Lead Finder

Lead Finder software is a computational chemistry application for modeling protein-ligand interactions. Lead Finder can be used in molecular docking studies and for the quantitative evaluation of ligand binding and biological activity. It is free for individual non-commercial academic users.

About

Lead-Finder[1] software is an integrated solution for simulating structure and binding affinity of protein-ligand complexes. The software combines automatic processing of protein structures, extra precision protein-ligand docking and calculation of free energy of ligand binding. Original docking algorithm provides a fast rate of calculations, which can be easily adjusted from more rapid (for virtual screening applications) to slightly more slow and robust, while unique scoring function implemented in Lead-Finder provides unsurpassed accuracy of calculations. Lead-Finder is intended to meet the requirements of computational and medicinal chemists involved in drug discovery, pharmacologists and toxicologists involved in the evaluation of ADMET properties in silico, and biochemists and enzymologists working on modeling protein-ligand interactions, enzyme specificity and rational enzyme design. Efficiency of ligand docking and binding energy estimations achieved by Lead-Finder are due to docking algorithm and extra precision representation of protein-ligand interactions.

Docking algorithm

From mathematical point of view ligand docking represents a search for global minimum on the multidimensional surface describing the free energy of protein-ligand binding. With ligands having up to 15-20 degrees of freedom (freely rotatable bonds) and complex nature of energy surface, global optimum search represents generally unsolved scientific task. To tackle this computationally challenging problem Lead-Finder applies unique approach combining genetic algorithm search, local optimization procedures, and a smart exploitation of the knowledge generated during the search run. Rational combination of different optimization strategies makes Lead Finder efficient in terms of coarse sampling of ligand's phase space and refinement of promising solutions.

Scoring function

Extra precise representation of protein-ligand interactions implemented in Lead-Finder scoring function is the second (in addition to docking algorithm) component of successful ligand docking. Lead-Finder scoring function is based on a semi-empiric molecular mechanical functional, which explicitly accounts for different types of molecular interactions. Individual energy contributions are scaled with empiric coefficients to fit particular purposes: accurate binding energy predictions, correct energy-ranking of docked ligand poses, correct rank-ordering of active and inactive compounds during virtual screening experiments. For these reasons three distinct types of scoring functions based on the same set of energy contributions but different sets of energy-scaling coefficients are used by Lead-Finder.

Docking success rate

Docking success rate was benchmarked as a percentage of correctly docked ligands (for which top-scored pose was within 2 Å RMSD from the reference ligand coordinates) for a set of protein-ligand complexes extracted from PDB. A set of 407 protein-ligand complexes was used for current docking success rate measurements. This set of complexes was combined from test sets used in original benchmarking studies of such docking programs as: FlexX,[2] Glide SP,[3] Glide XP,[4] Gold,[5][6][7] LigandFit,[8] MolDock,[9] Surflex.[10]

Accuracy of binding energy estimations

The ability of Lead-Finder to estimate free energy of protein-ligand binding was benchmarked against the set of 330 diverse protein-ligand complexes, which is currently the most extensive benchmarking study of such kind. Lead-Finder demonstrated unique precision of binding energy prediction (RMSD = 1.5 kcal/mol) combined with high speed of calculations (less than one second per compound on average).

gollark: If a rule is going to be enforced randomly, it shouldn't be there.
gollark: Then delete it.
gollark: <@!356107472269869058> Databases good, lack of databases less good.
gollark: Oh yes, rule 9.
gollark: don't the rules say to use english because the staff speak it?

References
  1. Stroganov O (2008). "Lead Finder: An Approach To Improve Accuracy of Protein−Ligand Docking, Binding Energy Estimation, and Virtual Screening". J. Chem. Inf. Model. 48 (12): 2371–2385. doi:10.1021/ci800166p.
  2. M. Rarey; B. Kramer; T. Lengauer (1997). "Multiple automatic base selection: Protein-ligand docking based on incremental construction without manual intervention". J Comp Aid Mol Des. 11 (4): 369–384. doi:10.1023/A:1007913026166.
  3. R. A. Friesner; R. B. Murphy; M. P. Repasky; L. L. Frye; J. R. Greenwood; T. A. Halgren; P. C. Sanschagrin; D. T. Mainz (2004). "Glide: A New Approach for Rapid, Accurate Docking and Scoring. 1. Method and Assessment of Docking Accuracy". Journal of Medicinal Chemistry. 47 (7): 1739–1749. doi:10.1021/jm0306430. PMID 15027865.
  4. R. A. Friesner; J. L. Banks; R. B. Murphy; T. A. Halgren; J. J. Klicic; D. T. Mainz; M. P. Repasky; E. H. Knoll; M. Shelley; J. K. Perry; D. E. Shaw; P. Francis; P. S. Shenkin (2006). "Glide: extra Precision Glide: Docking and Scoring incorporating a Model of Hydrophobic Enclosure for Protein-Ligand Complexes". Journal of Medicinal Chemistry. 49 (21): 6177–6196. CiteSeerX 10.1.1.619.3600. doi:10.1021/jm051256o. PMID 17034125.
  5. G. Jones; P. Willett; R. C. Glen; A. R. Leach; R. Taylor (1997). "Development and Validation of a Genetic Algorithm for Flexible Docking". J Mol Biol. 267 (3): 727–748. CiteSeerX 10.1.1.130.3377. doi:10.1006/jmbi.1996.0897. PMID 9126849.
  6. M. J. Hartshorn; M. L. Verdonk; G. Chessari; S. C. Brewerton; W.T..M. Mooij; P. N. Mortenson; C. W. Murray (2007). "Diverse, High-Quality Test Set for the Validation of Protein-Ligand Docking Performance". Journal of Medicinal Chemistry. 50 (4): 726–741. doi:10.1021/jm061277y. PMID 17300160.
  7. J.W.M. Nissink; C. Murray; M. Hartshorn; M. L. Verdonk; J. C. Cole; R. Taylor (2002). "A New Test Set for Validating Predictions of Protein-Ligand Interaction". Proteins: Structure, Function, and Genetics. 49 (4): 457–471. doi:10.1002/prot.10232. PMID 12402356.
  8. C. M. Venkatachalam; X. Jiang; T. Oldfield; M. Waldman (2003). "LigandFit: a novel method for the shape-directed rapid docking of ligands to protein active sites". J Mol Graph Model. 21 (4): 289–307. doi:10.1016/s1093-3263(02)00164-x.
  9. R. Thomsen; M. H. Christensen (2006). "MolDock: A new technique for high-accuracy molecular docking". Journal of Medicinal Chemistry. 49 (11): 3315–3321. CiteSeerX 10.1.1.116.2126. doi:10.1021/jm051197e. PMID 16722650.
  10. A. N. Jain (2003). "Surflex: Fully Automatic Flexible Molecular Docking Using a Molecular Similarity-Based Search Engine". Journal of Medicinal Chemistry. 46 (4): 499–511. doi:10.1021/jm020406h. PMID 12570372.
This article is issued from Wikipedia. The text is licensed under Creative Commons - Attribution - Sharealike. Additional terms may apply for the media files.