Probing the early stages of prion protein (PrP) aggregation with atomistic molecular dynamics simulations

Engineering of conserved residues near antibody heavy chain complementary determining region 3 (HCDR3) improves both affinity and stability

Affinity and stability are crucial parameters in antibody development and engineering approaches. Although improvement in both metrics is desirable, trade-offs are almost unavoidable. Heavy chain complementarity determining region 3 (HCDR3) is the best-known region for antibody affinity but its impact on stability is often neglected. Here, we present a mutagenesis study of conserved residues near HCDR3 to elicit the role of this region in the affinity-stability trade-off. These key residues are positioned around the conserved salt bridge between VH-K94 and VH-D101 which is crucial for HCDR3 integrity. We show that the additional salt bridge at the stem of HCDR3 (VH-K94:VH-D101:VH-D102) has an extensive impact on this loop's conformation, therefore simultaneous improvement in both affinity and stability. We find that the disruption of π-π stacking near HCDR3 (VH-Y100E:VL-Y49) at the VH-VL interface cause an irrecoverable loss in stability even if it improves the affinity. Molecular simulations of putative rescue mutants exhibit complex and often non-additive effects. We confirm that our experimental measurements agree with the molecular dynamic simulations providing detailed insights for the spatial orientation of HCDR3. VH-V102 right next to HCDR3 salt bridge might be an ideal candidate to overcome affinity-stability trade-off.

Homodimeric complexes of the 90-231 human prion: a multilayered computational study based on FMO/GRID-DRY approach

The molecular interaction properties and aggregation capabilities disclosed by PrP-E200K, a pathogenic mutant of the human prion protein, were investigated in detail using multilayered computational approaches. In a previous work, we reported that the electrostatic complementarity between region1 (negative) and region3 (positive) has been assumed to lead to a head-to tail interaction between 120 and 231 PrP-E200K units and to initiation of the aggregation process. In this work, we extended the PrP-E200K structure by including the unstructured 90–120 segment which was found to assume different conformations. Plausible models of 90–231 PrP-E200K dimers were calculated and analyzed in depth to identify the nature of the involved protein–protein interactions. The unstructured 90–120 segment was found to extend the positively charged region3 involved in the association of PrP-E200K units which resulted to be driven by hydrophobic interactions. The combination of molecular dynamics, protein–protein docking, grid-based mapping, and fragment molecular orbital approaches allowed us to provide a plausible mechanism of the early state of 90–231 PrP-E200K aggregation, considered a preliminary step of amyloid conversion.

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.

Comparison of the pH- and thermally-induced fluctuations of a therapeutic antibody Fab fragment by molecular dynamics simulation

Successful development of protein therapeutics depends critically on achieving stability under a range of conditions. A deeper understanding of the drivers of instability across different stress conditions, will enable the engineering of more robust protein scaffolds. We compared the impacts of low pH and high temperature stresses on the structure of a humanized antibody fragment (Fab) A33, using atomistic molecular dynamics simulations, using a recent 2.5 Å crystal structure. This revealed that low-pH induced the loss of native contacts in the domain C_L. By contrast, thermal stress led to 5-7% loss of native contacts in all four domains, and simultaneous loss of >30% of native contacts in the V_L-V_H and C_L-C_H interfaces. This revealed divergent destabilising pathways under the two different stresses. The underlying cause of instability was probed using FoldX and Rosetta mutation analysis, and packing density calculations. These agreed that mutations in the C_L domain, and C_L-C_H1 interface have the greatest potential for stabilisation of Fab A33. Several key salt bridge losses underpinned the conformational change in C_L at low pH, whereas at high temperature, salt bridges became more dynamic, thus contributing to an overall destabilization. Lastly, the unfolding events at the two stress conditions exposed different predicted aggregation-prone regions (APR) to solvent, which would potentially lead to different aggregation mechanisms. Overall, our results identified the early stages of unfolding and stability-limiting regions of Fab A33, and the V_H and C_L domains as interesting future targets for engineering stability to both pH- and thermal-stresses simultaneously.

Molecular Simulations Guidelines for Biological Nanomaterials: From Peptides to Membranes

In studying biological processes and focusing on the molecular mechanisms at the basis of these, molecular dynamics (MD) simulations have demonstrated to be a very useful tool for the past 50 years. This suite of computational methods calculates the time-dependent evolution of a molecular system using physics-based first principles. In this chapter, we give a brief introduction to the theory and practical use of molecular dynamics simulations, highlighting the different models and algorithms that have been developed to tackle specific problems, with a special focus on classical force fields. Some examples of how simulations have been used in the past will help the reader in discerning their power, limitations, and significance.

Insight From Animals Resistant to Prion Diseases: Deciphering the Genotype - Morphotype - Phenotype Code for the Prion Protein

Prion diseases are a group of neurodegenerative diseases endemic in humans and several ruminants caused by the misfolding of native prion protein (PrP) into pathological conformations. Experimental work and the mad-cow epidemic of the 1980s exposed a wide spectrum of animal susceptibility to prion diseases, including a few highly resistant animals: horses, rabbits, pigs, and dogs/canids. The variable susceptibility to disease offers a unique opportunity to uncover the mechanisms governing PrP misfolding, neurotoxicity, and transmission. Previous work indicates that PrP-intrinsic differences (sequence) are the main contributors to disease susceptibility. Several residues have been cited as critical for encoding PrP conformational stability in prion-resistant animals, including D/E159 in dog, S167 in horse, and S174 in rabbit and pig PrP (all according to human numbering). These amino acids alter PrP properties in a variety of assays, but we still do not clearly understand the structural correlates of PrP toxicity. Additional insight can be extracted from comparative structural studies, followed by molecular dynamics simulations of selected mutations, and testing in manipulable animal models. Our working hypothesis is that protective amino acids generate more compact and stable structures in a C-terminal subdomain of the PrP globular domain. We will explore this idea in this review and identify subdomains within the globular domain that may hold the key to unravel how conformational stability and disease susceptibility are encoded in PrP.

Atomic insights into the effects of pathological mutants through the disruption of hydrophobic core in the prion protein

Destabilization of prion protein induces a conformational change from normal prion protein (PrP^C) to abnormal prion protein (PrP^SC). Hydrophobic interaction is the main driving force for protein folding, and critically affects the stability and solvability. To examine the importance of the hydrophobic core in the PrP, we chose six amino acids (V176, V180, T183, V210, I215, and Y218) that make up the hydrophobic core at the middle of the H2-H3 bundle. A few pathological mutants of these amino acids have been reported, such as V176G, V180I, T183A, V210I, I215V, and Y218N. We focused on how these pathologic mutations affect the hydrophobic core and thermostability of PrP. For this, we ran a temperature-based replica-exchange molecular dynamics (T-REMD) simulation, with a cumulative simulation time of 28 μs, for extensive ensemble sampling. From the T-REMD ensemble, we calculated the protein folding free energy difference between wild-type and mutant PrP using the thermodynamic integration (TI) method. Our results showed that pathological mutants V176G, T183A, I215V, and Y218N decrease the PrP stability. At the atomic level, we examined the change in pair-wise hydrophobic interactions from valine-valine to valine-isoleucine (and vice versa), which is induced by mutation V180I, V210I (I215V) at the 180^th-210^th (176^th-215^th) pair. Finally, we investigated the importance of the π-stacking between Y218 and F175.