XineProtein
XineProtein is a closed-loop AI platform for designing, folding, evaluating, and optimizing novel proteins. From therapeutic antibodies to industrial enzymes — rational protein engineering at computational scale.
Directed evolution campaigns test thousands of random variants to find improvements. It's slow, expensive, and leaves most of the sequence space unexplored.
Traditional approaches test thousands of variants to find a handful of improvements. Most of the experimental budget is wasted on dead ends.
AlphaFold predicts what a protein looks like. But designing a new protein with specific function requires a fundamentally different approach.
Physical characterization of each protein variant is expensive. Computational pre-screening can eliminate 90% of experimental cost.
Specify your target protein function: binding target, catalytic activity, stability requirements. Upload a starting sequence or describe the desired fold.
Our protein language model generates thousands of sequence variants, each predicted to improve your target function while maintaining foldability and expression.
Instant structure prediction validates each variant folds correctly. GPU-accelerated MD simulations assess stability, dynamics, and binding in realistic conditions.
Multi-objective optimization refines variants over 100+ autonomous cycles. Top candidates are exported as codon-optimized DNA sequences ready for synthesis.
Our protein language model understands the relationship between sequence, structure, and function. Trained on billions of protein sequences, it predicts the effect of any mutation without explicit structural modeling.
A specialized module for designing therapeutic antibodies. Optimize CDR loops, predict humanization outcomes, and assess developability — all computationally.
Export designed protein variants directly to your synthesis partner. Our platform generates everything needed to go from silico to vitro.
XineProtein outputs are designed to move directly into synthesis, expression screening, and functional characterization.
Codon-optimized constructs and plate layouts reduce handoff friction for synthesis partners.
Recommended SPR, BLI, DSF, activity, or expression assays match the design objective.
Aggregation, immunogenicity, expression, and stability risks are surfaced before lab spend.
Optimize CDRs, humanization, developability, and affinity.
Improve activity, thermostability, solvent tolerance, and substrate scope.
Design scaffold proteins and biologics against challenging targets.
Balance performance, expression, and manufacturing constraints.
Protein engineering campaigns work best when AI-generated variants are reviewed alongside domain intuition and assay realities.
Yes. Campaigns can begin from a known sequence, structure, homolog family, or desired functional specification.
Yes. XineProtein includes antibody-specific CDR design, humanization, developability, and pairing workflows.
Assay results can be imported to update objectives and guide the next generation of variants.