Computational Modeling and Simulation of the Bcl-2 Family: Paving the Way for Rational Drug Design

AbstractThis is a brief review of the computational modeling of protein-ligand interactions using a recently developed fully polarizable continuum model (FPCM) and rational drug design. Computational modeling has become a powerful tool in understanding detailed protein-ligand interactions at molecular level and in rational drug design. To study the binding of a protein with multiple molecular species of a ligand, one must accurately determine both the relative free energies of all of the molecular species in solution and the corresponding microscopic binding free energies for all of the molecular species binding with the protein. In this paper, we aim to provide a brief overview of the recent development in computational modeling of the solvent effects on the detailed protein-ligand interactions involving multiple molecular species of a ligand related to rational drug design. In particular, we first briefly discuss the main challenges in computational modeling of the detailed protein-ligand interactions involving the multiple molecular species and then focus on the FPCM model and its applications. The FPCM method allows accurate determination of the solvent effects in the first-principles quantum mechanism (QM) calculations on molecules in solution. The combined use of the FPCM-based QM calculations and other computational modeling and simulations enables us to accurately account for a protein binding with multiple molecular species of a ligand in solution. Based on the computational modeling of the detailed protein-ligand interactions, possible new drugs may be designed rationally as either small-molecule ligands of the protein or engineered proteins that bind/metabolize the ligand. The computational drug design has successfully led to discovery and development of promising drugs.

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Investigating the Conformation of S100β Protein Under Physiological Parameters Using Computational Modeling: A Clue for Rational Drug Design

The Open Biomedical Engineering Journal ◽

10.2174/1874120701812010073 ◽

2018 ◽

Vol 12 (1) ◽

pp. 73-73

Author(s):

Elvis K. Tiburu ◽

Ibrahim Issah ◽

Mabel Darko ◽

Robert E. Armah-Sekum ◽

Stephen O. A. Gyampo ◽

...

Keyword(s):

Drug Design ◽

Computational Modeling ◽

Physiological Parameters ◽

Rational Drug Design ◽

S100β Protein ◽

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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Computational Modeling and Simulation of Film-Condensation

10.21236/ada578521 ◽

2013 ◽

Author(s):

Atul Sharma ◽

Amit Agrawal

Keyword(s):

Computational Modeling ◽

Modeling And Simulation ◽

Film Condensation ◽

Computational Modeling And Simulation

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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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