VS-APPLE: A Virtual Screening Algorithm Using Promiscuous Protein–Ligand Complexes

Okuno, Tatsuya; Kato, Koya; Terada, Tomoki P.; Sasai, Masaki; Chikenji, George

doi:10.1021/acs.jcim.5b00134

Cited by 8 publications

(14 citation statements)

References 44 publications

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“…Proteins and their bound ligands were superimposed by the protein structure alignment program MICAN 48 against a modeled Yes structure. The Enamine library compounds were compared with the multiple-template model using the geometric hashing technique 49 , where scoring was defined as the number of coincident atoms minus the protein-compound crash penalty, to identify potential hit compounds 50 . Group 7 (G7) : MD simulation and fragment molecular orbital calculation of the Src-dasatinib complex structure were performed to identify residues that interact with dasatinib with high retention or interaction energy, respectively.…”

Section: Details Of the Contestmentioning

confidence: 99%

Identification of potential inhibitors based on compound proposal contest: Tyrosine-protein kinase Yes as a target

Chiba

Ikeda²,

Ishida

et al. 2015

Sci Rep

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A search of broader range of chemical space is important for drug discovery. Different methods of computer-aided drug discovery (CADD) are known to propose compounds in different chemical spaces as hit molecules for the same target protein. This study aimed at using multiple CADD methods through open innovation to achieve a level of hit molecule diversity that is not achievable with any particular single method. We held a compound proposal contest, in which multiple research groups participated and predicted inhibitors of tyrosine-protein kinase Yes. This showed whether collective knowledge based on individual approaches helped to obtain hit compounds from a broad range of chemical space and whether the contest-based approach was effective.

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Section: Details Of the Contestmentioning

confidence: 99%

Identification of potential inhibitors based on compound proposal contest: Tyrosine-protein kinase Yes as a target

Chiba

Ikeda²,

Ishida

et al. 2015

Sci Rep

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“…Please see Ref. [ 8 ] for more detailed explanation of the method. Also explained in this section is a subset of DUD data set used for the performance test in the present paper.…”

Section: Methodsmentioning

confidence: 99%

“…Recently, the present authors developed a VS method, VS-APPLE (Virtual Screening Algorithm using Promiscuous Protein-Ligand complExes) [ 8 ] which utilizes the structure data of multiple protein-ligand complexes. In VS-APPLE, structures of protein-ligand complexes are superposed so as to maximize the structural overlap between the target protein and proteins in complexes.…”

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confidence: 99%

“…In Ref. [ 8 ], the performance of VS-APPLE was tested by using a filtered, clustered version [ 9 , 10 ] of the Directory of Useful Decoys (DUD) data set [ 11 ]. In Area Under the Curve (AUC) analyses [ 12 , 13 ] of this data set, VS-APPLE showed a comparable performance to a VS method Glide [ 15 – 17 ] and outperformed other popular methods such as ROCS [ 18 – 20 ], BABEL [ 21 ], DOCK [ 10 , 22 ], and GOLD [ 22 ].…”

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confidence: 99%

“…A further merit of VS-APPLE is its fast computational speed: It was shown that VS-APPLE was about three times faster than Glide by using parameters given in Ref. [ 8 ]. Because it is necessary to examine a combinatorially large number of compounds for drug design, which often exceeds 10 millions, the computational speed of VS method is indeed an important subject.…”

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confidence: 99%

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Importance of consensus region of multiple-ligand templates in a virtual screening method

et al. 2016

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We discuss methods and ideas of virtual screening (VS) for drug discovery by examining the performance of VS-APPLE, a recently developed VS method, which extensively utilizes the tendency of single binding pockets to bind diversely different ligands, i.e. promiscuity of binding pockets. In VS-APPLE, multiple ligands bound to a pocket are spatially arranged by maximizing structural overlap of the protein while keeping their relative position and orientation with respect to the pocket surface, which are then combined into a multiple-ligand template for screening test compounds. To greatly reduce the computational cost, comparison of test compound structures are made only with limited regions of the multiple-ligand template. Even when we use the narrow regions with most densely populated atoms for the comparison, VSAPPLE outperforms other conventional VS methods in terms of Area Under the Curve (AUC) measure. This region with densely populated atoms corresponds to the consensus region among multiple ligands. It is typically observed that expansion of the sampled region including more atoms improves screening efficiency. However, for some target proteins, considering only a small consensus region is enough for the effective screening of test compounds. These results suggest that the performance test of VS methods sheds light on the mechanisms of protein-ligand interactions, and elucidation of the protein-ligand interactions should further help improvement of VS methods.

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An iterative compound screening contest method for identifying target protein inhibitors using the tyrosine-protein kinase Yes

Chiba

Ishida

Ikeda³

et al. 2017

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We propose a new iterative screening contest method to identify target protein inhibitors. After conducting a compound screening contest in 2014, we report results acquired from a contest held in 2015 in this study. Our aims were to identify target enzyme inhibitors and to benchmark a variety of computer-aided drug discovery methods under identical experimental conditions. In both contests, we employed the tyrosine-protein kinase Yes as an example target protein. Participating groups virtually screened possible inhibitors from a library containing 2.4 million compounds. Compounds were ranked based on functional scores obtained using their respective methods, and the top 181 compounds from each group were selected. Our results from the 2015 contest show an improved hit rate when compared to results from the 2014 contest. In addition, we have successfully identified a statistically-warranted method for identifying target inhibitors. Quantitative analysis of the most successful method gave additional insights into important characteristics of the method used.

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VS-APPLE: A Virtual Screening Algorithm Using Promiscuous Protein–Ligand Complexes

Cited by 8 publications

References 44 publications

Identification of potential inhibitors based on compound proposal contest: Tyrosine-protein kinase Yes as a target

Identification of potential inhibitors based on compound proposal contest: Tyrosine-protein kinase Yes as a target

Importance of consensus region of multiple-ligand templates in a virtual screening method

An iterative compound screening contest method for identifying target protein inhibitors using the tyrosine-protein kinase Yes

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