Carter Mitchell, Chief Scientific Officer at Kemp Proteins, brings scientific rigor and an artist’s imagination to the world of protein design and production. In this episode, recorded at the Advanced Lateral Flow Conference, we explore how his company is pushing the boundaries of protein expression, quality, and analysis using tools that merge machine learning, automation, and human creativity.A company reborn through precision and innovationKemp Proteins has deep roots in r...
Carter Mitchell, Chief Scientific Officer at Kemp Proteins, brings scientific rigor and an artist’s imagination to the world of protein design and production. In this episode, recorded at the Advanced Lateral Flow Conference, we explore how his company is pushing the boundaries of protein expression, quality, and analysis using tools that merge machine learning, automation, and human creativity.
A company reborn through precision and innovationKemp Proteins has deep roots in recombinant protein production, tracing back over 30 years to a company that began with insect-cell expression systems. After a rocky acquisition phase, the company was revived with renewed focus under CEO Mike Keefe, this time with a modern quality management system and new emphasis on antibodies and engineering solutions for diagnostics, therapeutics, and vaccines.
Carter, a self described protein nerd, joined around that time, bringing expertise in structural biology, protein engineering, and quantitative analytics and a mission to integrate AI into the company’s core processes.
Why insect cells still matterI knew that people used insect cells but I didn’t know why. Mitchell explains how insect cells, long used in protein production, still offer unique advantages. Unlike E. coli, insect cells can perform post-translational modifications, such as glycosylation—key for producing proteins that resemble their natural human counterparts. While mammalian systems like HEK293 have since made expression “paint-by-numbers” simple, Carter notes that insect systems still excel when complexity and authenticity matter. “It’s about having multiple expression capabilities,” he says, “so you can choose the right one for the problem at hand.”
Four questions that guide every projectCarter’s approach to solving client challenges starts with four questions:
* What is the protein?
* What information is available?
* What’s the intended use?
* What’s the scale?
From there, the team tailors both the process and the system to ensure reproducibility and regulatory readiness, whether the goal is a diagnostic reagent or a therapeutic protein. As an aside, manufacturing kilograms of protein still blows my mind.
As Carter puts it: “Regulators don’t want to see a smear on an SDS page. We think like regulators, anticipate their questions, and design out variability before it becomes a problem.”
From data lake to digital expert: ProtIQThe centerpiece of Carter’s innovation is ProtIQ, an internal expert system that combines structured data, AI models, and domain expertise into a 200–300-page report for every target protein. Initially, these reports were for experts, but Carter’s team is now transforming them into an interactive chatbot interface so anyone on the team can query the data conversationally.
“If a technician can ask, ‘What’s the isoelectric point?’ or ‘Does it have a secretory tag?’ and get an immediate answer, they’re empowered,” he says.It’s part of a broader effort to turn technicians into scientists, helping them engage more deeply with data, notice anomalies early, and contribute to process improvement.
Predicting protein liabilities before they happenUsing sequence analysis and AI-assisted visualization, Kemp Proteins can predict potential degradation sites or stability issues before production even begins. Carter’s team also models how viral variants like influenza strains might evolve over time, identifying changes in glycosylation patterns that could impact diagnostic binding. “We’re actually collaborating with the FDA on this,” he adds.
When science meets artCarter looks at protein structure like art. A lifelong painter and flamenco guitarist, he traces his fascination with structure to his mother’s art studio and his childhood encounters with crystals in Texas soil. That visual mindset drives how he thinks about molecules: “Art flattens multi-dimensional space to describe motion. That’s what we do in AI and machine learning, flattening complexity into something interpretable.”
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