Sakthivel, N. C. | Search bioRxiv

bioRxiv2024

Increased 'selfness' in the tumor emerges as a possible immune sculpting mechanism: A pan-cancer data analysis of 32 solid tumors in TCGA

Tumors pose a unique challenge to the immune system since they straddle the boundary between self and non-self. T-cells recognize tumors that contain non-self neo-antigens. They can also recognize tumors that contain aberrantly expressed self-antigens, highlighting the importance of the central tolerance and the tuning of the T-cell repertoire in the thymus. Therefore, the similarity to the thymic expression profiles must have information in it to influence the T-cell repertoire and what self-peptides are recognized. We utilize this principle in a pan-cancer analysis and develop a thymus-like or selfness score (TLS) based on the gene-expression similarity to thymi, indicative of recognizability of tumors by T-cells. We show that the TLS is indicative of patient survival in 8 different TCGA cohorts, indicating gene expression modulation to mimic that in thymi as a potential immune sculpting mechanism. Surprisingly, we also see an inverse relationship between TLS and the degree of immune infiltration.

immunology↗

bioRxiv2022

Fast Local Alignment of Protein Pockets (FLAPP): A system-compiled program for large-scale binding site alignment

Protein function is a direct consequence of its sequence, structure and the arrangement at the binding site. Bioinformatics using sequence analysis is typically used to gain a first insight into protein function. Protein structures, on the other hand, provide a higher resolution platform into understanding functions. As the protein structural information is increasingly becoming available through experimental structure determination and through advances in computational methods for structure prediction, the opportunity to utilize this data is also increasing. Structural analysis of small molecule ligand binding sites in particular provide a direct and more accurate window to infer protein function. However it remains a poorly utilized resource due to the huge computational cost of existing methods that make large scale structural comparisons of binding sites prohibitive. Here we present an algorithm called FLAPP that produces very rapid atomic level alignments. By combining clique matching in graphs and the power of modern CPU architectures, FLAPP aligns a typical pair of binding site binding sites at ~12.5 milliseconds using a single CPU core, ~ 1 millisecond using 12 cores on a standard desktop machine, and performs a PDB-wide scan in 1-2 minutes. We perform rigorous validation of the algorithm at multiple levels of complexity and show that FLAPP provides accurate alignments. We also present a case study involving vitamin B12 binding sites to showcase the usefulness of FLAPP for performing an exhaustive alignment based PDB-wide scan. We expect this tool will be invaluable to the scientific community to quickly align millions of site pairs on a normal desktop machine to gain insights into protein function and drug discovery for drug target and off-target identification, and polypharmacology.

bioinformatics↗