DOCSLIB.ORG

Controlling Entropy to Tune the Functions of Intrinsically Disordered

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Controlling Entropy to Tune the Functions of Intrinsically Disordered Available online at www.sciencedirect.com ScienceDirect Controlling entropy to tune the functions of intrinsically disordered regions 1 1 Tilman Flock , Robert J Weatheritt , Natasha S Latysheva and M Madan Babu Intrinsically disordered regions (IDRs) are fundamental units of extensive functional regions within polypeptide seg- protein function and regulation. Despite their inability to form a ments that do not form stable tertiary structure. These unique stable tertiary structure in isolation, many IDRs adopt a regions, referred to as intrinsically disordered regions defined conformation upon binding and achieve their function (IDRs), exist in over 35% of human proteins [2,3] func- through their interactions with other biomolecules. However, this tioning in all major cellular processes with significant requirement for IDR functionality seems to be at odds with the enrichment in signalling and regulation [4,5 ]. Although high entropic cost they must incur upon binding an interaction IDRs are capable of performing functions comparable to partner. How is this seeming paradox resolved? While increasing structured regions, they vary significantly in their amino the enthalpy of binding is one approach to compensate for this acid composition [6] and their biophysical properties entropic cost, growing evidence suggests that inherent features [7 ]. Together this suggests that IDRs are independent of IDRs, for instance repeating linear motifs, minimise the units of protein function, capable of achieving biological entropic cost of binding. Moreover, this control of entropic cost function in a manner that is distinct from that of struc- can be carefully modulated by a range of regulatory tured regions [1 ,7 ]. mechanisms, such as alternative splicing and post-translational modifications, which enable allosteric communication and Energy landscape of intrinsically disordered rheostat-like tuning of IDR function. In that sense, the high regions in proteins entropic cost of IDR binding can be advantageous by providing In contrast to structured domains that are primarily gov- tunability to protein function. In addition to biological regulatory erned by non-covalent tertiary contacts between second- mechanisms, modulation of entropy can also be controlled by ary structural elements, IDRs do not adopt a well-defined environmental factors, such as changes in temperature, redox- tertiary structure. Instead they are in a dynamic equi- potential and pH. These principles are extensively exploited by a librium between different sets of conformational states number of organisms, including pathogens. They can also be [8] (Figure 1). The probability of finding an IDR in a utilised in bioengineering, synthetic biology and in certain state within this conformational (or statistical) pharmaceutical applications such as increasing bioavailability of ensemble is related to its conformational free energy protein therapeutics. landscape [7 ,8]. The native state of IDRs is therefore Addresses best described in terms of a relatively flat but rugged MRC Laboratory of Molecular Biology, Francis Crick Avenue, Cambridge energy landscape with numerous local energy minima CB2 0QH, UK separated by low energetic barriers [9,10]. This strongly contrasts with the energy landscape of a structured Corresponding authors: Weatheritt, Robert J (rweather@mrc- lmb.cam.ac.uk), Babu, M Madan ([email protected]) domain, which exhibits a well-defined minimum energy 1 These authors contributed equally to this work. state [7 ], resulting in one or a few clearly favoured conformational states (Figure 1). Because of their flat Current Opinion in Structural Biology 2014, 26:62–72 energy landscape, IDRs can exist in multiple energeti- cally similar but conformationally different states. In This review comes from a themed issue on Sequences and topology biophysical terms, this means that IDRs exhibit high Edited by L Aravind and Christine Orengo conformational entropy (Figure 1). When participating in binding events with other biomolecules, IDRs must pay an entropic cost due to transitioning from a large set of http://dx.doi.org/10.1016/j.sbi.2014.05.007 conformations in the free form to a more restricted 0959-440X/# 2014 Elsevier Ltd. All rights reserved. arrangement in the bound form [11]. How do IDRs exhibit functional properties when the potential entropic penalty of engaging in functionality is likely to be high? Recent studies have suggested that the large entropy loss due to a disorder-to-order transition upon binding can be Introduction counter-balanced by a comparable gain in enthalpy of There is an increasing appreciation that structure is not an binding [12–14]. In many cases, this has been suggested essential prerequisite for many kinds of protein function to result in an interaction that is characterised by high [1 ]. An ever-growing number of studies have identified specificity but low affinity of binding. IDRs achieve this Current Opinion in Structural Biology 2014, 26:62–72 www.sciencedirect.com Exploiting entropy for biomolecular interaction Flock et al. 63 Figure 1 Native Disordered State Preferred Conformations Ordered State Free Energy Free Energy Free Energy Free Energy Landscape Ordered Disordered Statistical Ensemble Single conformation dominates Many different conformations sampled Current Opinion in Structural Biology The free energy landscape and conformational ensembles of IDRs can be dynamically tuned. The free energy landscape schematically represents any possible conformation (x–y axes) a protein can adopt, as well as its respective free energy (z-axis) ( free energy landscape, upper row), and thus describes the probability of finding any particular conformation in an ensemble of conformational states (statistical ensemble, lower row). Whereas structured proteins have energy landscapes with a clearly preferred conformation (global minimum, right panel), disordered proteins in their native form have a rugged, flat energy landscape allowing multiple conformations to be thermodynamically favourable and to be found in the statistical ensemble (left panel). Several factors can modulate the free energy landscape of IDRs and restrict the statistical ensemble to fewer, more defined conformations (middle panel). As conformational entropy decreases, so does the number of observed conformational states. In the most ordered state, the disordered protein landscape turns into the funnel-shaped landscape of structured proteins, shifting the statistical conformational ensemble towards a predominant conformation (right panel). This fine-tuning of the statistical ensemble of IDRs enables specific functions and tunability. by having an extensive interaction interface capable of IDRs upon binding as a disadvantage, it may be regarded participating in a large number of weak non-covalent as highly advantageous in many cases. This is because contacts [12,14]. However, there are many examples of modulation of the entropic cost enables IDRs to dyna- IDRs binding with a limited interaction surface area and/ mically change their energy landscapes. This results in or in their disordered state [15 ,16]. So what other the creation of a wide range of regulatory functions that additional mechanisms allow IDRs to overcome the can be actively and intricately tuned by intrinsic and entropic penalty of binding and become functional? In extrinsic factors. this article, we discuss the role of entropic contribution to free energy (DG = DH À TDS; see Box 1) in IDR function How inherent features of IDRs permit and highlight that minimising the relative entropic cost exploitation of entropy for functionality upon binding is a prerequisite for functions mediated by What are the potential ways to counter-balance the several IDRs. We further discuss that rather than con- entropic cost of binding of IDRs and modulate their sidering the potentially large entropic cost incurred by free energy landscape? Deriving from the definition of www.sciencedirect.com Current Opinion in Structural Biology 2014, 26:62–72 64 Sequences and topology Box 1 Thermodynamic properties in the context of polypeptides conformational entropy at the motif interface is limited. and their ligands. Since many motifs do not form secondary structure Relative enthalpy (DH) describes the change in internal energy of elements upon binding, the region mediating the the system as well as the impact on its surrounding environment. In interaction remains flexible, permitting extensive back- protein folding, this is mainly determined by electrostatic interactions bone-mediated hydrogen bonds [18 ,19] and thereby and non-covalent bonds between amino acids within secondary maximising the potential enthalpy of binding within structural elements of structured domains. Relative entropy (DS) is a measure of the change in randomness the limited interface of the motif. When motifs do have (number of distinct states) of a system. In biological systems, the main secondary structure, it is typically preformed and rigid in factors contributing to entropy are the degree of conformational the native state (e.g. polyproline stretches), minimising freedom of the polypeptide chain, entropy of complex formation (e.g. the loss of conformational entropy upon binding [15 ,20]. binding of ligand to the polypeptide), and entropy changes due to the Secondly, the interaction partners of motifs tend to have rearrangement of surrounding water (solvent) molecules. Configurational entropy (DSconfig) of a protein is related to the rigid interfaces, which ensure that
Load more

Recommended publications
  • A Network of Molecular Switches Controls the Activation of the Two-Component Response Regulator Ntrc
    ARTICLE Received 8 Aug 2014 | Accepted 26 Apr 2015 | Published 15 Jun 2015 DOI: 10.1038/ncomms8283 A network of molecular switches controls the activation of the two-component response regulator NtrC Dan K. Vanatta1, Diwakar Shukla1,2,3, Morgan Lawrenz1 & Vijay S. Pande1,2 Recent successes in simulating protein structure and folding dynamics have demonstrated the power of molecular dynamics to predict the long timescale behaviour of proteins. Here, we extend and improve these methods to predict molecular switches that characterize conformational change pathways between the active and inactive state of nitrogen regulatory protein C (NtrC). By employing unbiased Markov state model-based molecular dynamics simulations, we construct a dynamic picture of the activation pathways of this key bacterial signalling protein that is consistent with experimental observations and predicts new mutants that could be used for validation of the mechanism. Moreover, these results suggest a novel mechanistic paradigm for conformational switching. 1 Department of Chemistry, Stanford University, Stanford, California 94305, USA. 2 SIMBIOS NIH Center for Biomedical Computation, Stanford University, Stanford, California 94305,USA. 3 Department of Chemical and Bimolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illionios 61801, USA. Correspondence and requests for materials should be addressed to V.S.P. (email: [email protected]). NATURE COMMUNICATIONS | 6:7283 | DOI: 10.1038/ncomms8283 | www.nature.com/naturecommunications 1 & 2015 Macmillan Publishers Limited. All rights reserved. ARTICLE NATURE COMMUNICATIONS | DOI: 10.1038/ncomms8283 roteins involved in cellular signalling change their con- Results formation in response to changes in environment (input Simulations reveal molecular switches for NtrC activation. Psignal), such as ligand binding or chemical modification, MSMs for NtrC were built using snapshots along the simulated to control downstream cellular processes (output signal)1.
    [Show full text]
  • Spring 2013 Lecture 23
    CHM333 LECTURES 23: 3/25/13 SPRING 2013 Professor Christine Hrycyna LIPIDS III EFFECT OF CHOLESTEROL ON MEMBRANES: - Bulky rigid molecule - Moderates fluidity of membranes – both increases and decreases o Cholesterol in membranes DECREASES fluidity because it is rigid o Prevents crystallization (making solid) of fatty acyl side chains by fitting between them. Disrupts close packing of fatty acyl chains. Therefore, INCREASED fluidity BIOLOGICAL MEMBRANES CONTAIN PROTEINS AS WELL AS LIPIDS: - Proteins are 20-80% of cell membrane - Rest is lipid or carbohydrate; supramolecular assembly of lipid, protein and carbohydrate - Proteins are also distributed asymmetrically - TWO classes of Membrane Proteins: o Integral Membrane Proteins o Peripheral Membrane Proteins 178 CHM333 LECTURES 23: 3/25/13 SPRING 2013 Professor Christine Hrycyna - INTEGRAL MEMBRANE PROTEINS o Located WITHIN the lipid bilayer o Usually span the bilayer one or more times – called transmembrane (TM) proteins o Hydrophobic amino acids interact with fatty acid chains in the hydrophobic core of the membrane o Can be removed from the membrane with detergents like SDS – need to disrupt the hydrophobic interactions § Membrane Disruption Animation: o http://www.youtube.com/watch?v=AHT37pvcjc0 o Function: § Transporters – moving molecules into or out of cells or cell membranes § Receptors – transmitting signals from outside of the cell to the inside - β Barrel Integral Membrane Proteins § Barrel-shaped membrane protein that is made up of antiparallel β-strands with hydrophilic (interior) and hydrophobic (facing lipid tails). § So far found only in outer membranes of Gram-negative bacteria, cell wall of Gram-positive bacteria, and outer membranes of mitochondria and chloroplasts. 179 CHM333 LECTURES 23: 3/25/13 SPRING 2013 Professor Christine Hrycyna - α-Helical Membrane Proteins - Can cross the membrane once or many times and have multiple transmembrane segments.
    [Show full text]
  • Site-Specific Tryptophan Labels Reveal Local Microsecond
    molecules Article Site-Specific Tryptophan Labels Reveal Local Microsecond–Millisecond Motions of Dihydrofolate Reductase Morgan B. Vaughn 1,*, Chloe Biren 2, Qun Li 2, Ashwin Ragupathi 2 and R. Brian Dyer 2,* 1 Division of Natural Sciences, Oglethorpe University, Atlanta, GA 30319, USA 2 Department of Chemistry, Emory University, Atlanta, GA 30322, USA; [email protected] (C.B.); [email protected] (Q.L.); [email protected] (A.R.) * Correspondence: [email protected] (M.B.V.); [email protected] (R.B.D.); Tel.: +1-404-504-1214 (M.B.V.); +1-404-727-6637 (R.B.D.) Academic Editor: Rafik Karaman Received: 24 July 2020; Accepted: 21 August 2020; Published: 22 August 2020 Abstract: Many enzymes are known to change conformations during their catalytic cycle, but the role of these protein motions is not well understood. Escherichia coli dihydrofolate reductase (DHFR) is a small, flexible enzyme that is often used as a model system for understanding enzyme dynamics. Recently, native tryptophan fluorescence was used as a probe to study micro- to millisecond dynamics of DHFR. Yet, because DHFR has five native tryptophans, the origin of the observed conformational changes could not be assigned to a specific region within the enzyme. Here, we use DHFR mutants, each with a single tryptophan as a probe for temperature jump fluorescence spectroscopy, to further inform our understanding of DHFR dynamics. The equilibrium tryptophan fluorescence of the mutants shows that each tryptophan is in a different environment and that wild-type DHFR fluorescence is not a simple summation of all the individual tryptophan fluorescence signatures due to tryptophan–tryptophan interactions.
    [Show full text]
  • Predicting Protein-Membrane Interfaces of Peripheral Membrane
    bioRxiv preprint doi: https://doi.org/10.1101/2021.06.28.450157; this version posted June 29, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Predicting protein-membrane interfaces of pe- ripheral membrane proteins using ensemble machine learning Alexios Chatzigoulas1,2,* and Zoe Cournia1,* 1Biomedical Research Foundation, Academy of Athens, 4 Soranou Ephessiou, 11527 Athens, Greece, 2Depart- ment of Informatics and Telecommunications, National and Kapodistrian University of Athens, 15784 Athens, Greece *To whom correspondence should be addressed. Abstract Motivation: Abnormal protein-membrane attachment is involved in deregulated cellular pathways and in disease. Therefore, the possibility to modulate protein-membrane interactions represents a new promising therapeutic strategy for peripheral membrane proteins that have been considered so far undruggable. A major obstacle in this drug design strategy is that the membrane binding domains of peripheral membrane proteins are usually not known. The development of fast and efficient algorithms predicting the protein-membrane interface would shed light into the accessibility of membrane-protein interfaces by drug-like molecules. Results: Herein, we describe an ensemble machine learning methodology and algorithm for predicting membrane-penetrating residues. We utilize available experimental data in the literature for training 21 machine learning classifiers and a voting classifier. Evaluation of the ensemble classifier accuracy pro- duced a macro-averaged F1 score = 0.92 and an MCC = 0.84 for predicting correctly membrane-pen- etrating residues on unknown proteins of an independent test set.
    [Show full text]
  • Conformational Proofreading: the Impact of Conformational
    Conformational Proofreading: The Impact of Conformational Changes on the Specificity of Molecular Recognition Yonatan Savir1 and Tsvi Tlusty1,* 1Department of Physics of Complex Systems, the Weizmann Institute of Science, Rehovot, Israel, 76100 *Corresponding author. E-mail: [email protected] Abstract To perform recognition, molecules must locate and specifically bind their targets within a noisy biochemical environment with many look-alikes. Molecular recognition processes, especially the induced-fit mechanism, are known to involve conformational changes. This arises a basic question: does molecular recognition gain any advantage by such conformational changes? By introducing a simple statistical-mechanics approach, we study the effect of conformation and flexibility on the quality of recognition processes. Our model relates specificity to the conformation of the participant molecules and thus suggests a possible answer: Optimal specificity is achieved when the ligand is slightly off target, that is a conformational mismatch between the ligand and its main target improves the selectivity of the process. This indicates that deformations upon binding serve as a conformational proofreading mechanism, which may be selected for via evolution. Introduction Practically all biological systems rely on the ability of bio-molecules to specifically recognize each other. Examples are antibodies targeting antigens, regulatory proteins binding DNA and enzymes catalyzing their substrates. These and other molecular recognizers must locate and preferentially interact with their specific targets among a vast variety of molecules that are often structurally similar. This task is further complicated by the inherent noise in the biochemical environment, whose magnitude is comparable with that of the non- covalent binding interactions [1-3].
    [Show full text]
  • M.Sc. [Botany] 346 13
    cover page as mentioned below: below: mentioned Youas arepage instructedcover the to updateupdate to the coverinstructed pageare asYou mentioned below: Increase the font size of the Course Name. Name. 1. IncreaseCourse the theof fontsize sizefont ofthe the CourseIncrease 1. Name. use the following as a header in the Cover Page. Page. Cover 2. the usein the followingheader a as as a headerfollowing the inuse the 2. Cover Page. ALAGAPPAUNIVERSITY UNIVERSITYALAGAPPA [Accredited with ’A+’ Grade by NAAC (CGPA:3.64) in the Third Cycle Cycle Third the in (CGPA:3.64) [AccreditedNAAC by withGrade ’A+’’A+’ Gradewith by NAAC[Accredited (CGPA:3.64) in the Third Cycle and Graded as Category–I University by MHRD-UGC] MHRD-UGC] by University and Category–I Graded as as Graded Category–I and University by MHRD-UGC] M.Sc. [Botany] 003 630 – KARAIKUDIKARAIKUDI – 630 003 346 13 EDUCATION DIRECTORATEDISTANCE OF OF DISTANCEDIRECTORATE EDUCATION BIOLOGICAL TECHNIQUES IN BOTANY I - Semester BOTANY IN TECHNIQUES BIOLOGICAL M.Sc. [Botany] 346 13 cover page as mentioned below: below: mentioned Youas arepage instructedcover the to updateupdate to the coverinstructed pageare asYou mentioned below: Increase the font size of the Course Name. Name. 1. IncreaseCourse the theof fontsize sizefont ofthe the CourseIncrease 1. Name. use the following as a header in the Cover Page. Page. Cover 2. the usein the followingheader a as as a headerfollowing the inuse the 2. Cover Page. ALAGAPPAUNIVERSITY UNIVERSITYALAGAPPA [Accredited with ’A+’ Grade by NAAC (CGPA:3.64) in the Third Cycle Cycle Third the in (CGPA:3.64) [AccreditedNAAC by withGrade ’A+’’A+’ Gradewith by NAAC[Accredited (CGPA:3.64) in the Third Cycle and Graded as Category–I University by MHRD-UGC] MHRD-UGC] by University and Category–I Graded as as Graded Category–I and University by MHRD-UGC] M.Sc.
    [Show full text]
  • Cell Signaling: G-Protein Coupled Receptors and the Β
    Cell Signaling: G-Protein Coupled Receptors and the β2-Adrenergic Receptor Dianna Amasino, Axel Glaubitz, Susan Huang, Joy Li, Hsien-Yu Shih, Junyao Song, Xiao Zhu Madison West High School, Madison, WI 53726 Advisor: Basudeb Bhattacharyya, University of Wisconsin, Madison, WI 53706 Mentors: Dr. Dave Nelson and Peter Vander Velden, University of Wisconsin, Madison, WI 53706 Introduction Mechanism of the β adrenergic receptor: an example of GPCR signal A conserved structure and mechanism G protein-coupled receptors (GPCRs) are the largest transduction family of integral membrane proteins coded by the human genome. GPCRs are important for signal transduction with the general structural characteristic of a plasma membrane receptor with seven transmembrane segments (Figure 1). One example of a GPCR targeted by pharmaceutical companies is the β2-adrenergic receptor. Adrenergic receptors are found through out the body and are triggered by the hormone epinephrine (also known as adrenaline, hence the name adrenergic). When epinephrine binds to the receptors, it causes a slight conformational change within the receptor. This Cherezov, et al., 2007 change then triggers activation of a G-protein, which induces a response within the cell (for example, muscle contraction). When this signal transduction event Figure 4. Overlay of bovine rhodopsin (cyan) and the functions normally in the body, it helps regulate heart rate human β2-adrenergic receptor (orange) and blood pressure and is important for the “fight or Figure 4 above demonstrates how conserved the structure, flight” response. Beta blockers are medically used to and hence the mechannsm, of signal transduction using bind to adrenergic receptors, manipulating the hormone’s GPCRs really is.
    [Show full text]
  • Membrane Protein Structure Determination and Characterisation by Solution and Solid-State NMR
    biology Review Membrane Protein Structure Determination and Characterisation by Solution and Solid-State NMR Vivien Yeh , Alice Goode and Boyan B. Bonev * School of Life Sciences, University of Nottingham, Nottingham NG7 2UH, UK; [email protected] (V.Y.); [email protected] (A.G.) * Correspondence: [email protected] Received: 21 October 2020; Accepted: 11 November 2020; Published: 12 November 2020 Simple Summary: Cells, life’s smallest units, are defined within the enclosure of thin, continuous membranes, which confine the molecular machinery required for the life and replication of cells. Crucially, membranes of cells establish and actively maintain distinctly different environments inside cells, including electrical and solute gradients vital to normal cellular functions. Membrane proteins are in charge of transport, electrical polarisation, signalling, membrane remodelling and other important functions. As such, membrane proteins are key drug targets and understanding their structure and function is essential to drug development and cellular control. Membrane proteins have physical characteristics that make such studies very challenging. Nuclear magnetic resonance is one advanced tool that enables structural studies of membrane proteins and their interactions at the atomic level of detail. We discuss the applications of NMR in solution and solid state to membrane protein studies alongside new developments in signal and sensitivity enhancement through dynamic nuclear polarisation. Abstract: Biological membranes define the interface of life and its basic unit, the cell. Membrane proteins play key roles in membrane functions, yet their structure and mechanisms remain poorly understood. Breakthroughs in crystallography and electron microscopy have invigorated structural analysis while failing to characterise key functional interactions with lipids, small molecules and membrane modulators, as well as their conformational polymorphism and dynamics.
    [Show full text]
  • Biomodel Engineering for Multiscale Systems Biology
    BioModel Engineering for Multiscale Systems Biology Monika Heinera, David Gilbertb aDepartment of Computer Science, Brandenburg University of Technology Postbox 10 13 44, 03013 Cottbus, Germany [email protected] bSchool of Information Systems, Computing and Mathematics Brunel University, Uxbridge, Middlesex UB8 3PH, UK [email protected] Abstract We discuss some motivational challenges arising from the need to model and analyse complex biological systems at multiple scales (spatial and temporal), and present a biomodel engineering framework to address some of these issues within the context of multiscale Systems Biology. Our methodology is based on a structured family of Petri net classes which enables the investigation of a given system using various modelling abstractions: qualitative, stochastic, continuous and hybrid, optionally in a spatial context. We illustrate our approach with case studies demonstrating hierarchical flattening, treatment of space, and hierarchical organisation of space. Keywords: BioModel Engineering; multiscale modelling; Systems Biology; Synthetic Biology; biomolecular networks; coloured qualitative/stochastic/continuous/hybrid Petri nets; model checking. 1. Motivation BioModel Engineering. Biology is increasingly becoming an informa- tional science. This revolution has been driven by technological advances which have supported the development of studies at many levels of intra- and intercellular activity. These advances have facilitated the analysis of how the components of a biological
    [Show full text]
  • Conformational Changes in Ligand Binding Processes
    Conformational Changes in Ligand Binding Processes Conformational Changes in Ligand Binding Processes Dissertation for the award of the degree Doctor rerum naturalium of the Georg-August-Universit¨atG¨ottingen within the doctoral program IMPRS-PBCS of the Georg-August University School of Science (GAUSS) Submitted by B´elaVoß from Hamburg G¨ottingen,2014 • Thesis Committee: { Prof. Dr. Helmut Grubm¨uller Department for Theoretical and Computational Biophysics Max-Planck-Institute for Biophysical Chemistry { Prof. Dr. J¨orgEnderlein Third Institute of Physics Faculty of Physics Georg-August-Universit¨atG¨ottingen { Prof. Dr. Marina Benatti Electron Spin Resonance Spectroscopy Group Max-Planck-Institute for Biophysical Chemistry • Examination Board: { Prof. Dr. Helmut Grubm¨uller(supervisor, reviewer) Department for Theoretical and Computational Biophysics Max-Planck-Institute for Biophysical Chemistry { Prof. Dr. J¨orgEnderlein (reviewer) Third Institute of Physics Faculty of Physics Georg-August-Universit¨atG¨ottingen { Prof. Dr. Marina Benatti Electron Spin Resonance Spectroscopy Group Max-Planck-Institute for Biophysical Chemistry { Prof. Dr. Sarah K¨oster CRC Research Group \Nanoscale Imaging of Cellular Dynamics" Faculty of Physics Georg-August-Universit¨atG¨ottingen { Dr. Thomas Burg Biological Micro- and Nanotechnology Group Max-Planck-Institute for Biophysical Chemistry { Prof. Dr. Markus M¨uller Institute for Theoretical Physics Faculty of Physics Georg-August-Universit¨atG¨ottingen Date of the disputation: 2015-01-30 iv Hiermit erkl¨areich, dass ich die vorliegende Arbeit selbstst¨andigverfasst und keine anderen als die angegebenen Quellen und Hilfsmittel verwendet habe. G¨ottingen,den 26.11.2014 B´elaVoß v Contents List of Abbreviations xi I. Opening 1 1. Introduction 3 2. Theoretical Background 5 2.1.
    [Show full text]
  • Induced Fit, Conformational Selection and Independent Dynamic Segments: an Extended View of Binding Events
    Opinion Cover story in Trends in Biochemical Sciences 35, 539-546 (2010) Induced fit, conformational selection and independent dynamic segments: an extended view of binding events Peter Csermely1,a, Robin Palotai1 and Ruth Nussinov2,3 1Department of Medical Chemistry, Semmelweis University, P O Box 260., H-1444 Budapest 8, Hungary 2Center for Cancer Research Nanobiology Program, SAIC-Frederick, Inc., NCI-Frederick, Frederick, MD 21702, USA 3Sackler Institute of Molecular Medicine, Department of Human Genetics and Molecular Medicine, Sackler School of Medicine, Tel Aviv University, Tel Aviv 69978, Israel Single molecule and NMR measurements of protein dynamics increasingly uncover the complexity of binding scenarios. Here we describe an extended conformational selection model which embraces a repertoire of selection and adjustment processes. Induced fit can be viewed as a subset of this repertoire, whose contribution is affected by the bond- types stabilizing the interaction and the differences between the interacting partners. We argue that protein segments whose dynamics are distinct from the rest of the protein (‘discrete breathers’) can govern conformational transitions and allosteric propagation that accompany binding processes, and as such may be more sensitive to mutational events. Additionally, we highlight the dynamic complexity of binding scenarios as they relate to events such as aggregation and signalling, and the crowded cellular environment. The induced fit and the original conformational selection models The prevailing view of binding mechanisms has evolved from the early ‘lock-and-key’ hypothesis [1] to the now popular ‘induced fit’ model [2]. According to the induced fit scenario, the interaction between a protein and a rigid binding partner induces a conformational change in the protein.
    [Show full text]
  • Binding Mechanism and Dynamic Conformational Change of C Subunit
    Binding mechanism and dynamic conformational PNAS PLUS change of C subunit of PKA with different pathways Wen-Ting Chua,1, Xiakun Chub,1, and Jin Wanga,c,2 aState Key Laboratory of Electroanalytical Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun, Jilin 130022, China; bInstituto Madrileno˜ de Estudios Avanzados en Nanociencia (IMDEA Nanociencia), Campus Universitario de Cantoblanco, 28049 Madrid, Spain; and cDepartment of Chemistry and Physics, State University of New York at Stony Brook, Stony Brook, NY 11794 Edited by Jose´ N. Onuchic, Rice University, Houston, TX, and approved July 31, 2017 (received for review February 22, 2017) The catalytic subunit of PKA (PKAc) exhibits three major confor- (12). However, because of the highly dynamic characteristics, the mational states (open, intermediate, and closed) during the bio- structure of PKAc is elusive. The first crystallographic structure catalysis process. Both ATP and substrate/inhibitor can effectively of PKAc was successfully solved by Knighton et al. (7) via cocrys- induce the conformational changes of PKAc from open to closed talizing a high-affinity protein binding inhibitor (PKI), a 20-aa states. Aiming to explore the mechanism of this allosteric regu- peptide of heat stable protein, including a high-affinity pseu- lation, we developed a coarse-grained model and analyzed the dosubstarte region and a nuclear export signal (13). The PKAc dynamics of conformational changes of PKAc during binding by dynamics are found to be quenched by the high-affinity binding performing molecular dynamics simulations for apo PKAc, binary interactions from PKI, making the structure of PKAc accessi- PKAc (PKAc with ATP,PKAc with PKI), and ternary PKAc (PKAc with ble (14, 15).
    [Show full text]