Novel KIF26A variants associated with pediatric intestinal pseudo-obstruction (PIPO) and brain developmental defects.

Pediatric intestinal pseudo-obstruction (PIPO) is a rare congenital disorder of the enteric nervous system with distal colon aganglionosis potentially leading to intestinal obstruction. Recently, biallelic variants in KIF26A, encoding a crucial motor protein for the migration and differentiation of enteric neural crest cells, have been associated with a neurodevelopmental condition featuring cortical defects and PIPO-like features, though in absence of aganglionosis. So far, only 10 patients have been reported. In this study, we investigated three subjects with congenital hydrocephalus, neurodevelopmental impairment, and intestinal obstruction megacolon syndrome. Brain MRI revealed malformations within cortical dysplasia spectrum, including polymicrogyria and heterotopia. Pathology study of the intestine revealed aganglionosis and elevated acetylcholinesterase activity in parasympathetic nerve fibers. Through trio-exome sequencing (ES), we detected four novel biallelic KIF26A variants, including two missense changes (#1) and two distinct homozygous truncating variants in (#2 and #3). All variants are rare and predicted to be deleterious according to in silico tools. To characterize the impact of the missense variants, we performed 3D protein modeling using Alphafold3 and YASARA. Mutants exhibited increased energy scores compared to wild-type protein, supporting a significant structural destabilization of the protein. Our study expands the genotype and phenotype spectrum of the emerging KIF26A-related disorder.

Predicting monoclonal antibody binding sequences from a sparse sampling of all possible sequences.

Previous work has shown that binding of target proteins to a sparse, unbiased sample of all possible peptide sequences is sufficient to train a machine learning model that can then predict, with statistically high accuracy, target binding to any possible peptide sequence of similar length. Here, highly sequence-specific molecular recognition is explored by measuring binding of 8 monoclonal antibodies (mAbs) with specific linear cognate epitopes to an array containing 121,715 near-random sequences about 10 residues in length. Network models trained on resulting sequence-binding values are used to predict the binding of each mAb to its cognate sequence and to an in silico generated one million random sequences. The model always ranks the binding of the cognate sequence in the top 100 sequences, and for 6 of the 8 mAbs, the cognate sequence ranks in the top ten. Practically, this approach has potential utility in selecting highly specific mAbs for therapeutics or diagnostics. More fundamentally, this demonstrates that very sparse random sampling of a large amino acid sequence spaces is sufficient to generate comprehensive models predictive of highly specific molecular recognition.