Protein–Protein Docking with Large-Scale Backbone Flexibility Using Coarse-Grained Monte-Carlo Simulations
Abstract
:1. Introduction
2. Results
3. Discussion
4. Methods
4.1. Docking Simulation Protocol
- Preparing input structures of a protein-ligand and a protein-receptor. The protocol requires the input of two protein structures (single- or multi-chain) in the PDB format. One of them has to be indicated as a ligand and the second as a receptor. The ligand undergoes large conformational fluctuations, translations, and rotations around the receptor within the proposed protocol. The “ligand” should be a smaller protein because the computational cost of searching its conformational space rapidly grows with the chain length. That is because the motion of the entire structure (including fold relaxation, rotation, and translation of the entire molecule) is simulated by a random sequence of local moves. The accuracy of such sampling is acceptable for not too-large proteins. On the other hand, treating the “ligand” as a fully flexible object allows approximate studies of entire docking trajectories. In some cases, it would be perhaps worth treating a larger protein (but not too large) as a flexible “ligand”, although this was out of range of the present studies.
- Generating starting structures. Starting conformations are built using C-alpha coordinates only (in the CABS model C-alpha traces define the position of other united pseudo-atoms, see details [29]). The algorithm places the protein-ligand center at 20 random positions around the protein receptor at the approximate distance of 20 Å from the protein receptor’s surface. Next, these protein-ligand systems are used as starting conformations for the 20 replicas in the REMC CABS sampling scheme (each replica starts from a different ligand-receptor arrangement).
- Docking simulations using CABS coarse-grained model and REMC dynamics. During simulations, the protein receptor structure is kept close to the starting structure using distance restraints. Distance restraints are generated using the input coordinates of the C-alpha atoms. Two residues are automatically restrained if two conditions are met. First, their separation along the sequence has to be at least five residues. Second, the distance between their C-alpha atoms must be within the range of 5–15 Å. During simulations, the receptor restraints imply small-scale fluctuations of the protein receptor backbone in the range of 1 Å and, accordingly, more significant fluctuations of the side-chain atoms. A similar restraints scheme is applied to the protein-ligand but with tenfold weaker weights. During simulations, the ligand moves freely within the vicinity of the receptor and internal restraint allows for large-scale fluctuations of its structure. Usually, the ligand fluctuations are within the range of 2 and 12 Å to the input structure although folding-unfolding events are possible at highest temperatures. The docking simulation is conducted using CABS REMC pseudo-dynamics with simulated annealing. In this work, 20 replicas and 20 annealing steps have been used. All the REMC scheme parameters have been adjusted to allow for large-scale conformational transitions, rotations, and translations of the protein-ligand in a reasonable computational time. The modeling protocol collects trajectories from all 20 replicas. The protocol saves only a small fraction (2%) of the generated models for further analysis i.e., 500 models from each replica, thus 10,000 models in total.
- Reconstructing to CABS coarse-grained representation. The set of 10,000 models in C-alpha traces are reconstructed to complete CABS model representation using CABS algorithm [29]. In CABS, positions of C-beta and Side-Chain united atoms are defined by the positions of the three consecutive C-alpha atoms and the amino acid identity (the most probable positions from the PDB database are used).
- Clustering of contact maps. First, for all of the 10,000 models the contact maps between the receptor and the ligand proteins are calculated. Two residues are considered to form a contact if their Side Chain pseudoatoms are at most 6 Å apart (for Alanines the C-beta atoms are considered as the Side Chain; for Glycines—it’s the C-alpha atoms). Next, the algorithm sorts the models according to the number of the receptor-ligand contacts, and the set of top 1000 is kept for further processing. This way the transient and weakly bound complexes are removed from the solutions pool. In the next step, the 1000 contact maps are clustered together to identify the most frequently occurring contact patterns. The complete link hierarchical clustering was used with the Jaccard index as the distance metric between contact maps. Finally, the identified clusters are ranked according to their density, defined as the number of the cluster members divided by the average metric between them.
4.2. Results Analysis and Quality Metrics
4.3. Dataset
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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X-ray Data (Number of Residues) | Ligand Flexibility | Results—Best from All Models | Results—Best from 10 Top-Scored Models | |||||||
---|---|---|---|---|---|---|---|---|---|---|
Receptor * | Ligand * | Complex | RMSD ** | Average LoRMSD | iRMSD | LRMSD | fNAT | iRMSD | LRMSD | fNAT |
Low-flexibility cases | ||||||||||
5CHA (238) | 2OVO (53) | 1CHO | 0.62 | 4.84 | 2.65 | 6.95 | 0.48 | 2.96 | 10.93 | 0.18 |
2PKA (232) | 6PTI (56) | 2KAI | 0.91 | 4.64 | 3.32 | 11.34 | 0.19 | 4.75 | 15.76 | 0.12 |
1CHG (245) | 1HPT (56) | 1CGI | 1.53 | 5.24 | 2.76 | 4.13 | 0.37 | 6.18 | 14.15 | 0.09 |
2PTN (223) | 6PTI (58) | 2PTC | 0.31 | 5.23 | 2.97 | 11.86 | 0.29 | 4.39 | 15.93 | 0.15 |
1SUP (275) | 2CI2 (64) | 2SNI | 0.37 | 3.89 | 1.09 | 3.86 | 0.69 | 2.81 | 9.09 | 0.46 |
2ACE (532) | 1FSC (61) | 1FSS | 0.76 | 4.48 | 3.41 | 7.20 | 0.25 | 15.03 | 32.56 | 0.03 |
1MAA (533) | 1FSC (61) | 1MAH | 0.60 | 4.58 | 2.49 | 3.89 | 0.45 | 11.25 | 24.43 | 0.06 |
1A2P (108) | 1A19 (89) | 1BRS | 0.47 | 3.33 | 1.94 | 4.19 | 0.64 | 4.01 | 8.74 | 0.14 |
1CCP (294) | 1YCC (103) | 2PCC | 0.39 | 4.18 | 3.13 | 10.19 | 0.25 | 11.89 | 26.68 | 0.08 |
1SUP (275) | 3SSI (107) | 2SIC | 0.39 | 4.01 | 4.03 | 18.96 | 0.23 | 4.77 | 19.40 | 0.12 |
1VFA (223) | 1LZA (129) | 1VFB | 0.59 | 3.72 | 4.61 | 15.07 | 0.11 | 17.45 | 37.15 | 0.00 |
1MLB (432) | 1LZA (129) | 1MLC | 0.85 | 3.74 | 2.82 | 10.47 | 0.36 | 8.04 | 33.09 | 0.04 |
Medium-flexibility cases | ||||||||||
1CHG (226) | 1HPT (56) | 1CGI | 2.02 | 5.80 | 2.46 | 3.21 | 0.44 | 5.86 | 10.72 | 0.12 |
5C2B (241) | 4ZAI (80) | 5CBA | 1.49 | 4.51 | 2.48 | 7.64 | 0.42 | 9.34 | 16.01 | 0.10 |
5P2 (166) | 1LXD (87) | 1LFD | 1.79 | 4.12 | 2.87 | 6.76 | 0.27 | 12.47 | 24.24 | 0.00 |
1R6C (142) | 2W9R (97) | 1R6Q | 1.67 | 9.27 | 7.95 | 11.97 | 0.14 | 13.71 | 35.71 | 0.00 |
1JXQ (242) | 2OPY (106) | 1NW9 | 1.97 | 4.09 | 7.05 | 8.69 | 0.23 | 9.33 | 17.55 | 0.00 |
1IAS (330) | 1D6O (107) | 1B6C | 1.96 | 4.65 | 4.72 | 10.74 | 0.14 | 12.24 | 23.99 | 0.00 |
5E56 (116) | 5E03 (113) | 5E5M | 1.56 | 4.16 | 3.83 | 9.09 | 0.23 | 10.96 | 20.00 | 0.00 |
2HRA (180) | 2HQT (115) | 2HRK | 2.03 | 7.27 | 3.55 | 10.17 | 0.26 | 10.81 | 32.54 | 0.00 |
4BLM (256) | 4M3J (116) | 4M3K | 1.77 | 4.41 | 4.96 | 7.41 | 0.10 | 13.75 | 27.11 | 0.03 |
1E78 (578) | 5VNV (120) | 5VNW | 1.49 | 3.81 | 5.93 | 22.23 | 0.10 | 23.89 | 70.83 | 0.00 |
3BX8 (167) | 3OSK (121) | 3BX7 | 1.63 | 4.63 | 4.94 | 17.46 | 0.28 | 6.22 | 20.32 | 0.12 |
6ETL (124) | 4POY (121) | 4POU | 1.83 | 4.01 | 2.91 | 10.16 | 0.50 | 6.55 | 19.65 | 0.25 |
4FUD (246) | 5HDO (126) | 5HGG | 0.84 | 4.22 | 3.59 | 12.52 | 0.19 | 13.00 | 29.3 | 0.00 |
3TGR (346) | 3R0M (127) | 3RJQ | 0.79 | 4.00 | 5.32 | 16.98 | 0.13 | 12.77 | 33.94 | 0.00 |
6EY5 (585) | 5FWO (129) | 6EY6 | 1.90 | 3.86 | 3.83 | 6.03 | 0.14 | 12.89 | 27.61 | 0.00 |
1SZ7 (159) | 2BJN (141) | 2CFH | 1.55 | 5.13 | 1.98 | 4.01 | 0.71 | 2.82 | 5.50 | 0.63 |
3V6F (437) | 3KXS (142) | 3V6Z | 1.83 | 7.11 | 6.12 | 16.68 | 0.15 | 6.66 | 20.06 | 0.06 |
3CPI (437) | 1G16 (156) | 3CPH | 2.12 | 4.34 | 4.87 | 15.64 | 0.09 | 15.02 | 27.88 | 0.00 |
1QJB (460) | 1KUY (166) | 1IB1 | 2.09 | 4.22 | 6.56 | 14.83 | 0.13 | 16.10 | 46.26 | 0.00 |
1IAM (185) | 1MQ9 (173) | 1MQ8 | 1.76 | 4.22 | 4.93 | 14.99 | 0.21 | 26.17 | 70.50 | 0.00 |
3HI5 (430) | 1MJN (179) | 3HI6 | 1.65 | 3.77 | 5.79 | 23.30 | 0.21 | 19.38 | 49.77 | 0.00 |
2G75 (429) | 2GHV (183) | 2DD8 | 2.19 | 5.37 | 5.73 | 13.78 | 0.09 | 17.20 | 34.33 | 0.00 |
1A12 (401) | 1QG4 (202) | 1I2M | 2.12 | 4.19 | 2.84 | 6.43 | 0.51 | 3.58 | 6.97 | 0.47 |
1N0V (825) | 1XK9 (204) | 1ZM4 | 2.11 | 3.54 | 8.82 | 28.17 | 0.04 | 11.05 | 48.14 | 0.00 |
4EBQ (429) | 4E9O (230) | 4ETQ | 0.47 | 3.72 | 7.12 | 14.74 | 0.20 | 8.68 | 19.61 | 0.07 |
1S3X (380) | 1XQR (259) | 1XQS | 1.77 | 5.44 | 5.63 | 26.14 | 0.11 | 15.88 | 30.51 | 0.00 |
3HEC (329) | 3FYK (282) | 2OZA | 1.89 | 4.29 | 4.35 | 9.32 | 0.33 | 11.24 | 18.8 | 0.03 |
6A0X (437) | 2FK0 (322) | 6A0Z | 1.28 | 5.75 | 5.75 | 25.59 | 0.16 | 11.43 | 31.39 | 0.00 |
Highly flexible cases | ||||||||||
1CL0 (316) | 2TIR (108) | 1F6M | 4.9 | 3.83 | 7.02 | 11.34 | 0.10 | 11.92 | 18.06 | 0.00 |
1 × 9Y (346) | 1NYC (110) | 1PXV | 2.63 | 4.86 | 5.74 | 14.10 | 0.07 | 7.46 | 16.31 | 0.02 |
1JZO (431) | 1JPE (116) | 1JZD | 2.71 | 4.65 | 4.98 | 8.13 | 0.28 | 13.38 | 34.05 | 0.00 |
5D7S (423) | 2GMF (121) | 5C7X | 2.26 | 4.17 | 4.12 | 13.61 | 0.34 | 4.69 | 16.74 | 0.20 |
1FCH (302) | 1C44 (123) | 2C0L | 2.62 | 5.51 | 5.02 | 5.54 | 0.21 | 10.24 | 24.74 | 0.00 |
1YWH (268) | 2I9A (123) | 2I9B | 3.79 | 7.14 | 5.79 | 17.59 | 0.14 | 6.92 | 33.23 | 0.05 |
3L88 (550) | 1CKL (126) | 3L89 | 2.51 | 9.86 | 4.83 | 10.90 | 0.17 | 17.84 | 31.87 | 0.00 |
1ZM8 (239) | 1J57 (143) | 2O3B | 3.13 | 6.20 | 4.76 | 16.43 | 0.18 | 15.34 | 31.95 | 0.00 |
1G0Y (310) | 1ILR (145) | 1IRA | 8.38 | 4.07 | 12.97 | 22.24 | 0.08 | 15.86 | 25.46 | 0.05 |
1QUP (219) | 2JCW (153) | 1JK9 | 2.51 | 9.40 | 8.07 | 13.85 | 0.10 | 17.41 | 30.74 | 0.00 |
1SYQ (259) | 3MYI (163) | 1RKE | 4.25 | 4.15 | 5.26 | 6.43 | 0.38 | 16.11 | 34.67 | 0.00 |
2II0 (463) | 1CTQ (166) | 1BKD | 2.86 | 4.51 | 4.80 | 7.33 | 0.14 | 19.96 | 39.32 | 0.00 |
1ERN (416) | 1BUY (166) | 1EER | 2.44 | 5.22 | 12.97 | 13.18 | 0.02 | 17.12 | 30.73 | 0.00 |
3AVE (419) | 1FNL (173) | 1E4K | 2.60 | 5.32 | 3.44 | 10.07 | 0.43 | 7.59 | 24.33 | 0.13 |
1R8M (195) | 1HUR (180) | 1R8S | 3.73 | 5.50 | 6.67 | 13.41 | 0.09 | 15.15 | 25.10 | 0.00 |
1QFK (348) | 1TFH (182) | 1FAK | 6.18 | 5.64 | 8.97 | 15.57 | 0.16 | 15.59 | 34.46 | 0.00 |
1F59 (440) | 1QG4 (202) | 1IBR | 2.54 | 5.01 | 6.65 | 14.36 | 0.14 | 16.41 | 33.07 | 0.00 |
4DVB (427) | 4DVA (246) | 4DW2 | 2.27 | 3.85 | 6.61 | 21.91 | 0.14 | 9.94 | 29.27 | 0.00 |
1NG1 (294) | 2IYL (271) | 2J7P | 2.67 | 4.51 | 8.87 | 18.77 | 0.11 | 18.46 | 48.05 | 0.00 |
1UX5 (411) | 2FXU (360) | 1Y64 | 4.69 | 4.15 | 6.42 | 13.50 | 0.27 | 15.50 | 36.42 | 0.00 |
1D0N (729) | 1IJJ (371) | 1H1V | 6.62 | 3.44 | 7.92 | 31.14 | 0.36 | 29.12 | 65.07 | 0.03 |
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Kurcinski, M.; Kmiecik, S.; Zalewski, M.; Kolinski, A. Protein–Protein Docking with Large-Scale Backbone Flexibility Using Coarse-Grained Monte-Carlo Simulations. Int. J. Mol. Sci. 2021, 22, 7341. https://doi.org/10.3390/ijms22147341
Kurcinski M, Kmiecik S, Zalewski M, Kolinski A. Protein–Protein Docking with Large-Scale Backbone Flexibility Using Coarse-Grained Monte-Carlo Simulations. International Journal of Molecular Sciences. 2021; 22(14):7341. https://doi.org/10.3390/ijms22147341
Chicago/Turabian StyleKurcinski, Mateusz, Sebastian Kmiecik, Mateusz Zalewski, and Andrzej Kolinski. 2021. "Protein–Protein Docking with Large-Scale Backbone Flexibility Using Coarse-Grained Monte-Carlo Simulations" International Journal of Molecular Sciences 22, no. 14: 7341. https://doi.org/10.3390/ijms22147341