Evaluation of an inverse molecular design algorithm in a model binding site

@article{citeulike:3377004, abstract = {Computational molecular design is a useful tool in modern drug discovery. Virtual screening is an approach that docks and then scores individual members of compound libraries. In contrast to this forward approach, inverse approaches construct compounds from fragments, such that the computed affinity, or a combination of relevant properties, is optimized. We have recently developed a new inverse approach to drug design based on the dead-end elimination and A* algorithms employing a physical potential function. This approach has been applied to combinatorially constructed libraries of small-molecule ligands to design high-affinity HIV-1 protease inhibitors (Altman et al., J Am Chem Soc 2008;130:6099-6013). Here we have evaluated the new method using the well-studied W191G mutant of cytochrome c peroxidase. This mutant possesses a charged binding pocket and has been used to evaluate other design approaches. The results show that overall the new inverse approach does an excellent job of separating binders from nonbinders. For a few individual cases, scoring inaccuracies led to false positives. The majority of these involve erroneous solvation energy estimation for charged amines, anilinium ions, and phenols, which has been observed previously for a variety of scoring algorithms. Interestingly, although inverse approaches are generally expected to identify some but not all binders in a library, due to limited conformational searching, these results show excellent coverage of the known binders while still showing strong discrimination of the nonbinders. Proteins 2008. {\copyright} 2008 Wiley-Liss, Inc.}, address = {Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts; Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts; Department of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts; Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts}, author = {Huggins, David J. and Altman, Michael D. and Tidor, Bruce}, citeulike-article-id = {3377004}, doi = {10.1002/prot.22226}, journal = {Proteins: Structure, Function, and Bioinformatics}, keywords = {docking}, number = {9999}, pages = {NA+}, posted-at = {2008-10-05 18:59:32}, priority = {1}, title = {Evaluation of an inverse molecular design algorithm in a model binding site}, url = {http://dx.doi.org/10.1002/prot.22226}, volume = {9999}, year = {2008} }

TY - JOUR ID - citeulike:3377004 L3 - citeulike-article-id:3377004 N2 - Computational molecular design is a useful tool in modern drug discovery. Virtual screening is an approach that docks and then scores individual members of compound libraries. In contrast to this forward approach, inverse approaches construct compounds from fragments, such that the computed affinity, or a combination of relevant properties, is optimized. We have recently developed a new inverse approach to drug design based on the dead-end elimination and A* algorithms employing a physical potential function. This approach has been applied to combinatorially constructed libraries of small-molecule ligands to design high-affinity HIV-1 protease inhibitors (Altman et al., J Am Chem Soc 2008;130:6099-6013). Here we have evaluated the new method using the well-studied W191G mutant of cytochrome c peroxidase. This mutant possesses a charged binding pocket and has been used to evaluate other design approaches. The results show that overall the new inverse approach does an excellent job of separating binders from nonbinders. For a few individual cases, scoring inaccuracies led to false positives. The majority of these involve erroneous solvation energy estimation for charged amines, anilinium ions, and phenols, which has been observed previously for a variety of scoring algorithms. Interestingly, although inverse approaches are generally expected to identify some but not all binders in a library, due to limited conformational searching, these results show excellent coverage of the known binders while still showing strong discrimination of the nonbinders. Proteins 2008. © 2008 Wiley-Liss, Inc. IS - 9999 JF - Proteins: Structure, Function, and Bioinformatics AD - Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts; Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts; Department of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts; Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts TI - Evaluation of an inverse molecular design algorithm in a model binding site VL - 9999 SP - NA KW - docking AU - Huggins, David AU - Altman, Michael AU - Tidor, Bruce PY - 2008/// UR - http://dx.doi.org/10.1002/prot.22226 ER -