Ligand Binding Mode Prediction by Docking: Mdm2/Mdmx Inhibitors as a Case Study

Bharatham, Nagakumar; Bharatham, Kavitha; Shelat, Anang A.; Bashford, Donald

doi:10.1021/ci4004656

Cited by 37 publications

(40 citation statements)

References 77 publications

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“…However, the inter-molecular 1 H- 1 H NOEs were generally of lower intensity for the complexes with the SJ compounds versus that with p53-TAD1, suggesting that some of the small molecules do not bind as deeply within the hydrophobic groove of MdmX as does the p53 peptide. Our solution structures (Figure 3, Table 2) showed that the para-chloro-phenyl group of the SJ compounds bound within the pocket on MdmX that was occupied by W23 of p53-TAD1 (the “W23 pocket”) and that the adjacent substituent on the 4 position of the diazole ring ( e.g., meta-chloro-phenyl in SJ295) bound within the “L26 pocket” 13 (Figure 3). The substituents at this position in the different SJ compounds exhibited different patterns of inter-molecular NOEs but, in all cases, the numbers of NOEs were sufficient to uniquely position these moieties within the L26 pocket of MdmX.…”

Section: Resultsmentioning

confidence: 97%

Monitoring Ligand-Induced Protein Ordering in Drug Discovery

Grace

Ban

Min

et al. 2016

Journal of Molecular Biology

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While the gene for p53 is mutated in many human cancers causing loss of function, many others maintain a wild-type gene but exhibit reduced p53 tumor suppressor activity through overexpression of the negative regulators, Mdm2 and/or MdmX. For the latter mechanism of loss of function, the activity of endogenous p53 can be restored through inhibition of Mdm2 or MdmX with small molecules. We previously reported a series of compounds based upon the Nutlin-3 chemical scaffold that bind to both MdmX and Mdm21. Here we present the first solution structures based on data from NMR spectroscopy for MdmX in complex with four of these compounds and compare them with the MdmX:p53 complex. A p53-derived peptide binds with high affinity (Kd value of 150 nM) and causes the formation of an extensive network of hydrogen bonds within MdmX; this constitutes the induction of order within MdmX through ligand binding. In contrast, the compounds bind more weakly (Kd values from 600 nM to 12 μM) and induce an incomplete hydrogen bond network within MdmX. Despite relatively weak binding, the four compounds activated p53 and induced p21Cip1 expression in retinoblastoma cell lines that overexpress MdmX, suggesting that they specifically target MdmX and/or Mdm2. Our results document structure-activity relationships for lead-like small molecules targeting MdmX and suggest a strategy for their further optimization in the future by using NMR spectroscopy to monitor small molecule-induced protein order as manifested through hydrogen bond formation.

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Section: Resultsmentioning

confidence: 97%

Monitoring Ligand-Induced Protein Ordering in Drug Discovery

Grace

Ban

Min

et al. 2016

Journal of Molecular Biology

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“…NY2264 is an inhibitor targeting Myc, an IDP, and Myc-associated factor X (MAX) interaction [100]. Nutlin, an inhibitor of the p53-MDM2 interaction, binds to MDM2 and releases p53, which has a long IDR at its N-terminus [101, 102]. …”

Section: Intrinsically Disordered Proteins As Drug Targetsmentioning

confidence: 99%

In silico structure-based approaches to discover protein-protein interaction-targeting drugs

Shin

Christoffer

Kihara

2017

Methods

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A core concept behind modern drug discovery is finding a small molecule that modulates a function of a target protein. This concept has been successfully applied since the mid-1970s. However, the efficiency of drug discovery is decreasing because the druggable target space in the human proteome is limited. Recently, protein-protein interaction (PPI) has been identified as an emerging target space for drug discovery. PPI plays a pivotal role in biological pathways including diseases. Current human interactome research suggests that the number of PPIs ranges from 130,000 to 650,000, and only a small number of them have been targeted as drug targets. For traditional drug targets, in silico structure-based methods have been successful in many cases. However, their performance suffers on PPI interfaces because PPI interfaces are different in five major aspects: From a geometric standpoint, they have relatively large interface regions, flat geometry, and the interface surface shape tends to fluctuate upon binding. Also, their interactions are dominated by hydrophobic atoms, which is different from traditional binding-pocket-targeted drugs. Finally, PPI targets usually lack natural molecules that bind to the target PPI interface. Here, we first summarize characteristics of PPI interfaces and their known binders. Then, we will review existing in silico structure-based approaches for discovering small molecules that bind to PPI interfaces.

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“…The scoring function is used to evaluate the affinity between the receptor and the ligand for each conformation [20]. Scoring functions express the geometric complementarity and the energy strength of the interaction based on the physicochemical characteristics of the amino acids in contact with each other [21].…”

Section: Introductionmentioning

confidence: 99%

An Efficient ABC_DE_Based Hybrid Algorithm for Protein–Ligand Docking

Guan

Zhao

2018

IJMS

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Protein–ligand docking is a process of searching for the optimal binding conformation between the receptor and the ligand. Automated docking plays an important role in drug design, and an efficient search algorithm is needed to tackle the docking problem. To tackle the protein–ligand docking problem more efficiently, An ABC_DE_based hybrid algorithm (ADHDOCK), integrating artificial bee colony (ABC) algorithm and differential evolution (DE) algorithm, is proposed in the article. ADHDOCK applies an adaptive population partition (APP) mechanism to reasonably allocate the computational resources of the population in each iteration process, which helps the novel method make better use of the advantages of ABC and DE. The experiment tested fifty protein–ligand docking problems to compare the performance of ADHDOCK, ABC, DE, Lamarckian genetic algorithm (LGA), running history information guided genetic algorithm (HIGA), and swarm optimization for highly flexible protein–ligand docking (SODOCK). The results clearly exhibit the capability of ADHDOCK toward finding the lowest energy and the smallest root-mean-square deviation (RMSD) on most of the protein–ligand docking problems with respect to the other five algorithms.

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Ligand Binding Mode Prediction by Docking: Mdm2/Mdmx Inhibitors as a Case Study

Cited by 37 publications

References 77 publications

Monitoring Ligand-Induced Protein Ordering in Drug Discovery

Monitoring Ligand-Induced Protein Ordering in Drug Discovery

In silico structure-based approaches to discover protein-protein interaction-targeting drugs

An Efficient ABC_DE_Based Hybrid Algorithm for Protein–Ligand Docking

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