Learning the shape of protein microenvironments with a holographic convolutional neural network.

Proteins play a central role in biology from immune recognition to brain activity. While major advances in machine learning have improved our ability to predict protein structure from sequence, determining protein function from its sequence or structure remains a major challenge. Here, we introduce holographic convolutional neural network (H-CNN) for proteins, which is a physically motivated machine learning approach to model amino acid preferences in protein structures. H-CNN reflects physical interactions in a protein structure and recapitulates the functional information stored in evolutionary data. H-CNN accurately predicts the impact of mutations on protein stability and binding of protein complexes. Our interpretable computational model for protein structure-function maps could guide design of novel proteins with desired function.

Loss of LDAH associated with prostate cancer and hearing loss.

Great strides in gene discovery have been made using a multitude of methods to associate phenotypes with genetic variants, but there still remains a substantial gap between observed symptoms and identified genetic defects. Herein, we use the convergence of various genetic and genomic techniques to investigate the underpinnings of a constellation of phenotypes that include prostate cancer (PCa) and sensorineural hearing loss (SNHL) in a human subject. Through interrogation of the subject's de novo, germline, balanced chromosomal translocation, we first identify a correlation between his disorders and a poorly annotated gene known as lipid droplet associated hydrolase (LDAH). Using data repositories of both germline and somatic variants, we identify convergent genomic evidence that substantiates a correlation between loss of LDAH and PCa. This correlation is validated through both in vitro and in vivo models that show loss of LDAH results in increased risk of PCa and, to a lesser extent, SNHL. By leveraging convergent evidence in emerging genomic data, we hypothesize that loss of LDAH is involved in PCa and other phenotypes observed in support of a genotype-phenotype association in an n-of-one human subject.

Describing sequencing results of structural chromosome rearrangements with a suggested next-generation cytogenetic nomenclature.

With recent rapid advances in genomic technologies, precise delineation of structural chromosome rearrangements at the nucleotide level is becoming increasingly feasible. In this era of "next-generation cytogenetics" (i.e., an integration of traditional cytogenetic techniques and next-generation sequencing), a consensus nomenclature is essential for accurate communication and data sharing. Currently, nomenclature for describing the sequencing data of these aberrations is lacking. Herein, we present a system called Next-Gen Cytogenetic Nomenclature, which is concordant with the International System for Human Cytogenetic Nomenclature (2013). This system starts with the alignment of rearrangement sequences by BLAT or BLAST (alignment tools) and arrives at a concise and detailed description of chromosomal changes. To facilitate usage and implementation of this nomenclature, we are developing a program designated BLA(S)T Output Sequence Tool of Nomenclature (BOSToN), a demonstrative version of which is accessible online. A standardized characterization of structural chromosomal rearrangements is essential both for research analyses and for application in the clinical setting.