TRAPP webserver: predicting protein binding site flexibility and detecting transient binding pockets

Stank, Antonia; Kokh, Daria B.; Horn, Max; Sizikova, Elena; Neil, Rebecca; Panecka, Joanna; Richter, Stefan; Wade, Rebecca C.

doi:10.1093/nar/gkx277

Cited by 50 publications

(36 citation statements)

References 27 publications

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“…One of each five frames of the trajectories were saved into a new PDB format trajectory and were taken to further analysis using TRAnsient Pockets in Proteins (TRAPP) software 46 , which allows the simulation, analysis, and visualisation of protein cavity dynamics for detection of transient sub-pockets using protein motion trajectories or ensembles of protein structures obtained either from experiments or from simulations. The catalytic pocket was also analysed using this software.…”

Section: Methodsmentioning

confidence: 99%

Identification of new allosteric sites and modulators of AChE through computational and experimental tools

Roca

Requeña

Sebastián-Pérez

et al. 2018

Journal of Enzyme Inhibition and Medicinal Chemistry

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Allosteric sites on proteins are targeted for designing more selective inhibitors of enzyme activity and to discover new functions. Acetylcholinesterase (AChE), which is most widely known for the hydrolysis of the neurotransmitter acetylcholine, has a peripheral allosteric subsite responsible for amyloidosis in Alzheimer’s disease through interaction with amyloid β-peptide. However, AChE plays other non-hydrolytic functions. Here, we identify and characterise using computational tools two new allosteric sites in AChE, which have allowed us to identify allosteric inhibitors by virtual screening guided by structure-based and fragment hotspot strategies. The identified compounds were also screened for in vitro inhibition of AChE and three were observed to be active. Further experimental (kinetic) and computational (molecular dynamics) studies have been performed to verify the allosteric activity. These new compounds may be valuable pharmacological tools in the study of non-cholinergic functions of AChE.

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Section: Methodsmentioning

confidence: 99%

Identification of new allosteric sites and modulators of AChE through computational and experimental tools

Roca

Requeña

Sebastián-Pérez

et al. 2018

Journal of Enzyme Inhibition and Medicinal Chemistry

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“…500 protein conformations from the last 80 ns of the accelerated MD simulation were samples and used to confirm the existence of the identified pockets and to evaluate their druggability. Sampled protein conformations were used as inputs for TRAnsient Pockets in Proteins (TRAPP) -a powerful machine-learning tool for binding pocket detection and druggability assessment [47][48] . In its most recent implementation, TRAPP offers the possibility of assessing the druggability of the detected pockets using either a logistic regression model (TRAPP-LR) and/or a convolutional neural network model (TRAPP-CNN), deep learning model 49 .…”

Section: Accelerated MD Simulationsmentioning

confidence: 99%

A “Deep Dive” into the SARS-Cov-2 Polymerase Assembly: Identifying Novel Allosteric Sites and Analyzing the Hydrogen Bond Networks and Correlated Dynamics

Barakat

Ahmed

Tabana

et al. 2020

Preprint

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Replication of the SARS-CoV-2 genome is a fundamental step in the virus life cycle and inhibiting the SARS-CoV2 replicase machinery has been proven recently as a promising approach in combating the virus. Despite this recent success, there are still several aspects related to the structure, function and dynamics of the CoV-2 polymerase that still need to be addressed. This includes understanding the dynamicity of the various polymerase subdomains, analyzing the hydrogen bond networks at the active site and at the template entry in the presence of water, studying the binding modes of the nucleotides at the active site, highlighting positions for acceptable nucleotides' substitutions that can be tolerated at different positions within the nascent RNA strand, identifying possible allosteric sites within the polymerase structure and studying their correlated dynamics relative to the catalytic site. Here, we combined various cutting-edge modelling tools with the recently resolved SARS-CoV-2 cryo-EM polymerase structures to fill this gap in knowledge. Our findings provide a detailed analysis of the hydrogen bond networks at various parts of the polymerase structure and suggest possible nucleotides' substitutions that can be tolerated by the polymerase complex. We also report here three "druggable" allosteric sites within the nsp12 RdRp that can be targeted by small molecule inhibitors. Our correlated motion analysis shows that the dynamics within one of the newly identified sites are linked to the active site, indicating that targeting this site can significantly impact the catalytic activity of the SARS-CoV-2 polymerase.

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“…The remaining parameters for applying L-RIP followed the default settings on the TRAPP webserver. 33,35 Before druggability analysis, all snapshots in the trajectory were superimposed by fitting all binding site heavy atoms that were within 4…”

Section: Model Testing Using a Trajectory From An L-rip Simulationmentioning

confidence: 99%

Druggability Assessment in TRAPP using Machine Learning Approaches

Yuan

Han

Richter

et al. 2019

Preprint

Self Cite

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Accurate protein druggability predictions are important for the selection of drug targets in the early stages of drug discovery. Due to the flexible nature of proteins, the druggability of a binding pocket may vary due to conformational changes. We have therefore developed two statistical models, a logistic regression model (TRAPP-LR) and a convolutional neural network model (TRAPP-CNN), for predicting druggability and how it varies with changes in the spatial and physicochemical properties of a binding pocket. These models are integrated into TRAPP (TRAnsient Pockets in Proteins), a tool for the analysis of binding pocket variations along a protein motion trajectory.The models, which were trained on publicly available and self-augmented data sets, show equivalent or superior performance to existing methods on test sets of protein crystal structures, and have sufficient sensitivity to identify potentially druggable protein conformations in trajectories from molecular dynamics simulations. Visualization of the evidence for the decisions of the models in TRAPP facilitates identification of the factors affecting the druggability of protein binding pockets.

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TRAPP webserver: predicting protein binding site flexibility and detecting transient binding pockets

Cited by 50 publications

References 27 publications

Identification of new allosteric sites and modulators of AChE through computational and experimental tools

Identification of new allosteric sites and modulators of AChE through computational and experimental tools

A “Deep Dive” into the SARS-Cov-2 Polymerase Assembly: Identifying Novel Allosteric Sites and Analyzing the Hydrogen Bond Networks and Correlated Dynamics

Druggability Assessment in TRAPP using Machine Learning Approaches

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