scholarly journals Binding Mode Prediction of Evodiamine within Vanilloid Receptor TRPV1

2012 ◽  
Vol 13 (7) ◽  
pp. 8958-8969 ◽  
Author(s):  
Zhanli Wang ◽  
Lidan Sun ◽  
Hui Yu ◽  
Yanhui Zhang ◽  
Wuzhuang Gong ◽  
...  
Keyword(s):  
Binding Mode ◽  
Vanilloid Receptor ◽  
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.

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.

scholarly journals DNA-binding residues and binding mode prediction with binding-mechanism concerned models

BMC Genomics ◽  
2009 ◽  
Vol 10 (Suppl 3) ◽  
pp. S23 ◽  
Author(s):  
Yu-Feng Huang ◽  
Chun-Chin Huang ◽  
Yu-Cheng Liu ◽  
Yen-Jen Oyang ◽  
Chien-Kang Huang
Keyword(s):  
Dna Binding ◽  
Binding Mode ◽  
Binding Mechanism ◽  
Binding Mode Prediction ◽  
Binding Residues

scholarly journals Comparative analysis of ChIP-exo peak-callers: impact of data quality, read duplication and binding subtypes

2019 ◽  
Author(s):  
Vasudha Sharma ◽  
Sharmistha MAJUMDAR
Keyword(s):  
Chromatin Immunoprecipitation ◽  
Binding Mode ◽  
Dna Interaction ◽  
Binding Mode Prediction ◽  
Direct Binding ◽  
Single Base Pair ◽  
Self Learning ◽  
Peak Caller ◽  
Better Than

Abstract Background: ChIP (Chromatin immunoprecipitation)-exo has emerged as an important and versatile improvement over conventional ChIP-seq as it reduces the level of noise, maps the transcription factor (TF) binding location in a very precise manner, upto single base-pair resolution, and enables binding mode prediction. Availability of numerous peak-callers for analyzing ChIP-exo reads has motivated the need to assess their performance and report which tool executes reasonably well for the task. Results: This study has focussed on comparing peak-callers that report direct binding events with those that report indirect binding events. The effect of strandedness of reads and duplication of data on the performance of peak-callers has been investigated. The number of peaks reported by each peak-caller is compared followed by a comparison of the annotated motifs present in the reported peaks. The significance of peaks is assessed based on the presence of a motif in top peaks. Indirect binding tools have been compared on the basis of their ability to identify annotated motifs and predict mode of protein-DNA interaction. Conclusion: By studying the output of the peak-callers investigated in this study, it is concluded that the tools that use self-learning algorithms, i.e. the tools that estimate all the essential parameters from the aligned reads, perform better than the algorithms which require formation of peak-pairs. The latest tools that account for indirect binding of TFs appear to be an upgrade over the available tools, as they are able to reveal valuable information about the mode of binding in addition to direct binding. Furthermore, the quality of ChIP-exo reads have important consequences on the output of data analysis.

scholarly journals Comparative analysis of ChIP-exo peak-callers: impact of data quality, read duplication and binding subtypes

2019 ◽  
Author(s):  
Vasudha Sharma ◽  
Sharmistha Majumdar
Keyword(s):  
Chromatin Immunoprecipitation ◽  
Binding Mode ◽  
Dna Interaction ◽  
Binding Mode Prediction ◽  
Direct Binding ◽  
Single Base Pair ◽  
Self Learning ◽  
Peak Caller ◽  
Better Than

AbstractBackgroundChIP (Chromatin immunoprecipitation)-exo has emerged as an important and versatile improvement over conventional ChIP-seq as it reduces the level of noise, maps the transcription factor (TF) binding location in a very precise manner, upto single base-pair resolution, and enables binding mode prediction. Availability of numerous peak-callers for analyzing ChIP-exo reads has motivated the need to assess their performance and report which tool executes reasonably well for the task.ResultsThis study has focussed on comparing peak-callers that report direct binding events with those that report indirect binding events. The effect of strandedness of reads and duplication of data on the performance of peak-callers has been investigated. The number of peaks reported by each peak-caller is compared followed by a comparison of the annotated motifs present in the reported peaks. The significance of peaks is assessed based on the presence of a motif in top peaks. Indirect binding tools have been compared on the basis of their ability to identify annotated motifs and predict mode of protein-DNA interaction.ConclusionBy studying the output of the peak-callers investigated in this study, it is concluded that the tools that use self-learning algorithms, i.e. the tools that estimate all the essential parameters from the aligned reads, perform better than the algorithms which require formation of peak-pairs. The latest tools that account for indirect binding of TFs appear to be an upgrade over the available tools, as they are able to reveal valuable information about the mode of binding in addition to direct binding. Furthermore, the quality of ChIP-exo reads have important consequences on the output of data analysis.

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