Schwartz, A. S. | Search bioRxiv

bioRxiv2026

Discovery and Targeting of a Cryptic Human Proteome

First-in-class therapeutics require first-in-class biology. Yet despite decades of genomic and proteomic cataloging, vast regions of the human transcriptome remain dark and their encoded proteins invisible. Here we present RyboCypher, an integrated RNA-sequencing and AI-assisted proteogenomics platform that systematically maps the RyboCypher-derived "dark" transcriptome to unannotated peptides, predicting and empirically identifying cryptic proteins across the uncharted genome. Applied to cancer cell lines, patient tumors, and matched healthy tissues, RyboCypher resolved [~]8.3 million dark RNA isoforms and [~]16 million candidate ORFs. Interrogating these against [~]0.5 billion MS/MS spectra from cellular proteomics, membrane proteomics, and immunopeptidomics datasets (comprising a total of >8,000 raw MS data files ([~]7TB of MS data), derived from 2,229 patient samples), we empirically identified [~]80,000 cryptic peptides ([~]10,000 cancer-associated or cancer-upregulated) at <1% FDR. Altogether, these datasets establish the CypherAtlas, a comprehensive proteogenomic atlas of an unreported proteome comprising thousands of novel proteins, including membrane proteins with targetable extracellular domains, and intracellular proteins accessible through antigen presentation. By linking dark-RNA transcripts, predicted proteins, and patient-level metadata across RyboDyns proprietary experimental data, CypherAtlas further provides the training substrate for multi-modal models such as DarkCypher, which is being developed to prioritize cryptic targets and to forecast their expression in new patient samples. As proof of therapeutic potential, we disclose evidence for a cancer-associated, cryptic protein expressed from the YBX1 locus, (cryptic YBX1; cYBX1) and demonstrate selective in vitro tumor cell killing through a cryptic peptide-MHC (pMHC) complex derived from this protein with a TCR-mimic (TCRm) antibody when formatted as antibody drug conjugates (ADCs). Together, RyboCypher and CypherAtlas establish the dark proteome as a vast and previously inaccessible reservoir of novel targetable biology, laying the foundation for the next generation of first-in-class therapeutics. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=108 SRC="FIGDIR/small/743124v1_ufig1.gif" ALT="Figure 1"> View larger version (28K): org.highwire.dtl.DTLVardef@104c385org.highwire.dtl.DTLVardef@1c065ccorg.highwire.dtl.DTLVardef@5b717forg.highwire.dtl.DTLVardef@1788694_HPS_FORMAT_FIGEXP M_FIG C_FIG

genomics↗

bioRxiv2019

An improved pig reference genome sequence to enable pig genetics and genomics research

The domestic pig (Sus scrofa) is important both as a food source and as a biomedical model with high anatomical and immunological similarity to humans. The draft reference genome (Sscrofa10.2) of a purebred Duroc female pig established using older clone-based sequencing methods was incomplete and unresolved redundancies, short range order and orientation errors and associated misassembled genes limited its utility. We present two annotated highly contiguous chromosome-level genome assemblies created with more recent long read technologies and a whole genome shotgun strategy, one for the same Duroc female (Sscrofa11.1) and one for an outbred, composite breed male (USMARCv1.0). Both assemblies are of substantially higher (>90-fold) continuity and accuracy than Sscrofa10.2. These highly contiguous assemblies plus annotation of a further 11 short read assemblies provide an unprecedented view of the genetic make-up of this important agricultural and biomedical model species. We propose that the improved Duroc assembly (Sscrofa11.1) become the reference genome for genomic research in pigs.

genomics↗

bioRxiv2018

Deep Semantic Protein Representation for Annotation, Discovery, and Engineering

Computational assignment of function to proteins with no known homologs is still an unsolved problem. We have created a novel, function-based approach to protein annotation and discovery called D-SPACE (Deep Semantic Protein Annotation Classification and Exploration), comprised of a multi-task, multi-label deep neural network trained on over 70 million proteins. Distinct from homology and motif-based methods, D-SPACE encodes proteins in high-dimensional representations (embeddings), allowing the accurate assignment of over 180,000 labels for 13 distinct tasks. The embedding representation enables fast searches for functionally related proteins, including homologs undetectable by traditional approaches. D-SPACE annotates all 109 million proteins in UniProt in under 35 hours on a single computer and searches the entirety of these in seconds. D-SPACE further quantifies the relative functional effect of mutations, facilitating rapid in silico mutagenesis for protein engineering applications. D-SPACE incorporates protein annotation, search, and other exploratory efforts into a single cohesive model.

bioinformatics↗