Providing a comprehensive modeling solution for biologics
The Advantages of BioLuminate
While there have previously been some tools to model a few facets of biological systems, Schrödinger’s BioLuminate is the first comprehensive user interface and the lynchpin product of the Biologics Suite that is designed from the ground up, with significant user input, to specifically address the key questions associated with the molecular design of biologics. BioLuminate leverages industry-leading simulations while logically organizes tasks and workflows.
Building on a solid foundation of comprehensive protein modeling tools, BioLuminate provides access to additional advanced tools for protein engineering, analysis of protein-protein interfaces, and antibody modeling.
BioLuminate offers a state of the art protein-protein docking program, with modes for antibody and multimer docking.
BioLuminate contains a complete set of homology modeling and protein sequence analysis tools, including advanced loop predictions, annotation capabilities, chimeric model building, and interactive protein structure quality analysis.
BioLuminate generates protein aggregation propensity surfaces and performs residue-based property predictions including binding energy, thermal stability, solvent-accessible surface area, hydrophilicity, and hydrophobicity. In addition, cysteine scanning automatically identifies potential mutations that can result in disulfide bridges. Reactive hot spots prone to proteolysis, glycosylation, deamidation, and oxidation are also detected.
BioLuminate offers an antibody-specific homology modeling workflow including automated prediction of CDR loops from sequence. BioLuminate includes a curated antibody database with tools to add in-house antibody structures.
BioLuminate can access the full suite of Schrödinger simulation tools to perform advanced computational analyses such as helical stability/melting analysis from molecular dynamics (MD) simulations, free energy perturbation (FEP) calculation of binding affinity and protein stability, large-scale low-mode search for domain movement, and QM/MM prediction of binding site reactivity.
Citations and Acknowledgements
Biologics Suite 2017-1: BioLuminate, Schrödinger, LLC, New York, NY, 2017
ö Zhu, K.; Day, T.; Warshaviak, D.; Murrett, C.; Friesner, R.; Pearlman, D., "Antibody structure determination using a combination of homology modeling, energy-based refinement, and loop prediction," Proteins, 2014, 82(8), 1646–1655
ö Salam, N.K.; Adzhigirey, M.; Sherman, W.; Pearlman, D.A., "Structure-based approach to the prediction of disulfide bonds in proteins," Protein Eng. Des. Sel., 2014, 27(10), 365-74
ö Beard, H.; Cholleti, A.; Pearlman, D.; Sherman, W.; Loving, K.A., "Applying physics-based scoring to calculate free energies of binding for single amino acid mutations in protein-protein complexes," PLoS ONE, 2013, 8(12), e82849