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Searching within large databases of 3D chemical structures for those compounds which satisfy both the chemical and geometric requirements specified in the 3D search query
The search typically reflects the chemical and geometric requirements for a ligand to interact favorably with a particular bio-receptor
That is, the search query usually reflects “the pharmacophore”
3D Searching review articles VanDrie, J. H. “3D Database Searching in Drug Discovery,”
http://www.netsci.org/Science/Cheminform/feature06.html Güner, O. F. and Henry, D. R. “Three-dimensional Structure Searching,” in
The Encyclopedia of Computational Chemistry; Schleyer, P. v. R.; Allinger, N. L. Clark, T.; Gasteiger, J.; Kollman, P. A.; Schaefer III, H. F.; Schreiner, P. R. (Eds.): John Wiley & Sons: Chichester, 1998, vol 5, pp 2988-3003.
Kurogi, Y. and Güner, O. F. “Pharmacophore Modeling and Three-dimensional Database Searching for Drug Design Using Catalyst,” Curr. Med. Chem. 2001, 8, 1035-1055.
Searching Software – 2D vs 3D2D Substructure (and similarity) searching User specifies atoms and how they are connected
Users can’t discard undesired “hits” based on 3D geometry of those hits (no 3D information allowed)
Therefore, user must discard undesired hits based on connectivity and, thereby, pre-determines a large fraction of sub-structural information about hits
3D Searching User specifies atom-types and their relative
position in 3D-spaceUser does not specify how atoms are connectedUser does not pre-determine “chemistry” of hits
Converts CONnection table to 3D CoORDinates 2D CT contains information about connectivity per se 2.5D Contains additional information about stereochemistry
Handles almost all “drug-sized” compounds
Handles input/output in a wide variety of ways
Very fast
Good to excellent structures
Limitations No inorganics or metallo-organics Single low energy conformation Not intended for macrocycles, polymers, or other highly flexible
structures for which 3D structure is determined by extrinsic rather than intrinsic forces
Uses rule-based “chemical intuition” when applicable (most acyclic substructures)
Uses pseudo-molecular mechanics approach when “intuition” not applicable (most cyclic substructures) A novel strain function is minimized Strain is a function defined such that minimization is performed over
a single, composite variable
Initial structure is checked for close-contacts; dihedrals causing close-contacts (or all acyclic dihedrals) are then relaxed by ultra-fast MM optimization Carried out in torsion space, using analytical gradients, and with
General database management software There are many examples… all chemistry ignorant (data
“name” oriented)
Chemical database management software Accelrys, CAS, Daylight, MDL, Tripos, Oracle*
Unique, structure-related storage key Searchable by structure, as well as name, etc. Searchable by 2D substructure keys Searchable by 3D substructure keys Integrated queries (including biological, chemical data, etc.)
Some include 3D shape based searches Some interface with other modeling and analysis software tools
Bit strings (a.k.a. “fingerprints”) – is a particular feature present? Yes or no?
2D Substructural keys
3D object/distance keys Object: atom, lone-pair, ring-centroid, projected point, etc. Distance (rigid): single distance bins Distance (flexible): ranges of bins 3D shape-based keys 3, 4-point pharmacophore keys
Screen from MACCS-3D displaying a hit retrieved from MDDR-3D based on a CNS active drugs pharmacophore from: Lloyd, E.J. and Andrews, P.R. J. Med. Chem. 1986, 29, 453.
Screen from ISIS/3D displaying a dopamine antagonist pharmacophore proposed by Martin, Y.C. Tetrahedron Comput. Meth. 1990, 3, 15-25; and a hit retrieved from MDDR-3D.
A UNITY query based on a set of muscarinic M3 receptor antagonists (Marriott, D.P., Dougall, I.G., Meghani, P., Liu, Y., and Flower, D.R. J. Med. Chem. 1999, 42, 3210) developed using DISCOtech and refined via Tripos’ Pharmacophore Model Analysis tools.
Screen from Catalyst displaying a hit retrieved from Maybridge database based on an angiotensin II blockers pharmacophore developed by Peter Sprague. The conformation of the hit with the highest score is shown overlaid with the original query.
Addressing Conformational Flexibility – in the Database
Store multiple conformations of each compound Too many to store (33 = 27, 124 = 20,736) Too many to search Still no guarantee that bound conformation is amongst those
stored
Example reference: Murrall, N. W.; Davies, E. K. “Conformational Freedom in 3-D
Databases,” J. Chem. Inf. Comput. Sci., 1990, 30, 312-316.
Addressing Conformational Flexibility – in Search QueryBuild conformational flexibility into query Specify ranges for geometric constraints
Increasing range increase false positives
Decreasing range increase false negatives
Specify “hinge” points Differentiate parts of the query dealing with flexible regions
Generality of query may be compromised
Example references: Güner, O. F.; Henry, D. R.; Pearlman, R. S. “Use of Flexible Queries for
Searching Conformationally Flexible Molecules in Databases of Three-Dimensional Structures,” J. Chem. Inf. Comput. Sci. 1992, 32, 101-109.
Güner, O. F.; Henry, D. R.; Moock, T. E.; Pearlman, R. S. “Flexible Queries in 3D Searching. 2. Techniques in 3D Query Formulation,” Tetrahedron Comp. Meth. 1990, 3(6C), 557-563.
Addressing Conformational Flexibility – during Search
Explore conformational flexibility at search-time Rigid: does this conformation match query? Flexible: could this compound match query? 2-phase process:
First rapid screen based upon max/min distance keys
Then, slower conformational search
Ensures that no hits are missed (however, it is important note local minima problem)
Example references: Moock, T. E.; Henry, D. R.; Ozkabak, A. G.; Alamgir, M. “Conformational
Searching in ISIS/3D Databases,” J. Chem. Inf. Comput. Sci., 1994, 34, 184-189.
Hurst, T. “Flexible 3D Searching: the Directed Tweak Technique,” J. Chem. Inf. Comput. Sci., 1994, 34, 190-196.
A. Smellie, S.L. Teig, and P. Towbin, "Poling: Promoting Conformational Coverage", J. Comp. Chem., 1995, 16, 171-187.
A. Smellie, S.D. Kahn, and S. Teig, "An Analysis of Conformational Coverage 1. Validation and Estimation of Coverage", J. Chem. Inf. Comput. Sci., 1995, 35, 285-294.
A. Smellie, S.D. Kahn, and S. Teig, "An Analysis of Conformational Coverage 2. Applications of Conformational Models" , J. Chem. Inf. Comput. Sci., 1995, 35, 295-304.
– The most commonly used approach is to store multiple diverse conformations in the database and perform a flexible search
• ISIS/3D – performs a random kick before giving up on a conformation
• UNITY – performs user defined number of kicks
• Catalyst – uses “poling” algorithm to store multiple conformations (av. >30 conf.s)
Object-1: Zn-ligand (sulfhydryl or carboxylate oxygen)
Object-2: H-bond acceptor (N, O, or F)
Object-3: anion (--CS-, --COO-, --SO4-2, or –-PO4-3)
Object-4: indicates direction of lone-pair on object-2
Object-5: “central” atom in anion labeled object-3
Mayer, D.; Naylor, C. B.; Motoc, I.; Marshall, G. R. “A unique geometry of the active site of angiotensin-converting enzyme consistent with structure-activity studies,” J. Comput.-Aided Mol. Des. 1987, 1(1), 3-16.
3D Searching works but requires a team effort: Laboratory synthesis and testing (and/or HTS) Molecular modeling for query refinement Tight interface between modeling and searching software Hit list analysis, prioritization, post processing
3D Searching can spark chemists’ imagination
The more information provided by chemists, the more information returned by 3D search