Affold structures is listed: CA: Carbonic anhydrase II (1ydb, 1yda, 1ydd

Affold structures is listed: CA: Carbonic anhydrase II (1ydb, 1yda, 1ydd), HP: HIV-1 protease (1met, 1meu, 1mes), S1: Streptavidin test 1 (1swe, 1n43). doi:10.1371/journal.pone.0052505.gproblems are probably harder to address than the more complicated test cases dealt with in this study, so that we do not expect that current methods can tackle them with much success. Some apparent problems of POCKETOPTIMIZER, however, such as the occurrence of unresolvable steric clashes between ligand and side chains should be mendable by better sampling of the conformational space and the introduction of backbone flexibility [36] [37?8]. It is conceivable that a continuous minimization step at the end of the design calculation could also be beneficial. In conclusion, it seems that although POCKETOPTIMIZER performs well, and even better in some respects than the state-of-theart method ROSETTA, there is still room for improvement in computational design of protein-ligand binding. Our study highlights the usefulness of benchmark data sets and systematic testing in order to arrive at an informed assessment of computational design methods. In fact it would be interesting to test other available protein design schemes using our benchmark. A comparison of their performance should be very informative. Further, the benchmark will be useful in future test of parts of our modular design pipeline, e.g. by exchanging the force-field in POCKETOPTIMIZER its contribution can be tested rather than the overall design approach. When we started to compile our benchmark set, we were hoping for considerably more test cases. The fact that out of the 6,protein structures currently contained in the PDBbind database, only ten suitable test cases could be extracted (twelve if the double cases of neuroaminidase and streptavidin are counted), 23977191 was rather surprising to us. This emphasizes the need for more benchmark data. Thus, an explicit effort to systematically create experimental and structural data is required. For protein-ligand interaction design it would be desirable to have data that covers many mutations of several pocket positions, ideally also of a set of different proteins.Materials and Methods Benchmark SetThe basis for the benchmark set is the PDBbind database. It contains a set of crystal structures of proteins complexed with small ligands, and the corresponding experimentally determined binding affinity. [34]. Our analysis is based on release 2010. First, we aligned the sequences of all proteins in the database to each other, using the Needleman-Wunsch algorithm [39] as implemented in the EMBOSS suite [40]. The proteins were then clustered with single linkage clustering, a link was assumed if the sequence identity was 95 . One cluster was assumed to contain structures of variants of the same protein with some mutations. Several descriptors were calculated for the protein-ligand complexes. If theComputational Design of Binding Pocketscrystal FCCP chemical information structure contains water molecules in the binding pocket, waters that have a high probability to play a role in binding were identified and counted. This was done with the tool WATERFINDER included in CADDSuite [28?1] that estimates the strength of binding of a water molecule observed in a crystal structure to the protein. The number of rotatable bonds in the ligand is used as a measure of ligand size and flexibility. The Potassium clavulanate ligands of all proteins in a cluster were pairwise compared using ligand fingerprints as implemented in Ope.Affold structures is listed: CA: Carbonic anhydrase II (1ydb, 1yda, 1ydd), HP: HIV-1 protease (1met, 1meu, 1mes), S1: Streptavidin test 1 (1swe, 1n43). doi:10.1371/journal.pone.0052505.gproblems are probably harder to address than the more complicated test cases dealt with in this study, so that we do not expect that current methods can tackle them with much success. Some apparent problems of POCKETOPTIMIZER, however, such as the occurrence of unresolvable steric clashes between ligand and side chains should be mendable by better sampling of the conformational space and the introduction of backbone flexibility [36] [37?8]. It is conceivable that a continuous minimization step at the end of the design calculation could also be beneficial. In conclusion, it seems that although POCKETOPTIMIZER performs well, and even better in some respects than the state-of-theart method ROSETTA, there is still room for improvement in computational design of protein-ligand binding. Our study highlights the usefulness of benchmark data sets and systematic testing in order to arrive at an informed assessment of computational design methods. In fact it would be interesting to test other available protein design schemes using our benchmark. A comparison of their performance should be very informative. Further, the benchmark will be useful in future test of parts of our modular design pipeline, e.g. by exchanging the force-field in POCKETOPTIMIZER its contribution can be tested rather than the overall design approach. When we started to compile our benchmark set, we were hoping for considerably more test cases. The fact that out of the 6,protein structures currently contained in the PDBbind database, only ten suitable test cases could be extracted (twelve if the double cases of neuroaminidase and streptavidin are counted), 23977191 was rather surprising to us. This emphasizes the need for more benchmark data. Thus, an explicit effort to systematically create experimental and structural data is required. For protein-ligand interaction design it would be desirable to have data that covers many mutations of several pocket positions, ideally also of a set of different proteins.Materials and Methods Benchmark SetThe basis for the benchmark set is the PDBbind database. It contains a set of crystal structures of proteins complexed with small ligands, and the corresponding experimentally determined binding affinity. [34]. Our analysis is based on release 2010. First, we aligned the sequences of all proteins in the database to each other, using the Needleman-Wunsch algorithm [39] as implemented in the EMBOSS suite [40]. The proteins were then clustered with single linkage clustering, a link was assumed if the sequence identity was 95 . One cluster was assumed to contain structures of variants of the same protein with some mutations. Several descriptors were calculated for the protein-ligand complexes. If theComputational Design of Binding Pocketscrystal structure contains water molecules in the binding pocket, waters that have a high probability to play a role in binding were identified and counted. This was done with the tool WATERFINDER included in CADDSuite [28?1] that estimates the strength of binding of a water molecule observed in a crystal structure to the protein. The number of rotatable bonds in the ligand is used as a measure of ligand size and flexibility. The ligands of all proteins in a cluster were pairwise compared using ligand fingerprints as implemented in Ope.