BioLuminate®

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 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 organizing 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.

Protein-protein docking:
BioLuminate provides a graphical interface for the state of the art protein-protein docking program PIPER, with modes for antibody and multimer docking.

Protein modeling:
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.

Protein engineering:
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.

Antibody modeling:
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.

Advanced simulations:
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

Schrödinger Release 2021-4: BioLuminate, Schrödinger, LLC, New York, NY, 2021.

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

· "Accurate Prediction of Protein Thermodynamic Stability Changes upon Residue Mutation using Free Energy Perturbation"

Guido Scarabelli, Eliud O. Oloo, Johannes K.X. Maier, Agustina Rodriguez-Granillo, JMB, 2022, 434(2),

· "A Descriptor Set for Quantitative Structure-Property Relationship Prediction in Biologics"

Kannan Sankar, Kyle Trainor, Levi L. Blazer, Jarrett J. Adams, Sachdev S. Sidhu, Tyler Day, Elizabeth Meiering, Johannes K. X. Maier, Mol Inform, 2022, ,

· "Large-Scale In Vitro Functional Testing and Novel Variant Scoring via Protein Modeling Provide Insights Into Alkaline Phosphatase Activity in Hypophosphatasia"

Del Angel, G.; Reynders, J.; Negron, C.; Steinbrecher, T.; Mornet, E., Hum Mutat., 2020, DOI: 10.1002/humu.24010,

· "Predicting mutations deleterious to function in beta-lactamase TEM1 using MM-GBSA"

Negron, C.; Pearlman, D.A.; del Angel, G., PLoS ONE, 2019, 14(3), e0214015

· "Relative Binding Affinity Prediction of Charge-Changing Sequence Mutations with FEP in Protein–Protein Interfaces"

Clark, A.J.; Negron, C.; Hauser, K.; Sun, M.; Wang, L.; Abel, R.; Friesner, R.A., Journal of Molecular Biology, 2019, ,

· "AggScore: Prediction of aggregation-prone regions in proteins based on the distribution of surface patches"

Sankar, K.; Krystek, S.R. Jr; Carl, S.M.; Day, T.; Maier, J.K.X., Proteins, 2018, 86(11), 1147-1156

· "Selection of Nanobodies that Block the Enzymatic and Cytotoxic Activities of the Binary Clostridium Difficile Toxin CDT"

Unger, M; Eichhoff, AM; Schumacher, L; Strysio, M; Menzel, M; Schwan, C; Alzogaray, V; Zylberman, V; Seman, M; Brandner, J; Rohde, H; Zhu, K; Haag, F; Mittrücker, H; Goldbaum, F; Aktories, K; Koch-Nolte, F., Scientific Reports, 2015, 5(7850), 1-10

"Crystal Structure of Antagonist Bound Human Lysophosphatidic Acid Receptor 1"

Chrencik, J.E.; Roth, C.B.; Terakado, M.; Kurata, H.; Omi, R.; Kihara, Y.; Warshaviak, D.; Nakade, S.; Asmar-Rovira, G.; Mileni, M.; Mizuno, H.; Griffith, M.T.; Rodgers, C.; Han, G.W.; Velasquez, J.; Chun, J.; Stevens, R.C.; Hanson, M.A., Cell, 2015, 161(7), 1633-1643

"Sequence Selectivity of Macrolide-Induced Translational Attenuation"

Davis, A.R; Gohara, D.W.; and Yap, M.F., PNAS, 2014, 111(43), 15379-15384

· "Structure-based approach to the prediction of disulfide bonds in proteins"

Salam, N.K.; Adzhigirey, M.; Sherman, W.; Pearlman, D.A., Protein Eng. Des. Sel., 2014, 27(10), 365-74

· "Mechanistic and Computational Studies on C-N Bond Oxidation in D-Amino Acids Catalyzed by D-Arginine Dehydrogenase Y53F and Y249F (584.4)"

Gannavaram, S.; Sirin, S.; Gadda, G., FASEB J., 2014, 28(1), Supplement 584.4

· "Antibody Structure Determination Using a Combination of Homology Modeling, Energy-Based Refinement, and Loop Prediction"

Zhu, K.; Day, T.; Warshaviak, D.; Murrett, C.; Friesner, R.; Pearlman, D., Proteins, 2014, 82(8), 1646–1655

· "Mechanistic and Computational Studies of the Reductive Half-Reaction of Tyrosine to Phenylalanine Active Site Variants of d-Arginine Dehydrogenase"

Gannavaram, S.; Sirin, S.; Sherman, W.; Gadda, G., Biochemistry, 2014, 53(41), 6574-6583

· "Physics-Based Enzyme Design: Predicting Binding Affinity and Catalytic Activity"

Sirin, S.; Pearlman, D.A.; Sherman, W., Proteins, 2014, 82(12), 3397-409

· "A Computational Approach to Enzyme Design: Predicting ω-Aminotransferase Catalytic Activity Using Docking and MM-GBSA Scoring"

Sirin, S.; Kumar, R.; Martinez, C.; Karmilowicz, M.J.; Ghosh, P.; Abramov, Y.A.; Martin, V.; Sherman, W., J. Chem. Inf. Model., 2014, 54(8), 2334-2346

· "Ab initio structure prediction of the antibody hypervariable H3 loop"

Zhu, K.; Day, T., Proteins, 2013, 81(6), 1081-1089

· "Applying physics-based scoring to calculate free energies of binding for single amino acid mutations in protein-protein complexes"

Beard, H.; Cholleti, A.; Pearlman, D.; Sherman, W.; Loving, K.A., PLoS ONE, 2013, 8(12), e82849

· "Improved docking of polypeptides with Glide"

Tubert-Brohman, I.; Sherman, W.; Repasky, M.; Beuming, T., J. Chem. Inf. Model., 2013, 53(7), 1689-1699

· "Probing the α-Helical Structural Stability of Stapled p53 Peptides: Molecular Dynamics Simulations and Analysis"

Guo, Z.; Mohanty, U.; Noehre, J.; Sawyer, T. K.; Sherman, W.; Krilov, G., Chem. Biol. Drug Des., 2010, 75, 348-359
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