating stability to design optimal recognition properties into future de novo switches and sensors built through chemical rescue of structure.
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Affiliation(s)
- Yan Xia
- Department of Molecular Biosciences, ‡Department of Chemistry, §Protein Structure Laboratory, and ∥Center for Bioinformatics, University of Kansas , Lawrence, Kansas 66045, United States
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38
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Zhu X, Ericksen SS, Demerdash ONA, Mitchell JC. Data-driven models for protein interaction and design. Proteins 2013; 81:2221-8. [PMID: 24038640 DOI: 10.1002/prot.24405] [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] [Received: 06/11/2013] [Revised: 08/12/2013] [Accepted: 08/21/2013] [Indexed: 12/13/2022]
Abstract
We describe methods and results for four new types of challenge in the Critical Assessment of PRedicted Interactions (CAPRI). Two new challenges asked predictors to create models related to protein interface design. The first of these was to distinguish binding interfaces from designed nonbinding interfaces. The second was to predict the effects of all single-point mutations on hemagglutinin binding to two small designed proteins. Two additional challenges asked predictors to submit high-resolution structures for interface-bound crystallographic waters and for binding heparin to a putative glycosylase.
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39
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Erman B. A fast approximate method of identifying paths of allosteric communication in proteins. Proteins 2013; 81:1097-101. [PMID: 23508936 DOI: 10.1002/prot.24284] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2013] [Revised: 02/22/2013] [Accepted: 03/05/2013] [Indexed: 11/07/2022]
Abstract
Fluctuations of the distance between a pair of residues i and j may be correlated with the fluctuations of the distance between another pair k and l. In this case, information may be transmitted among these four residues. Allosteric activity is postulated to proceed through such correlated paths. In this short communication a fast method for calculating correlations among all possible pairs ij and kl leading to a pathway of correlated residues of a protein is proposed. The method is based on the alpha carbon centered Gaussian Network Model. The model is applied to Glutamine Amidotransferase and pathways of allosteric activity are identified and compared with literature.
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Affiliation(s)
- Burak Erman
- Department of Chemical and Biological Engineering, Koc University, Istanbul, Turkey.
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40
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Daily MD, Yu H, Phillips GN, Cui Q. Allosteric activation transitions in enzymes and biomolecular motors: insights from atomistic and coarse-grained simulations. Top Curr Chem (Cham) 2013; 337:139-64. [PMID: 23468286 PMCID: PMC3976962 DOI: 10.1007/128_2012_409] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
The chemical step in enzymes is usually preceded by a kinetically distinct activation step that involves large-scale conformational transitions. In "simple" enzymes this step corresponds to the closure of the active site; in more complex enzymes, such as biomolecular motors, the activation step is more complex and may involve interactions with other biomolecules. These activation transitions are essential to the function of enzymes and perturbations in the scale and/or rate of these transitions are implicated in various serious human diseases; incorporating key flexibilities into engineered enzymes is also considered a major remaining challenge in rational enzyme design. Therefore it is important to understand the underlying mechanism of these transitions. This is a significant challenge to both experimental and computational studies because of the allosteric and multi-scale nature of such transitions. Using our recent studies of two enzyme systems, myosin and adenylate kinase (AK), we discuss how atomistic and coarse-grained simulations can be used to provide insights into the mechanism of activation transitions in realistic systems. Collectively, the results suggest that although many allosteric transitions can be viewed as domain displacements mediated by flexible hinges, there are additional complexities and various deviations. For example, although our studies do not find any evidence for "cracking" in AK, our results do underline the contribution of intra-domain properties (e.g., dihedral flexibility) to the rate of the transition. The study of mechanochemical coupling in myosin highlights that local changes important to chemistry require stabilization from more extensive structural changes; in this sense, more global structural transitions are needed to activate the chemistry in the active site. These discussions further emphasize the importance of better understanding factors that control the degree of co-operativity for allosteric transitions, again hinting at the intimate connection between protein stability and functional flexibility. Finally, a number of topics of considerable future interest are briefly discussed.
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Affiliation(s)
- Michael D Daily
- Pacific Northwest National Laboratory, Richland, Washington, 99352, USA
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41
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Impact of mutations on the allosteric conformational equilibrium. J Mol Biol 2012; 425:647-61. [PMID: 23228330 DOI: 10.1016/j.jmb.2012.11.041] [Citation(s) in RCA: 41] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2012] [Revised: 11/27/2012] [Accepted: 11/30/2012] [Indexed: 11/21/2022]
Abstract
Allostery in a protein involves effector binding at an allosteric site that changes the structure and/or dynamics at a distant, functional site. In addition to the chemical equilibrium of ligand binding, allostery involves a conformational equilibrium between one protein substate that binds the effector and a second substate that less strongly binds the effector. We run molecular dynamics simulations using simple, smooth energy landscapes to sample specific ligand-induced conformational transitions, as defined by the effector-bound and effector-unbound protein structures. These simulations can be performed using our web server (http://salilab.org/allosmod/). We then develop a set of features to analyze the simulations and capture the relevant thermodynamic properties of the allosteric conformational equilibrium. These features are based on molecular mechanics energy functions, stereochemical effects, and structural/dynamic coupling between sites. Using a machine-learning algorithm on a data set of 10 proteins and 179 mutations, we predict both the magnitude and the sign of the allosteric conformational equilibrium shift by the mutation; the impact of a large identifiable fraction of the mutations can be predicted with an average unsigned error of 1k(B)T. With similar accuracy, we predict the mutation effects for an 11th protein that was omitted from the initial training and testing of the machine-learning algorithm. We also assess which calculated thermodynamic properties contribute most to the accuracy of the prediction.
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42
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Panjkovich A, Daura X. Exploiting protein flexibility to predict the location of allosteric sites. BMC Bioinformatics 2012; 13:273. [PMID: 23095452 PMCID: PMC3562710 DOI: 10.1186/1471-2105-13-273] [Citation(s) in RCA: 66] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2012] [Accepted: 10/17/2012] [Indexed: 01/11/2023] Open
Abstract
BACKGROUND Allostery is one of the most powerful and common ways of regulation of protein activity. However, for most allosteric proteins identified to date the mechanistic details of allosteric modulation are not yet well understood. Uncovering common mechanistic patterns underlying allostery would allow not only a better academic understanding of the phenomena, but it would also streamline the design of novel therapeutic solutions. This relatively unexplored therapeutic potential and the putative advantages of allosteric drugs over classical active-site inhibitors fuel the attention allosteric-drug research is receiving at present. A first step to harness the regulatory potential and versatility of allosteric sites, in the context of drug-discovery and design, would be to detect or predict their presence and location. In this article, we describe a simple computational approach, based on the effect allosteric ligands exert on protein flexibility upon binding, to predict the existence and position of allosteric sites on a given protein structure. RESULTS By querying the literature and a recently available database of allosteric sites, we gathered 213 allosteric proteins with structural information that we further filtered into a non-redundant set of 91 proteins. We performed normal-mode analysis and observed significant changes in protein flexibility upon allosteric-ligand binding in 70% of the cases. These results agree with the current view that allosteric mechanisms are in many cases governed by changes in protein dynamics caused by ligand binding. Furthermore, we implemented an approach that achieves 65% positive predictive value in identifying allosteric sites within the set of predicted cavities of a protein (stricter parameters set, 0.22 sensitivity), by combining the current analysis on dynamics with previous results on structural conservation of allosteric sites. We also analyzed four biological examples in detail, revealing that this simple coarse-grained methodology is able to capture the effects triggered by allosteric ligands already described in the literature. CONCLUSIONS We introduce a simple computational approach to predict the presence and position of allosteric sites in a protein based on the analysis of changes in protein normal modes upon the binding of a coarse-grained ligand at predicted cavities. Its performance has been demonstrated using a newly curated non-redundant set of 91 proteins with reported allosteric properties. The software developed in this work is available upon request from the authors.
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Affiliation(s)
- Alejandro Panjkovich
- Institute of Biotechnology and Biomedicine, Universitat Autònoma de Barcelona, 08193 Cerdanyola del Vallès, Spain
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43
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The emergence of protein complexes: quaternary structure, dynamics and allostery. Colworth Medal Lecture. Biochem Soc Trans 2012; 40:475-91. [PMID: 22616857 DOI: 10.1042/bst20120056] [Citation(s) in RCA: 64] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
All proteins require physical interactions with other proteins in order to perform their functions. Most of them oligomerize into homomers, and a vast majority of these homomers interact with other proteins, at least part of the time, forming transient or obligate heteromers. In the present paper, we review the structural, biophysical and evolutionary aspects of these protein interactions. We discuss how protein function and stability benefit from oligomerization, as well as evolutionary pathways by which oligomers emerge, mostly from the perspective of homomers. Finally, we emphasize the specificities of heteromeric complexes and their structure and evolution. We also discuss two analytical approaches increasingly being used to study protein structures as well as their interactions. First, we review the use of the biological networks and graph theory for analysis of protein interactions and structure. Secondly, we discuss recent advances in techniques for detecting correlated mutations, with the emphasis on their role in identifying pathways of allosteric communication.
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44
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Laine E, Auclair C, Tchertanov L. Allosteric communication across the native and mutated KIT receptor tyrosine kinase. PLoS Comput Biol 2012; 8:e1002661. [PMID: 22927810 PMCID: PMC3426562 DOI: 10.1371/journal.pcbi.1002661] [Citation(s) in RCA: 46] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2012] [Accepted: 07/12/2012] [Indexed: 11/18/2022] Open
Abstract
A fundamental goal in cellular signaling is to understand allosteric communication, the process by which signals originated at one site in a protein propagate dependably to affect remote functional sites. Here, we describe the allosteric regulation of the receptor tyrosine kinase KIT. Our analysis evidenced that communication routes established between the activation loop (A-loop) and the distant juxtamembrane region (JMR) in the native protein were disrupted by the oncogenic mutation D816V positioned in the A-loop. In silico mutagenesis provided a plausible way of restoring the protein communication detected in the native KIT by introducing a counter-balancing second mutation D792E. The communication patterns observed in the native and mutated KIT correlate perfectly with the structural and dynamical features of these proteins. Particularly, a long-distance effect of the D816V mutation manifested as an important structural re-organization of the JMR in the oncogenic mutant was completely vanished in the double mutant D816V/D792E. This detailed characterization of the allosteric communication in the different forms of KIT, native and mutants, was performed by using a modular network representation composed of communication pathways and independent dynamic segments. Such representation permits to enrich a purely mechanistic interaction-based model of protein communication by the introduction of concerted local atomic fluctuations. This method, validated on KIT receptor, may guide a rational modulation of the physiopathological activities of other receptor tyrosine kinases. The majority of functionally important biological processes are regulated by allosteric communication within individual proteins and across protein complexes. Receptor tyrosine kinases (RTKs) control signal transduction pathways and consequently represent a typical paradigm. The mutation-induced deregulation of RTK activity impairs crucial cellular physiological functions and causes serious human diseases. The present study focuses on the allosteric communication across the three-dimensional structure of the RTK KIT cytoplasmic region. Combining a mechanistic model of information transmission with the analysis of concerted local atomic fluctuations we examined and compared the communication profiles in the native and D816V-mutated proteins. This approach permitted to localize and visualize communication routes in the native KIT and revealed that these routes were disrupted in the mutant D816V. We proposed in silico mutagenesis as a mean to restore the communication detected in the native KIT. Our work sheds light on the allosteric communication in RTKs, a phenomenon playing an essential role in signaling pathways albeit experiments do not provide the atomic details of the path followed in going from one structural element to the other. A rational understanding of the molecular determinants underlying the effects of disease-related kinase mutations may contribute to the improvement of targeted therapies.
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Affiliation(s)
- Elodie Laine
- LBPA, CNRS - ENS de Cachan, LabEx LERMIT, Cachan, France
| | | | - Luba Tchertanov
- LBPA, CNRS - ENS de Cachan, LabEx LERMIT, Cachan, France
- * E-mail:
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45
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Qi Y, Wang Q, Tang B, Lai L. Identifying Allosteric Binding Sites in Proteins with a Two-State Go̅ Model for Novel Allosteric Effector Discovery. J Chem Theory Comput 2012; 8:2962-71. [DOI: 10.1021/ct300395h] [Citation(s) in RCA: 40] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Affiliation(s)
- Yifei Qi
- BNLMS,
State Key Laboratory for Structural Chemistry of Unstable and Stable
Species, and Peking−Tsinghua Center for Life Sciences at College
of Chemistry and Molecular Engineering and ‡Center for Quantitative Biology, Peking University, Beijing 100871, China
| | - Qian Wang
- BNLMS,
State Key Laboratory for Structural Chemistry of Unstable and Stable
Species, and Peking−Tsinghua Center for Life Sciences at College
of Chemistry and Molecular Engineering and ‡Center for Quantitative Biology, Peking University, Beijing 100871, China
| | - Bo Tang
- BNLMS,
State Key Laboratory for Structural Chemistry of Unstable and Stable
Species, and Peking−Tsinghua Center for Life Sciences at College
of Chemistry and Molecular Engineering and ‡Center for Quantitative Biology, Peking University, Beijing 100871, China
| | - Luhua Lai
- BNLMS,
State Key Laboratory for Structural Chemistry of Unstable and Stable
Species, and Peking−Tsinghua Center for Life Sciences at College
of Chemistry and Molecular Engineering and ‡Center for Quantitative Biology, Peking University, Beijing 100871, China
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46
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Deckert K, Budiardjo SJ, Brunner LC, Lovell S, Karanicolas J. Designing allosteric control into enzymes by chemical rescue of structure. J Am Chem Soc 2012; 134:10055-60. [PMID: 22655749 DOI: 10.1021/ja301409g] [Citation(s) in RCA: 35] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Ligand-dependent activity has been engineered into enzymes for purposes ranging from controlling cell morphology to reprogramming cellular signaling pathways. Where these successes have typically fused a naturally allosteric domain to the enzyme of interest, here we instead demonstrate an approach for designing a de novo allosteric effector site directly into the catalytic domain of an enzyme. This approach is distinct from traditional chemical rescue of enzymes in that it relies on disruption and restoration of structure, rather than active site chemistry, as a means to achieve modulate function. We present two examples, W33G in a β-glycosidase enzyme (β-gly) and W492G in a β-glucuronidase enzyme (β-gluc), in which we engineer indole-dependent activity into enzymes by removing a buried tryptophan side chain that serves as a buttress for the active site architecture. In both cases, we observe a loss of function, and in both cases we find that the subsequent addition of indole can be used to restore activity. Through a detailed analysis of β-gly W33G kinetics, we demonstrate that this rescued enzyme is fully functionally equivalent to the corresponding wild-type enzyme. We then present the apo and indole-bound crystal structures of β-gly W33G, which together establish the structural basis for enzyme inactivation and rescue. Finally, we use this designed switch to modulate β-glycosidase activity in living cells using indole. Disruption and recovery of protein structure may represent a general technique for introducing allosteric control into enzymes, and thus may serve as a starting point for building a variety of bioswitches and sensors.
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Affiliation(s)
- Katelyn Deckert
- Department of Molecular Biosciences, University of Kansas, 1200 Sunnyside Avenue, Lawrence, Kansas 66045-7534, USA
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47
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Demerdash ONA, Mitchell JC. Density-cluster NMA: A new protein decomposition technique for coarse-grained normal mode analysis. Proteins 2012; 80:1766-79. [PMID: 22434479 DOI: 10.1002/prot.24072] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2011] [Revised: 02/13/2012] [Accepted: 03/12/2012] [Indexed: 11/10/2022]
Abstract
Normal mode analysis has emerged as a useful technique for investigating protein motions on long time scales. This is largely due to the advent of coarse-graining techniques, particularly Hooke's Law-based potentials and the rotational-translational blocking (RTB) method for reducing the size of the force-constant matrix, the Hessian. Here we present a new method for domain decomposition for use in RTB that is based on hierarchical clustering of atomic density gradients, which we call Density-Cluster RTB (DCRTB). The method reduces the number of degrees of freedom by 85-90% compared with the standard blocking approaches. We compared the normal modes from DCRTB against standard RTB using 1-4 residues in sequence in a single block, with good agreement between the two methods. We also show that Density-Cluster RTB and standard RTB perform well in capturing the experimentally determined direction of conformational change. Significantly, we report superior correlation of DCRTB with B-factors compared with 1-4 residue per block RTB. Finally, we show significant reduction in computational cost for Density-Cluster RTB that is nearly 100-fold for many examples.
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Affiliation(s)
- Omar N A Demerdash
- Medical Scientist Training Program, University of Wisconsin-Madison, Madison, Wisconsin, USA
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48
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Namboodiri S, Giuliani A, Nair AS, Dhar PK. Looking for a sequence based allostery definition: a statistical journey at different resolution scales. J Theor Biol 2012; 304:211-8. [PMID: 22484347 DOI: 10.1016/j.jtbi.2012.03.005] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2011] [Revised: 02/29/2012] [Accepted: 03/03/2012] [Indexed: 11/18/2022]
Abstract
The aim of this work was to detect allosteric hotspots signatures characterizing protein regions acting as the 'key drivers' of global allosteric conformational change. We computationally estimated the relative strength of intra-molecular interaction in allosteric proteins between two putative allostery-susceptible sites using a co-evolution model based upon the optimization of the cross-correlation in terms of free-energy-transfer hydrophobicity scale (Tanford scale) distribution along the chain. Cross-Recurrence Quantification Analysis (Cross-RQA) applied on the sequences of allostery susceptible sites showed evidence of strong interaction amongst allosteric susceptible sites. This could be due to transient weak molecular bonds between allostery susceptible patches enabling regions far-apart to come together. Further, using a large protein dataset, by comparing allosteric protein set with a randomly generated sequence population as well as a generic protein set, we reconfirmed our earlier findings that hydrophobicity patterning (as formalized by Recurrence Quantification Analysis (RQA) descriptors) may serve as determinant of allostery and its relevance in the transmission of allosteric conformational change. We applied RQA to free-energy-transfer hydrophobicity-transformed amino acid sequences of the allostery dataset to extract allostery specific global sequence features. These free-energy-transfer hydrophobicity-based RQA markers proved to be representative of allosteric signatures and not related to the differences between randomly generated and real proteins. These free-energy-transfer hydrophobicity-based RQA markers when evaluated by pattern recognition tools could distinguish allosteric proteins with 92% accuracy.
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Affiliation(s)
- Saritha Namboodiri
- State Inter University Centre of Excellence in Bioinformatics, University of Kerala, Kariyavattom Campus, Thiruvananthapuram, Kerala, India
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49
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Dixit A, Verkhivker GM. Computational modeling of allosteric communication reveals organizing principles of mutation-induced signaling in ABL and EGFR kinases. PLoS Comput Biol 2011; 7:e1002179. [PMID: 21998569 PMCID: PMC3188506 DOI: 10.1371/journal.pcbi.1002179] [Citation(s) in RCA: 63] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2011] [Accepted: 07/16/2011] [Indexed: 12/15/2022] Open
Abstract
The emerging structural information about allosteric kinase complexes and the growing number of allosteric inhibitors call for a systematic strategy to delineate and classify mechanisms of allosteric regulation and long-range communication that control kinase activity. In this work, we have investigated mechanistic aspects of long-range communications in ABL and EGFR kinases based on the results of multiscale simulations of regulatory complexes and computational modeling of signal propagation in proteins. These approaches have been systematically employed to elucidate organizing molecular principles of allosteric signaling in the ABL and EGFR multi-domain regulatory complexes and analyze allosteric signatures of the gate-keeper cancer mutations. We have presented evidence that mechanisms of allosteric activation may have universally evolved in the ABL and EGFR regulatory complexes as a product of a functional cross-talk between the organizing αF-helix and conformationally adaptive αI-helix and αC-helix. These structural elements form a dynamic network of efficiently communicated clusters that may control the long-range interdomain coupling and allosteric activation. The results of this study have unveiled a unifying effect of the gate-keeper cancer mutations as catalysts of kinase activation, leading to the enhanced long-range communication among allosterically coupled segments and stabilization of the active kinase form. The results of this study can reconcile recent experimental studies of allosteric inhibition and long-range cooperativity between binding sites in protein kinases. The presented study offers a novel molecular insight into mechanistic aspects of allosteric kinase signaling and provides a quantitative picture of activation mechanisms in protein kinases at the atomic level. Despite recent progress in computational and experimental studies of dynamic regulation in protein kinases, a mechanistic understanding of long-range communication and mechanisms of mutation-induced signaling controlling kinase activity remains largely qualitative. In this study, we have performed a systematic modeling and analysis of allosteric activation in ABL and EGFR kinases at the increasing level of complexity - from catalytic domain to multi-domain regulatory complexes. The results of this study have revealed organizing structural and mechanistic principles of allosteric signaling in protein kinases. Although activation mechanisms in ABL and EGFR kinases have evolved through acquisition of structurally different regulatory complexes, we have found that long-range interdomain communication between common functional segments (αF-helix and αC-helix) may be important for allosteric activation. The results of study have revealed molecular signatures of activating cancer mutations and have shed the light on general mechanistic aspects of mutation-induced signaling in protein kinases. An advanced understanding and further characterization of molecular signatures of kinase mutations may aid in a better rationalization of mutational effects on clinical outcomes and facilitate molecular-based therapeutic strategies to combat kinase mutation-dependent tumorigenesis.
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Affiliation(s)
- Anshuman Dixit
- Department of Pharmaceutical Chemistry, School of Pharmacy, The University of Kansas, Lawrence, Kansas, United States of America
| | - Gennady M. Verkhivker
- Department of Pharmaceutical Chemistry, School of Pharmacy, The University of Kansas, Lawrence, Kansas, United States of America
- Department of Pharmacology, University of California San Diego, La Jolla, California, United States of America
- * E-mail:
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50
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Maksay G. Allostery in pharmacology: Thermodynamics, evolution and design. PROGRESS IN BIOPHYSICS AND MOLECULAR BIOLOGY 2011; 106:463-73. [DOI: 10.1016/j.pbiomolbio.2011.01.001] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/03/2011] [Accepted: 01/03/2011] [Indexed: 12/13/2022]
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