Unifying structural descriptors for biological and bioinspired nanoscale complexes

Abstract

Biomimetic nanoparticles are known to serve as nanoscale adjuvants, enzyme mimics and amyloid fibrillation inhibitors. Their further development requires better understanding of their interactions with proteins. The abundant knowledge about proteinprotein interactions can serve as a guide for designing protein-nanoparticle assemblies, but the chemical and biological inputs used in computational packages for protein-protein interactions are not applicable to inorganic nanoparticles. Analysing chemical, geometrical and graph-theoretical descriptors for protein complexes, we found that geometrical and graph-theoretical descriptors are uniformly applicable to biological and inorganic nanostructures and can predict interaction sites in protein pairs with accuracy >80% and classification probability ~90%. We extended the machine-learning algorithms trained on proteinprotein interactions to inorganic nanoparticles and found a nearly exact match between experimental and predicted interaction sites with proteins. These findings can be extended to other organic and inorganic nanoparticles to predict their assemblies with biomolecules and other chemical structures forming lock-and-key complexes.

Biomimetic mineralization based on self-assembling peptides

“…Recently, Kotov et al extended the machine-learning algorithms trained on protein–protein interactions to inorganic nanoparticles. 324 In addition, machine learning methods can be used to design peptide sequences with high self-assembly propensity, which can self-assemble into hydrogels. 325 Based on the above research studies, we believe that machine learning strategies for designing peptide sequences during mineralization should be developed, which is promising, although not yet directly relevant.…”

Bioinspired chiral inorganic nanomaterials

“…For example, nanoparticle interactions in the body can be represented by the lock and key interaction model to analyse the effect of nanoparticle morphology and chirality on the dynamics of targeting ligand-receptor interactions, such as in immune bone marrow cells and cancer cell membranes. Single-nanoparticle orientation studies 228 and multiscale structural descriptors 229 can be applied to assess such lock and key interactions. Single nanoparticle-level observations of nanocatalysts have also provided insight into catalytic mechanisms 230 .…”

“…The graph theory representations and related complexity index calculations can be used as a guide for the engineering of biomimetic chiral particles and their assemblies. In practice, graph theory enables direct parametrization of atomic and nanoscale geometries, which, in turn, enables the unification of structural descriptors for biological and abiological nanostructures 229 . Combining graph theory descriptors with chirality measures extracted from the protein data bank and electron microscopy images would open the road for the engineering of chiral nanostructures for targeted formation of protein-nanoparticle complexes that mimic protein-protein complexes with lock and key matches of their chirality and concavity.…”

Molecular Architecture and Helicity of Bacterial Amyloid Nanofibers: Implications for the Design of Nanoscale Antibiotics

“…The 2β PSMα1 amyloid nanofiber model can be used to study nanofiber−antimicrobial interactions to elucidate a mechanism for biofilm manipulation using man-made antiamyloid biomimetic nanostructures, as well as to explore the propagation of vibrations across functional amyloids anchored to bacterial membranes. 44,45,50,51 In addition to machine learning techniques recently developed by our team, 46,53 this study provides a structural understanding of amyloid fibers that can inform a set of MD-based design principles and can usher in an era of tailor-made NPs as nanobiotics as high-efficacy antibacterial agents.…”