Computational fragment-based screening using RosettaLigand: the SAMPL3 challenge

Kumar, Ashutosh; Zhang, Kam Y. J.

doi:10.1007/s10822-011-9523-0

Cited by 16 publications

(10 citation statements)

References 55 publications

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“…Our study found that results from RosettaLigand [147][148][149] display a similar performance for fragment like ligands as for druglike ligands. The chances of success in a fragment screening process could be increased significantly with careful selection of receptor structures, protein flexibility, sufficient conformational sampling within binding pocket and accurate assignment of ligand and protein partial charges [150]. There are number of studies reported in literature that identified potent fragment molecules utilizing structure based docking followed by experimental validation [140,[151][152][153].…”

Section: Screening Of Fragment Libraries By Computational Methodsmentioning

confidence: 99%

Fragment Based Drug Design: From Experimental to Computational Approaches

Kumar

Voet²,

Zhang

2012

CMC

151

102

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Fragment based drug design has emerged as an effective alternative to high throughput screening for the identification of lead compounds in drug discovery in the past fifteen years. Fragment based screening and optimization methods have achieved credible success in many drug discovery projects with one approved drug and many more compounds in clinical trials. The fragment based drug design starts with the identification of fragments or low molecular weight compounds that generally bind with weak affinity to the target of interest. The fragments that form high quality interactions are then optimized to lead compounds with high affinity and selectivity. The weak affinity of fragments for their target requires the use of biophysical techniques such as nuclear magnetic resonance, X-ray crystallography or surface plasmon resonance to identify hits. These techniques are very sensitive and some of them provide detailed protein fragment interaction information that is important for fragment to lead optimization. Despite the huge advances in technology in the past years, experimental methods of fragment screening suffer several challenges such as low throughput, high cost of instruments and experiments, high protein and fragment concentration requirements. To address challenges posed by experimental screening approaches, computational methods were developed that play an important role in fragment library design, fragment screening and optimization of initial fragment hits. The computational approaches of fragment screening and optimization are most useful when they are used in combination with experimental approaches. The use of virtual fragment based screening in combination with experimental methods has fostered the application of fragment based drug design to important biological targets including protein-protein interactions and membrane proteins such as GPCRs. This review provides an overview of experimental and computational screening approaches used in fragment based drug discovery with an emphasis on recent successes achieved in discovering potent lead molecules using these approaches.

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Section: Screening Of Fragment Libraries By Computational Methodsmentioning

confidence: 99%

Fragment Based Drug Design: From Experimental to Computational Approaches

Kumar

Voet²,

Zhang

2012

CMC

151

102

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“…Kumar and Zhang evaluated their VS protocol with RosettaLigand by screening a 500-fragment Maybridge library. [106] They highlighted the importance of an appropriate method for the calculation of partial charges. They also confirmed that using multiple receptor ensembles in docking does not always yield better enrichment than individual receptors, a finding previously observed in other studies.…”

Section: Fragment Dockingmentioning

confidence: 99%

Improvements, trends, and new ideas in molecular docking: 2012–2013 in review

Yuriev

Holien

Ramsland

2015

J of Molecular Recognition

239

165

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Molecular docking is a computational method for predicting the placement of ligands in the binding sites of their receptor(s). In this review, we discuss the methodological developments that occurred in the docking field in 2012 and 2013, with a particular focus on the more difficult aspects of this computational discipline. The main challenges and therefore focal points for developments in docking, covered in this review, are receptor flexibility, solvation, scoring, and virtual screening. We specifically deal with such aspects of molecular docking and its applications as selection criteria for constructing receptor ensembles, target dependence of scoring functions, integration of higher-level theory into scoring, implicit and explicit handling of solvation in the binding process, and comparison and evaluation of docking and scoring methods.

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“…Affinity prediction results in some cases were reasonable, however[49]. The SAMPL3 protein-ligand challenge involved predicting binding of a series of fragments to trypsin [39], and a number of groups participated[30, 50, 31, 49, 2], in some cases achieving rather good enrichment for screening [50, 49] and good correlations between predicted binding strength and measured affinity[50], though the test was still challenging[31]. …”

Section: Introductionmentioning

confidence: 99%

Blind prediction of HIV integrase binding from the SAMPL4 challenge

Mobley

Liu

Lim

et al. 2014

J Comput Aided Mol Des

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Here, we give an overview of the protein-ligand binding portion of the SAMPL4 challenge, which focused on predicting binding of HIV integrase inhibitors in the catalytic core domain. The challenge encompassed three components – a small “virtual screening” challenge, a binding mode prediction component, and a small affinity prediction component. Here, we give summary results and statistics concerning the performance of all submissions at each of these challenges. Virtual screening was particularly challenging here in part because, in contrast to more typical virtual screening test sets, the inactive compounds were tested because they were thought to be likely binders, so only the very top predictions performed significantly better than random. Pose prediction was also quite challenging, in part because inhibitors in the set bind to three different sites, so even identifying the correct binding site was challenging. Still, the best methods managed low RMSD predictions in many cases. Here, we give an overview of results, highlight some features of methods which worked particularly well, and refer the interested reader to papers in this issue which describe specific submissions for additional details.

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Computational fragment-based screening using RosettaLigand: the SAMPL3 challenge

Cited by 16 publications

References 55 publications

Fragment Based Drug Design: From Experimental to Computational Approaches

Fragment Based Drug Design: From Experimental to Computational Approaches

Improvements, trends, and new ideas in molecular docking: 2012–2013 in review

Blind prediction of HIV integrase binding from the SAMPL4 challenge

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