Sarah Rauscher

CHARMM36m: an improved force field for folded and intrinsically disordered proteins

The all-atom additive CHARMM36 protein force field is widely used in molecular modeling and simulations. We present its refinement, CHARMM36m (http://mackerell.umaryland.edu/charmm_ff.shtml), with improved accuracy in generating polypeptide backbone conformational ensembles for intrinsically disordered peptides and proteins.

Structural Ensembles of Intrinsically Disordered Proteins Depend Strongly on Force Field: A Comparison to Experiment.

Intrinsically disordered proteins (IDPs) are notoriously challenging to study both experimentally and computationally. The structure of IDPs cannot be described by a single conformation but must instead be described as an ensemble of interconverting conformations. Atomistic simulations are increasingly used to obtain such IDP conformational ensembles. Here, we have compared the IDP ensembles generated by eight all-atom empirical force fields against primary small-angle X-ray scattering (SAXS) and NMR data. Ensembles obtained with different force fields exhibit marked differences in chain dimensions, hydrogen bonding, and secondary structure content. These differences are unexpectedly large: changing the force field is found to have a stronger effect on secondary structure content than changing the entire peptide sequence. The CHARMM 22* ensemble performs best in this force field comparison: it has the lowest error in chemical shifts and J-couplings and agrees well with the SAXS data. A high population of left-handed α-helix is present in the CHARMM 36 ensemble, which is inconsistent with measured scalar couplings. To eliminate inadequate sampling as a reason for differences between force fields, extensive simulations were carried out (0.964 ms in total); the remaining small sampling uncertainty is shown to be much smaller than the observed differences. Our findings highlight how IDPs, with their rugged energy landscapes, are highly sensitive test systems that are capable of revealing force field deficiencies and, therefore, contributing to force field development.

Accelerating Convergence in Molecular Dynamics Simulations of Solutes in Lipid Membranes by Conducting a Random Walk along the Bilayer Normal.

All molecular dynamics simulations are susceptible to sampling errors, which degrade the accuracy and precision of observed values. The statistical convergence of simulations containing atomistic lipid bilayers is limited by the slow relaxation of the lipid phase, which can exceed hundreds of nanoseconds. These long conformational autocorrelation times are exacerbated in the presence of charged solutes, which can induce significant distortions of the bilayer structure. Such long relaxation times represent hidden barriers that induce systematic sampling errors in simulations of solute insertion. To identify optimal methods for enhancing sampling efficiency, we quantitatively evaluate convergence rates using generalized ensemble sampling algorithms in calculations of the potential of mean force for the insertion of the ionic side chain analog of arginine in a lipid bilayer. Umbrella sampling (US) is used to restrain solute insertion depth along the bilayer normal, the order parameter commonly used in simulations of molecular solutes in lipid bilayers. When US simulations are modified to conduct random walks along the bilayer normal using a Hamiltonian exchange algorithm, systematic sampling errors are eliminated more rapidly and the rate of statistical convergence of the standard free energy of binding of the solute to the lipid bilayer is increased 3-fold. We compute the ratio of the replica flux transmitted across a defined region of the order parameter to the replica flux that entered that region in Hamiltonian exchange simulations. We show that this quantity, the transmission factor, identifies sampling barriers in degrees of freedom orthogonal to the order parameter. The transmission factor is used to estimate the depth-dependent conformational autocorrelation times of the simulation system, some of which exceed the simulation time, and thereby identify solute insertion depths that are prone to systematic sampling errors and estimate the lower bound of the amount of sampling that is required to resolve these sampling errors. Finally, we extend our simulations and verify that the conformational autocorrelation times estimated by the transmission factor accurately predict correlation times that exceed the simulation time scale-something that, to our knowledge, has never before been achieved.

STRUCTURAL DISORDER AND PROTEIN ELASTICITY

An emerging class of disordered proteins underlies the elasticity of many biological tissues. Elastomeric proteins are essential to the function of biological machinery as diverse as the human arterial wall, the capture spiral of spider webs and the jumping mechanism of fleas. In this chapter, we review what is known about the molecular basis and the functional role of structural disorder in protein elasticity. In general, the elastic recoil of proteins is due to a combination of internal energy and entropy. In rubber-like elastomeric proteins, the dominant driving force is the increased entropy of the relaxed state relative to the stretched state. Aggregates of these proteins are intrinsically disordered or fuzzy, with high polypeptide chain entropy. We focus our discussion on the sequence, structure and function of five rubber-like elastomeric proteins, elastin, resilin, spider silk, abductin and ColP. Although we group these disordered elastomers together into one class of proteins, they exhibit a broad range of sequence motifs, mechanical properties and biological functions. Understanding how sequence modulates both disorder and elasticity will help advance the rational design of elastic biomaterials such as artificial skin and vascular grafts.

Molecular simulations of protein disorder.

Protein disorder is abundant in proteomes throughout all kingdoms of life and serves many biologically important roles. Disordered states of proteins are challenging to study experimentally due to their structural heterogeneity and tendency to aggregate. Computer simulations, which are not impeded by these properties, have recently emerged as a useful tool to characterize the conformational ensembles of intrinsically disordered proteins. In this review, we provide a survey of computational studies of protein disorder with an emphasis on the interdisciplinary nature of these studies. The application of simulation techniques to the study of disordered states is described in the context of experimental and bioinformatics approaches. Experimental data can be incorporated into simulations, and simulations can provide predictions for experiment. In this way, simulations have been integrated into the existing methodologies for the study of disordered state ensembles. We provide recent examples of simulations of disordered states from the literature and our own work. Throughout the review, we emphasize important predictions and biophysical understanding made possible through the use of simulations. This review is intended as both an overview and a guide for structural biologists and theoretical biophysicists seeking accurate, atomic-level descriptions of disordered state ensembles.

Simulated Tempering Distributed Replica Sampling, Virtual Replica Exchange, and Other Generalized-Ensemble Methods for Conformational Sampling.

Generalized-ensemble algorithms in temperature space have become popular tools to enhance conformational sampling in biomolecular simulations. A random walk in temperature leads to a corresponding random walk in potential energy, which can be used to cross over energetic barriers and overcome the problem of quasi-nonergodicity. In this paper, we introduce two novel methods: simulated tempering distributed replica sampling (STDR) and virtual replica exchange (VREX). These methods are designed to address the practical issues inherent in the replica exchange (RE), simulated tempering (ST), and serial replica exchange (SREM) algorithms. RE requires a large, dedicated, and homogeneous cluster of CPUs to function efficiently when applied to complex systems. ST and SREM both have the drawback of requiring extensive initial simulations, possibly adaptive, for the calculation of weight factors or potential energy distribution functions. STDR and VREX alleviate the need for lengthy initial simulations, and for synchronization and extensive communication between replicas. Both methods are therefore suitable for distributed or heterogeneous computing platforms. We perform an objective comparison of all five algorithms in terms of both implementation issues and sampling efficiency. We use disordered peptides in explicit water as test systems, for a total simulation time of over 42 μs. Efficiency is defined in terms of both structural convergence and temperature diffusion, and we show that these definitions of efficiency are in fact correlated. Importantly, we find that ST-based methods exhibit faster temperature diffusion and correspondingly faster convergence of structural properties compared to RE-based methods. Within the RE-based methods, VREX is superior to both SREM and RE. On the basis of our observations, we conclude that ST is ideal for simple systems, while STDR is well-suited for complex systems.

Binding of inositol stereoisomers to model amyloidogenic peptides.

The self-aggregation of proteins into amyloid fibrils is a pathological hallmark of numerous incurable diseases such as Alzheimers disease. scyllo-Inositol is a stereochemistry-dependent in vitro inhibitor of amyloid formation. As the first step to elucidate its mechanism of action, we present molecular dynamics simulations of scyllo-inositol and its inactive stereoisomer, chiro-inositol, with simple peptide models, alanine dipeptide (ADP) and (Gly-Ala)(4). We characterize molecular interactions and compute equilibrium binding constants between inositol and ADP as well as, successively, monomers, amorphous aggregates, and fibril-like β-sheet aggregates of (Gly-Ala)(4). Inositol interacts weakly with all peptide systems considered, with millimolar to molar affinities, and displaces the conformational equilibria of ADP but not of the (Gly-Ala)(4) systems. However, scyllo- and chiro-inositol adopt different binding modes on the surface of β-sheet aggregates. These results suggest that inositol does not inhibit amyloid formation by breaking up preformed aggregates but rather by binding to the surface of prefibrillar aggregates.

The liquid structure of elastin

The protein elastin imparts extensibility, elastic recoil, and resilience to tissues including arterial walls, skin, lung alveoli, and the uterus. Elastin and elastin-like peptides are hydrophobic, disordered, and undergo liquid-liquid phase separation upon self-assembly. Despite extensive study, the structure of elastin remains controversial. We use molecular dynamics simulations on a massive scale to elucidate the structural ensemble of aggregated elastin-like peptides. Consistent with the entropic nature of elastic recoil, the aggregated state is stabilized by the hydrophobic effect. However, self-assembly does not entail formation of a hydrophobic core. The polypeptide backbone forms transient, sparse hydrogen-bonded turns and remains significantly hydrated even as self-assembly triples the extent of non-polar side chain contacts. Individual chains in the assembly approach a maximally-disordered, melt-like state which may be called the liquid state of proteins. These findings resolve long-standing controversies regarding elastin structure and function and afford insight into the phase separation of disordered proteins.

The conformational ensemble of the β-casein phosphopeptide reveals two independent intrinsically disordered segments.

The β-casein phosphopeptide 1-25 (βCPP) is involved in calcium binding, cellular transduction, and dental remineralization. Though the net charge of 13e suggests an intrinsically disordered peptide, it has been shown to possibly maintain partial structure. To investigate the nature and extent of its conformational disorder, 100 independent molecular dynamics simulations (cumulative time of 30 μs) were conducted in explicit water with 0.1 M sodium chloride. βCPP adopted an ensemble of conformations (Rg = 8.61 ± 0.06 Å) stabilized primarily by ionic interactions and less so by hydrogen bonding (HB). Intramolecular contact maps showed a lack of interaction between the peptides head (RELEELNVPGEIVEΣ) and tail (ΣΣΣEESITR) segments, suggesting their conformational independence. While many backbone HB interactions were observed between the amino acids in each segment, there was no persistent secondary structure evident. Our findings provide a framework for further investigation of βCPPs conformation and mechanism of action upon binding to calcium phosphate.

Simulated tempering distributed replica sampling: A practical guide to enhanced conformational sampling

Simulated tempering distributed replica sampling (STDR) is a generalized-ensemble method designed specifically for simulations of large molecular systems on shared and heterogeneous computing platforms [Rauscher, Neale and Pomes (2009) J. Chem. Theor. Comput. 5, 2640]. The STDR algorithm consists of an alternation of two steps: (1) a short molecular dynamics (MD) simulation; and (2) a stochastic temperature jump. Repeating these steps thousands of times results in a random walk in temperature, which allows the system to overcome energetic barriers, thereby enhancing conformational sampling. The aim of the present paper is to provide a practical guide to applying STDR to complex biomolecular systems. We discuss the details of our STDR implementation, which is a highly-parallel algorithm designed to maximize computational efficiency while simultaneously minimizing network communication and data storage requirements. Using a 35-residue disordered peptide in explicit water as a test system, we characterize the efficiency of the STDR algorithm with respect to both diffusion in temperature space and statistical convergence of structural properties. Importantly, we show that STDR provides a dramatic enhancement of conformational sampling compared to a canonical MD simulation.