Skip to main content
Schrödinger
  • ホーム
  • 製品とサービス
      • Small-Molecule Drug Discovery Suite
      • Biologics Suite
      • Materials Science Suite
      • Discovery Informatics Suite
      • PyMOL
      • All Software Applications
      • Discovery Services and Collaborations
      • IT Services
      • Request Trial License
      • Request Sales Quote
      • Request Web Account
      • サイエンス
          • Biologics Design
          • Catalysis and Chemical Reactivity
          • DFT-based pKa Prediction
          • Docking and Scoring
          • Force Field
          • Free Energy Methods (FEP)
          • Machine Learning & QSPR for Materials
          • Organic Electronics
          • Shape-based Screening
          • Water Thermodynamics
          • Publications
          • Citations
          • サポート
              • Contact Support
              • Documentation
              • Knowledge Base
              • Known Issues
              • License information
              • Python API
              • Scripts
              • Seminars
              • Supported Platforms
              • Training
              • ダウンロード
                  • Product Suites Downloads
                  • KNIME Workflows
                  • Free Maestro
                  >> SEARCH BY TOPIC
                  SEARCH BY TOPIC:(Select one or more)
                  > BACK TO KEYWORD SEARCH
                  >

                  BioLuminate

                  バイオ医薬品創出やタンパク質モデリングのための包括的なソリューション

                   Crystal structure of a matured therapeutic antibody bound to the I-domain of the integrin VLA1 (PDB ID 1MHP). 

                  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.

                  • Features
                  • Publications
                  • Training Material

                  Protein-protein docking:
                  BioLuminate offers a state of the art protein-protein docking program, 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 2016-4: BioLuminate, Schrödinger, LLC, New York, NY, 2016

                  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

                  ö "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

                  ö "Physics-based enzyme design: Predicting binding affinity and catalytic activity"

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

                  ö "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

                  ö "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

                  Tutorials

                  • Homology Modeling with BioLuminate

                  Lectures

                  • General Introduction to Antibody Modeling

                  Find more helpful resources in the Training Center
                  Download
                  Download Software
                  Request Trial
                  Request Trial
                  Recent Publications

                    Transforming drug discovery and materials research.

                  Copyright © 2018 Schrödinger, LLC

                  • Privacy Policies
                  • Terms of Use
                  • FCOI Policy
                  • EULA
                  TwitterGoogle PlusLinked InYoutube