Optimising Elastic Network Models for Protein Dynamics and Allostery: Spatial and Modal Cut-offs and Backbone Stiffness.

The family of coarse-grained models for protein dynamics known as Elastic Network Models (ENMs) require careful choice of parameters to represent well experimental measurements or fully-atomistic simulations. The most basic ENM that represents each protein residue by a node at the position of its C-alpha atom, all connected by springs of equal stiffness, up to a cut-off in distance. Even at this level a choice is required of the optimum cut-off distance and the upper limit of elastic normal modes taken in any sum for physical properties, such as dynamic correlation or allosteric effects on binding. Additionally, backbone-enhanced ENM (BENM) may improve the model by allocating a higher stiffness to springs that connect along the protein backbone. This work reports on the effect of varying these three parameters (distance and mode cutoffs, backbone stiffness) on the dynamical structure of three proteins, Catabolite Activator Protein (CAP), Glutathione S-transferase (GST), and the SARS-CoV-2 Main Protease (M pro ). Our main results are: (1) balancing B-factor and dispersion-relation predictions, a near-universal optimal value of 8.5 Å is advisable for ENMs; (2) inhomogeneity in elasticity brings the first mode containing spatial structure not well-resolved by the ENM typically within the first 20; (3) the BENM only affects modes in the upper third of the distribution, and, additionally to the ENM, is only able to model the dispersion curve better in this vicinity; (4) BENM does not typically affect fluctuation-allostery, which also requires careful treatment of the effector binding to the host protein to capture.

Erratum: Physics of Brains.

[This corrects the article DOI: 10.1016/j.isci.2021.102877.].

Membraneless organelles formed by liquid-liquid phase separation increase bacterial fitness.

Liquid-liquid phase separation is emerging as a crucial phenomenon in several fundamental cell processes. A range of eukaryotic systems exhibit liquid condensates. However, their function in bacteria, which, in general, lack membrane-bound compartments, remains less clear. Here, we used high-resolution optical microscopy to observe single bacterial aggresomes, nanostructured intracellular assemblies of proteins, to undercover their role in cell stress. We find that proteins inside aggresomes are mobile and undergo dynamic turnover, consistent with a liquid state. Our observations are in quantitative agreement with phase-separated liquid droplet formation driven by interacting proteins under thermal equilibrium that nucleate following diffusive collisions in the cytoplasm. We have found aggresomes in multiple species of bacteria and show that these emergent, metastable liquid-structured protein assemblies increase bacterial fitness by enabling cells to tolerate environmental stresses.

Stretching of Bombyx mori Silk Protein in Flow.

The flow-induced self-assembly of entangled Bombyx mori silk proteins is hypothesised to be aided by the 'registration' of aligned protein chains using intermolecularly interacting 'sticky' patches. This suggests that upon chain alignment, a hierarchical network forms that collectively stretches and induces nucleation in a precisely controlled way. Through the lens of polymer physics, we argue that if all chains would stretch to a similar extent, a clear correlation length of the stickers in the direction of the flow emerges, which may indeed favour such a registration effect. Through simulations in both extensional flow and shear, we show that there is, on the other hand, a very broad distribution of protein-chain stretch, which suggests the registration of proteins is not directly coupled to the applied strain, but may be a slow statistical process. This qualitative prediction seems to be consistent with the large strains (i.e., at long time scales) required to induce gelation in our rheological measurements under constant shear. We discuss our perspective of how the flow-induced self-assembly of silk may be addressed by new experiments and model development.

Power Law Stretching of Associating Polymers in Steady-State Extensional Flow.

We present a tube model for the Brownian dynamics of associating polymers in extensional flow. In linear response, the model confirms the analytical predictions for the sticky diffusivity by Leibler-Rubinstein-Colby theory. Although a single-mode Doi-Edwards-Marrucci-Grizzuti approximation accurately describes the transient stretching of the polymers above a "sticky" Weissenberg number (product of the strain rate with the sticky-Rouse time), the preaveraged model fails to capture a remarkable development of a power law distribution of stretch in steady-state extensional flow: while the mean stretch is finite, the fluctuations in stretch may diverge. We present an analytical model that shows how strong stochastic forcing drives the long tail of the distribution, gives rise to rare events of reaching a threshold stretch, and constitutes a framework within which nucleation rates of flow-induced crystallization may be understood in systems of associating polymers under flow. The model also exemplifies a wide class of driven systems possessing strong, and scaling, fluctuations.

Computational analysis of dynamic allostery and control in the SARS-CoV-2 main protease.

The COVID-19 pandemic caused by the novel coronavirus SARS-CoV-2 has no publicly available vaccine or antiviral drugs at the time of writing. An attractive coronavirus drug target is the main protease (Mpro, also known as 3CLpro) because of its vital role in the viral cycle. A significant body of work has been focused on finding inhibitors which bind and block the active site of the main protease, but little has been done to address potential non-competitive inhibition, targeting regions other than the active site, partly because the fundamental biophysics of such allosteric control is still poorly understood. In this work, we construct an elastic network model (ENM) of the SARS-CoV-2 Mpro homodimer protein and analyse its dynamics and thermodynamics. We found a rich and heterogeneous dynamical structure, including allosterically correlated motions between the homodimeric protease's active sites. Exhaustive 1-point and 2-point mutation scans of the ENM and their effect on fluctuation free energies confirm previously experimentally identified bioactive residues, but also suggest several new candidate regions that are distant from the active site, yet control the protease function. Our results suggest new dynamically driven control regions as possible candidates for non-competitive inhibiting binding sites in the protease, which may assist the development of current fragment-based binding screens. The results also provide new insights into the active biophysical research field of protein fluctuation allostery and its underpinning dynamical structure.

Silk Protein Solution: A Natural Example of Sticky Reptation.

Silk is one of the most intriguing examples of biomolecular self-assembly, yet little is understood of molecular mechanisms behind the flow behavior generating these complex high-performance fibers. This work applies the polymer physics of entangled solution rheology to present a first microphysical understanding of silk in the linear viscoelastic regime. We show that silk solutions can be approximated as reptating polymers with "sticky" calcium bridges whose strength can be controlled through the potassium concentration. This approach provides a new window into critical microstructural parameters, in particular identifying the mechanism by which potassium and calcium ions are recruited as a powerful viscosity control in silk. Our model constitutes a viable starting point to understand not only the "flow-induced self-assembly" of silk fibers but also a broader range of phenomena in the emergent field of material-focused synthetic biology.

How proteins' negative cooperativity emerges from entropic optimisation of versatile collective fluctuations.

The fact that allostery, a nonlocal signaling between distant binding sites, can arise mainly from the entropy balance of collective thermal modes, without conformational changes, is by now well known. However, the propensity to generate negative cooperativity is still unclear. Starting from an elastic-network picture of small protein complexes, in which effector binding is modeled by locally altering interaction strengths in lieu of adding a node-spring pair, we elucidate mechanisms particularly for such negative cooperativity. The approach via a few coupled harmonic oscillators with internal elastic strengths allows us to trace individual eigenmodes, their frequencies, and their statistical weights through successive bindings. We find that the alteration of the oscillators' couplings is paramount to covering both signs of allostery. Binding-modified couplings create a rich set of eigenmodes individually for each binding state, modes inaccessible to an ensemble of noninteracting units. The associated shifts of collective-mode frequencies, nonuniform with respect to modes and binding states, result in an enhanced optimizability, reflected by a subtle phase map of allosteric behaviors.

Morphology formation in binary mixtures upon gradual destabilisation.

Spontaneous liquid-liquid phase separation is commonly understood in terms of phenomenological mean-field theories. These theories correctly predict the structural features of the fluid at sufficiently long time scales and wavelengths. However, these conditions are not met in various examples in biology and materials science where the mixture is slowly destabilised, and phase separation is strongly affected by critical thermal fluctuations. We propose a mechanism of pretransitional structuring of a mixture that approaches the miscibility gap and predict scaling relations that describe how the characteristic feature size of the emerging morphology decreases with an increasing quench rate. These predictions quantitatively agree with our kinetic Monte Carlo and molecular dynamics simulations of a phase-separating binary mixture, as well as with previously reported experimental observations. We discuss how these predictions are affected by non-conserved order parameters (e.g., due to chemical reactions or alignment of liquid-crystalline molecules), hydrodynamics and active transport.

Securing the future of research computing in the biosciences.

Improvements in technology often drive scientific discovery. Therefore, research requires sustained investment in the latest equipment and training for the researchers who are going to use it. Prioritising and administering infrastructure investment is challenging because future needs are difficult to predict. In the past, highly computationally demanding research was associated primarily with particle physics and astronomy experiments. However, as biology becomes more quantitative and bioscientists generate more and more data, their computational requirements may ultimately exceed those of physical scientists. Computation has always been central to bioinformatics, but now imaging experiments have rapidly growing data processing and storage requirements. There is also an urgent need for new modelling and simulation tools to provide insight and understanding of these biophysical experiments. Bioscience communities must work together to provide the software and skills training needed in their areas. Research-active institutions need to recognise that computation is now vital in many more areas of discovery and create an environment where it can be embraced. The public must also become aware of both the power and limitations of computing, particularly with respect to their health and personal data.

Allostery in Its Many Disguises: From Theory to Applications.

Allosteric regulation plays an important role in many biological processes, such as signal transduction, transcriptional regulation, and metabolism. Allostery is rooted in the fundamental physical properties of macromolecular systems, but its underlying mechanisms are still poorly understood. A collection of contributions to a recent interdisciplinary CECAM (Center Européen de Calcul Atomique et Moléculaire) workshop is used here to provide an overview of the progress and remaining limitations in the understanding of the mechanistic foundations of allostery gained from computational and experimental analyses of real protein systems and model systems. The main conceptual frameworks instrumental in driving the field are discussed. We illustrate the role of these frameworks in illuminating molecular mechanisms and explaining cellular processes, and describe some of their promising practical applications in engineering molecular sensors and informing drug design efforts.

Ligand-regulated oligomerisation of allosterically interacting proteins.

The binding of ligands to distinct sites at proteins or at protein clusters is often cooperative or anti-cooperative due to allosteric signalling between those sites. The allostery is usually attributed to a configurational change of the proteins from a relaxed to a configurationally different tense state. Alternatively, as originally proposed by Cooper and Dryden, a tense state may be achieved by merely restricting the thermal vibrations of the protein around its mean configuration. In this work, we provide theoretical tools to investigate fluctuation allostery using cooling and titration experiments in which ligands regulate dimerisation, or ring or chain formation. We discuss in detail how ligands may regulate the supramolecular (co)polymerisation of liganded and unliganded proteins.

The 'allosteron' model for entropic allostery of self-assembly.

Using the simple 'allosteron' model, we show that it is possible, in principle, to elicit pathways by which fluctuation allostery affects self-assembly of protein complexes. We treat the cases of (i) protein fibrils and nucleation, (ii) n-mer protein complexes, and (iii) weakly attractive allosteric interactions in protein-like soft nanoscale objects that can be tuned to define exclusive self-associating families.This article is part of a discussion meeting issue 'Allostery and molecular machines'.

Peptide strand length controls the energetics of self-assembly and morphology of ß-sheet fibrils.

Self-assembling peptides can be used as versatile, natural, and multifunctional building blocks to produce a variety of well-defined nanostructures, materials and devices for applications in medicine and nanotechnology. Here, we concentrate on the 1D self-assembly of de novo designed Px-2 peptide ß-strands into anti-parallel ß-sheet tapes and higher order aggregates. We study six members of the Px-2 family, ranging from 3 amino acids (aa) to 13 aa in length, using a range of complementary experimental techniques, computer simulation and theoretical statistical mechanics. The critical concentration for self-assembly (c*) is found to increase systematically with decreasing peptide length. The shortest peptide found to self-assemble into soluble ß-tapes in water is a 5 amino acid residue peptide. These investigations help decipher the role of the peptide length in controlling self-assembly, aggregate morphology, and material properties. By extracting free energies from these data using a statistical mechanical analysis and combining the results with computer simulations at the atomistic level, we can extract the entropy of association for individual ß-strands.

Bow-shaped caustics from conical prisms: a 13th-century account of rainbow formation from Robert Grosseteste's De iride.

The rainbow has been the subject of discussion across a variety of historical periods and cultures, and numerous optical explanations have been suggested. Here, we further explore the scientific treatise De iride [On the Rainbow] written by Robert Grosseteste in the 13th century. Attempting to account for the shape of the rainbow, Grosseteste bases his explanation on the optical properties of transparent cones, which he claims can give rise to arc-shaped projections through refraction. By stating that atmospheric phenomena are reducible to the geometric optics of a conical prism, the De iride lays out a coherent and testable hypothesis. Through both physical experiment and physics-based simulation, we present a novel characterization of cone-light interactions, demonstrating that transparent cones do indeed give rise to bow-shaped caustics-a nonintuitive phenomenon that suggests Grosseteste's theory of the rainbow is likely to have been grounded in observation.

Are there ergodic limits to evolution? Ergodic exploration of genome space and convergence.

We examine the analogy between evolutionary dynamics and statistical mechanics to include the fundamental question of ergodicity-the representative exploration of the space of possible states (in the case of evolution this is genome space). Several properties of evolutionary dynamics are identified that allow a generalization of the ergodic dynamics, familiar in dynamical systems theory, to evolution. Two classes of evolved biological structure then arise, differentiated by the qualitative duration of their evolutionary time scales. The first class has an ergodicity time scale (the time required for representative genome exploration) longer than available evolutionary time, and has incompletely explored the genotypic and phenotypic space of its possibilities. This case generates no expectation of convergence to an optimal phenotype or possibility of its prediction. The second, more interesting, class exhibits an evolutionary form of ergodicity-essentially all of the structural space within the constraints of slower evolutionary variables have been sampled; the ergodicity time scale for the system evolution is less than the evolutionary time. In this case, some convergence towards similar optima may be expected for equivalent systems in different species where both possess ergodic evolutionary dynamics. When the fitness maximum is set by physical, rather than co-evolved, constraints, it is additionally possible to make predictions of some properties of the evolved structures and systems. We propose four structures that emerge from evolution within genotypes whose fitness is induced from their phenotypes. Together, these result in an exponential speeding up of evolution, when compared with complete exploration of genomic space. We illustrate a possible case of application and a prediction of convergence together with attaining a physical fitness optimum in the case of invertebrate compound eye resolution.

Dynamic Transmission of Protein Allostery without Structural Change: Spatial Pathways or Global Modes?

We examine the contrast between mechanisms for allosteric signaling that involve structural change, and those that do not, from the perspective of allosteric pathways. In particular we treat in detail the case of fluctuation-allostery by which amplitude modulation of the thermal fluctuations of the elastic normal modes conveys the allosteric signal, and address the question of what an allosteric pathway means in this case. We find that a perturbation theory of thermal elastic solids and nonperturbative approach (by super-coarse-graining elasticity into internal bending modes) have opposite signatures in their structure of correlated pathways. We illustrate the effect from analysis of previous results from GlxR of Corynebacterium glutamicum, an example of the CRP/FNR transcription family of allosteric homodimers. We find that the visibility of both correlated pathways and disconnected sites of correlated motion in this protein suggests that mechanisms of local elastic stretch and bend are recruited for the purpose of creating and controlling allosteric cooperativity.