Albert H. Mao

Net charge per residue modulates conformational ensembles of intrinsically disordered proteins

Intrinsically disordered proteins (IDPs) adopt heterogeneous ensembles of conformations under physiological conditions. Understanding the relationship between amino acid sequence and conformational ensembles of IDPs can help clarify the role of disorder in physiological function. Recent studies revealed that polar IDPs favor collapsed ensembles in water despite the absence of hydrophobic groups—a result that holds for polypeptide backbones as well. By studying highly charged polypeptides, a different archetype of IDPs, we assess how charge content modulates the intrinsic preference of polypeptide backbones for collapsed structures. We characterized conformational ensembles for a set of protamines in aqueous milieus using molecular simulations and fluorescence measurements. Protamines are arginine-rich IDPs involved in the condensation of chromatin during spermatogenesis. Simulations based on the ABSINTH implicit solvation model predict the existence of a globule-to-coil transition, with net charge per residue serving as the discriminating order parameter. The transition is supported by quantitative agreement between simulation and experiment. Local conformational preferences partially explain the observed trends of polymeric properties. Our results lead to the proposal of a schematic protein phase diagram that should enable prediction of polymeric attributes for IDP conformational ensembles using easily calculated physicochemical properties of amino acid sequences. Although sequence composition allows the prediction of polymeric properties, interresidue contact preferences of protamines with similar polymeric attributes suggest that certain details of conformational ensembles depend on the sequence. This provides a plausible mechanism for specificity in the functions of IDPs.

Role of Backbone−Solvent Interactions in Determining Conformational Equilibria of Intrinsically Disordered Proteins

Intrinsically disordered proteins (IDPs) are functional proteins that do not fold into well-defined three-dimensional structures under physiological conditions. IDP sequences have low hydrophobicity, and hence, recent experiments have focused on quantitative studies of conformational ensembles of archetypal IDP sequences such as polyglutamine and glycine-serine block copolypeptides. Results from these experiments show that, despite the absence of hydrophobic residues, polar IDPs prefer ensembles of collapsed structures in aqueous milieus. Do these preferences originate in interactions that are unique to polar sidechains? The current study addresses this issue by analyzing conformational equilibria for polyglycine and a glycine-serine block copolypeptide in two environments, namely, water and 8 M urea. Polyglycine, a poly secondary-amide, has no sidechains and is a useful model system for generic polypeptide backbones. Results based on large-scale molecular dynamics simulations show that polyglycine forms compact, albeit disordered, globules in water and swollen, disordered coils in 8 M urea. There is minimal overlap between conformational ensembles in the two environments. Analysis of order parameters derived from theories for flexible polymers show that water at ambient temperatures is a poor solvent for generic polypeptide backbones. Therefore, the experimentally observed preferences for polyglutamine and glycine-serine block copolypeptides must originate, at least partially, in polypeptide backbones. A preliminary analysis of the driving forces that lead to distinct conformational preferences for polyglycine in two different environments is presented. Implications for describing conformational ensembles of generic IDP sequences are also discussed.

Describing Sequence-Ensemble Relationships for Intrinsically Disordered Proteins

Intrinsically disordered proteins participate in important protein-protein and protein-nucleic acid interactions and control cellular phenotypes through their prominence as dynamic organizers of transcriptional, post-transcriptional and signalling networks. These proteins challenge the tenets of the structure-function paradigm and their functional mechanisms remain a mystery given that they fail to fold autonomously into specific structures. Solving this mystery requires a first principles understanding of the quantitative relationships between information encoded in the sequences of disordered proteins and the ensemble of conformations they sample. Advances in quantifying sequence-ensemble relationships have been facilitated through a four-way synergy between bioinformatics, biophysical experiments, computer simulations and polymer physics theories. In the present review we evaluate these advances and the resultant insights that allow us to develop a concise quantitative framework for describing the sequence-ensemble relationships of intrinsically disordered proteins.

Crystal lattice properties fully determine short-range interaction parameters for alkali and halide ions

Accurate models of alkali and halide ions in aqueous solution are necessary for computer simulations of a broad variety of systems. Previous efforts to develop ion force fields have generally focused on reproducing experimental measurements of aqueous solution properties such as hydration free energies and ion-water distribution functions. This dependency limits transferability of the resulting parameters because of the variety and known limitations of water models. We present a solvent-independent approach to calibrating ion parameters based exclusively on crystal lattice properties. Our procedure relies on minimization of lattice sums to calculate lattice energies and interionic distances instead of equilibrium ensemble simulations of dense fluids. The gain in computational efficiency enables simultaneous optimization of all parameters for Li+, Na+, K+, Rb+, Cs+, F-, Cl-, Br-, and I- subject to constraints that enforce consistency with periodic table trends. We demonstrate the method by presenting lattice-derived parameters for the primitive model and the Lennard-Jones model with Lorentz-Berthelot mixing rules. The resulting parameters successfully reproduce the lattice properties used to derive them and are free from the influence of any water model. To assess the transferability of the Lennard-Jones parameters to aqueous systems, we used them to estimate hydration free energies and found that the results were in quantitative agreement with experimentally measured values. These lattice-derived parameters are applicable in simulations where coupling of ion parameters to a particular solvent model is undesirable. The simplicity and low computational demands of the calibration procedure make it suitable for parametrization of crystallizable ions in a variety of force fields.

Unmasking Functional Motifs Within Disordered Regions of Proteins

The application of a computational approach to identify short linear motifs may enable the engineering of signaling networks. Eukaryotic proteins often possess long stretches that fail to adopt well-defined, three-dimensional structures. These intrinsically disordered regions are associated with cell signaling through the enrichment of hub proteins of networks and as targets for posttranslational modifications. Although disordered regions are readily identified because of their distinct sequence characteristics, it is difficult to predict the functions associated with these regions. This is because disordered regions often house short (two- to five-residue) linear motifs that mediate intermolecular interactions. Predicting their function requires the ability to identify the functionally relevant motifs. If one assumes that functional motifs are highly conserved as compared to background sequence contexts, then a suitable comparative genomics approach proves to be powerful in unmasking functional motifs that are part of disordered regions. This approach has successfully identified known functional motifs and predicted a set of new motifs that might yield important insights regarding previously unknown functionalities for disordered regions. Given knowledge of highly conserved motifs, one can assess whether the rapidly changing sequence contexts are actuators of the functionalities of short linear motifs within disordered regions. This should have important implications for engineering and targeting hub proteins in signaling networks.

Improved Atomistic Monte Carlo Simulations Demonstrate That Poly-l-Proline Adopts Heterogeneous Ensembles of Conformations of Semi-Rigid Segments Interrupted by Kinks

Poly-L-proline (PLP) polymers are useful mimics of biologically relevant proline-rich sequences. Biophysical and computational studies of PLP polymers in aqueous solutions are challenging because of the diversity of length scales and the slow time scales for conformational conversions. We describe an atomistic simulation approach that combines an improved ABSINTH implicit solvation model, with conformational sampling based on standard and novel Metropolis Monte Carlo moves. Refinements to forcefield parameters were guided by published experimental data for proline-rich systems. We assessed the validity of our simulation results through quantitative comparisons to experimental data that were not used in refining the forcefield parameters. Our analysis shows that PLP polymers form heterogeneous ensembles of conformations characterized by semirigid, rod-like segments interrupted by kinks, which result from a combination of internal cis peptide bonds, flexible backbone ψ angles, and the coupling between ring puckering and backbone degrees of freedom.

Exact recording of Metropolis–Hastings-class Monte Carlo simulations using one bit per sample

Abstract The Metropolis–Hastings (MH) algorithm is the prototype for a class of Markov chain Monte Carlo methods that propose transitions between states and then accept or reject the proposal. These methods generate a correlated sequence of random samples that convey information about the desired probability distribution. Deciding how this information gets recorded is an important step in the practical design of MH-class algorithm implementations. Many implementations discard most of this information in order to reduce demands on storage capacity and disk writing throughput. Here, we describe how recording a bit string containing 1ʼs for acceptance and 0ʼs for rejection allows the full sample sequence to be recorded with no information loss, facilitating decoupling of simulation design from the constraints of data analysis. The recording uses only one bit per sample, which is an upper bound on the rate at which information about the desired distribution is acquired. We also demonstrate the method and quantify its benefits on a nontrivial colloidal system of charged particles in the canonical ensemble. The method imposes no restrictions on the system or simulation design and is compatible with descendants of the MH algorithm.

Accurate Atomistic Modeling of Conformational Equilibria of Proline-Rich Sequences

Proline-rich regions (PRRs) are ubiquitous in biology. They occur as modules for protein-protein interactions, as linkers, spacers, and bristles in intrinsically disordered proteins, and as flanking sequences that modulate interactions of aggregation-prone regions. Proline is unique in many respects: a conformationally restricted N-substituted imino acid, it lacks a hydrogen bond donor and its peptide bond has a greater likelihood of sampling the cis isomer. Under the assumption of persistent conformational rigidity, polyproline has been used as a spectroscopic ruler for calibrating expectations from Forster resonance energy transfer experiments. However, recent studies have raised questions regarding assumptions of conformational rigidity, specifically concerning cis-to-trans ratios for prolyl peptide bonds in PRRs and its implications for intrinsic flexibilities of PRRs. Additional questions arise with respect to the coupling between ring puckering and prolyl peptide bond isomerization. Atomistic simulations can in theory shed light on this important topic. However, simulations are plagued by difficulties on two fronts viz., the inaccuracies of molecular mechanics parameters and the inaccessibility of cis-to-trans isomerization to traditional simulations that rely on the use of molecular dynamics methods.Here, we present a series of novel sampling moves in a Monte Carlo simulation framework using the ABSINTH implicit solvation model. We use experimental data for small peptide systems to calibrate torsional parameters in the OPLS-AA/L forcefield. Our parameterization successfully reproduces observed puckered state ratios for prolyl rings and the coupling between ring puckering and the cis versus trans state of prolyl peptide bonds. Improvements to forcefield parameters and the improved Monte Carlo moves were tested for their ability to reproduce a range of experimental data for different PRRs including polyproline. Analysis of sampled ensembles suggests that prolyl peptide bonds have a higher than previously appreciated likelihood of sampling cis isomers in PRRs.