Self-assembling properties of peptides derived from TDP-43 C-terminal fragment.

Two highly fibrillogenic peptide sequences (MNFGAFSINP and EDLIIKGISV) were previously reported in the C-terminal fragment (CTF) of TDP-43 (220-414), a protein recently implicated in neuro-degenerative diseases such as amyotrophic lateral sclerosis (ALS) and frontotemporal lobar degeneration (FTLD-U). It was observed that the sequences MNFGAFS and EDLIIKG harbor their respective fibrillogenic domains. Here, the self-assembling properties of peptides obtained by systematic deletion of residues from these two sequences were investigated with the help of light scattering, thioflavin T fluorescence, transmission electron microscopy, and circular dichroism spectroscopy. It was found that the pentapeptide NFGAF and the tetrapeptide DLII are the shortest fibrillogenic sequences from MNFGAFS and EDLIIKG, respectively. Structure function studies revealed that self-assembly of the peptides is largely governed by hydrophobic interactions. Both NFGAF and DLII formed hydrogels based on a complex fibrillar network, at relatively low concentrations, and of remarkable strength and stability. Of particular interest was DLII, a rare aliphatic tetrapeptide that formed a hydrogel at a concentration of 1 mg/mL in less than an hour. Interestingly, various other tetrapeptides based on DLII (YLII, KLII, NLII, and LIID) also formed hydrogels of comparable physical properties, suggesting that an amphipathic peptide design based on the hydrophobic LII motif and a single residue polar terminus is highly favorable for hydrogelation. Peptides discovered in this study, especially DLII and its variants, are some of the shortest ever reported to show such structural and functional features, suggesting that they can be useful templates for the design of peptide-based soft materials.

Significance of Artificial Intelligence in the Study of Virus-Host Cell Interactions.

A highly critical event in a virus's life cycle is successfully entering a given host. This process begins when a viral glycoprotein interacts with a target cell receptor, which provides the molecular basis for target virus-host cell interactions for novel drug discovery. Over the years, extensive research has been carried out in the field of virus-host cell interaction, generating a massive number of genetic and molecular data sources. These datasets are an asset for predicting virus-host interactions at the molecular level using machine learning (ML), a subset of artificial intelligence (AI). In this direction, ML tools are now being applied to recognize patterns in these massive datasets to predict critical interactions between virus and host cells at the protein-protein and protein-sugar levels, as well as to perform transcriptional and translational analysis. On the other end, deep learning (DL) algorithms-a subfield of ML-can extract high-level features from very large datasets to recognize the hidden patterns within genomic sequences and images to develop models for rapid drug discovery predictions that address pathogenic viruses displaying heightened affinity for receptor docking and enhanced cell entry. ML and DL are pivotal forces, driving innovation with their ability to perform analysis of enormous datasets in a highly efficient, cost-effective, accurate, and high-throughput manner. This review focuses on the complexity of virus-host cell interactions at the molecular level in light of the current advances of ML and AI in viral pathogenesis to improve new treatments and prevention strategies.

Delineation of the core aggregation sequences of TDP-43 C-terminal fragment.

Ubiquitinated cytoplasmic inclusions of TDP-43 and its C-terminal cleavage products are the pathological hallmarks of amyotrophic lateral sclerosis and frontotemporal lobar degeneration with ubiquitinated inclusions. The C-terminal fragments (CTFs) of TDP-43 are increasingly considered to play an important role in its aggregation and in disease. Here, we employed a set of synthetic peptides spanning the length of the TDP-43 CTF (220-414) in order to find out its core aggregation domains. Two regions, one in the RRM-2 domain (246-255) and the other in the C-terminal domain (311-320) of TDP-43, stand out as highly aggregation prone. Studies done on recombinant purified TDP-43 CTF and its three mutants, in which these sequences were deleted individually and together, suggested that the 311-320 region has a more crucial role to play than the 246-255 in its aggregation. The study helps in defining specific peptide sequences that might form the core of TDP-43 aggregation. Identification of these sequences could help in designing peptide based inhibitors of TDP-43 aggregation.