Dual Role of Anionic Lipids in Amyloid Aggregation

Neurodegenerative diseases, such as Alzheimer's, Parkinson's, and Huntington's, affect millions worldwide and share a common feature: the aggregation of intrinsically disordered proteins into toxic oligomers that interact with cell membranes. In Alzheimer's disease (AD), amyloid-beta (Aβ) peptides accumulate and bind to plasma membranes, potentially disrupting cellular function. The complex interplay between amyloidogenic peptides and lipid membranes, particularly the role of anionic lipids, is crucial in disease pathogenesis but challenging to characterize experimentally. The literature presents conflicting results on the influence of anionic lipids on peptide aggregation kinetics, highlighting a knowledge gap. To address this, we used coarse-grained molecular dynamics (CG-MD) simulations to study interactions between a model amyloidogenic peptide, amyloid-β's K16LVFFAE22 fragment (Aβ16-22), and mixed lipid bilayers. We used phosphatidylserine (PS) and phosphatidylcholine (PC) as representative anionic and zwitterionic lipids, respectively, examining the mixed bilayer compositions of 0% PS-100% PC, 10% PS-90% PC, and 30% PS-70% PC. Our simulations revealed that membranes enriched in anionic lipids enhance peptide adsorption and interaction kinetics. The aggregation dynamics was modulated by two competing factors: increased local peptide concentration near negatively charged membranes, which promoted aggregation, and peptide-lipid interactions, which slowed it down. Higher percentages of anionic lipids led to smaller and more ordered aggregates and enhanced lipid demixing, leading to the formation of PS clusters. These findings contribute to understanding membrane-mediated peptide aggregation in neurodegenerative disorders, potentially guiding new therapeutic strategies targeting the early stages of protein aggregation in various neurodegenerative diseases.

Modulation of Aβ 16-22 aggregation by glucose

The self-assembly of amyloid-beta (Aβ) peptides into fibrillar structures in the brain is a signature of Alzheimer's disease. Recent studies have reported correlations between Alzheimer's disease and type-2 diabetes. Structurally, hyperglycemia induces covalent protein crosslinkings by advanced glycation end products (AGE), which can affect the stability of Aβ oligomers. In this work, we leverage physics-based coarse-grained molecular simulations to probe alternate thermodynamic pathways that affect peptide aggregation propensities at varying concentrations of glucose molecules. Similar to previous experimental reports, our simulations show a glucose concentration-dependent increase in Aβ aggregation rates, without changes in the overall secondary structure content. We discovered that glucose molecules prefer partitioning onto the aggregate-water interface at a specific orientation, resulting in a loss of molecular rotational entropy. This effectively hastens the aggregation rates, as peptide self-assembly can reduce the available surface area for peptide-glucose interactions. This work introduces a new thermodynamic-driven pathway, beyond chemical cross-linking, that can modulate Aβ aggregation.

The effect of monomer polarizability on the stability and salt partitioning in model coacervates

Coacervation of charged polymer chains has been a topic of major interest in both polymer and biological sciences, as it is a subset of a phenomenon called liquid-liquid phase separation (LLPS). In this process the polymer-rich phase separates from the polymer-lean supernatant while still maintaining its liquid-like properties. LLPS has been shown to play a crucial role in cellular homeostasis by driving the formation of membraneless organelles. It also has the potential to be harnessed to aid in novel therapeutical applications. Recent studies have demonstrated that there is no one simple mechanism which drives LLPS, which is instead a result of the combined effect of electrostatic, dipolar, hydrophobic, and other weak interactions. Using coarse-grained polymer simulations we investigate the relatively unexplored effects of monomer polarizability and spatially varying dielectric constant on LLPS propensity, and these factors affect the properties of the resulting condensates. In order to produce spatial variations in the dielectric constant, all our simulations include explicit solvent and counterions. We demonstrate that polarizability has only a minor effect on the bulk behaviour of the condensates but plays a major role when ion partitioning and microstructure are considered. We observe that the major contribution comes from the nature of the neutral blocks as endowing them with an induced dipole changes their character from hydrophobic to hydrophilic. We hypothesize that the results of this work can aid in guiding future studies concerned with LLPS by providing a general framework and by highlighting important factors which influence LLPS.

Side Chain Geometry Determines the Fibrillation Propensity of a Minimal Two-Beads-per-Residue Peptide Model

The molecular mechanism of fibrillation is an important issue for understanding peptide aggregation. In our previous work, we demonstrated that the interchain attraction and intrachain bending stiffness control the aggregation kinetics and transient aggregate morphologies of a one-bead-per-residue implicit solvent peptide model. However, that model did not lead to fibrillation. In this work, we study the molecular origin of fibril formation using a two-beads-per-residue model, where one bead represents the backbone residue atoms and the other the side chain atoms. We show that the side chain geometry determines the fibrillation propensity that is further modulated by the modified terminal beads. This allows us to bring out the effects of side chain geometry and terminal capping on the fibrillation propensity. Our model does not assume a secondary structure and is, perhaps, the simplest bead-based chain model leading to fibrillation.

Transferable and Polarizable Coarse Grained Model for Proteins─ProMPT

The application of classical molecular dynamics (MD) simulations at atomic resolution (fine-grained level, FG), to most biomolecular processes, remains limited because of the associated computational complexity of representing all the atoms. This problem is magnified in the presence of protein-based biomolecular systems that have a very large conformational space, and MD simulations with fine-grained resolution have slow dynamics to explore this space. Current transferable coarse grained (CG) force fields in literature are either limited to only peptides with the environment encoded in an implicit form or cannot capture transitions into secondary/tertiary peptide structures from a primary sequence of amino acids. In this work, we present a transferable CG force field with an explicit representation of the environment for accurate simulations with proteins. The force field consists of a set of pseudoatoms representing different chemical groups that can be joined/associated together to create different biomolecular systems. This preserves the transferability of the force field to multiple environments and simulation conditions. We have added electronic polarization that can respond to environmental heterogeneity/fluctuations and couple it to protein's structural transitions. The nonbonded interactions are parametrized with physics-based features such as solvation and partitioning free energies determined by thermodynamic calculations and matched with experiments and/or atomistic simulations. The bonded potentials are inferred from corresponding distributions in nonredundant protein structure databases. We present validations of the CG model with simulations of well-studied aqueous protein systems with specific protein fold types─Trp-cage, Trpzip4, villin, WW-domain, and β-α-β. We also explore the applications of the force field to study aqueous aggregation of Aβ 16-22 peptides.

Zinc Binding to NAP-Type Neuroprotective Peptides: Nuclear Magnetic Resonance Studies and Molecular Modeling

Aggregation of amyloid-β peptides (Aβ) is a hallmark of Alzheimer’s disease (AD), which is affecting an increasing number of people. Hence, there is an urgent need to develop new pharmaceutical treatments which could be used to prevent the AD symptomatology. Activity-dependent neuroprotective protein (ADNP) was found to be deficient in AD, whereas NAP, an 8-amino-acid peptide (¹NAPVSIPQ⁸) derived from ADNP, was shown to enhance cognitive function. The higher tendency of zinc ion to induce Aβ aggregation and formation of amorphous aggregates is also well-known in the scientific literature. Although zinc binding to Aβ peptides was extensively investigated, there is a shortage of knowledge regarding the relationship between NAP peptide and zinc ions. Therefore, here, we investigated the binding of zinc ions to the native NAP peptide and its analog obtained by replacing the serine residue in the NAP sequence with tyrosine (¹NAPVYIPQ⁸) at various molar ratios and pH values by mass spectrometry (MS) and nuclear magnetic resonancespectroscopy (NMR). Matrix-assisted laser desorption/ionization time-of-flight (MALDI ToF) mass spectrometry confirmed the binding of zinc ions to NAP peptides, while the chemical shift of Asp¹, observed in ¹H-NMR spectra, provided direct evidence for the coordinating role of zinc in the N-terminal region. In addition, molecular modeling has also contributed largely to our understanding of Zn binding to NAP peptides.

Effects of applied surface-tension on membrane-assisted Aβ aggregation

Accumulation of protein-based (Aβ) aggregates on cellular membranes with varying structural properties is commonly recognized as the key step in Alzheimer's pathogenesis. But experimental and computational challenges have made this biophysical characterization difficult. In particular, studies connecting biological membrane organization and Aβ aggregation are limited. While experiments have suggested that an increased membrane curvature results in faster Aβ peptide aggregation in the context of Alzheimer's disease, a mechanistic explanation for this relation is missing. In this work, we are leveraging molecular simulations with a physics-based coarse grained model to address and understand the relationships between curved cellular membranes and aggregation of a model template peptide Aβ 16-22. In agreement with experimental results, our simulations also suggest a positive correlation between increased peptide aggregation and membrane curvature. More curved membranes have higher lipid packing defects that engage peptide hydrophobic groups and promote faster diffusion leading to peptide fibrillar structures. In addition, we curated the effects of peptide aggregation on the membrane's structure and organization. Interfacial peptide aggregation results in heterogeneous headgroup-peptide interactions and an induced crowding effect at the lipid headgroup region, leading to a more ordered headgroup region and disordered lipid-tails at the membrane core. This work presents a mechanistic and morphological overview of the relationships between the biomembrane local structure and organization, and Aβ peptide aggregation.

Machine Learning Feature Selection for Predicting High Concentration Therapeutic Antibody Aggregation

Protein aggregation can hinder the development, safety and efficacy of therapeutic antibody-based drugs. Developing a predictive model that evaluates aggregation behaviors during early stage development is therefore desirable. Machine learning is a widely used tool to train models that predict data with different attributes. However, most machine learning techniques require more data than is typically available in antibody development. In this work, we describe a rational feature selection framework to develop accurate models with a small number of features. We applied this framework to predict aggregation behaviors of 21 approved monospecific monoclonal antibodies at high concentration (150 mg/mL), yielding a correlation coefficient of 0.71 on validation tests with only two features using a linear model. The nearest neighbors and support vector regression models further improved the performance, which have correlation coefficients of 0.86 and 0.80, respectively. This framework can be extended to train other models that predict different physical properties.

Tumorigenic p53 mutants undergo common structural disruptions including conversion to α-sheet structure

The p53 protein is a commonly studied cancer target because of its role in tumor suppression. Unfortunately, it is susceptible to mutation-associated loss of function; approximately 50% of cancers are associated with mutations to p53, the majority of which are located in the central DNA-binding domain. Here, we report molecular dynamics simulations of wild-type (WT) p53 and 20 different mutants, including a stabilized pseudo-WT mutant. Our findings indicate that p53 mutants tend to exacerbate latent structural-disruption tendencies, or vulnerabilities, already present in the WT protein, suggesting that it may be possible to develop cancer therapies by targeting a relatively small set of structural-disruption motifs rather than a multitude of effects specific to each mutant. In addition, α-sheet secondary structure formed in almost all of the proteins. α-Sheet has been hypothesized and recently demonstrated to play a role in amyloidogenesis, and its presence in the reported p53 simulations coincides with the recent re-consideration of cancer as an amyloid disease.

Computational insights into lipid assisted peptide misfolding and aggregation in neurodegeneration

An overview of recent advances in computational investigation of peptide–lipid interactions in neurodegeneration – Alzheimer's, Parkinson's and Huntington's disease.

Pathways of amyloid-beta absorption and aggregation in a membranous environment

Aggregation of misfolded oligomeric amyloid-beta (Aβ) peptides on lipid membranes has been identified as a primary event in Alzheimer's pathogenesis. However, the structural and dynamical features of this membrane assisted Aβ aggregation have not been well characterized. The microscopic characterization of dynamic molecular-level interactions in peptide aggregation pathways has been challenging both computationally and experimentally. In this work, we explore differential patterns of membrane-induced Aβ 16-22 (K-L-V-F-F-A-E) aggregation from the microscopic perspective of molecular interactions. Physics-based coarse-grained molecular dynamics (CG-MD) simulations were employed to investigate the effect of lipid headgroup charge - zwitterionic (1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine: POPC) and anionic (1-palmitoyl-2-oleoyl-sn-glycero-3-phospho-l-serine: POPS) - on Aβ 16-22 peptide aggregation. Our analyses present an extensive overview of multiple pathways for peptide absorption and biomechanical forces governing peptide folding and aggregation. In agreement with experimental observations, anionic POPS molecules promote extended configurations in Aβ peptides that contribute towards faster emergence of ordered β-sheet-rich peptide assemblies compared to POPC, suggesting faster fibrillation. In addition, lower cumulative rates of peptide aggregation in POPS due to higher peptide-lipid interactions and slower lipid diffusion result in multiple distinct ordered peptide aggregates that can serve as nucleation seeds for subsequent Aβ aggregation. This study provides an in-silico assessment of experimentally observed aggregation patterns, presents new morphological insights and highlights the importance of lipid headgroup chemistry in modulating the peptide absorption and aggregation process.

Looking at the Disordered Proteins through the Computational Microscope

Intrinsically disordered proteins (IDPs) have attracted wide interest over the past decade due to their surprising prevalence in the proteome and versatile roles in cell physiology and pathology. A large selection of IDPs has been identified as potential targets for therapeutic intervention. Characterizing the structure-function relationship of disordered proteins is therefore an essential but daunting task, as these proteins can adapt transient structure, necessitating a new paradigm for connecting structural disorder to function. Molecular simulation has emerged as a natural complement to experiments for atomic-level characterizations and mechanistic investigations of this intriguing class of proteins. The diverse range of length and time scales involved in IDP function requires performing simulations at multiple levels of resolution. In this Outlook, we focus on summarizing available simulation methods, along with a few interesting example applications. We also provide an outlook on how these simulation methods can be further improved in order to provide a more accurate description of IDP structure, binding, and assembly.

The Theory of Ultra-Coarse-Graining. 3. Coarse-Grained Sites with Rapid Local Equilibrium of Internal States

When viewed through a coarse-grained lens, important molecular and biophysical systems can appear to undergo discrete, switch-like state changes in addition to more continuous configurational motions. One of our recent papers described a theory for bottom-up coarse-graining of the equilibrium statistics of models with such behavior, called ultra-coarse-grained (UCG) models, and a follow up paper described an implementation when the states of the coarse-grained sites or "beads" change rarely. However, not all systems with this discrete behavior fall under that special limit. This article develops the general UCG theory for the opposite limit, that is, where the internal states of the CG particles or beads adjust rapidly so as to always remain effectively at quasi-equilibrium no matter what the positions of the coarse-grained particles. This rapid local equilibrium allows ultra-coarse-graining to mix standard coarse-grained force fields by using local order parameters to control the degree of mixing, which adds an environmental dependence and many-body effects to the coarse-grained model while requiring minimal new coding. This article first presents the definition of such UCG force fields as well as their fitting procedures from atomistic-scale data, and then it presents three examples of UCG simulations with an approach that we call UCG with rapid local equilibrium (UCG-RLE). We then present an application of UCG-RLE using the full bottom-up methodology to coarse-grain and simulate cooperative hydrophobic association of neopentane in methanol solvent. UCG-RLE force matching does a superior job of matching solute-solute correlation functions and solute cluster size distributions compared to the more standard force-matched models not having coarse-grained sites with discrete internal states.