Biologics Design

Advances in Biologics Modeling with BioLuminate

Background

Over the past decade, the growth of biopharmaceuticals (biologics) as a fraction of total drug sales has been nothing short of spectacular. With this growth, we have witnessed concomitant increases in the resources applied to the discovery and development of new biologics within the pharmaceutical industry. In recognition of this shift, Schrödinger has affirmed its commitment to advancing the field of biologics modeling, and this commitment is reflected by a number of important publications in this area over the past few years, as well as the development of a number of new computational tools. In this article we highlight these publications and tools.

First, it is useful to provide an overview of BioLuminate, Schrödinger’s biologics modeling platform. BioLuminate is a comprehensive modeling package for biologics, with advanced simulation methods deployed through an intuitive user interface that is specifically designed for biologics. BioLuminate is the first comprehensive integrated modeling package to specifically address the key questions associated with the molecular design of biologics. BioLuminate provides access to tools for protein engineering, residue/alanine scanning, analysis of protein-protein interfaces, antibody modeling, protein aggregation prediction, identification of reactive hotspots (proteolysis, glycosylation, deamidation, and oxidation), and more. BioLuminate also serves as the entry point to PIPER, the protein-protein docking tool developed by the Vajda group at Boston University.1 The core algorithms in PIPER consistently perform best in CAPRI competitions when compared with other automated protein-protein docking servers (note: ClusPro is the automated server based on the algorithms in PIPER).2 For a more complete listing of BioLuminate features, please visit the product website.

In the rest of this article, we will focus on 4 primary application areas:

     1. Antibody modeling
     2. Protein-protein binding
     3. Protein stabilization
     4. Enzyme design

Conclusion


As suggested by the examples above, we are making great strides forward in the development and application of structure-based methods for biopharmaceutical design. These improvements reflect both the availability of an integrated software platform for such calculations (BioLuminate), and methodological improvements in approaches for specific problems. Looking ahead, we anticipate that these tools will become increasingly important to biologics design, where the integration of computational approaches during discovery and development is still in the ascendancy.


References
1. Kozakov, D.; Brenke, R.; Comeau, S.R.; Vajda, S. PIPER: An FFT-based protein docking program with pairwise potentials. Proteins, 2006, 65(2), 392–406

2. Janin, J. Protein–protein docking tested in blind predictions: the CAPRI experiment. Mol. BioSyst., 2010, 6, 2351-2362

3. Zhu, K. and Day, T. Ab initio structure prediction of the antibody hypervariable H3 loop. Proteins, 2013, 81(6), 1081-108

4. Zhu, K.; Day, T.; Warshaviak, D.; Murrett, C.; Friesner, R.; Pearlman, D.A. Antibody structure determination using a combination of homology modeling, energy-based refinement, and loop prediction. Proteins, 2014, 82(8), 1646–1655

5. Jorgensen, W.L.; Maxwell, D.S.; Tirado-Rives, J. Development and testing of the OPLS all-atom force field on conformational energetics and properties of organic liquids. J Am Chem Soc, 1996, 118, 11225–11236

6. Shivakumar, D.; Williams, J.; Wu, Y.; Damm, W.; Shelley, J.; Sherman, W.; Prediction of absolute solvation free energies using molecular dynamics free energy perturbation and the OPLS force field. J Chem Theory Comput, 2010, 6, 1509–1519

7. Li, J.; Abel, R.; Zhu, K.; Cao, Y.; Zhao, S.; Friesner, R. The VSGB 2.0 model: A next generation energy model for high resolution protein structure modeling. Proteins: Structure, Function, and Bioinformatics, 2011, 79(10), 2794–2812

8. 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. doi:10.1371/journal.pone.0082849

9. Salam, N.; Adzhigirey, M.; Sherman, W.; and Pearlman, D.A. Structure-based approach to the prediction of disulfide bonds in proteins. PEDS, 2014, 27(10), 365-374

10. Chrencik et al. Crystal Structure of Antagonist Bound Human Lysophosphatidic Acid Receptor 1. Cell, 2015, 161(7), 1633–1643

11. Sirin, S.; Pearlman, D.A.; Sherman, W. Physics-based enzyme design: Predicting binding affinity and catalytic activity. Proteins, 2014, 82(12), 3397-409

12. Gannavaram, S.; Sirin, S.; Sherman, W.; Gadda, G. Mechanistic and Computational Studies of the Reductive Half-Reaction of Tyrosine to Phenylalanine Active Site Variants of d-Arginine Dehydrogenase. Biochemistry, 2014, 53(41), 6574-6583

13. Gannavaram, S.; Sirin, S.; Gadda, G. Mechanistic and computational studies on C-N bond oxidation in D-amino acids catalyzed by D-arginine dehydrogenase Y53F and Y249F (584.4). FASEB J., 2014, 28(1), Supplement 584.4

14. Sirin, S.; Kumar, R.; Martinez, C.; Karmilowicz, M.J.; Ghosh, P.; Abramov, Y.A.; Martin, V.; Sherman, W. A Computational Approach to Enzyme Design: Predicting ω-Aminotransferase Catalytic Activity Using Docking and MM-GBSA Scoring. J. Chem. Inf. Model., 2014, 54(8), 2334-2346

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