Unveiling the binding mode of adenosine deaminase inhibitors to the active site of the enzyme: implication for rational drug design

Background: Human African trypanosomiasis is a fatal disease prevalent in approximately 36 sub-Saharan countries. Emerging reports of drug resistance in Trypanosoma brucei are a serious cause of concern as only limited drugs are available for the treatment of the disease. Pteridine reductase is an enzyme of Trypanosoma brucei. Methods: It plays a critical role in the pterin metabolic pathway that is absolutely essential for its survival in the human host. The success of finding a potent inhibitor in structure-based drug design lies within the ability of computational tools to efficiently and accurately dock a ligand into the binding cavity of the target protein. Here we report the computational characterization of Trypanosoma brucei pteridine reductase (Tb-PR) active-site using twenty-four high-resolution co-crystal structures with various drugs. Structurally, the Tb-PR active site can be grouped in two clusters; one with high Root Mean Square Deviation (RMSD) of atomic positions and another with low RMSD of atomic positions. These clusters provide fresh insight for rational drug design against Tb-PR. Henceforth, the effect of several factors on docking accuracy, including ligand and protein flexibility were analyzed using Fred. Results: The online server was used to analyze the side chain flexibility and four proteins were selected on the basis of results. The proteins were subjected to small-scale virtual screening using 85 compounds, and statistics were calculated using Bedroc and roc curves. The enrichment factor was also calculated for the proteins and scoring functions. The best scoring function was used to understand the ligand protein interactions with top common compounds of four proteins. In addition, we made a 3D structural comparison between the active site of Tb-PR and Leishmania major pteridine reductase (Lm- PR). We described key structural differences between Tb-PR and Lm-PR that can be exploited for rational drug design against these two human parasites. Conclusion: The results indicated that relying just on re-docking and cross-docking experiments for virtual screening of libraries isn’t enough and results might be misleading. Hence it has been suggested that small scale virtual screening should be performed prior to large scale screening.

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Computational Methods Applied to Rational Drug Design

The Open Medicinal Chemistry Journal ◽

10.2174/1874104501610010007 ◽

2016 ◽

Vol 10 (1) ◽

pp. 7-20 ◽

Cited By ~ 20

Author(s):

David Ramírez

Keyword(s):

Drug Design ◽

Small Molecules ◽

De Novo ◽

Relevant Information ◽

Binding Mode ◽

Rational Drug Design ◽

Computational Techniques ◽

Computational Tools ◽

Rational Drug ◽

Number Of Publications

Due to the synergic relationship between medical chemistry, bioinformatics and molecular simulation, the development of new accurate computational tools for small molecules drug design has been rising over the last years. The main result is the increased number of publications where computational techniques such as molecular docking,de novodesign as well as virtual screening have been used to estimate the binding mode, site and energy of novel small molecules. In this work I review some tools, which enable the study of biological systems at the atomistic level, providing relevant information and thereby, enhancing the process of rational drug design.

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Crystal Structure of Lumazine Synthase fromMycobacterium tuberculosisas a Target for Rational Drug Design: Binding Mode of a New Class of Purinetrione Inhibitors†,‡

Biochemistry ◽

10.1021/bi047848a ◽

2005 ◽

Vol 44 (8) ◽

pp. 2746-2758 ◽

Cited By ~ 43

Author(s):

Ekaterina Morgunova ◽

Winfried Meining ◽

Boris Illarionov ◽

Ilka Haase ◽

Guangyi Jin ◽

...

Keyword(s):

Crystal Structure ◽

Drug Design ◽

Binding Mode ◽

Rational Drug Design ◽

New Class ◽

Lumazine Synthase ◽

Rational Drug

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Improving the Accuracy of Protein-Ligand Binding Affinity Prediction by Deep Learning Models: Benchmark and Model

10.26434/chemrxiv.9866912 ◽

2019 ◽

Author(s):

Mohammad Rezaei ◽

Yanjun Li ◽

Xiaolin Li ◽

Chenglong Li

Keyword(s):

Deep Learning ◽

Drug Design ◽

Binding Affinity ◽

Benchmark Dataset ◽

Rational Drug Design ◽

Learning Models ◽

Structure Based Drug Design ◽

Binding Affinity Prediction ◽

Affinity Prediction ◽

Rational Drug

Introduction: The ability to discriminate among ligands binding to the same protein target in terms of their relative binding affinity lies at the heart of structure-based drug design. Any improvement in the accuracy and reliability of binding affinity prediction methods decreases the discrepancy between experimental and computational results. Objectives: The primary objectives were to find the most relevant features affecting binding affinity prediction, least use of manual feature engineering, and improving the reliability of binding affinity prediction using efficient deep learning models by tuning the model hyperparameters. Methods: The binding site of target proteins was represented as a grid box around their bound ligand. Both binary and distance-dependent occupancies were examined for how an atom affects its neighbor voxels in this grid. A combination of different features including ANOLEA, ligand elements, and Arpeggio atom types were used to represent the input. An efficient convolutional neural network (CNN) architecture, DeepAtom, was developed, trained and tested on the PDBbind v2016 dataset. Additionally an extended benchmark dataset was compiled to train and evaluate the models. Results: The best DeepAtom model showed an improved accuracy in the binding affinity prediction on PDBbind core subset (Pearson’s R=0.83) and is better than the recent state-of-the-art models in this field. In addition when the DeepAtom model was trained on our proposed benchmark dataset, it yields higher correlation compared to the baseline which confirms the value of our model. Conclusions: The promising results for the predicted binding affinities is expected to pave the way for embedding deep learning models in virtual screening and rational drug design fields.

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Rational Drug Design Approach of Receptor Tyrosine Kinase Type III Inhibitors

Current Medicinal Chemistry ◽

10.2174/0929867325666180622143548 ◽

2020 ◽

Vol 26 (42) ◽

pp. 7623-7640 ◽

Cited By ~ 1

Author(s):

Cheolhee Kim ◽

Eunae Kim

Keyword(s):

Drug Design ◽

Tyrosine Kinase ◽

Receptor Tyrosine Kinase ◽

Synergistic Effects ◽

Rational Drug Design ◽

Computational Techniques ◽

Biological Targets ◽

Type Iii ◽

Theoretical Approaches ◽

Rational Drug

: Rational drug design is accomplished through the complementary use of structural biology and computational biology of biological macromolecules involved in disease pathology. Most of the known theoretical approaches for drug design are based on knowledge of the biological targets to which the drug binds. This approach can be used to design drug molecules that restore the balance of the signaling pathway by inhibiting or stimulating biological targets by molecular modeling procedures as well as by molecular dynamics simulations. Type III receptor tyrosine kinase affects most of the fundamental cellular processes including cell cycle, cell migration, cell metabolism, and survival, as well as cell proliferation and differentiation. Many inhibitors of successful rational drug design show that some computational techniques can be combined to achieve synergistic effects.

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Recent Advances in the Rational Drug Design Based on Multi-target Ligands

Current Medicinal Chemistry ◽

10.2174/0929867327666200102120652 ◽

2020 ◽

Vol 27 (28) ◽

pp. 4720-4740 ◽

Cited By ~ 3

Author(s):

Ting Yang ◽

Xin Sui ◽

Bing Yu ◽

Youqing Shen ◽

Hailin Cong

Keyword(s):

Drug Resistance ◽

Clinical Practice ◽

Side Effects ◽

Drug Design ◽

Rational Drug Design ◽

Health Conditions ◽

Pharmacokinetic Parameters ◽

Single Target ◽

Rational Drug ◽

Huge Challenge

Multi-target drugs have gained considerable attention in the last decade owing to their advantages in the treatment of complex diseases and health conditions linked to drug resistance. Single-target drugs, although highly selective, may not necessarily have better efficacy or fewer side effects. Therefore, more attention is being paid to developing drugs that work on multiple targets at the same time, but developing such drugs is a huge challenge for medicinal chemists. Each target must have sufficient activity and have sufficiently characterized pharmacokinetic parameters. Multi-target drugs, which have long been known and effectively used in clinical practice, are briefly discussed in the present article. In addition, in this review, we will discuss the possible applications of multi-target ligands to guide the repositioning of prospective drugs.

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