Potent, selective inhibitors of protein tyrosine phosphatase 1B

Xin, Zhili; Oost, Thorsten; Abad‐Zapatero, Celerino; Hajduk, Philip J.; Pei, Zhonghua; Szczepankiewicz, Bruce G.; Hutchins, Charles W.; Ballaron, Steve J.; Stashko, Mike; Lubben, Tom; Trevillyan, James M.; Jirousek, Mike R; Liu, Gang

doi:10.1016/s0960-894x(03)00302-0

Cited by 51 publications

(29 citation statements)

References 16 publications

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“…The first group contains the sets that are based on crystal structures. These are structures for the targets PTP-1B (PDB ID: 1NZ7 [21] ) and CHK1 (PDB ID: 2YWP [22] ) taken from the supporting information of a study by Brown and Muchmore. [19] Because all compounds were provided aligned in one single protein structure, which might lead to inaccuracies due to side chain flexibility, we minimized the structures with the program MOE.…”

Section: Preparation Of Training Setsmentioning

confidence: 99%

“…To ensure independence of the external test sets, the DUD2 sets were tested for intersections with the training sets. Because no benchmarked test sets are available for the targets PTP-1B (PDB ID: 1NZ7 [21] ) and CHK1 (PDB ID: 2YWP [22] ), we collected active and decoy sets by following principles similar to those used for the DUD2 data sets. The decoys were collected so that the distributions of a number of descriptors are similar to those of the active compounds.…”

Section: Preparation Of Training Setsmentioning

confidence: 99%

“…The decoys were collected so that the distributions of a number of descriptors are similar to those of the active compounds. In a first step, the actives were collected from the BindingDB [21,22] data base and were tested against the training sets to avoid intersections. Then, the active sets were used to calculate the following descriptors: molecular weight, log P, number of H-bond acceptors, number of H-bond donors, TPSA, number of rotatable bonds, volume, number of atoms, number of bonds, number of aromatic atoms, number of halogen atoms, number of ring systems, number of aromatic ring systems, number of rings, number of aromatic rings, maximum connected rotatable bonds (CRTB), and formal charge.…”

Section: Preparation Of Training Setsmentioning

confidence: 99%

“…[21,22] [b] Collected from the ZINC [30] database with descriptors in the range of mean values AE 2 SD with respect to the active compounds, as described in the Experimental Section.…”

Section: Targetmentioning

confidence: 99%

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From Activity Cliffs to Target‐Specific Scoring Models and Pharmacophore Hypotheses

2011

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The role of activity cliffs in drug discovery projects is certainly two-edged: on the one hand, they often lead to the failure of QSAR modeling techniques; on the other, they are highly valuable for identifying key aspects of SARs. In the presence of activity cliffs the results of purely ligand-based QSAR approaches often remain puzzling, and the resulting models have limited predictive power. Herein we present a new approach for the identification of structure-based activity cliffs (ISAC). It uses the valuable information of activity cliffs in a structure-based design scenario by analyzing interaction energies of protein-ligand complexes. Using the relative frequency at which a protein atom is involved in activity cliff events, we introduce a novel visualization of hot spots in the active site of a protein. The ISAC approach supports the medicinal chemist in elucidating the key interacting atoms of the binding site and facilitates the development of pharmacophore hypotheses. The hot spot visualization can be applied to small data sets in early project phases as well as in the lead optimization process. Based on the ISAC approach, we developed a method to derive target-specific scoring functions and pharmacophore constraints, which were validated on independent external data sets in virtual screening experiments. The activity-cliff-based approach shows an improved enrichment over the generic empirical scoring function for various protein targets in the validation set.

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Section: Preparation Of Training Setsmentioning

confidence: 99%

Section: Preparation Of Training Setsmentioning

confidence: 99%

Section: Preparation Of Training Setsmentioning

confidence: 99%

“…[21,22] [b] Collected from the ZINC [30] database with descriptors in the range of mean values AE 2 SD with respect to the active compounds, as described in the Experimental Section.…”

Section: Targetmentioning

confidence: 99%

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From Activity Cliffs to Target‐Specific Scoring Models and Pharmacophore Hypotheses

2011

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“…This preference was confirmed by some initial chemical couplings to compound 3, where it was found that the amino acid phenylalanine was 24-fold times more potent than the phenyl group alone ( Table 2 of ref. [27]). This initial observation prompted the preparation of a library of different L-amino acids to be coupled to the core template (6).…”

Section: The Search For Potency and Selectivitymentioning

confidence: 99%

Synergistic Use of Protein Crystallography and Solution‐phase NMR Spectroscopy in Structure‐based Drug Design: Strategies and Tactics

Abad‐Zapatero

Stamper

Stoll

2006

Fragment‐based Approaches in Drug Discovery

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Structural biology is an integral part of the drug discovery process in the pharmaceutical industry. Two complementary methods dominate among the experimental techniques capable of providing detailed three-dimensional (3D) structural information of therapeutically relevant targets, most often with bound ligands: protein crystallography and solution-phase NMR spectroscopy. The structural information these techniques provide continues to grow in depth and breadth, impacting every step of drug discovery from lead identification to preclinical testing ( Fig. 12.1). Used together, protein crystallography and solution-phase NMR can significantly increase both the pace of discovery and the quality of the resulting compounds that are moved forward into development.The benefits to drug discovery of rapidly available, high-resolution 3D structural information provided by protein crystallography are well established [1,2]. While the technique's success depends on diffraction-quality protein crystals, significant advances in protein biochemistry and protein crystallization technologies have dramatically increased the number of targets amenable to structure determination by X-ray diffraction. Driven in large part by a number of worldwide structural genomics efforts underway in both academia and industry [3], these advances include robotic systems for cloning, expression, protein purification and crystallization [4]. In addition, continued hardware and software developments, both at synchrotron and in-house X-ray sources, have decreased the time of de novo structure solution from months to days, while co-crystal structures are routinely obtained within hours. Thus, once diffraction-quality crystals are obtained, structure determination is not a bottleneck in the discovery process. In fact, a number of drugs on the market were discovered in large part by routine use of structural information provided by protein crystallography [2, 5], a clear indication that this technique has become a driver of the design process and not a retrospective analysis tool.In academia, solution-phase NMR spectroscopy continues to develop as a technique for obtaining 3D structural information of macromolecules including target-ligand complexes. Recent advances have made NMR structural work possible 249 Fragment-based Approaches in Drug Discovery. Edited

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