Emerging computational approaches for the study of protein allostery

Allosteric regulation of protein function is key in controlling cellular processes so its underlying mechanisms are of primary concern to research in areas spanning protein engineering and drug design. However, due to the complex nature of allosteric mechanisms, a clear and predictive understanding of the relationship between protein structure and allosteric function remains elusive. Well established experimental approaches are available to offer a limited degree of characterization of mechanical properties within proteins, but the analytical capabilities of computational methods are evolving rapidly in their ability to accurately define the subtle and concerted structural dynamics that comprise allostery. This review includes a brief overview of allostery in proteins and an exploration of relevant experimental methods. An explanation of the transition from experimental toward computational methods for allostery is discussed, followed by a review of existing and emerging methods.     

Exploring and exploiting allostery: Models, evolution, and drug targeting

The concept of allostery was elaborated almost 50years ago by Monod and coworkers to provide a framework for interpreting experimental studies on the regulation of protein function. In essence, binding of a ligand at an allosteric site affects the function at a distant site exploiting protein flexibility and reshaping protein energy landscape. Both monomeric and oligomeric proteins can be allosteric. In the past decades, the behavior of allosteric systems has been analyzed in many investigations while general theoretical models and variations thereof have been steadily proposed to interpret the experimental data. Allostery has been established as a fundamental mechanism of regulation in all organisms, governing a variety of processes that range from metabolic control to receptor function and from ligand transport to cell motility. A number of studies have shed light on how evolutionary pressures have favored and molded the development of allosteric features in specific macromolecular systems. The widespread occurrence of allostery has been recently exploited for the development and design of allosteric drugs that bind to either physiological or non-physiological allosteric sites leading to gain of function or loss of function. This article is part of a Special Issue entitled: Protein Dynamics: Experimental and Computational Approaches.     

Engineering and design of ligand-induced conformational change in proteins

The ability to manipulate ligand-induced conformational change, although representing a major challenge to the protein engineer, is an essential end point in efforts to produce novel functional proteins for biotechnology and therapeutic applications. Progress towards this goal requires determining not only what factors control the fold and stability of a protein, but also how ligand binding alters the complex conformational/energetic landscape. Important strides are being made on several fronts, including understanding the origin of long-range effects and allosteric structural mechanisms, using both experimental and theoretical approaches.     

Distinct allosteric pathways in imidazole glycerol phosphate synthase from yeast and bacteria

Understanding the relationship between protein structures and their function is still an open question that becomes very challenging when allostery plays an important functional role. Allosteric proteins, in fact, exploit different ranges of motions (from sidechain local fluctuations to long-range collective motions) to effectively couple distant binding sites, and of particular interest is whether allosteric proteins of the same families with similar functions and structures also necessarily share the same allosteric mechanisms. Here, we compared the early dynamics initiating the allosteric communication of a prototypical allosteric enzyme from two different organisms, i.e., the imidazole glycerol phosphate synthase (IGPS) enzymes from the thermophilic bacteria and the yeast, working at high and room temperatures, respectively. By combining molecular dynamics simulations and network models derived from graph theory, we found rather distinct early allosteric dynamics in the IGPS from the two organisms, involving significatively different allosteric pathways in terms of both local and collective motions. Given the successful prediction of key allosteric residues in the bacterial IGPS, whose mutation disrupts its allosteric communication, the outcome of this study paves the way for future experimental studies on the yeast IGPS that could foster therapeutic applications by exploiting the control of IGPS enzyme allostery.     

X-ray solution scattering (SAXS) combined with crystallography and computation: defining accurate macromolecular structures, conformations and assemblies in solution

Crystallography supplies unparalleled detail on structural information critical for mechanistic analyses; however, it is restricted to describing low energy conformations of macromolecules within crystal lattices. Small angle X-ray scattering (SAXS) offers complementary information about macromolecular folding, unfolding, aggregation, extended conformations, flexibly linked domains, shape, conformation, and assembly state in solution, albeit at the lower resolution range of about 50 A to 10 A resolution, but without the size limitations inherent in NMR and electron microscopy studies. Together these techniques can allow multi-scale modeling to create complete and accurate images of macromolecules for modeling allosteric mechanisms, supramolecular complexes, and dynamic molecular machines acting in diverse processes ranging from eukaryotic DNA replication, recombination and repair to microbial membrane secretion and assembly systems. This review addresses both theoretical and practical concepts, concerns and considerations for using these techniques in conjunction with computational methods to productively combine solution scattering data with high-resolution structures. Detailed aspects of SAXS experimental results are considered with a focus on data interpretation tools suitable to model protein and nucleic acid macromolecular structures, including membrane protein, RNA, DNA, and protein-nucleic acid complexes. The methods discussed provide the basis to examine molecular interactions in solution and to study macromolecular flexibility and conformational changes that have become increasingly relevant for accurate understanding, simulation, and prediction of mechanisms in structural cell biology and nanotechnology.     

Computational modeling of allosteric communication reveals organizing principles of mutation-induced signaling in ABL and EGFR kinases

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.     

Emerging Methods and Applications to Decrypt Allostery in Proteins and Nucleic Acids

Many large protein-nucleic acid complexes exhibit allosteric regulation. In these systems, the propagation of the allosteric signaling is strongly coupled to conformational dynamics and catalytic function, challenging state-of-the-art analytical methods. Here, we review established and innovative approaches used to elucidate allosteric mechanisms in these complexes. Specifically, we report network models derived from graph theory and centrality analyses in combination with molecular dynamics (MD) simulations, introducing novel schemes that implement the synergistic use of graph theory with enhanced simulations methods and ab-initio MD. Accelerated MD simulations are used to construct “enhanced network models”, describing the allosteric response over long timescales and capturing the relation between allostery and conformational changes. “Ab-initio network models” combine graph theory with ab-initio MD and quantum mechanics/molecular mechanics (QM/MM) simulations to describe the allosteric regulation of catalysis by following the step-by-step dynamics of biochemical reactions. This approach characterizes how the allosteric regulation changes from reactants to products and how it affects the transition state, revealing a tense-to-relaxed allosteric regulation along the chemical step. Allosteric models and applications are showcased for three paradigmatic examples of allostery in protein-nucleic acid complexes: (i) the nucleosome core particle, (ii) the CRISPR-Cas9 genome editing system and (iii) the spliceosome. These methods and applications create innovative protocols to determine allosteric mechanisms in protein-nucleic acid complexes that show tremendous promise for medicine and bioengineering.     

无序蛋白质的判定及其结构、功能和进化特征

固有无序蛋白质(intrinsically disordered proteins,IDPs)是天然条件下自身不能折叠为明确唯一的空间结构,却具有生物学功能的一类新发现的蛋白质.这类蛋白质的发现是对传统的"结构-功能"关系认识模式的挑战.本文首先总结了无序蛋白质的实验鉴定手段、预测方法、数据库;并介绍了无序蛋白质结构(包括一级结构、二级结构、结构域无序性及变构效应)和功能特征;然后重点总结了无序蛋白质在进化角度研究的进展,包括无序区域产生的进化机制、进化速率,蛋白无序性的进化在蛋白质功能进化及生物学复杂性增加等方面的重要作用;最后展望了无序蛋白质在医药方面的应用前景.本文对于深入认识无序蛋白质的形成机制、结构和功能特征及其潜在的临床应用前景具有重要意义.     

透明质酸酶的研究进展

透明质酸酶是能降解透明质酸及部分糖胺聚糖的一类糖苷酶,可应用于医疗和美容等领域。透明质酸酶也可用于制备小分子糖胺寡糖,许多研究发现小分子糖胺寡糖具有比大分子糖胺聚糖更高的生物免疫活性。为便于研究人员对透明质酸酶进行进一步的基础研究及应用研究,本文介绍了透明质酸和透明质酸酶,梳理了透明质酸酶的分类、结构和催化机理,归纳总结了透明质酸酶的酶活力测定方法、重组表达、酶学性质和应用,展望了透明质酸酶的研究方向。     

Allosteric regulation and catalysis emerge via a common route

Allosteric regulation of protein function is a mechanism by which an event in one place of a protein structure causes an effect at another site, much like the behavior of a telecommunications network in which a collection of transmitters, receivers and transceivers communicate with each other across long distances. For example, ligand binding or an amino acid mutation at an allosteric site can alter enzymatic activity or binding affinity in a distal region such as the active site or a second binding site. The mechanism of this site-to-site communication is of great interest, especially since allosteric effects must be considered in drug design and protein engineering. In this review, conformational mobility as the common route between allosteric regulation and catalysis is discussed. We summarize recent experimental data and the resulting insights into allostery within proteins, and we discuss the nature of future studies and the new applications that may result from increased understanding of this regulatory mechanism.     

Engineered Allosteric Regulation of Protein Function

Allosteric regulation of proteins has been utilized to study various aspects of cell signaling, from unicellular events to organism-wide phenotypes. However, traditional methods of allosteric regulation, such as constitutively active mutants and inhibitors, lack tight spatiotemporal control. This often leads to unintended signaling consequences that interfere with data interpretation. To overcome these obstacles, researchers employed protein engineering approaches that enable tight control of protein function through allosteric mechanisms. These methods provide high specificity as well as spatial and temporal precision in regulation of protein activity in vitro and in vivo. In this review, we focus on the recent advancements in engineered allosteric regulation and discuss the various bioengineered allosteric techniques available now, from chimeric GPCRs to chemogenetic and optogenetic switches. We highlight the benefits and pitfalls of each of these techniques as well as areas in which future improvements can be made. Additionally, we provide a brief discussion on implementation of engineered allosteric regulation approaches, demonstrating that these tools can shed light on elusive biological events and have the potential to be utilized in precision medicine.     

Adaptive machine learning for protein engineering

Machine-learning models that learn from data to predict how protein sequence encodes function are emerging as a useful protein engineering tool. However, when using these models to suggest new protein designs, one must deal with the vast combinatorial complexity of protein sequences. Here, we review how to use a sequence-to-function machine-learning surrogate model to select sequences for experimental measurement. First, we discuss how to select sequences through a single round of machine-learning optimization. Then, we discuss sequential optimization, where the goal is to discover optimized sequences and improve the model across multiple rounds of training, optimization, and experimental measurement.     

SELEX experiments: new prospects, applications and data analysis in inferring regulatory pathways

Systematic Evolution of Ligands by EXponential enrichment (SELEX) is an experimental procedure that allows extraction, from an initially random pool of oligonucleotides, of the oligomers with a desired binding affinity for a given molecular target. The procedure can be used to infer the strongest binders for a given DNA or RNA binding protein, and the highest affinity binding sequences isolated through SELEX can have numerous research, diagnostic and therapeutic applications. Recently, important new modifications of the SELEX protocol have been proposed. In particular, a modification of the standard SELEX procedure allows generating a dataset from which protein-DNA interaction parameters can be determined with unprecedented accuracy. Another variant of SELEX allows investigating interactions of a protein with nucleic-acid fragments derived from the entire genome of an organism. We review here different SELEX-based methods, with particular emphasis on the experimental design and on the applications aimed at inferring protein-DNA interactions. In addition to the experimental issues, we also review relevant methods of data analysis, as well as theoretical modeling of SELEX.     

Applications of calorimetric methods to drug discovery and the study of protein interactions

Recent studies report the application of isothermal titration calorimetry and differential scanning calorimetry to the study of protein-ligand interactions, allosteric cooperativity and aspects of protein folding. New methods of data analysis compare alternative methods for determining ligand binding enthalpy and analyze potential sources of error in the experimental measurement of other thermodynamic parameters. Several reports examine issues concerning drug design and the correlation of thermodynamic and X-ray structural data. New instruments allow volumetric effects in biochemical systems to be evaluated calorimetrically and to substantially expand the throughput of differential scanning calorimetry measurements in drug discovery and other high-throughput applications.     

Coupling between global dynamics and signal transduction pathways: a mechanism of allostery for chaperonin GroEL

Despite significant efforts toward understanding the molecular basis of allosteric communication, the mechanisms by which local energetic and conformational changes cooperatively diffuse from ligand-binding sites to distal regions across the 3-dimensional structure of allosteric proteins remain to be established. Recent experimental and theoretical evidence supports the view that allosteric communication is facilitated by the intrinsic ability of the biomolecules to undergo collective changes in structure, triggered by ligand binding. Two groups of studies recently proved to provide insights into such intrinsic, structure-induced effects: elastic network models that permit us to visualize the cooperative changes in conformation that are most readily accessible near native state conditions, and information-theoretic approaches that elucidate the most efficient pathways of signal transmission favored by the overall architecture. Using a combination of these two approaches, we highlight, by way of application to the bacterial chaperonin complex GroEL-GroES, how the most cooperative modes of motion play a role in mediating the propagation of allosteric signals. A functional coupling between the global dynamics sampled under equilibrium conditions and the signal transduction pathways inherently favored by network topology appears to control allosteric effects.     

The energy landscape analysis of cancer mutations in protein kinases

The growing interest in quantifying the molecular basis of protein kinase activation and allosteric regulation by cancer mutations has fueled computational studies of allosteric signaling in protein kinases. In the present study, we combined computer simulations and the energy landscape analysis of protein kinases to characterize the interplay between oncogenic mutations and locally frustrated sites as important catalysts of allostetric kinase activation. While structurally rigid kinase core constitutes a minimally frustrated hub of the catalytic domain, locally frustrated residue clusters, whose interaction networks are not energetically optimized, are prone to dynamic modulation and could enable allosteric conformational transitions. The results of this study have shown that the energy landscape effect of oncogenic mutations may be allosteric eliciting global changes in the spatial distribution of highly frustrated residues. We have found that mutation-induced allosteric signaling may involve a dynamic coupling between structurally rigid (minimally frustrated) and plastic (locally frustrated) clusters of residues. The presented study has demonstrated that activation cancer mutations may affect the thermodynamic equilibrium between kinase states by allosterically altering the distribution of locally frustrated sites and increasing the local frustration in the inactive form, while eliminating locally frustrated sites and restoring structural rigidity of the active form. The energy landsape analysis of protein kinases and the proposed role of locally frustrated sites in activation mechanisms may have useful implications for bioinformatics-based screening and detection of functional sites critical for allosteric regulation in complex biomolecular systems.     

The automatic discovery of structural principles describing protein fold space

The study of protein structure has been driven largely by the careful inspection of experimental data by human experts. However, the rapid determination of protein structures from structural-genomics projects will make it increasingly difficult to analyse (and determine the principles responsible for) the distribution of proteins in fold space by inspection alone. Here, we demonstrate a machine-learning strategy that automatically determines the structural principles describing 45 folds. The rules learnt were shown to be both statistically significant and meaningful to protein experts. With the increasing emphasis on high-throughput experimental initiatives, machine-learning and other automated methods of analysis will become increasingly important for many biological problems.     

Enzymes: An integrated view of structure, dynamics and function

Microbes utilize enzymes to perform a variety of functions. Enzymes are biocatalysts working as highly efficient machines at the molecular level. In the past, enzymes have been viewed as static entities and their function has been explained on the basis of direct structural interactions between the enzyme and the substrate. A variety of experimental and computational techniques, however, continue to reveal that proteins are dynamically active machines, with various parts exhibiting internal motions at a wide range of time-scales. Increasing evidence also indicates that these internal protein motions play a role in promoting protein function such as enzyme catalysis. Moreover, the thermodynamical fluctuations of the solvent, surrounding the protein, have an impact on internal protein motions and, therefore, on enzyme function. In this review, we describe recent biochemical and theoretical investigations of internal protein dynamics linked to enzyme catalysis. In the enzyme cyclophilin A, investigations have lead to the discovery of a network of protein vibrations promoting catalysis. Cyclophilin A catalyzes peptidyl-prolyl cis/trans isomerization in a variety of peptide and protein substrates. Recent studies of cyclophilin A are discussed in detail and other enzymes (dihydrofolate reductase and liver alcohol dehydrogenase) where similar discoveries have been reported are also briefly discussed. The detailed characterization of the discovered networks indicates that protein dynamics plays a role in rate-enhancement achieved by enzymes. An integrated view of enzyme structure, dynamics and function have wide implications in understanding allosteric and co-operative effects, as well as protein engineering of more efficient enzymes and novel drug design.