scholarly journals Significance of Data Selection in Deep Learning for Reliable Binding Mode Prediction of Ligands in the Active Site of CYP3A4

2019 ◽  
Vol 67 (11) ◽  
pp. 1183-1190
Author(s):  
Atsuko Sato ◽  
Naoki Tanimura ◽  
Teruki Honma ◽  
Akihiko Konagaya
Keyword(s):  
Deep Learning ◽  
Active Site ◽  
Binding Mode ◽  
Data Selection ◽  
Binding Mode Prediction

scholarly journals Exploring aromatic cage flexibility of the histone methyllysine reader protein Spindlin1 and its impact on binding mode prediction: an in silico study

2021 ◽  
Author(s):  
Chiara Luise ◽  
Dina Robaa ◽  
Wolfgang Sippl
Keyword(s):  
Molecular Dynamic ◽  
In Silico ◽  
Binding Pocket ◽  
Binding Mode ◽  
Molecular Dynamic Simulations ◽  
Flexible Docking ◽  
Dynamic Simulations ◽  
Binding Mode Prediction ◽  
Aromatic Cage ◽  
The Right

AbstractSome of the main challenges faced in drug discovery are pocket flexibility and binding mode prediction. In this work, we explored the aromatic cage flexibility of the histone methyllysine reader protein Spindlin1 and its impact on binding mode prediction by means of in silico approaches. We first investigated the Spindlin1 aromatic cage plasticity by analyzing the available crystal structures and through molecular dynamic simulations. Then we assessed the ability of rigid docking and flexible docking to rightly reproduce the binding mode of a known ligand into Spindlin1, as an example of a reader protein displaying flexibility in the binding pocket. The ability of induced fit docking was further probed to test if the right ligand binding mode could be obtained through flexible docking regardless of the initial protein conformation. Finally, the stability of generated docking poses was verified by molecular dynamic simulations. Accurate binding mode prediction was obtained showing that the herein reported approach is a highly promising combination of in silico methods able to rightly predict the binding mode of small molecule ligands in flexible binding pockets, such as those observed in some reader proteins.

Data-driven method for training data selection for deep learning

2021 ◽  
Author(s):  
C. Lacombe ◽  
I. Hammoud ◽  
J. Messud ◽  
H. Peng ◽  
T. Lesieur ◽  
...  
Keyword(s):  
Deep Learning ◽  
Training Data ◽  
Data Selection ◽  
Data Driven ◽  
Selection For ◽  
Training Data Selection

Recent insights into lytic polysaccharide monooxygenases (LPMOs)

2018 ◽  
Vol 46 (6) ◽  
pp. 1431-1447 ◽  
Author(s):  
Tobias Tandrup ◽  
Kristian E. H. Frandsen ◽  
Katja S. Johansen ◽  
Jean-Guy Berrin ◽  
Leila Lo Leggio
Keyword(s):  
Active Site ◽  
Room Temperature ◽  
Experimental Studies ◽  
Binding Mode ◽  
Structural Features ◽  
Oxygen Species ◽  
Animal Kingdom ◽  
X Ray ◽  
Lytic Polysaccharide Monooxygenases ◽  
Polysaccharide Monooxygenases

Lytic polysaccharide monooxygenases (LPMOs) are copper enzymes discovered within the last 10 years. By degrading recalcitrant substrates oxidatively, these enzymes are major contributors to the recycling of carbon in nature and are being used in the biorefinery industry. Recently, two new families of LPMOs have been defined and structurally characterized, AA14 and AA15, sharing many of previously found structural features. However, unlike most LPMOs to date, AA14 degrades xylan in the context of complex substrates, while AA15 is particularly interesting because they expand the presence of LPMOs from the predominantly microbial to the animal kingdom. The first two neutron crystallography structures have been determined, which, together with high-resolution room temperature X-ray structures, have putatively identified oxygen species at or near the active site of LPMOs. Many recent computational and experimental studies have also investigated the mechanism of action and substrate-binding mode of LPMOs. Perhaps, the most significant recent advance is the increasing structural and biochemical evidence, suggesting that LPMOs follow different mechanistic pathways with different substrates, co-substrates and reductants, by behaving as monooxygenases or peroxygenases with molecular oxygen or hydrogen peroxide as a co-substrate, respectively.

New hypotheses for the binding mode of 4- and 7-substituted indazoles in the active site of neuronal nitric oxide synthase

2012 ◽  
Vol 20 (17) ◽  
pp. 5296-5304 ◽  
Author(s):  
Elodie Lohou ◽  
Jana Sopkova-de Oliveira Santos ◽  
Pascale Schumann-Bard ◽  
Michel Boulouard ◽  
Silvia Stiebing ◽  
...  
Keyword(s):  
Nitric Oxide ◽  
Nitric Oxide Synthase ◽  
Active Site ◽  
Neuronal Nitric Oxide Synthase ◽  
Binding Mode ◽  
Oxide Synthase

scholarly journals Dissimilar Ligands Bind in a Similar Fashion: A Guide to Ligand Binding-Mode Prediction with Application to CELPP Studies

2021 ◽  
Vol 22 (22) ◽  
pp. 12320
Author(s):  
Xianjin Xu ◽  
Xiaoqin Zou
Keyword(s):  
Molecular Similarity ◽  
Complex Structure ◽  
Binding Mode ◽  
Molecular Structures ◽  
Complex Structures ◽  
Binding Modes ◽  
Ligand Complex ◽  
Systematic Analysis ◽  
Binding Mode Prediction ◽  
Similarity Principle

The molecular similarity principle has achieved great successes in the field of drug design/discovery. Existing studies have focused on similar ligands, while the behaviors of dissimilar ligands remain unknown. In this study, we developed an intercomparison strategy in order to compare the binding modes of ligands with different molecular structures. A systematic analysis of a newly constructed protein–ligand complex structure dataset showed that ligands with similar structures tended to share a similar binding mode, which is consistent with the Molecular Similarity Principle. More importantly, the results revealed that dissimilar ligands can also bind in a similar fashion. This finding may open another avenue for drug discovery. Furthermore, a template-guiding method was introduced for predicting protein–ligand complex structures. With the use of dissimilar ligands as templates, our method significantly outperformed the traditional molecular docking methods. The newly developed template-guiding method was further applied to recent CELPP studies.

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