When theory meets experiment: the PD-1 challenge

Applying atomistic computational modeling to drug discovery has proven to be a hugely successful approach, allowing drug–receptor interactions to be predicted and drugs to be optimized for potency, selectivity, and safety. However, when it comes to predicting protein–protein interactions and to rationally designing regulators of these interactions, computational tools often fail. Here, we report one of the rare instances where state-of-the-art computer simulations, guided by experiment, were able to correctly predict one of the most sophisticated protein–protein interactions. We revisit our previous discovery of the complex of human PD-1 with the ligand PD-L1 and compare our earlier findings with the recently published crystal structure of the same complex. Side-by-side comparison of the model of the complex with its crystal structure reveals outstanding agreement and suggests that our protein–protein prediction workflow could be applied to similar problems.

[1]  Philippe Roche,et al.  2P2Idb: a structural database dedicated to orthosteric modulation of protein–protein interactions , 2012, Nucleic Acids Res..

[2]  Vasant Honavar,et al.  HomPPI: a class of sequence homology based protein-protein interface prediction methods , 2011, BMC Bioinformatics.

[3]  Mieczyslaw Torchala,et al.  The scoring of poses in protein-protein docking: current capabilities and future directions , 2013, BMC Bioinformatics.

[4]  A. Bonvin,et al.  The HADDOCK web server for data-driven biomolecular docking , 2010, Nature Protocols.

[5]  L. Zitvogel,et al.  Targeting PD-1/PD-L1 interactions for cancer immunotherapy , 2012, Oncoimmunology.

[6]  Z. Weng,et al.  ZDOCK: An initial‐stage protein‐docking algorithm , 2003, Proteins.

[7]  Jeffrey J. Gray,et al.  Protein-protein docking with simultaneous optimization of rigid-body displacement and side-chain conformations. , 2003, Journal of molecular biology.

[8]  Rebecca C. Wade,et al.  Protein‐Protein Docking , 2001 .

[9]  Dong Xu,et al.  Computational analyses of high-throughput protein-protein interaction data. , 2003, Current protein & peptide science.

[10]  K. Zak,et al.  Structure of the Complex of Human Programmed Death 1, PD-1, and Its Ligand PD-L1. , 2015, Structure.

[11]  Albert Chan,et al.  PIPE: a protein-protein interaction prediction engine based on the re-occurring short polypeptide sequences between known interacting protein pairs , 2006, BMC Bioinformatics.

[12]  Wenping Liu,et al.  Molecular dynamics simulations elucidate conformational selection and induced fit mechanisms in the binding of PD-1 and PD-L1. , 2017, Molecular bioSystems.

[13]  A. Mackensen,et al.  Interaction of PD-L1 on tumor cells with PD-1 on tumor-specific T cells as a mechanism of immune evasion: implications for tumor immunotherapy , 2005, Cancer Immunology, Immunotherapy.

[14]  J. Allison.  Immune Checkpoint Blockade in Cancer Therapy , 2008 .

[15]  Shoshana J Wodak,et al.  Prediction of protein-protein interactions: the CAPRI experiment, its evaluation and implications. , 2004, Current opinion in structural biology.

[16]  Alexandre M J J Bonvin,et al.  Strengths and weaknesses of data‐driven docking in critical assessment of prediction of interactions , 2010, Proteins.

[17]  V. S. Rao,et al.  Protein-Protein Interaction Detection: Methods and Analysis , 2014, International journal of proteomics.

[18]  Gerhard Klebe,et al.  Fconv: Format Conversion, Manipulation and Feature Computation of Molecular Data , 2011, Bioinform..

[19]  B. Honig,et al.  Structure-based prediction of protein-protein interactions on a genome-wide scale , 2012, Nature.

[20]  M. Sternberg,et al.  The relationship between the flexibility of proteins and their conformational states on forming protein-protein complexes with an application to protein-protein docking. , 2005, Journal of molecular biology.

[21]  C. Drake,et al.  Immune checkpoint blockade: a common denominator approach to cancer therapy. , 2015, Cancer cell.

[22]  Holger Gohlke,et al.  The Amber biomolecular simulation programs , 2005, J. Comput. Chem..

[23]  A. Eggermont,et al.  Drug of the year: programmed death-1 receptor/programmed death-1 ligand-1 receptor monoclonal antibodies. , 2013, European journal of cancer.

[24]  Marawan Ahmed,et al.  Human PD-1 binds differently to its human ligands: a comprehensive modeling study. , 2015, Journal of molecular graphics & modelling.

[25]  Holger Gohlke,et al.  MMPBSA.py: An Efficient Program for End-State Free Energy Calculations. , 2012, Journal of chemical theory and computation.

[26]  C. Dominguez,et al.  HADDOCK: a protein-protein docking approach based on biochemical or biophysical information. , 2003, Journal of the American Chemical Society.

[27]  Joël Janin,et al.  Protein-protein docking tested in blind predictions: the CAPRI experiment. , 2010, Molecular bioSystems.

[28]  John E. Ladbury,et al.  Structure and Interactions of the Human Programmed Cell Death 1 Receptor* , 2013, The Journal of Biological Chemistry.

[29]  Laxmikant V. Kalé,et al.  Scalable molecular dynamics with NAMD , 2005, J. Comput. Chem..

[30]  Jürgen Bajorath,et al.  Molecular Modeling and Functional Mapping of B7-H1 and B7-DC Uncouple Costimulatory Function from PD-1 Interaction , 2003, The Journal of experimental medicine.

[31]  Arnaud Droit,et al.  Experimental and bioinformatic approaches for interrogating protein-protein interactions to determine protein function. , 2005, Journal of molecular endocrinology.

[32]  Lauren Wickstrom,et al.  Evaluating the performance of the ff99SB force field based on NMR scalar coupling data. , 2009, Biophysical journal.

[33]  Oliviero Carugo,et al.  Computational approaches to protein-protein interaction , 2004, Journal of Structural and Functional Genomics.

[34]  J. Janin.  Assessing predictions of protein–protein interaction: The CAPRI experiment , 2005, Protein science : a publication of the Protein Society.