Design of protein-ligand binding based on the molecular-mechanics energy model - PubMed (original) (raw)
Design of protein-ligand binding based on the molecular-mechanics energy model
F Edward Boas et al. J Mol Biol. 2008.
Abstract
While the molecular-mechanics field has standardized on a few potential energy functions, computational protein design efforts are based on potentials that are unique to individual laboratories. Here we show that a standard molecular-mechanics potential energy function without any modifications can be used to engineer protein-ligand binding. A molecular-mechanics potential is used to reconstruct the coordinates of various binding sites with an average root-mean-square error of 0.61 A and to reproduce known ligand-induced side-chain conformational shifts. Within a series of 34 mutants, the calculation can always distinguish between weak (K(d)>1 mM) and tight (K(d)<10 microM) binding sequences. Starting from partial coordinates of the ribose-binding protein lacking the ligand and the 10 primary contact residues, the molecular-mechanics potential is used to redesign a ribose-binding site. Out of a search space of 2 x 10(12) sequences, the calculation selects a point mutant of the native protein as the top solution (experimental K(d)=17 microM) and the native protein as the second best solution (experimental K(d)=210 nM). The quality of the predictions depends on the accuracy of the generalized Born electrostatics model, treatment of protonation equilibria, high-resolution rotamer sampling, a final local energy minimization step, and explicit modeling of the bound, unbound, and unfolded states. The application of unmodified molecular-mechanics potentials to protein design links two fields in a mutually beneficial way. Design provides a new avenue for testing molecular-mechanics energy functions, and future improvements in these energy functions will presumably lead to more accurate design results.
Figures
Figure 1
Simplified schematic of the protein design algorithm. (a) Setting up a design calculation. The design calculation is based on a scaffold protein (gray) with a known crystal structure, and a set of design positions (red). Possible ligand poses (green) and side chain conformations (blue) for each amino acid at each position are constructed. The right panel shows multiple side chain rotamers modeled at one design position, and two alternative ligand poses. Interaction energies between the possible ligand poses and the possible side chain conformations are precomputed. (b) Running a design calculation. The design procedure involves separate sequence optimization (to find sequences that bind ribose) and structural optimization (to determine the binding constant and stability of each sequence). In the RBP-ribose redesign, we search a space of 2×1012 sequences and an average of 5×1028 conformations per sequence.
Figure 2
Higher rotamer resolution improves structural predictions for the RBP binding site (PDB code: 2DRI). Δ Energy is the difference in potential energy between the calculated structure and the crystal structure, after both have been subjected to local energy minimization. RMS error is the root-mean-square deviation between the calculated and crystallographic coordinates of the repacked atoms, comprising the ligand and ten active site side chains. The phenylalanine rotamers from each rotamer library are shown to illustrate the sampling resolution. The lowest resolution rotamer library shown is the Richardson penultimate rotamer library with protonation states added for His, Asp, and Glu. The other rotamer libraries were derived by clustering side chain conformations in high resolution crystal structures from the Protein Data Bank (see Supporting Information).
Figure 3
Prediction of binding site coordinates. Starting from crystal structures stripped of the ligand and the contacting residues, the active site was reconstructed by finding the lowest energy arrangement of the ligand and side chains. For ABP-arabinose (PBD code: 6ABP), the coordinates of the arabinose and 15 contacting residues (10, 14, 16, 17, 64, 89, 90, 108, 145, 147, 151, 204, 205, 232, 259) were predicted using 6028 rotamers per position and 4111 ligand poses. For RBP-ribose (PDB code: 2DRI), the coordinates of ribose and 10 contacting residues (13, 15, 16, 89, 90, 141, 164, 190, 215, 235) were predicted using 5449 rotamers per position, and 4639 ligand poses.
Figure 4
Prediction of binding site coordinates for bevacizumab-VEGF (1BJ1), unbound VEGF (2VPF), and unbound RBP (1URP). For bevacizumab-VEGF, the following 23 residues were repacked, using 6028 rotamers per position: V17, V21, W48, W79, W81, W82, W83, W91, W93, H28, H30, H31, H32, H54, H55, H99, H101, H102, H103, H105, H106, H107, H108. V and W are VEGF chains, H and L are antibody heavy and light chains. For unbound VEGF and RBP, the same set of residues were predicted as the bound structure.
Figure 5
Prediction of side chain conformational shifts in RBP upon binding ribose, or VEGF upon binding bevacizumab. The five largest experimentally observed conformational shifts are shown for each protein. The residues were superimposed by aligning the backbone amide nitrogen, alpha carbon, and carbonyl carbon. * denotes correct predictions, where the unbound/bound predictions are closest to the unbound/bound crystallographic coordinates, respectively.
Figure 6
Predicting dissociation energies. (a) Calculated stability and dissociation energy distinguish the native sequence (×) from 1000 scrambled sequences (♦) for ABP and RBP. Sequences predicted to be more then 10 kcal/mol destabilized relative to the native are shown in gray. (b) Predicting relative dissociation energies of mutants. The graph shows data on mutants of ABP binding to arabinose. Experimental data are from reference and from measurements reported in Supporting Table 6. An experimental dissociation energy of zero means that there was no detectable binding. Calculations were performed using 6ABP as the scaffold structure for both the bound and unbound states, with 6028 rotamers per position. Coordinates of the fifteen primary ligand contacts and of residues 20 and 235 were optimized. The circled points are predicted to be destabilized by more than 10 kcal/mol relative to the native.
Figure 7
Redesigning the ribose binding site in RBP. Positions identical to the native are highlighted in yellow. The figure shows the best sequence as a function of the number of sequences considered, using either the mean field dissociation energy as the criterion (blue trajectories) or alternatively the dissociation energy calculated using minimized structures (red trajectories). All sequences with a mean field dissociation energy greater than 30 kcal/mol (corresponding to −7.5 kcal/mol relative to the native sequence, dashed line) were locally energy minimized to generate the red trajectory. Sequence 8871 is the top sequence when ranked by mean field dissociation energy (corresponding to Table 1b), and sequence 8888 is the top sequence when ranked by minimized dissociation energy (corresponding to Table 1d). The native sequence was found out of a possible 2×1012 sequences after 8964 sequence evaluations. Dissociation and unfolding energies are reported in kcal/mol, relative to the native sequence. The number of protein-ligand hydrogen bonds was determined using bndlst. Shape complementarity (which ranges from 0 for perfectly non-complementary surfaces to 1 for perfectly complementary surfaces) was calculated using sc. Backbone coordinates for the bound state are from 2DRI, and backbone coordinates for the unbound state are from 1URP.
Similar articles
- Ligand-induced conformational changes in a thermophilic ribose-binding protein.
Cuneo MJ, Beese LS, Hellinga HW. Cuneo MJ, et al. BMC Struct Biol. 2008 Nov 19;8:50. doi: 10.1186/1472-6807-8-50. BMC Struct Biol. 2008. PMID: 19019243 Free PMC article. - Stability of Ligand-induced Protein Conformation Influences Affinity in Maltose-binding Protein.
van den Noort M, de Boer M, Poolman B. van den Noort M, et al. J Mol Biol. 2021 Jul 23;433(15):167036. doi: 10.1016/j.jmb.2021.167036. Epub 2021 May 4. J Mol Biol. 2021. PMID: 33957147 - Analysis of ligand binding to a ribose biosensor using site-directed mutagenesis and fluorescence spectroscopy.
Vercillo NC, Herald KJ, Fox JM, Der BS, Dattelbaum JD. Vercillo NC, et al. Protein Sci. 2007 Mar;16(3):362-8. doi: 10.1110/ps.062595707. Epub 2007 Jan 22. Protein Sci. 2007. PMID: 17242374 Free PMC article. - ABC transporter architecture and mechanism: implications from the crystal structures of BtuCD and BtuF.
Locher KP, Borths E. Locher KP, et al. FEBS Lett. 2004 Apr 30;564(3):264-8. doi: 10.1016/S0014-5793(04)00289-3. FEBS Lett. 2004. PMID: 15111107 Review. - Periplasmic binding proteins: a versatile superfamily for protein engineering.
Dwyer MA, Hellinga HW. Dwyer MA, et al. Curr Opin Struct Biol. 2004 Aug;14(4):495-504. doi: 10.1016/j.sbi.2004.07.004. Curr Opin Struct Biol. 2004. PMID: 15313245 Review.
Cited by
- Ribose-Binding Protein Mutants With Improved Interaction Towards the Non-natural Ligand 1,3-Cyclohexanediol.
Tavares D, van der Meer JR. Tavares D, et al. Front Bioeng Biotechnol. 2021 Jul 23;9:705534. doi: 10.3389/fbioe.2021.705534. eCollection 2021. Front Bioeng Biotechnol. 2021. PMID: 34368100 Free PMC article. - Toward the Accuracy and Speed of Protein Side-Chain Packing: A Systematic Study on Rotamer Libraries.
Huang X, Pearce R, Zhang Y. Huang X, et al. J Chem Inf Model. 2020 Jan 27;60(1):410-420. doi: 10.1021/acs.jcim.9b00812. Epub 2019 Dec 31. J Chem Inf Model. 2020. PMID: 31851497 Free PMC article. - Computational redesign of the Escherichia coli ribose-binding protein ligand binding pocket for 1,3-cyclohexanediol and cyclohexanol.
Tavares D, Reimer A, Roy S, Joublin A, Sentchilo V, van der Meer JR. Tavares D, et al. Sci Rep. 2019 Nov 15;9(1):16940. doi: 10.1038/s41598-019-53507-5. Sci Rep. 2019. PMID: 31729460 Free PMC article. - Rosetta:MSF: a modular framework for multi-state computational protein design.
Löffler P, Schmitz S, Hupfeld E, Sterner R, Merkl R. Löffler P, et al. PLoS Comput Biol. 2017 Jun 12;13(6):e1005600. doi: 10.1371/journal.pcbi.1005600. eCollection 2017 Jun. PLoS Comput Biol. 2017. PMID: 28604768 Free PMC article. - Small Molecule-Induced Domain Swapping as a Mechanism for Controlling Protein Function and Assembly.
Karchin JM, Ha JH, Namitz KE, Cosgrove MS, Loh SN. Karchin JM, et al. Sci Rep. 2017 Mar 13;7:44388. doi: 10.1038/srep44388. Sci Rep. 2017. PMID: 28287617 Free PMC article.
References
- Snow CD, Sorin EJ, Rhee YM, Pande VS. How well can simulation predict protein folding kinetics and thermodynamics? Annu. Rev. Biophys. Biomol. Struct. 2005;34:43–69. - PubMed
- Ren PY, Ponder JW. Consistent treatment of inter- and intramolecular polarization in molecular mechanics calculations. J. Comput. Chem. 2002;23:1497–1506. - PubMed
- Friesner RA. Modeling polarization in proteins and protein-ligand complexes: Methods and preliminary results. Adv. Protein Chem. 2006;72:79–104. - PubMed
- Maple JR, Cao YX, Damm WG, Halgren TA, Kaminski GA, Zhang LY, Friesner RA. A polarizable force field and continuum solvation methodology for modeling of protein-ligand interactions. Journal of Chemical Theory and Computation. 2005;1:694–715. - PubMed
- Mackerell AD., Jr Empirical force fields for biological macromolecules: overview and issues. J. Comput. Chem. 2004;25:1584–1604. - PubMed
Publication types
MeSH terms
Substances
Grants and funding
- DP1 OD000429-03/OD/NIH HHS/United States
- T32 GM007365/GM/NIGMS NIH HHS/United States
- 1-DP1-0D000429/DP/NCCDPHP CDC HHS/United States
- R01 GM068126/GM/NIGMS NIH HHS/United States
- DP1 OD000429-02/OD/NIH HHS/United States
- DP1 OD000429/OD/NIH HHS/United States
- DP1 OD000429-01/OD/NIH HHS/United States
- GM068126-01/GM/NIGMS NIH HHS/United States
- 5T32 GM07365-28/GM/NIGMS NIH HHS/United States
LinkOut - more resources
Full Text Sources