Principles for optimal cooperativity in allosteric materials

Allosteric proteins transmit a mechanical signal induced by binding a ligand. However, understanding the nature of the information transmitted and the architectures optimizing such transmission remains a challenge. Here we show using an {\it in-silico} evolution scheme and theoretical arguments that architectures optimized to be cooperative, which propagate efficiently energy, {qualitatively} differ from previously investigated materials optimized to propagate strain. Although we observe a large diversity of functioning cooperative architectures (including shear, hinge and twist designs), they all obey the same principle {of displaying a {\it mechanism}, i.e. an extended {soft} mode}. We show that its optimal frequency decreases with the spatial extension $L$ of the system as $L^{-d/2}$, where $d$ is the spatial dimension. For these optimal designs, cooperativity decays logarithmically with $L$ for $d=2$ and does not decay for $d=3$. Overall our approach leads to a natural explanation for several observations in allosteric proteins, and { indicates an experimental path to test if allosteric proteins lie close to optimality}.Comment: 11 pages, 9 figures in the main text, 9 pages 9 figures in the supplemental materia

Allostery and cooperativity in multimeric proteins: bond-to-bond propensities in ATCase

Aspartate carbamoyltransferase (ATCase) is a large dodecameric enzyme with six active sites that exhibits allostery: its catalytic rate is modulated by the binding of various substrates at distal points from the active sites. A recently developed method, bond-to-bond propensity analysis, has proven capable of predicting allosteric sites in a wide range of proteins using an energy-weighted atomistic graph obtained from the protein structure and given knowledge only of the location of the active site. Bond-to-bond propensity establishes if energy fluctuations at given bonds have significant effects on any other bond in the protein, by considering their propagation through the protein graph. In this work, we use bond-to-bond propensity analysis to study different aspects of ATCase activity using three different protein structures and sources of fluctuations. First, we predict key residues and bonds involved in the transition between inactive (T) and active (R) states of ATCase by analysing allosteric substrate binding as a source of energy perturbations in the protein graph. Our computational results also indicate that the effect of multiple allosteric binding is non linear: a switching effect is observed after a particular number and arrangement of substrates is bound suggesting a form of long range communication between the distantly arranged allosteric sites. Second, cooperativity is explored by considering a bisubstrate analogue as the source of energy fluctuations at the active site, also leading to the identification of highly significant residues to the T ↔ R transition that enhance cooperativity across active sites. Finally, the inactive (T) structure is shown to exhibit a strong, non linear communication between the allosteric sites and the interface between catalytic subunits, rather than the active site. Bond-to-bond propensity thus offers an alternative route to explain allosteric and cooperative effects in terms of detailed atomistic changes to individual bonds within the protein, rather than through phenomenological, global thermodynamic arguments

Frustration in Biomolecules

Biomolecules are the prime information processing elements of living matter. Most of these inanimate systems are polymers that compute their structures and dynamics using as input seemingly random character strings of their sequence, following which they coalesce and perform integrated cellular functions. In large computational systems with a finite interaction-codes, the appearance of conflicting goals is inevitable. Simple conflicting forces can lead to quite complex structures and behaviors, leading to the concept of "frustration" in condensed matter. We present here some basic ideas about frustration in biomolecules and how the frustration concept leads to a better appreciation of many aspects of the architecture of biomolecules, and how structure connects to function. These ideas are simultaneously both seductively simple and perilously subtle to grasp completely. The energy landscape theory of protein folding provides a framework for quantifying frustration in large systems and has been implemented at many levels of description. We first review the notion of frustration from the areas of abstract logic and its uses in simple condensed matter systems. We discuss then how the frustration concept applies specifically to heteropolymers, testing folding landscape theory in computer simulations of protein models and in experimentally accessible systems. Studying the aspects of frustration averaged over many proteins provides ways to infer energy functions useful for reliable structure prediction. We discuss how frustration affects folding, how a large part of the biological functions of proteins are related to subtle local frustration effects and how frustration influences the appearance of metastable states, the nature of binding processes, catalysis and allosteric transitions. We hope to illustrate how Frustration is a fundamental concept in relating function to structural biology.Comment: 97 pages, 30 figure

On the expanding terminology in the GPCR field: The meaning of receptor mosaics and receptor heteromers

The oligomerization of G protein-coupled receptors (GPCRs) is a fact that deserves further attention as increases both the complexity and diversity of the receptor-mediated signal transduction, thus enriching the cell signaling. Consequently, in the present review we tackle among others the problems concerning the terminology used to describe aspects surrounding the GPCRs oligomerization phenomenon. Therefore, the theoretical implications of the GPCR oligomerization will be briefly discussed together with possible implications of this phenomenon especially for new strategies in drug development

Agonist-Induced Conformational Changes in the NMDA Receptor

NMDA receptors are ligand-gated ion channels that mediate a number of physiological and pathological phenomena within the mammalian central nervous system. Under the typical course of activation, these receptors bind to glycine and glutamate molecules and undergo a series of conformational changes that results in the opening of a cation-permeable pore in the neuronal plasma membrane. Various aspects of NMDA receptor function are not fully understood, including the phenomenon of negative cooperativity between the glycine- and glutamate-binding sites of the receptor and the mechanism controlling partial agonism. Past studies utilizing static structural snapshots of the receptor or isolated domains of the receptor have provided insufficient insights to fully understand these issues. Herein, I have conducted Förster Resonance Energy Transfer measurements on individual NMDA receptor molecules to observe their conformational landscape under various conditions. These studies have revealed changes in conformation of the receptor that underlie negative cooperativity and partial agonism, thereby affording new insights into the mechanisms controlling these processes

MODULATION OF PROTEIN DYNAMICS BY LIGAND BINDING AND SOLVENT COMPOSITION

Many proteins undergo conformational switching in order to perform their cellular functions. A multitude of factors may shift the energy landscape and alter protein dynamics with varying effects on the conformations they explore. We apply atomistic molecular dynamics simulations to a variety of biomolecular systems in order to investigate how factors such as pressure, the chemical environment, and ligand binding at distant binding pockets affect the structure and dynamics of these protein systems. Further, we examine how such changes should be characterized. We first investigate how pressure and solvent modulate ligand access to the active site of a bacterial lipase by probing the dynamics in a variety of pressures and DMSO-water solvent mixtures. By measuring the gorge leading to the binding pocket we find small amounts of DMSO and high atmospheric pressure optimize the ability of lipids to reach the catalytic interior. Next, we examine the allosteric mechanism behind cooperative and anti-cooperative binding of nuclear hormone receptor RXR and two of its binding partners (TR and CAR). We detail why ligands of the RXR:TR (9c and t3) complex bind anti-cooperatively while ligands of RXR:CAR (9c and tcp) bind cooperatively. Finally, we describe how an intrinsically disordered protein, α-synuclein, alters its conformational dynamics in a pH-dependent manner increasing the likelihood of pathogenic aggregation and neurodegenerative disease at low pH. In each case, we apply contact analysis to uncover the collective motions underlying conformational change triggered by environmental factors or ligand binding

Approaches for studying allostery using network theory

Allostery is the process whereby binding of a substrate at a site other than the active site modulates the function of a protein. Allostery is thus one of the myriad of biological processes that keeps cells under tight regulatory control, specifically one that acts at the level of the protein rather than through changes in gene transciption or translation of mRNA. Despite over 50 years of investigation, allostery has remained a difficult phenomenon to elucidate. Structural changes are often too subtle for many experimental methods to capture and it has become increasingly obvious that a range of timescales are involved, from extremely fast pico- to nanosecond local fluctuations all the way up to the millisecond or even second timescales over which the biological effects of allostery are observed. As a result, computational methods have arisen to become a powerful means of studying allostery, aided greatly by the staggering increases in computational power over the last 70 years. A field that has experienced a surge in interest over the last 20 years or so is \emph{network theory}, perhaps stimulated by the development of the internet and the Web, two examples of immensely important networks in our everyday life. One of the reasons for the popularity of networks in modelling is their comparative simplicity: a network consists of \emph{nodes}, representing a set of objects in a system, and \emph{edges}, that capture the relations between them. In this thesis, we both apply existing ideas and methods from network theory and develop new computational network methods to study allostery in proteins. We attempt to tackle this problem in three distinct ways, each representing a protein using a different form of a network. Our initial work follows on logically from previous work in the group, representing proteins as \emph{graphs} where atoms are nodes and bonds are energy weighted edges. In effect we disregard the 3-dimensional structure of the protein and instead focus on how the bond \emph{connectivity} can be used to explain potential long range communication between allosteric and active sites in a multimeric protein. We then focus on a class of protein models known as \emph{elastic network models}, in which our edges now correspond to mechanical Hooke springs between either atoms or residues, in order to attempt to understand the physical, mechanistic basis of allostery.Open Acces

Bioinformatics and mathematical modelling in the study of receptor-receptor interactions and receptor oligomerization: focus on adenosine receptors.

none8sìThe concept of intra-membrane receptor-receptor interactions (RRIs) between different types of G protein-coupled receptors (GPCRs) and evidence for their existence was introduced by Agnati and Fuxe in 1980/81 through the biochemical analysis of the effects of neuropeptides on the binding characteristics of monoamine receptors in membrane preparations from discrete brain regions and functional studies of the interactions between neuropeptides and monoamines in the control of specific functions such as motor control and arterial blood pressure control in animal models. Whether GPCRs can form high-order structures is still a topic of an intense debate. Increasing evidence, however, suggests that the hypothesis of the existence of high-order receptor oligomers is correct. A fundamental consequence of the view describing GPCRs as interacting structures, with the likely formation at the plasma membrane of receptor aggregates of multiple receptors (Receptor Mosaics) is that it is no longer possible to describe signal transduction simply as the result of the binding of the chemical signal to its receptor, but rather as the result of a filtering/integration of chemical signals by the Receptor Mosaics (RMs) and membrane-associated proteins. Thus, in parallel with experimental research, significant efforts were spent in bioinformatics and mathematical modelling. We review here the main approaches that have been used to assess the interaction interfaces allowing the assembly of GPCRs and to shed some light on the integrative functions emerging from the complex behaviour of these RMs. Particular attention was paid to the RMs generated by adenosine A(2A), dopamine D-2, cannabinoid CB1, and metabotropic glutamate mGlu(5) receptors (A(2A). D-2, CB1, and mGlu(5), respectively), and a possible approach to model the interplay between the D-2-A(2A)-CB1 and D-2-A(2A)-mGlu(5) trimers is proposed. This article is part of a Special Issue entitled: "Adenosine Receptors". (C) 2010 Elsevier B.V. All rights reserved.openD. GUIDOLIN; F. CIRUELA; S. GENEDANI; M. GUESCINI; C. TORTORELLA; G. ALBERTIN; K. FUXE; L.F. AGNATID., Guidolin; F., Ciruela; S., Genedani; Guescini, Michele; C., Tortorella; G., Albertin; K., Fuxe; L. F., Agnat

Disordered proteins and network disorder in network descriptions of protein structure, dynamics and function. Hypotheses and a comprehensive review

During the last decade, network approaches became a powerful tool to describe protein structure and dynamics. Here we review the links between disordered proteins and the associated networks, and describe the consequences of local, mesoscopic and global network disorder on changes in protein structure and dynamics. We introduce a new classification of protein networks into ‘cumulus-type’, i.e., those similar to puffy (white) clouds, and ‘stratus-type’, i.e., those similar to flat, dense (dark) low-lying clouds, and relate these network types to protein disorder dynamics and to differences in energy transmission processes. In the first class, there is limited overlap between the modules, which implies higher rigidity of the individual units; there the conformational changes can be described by an ‘energy transfer’ mechanism. In the second class, the topology presents a compact structure with significant overlap between the modules; there the conformational changes can be described by ‘multi-trajectories’; that is, multiple highly populated pathways. We further propose that disordered protein regions evolved to help other protein segments reach ‘rarely visited’ but functionally-related states. We also show the role of disorder in ‘spatial games’ of amino acids; highlight the effects of intrinsically disordered proteins (IDPs) on cellular networks and list some possible studies linking protein disorder and protein structure networks