Understanding the role of hydrophobic patches in protein disaggregation

Elucidating the Molecular Basis of 14-3-3 Interaction with α-Synuclein: Insights from Molecular Dynamics Simulations and the Design of a Novel Protein-Protein Interaction Inhibitor

Parkinson's disease is a widespread age-related neurodegenerative disorder characterized by the loss of dopaminergic neurons in the midbrain along with the appearance of protein aggregates, termed as "Lewy bodies" in the surviving neuronal cells. The components of Lewy bodies include proteins such as α-synuclein, 14-3-3, Parkin, and LRRK2, along with other cellular organelles, which, in their native state, perform a plethora of vital biological functions within the human biome. Formation of these aggregates renders these components inactive, thereby interfering with homeostasis. In this regard, the current study attempts to investigate the complexation behavior of all human-based 14-3-3 isoforms with α-synuclein via a combination of classical and enhanced sampling techniques and thereby determine the causality of these protein-protein interactions. The study indicated that upon complexation, the aggregation propensity of both 14-3-3 and α-synuclein increases, and this increment is propelled by the interfacial residues on either protein. Furthermore, mutagenesis studies revealed that Lys214 of 14-3-3 (henceforth termed K214A) is crucial for the formation of this binary complex. Principal component analysis combined with clustering studies unveiled the stability of these complexes in terms of their conformational distribution across the entire MD trajectory. For K214A, these clustered states were sparsely located, thereby making the transitions between them slightly difficult. Dynamic cross-correlation maps (DCCM) revealed the role of residues in the range 80-130 of 14-3-3 having a potential allosteric role in driving this complexation process. Finally, a novel peptide-based supramolecular inhibitor was designed, which exhibited higher proficiency in limiting the 14-3-3/α-synuclein interaction compared to the previous inhibitor model. It was also revealed that the presence of this inhibitor induces structural rigidity in α-synuclein, making changes in its conformations extremely difficult, as observed through Umbrella Sampling studies. Based on available information, the current study provides an insight into the molecular-level understanding of protein-protein interactions underlying Parkinson's disease and adds on to the methods of devising novel therapeutic approaches to treat the same.

Advanced computational approaches to understand protein aggregation

Protein aggregation is a widespread phenomenon implicated in debilitating diseases like Alzheimer's, Parkinson's, and cataracts, presenting complex hurdles for the field of molecular biology. In this review, we explore the evolving realm of computational methods and bioinformatics tools that have revolutionized our comprehension of protein aggregation. Beginning with a discussion of the multifaceted challenges associated with understanding this process and emphasizing the critical need for precise predictive tools, we highlight how computational techniques have become indispensable for understanding protein aggregation. We focus on molecular simulations, notably molecular dynamics (MD) simulations, spanning from atomistic to coarse-grained levels, which have emerged as pivotal tools in unraveling the complex dynamics governing protein aggregation in diseases such as cataracts, Alzheimer's, and Parkinson's. MD simulations provide microscopic insights into protein interactions and the subtleties of aggregation pathways, with advanced techniques like replica exchange molecular dynamics, Metadynamics (MetaD), and umbrella sampling enhancing our understanding by probing intricate energy landscapes and transition states. We delve into specific applications of MD simulations, elucidating the chaperone mechanism underlying cataract formation using Markov state modeling and the intricate pathways and interactions driving the toxic aggregate formation in Alzheimer's and Parkinson's disease. Transitioning we highlight how computational techniques, including bioinformatics, sequence analysis, structural data, machine learning algorithms, and artificial intelligence have become indispensable for predicting protein aggregation propensity and locating aggregation-prone regions within protein sequences. Throughout our exploration, we underscore the symbiotic relationship between computational approaches and empirical data, which has paved the way for potential therapeutic strategies against protein aggregation-related diseases. In conclusion, this review offers a comprehensive overview of advanced computational methodologies and bioinformatics tools that have catalyzed breakthroughs in unraveling the molecular basis of protein aggregation, with significant implications for clinical interventions, standing at the intersection of computational biology and experimental research.

Nano-Osmolyte Conjugation: Tailoring the Osmolyte-Protein Interactions at the Nanoscale

Osmolytes are small organic compounds accumulated at higher concentrations in the cell under various stress conditions like high temperature, high salt, high pressure, etc. Osmolytes mainly include four major classes of compounds including sugars, polyols, methylamines, and amino acids and their derivatives. In addition to their ability to maintain protein stability and folding, these osmolytes, also termed as chemical chaperones, can prevent protein misfolding and aggregation. Although being efficient protein folders and stabilizers, these osmolytes exhibit certain unavoidable limitations such as nearly molar concentrations of osmolytes being required for their effect, which is quite difficult to achieve inside a cell or in the extracellular matrix due to nonspecificity and limited permeability of the blood-brain barrier system and reduced bioavailability. These limitations can be overcome to a certain extent by using smart delivery platforms for the targeted delivery of osmolytes to the site of action. In this context, osmolyte-functionalized nanoparticles, termed nano-osmolytes, enhance the protein stabilization and chaperone efficiency of osmolytes up to 105 times in certain cases. For example, sugars, polyols, and amino acid functionalized based nano-osmolytes have shown tremendous potential in preventing protein aggregation. The enhanced potential of nano-osmolytes can be attributed to their high specificity at low concentrations, high tunability, amphiphilicity, multivalent complex formation, and efficient drug delivery system. Keeping in view the promising potential of nano-osmolytes conjugation in tailoring the osmolyte-protein interactions, as compared to their molecular forms, the present review summarizes the recent advancements of the nano-osmolytes that enhance the protein stability/folding efficiency and ability to act as artificial chaperones with increased potential to prevent protein misfolding disorders. Some of the potential nano-osmolyte aggregation inhibitors have been highlighted for large-scale screening with future applications in aggregation disorders. The synthesis of nano-osmolytes by numerous approaches and future perspectives are also highlighted.

Assessment of Enzyme Functionality at Metal-Organic Framework Interfaces Developed through Molecular Simulations

The catalytic efficiency and unrivaled selectivity with which enzymes convert substrates to products have been tapped for widespread chemical transformations within biomedical technology, biofuel production, gas sensing, and the upgrading of commodity chemicals, just to name a few. However, the feasibility of enzymes implementation is challenged by the lack of reusability and loss of native catalytic activity due to the irreversible biocatalyst denaturation at high temperatures and in the presence of industrial solvents. Enzyme immobilization, a prerequisite for enzyme reusability, offers controllable strategies for increased functional viability of the biocatalyst in a synthetic environment. Herein we used molecular dynamics (MD) simulations and probed the noncovalent interactions between model enzymes of technological interest, i.e., carbonic anhydrase (CA) and myeloperoxidase (MPO), with selected metal-organic frameworks (MOFs; MIL-160 and ZIF-8) of proven industrial implementation. We found that the CA and MPO can bind to MIL-160 at optimal binding energies of 201 and 501 kJ mol^-1, respectively, that are strongly influenced by the increased incidence of hydrogen bonding between enzymes and the frameworks. The free energy of binding of enzymes to ZIF-8, on the other hand, was found to be less strongly influenced by hydrogen bonding networks relative to the occurrence of hydrophobic-hydrophobic interactions that yielded 106 kJ mol^-1 for CA and 201 kJ mol^-1 for MPO.