Physics:Lead Finder

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Short description: Computational chemistry application

Lead Finder is a computational chemistry tool designed for modeling protein-ligand interactions. This application is useful for conducting molecular docking studies and quantitatively assessing ligand binding and biological activity. Lead Finder offers free access to individual users, especially those in non-commercial and academic settings.

About

The original docking algorithm integrated into Lead Finder offers rapid calculation capabilities, which can be easily tailored for swifter virtual screening applications or adjusted for slightly slower but more robust analyses.[1]

Lead Finder caters to the needs of computational and medicinal chemists engaged in drug discovery, pharmacologists, and toxicologists involved in in silico assessment of ADMET properties. Additionally, it is a valuable tool for biochemists and enzymologists working on modeling protein-ligand interactions, enzyme specificity, and rational enzyme design. Lead Finder's efficiency in ligand docking and binding energy estimation is a result of its advanced docking algorithm and the precision with which it represents protein-ligand interactions.[2]

Docking algorithm

From a mathematical perspective, ligand docking involves the exploration of a multidimensional surface that describes the free energy associated with protein-ligand binding. This surface can be highly complex, with ligands possessing as many as 15-20 degrees of freedom, such as freely rotatable bonds.

Lead Finder employs a distinctive approach that combines the use of genetic algorithm search, local optimization techniques, and a utilization of knowledge gathered during the search process.

Scoring function

The Lead Finder scoring function, in addition to the docking algorithm, incorporates a critical element for the precise representation of protein-ligand interactions. This extra precision in modeling protein-ligand interactions is the second key component of successful ligand docking. The scoring function in Lead Finder is founded on a semi-empirical molecular mechanical model, designed to explicitly consider various types of molecular interactions.

In this scoring function, individual energy contributions are carefully adjusted with empirically derived coefficients tailored to specific objectives. These objectives include the accurate prediction of binding energies, the correct ranking of energy for docked ligand poses, and the accurate ordering of active and inactive compounds during virtual screening experiments. To achieve these goals, Lead Finder employs three distinct types of scoring functions. These scoring functions are based on the same set of energy contributions but utilize different sets of energy-scaling coefficients, thereby optimizing the software for various aspects of ligand docking and interaction analysis.[3]

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,[4] Glide SP,[5] Glide XP,[6] Gold,[7][8][9] LigandFit,[10] MolDock,[11] Surflex.[12]

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).

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. PMID 19007114. 
  2. ↑ "Benchmarking Lead Finder's Performance in Virtual Screening". https://www.biomoltech.com/science/benchmarking/virtual_screening_performance/. 
  3. ↑ Novikov, Fedor N.; Stroylov, Viktor S.; Zeifman, Alexey A.; Stroganov, Oleg V.; Kulkov, Val; Chilov, Ghermes G. (2012-05-09). "Lead Finder docking and virtual screening evaluation with Astex and DUD test sets". Journal of Computer-Aided Molecular Design 26 (6): 725–735. doi:10.1007/s10822-012-9549-y. ISSN 0920-654X. http://dx.doi.org/10.1007/s10822-012-9549-y. 
  4. ↑ 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. PMID 9334903. Bibcode: 1997JCAMD..11..369R. 
  5. ↑ 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. 
  6. ↑ R. A. Friesner; J. L. Banks; R. B. Murphy; T. A. Halgren; J. J. Klicic; D. T. Mainz; M. P. Repasky; E. H. Knoll et al. (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. doi:10.1021/jm051256o. PMID 17034125. 
  7. ↑ 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. doi:10.1006/jmbi.1996.0897. PMID 9126849. 
  8. ↑ 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. 
  9. ↑ 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. 
  10. ↑ 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. PMID 12479928. 
  11. ↑ R. Thomsen; M. H. Christensen (2006). "MolDock: A new technique for high-accuracy molecular docking". Journal of Medicinal Chemistry 49 (11): 3315–3321. doi:10.1021/jm051197e. PMID 16722650. 
  12. ↑ 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. 



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