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Evozyne

Evozyne is an AI-powered Science tool — AI protein engineering platform. Best for: Science. Pricing: Paid (GateOnAI Score: 46/100).

AI protein engineering platform

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Evozyne designs and optimizes protein sequences using AI-driven models that predict structure, stability, and functional activity. The platform offers a sequence generator that creates candidate variants from user‑defined constraints, a predictive engine that scores folding confidence and solubility, and a library design tool that formats oligo pools for synthesis. Users can visualize predicted 3‑D structures, run mutagenesis scans, and export results in FASTA or CSV formats. An integrated API lets lab automation software submit jobs and retrieve scores without manual steps. The system also logs version history for each design, enabling rollback to previous iterations. Target users include biotech R&D teams, academic protein engineers, and synthetic biology startups seeking rapid prototyping. Evozyne supports use cases such as enzyme redesign for higher turnover, antibody affinity maturation, and metabolic pathway optimization. The service operates on a paid‑only subscription model with tiered compute credits, so organizations scale usage by adding credits rather than per‑user fees. Compared with general‑purpose bioinformatics suites, Evozyne emphasizes end‑to‑end AI prediction and design rather than only sequence alignment. Enterprise plans provide dedicated support, SLA guarantees, and on‑premise deployment options for highly confidential projects. Key capabilities include an iterative design loop that refines variants based on experimental feedback, off‑target analysis to flag potential immunogenic motifs, and a collaborative workspace where multiple scientists share designs and comment on results. Cloud‑hosted compute ensures rapid turnaround, typically delivering scores within minutes for sequences under 500 residues. Export options cover standard formats for downstream modeling tools, and the platform complies with ISO‑27001 data‑security standards, making it suitable for regulated environments. A built‑in notebook interface lets researchers script custom analyses using Python, extending the platform beyond the default workflows.

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