Hydra

Hydra performs de novo protein sequence design using multi-objective optimization with dual energy functions to identify sequences predicted to fold into target structures.

Key Features:

• Multi-Objective Optimization: Uses two distinct energy functions to evaluate sequences simultaneously, addressing limitations of single-energy Monte Carlo or simulated annealing approaches.
• Ordered Sequence Space Transformation: Transforms an initially disordered discrete sequence space into a more ordered space using prior structural information from DSSP and PDB to accelerate search.
• Swarm Intelligence Algorithm: Applies a multiobjective swarm intelligence algorithm to navigate the ordered sequence space and identify optimal decoy sequences rapidly.
• Latent Quantitative Relationships: Exploits latent quantitative relationships between different amino acid types to enhance search efficiency and accuracy in identifying well-folded structures.

Scientific Applications:

• Diverse Fold Classes: Demonstrated efficacy across a range of protein targets encompassing various fold classes, producing well-folded structures in computational validation.
• Experimental Validation (1UBQ): Experimentally confirmed by solving the 1UBQ fold using NMR with a backbone RMSD of 1.074 Å from the native structure.
• Research Domains: Applicable to drug discovery, synthetic biology, and studies of protein folding mechanisms.

Methodology:

Simultaneous evaluation with dual energy functions; transformation of sequence space using prior DSSP and PDB structural information into an ordered representation; application of a multiobjective swarm intelligence algorithm within this ordered space while leveraging latent quantitative relationships among amino acid types.