Participated · Series B · Mar 2022

Arzeda builds a protein-design platform that uses biophysics-informed AI combined with generative methods (large language and diffusion models) trained on a proprietary dataset to design enzymes and proteins. The company targets non-biomedical, industrial and consumer applications—its first product is a natural stevia-based sweetener for an unnamed consumer brand, with pipeline projects including laundry detergents for Unilever and biodegradable materials co-developed with W. L. Gore. Arzeda also handles experimental validation and manufacturing of its designs and generates revenue from both sales of proteins/enzymes and end products made with contracted partners. The firm reports a 70-person team and says it is generating revenue, with customers including Unilever, W. L. Gore, AAK and the U.S. Department of Defense. It aims to scale production of its artificial sweetener and commercialize additional enzymes while working toward corporate-EBITDA-positive performance. Arzeda is a Seattle-based protein design company that engineers proteins and enzymes to replace ingredients with a high environmental footprint. The company uses a platform that combines physics-based computational protein design, machine learning, and lab automation to design and build new molecules. It works in partnership with Fortune 500 companies and industrial leaders to develop sustainable products for food & nutrition, advanced materials, and diagnostics & pharma industries globally. Led by CEO Alexandre Zanghellini, Arzeda intends to accelerate product development and commercialization of new enzymes. The company raised new capital to fund those commercialization and product development efforts. Arzeda leverages an advanced protein design technology and a proprietary platform to design enzymes and biological pathways for commercial production. The company develops a portfolio of enzymes and specialty chemicals targeting polymers, advanced materials and health and nutrition markets. It serves global manufacturers, including DuPont, Mitsubishi Chemical and INVISTA, and works to improve crop yield and resilience. Led by co‑founder and CEO Alexandre Zanghellini, Arzeda is expanding its product development pipeline. The company will use the new funding to expand the throughput capacity of its protein design platform and advance products toward commercial production. The close of the Series A comes as Arzeda gains traction in the synthetic biology and bioeconomy markets.

Participated · Series A · Dec 2020

Kenota Health develops a rapid allergy‑testing system that brings lab testing into the allergy specialist’s clinic, producing results in under 30 minutes from a few drops of blood via finger prick. The system is designed to allow physicians to test and treat more patients within a single clinic visit and to make pediatric sample collection faster and easier. Kenota aims to improve diagnosis speed and clarity for life‑threatening allergies and to support earlier access to immunotherapy. The company has consulted with the U.S. Food and Drug Administration from early development to align with regulatory needs. Kenota plans to use the new funding to complete development of its technology and begin the regulatory review process, with a market focus on the U.S. and Canada. Financially, the company raised a US$9M Series A and previously raised a US$2M seed round in 2016; it also won $25,000 in the 2014 Velocity Fund Finals.

Participated · Series A · Sep 2020

Catalog is a Boston startup founded in 2016 by MIT scientists, including CEO Hyunjun Park, building a synthetic DNA-powered data storage and computing platform. Its proprietary data-encoding scheme minimizes de novo DNA synthesis to enable practical DNA-based information storage and computation. Catalog’s custom DNA writer, Shannon, is designed to write over 10MB/sec and can store up to 1.63TB of compressed data in a single run. The platform enables data management, computation, and automation, and Catalog plans to invest in computation functions over the next two to three years. The company expects to commercialize the platform around 2025 and will continue supporting collaborators and partners in the DNA ecosystem. Catalog is a member of the DNA Storage Alliance, works with about a dozen co-development partners in the U.S. and Europe, and recently opened an office in Seoul for Asia expansion. Expected applications include fraud detection, image processing for defect discovery in manufacturing, and digital signal processing such as seismic processing. Catalog develops a DNA-based platform for large-scale digital data storage and computation. Its core product is a custom-developed DNA writer and storage system called Shannon that writes at over 10 Mb/sec, generates over a trillion identifiers in a single run, and can store up to 1.63 Tb of compressed data per run. The company converts binary data into combinations of pre-synthesized DNA molecules using technology similar to inkjet printers, developed in collaboration with Cambridge Consultants. Catalog says its approach can hold a million times more data in the same volume as magnetic and solid-state media. Founded in 2016 by MIT scientists and led by CEO and co-founder Hyunjun Park and newly appointed CTO Dave Turek, the company plans to use new funding for early product trials and continued research and development. Catalog has raised $21M to date, including the $10M Series A announced in this round. Catalog is developing commercially viable DNA data storage technology and is based in Boston, MA, operating out of the Harvard Life Lab. The company’s first-generation commercial product will provide an online interface through which customers submit data; Catalog will write that data into DNA using the company’s machine. Once written into DNA, the material can be stored by Catalog or at the customer’s own facility (for example in a commercial refrigerator). Catalog is focusing its initial commercial pilots on organizations in government (including intelligence and space), IT, entertainment, and other sectors. Founders include MIT scientists Hyunjun Park (CEO) and Nathaniel Roquet, and Devin Leake serves as Chief Science Officer. While developing the technology the team consulted with Eric Shaqfeh (Stanford) and David Weitz (Harvard). The company has raised $9M in funding.

Led · Seed · Sep 2019

A-Alpha Bio harnesses synthetic biology and machine learning via its AlphaSeq and AlphaBind platforms to measure, predict, and engineer protein–protein interactions. AlphaSeq enables rapid, quantitative measurement of millions of protein–protein binding affinities simultaneously, while AlphaBind predicts binding strength from sequence trained on the company’s interaction database. The company leverages these platforms for in-house therapeutic programs and partnerships with pharmaceutical companies and national laboratories. A-Alpha Bio is based in Seattle and was founded in 2017. To date it has measured nearly 10 million quantitative antibody–antigen binding affinities across three pathogen families, which has improved the accuracy of affinity‑predicting machine learning models and helped identify stabilized antigen variants. Recent funding from the Department of Defense will expand its work generating large-scale antibody–antigen datasets and training predictive models for multiple undisclosed pathogen families. A-Alpha Bio combines synthetic biology and machine learning to experimentally measure and predict protein-protein interaction affinities using its AlphaSeq and AlphaBind platforms. The company applies AlphaSeq to generate large-scale binding data and trains AlphaBind models to predict binding from sequence. It uses both platforms to discover and optimize therapeutics for its internal pipeline and in partnership with industry collaborators. A-Alpha Bio raised $22.4M in a Series A2 that brought total funding to $51M. The company said it will use the proceeds to further develop AlphaSeq and AlphaBind and to accelerate its oncology-focused internal pipeline. Led by CEO David Younger and based in Seattle, A-Alpha Bio has announced collaborations with Bristol Myers Squibb, Lawrence Livermore National Laboratory, and Gilead Sciences to advance molecular glue target discovery, antibody discovery for emerging biothreats, and next-generation HIV therapeutic options. A-Alpha Bio develops experimental and computational platforms to measure, predict, and engineer protein-protein interactions for therapeutic discovery. Its AlphaSeq platform can simultaneously measure millions of antibody-antigen interactions to determine affinity, specificity, cross-reactivity, and epitope engagement, and those binding data feed the AlphaBind machine-learning models. The company has measured tens of millions of interactions with Lawrence Livermore National Laboratory in support of Department of Defense work. Under a new $2.4M subcontract award, A-Alpha Bio will generate large-scale antibody-antigen binding data for the DOD’s GUIDE program and expand efforts to five prioritized pathogen families. The data and iterative AlphaSeq assays will be used to train and validate ML models to predict desirable antibody sequences and accelerate response to future biothreats. A-Alpha Bio is based in Seattle and was founded in 2017 at the University of Washington’s Institute for Protein Design and Center for Synthetic Biology. A-Alpha Bio has developed AlphaSeq, a proprietary platform that uses genetically engineered yeast to experimentally measure millions of protein-protein interaction affinities simultaneously at a library-on-library scale. The platform generates quantitative, multidimensional data on affinity, specificity, cross-reactivity and epitope to inform machine-learning models for antibody and antigen design. The company was awarded a $1.1M Department of Defense subcontract to generate binding data on antibody-antigen interactions for SARS-CoV-2 variants and to support a collaboration with Lawrence Livermore National Laboratory. Under the collaboration, A-Alpha Bio will synthesize multiple antibody libraries and measure binding strength against panels of coronavirus variants to elucidate relationships between antibody sequence and variant binding profiles. LLNL will use the AlphaSeq-generated data to refine ML models aimed at predicting antibody sequences with improved affinity to current and potential future variants. A-Alpha Bio was founded in 2017 and is based in Seattle. A-Alpha Bio provides cell-based tools for quantitative, high-throughput measurement of protein interactions through its AlphaSeq platform. The company aims to accelerate target discovery, library screening, and preclinical drug characterization. Its team includes synthetic biologists, structural biologists, and next-generation sequencing experts. A-Alpha Bio plans to build a new production laboratory and fill key roles across the organization. It intends to generate what it calls the largest-ever database of protein interaction measurements and to expand machine-learning capabilities to determine rules of protein binding and enable in silico modeling of complex protein interaction networks. The company announced a $20.0M Series A to fund these efforts.

Led · Series C · Sep 2018

uBiome began as a consumer gut-health testing company and has expanded its testing to more than 250,000 samples across gut, skin and vaginal microbiomes, which it says is the largest dataset of its kind. The company has published over 30 peer-reviewed papers and entered research partnerships with institutions including the CDC, Harvard, MIT and Stanford. Founder Jessica Richman says uBiome is moving into drug discovery and therapeutics, planning to commercialize drug-discovery efforts and develop patent assets. To support that work it will relocate its therapeutics unit into new Cambridge, Massachusetts headquarters while remaining San Francisco–based. uBiome is also bolstering its board with former Novartis CEO Joseph Jimenez to support the therapeutics push. Financially, the company has ample runway following a new $83 million Series C and a total of $105 million raised to date. uBiome develops consumer and clinical tests that sequence microbial DNA from fecal samples to profile the gut microbiome. It is launching a clinical screening product called SmartGut that uses 16S rRNA gene sequencing to identify dozens of microbial genera and species. The company operates a College of American Pathologists (CAP)–accredited clinical lab and says it has submitted testing methods to a peer‑reviewed journal. Customers can collect samples at home; a basic gut kit is listed for $89 on the site and collection takes about two minutes. uBiome reports a database of close to 100,000 gut samples, up from 2,500 four years earlier, and came out of Y Combinator in 2012. The company raised funding to support the SmartGut launch, lab operations, and team growth. uBiome sells consumer microbiome swab kits (three different swab-site kits) that users collect, ship back, and receive results from; the most basic kit is priced at $89 and is available via ubiome.com and Amazon Prime in the U.S. The company analyzes samples using patent-pending robot-run sample methods and machine-learning algorithms and says it holds the largest private dataset of human microbiome samples. Users receive a login to view results in about six weeks and can compare their microbiome to cohorts such as vegans, overweight people, or heavy drinkers. Shipping is free in the U.S. uBiome is Y Combinator–backed and previously ran an Indiegogo campaign that raised $351,193 against a $100,000 goal. The company states its goal is to generate large-scale microbiome data for researchers by crowdsourcing samples and analysis.