scholarly journals Comprehensive computational design of ordered peptide macrocycles

Science ◽  
2017 ◽  
Vol 358 (6369) ◽  
pp. 1461-1466 ◽  
Author(s):  
Parisa Hosseinzadeh ◽  
Gaurav Bhardwaj ◽  
Vikram Khipple Mulligan ◽  
Matthew D. Shortridge ◽  
Timothy W. Craven ◽  
...  
Keyword(s):  
Amino Acids ◽  
Computational Models ◽  
Computational Design ◽  
Rational Drug Design ◽  
Structural Space ◽  
Rational Drug ◽  
Macrocyclic Peptides ◽  
The Rich ◽  
Hydrophobic Cores ◽  
Stable Structures

Mixed-chirality peptide macrocycles such as cyclosporine are among the most potent therapeutics identified to date, but there is currently no way to systematically search the structural space spanned by such compounds. Natural proteins do not provide a useful guide: Peptide macrocycles lack regular secondary structures and hydrophobic cores, and can contain local structures not accessible with l-amino acids. Here, we enumerate the stable structures that can be adopted by macrocyclic peptides composed of l- and d-amino acids by near-exhaustive backbone sampling followed by sequence design and energy landscape calculations. We identify more than 200 designs predicted to fold into single stable structures, many times more than the number of currently available unbound peptide macrocycle structures. Nuclear magnetic resonance structures of 9 of 12 designed 7- to 10-residue macrocycles, and three 11- to 14-residue bicyclic designs, are close to the computational models. Our results provide a nearly complete coverage of the rich space of structures possible for short peptide macrocycles and vastly increase the available starting scaffolds for both rational drug design and library selection methods.

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

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

<b>Introduction:</b> 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.<br><b>Objectives:</b> 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.<br><b>Methods:</b> 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.<br><b>Results: </b>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.<br><b>Conclusions:</b> 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.

Rational Drug Design Approach of Receptor Tyrosine Kinase Type III Inhibitors

2020 ◽  
Vol 26 (42) ◽  
pp. 7623-7640 ◽  
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.

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

2020 ◽  
Vol 27 (28) ◽  
pp. 4720-4740 ◽  
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.

Rational Drug Design Paradigms: The Odyssey for Designing Better Drugs

2015 ◽  
Vol 18 (3) ◽  
pp. 238-256 ◽  
Author(s):  
Tahsin Kellici ◽  
Dimitrios Ntountaniotis ◽  
Eleni Vrontaki ◽  
George Liapakis ◽  
Panagiota Moutevelis-Minakakis ◽  
...  
Keyword(s):  
Drug Design ◽  
Rational Drug Design ◽  
Rational Drug

scholarly journals Evaluation of log P, pKa, and log D predictions from the SAMPL7 blind challenge

2021 ◽  
Author(s):  
Teresa Danielle Bergazin ◽  
Nicolas Tielker ◽  
Yingying Zhang ◽  
Junjun Mao ◽  
M. R. Gunner ◽  
...  
Keyword(s):  
Free Energy ◽  
Partition Coefficients ◽  
Physical Property ◽  
Rational Drug Design ◽  
Log P ◽  
Prediction Methods ◽  
Research Groups ◽  
Reasonable Accuracy ◽  
Statistical Assessment ◽  
Rational Drug

AbstractThe Statistical Assessment of Modeling of Proteins and Ligands (SAMPL) challenges focuses the computational modeling community on areas in need of improvement for rational drug design. The SAMPL7 physical property challenge dealt with prediction of octanol-water partition coefficients and pKa for 22 compounds. The dataset was composed of a series of N-acylsulfonamides and related bioisosteres. 17 research groups participated in the log P challenge, submitting 33 blind submissions total. For the pKa challenge, 7 different groups participated, submitting 9 blind submissions in total. Overall, the accuracy of octanol-water log P predictions in the SAMPL7 challenge was lower than octanol-water log P predictions in SAMPL6, likely due to a more diverse dataset. Compared to the SAMPL6 pKa challenge, accuracy remains unchanged in SAMPL7. Interestingly, here, though macroscopic pKa values were often predicted with reasonable accuracy, there was dramatically more disagreement among participants as to which microscopic transitions produced these values (with methods often disagreeing even as to the sign of the free energy change associated with certain transitions), indicating far more work needs to be done on pKa prediction methods.

scholarly journals The Kv1.3 K+ channel in the immune system and its “precision pharmacology” using peptide toxins

2021 ◽  
Vol 72 (1) ◽  
pp. 75-83
Author(s):  
Zoltan Varga ◽  
Gabor Tajti ◽  
Gyorgy Panyi
Keyword(s):  
T Cells ◽  
Immune System ◽  
Ion Channels ◽  
Rational Drug Design ◽  
Scorpion Toxin ◽  
Effector Memory ◽  
K Channel ◽  
Effector Memory T Cells ◽  
Human T Cells ◽  
Rational Drug

AbstractSince the discovery of the Kv1.3 voltage-gated K+ channel in human T cells in 1984, ion channels are considered crucial elements of the signal transduction machinery in the immune system. Our knowledge about Kv1.3 and its inhibitors is outstanding, motivated by their potential application in autoimmune diseases mediated by Kv1.3 overexpressing effector memory T cells (e.g., Multiple Sclerosis). High affinity Kv1.3 inhibitors are either small organic molecules (e.g., Pap-1) or peptides isolated from venomous animals. To date, the highest affinity Kv1.3 inhibitors with the best Kv1.3 selectivity are the engineered analogues of the sea anemone peptide ShK (e.g., ShK-186), the engineered scorpion toxin HsTx1[R14A] and the natural scorpion toxin Vm24. These peptides inhibit Kv1.3 in picomolar concentrations and are several thousand-fold selective for Kv1.3 over other biologically critical ion channels. Despite the significant progress in the field of Kv1.3 molecular pharmacology several progressive questions remain to be elucidated and discussed here. These include the conjugation of the peptides to carriers to increase the residency time of the peptides in the circulation (e.g., PEGylation and engineering the peptides into antibodies), use of rational drug design to create novel peptide inhibitors and understanding the potential off-target effects of Kv1.3 inhibition.

Sign in / Sign up

Export Citation Format

Share Document