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The proteome is the collection of all proteins in a cell, organism, or biological sample. The DNA in an organism’s genes encode all the proteins it can create, but the proteins themselves carry out most biological functions at the molecular, mechanistic level. Thus, we can use proteomic analyses to gain fundamental insights into how life works and leverage that knowledge to create new diagnostics, therapies, biotechnologies, and more.

A proteoform is any variant of a genetically encoded protein. Multiple mechanisms can give rise to these variants, including post-translational modifications and variations in amino acid sequence. Alternative splicing, phosphorylation, ubiquitination, etc are mechanisms that give rise to proteoforms. Proteoforms may have altered structure, function, interactions, or even solubility. There are exponentially more potential proteoforms than there are proteins.

Proteomics is the study of the proteome – the full collection of proteins in a cell, organism, or biological sample. Genomics, on the other hand, is the study of the genome – all the DNA in a cell, organism, or biological sample. Often genomics is used to infer what is happening at the protein level, but we know there is not always a direct correlation. Furthermore, while DNA is static and our genomes are the same from the day we’re born to the day we die, proteins are dynamic, their levels are constantly changing, and they directly carry out biological functions at the molecular level. Thus, studying the dynamic proteome is more challenging than studying the static genome but provides more actionable information on the real-time status of cells and organisms now.

Broadscale proteomics efforts aim to capture the identity and quantity of all proteins expressed in a given sample across the wide dynamic range of the proteome in an untargeted way. Often called “discovery proteomics,” these experiments require broad and precise protein coverage to ensure maximum insight. Broadscale discovery proteomics efforts are contrasted with “targeted proteomics” efforts that measure differences in a specified subset of proteins, often predetermined to play a role in disease or drug response through broadscale proteomics efforts.

PrISM is the theoretical framework used by the Nautilus^TM Proteome Analysis Platform to comprehensively quantify proteins across the wide dynamic range of the proteome in broadscale discovery proteomics experiments. PrISM repeatedly interrogates billions of single, intact protein molecules with a diverse array of novel affinity reagents called multi-affinity probes. These probes iteratively bind billions of proteins attached to our nano-fabricated protein arrays, and our machine learning algorithm decodes patterns generated from many binding cycles to identify each protein at the single-molecule level. Protein quantity is then determined by counting the number of times a protein is identified.

Multi-affinity probes are affinity reagents designed to bind short peptide sequences contained within many proteins (3-4 amino acids). On our platform, multi-affinity probes are labeled with fluorescent markers so probe binding can be assessed via fluorescence microscopy. Many multi-affinity probe binding events are used to decode protein identity on the Nautilus^TM Proteome Analysis Platform through our PrISM framework.

Single-molecule protein counting provides the most sensitive measurement of protein abundance achievable. This is important because small changes in protein abundance can disrupt the formation of protein complexes, alter biological signaling pathways, and change cellular function. Measuring intact (undigested) proteins is important because it simplifies analysis and precludes error-prone inference of protein identities and abundances from their parts (peptides). Additionally, it means that a single protein takes up a single spot for analysis. This contrasts with other technologies that analyze multiple peptides per protein at the single-molecule level, effectively decreasing their dynamic range. Additionally, analysis of intact proteins provides researchers with clear views of the proteoforms present in a sample – information that is lost by digesting full proteins into fragment peptides on other platforms.

There are two broad reasons we chose the name Nautilus. This very fitting name gets to:

1. Our goal – to enable voyages of discovery. Much like the Nautilus submarine in 20,000 Leagues Under the Sea and a variety of real-world Nautilus submarines, we aim to enable people to go further and deeper with their discoveries. Using our platform, we hope researchers can see parts of the proteome and biology that they’ve never seen before.
2. How we do it –technology at the intersection of math and biology. The Nautilus shell is a beautiful mixture of math and biology. The logarithmic regression of its chambers creates something that is complex, beautiful, and easy to grasp at the macro-level despite its complexity. In much the same way, our proteome decoding algorithms are formed at the cross-section of biological and mathematical knowledge. Using them, we aim to capture the complexity of the proteome and, through comprehensive analysis, provide graspable, actionable insights into biological function.

Omics-scale technologies aim to capture all the information contained in full sets of biological molecules like DNA (genomics), RNA (transcriptomics), and proteins (proteomics). Until recently, it was only possible to capture and store the data contained in the genome and transcriptome. While we have learned much about genetic diseases and biological variation from genomics and transcriptomics, DNA and RNA do not reveal the biological functions active in cells now. Proteins, on the other hand, carry out most biological functions, and proteomic technologies can provide robust analyses of active biological functions. Thankfully, we now have the technological capacity to capture, store, and analyze comprehensive proteomic data and thereby generate far deeper insights into biology than ever before. By making a proteomics platform that is accessible to all researchers, Nautilus hopes to enable researchers and physicians to apply proteomic insights to create new diagnostics, medicines, biotechnologies, and more.

Parag Mallick is Co-Founder and Chief Scientist at Nautilus, a tenured professor at Stanford University, and a professional magician. Parag and his collaborators have published hundreds of papers leveraging the latest proteomics technologies for groundbreaking work in cancer research, method development, biomarker discovery, and more. Parag co-founded Nautilus because he saw the incredible potential of proteomics but was frustrated by the lack of easy-to-use proteomics tools. Parag had spent years developing and optimizing mass spectrometry-based proteomics techniques but, looking to the future of proteomics, could only see current technologies providing incremental advances to the field. After spending a considerable time musing over various possible solutions, Parag changed tactics and tried to imagine entirely new ways to do proteomics. While on a solo road trip, his thoughts culminated in the idea that would form the underpinnings of Nautilus’ novel PrISM methodology for comprehensive and accessible proteome analysis at scale. Ever since, Parag and the entire Nautilus team have worked arduously to develop the Nautilus^TM Proteome Analysis Platform from this incredibly strong foundation.

Sujal Patel is Co-Founder and CEO of Nautilus. He previously founded and led the data storage company Isilon Systems through its successful acquisition by EMC Corporation. Along the way, Sujal built Isilon into the go-to data storage solution for bioinformaticians around the world. Parag Mallick was one of Isilon’s early customers while he was Director of Clinical Proteomics at Cedars-Sinai Medical Center. He worked with Sujal to manage vast amounts of multiomics data, and they developed a lasting friendship through this initial partnership. When Parag first had his idea for the Nautilus Platform, he reached out to Sujal as a trusted advisor who could thoroughly vet the idea. Over a marathon information session, Sujal was convinced of the incredible power of the nascent Nautilus Platform. Sujal invested his money, time, and expertise while also recruiting other pioneering investors to support Nautilus. More than that, he leveraged his knowledge of biology and years of software engineering expertise to establish the informational constraints under which the platform would identify proteins and built the computational foundations of the platform.