Analysis of variants associated with abnormal drug responses, genetics, and genomics in drug design

2022 ◽  
pp. 209-235
Author(s):  
Moyra Smith
Keyword(s):  
Drug Design ◽  
Drug Responses ◽  
Genetics And Genomics

Computational and Pharmacogenomic Insights on Hypertension Treatment: Rational Drug Design and Optimization Strategies

2019 ◽  
Vol 21 (1) ◽  
pp. 18-33 ◽  
Author(s):  
Lakshmanan Loganathan ◽  
Krishnasamy Gopinath ◽  
Vadivel Murugan Sankaranarayanan ◽  
Ritushree Kukreti ◽  
Kannan Rajendran ◽  
...  
Keyword(s):  
Drug Design ◽  
Drug Targets ◽  
Antihypertensive Drugs ◽  
Rational Drug Design ◽  
Hypertension Treatment ◽  
Genetic Traits ◽  
Drug Responses ◽  
Computational Screening ◽  
Rational Drug ◽  
Multiple Drugs

Background:: Hypertension is a prevalent cardiovascular complication caused by genetic and nongenetic factors. Blood pressure (BP) management is difficult because most patients become resistant to monotherapy soon after treatment initiation. Although many antihypertensive drugs are available, some patients do not respond to multiple drugs. Identification of personalized antihypertensive treatments is a key for better BP management. Objective:: This review aimed to elucidate aspects of rational drug design and other methods to develop better hypertension management. Results:: Among hypertension-related signaling mechanisms, the renin-angiotensin-aldosterone system is the leading genetic target for hypertension treatment. Identifying a single drug that acts on multiple targets is an emerging strategy for hypertension treatment, and could be achieved by discovering new drug targets with less mutated and highly conserved regions. Extending pharmacogenomics research to include patients with hypertension receiving multiple antihypertensive drugs could help identify the genetic markers of hypertension. However, available evidence on the role of pharmacogenomics in hypertension is limited and primarily focused on candidate genes. Studies on hypertension pharmacogenomics aim to identify the genetic causes of response variations to antihypertensive drugs. Genetic association studies have identified single nucleotide polymorphisms affecting drug responses. To understand how genetic traits alter drug responses, computational screening of mutagenesis can be utilized to observe drug response variations at the protein level, which can help identify new inhibitors and drug targets to manage hypertension. Conclusions:: Rational drug design facilitates the discovery and design of potent inhibitors. However, further research and clinical validation are required before novel inhibitors can be clinically used as antihypertensive therapies.

Kindler's Syndrome with Recurrent Neutropenia: Report of Two Cases from Saudi Arabia

2020 ◽  
Author(s):  
Yousef Binamer ◽  
Muzamil A. Chisti
Keyword(s):  
Autosomal Recessive ◽  
Common Mechanism ◽  
Sequencing Analysis ◽  
Loss Of Function ◽  
Skin Atrophy ◽  
Whole Exome ◽  
Infancy And Childhood ◽  
Genetics And Genomics ◽  
New Feature ◽  
Generation Sequencing

AbstractKindler syndrome (KS) is a rare photosensitivity disorder with autosomal recessive mode of inheritance. It is characterized by acral blistering in infancy and childhood, progressive poikiloderma, skin atrophy, abnormal photosensitivity, and gingival fragility. Besides these major features, many minor presentations have also been reported in the literature. We are reporting two cases with atypical features of the syndrome and a new feature of recurrent neutropenia. Whole exome sequencing analysis was done using next-generation sequencing which detected a homozygous loss-of-function (LOF) variant of FERMT1 in both patients. The variant is classified as a pathogenic variant as per the American College of Medical Genetics and Genomics guidelines. Homozygous LOF variants of FERMT1 are a common mechanism of KS and as such confirm the diagnosis of KS in our patients even though the presentation was atypical.

AutoLinker: Automatic Fragment Linking with Deep Conditional Transformer Neural Networks

2020 ◽  
Author(s):  
Yuyao Yang ◽  
Shuangjia Zheng ◽  
Shimin Su ◽  
Jun Xu ◽  
Hongming Chen
Keyword(s):  
Neural Networks ◽  
Drug Discovery ◽  
Drug Design ◽  
State Of The Art ◽  
The State ◽  
Generative Model ◽  
Lead Optimization ◽  
Scaffold Hopping ◽  
Reference Models ◽  
Lead Generation

Fragment based drug design represents a promising drug discovery paradigm complimentary to the traditional HTS based lead generation strategy. How to link fragment structures to increase compound affinity is remaining a challenge task in this paradigm. Hereby a novel deep generative model (AutoLinker) for linking fragments is developed with the potential for applying in the fragment-based lead generation scenario. The state-of-the-art transformer architecture was employed to learn the linker grammar and generate novel linker. Our results show that, given starting fragments and user customized linker constraints, our AutoLinker model can design abundant drug-like molecules fulfilling these constraints and its performance was superior to other reference models. Moreover, several examples were showcased that AutoLinker can be useful tools for carrying out drug design tasks such as fragment linking, lead optimization and scaffold hopping.

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.

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