Biology subjects

Correia, J. J.

Publications and source records attributed to Correia, J. J..

5 recordsLinked to original sources

bioRxiv2024

The Molecular Basis for Hydrodynamic Properties of PEGylated Human Serum Albumin

Polyethylene glycol conjugation provides a protective modification that enhances the pharmacokinetics and solubility of proteins for therapeutic use. A knowledge of the structural ensemble of these PEGylated proteins is necessary to understand the molecular details that contribute to their hydrodynamic and colligative properties. Because of the large size and dynamic flexibility of pharmaceutically important PEGylated proteins, the determination of structure is challenging. Here we demonstrate that structural ensembles, generated by coarse-grained simulations, can be analyzed with HullRad and used to predict sedimentation coefficients and concentration dependent hydrodynamic and diffusion nonideality coefficients of PEGylated proteins. A knowledge of these properties enhances the ability to design and analyze new modified protein therapeutics. STATEMENT OF SIGNIFICANCEProteins constitute a growing class of biotherapeutics. Chemical modification(s) with inert polymers are known to enhance the serum half-life and formulation of these biological therapeutics but the effects of modification on protein-protein interactions in solution have been difficult to predict. Here we describe methods for predicting the molecular basis for the hydrodynamic properties of polymer conjugated proteins that determine their solution behavior.

biophysics↗

bioRxiv2022

Revisiting Macromolecular Hydration with HullRadSAS

Hydration of biological macromolecules is important for their stability and function. Historically, attempts have been made to describe the degree of macromolecular hydration using a single parameter over a narrow range of values. Here, we describe a method to calculate two types of hydration: surface shell water and entrained water. A consideration of these two types of hydration helps to explain the "hydration problem" in hydrodynamics. The combination of these two types of hydration allows accurate calculation of hydrodynamic volume and related macromolecular properties such as sedimentation and diffusion coefficients, intrinsic viscosities, and the concentration dependent non-ideality identified with sedimentation velocity experiments.

biophysics↗

bioRxiv2022

Simulation of Gilbert Theory for Self-Association in Sedimentation Velocity Experiments: A Guide to Evaluate Best Fitting Models

There is a long tradition in the Biophysics community of using simulations as a means to understand macromolecular behavior in various physicochemical methods. This allows a rigorous means to interpret observations in terms of fundamental principles, including chemical equilibrium, reaction kinetics, transport processes and thermodynamics. Here we simulate data for the Gilbert Theory for self-association, a fundamental analytical ultracentrifuge (AUC) technique to understand the shape of sedimentation velocity reaction boundaries that involve reversible monomer-Nmer interactions. Simulating monomer-dimer through monomer-hexamer systems as a function of concentration about the equilibrium constant allows a visual means to differentiate reaction stoichiometry by determining end points and inflexion positions. Including intermediates (eg A1-A2-A3-A4-A5-A6) in the simulations reveals the smoothing of the reaction boundary and the removal of sharp inflexions between monomers and polymers. The addition of cooperativity restores sharp boundaries or peaks to the observation and allows more discrimination in the selection of possible fitting models. Thermodynamic nonideality adds additional features when applied across wide ranges of concentration that might be appropriate for high concentration therapeutic monoclonal antibody (mAb) solutions. This presentation serves as a tutorial for using modern AUC analysis software like SEDANAL for selecting potential fitting models.

biophysics↗

bioRxiv2022

Intrinsically disordered regions that drive phase separation form a robustly distinct protein class

Liquid-liquid phase separation (LLPS) of proteins is thought to be a primary driving force for the formation of membraneless organelles, which control a wide range of biological functions from stress response to ribosome biogenesis. LLPS of proteins in cells is primarily, though not exclusively, driven by intrinsically disordered (ID) domains. Accurate identification of ID regions (IDRs) that drive phase separation is important for testing the underlying mechanisms of phase separation, identifying biological processes that rely on phase separation, and designing sequences that modulate phase separation. To identify IDRs that drive phase separation, we first curated datasets of folded, ID, and phase-separating (PS) ID sequences. We then used these sequence sets to examine how broadly existing amino acids scales can be used to distinguish between the three classes of protein regions. We found that there are robust property differences between the classes and, consequently, that numerous combinations of amino acid property scales can be used to make robust predictions of LLPS. This result indicates that multiple, redundant mechanisms contribute to the formation of phase-separated droplets from IDRs. The top-performing scales were used to further optimize our previously developed predictor of PS IDRs, ParSe. We then modified ParSe to account for interactions between amino acids and obtained reasonable predictive power for mutations that have been designed to test the role of amino acid interactions in driving LLPS.

biophysics↗

bioRxiv2020

Beta Turn Propensity and Polymer Scaling Exponent Identify Intrinsically Disordered Proteins that Phase Separate

The complex cellular milieu can spontaneously de-mix in a process controlled in part by proteins that are intrinsically disordered (ID). A proteins propensity to de-mix is thought to be driven by the preference for protein-protein rather than protein-solvent interactions. The hydrodynamic size of monomeric proteins, as quantified by the polymer scaling exponent (v), is driven by a similar balance. We hypothesize that mean v, as predicted by the protein sequence, will be smaller for proteins with a strong propensity to de-mix. To test this hypothesis, we analyzed protein databases containing subsets that are either folded, disordered, or disordered and known to spontaneously phase separate. We find that the phase separating disordered proteins, on average, have lower calculated values of v compared to their non-phase separating counterparts. Moreover, these proteins have a higher sequence-predicted propensity for {beta}-turns. Using a simple, surface areabased model, we propose a physical mechanism for this difference: transient {beta}-turn structures reduce the desolvation penalty of forming a protein-rich phase and increase exposure of atoms involved in {pi}/sp2 electronic interactions. By this mechanism, {beta}-turns act as energetically favored nucleation points, which may explain the increased propensity for turns in ID regions (IDRs) that are utilized biologically for phase separation. Phase separating IDRs, non-phase separating IDRs, and folded regions could be distinguished by combining v and {beta}-turn propensity, and we propose a new algorithm, ParSe (partition sequence), for predicting phase separating protein regions. ParSe is able to accurately identify folded, disordered, and phase-separating protein regions from the primary sequence.

biophysics↗