Lian, A. | bioRxiv Science

bioRxiv2023

AF2BIND: Predicting ligand-binding sites using the pair representation of AlphaFold2

Predicting ligand-binding sites, particularly in the absence of previously resolved homologous structures, presents a significant challenge in structural biology. Here, we leverage the internal pairwise representation of AlphaFold2 (AF2) to train a model, AF2BIND, to accurately predict small-molecule-binding residues given only a target protein. AF2BIND uses 20 "bait" amino acids to optimally extract the binding signal in the absence of a small-molecule ligand. We find that the AF2 pair representation outperforms other neural-network representations for binding-site prediction. Moreover, unique combinations of the 20 bait amino acids are correlated with chemical properties of the ligand.

bioinformatics↗

bioRxiv2021

Customized de novo mutation detection for any variant calling pipeline: SynthDNM

MotivationAs sequencing technologies and analysis pipelines evolve, DNM calling tools must be adapted. Therefore, a flexible approach is needed that can accurately identify de novo mutations from genome or exome sequences from a variety of datasets and variant calling pipelines. ResultsHere, we describe SynthDNM, a random-forest based classifier that can be readily adapted to new sequencing or variant-calling pipelines by applying a flexible approach to constructing simulated training examples from real data. The optimized SynthDNM classifiers predict de novo SNPs and indels with robust accuracy across multiple methods of variant calling. AvailabilitySynthDNM is freely available on Github (https://github.com/james-guevara/synthdnm) Contactjsebat@ucsd.edu Supplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗