Adrian H. Elcock

Diffusion, Crowding & Protein Stability in a Dynamic Molecular Model of the Bacterial Cytoplasm

A longstanding question in molecular biology is the extent to which the behavior of macromolecules observed in vitro accurately reflects their behavior in vivo. A number of sophisticated experimental techniques now allow the behavior of individual types of macromolecule to be studied directly in vivo; none, however, allow a wide range of molecule types to be observed simultaneously. In order to tackle this issue we have adopted a computational perspective, and, having selected the model prokaryote Escherichia coli as a test system, have assembled an atomically detailed model of its cytoplasmic environment that includes 50 of the most abundant types of macromolecules at experimentally measured concentrations. Brownian dynamics (BD) simulations of the cytoplasm model have been calibrated to reproduce the translational diffusion coefficients of Green Fluorescent Protein (GFP) observed in vivo, and “snapshots” of the simulation trajectories have been used to compute the cytoplasms effects on the thermodynamics of protein folding, association and aggregation events. The simulation model successfully describes the relative thermodynamic stabilities of proteins measured in E. coli, and shows that effects additional to the commonly cited “crowding” effect must be included in attempts to understand macromolecular behavior in vivo.

Models of macromolecular crowding effects and the need for quantitative comparisons with experiment

In recent years significant effort has been devoted to exploring the potential effects of macromolecular crowding on protein folding and association phenomena. Theoretical calculations and molecular simulations have, in particular, been exploited to describe aspects of protein behavior in crowded and confined conditions and many aspects of the simulated behavior have reflected, at least at a qualitative level, the behavior observed in experiments. One major and immediate challenge for the theorists is to now produce models capable of making quantitatively accurate predictions of in vitro behavior. A second challenge is to derive models that explain results obtained from experiments performed in vivo, the results of which appear to call into question the assumed dominance of excluded-volume effects in vivo.

The Native 3D Organization of Bacterial Polysomes

Recent advances have led to insights into the structure of the bacterial ribosome, but little is known about the 3D organization of ribosomes in the context of translating polysomes. We employed cryoelectron tomography and a template-matching approach to map 70S ribosomes in vitrified bacterial translation extracts and in lysates of active E. coli spheroplasts. In these preparations, polysomal arrangements were observed in which neighboring ribosomes are densely packed and exhibit preferred orientations. Analysis of characteristic examples of polysomes reveals a staggered or pseudohelical organization of ribosomes along the mRNA trace, with the transcript being sequestered on the inside, the tRNA entrance sites being accessible, and the polypeptide exit sites facing the cytosol. Modeling of elongating nascent polypeptide chains suggests that this arrangement maximizes the distance between nascent chains on adjacent ribosomes, thereby reducing the probability of intermolecular interactions that would give rise to aggregation and limit productive folding.

Physicochemical Properties of Cells and Their Effects on Intrinsically Disordered Proteins (IDPs)

It has long been axiomatic that a protein’s structure determines its function. Intrinsically disordered proteins (IDPs) and disordered protein regions (IDRs) defy this structure–function paradigm. They do not exhibit stable secondary and/or tertiary structures and exist as dynamic ensembles of interconverting conformers with preferred, nonrandom orientations.1−4 The concept of IDPs and IDRs as functional biological units was initially met with skepticism. For a long time, disorder, intuitively implying chaos, had no place in our perception of orchestrated molecular events controlling cell biology. Over the past years, however, this notion has changed. Aided by findings that structural disorder constitutes an ubiquitous and abundant biological phenomenon in organisms of all phyla,5−7 and that it is often synonymous with function,8−11 disorder has become an integral part of modern protein biochemistry. Disorder thrives in eukaryotic signaling pathways12 and functions as a prominent player in many regulatory processes.13−15 Disordered proteins and protein regions determine the underlying causes of many neurodegenerative disorders and constitute the main components of amyloid fibrils.16 They further contribute to many forms of cancer, diabetes and to cardiovascular and metabolic diseases.17,18 Research into disordered proteins produced significant findings and established important new concepts. On the structural side, novel experimental and computational approaches identified and described disordered protein ensembles3,19,20 and led to terms such as secondary structure propensities, residual structural features, and transient long-range contacts.1,21 The discovery of coupled folding-and-binding reactions defined the paradigm of disorder-to-order transitions22 and high-resolution insights into the architectures of amyloid fibrils were obtained.23,24 On the biological side, we learned about the unexpected intracellular stability of disordered proteins, their roles in integrating post-translational protein modifications in cell signaling and about their functions in regulatory processes ranging from transcription to cell fate decisions.15,25,26 One open question remaining to be addressed is how these in vitro structural insights relate to biological in vivo effects. How do complex intracellular environments modulate the in vivo properties of disordered proteins and what are the implications for their biological functions (Figure ​(Figure11)?27−29 Figure 1 Intracellular complexity. (A) Left: Cryo-electron tomography slice of a mammalian cell. Middle: Close-up view of cellular structures colored according to their identities: Right: Three-dimensional surface representation of the same region. Yellow, endoplasmic ...

Computing ionization states of proteins with a detailed charge model

A convenient computational approach for the calculation of the p Kas of ionizable groups in a protein is described. The method uses detailed models of the charges in both the neutral and ionized form of each ionizable group. A full derivation of the theoretical framework is presented, as are details of its implementation in the UHBD program. Application to four proteins whose crystal structures are known shows that the detailed charge model improves agreement with experimentally determined pKas when a low protein dielectric constant is assumed, relative to the results with a simpler single‐site ionization model. It is also found that use of the detailed charge model increases the sensitivity of the computed pKas to the details of proton placement.

Identification of protein oligomerization states by analysis of interface conservation

The discrimination of true oligomeric protein–protein contacts from nonspecific crystal contacts remains problematic. Criteria that have been used previously base the assignment of oligomeric state on consideration of the area of the interface and/or the results of scoring functions based on statistical potentials. Both techniques have a high success rate but fail in more than 10% of cases. More importantly, the oligomeric states of several proteins are incorrectly assigned by both methods. Here we test the hypothesis that true oligomeric contacts should be identifiable on the basis of an increased degree of conservation of the residues involved in the interface. By quantifying the degree of conservation of the interface and comparing it with that of the remainder of the protein surface, we develop a new criterion that provides a highly effective complement to existing methods.

Calculation of Weak Protein-Protein Interactions: The pH Dependence of the Second Virial Coefficient

Interactions between proteins are often sufficiently weak that their study through the use of conventional structural techniques becomes problematic. Of the few techniques capable of providing experimental measures of weak protein-protein interactions, perhaps the most useful is the second virial coefficient, B(22), which quantifies a protein solutions deviations from ideal behavior. It has long been known that B(22) can in principle be computed, but only very recently has it been demonstrated that such calculations can be performed using protein models of true atomic detail (Biophys. J. 1998, 75:2469-2477). The work reported here extends these previous efforts in an attempt to develop a transferable energetic model capable of reproducing the experimental trends obtained for two different proteins over a range of pH and ionic strengths. We describe protein-protein interaction energies by a combination of three separate terms: (i) an electrostatic interaction term based on the use of effective charges, (ii) a term describing the electrostatic desolvation that occurs when charged groups are buried by an approaching protein partner, and (iii) a solvent-accessible surface area term that is used to describe contributions from van der Waals and hydrophobic interactions. The magnitude of the third term is governed by an adjustable, empirical parameter, gamma, that is altered to optimize agreement between calculated and experimental values of B(22). The model is applied separately to the proteins lysozyme and chymotrypsinogen, yielding optimal values of gamma that are almost identical. There are, however, clear difficulties in reproducing B(22) values at the extremes of pH. Explicit calculation of the protonation states of ionizable amino acids in the 200 most energetically favorable protein-protein structures suggest that these difficulties are due to a neglect of the protonation state changes that can accompany complexation. Proper reproduction of the pH dependence of B(22) will, therefore, almost certainly require that account be taken of these protonation state changes. Despite this problem, the fact that almost identical gamma values are obtained from two different proteins suggests that the basic energetic formulation used here, which can be evaluated very rapidly, might find use in dynamical simulations of weak protein-protein interactions at intermediate pH values.

Absolute Protein-Protein Association Rate Constants from Flexible, Coarse-Grained Brownian Dynamics Simulations: The Role of Intermolecular Hydrodynamic Interactions in Barnase-Barstar Association

Theory and computation have long been used to rationalize the experimental association rate constants of protein-protein complexes, and Brownian dynamics (BD) simulations, in particular, have been successful in reproducing the relative rate constants of wild-type and mutant protein pairs. Missing from previous BD studies of association kinetics, however, has been the description of hydrodynamic interactions (HIs) between, and within, the diffusing proteins. Here we address this issue by rigorously including HIs in BD simulations of the barnase-barstar association reaction. We first show that even very simplified representations of the proteins--involving approximately one pseudoatom for every three residues in the protein--can provide excellent reproduction of the absolute association rate constants of wild-type and mutant protein pairs. We then show that simulations that include intermolecular HIs also produce excellent estimates of association rate constants, but, for a given reaction criterion, yield values that are decreased by ∼35-80% relative to those obtained in the absence of intermolecular HIs. The neglect of intermolecular HIs in previous BD simulation studies, therefore, is likely to have contributed to the somewhat overestimated absolute rate constants previously obtained. Consequently, intermolecular HIs could be an important component to include in accurate modeling of the kinetics of macromolecular association events.

Sequence Selective Binding to the DNA Major Groove: Tris(1,10-phenanthroline) Metal Complexes Binding to Poly(dG-dC) and Poly(dA-dT)

Molecular modelling and energy minimisation calculations that incorporate solvent effects have been used to investigate the complexation of delta and lambda-[Ru(1,10-phenanthroline]2+ to DNA. The most stable binding geometry for both enantiomers is one in which a phenanthroline chelate is positioned in the major groove. The chelate is partially inserted between neighbouring base pairs, but is not intercalated. For delta, though not for lambda, a geometry with two chelates in the major groove is only slightly less favourable. Minor groove binding is shown to be no more favourable than external electrostatic binding. The optimised geometries of the DNA/[Ru(1,10-phenanthroline]2+ complexes enable published linear dichroism spectra to be used to determine the percentage of each enantiomer in the two most favourable major groove sites. For delta 57 +/- 15% and for lambda 82 +/- 7% of bound molecules are in the partially inserted site.

Rapid binding of a cationic active site inhibitor to wild type and mutant mouse acetylcholinesterase: Brownian dynamics simulation including diffusion in the active site gorge

It is known that anionic surface residues play a role in the long-range electrostatic attraction between acetylcholinesterase and cationic ligands. In our current investigation, we show that anionic residues also play an important role in the behavior of the ligand within the active site gorge of acetylcholinesterase. Negatively charged residues near the gorge opening not only attract positively charged ligands from solution to the enzyme, but can also restrict the motion of the ligand once it is inside of the gorge. We use Brownian dynamics techniques to calculate the rate constant kon, for wild type and mutant acetylcholinesterase with a positively charged ligand. These calculations are performed by allowing the ligand to diffuse within the active site gorge. This is an extension of previously reported work in which a ligand was allowed to diffuse only to the enzyme surface. By setting the reaction criteria for the ligand closer to the active site, better agreement with experimental data is obtained. Although a number of residues influence the movement of the ligand within the gorge, Asp74 is shown to play a particularly important role in this function. Asp74 traps the ligand within the gorge, and in this way helps to ensure a reaction.