Biology subjects

Movva, N. S. V.

Publications and source records attributed to Movva, N. S. V..

2 recordsLinked to original sources

bioRxiv2026

Cohort-stratified prioritization of CRISPR-Cas9 sgRNAs for HDR-mediated correction of TP53 hotspot codons in cancer

TP53 is mutated in roughly half of all human cancers. Eight recurrent missense substitutions in the DNA-binding domain (R175H, Y220C, G245S, R248Q, R248W, R249S, R273H, R282W) account for most of the mutational burden. Homology-directed repair (HDR) with a wild-type donor template is one of the few feasible routes to revert these alleles, but existing CRISPR sgRNA design tools rank candidates without reference to the cancer cohort being treated. We built a reproducible pipeline that prioritizes SpCas9 sgRNAs for HDR-mediated correction of TP53 hotspot codons. The pipeline uses NM 000546.6 from NCBI, GRCh38 off-target search via Cas-OFFinder with the published Doench-2016 CFD matrices, on-target Doench-2016 (Rule Set 2) scores from CRISPOR, and per-cohort hotspot prevalence from three TCGA Pan-Cancer Atlas studies (HGSOC, n = 523; PDAC, n = 179; CRC, n = 534) accessed through cBioPortal. We enumerate guides whose cut sites fall within {+/-}10 nt of each hotspot codon, exclude any candidate that fails to map to GRCh38, and score the remainder. The final set contains 21 SpCas9 NGG sgRNAs across the seven hotspots, with no PAM-desert residues. A single candidate at R248 (TP53-248-P-ad878223; spacer GCATGGGCGGCATGAACCGG, AGG PAM; off-target specificity 0.913 over 806 reference-genome hits) ranks first in all three cohorts and holds rank 1 in 97% of 147 weight settings tested. Four additional residues (R175, Y220, R273, R282) yield within-residue tier-1 picks robust in 100% of weight settings. Cohort-specific differences appear only in cross-residue ordering: R175 and R282 climb in CRC, consistent with the higher prevalence of R175H and R282W in colorectal tumors.

bioinformatics↗

bioRxiv2026

Computational Development of a GluN1 Synthetic Peptide Mimetic for Neutralization of Autoantibodies in Anti-NMDAR Autoimmune Encephalitis

Purpose/ObjectiveThis study aimed to design and computationally evaluate a synthetic GluN1-mimetic peptide as a decoy to bind and neutralize pathogenic autoantibodies in anti-NMDA receptor (NMDAR) encephalitis, a severe autoimmune neurological disorder affecting approximately 1.5 per million individuals annually. MethodsKey GluN1 epitope residues (351-390 of the amino-terminal domain) were identified from crystallographic evidence and patient-derived antibody binding studies. Multiple peptide variants were rationally designed to mimic the antibody-binding interface. AlphaFold2 was used to predict peptide structures. Rigid-body docking simulations were conducted with HADDOCK 2.4 to model peptide-antibody complexes, and binding affinities were quantified using PRODIGY. A scrambled peptide control was included to establish docking specificity. ResultsThe top-performing peptide demonstrated favorable predicted binding ({Delta}G = -21.5 kcal/mol, Kd = 1.7 x 10-{superscript 1} M) with an average pLDDT score of 90%, a buried surface area of 3,255.5 [A]{superscript 2}, and 18 intermolecular hydrogen bonds. Relative to the scrambled control ({Delta}G = -8.3 kcal/mol), the designed peptide showed substantially stronger predicted binding. Conclusion/ImplicationsThese results support the validity of an epitope-mimicry design strategy and establish a scalable computational framework for prioritizing peptide decoy candidates applicable to other antibody-mediated autoimmune disorders. Experimental validation remains necessary to confirm real-world efficacy.

bioinformatics↗