# Protein Engineering

Sparse autoencoders expand the dense 128-dimensional latent space at each ProteinMPNN encoder layer into an overcomplete sparse space, where individual dimensions correlate with protein features such as secondary structure, residue volume, residue shape, disulfide bridges and ligand coordination. On the Brolucizumab short-chain variable fragment, the mod=0, 0.5 and 1 candidates with branched-chain-amino-acid dimensions raised held lower RMSD at 433 K than the native sequence, the ProteinMPNN sequence and the GeoEvoBuilder sequence, while mod=2, the largest raise, unfolded substantially at that temperature; the unmodified mod=0 candidate improved too, showing the autoencoder itself shifts predictions.

Sparse autoencoders expand the dense 128-dimensional latent space at each ProteinMPNN encoder layer into an overcomplete sparse space, where individual dimensions correlate with protein features such as secondary structure, residue volume, residue shape, disulfide bridges and ligand coordination. On the Brolucizumab short-chain variable fragment, the mod=0, 0.5 and 1 candidates with branched-chain-amino-acid dimensions raised held lower RMSD at 433 K than the native sequence, the ProteinMPNN sequence and the GeoEvoBuilder sequence, while mod=2, the largest raise, unfolded substantially at that temperature; the unmodified mod=0 candidate improved too, showing the autoencoder itself shifts predictions.

UCB and Cradle announced a strategic collaboration that puts Cradle's AI-driven protein design platform inside selected UCB antibody discovery projects, spanning hit generation, lead optimization and preclinical readiness. UCB's digital antibody engineering program had relied on its own antibody discovery platform combined with computational science and a network of technology and research partners. Under the collaboration, experimental results feed back into program-specific models in a compounding "lab-in-the-loop" cycle used to optimize candidates across the full set of required properties.

UCB and Cradle announced a strategic collaboration that puts Cradle's AI-driven protein design platform inside selected UCB antibody discovery projects, spanning hit generation, lead optimization and preclinical readiness. UCB's digital antibody engineering program had relied on its own antibody discovery platform combined with computational science and a network of technology and research partners. Under the collaboration, experimental results feed back into program-specific models in a compounding "lab-in-the-loop" cycle used to optimize candidates across the full set of required properties.

Generative enzyme design has mostly optimized at the amino acid level, rarely accounting for coding sequences and host expression at once. EZPro-Fit places protein 3D structure, coding sequences and taxonomic information in one generative framework, pretrained on thermophilic microbial data and fine-tuned on highly expressed E. coli genes, so it generates host-adapted coding sequences directly from structure. On the industrial endoglucanase Cel5E, its variants N291D and D259E raised both melting temperature and catalytic activity in experiments.

Generative enzyme design has mostly optimized at the amino acid level, rarely accounting for coding sequences and host expression at once. EZPro-Fit places protein 3D structure, coding sequences and taxonomic information in one generative framework, pretrained on thermophilic microbial data and fine-tuned on highly expressed E. coli genes, so it generates host-adapted coding sequences directly from structure. On the industrial endoglucanase Cel5E, its variants N291D and D259E raised both melting temperature and catalytic activity in experiments.