Prediction of HIV‐1 Protease/Inhibitor Affinity using RosettaLigand

Predicting HIV‐1 protease/inhibitor binding affinity as the difference between the free energy of the inhibitor bound and unbound state remains difficult as the unbound state exists as an ensemble of conformations with various degrees of flap opening. We improve computational prediction of protease/inhibitor affinity by invoking the hypothesis that the free energy of the unbound state while difficult to predict is less sensitive to mutation. Thereby the HIV‐1 protease/inhibitor binding affinity can be approximated with the free energy of the bound state alone. Bound state free energy can be predicted from comparative models of HIV‐1 protease mutant/inhibitor complexes. Absolute binding energies are predicted with R = 0.71 and SE = 5.91 kJ/mol. Changes in binding free energy upon mutation can be predicted with R = 0.85 and SE = 4.49 kJ/mol. Resistance mutations that lower inhibitor binding affinity can thereby be recognized early in HIV‐1 protease inhibitor development.

[1]  Zhihai Liu,et al.  Comparative Assessment of Scoring Functions on a Diverse Test Set , 2009, J. Chem. Inf. Model..

[2]  Stephen F Martin,et al.  Preorganization in biological systems: Are conformational constraints worth the energy? , 2007 .

[3]  Roland L. Dunbrack,et al.  Bayesian statistical analysis of protein side‐chain rotamer preferences , 1997, Protein science : a publication of the Protein Society.

[4]  D. Baker,et al.  A simple physical model for binding energy hot spots in protein–protein complexes , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[5]  C. Simmerling,et al.  Solution structure of HIV-1 protease flaps probed by comparison of molecular dynamics simulation ensembles and EPR experiments. , 2008, Journal of the American Chemical Society.

[6]  M. Karplus,et al.  Effective energy function for proteins in solution , 1999, Proteins.

[7]  W. M. Westler,et al.  Protein conformational dynamics in the mechanism of HIV-1 protease catalysis , 2011, Proceedings of the National Academy of Sciences.

[8]  Ram Samudrala,et al.  Rappertk: a versatile engine for discrete restraint-based conformational sampling of macromolecules , 2007, BMC Structural Biology.

[9]  Mark L. Pearson,et al.  Complete nucleotide sequence of the AIDS virus, HTLV-III , 1985, Nature.

[10]  Tommy F. Liu,et al.  HIV-1 Protease and reverse-transcriptase mutations: correlations with antiretroviral therapy in subtype B isolates and implications for drug-resistance surveillance. , 2005, The Journal of infectious diseases.

[11]  B. Tidor,et al.  Novel Method for Probing the Specificity Binding Profile of Ligands: Applications to HIV Protease , 2008, Chemical biology & drug design.

[12]  D. Baker,et al.  Improved recognition of native‐like protein structures using a combination of sequence‐dependent and sequence‐independent features of proteins , 1999, Proteins.

[13]  P. Bradley,et al.  Toward High-Resolution de Novo Structure Prediction for Small Proteins , 2005, Science.

[14]  Pedro Alexandrino Fernandes,et al.  Protein–ligand docking: Current status and future challenges , 2006, Proteins.

[15]  X Chen,et al.  BindingDB: a web-accessible molecular recognition database. , 2001, Combinatorial chemistry & high throughput screening.

[16]  G. Fanucci,et al.  Interflap distances in HIV-1 protease determined by pulsed EPR measurements. , 2007, Journal of the American Chemical Society.

[17]  K. Misura,et al.  PROTEINS: Structure, Function, and Bioinformatics 59:15–29 (2005) Progress and Challenges in High-Resolution Refinement of Protein Structure Models , 2022 .

[18]  Irene T Weber,et al.  HIV-1 protease: structure, dynamics, and inhibition. , 2007, Advances in pharmacology.

[19]  A Wlodawer,et al.  Structure of complex of synthetic HIV-1 protease with a substrate-based inhibitor at 2.3 A resolution. , 1989, Science.

[20]  Jens Meiler,et al.  ROSETTALIGAND: Protein–small molecule docking with full side‐chain flexibility , 2006, Proteins.

[21]  Viktor Hornak,et al.  HIV-1 protease flaps spontaneously open and reclose in molecular dynamics simulations. , 2006, Proceedings of the National Academy of Sciences of the United States of America.

[22]  Supot Hannongbua,et al.  Accurate prediction of protonation state as a prerequisite for reliable MM‐PB(GB)SA binding free energy calculations of HIV‐1 protease inhibitors , 2008, J. Comput. Chem..

[23]  H. Scheraga,et al.  Monte Carlo-minimization approach to the multiple-minima problem in protein folding. , 1987, Proceedings of the National Academy of Sciences of the United States of America.

[24]  A. Wlodawer,et al.  Structure-based inhibitors of HIV-1 protease. , 1993, Annual review of biochemistry.

[25]  Holger Gohlke,et al.  Change in protein flexibility upon complex formation: Analysis of Ras‐Raf using molecular dynamics and a molecular framework approach , 2004, Proteins.

[26]  Haruki Nakamura,et al.  Announcing the worldwide Protein Data Bank , 2003, Nature Structural Biology.

[27]  D. Kern,et al.  Dynamic personalities of proteins , 2007, Nature.

[28]  H. Kagamiyama,et al.  Demonstration of the importance and usefulness of manipulating non-active-site residues in protein design. , 2001, Journal of biochemistry.

[29]  Roland L. Dunbrack,et al.  Backbone-dependent rotamer library for proteins. Application to side-chain prediction. , 1993, Journal of molecular biology.

[30]  Janet E. Jones.  On the determination of molecular fields. —II. From the equation of state of a gas , 1924 .

[31]  D. Baker,et al.  An orientation-dependent hydrogen bonding potential improves prediction of specificity and structure for proteins and protein-protein complexes. , 2003, Journal of molecular biology.

[32]  A Wlodawer,et al.  Inhibitors of HIV-1 protease: a major success of structure-assisted drug design. , 1998, Annual review of biophysics and biomolecular structure.