Frequently Asked Questions

CD Biosynsis offers AI-driven synthetic biology services including enzyme design, protein design, strain engineering, and comprehensive wet lab validation. Enginoma combines computational AI design with experimental verification to deliver functional biomolecules for pharmaceutical, industrial, and research applications.

The closed loop refers to our integrated workflow where AI designs proteins and enzymes, which are then experimentally validated in our wet lab facilities. The experimental results feed back into our AI models, continuously improving their accuracy and predictive power. This iterative process ensures that computational designs translate effectively to functional biomolecules.

Unlike traditional contract research organizations that focus solely on experimental execution, CD Biosynsis integrates AI-driven design with wet lab validation in a single platform. Our proprietary AI models generate optimized sequences before experimentation, reducing trial-and-error cycles and accelerating project timelines. The data flywheel from experiments continuously improves our AI capabilities, creating a compound advantage over time.

We serve a broad range of industries including pharmaceuticals (small molecule drugs, biologics, enzyme therapeutics), industrial enzymes (detergent, textile, pulp and paper), food and beverage processing, agricultural biotechnology, bioenergy and biofuels, environmental remediation, and diagnostics. Our AI-designed biomolecules are also used in academic research and synthetic biology tool development.

Yes, we maintain a portfolio of completed projects and success stories. Due to confidentiality agreements with our clients, detailed case studies are shared on a case-by-case basis under NDA. During your initial consultation, our scientific team can discuss relevant precedent projects in your application area. We also publish methodology papers and technical white papers that demonstrate our capabilities.

Absolutely. Our platform is designed to be accessible to researchers and companies regardless of computational background. You simply describe your biological or chemical problem, and our team handles the AI modeling and experimental work internally. We communicate results in clear, actionable terms with full documentation, making it easy to understand and apply the outcomes regardless of your technical background.

Purely computational providers can generate designs but cannot verify them experimentally. Purely experimental providers rely on traditional methods like random mutagenesis or rational design, which are time-consuming and have lower success rates. CD Biosynsis bridges both worlds: our AI generates informed design candidates that are then validated in our wet labs, with real experimental data feeding back to improve the AI. This closed-loop approach dramatically increases efficiency and success rates.

Yes, we provide comprehensive post-project support including technical consultation on implementation, troubleshooting guidance for scale-up or process integration, and follow-up optimization rounds if needed. Our team remains available for questions after project delivery. For ongoing collaborations, we offer retainer agreements that include regular check-ins and continuous optimization support.

The data flywheel is our continuous improvement mechanism where every experimental result—from successful designs and failed candidates—feeds back into training our AI models. This creates a compounding advantage: as we complete more projects, our models become increasingly accurate and predictive for your specific application type. You benefit from an AI system that learns from real experimental data, not just computational predictions.

Enginoma combines deeply re-engineered structural, sequence, and generative modules—performance-calibrated on our extensive experimental dataset—with proprietary activity and stability predictors optimized for specific therapeutic and industrial applications.

Our AI models are trained on a combination of publicly available protein databases (PDB, UniProt, BRENDA) and our proprietary experimental dataset accumulated from thousands of enzyme engineering and protein design projects. This continuous influx of real-world experimental data creates a data flywheel that progressively enhances model accuracy and predictive capabilities.

Our AI-assisted approach significantly improves design success compared to traditional directed evolution methods. For standard enzyme optimization projects, we typically achieve functional expression in the first design cycle for 70-85% of candidates, compared to 20-40% for traditional approaches. Specific success rates depend on target complexity, organism, and functional requirements.

We implement multiple quality assurance layers: computational filters for stability and expression, in silico validation of structural models, and mandatory experimental verification of all design candidates. Each deliverable includes comprehensive characterization data including activity assays, stability measurements, and structural confirmation where applicable.

Traditional directed evolution relies on random mutagenesis and selection, requiring hundreds to thousands of variants screened over many generations with limited improvement per round. AI design learns sequence-structure-function relationships from large datasets to propose targeted modifications in a single computational pass. Our approach reduces the experimental burden from months to weeks while achieving comparable or superior improvements, and can explore mutational space that random approaches would miss.

Yes, we can design enzymes for non-natural substrates and reactions using our proprietary enzyme activity prediction models combined with rational active site redesign. Our approach includes computational enzyme design for novel chemistry, directed evolution of natural enzymes toward new substrates, and mining enzyme superfamilies for catalytic promiscuity. We've successfully delivered enzymes for reactions not found in nature, including pesticide degradation, pharmaceutical intermediate synthesis, and specialty chemical production.

Requirements vary by project type. For enzyme optimization, we typically need the wild-type sequence or gene, known substrate(s), and an assay method. For de novo design, we need the target reaction chemistry and substrate structures. We can often start with minimal data—even 1-3 known active sequences from the same family can provide sufficient starting points. Our team will assess your specific inputs during the scoping call and advise on any additional data that would improve design success.

We can optimize virtually any enzyme or protein property depending on your application needs: catalytic efficiency (kcat/Km), substrate specificity, thermal stability, pH stability, organic solvent tolerance, storage stability, expression yield, specificity for non-natural substrates, and regio-/stereoselectivity. Multi-parameter optimization is supported, where we balance trade-offs between properties using Pareto-optimal design approaches.

Yes, our AI models are continuously retrained and updated using new experimental data from our wet lab operations. We maintain a dedicated ML engineering team that monitors model performance, incorporates new training data, and evaluates model improvements before deployment. As part of our data flywheel, every client project contributes to model refinement, benefiting both future clients and ongoing collaborations.

Generic structure predictors excel at fold geometry but were not built for enzyme engineering. Enginoma proprietary engines are performance-calibrated on experimental datasets for activity prediction, thermostability engineering, and multi-parameter optimization—delivering substantially better outcomes on enzyme design tasks than uncalibrated baseline tools.

Our standard wet lab validation cycle typically takes 2-4 weeks per design round. This includes gene synthesis (1 week), expression screening (1-1.5 weeks), and functional characterization (1-1.5 weeks). Parallel processing of multiple design candidates enables rapid iteration and optimization cycles.

Our wet lab capabilities include gene synthesis and cloning, protein expression in multiple host systems (E. coli, Pichia pastoris, Bacillus subtilis), enzyme activity assays, high-throughput screening, thermal stability characterization, and structural determination (X-ray crystallography, cryo-EM, NMR). We specialize in extremophile enzymes and engineered variants for industrial biocatalysis.

All experimental data from expression tests, activity assays, and stability measurements are standardized and integrated into our training pipeline. We track sequence-function relationships, identify design patterns that correlate with success, and actively update model weights and feature importance. This closed-loop learning ensures our AI models continuously improve with every project.

We support a comprehensive range of expression systems: E. coli (for rapid, high-yield production of bacterial and simple eukaryotic proteins), Pichia pastoris (for secreted eukaryotic proteins with disulfide bonds), Bacillus subtilis (for secreted industrial enzymes), mammalian cell lines (HEK293, CHO) for therapeutic proteins, insect cell/baculovirus systems for complex eukaryotic proteins, and cell-free synthesis systems for rapid prototyping. System selection is based on your protein complexity, yield requirements, and downstream application.

Our screening platform can process up to 10,000 variants per week using a combination of FACS-based sorting, 96/384-well microtiter plate assays, and automated liquid handling. We employ multiplexed assay formats that can measure multiple properties (activity, selectivity, stability) simultaneously. Throughput is scaled to project requirements—standard projects screen 100-500 variants per round, while directed evolution campaigns can scale to thousands.

Yes, we partner with leading structural biology facilities to offer full structural determination services: X-ray crystallography (1-2 week turnaround for crystallization, full structure refinement), cryo-EM (for large complexes or proteins difficult to crystallize), and NMR spectroscopy (for dynamics and ligand binding studies). For routine characterization, we provide Enginoma Structure modeling validated against wet-lab data as part of our standard deliverables.

Standard deliverables include purified protein (mg quantities based on your needs), activity assay data with kinetic parameters (Km, kcat, specificity), stability characterization (thermal, pH, solvent stability), sequence confirmation, expression/purification protocols, and a comprehensive technical report. For advanced projects, we can include structural data, site-directed mutagenesis results, substrate scope analysis, and scale-up feasibility data.

A complete design-validate cycle typically takes 4-6 weeks: gene synthesis and cloning (1-2 weeks), expression screening in 2-3 expression systems (1-2 weeks), protein purification (1 week), and functional characterization (1-2 weeks). For projects requiring multiple optimization rounds, we typically deliver top candidates every 4 weeks. De novo design projects may require 8-12 weeks for the first round due to additional computational time.

This is why we validate experimentally in every project. If initial designs underperform, our closed-loop process uses the experimental failure data to improve the next design round. We analyze why designs failed (expression, solubility, activity, stability) and incorporate these learnings into updated AI models. In practice, AI-designed candidates show significantly higher success rates than random mutagenesis, but iterative rounds with feedback are a standard part of the optimization process for challenging targets.

Yes, we provide fermentation optimization and scale-up services for industrial enzyme production. Our capabilities include shake flask optimization, bioreactor scale-up (1L to 1000L), fed-batch and continuous fermentation strategies, and downstream processing (cell disruption, clarification, purification). We work with you to optimize growth media, fermentation conditions, and downstream recovery to maximize product yield and reduce production costs.

Starting a project is simple: submit an inquiry through our 'Get a Quote' form or contact page with details about your enzyme engineering, protein design, or strain development needs. Our scientific team will review your requirements and respond within one business day with an initial consultation and project proposal outlining scope, timeline, and deliverables.

Project timelines vary by scope and complexity. Standard enzyme optimization projects typically run 6-12 weeks. De novo protein design projects may require 8-16 weeks depending on target complexity. Strain engineering projects typically span 12-24 weeks. We provide detailed timeline estimates during the initial project scoping phase.

We offer flexible engagement models tailored to your needs: fixed-price project quotes for well-defined deliverables, time-and-materials arrangements for exploratory or multi-round optimization projects, and annual platform access agreements for organizations with ongoing synthetic biology needs. We provide transparent pricing breakdowns in all proposals.

Yes. All client projects operate under strict confidentiality agreements. Project-specific sequences, experimental data, and methodology are isolated in secure environments and never shared between clients or published without explicit written consent. Our AI models are trained on aggregated and anonymized internal datasets, not on individual client project data.

You retain full ownership of all project-specific data, sequences, and resulting IP. The engineered sequences and experimental results delivered to you are yours to patent, license, or commercialize as you see fit. We retain rights to general methodological improvements and aggregated learnings, but never claim ownership of client-specific designs or confidential project information.

Yes, we offer expedited project options for time-sensitive needs. Rush timelines typically involve parallel processing of multiple design candidates, dedicated lab resources, and extended work hours. Expedite fees vary based on the degree of acceleration and resource availability. Contact us as early as possible for urgent projects—ideally 4+ weeks before your deadline—to discuss feasibility and timeline options.

Yes, flexibility is built into our project structure. You can redirect focus—such as changing target substrates, adding new optimization parameters, or pivoting to a related enzyme family—during scheduled milestone reviews. Mid-project pivots may involve scope adjustments and timeline modifications, which we discuss transparently. Our milestone-based approach ensures regular check-ins where strategic adjustments can be made.

We accept bank wire transfers, credit card payments (for smaller project deposits), and purchase orders for institutional clients. For large projects, we typically structure payments as milestone-based installments (e.g., 30% upfront, 40% at midpoint, 30% on delivery). We work with university technology transfer offices, pharmaceutical procurement departments, and startup finance teams to accommodate various payment workflows.

Absolutely, we welcome pilot projects to demonstrate capabilities before committing to larger engagements. Pilot projects typically focus on 1-3 enzyme targets with a single design-validation round, providing you with real experimental data to assess quality. Pilot pricing starts at $8,000-$15,000 depending on complexity. Many clients start with pilots, then expand to multi-round optimization programs after seeing results.

We work with clients globally across North America, Europe, Asia, and Australia. Collaboration is conducted entirely remotely via secure file sharing, video conferencing, and our project management portal. We accommodate different time zones for meetings and provide asynchronous updates. Sample shipping follows IATA regulations for biological materials, and we handle all customs documentation for international shipments.