Surface Patterning for Enhanced Protein Stability: Insights from Molecular Simulations

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.

In silico study of substrate chemistry effect on the tethering of engineered antibodies for SARS-CoV-2 detection: Amorphous silica vs gold

The influence of the properties of different solid substrates on the tethering of two antibodies, IgG1-CR3022 and IgG1-S309, which were specifically engineered for the detection of SARS-CoV-2, has been examined at the molecular level using conventional and accelerated Molecular Dynamics (cMD and aMD, respectively). Two surfaces with very different properties and widely used in immunosensors for diagnosis, amorphous silica and the most stable facet of the face-centered cubic gold structure, have been considered. The effects of such surfaces on the structure and orientation of the immobilized antibodies have been determined by quantifying the tilt and hinge angles that describe the orientation and shape of the antibody, respectively, and the dihedrals that measure the relative position of the antibody arms with respect to the surface. Results show that the interactions with amorphous silica, which are mainly electrostatic due to the charged nature of the surface, help to preserve the orientation and structure of the antibodies, especially of the IgG1-CR3022, indicating that the primary sequence of those antibodies also plays some role. Instead, short-range van der Waals interactions with the inert gold surface cause a higher degree tilting and fraying of the antibodies with respect to amorphous silica. The interactions between the antibodies and the surface also affect the correlation among the different angles and dihedrals, which increases with their strength. Overall, results explain why amorphous silica substrates are frequently used to immobilize antibodies in immunosensors.

Understanding the role of hydrophobic patches in protein disaggregation

Protein folding is a very complex process and, so far, the mechanism of folding still intrigues the research community. Despite a large conformational space available (O(10⁴⁷) for a 100 amino acid residue), most proteins fold into their native state within a very short time. While small proteins fold relatively fast (a few microseconds) large globular proteins may take as long as several milliseconds to fold. During the folding process, the protein synthesized in the ribosome is exposed to the crowded environment of the cell and is easily prone to misfolding and aggregation due to interactions with other proteins or biomacromolecules present within the cell. These large proteins, therefore, rely on chaperones for their folding and repair. Chaperones are known to have hydrophobic patchy domains that play a crucial role in shielding the protein against misfolding and disaggregation of aggregated proteins. In the current article, Monte Carlo simulations carried out in the framework of the hydrophobic-polar (H-P) lattice model indicate that hydrophobic patchy domains drastically reduce the inter-protein interactions and are efficient in disaggregating proteins. The effectiveness of the disaggregation depends on the size and distribution of these patches on the surface and also on the strength of the interaction between the protein and the surface. Further, our results indicate that when the patch is complementary to the exposed hydrophobic patch of the protein, protein disaggregation is accompanied by stabilization of the protein even relative to its bulk behavior due to favorable protein-surface interactions. We believe that these findings shed light on the role of the class of chaperones known as heat shock proteins (Hsps) on protein disaggregation and refolding.