A coarse-grained xDLVO model for colloidal protein-protein interactions

Computational Approaches to Predict Protein-Protein Interactions in Crowded Cellular Environments

Investigating protein-protein interactions is crucial for understanding cellular biological processes because proteins often function within molecular complexes rather than in isolation. While experimental and computational methods have provided valuable insights into these interactions, they often overlook a critical factor: the crowded cellular environment. This environment significantly impacts protein behavior, including structural stability, diffusion, and ultimately the nature of binding. In this review, we discuss theoretical and computational approaches that allow the modeling of biological systems to guide and complement experiments and can thus significantly advance the investigation, and possibly the predictions, of protein-protein interactions in the crowded environment of cell cytoplasm. We explore topics such as statistical mechanics for lattice simulations, hydrodynamic interactions, diffusion processes in high-viscosity environments, and several methods based on molecular dynamics simulations. By synergistically leveraging methods from biophysics and computational biology, we review the state of the art of computational methods to study the impact of molecular crowding on protein-protein interactions and discuss its potential revolutionizing effects on the characterization of the human interactome.

Effects of flexibility in coarse-grained models for bovine serum albumin and immunoglobulin G

We construct a coarse-grained, structure-based, low-resolution, 6-bead flexible model of bovine serum albumin (BSA, PDB: 4F5S), which is a popular example of a globular protein in biophysical research. The model is obtained via direct Boltzmann inversion using all-atom simulations of a single molecule, and its particular form is selected from a large pool of 6-bead coarse-grained models using two suitable metrics that quantify the agreement in the distribution of collective coordinates between all-atom and coarse-grained Brownian dynamics simulations of solutions in the dilute limit. For immunoglobulin G (IgG), a similar structure-based 12-bead model has been introduced in the literature [Chaudhri et al., J. Phys. Chem. B 116, 8045 (2012)] and is employed here to compare findings for the compact BSA molecule and the more anisotropic IgG molecule. We define several modified coarse-grained models of BSA and IgG, which differ in their internal constraints and thus account for a variation of flexibility. We study denser solutions of the coarse-grained models with purely repulsive molecules (achievable by suitable salt conditions) and address the effect of packing and flexibility on dynamic and static behavior. Translational and rotational self-diffusivity is enhanced for more elastic models. Finally, we discuss a number of effective sphere sizes for the BSA molecule, which can be defined from its static and dynamic properties. Here, it is found that the effective sphere diameters lie between 4.9 and 6.1 nm, corresponding to a relative spread of about ±10% around a mean of 5.5 nm.

Determination of specific and non-specific protein-protein interactions for beta-lactoglobulin by analytical ultracentrifugation and membrane osmometry experiments

Protein-protein interactions are essential for the understanding of biological processes. Specific protein aggregation is an important aspect for many biological systems. In particular, electrostatic interactions play the key role for protein-protein interactions, as many amino acids have pH-dependent charge states. Moreover, protein dissociation is directly related to the solution pH, ionic strength, temperature and protein concentration. The subtle interplay between different specific and non-specific interactions is demonstrated for beta-lactoglobulin (BLG) with a focus on low salt concentrations, thus mimicking technically relevant processing conditions. BLG is a well-characterized model system, proven to attain its monomer-dimer equilibrium strongly dependent upon the pH of the solution. In this manuscript, we present a unique combination of analytical ultracentrifugation and membrane osmometry experiments, which quantifies specific and non-specific interactions, i.e. in terms of the dimer dissociation constants and the second osmotic virial coefficient, at pH 3 and 7 and sodium chloride concentrations of 10 mM and 100 mM. This provides direct insight to protein-protein interactions for a system with a concentration-dependent monomer-dimer equilibrium. Moreover, using a coarse-grained extended DLVO model in combination with molecular dynamics simulations, we quantify non-specific monomer-monomer, monomer-dimer and dimer-dimer interactions as well as the binding free energy of BLG dimerization from theoretical calculations. The experimentally determined interactions are shown to be mainly governed by electrostatic interactions and further agree with free energy calculations. Our experimental protocol aims to determine non-specific and specific interactions for a dynamically interacting system and provides an understanding of protein-protein interactions for BLG at low salt concentrations.

Computational models for studying physical instabilities in high concentration biotherapeutic formulations

Computational prediction of the behavior of concentrated protein solutions is particularly advantageous in early development stages of biotherapeutics when material availability is limited and a large set of formulation conditions needs to be explored. This review provides an overview of the different computational paradigms that have been successfully used in modeling undesirable physical behaviors of protein solutions with a particular emphasis on high-concentration drug formulations. This includes models ranging from all-atom simulations, coarse-grained representations to macro-scale mathematical descriptions used to study physical instability phenomena of protein solutions such as aggregation, elevated viscosity, and phase separation. These models are compared and summarized in the context of the physical processes and their underlying assumptions and limitations. A detailed analysis is also given for identifying protein interaction processes that are explicitly or implicitly considered in the different modeling approaches and particularly their relations to various formulation parameters. Lastly, many of the shortcomings of existing computational models are discussed, providing perspectives and possible directions toward an efficient computational framework for designing effective protein formulations.

Understanding the interaction of proteins to ion exchange chromatographic supports: A surface energetics approach

Ion exchange chromatography is one of the most widely used chromatographic technique for the separation and purification of important biological molecules. Due to its wide applicability in separation processes, a targeted approach is required to suggest the effective binding conditions during ion exchange chromatography. A surface energetics approach was used to study the interaction of proteins to different types of ion exchange chromatographic beads. The basic parameters used in this approach are derived from the contact angle, streaming potential, and zeta potential values. The interaction of few model proteins to different anionic and cationic exchanger, with different backbone chemistry i.e., agarose and methacrylate, was performed. Generally, under binding conditions, it was observed that proteins having negative surface charges showed strong to lose interaction (20 kT for Hannilase to 0.5 kT for IgG) with different anionic exchangers (having different positive surface charges). On the contrary, anionic exchangers showed almost no interaction (0 - 0.1 kT) with the positively charged proteins. An inverse behavior was observed for the interaction of proteins to cationic exchangers. The outcome from these theoretical calculations can predict the binding behavior of different proteins under real ion exchange chromatographic conditions. This will ultimately propose a better bioprocess design for protein separation. This article is protected by copyright. All rights reserved.