Target
Structural definition of the target surface and relevant binding regions.
AI-Driven Protein Design Platform
Intelligent protein design.
Faster innovation.
AI-driven protein design for more stable and more producible proteins.
We combine bioinformatics, structural biology, physics-based modeling and modern AI methods in protein design to engineer optimized molecules. This increases the success probability of development projects, shortens timelines and makes the development process more efficient and cost-effective.
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In silico methodology
N° 0230+
Years scientific experience
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Data-driven insights
Network · Collaborations · Scientific Environment
Our Services
Three pillars of computational biotech
ProtoVela combines machine learning, bioinformatics, and decades of scientific expertise to deliver tangible results — without the cost or time burden of wet lab infrastructure.
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AI-Driven Protein Design
Intelligent algorithms for next-generation proteins.
ProtoVela optimizes proteins using modern in silico methods. Our workflows improve stability and expression and accelerate process development — from design through production to formulation.
Service · 01
N° 02
Bioinformatics & Structural Biology Services
Data-driven insights for molecular innovation.
Comprehensive bioinformatic analyses of client proteins, physics-based protein modeling and stability analyses of mutations — combined with generative AI from sequence space to structure. Including structure prediction for peptides, soluble proteins and membrane proteins, plus simulations and state-of-the-art modeling.
Service · 02
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Biotech Innovation Support
From feasibility to implementation — across the full development process.
AI-driven design of optimized biomolecule sequences plus advisory on downstream processing and the long-term stability of biomolecules — for efficient, fast product development.
Service · 03
Computational-to-experimental workflow
From the structural target to a prioritized candidate series
Computational design structures the search space, prioritizes promising candidates and prepares focused experimental validation.
Active pipelineBinder design
Pipeline for binder generation and testing
From the structural target through binder generation and optimization to experimental characterization using suitable binding assays.
Optimized binder candidates with a clear testing strategyITC · SPR and project-specific binding assays
Defined target surface
The target surface provides the reference for binder generation, positioning and evaluation.
Reference Project · Scientific Leadership
Hydrolase design — 14 days, eight winners
An anonymized example from industrial protein design experience: the ProtoVela-style design pipeline was applied to a hydrolase scaffold and delivered every selected sequence as an expressing, more stable variant.
Representative project experience led by ProtoVela's scientific leadership (Dr. Kornelius Zeth) in an earlier industrial context. Reproduced here to illustrate the design philosophy applied at ProtoVela.
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8 / 8
designs expressed
Every selected sequence was successfully expressed in the host system.
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+20 °C
higher melting point
Top design moved Tm from 54 °C to 75 °C — process-ready stability.
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3×
expression yield
Best variant produced up to three times the parent's titer.
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14 days
design turnaround
From client sequence to delivered, ranked sequence set.
Fig.02— Biomolecule
About ProtoVela
Pioneers in biotechnology and protein design
ProtoVela is an emerging biotechnology startup with deep expertise in protein structures, structure-based protein design, and bioinformatics.
We favor rational protein design over directed evolution, leveraging advanced in silico approaches to improve protein stability, expression, and handling. Our methods are built on years of scientific experience and are designed to deliver tangible results that can be validated by our partners in experimental environments.
What to expect
What does modern protein design actually deliver?
Classical directed evolution often meant screening hundreds of thousands of variants. With AI-driven design, a few dozen carefully selected candidates are usually enough to reach comparable or better results.
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~40 vs 100k
Variants to screen
Directed evolution used to require screening in the range of 100,000 variants. Modern AI-driven design typically narrows this to a few dozen carefully chosen sequences that actually go into expression.
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2–3×
Expression yield
Optimized sequences routinely reach two to three times the recombinant expression yield of the parent sequence in typical projects.
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10–100×
Activity gain
For enzymes and functional proteins, activity improvements of 10× to 100× are realistic — depending on target profile and starting protein.
Indicative ranges from project experience · results depend on system and target profile
Free intro call & Project Assessment
Assess first, then engineer — at no upfront cost.
Send us a non-confidential summary of your goal. After agreeing an NDA and secure data-transfer route where needed, we assess which optimization goals are realistic, how to prioritize them and what a fine-tuning project would look like. Only then do you decide whether to commission a paid engagement.
- Free intro call with our scientific leadership.
- Free project assessment — with secure sequence exchange after initial contact.
- Clear go / no-go recommendation with realistic expectations.
- Only the subsequent fine-tuning work is paid.
FAQ
Frequently asked questions
Quick answers about ProtoVela's protein engineering services, methods and partnerships.
Rational protein design uses structural and computational insight to engineer specific changes in a protein, while directed evolution relies on random mutation and screening. ProtoVela focuses on rational protein design powered by physics-based modeling and generative AI, which is faster, cheaper and produces IP-clean candidates without wet lab cycles.
Yes. Membrane protein structure prediction is a core specialty — including VDAC structural analysis and other challenging transporters where AlphaFold2 limitations are well documented. We combine physics-based protein modeling with curated structural data to deliver reliable models.
You do. ProtoVela works on a clear, contract-based IP ownership model: all generative protein design outputs delivered to a client are owned by the client. We do not retain shared rights to the designs.
Yes. We support biotech spin-offs and pharma teams as an embedded computational partner — from feasibility studies and target triage to long-term co-development of an AI-driven protein engineering platform.
De novo miniprotein therapeutics and antimicrobial peptide drug discovery — including overcoming bacterial efflux pumps with AMPs — are active focus areas, directly informed by Dr. Zeth's published research on membrane proteins.
ProtoVela UG is a protein engineering startup based in Regensburg, Bavaria, Germany, working with partners across Europe and globally.
Blog
Updates from research and practice.
Developments in structural biology, AI-assisted protein design and biotech ecosystems.
Protein Engineering & AI
From AlphaFold to Protein Design: Why the 2024 Nobel Prize Matters for Biotech
The 2024 Nobel Prize in Chemistry marked a turning point: computational protein design and protein structure prediction have become central technologies for modern biotech innovation.
5 minRead more →
Protein Engineering & AI
Directed Evolution and Rational Design: Two Complementary Paths to Better Proteins
Directed evolution and rational design are often framed as competing paradigms. In practice, strong protein engineering programs combine both.
4 minRead more →
Structural Biology
Seeing Biomolecules: Why Structural Biology Became a Strategic Biotech Capability
Cryo-EM, X-ray crystallography, NMR, mass spectrometry and computational modeling form the analytical backbone of structure-informed innovation.
4 minRead more →
Keep reading · Sitemap
Explore further
Five entry points into ProtoVela — from process and proof to the intro call.
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Expertise
Services, molecule classes and scientific application areas in detail.
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Why ProtoVela
Principles, optimisation targets and the eight-step design process.
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About
Team, location and scientific leadership behind ProtoVela.
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Blog
Field notes on AI, structural biology and protein design.
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Contact
Book a free intro call and Project Assessment.