Recent Trends in Drug Design and Discovery

2020 ◽  
Vol 20 (19) ◽  
pp. 1761-1770
Author(s):  
Devadasan Velmurugan ◽  
R. Pachaiappan ◽  
Chandrasekaran Ramakrishnan
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Molecular Modeling ◽  
Drug Design ◽  
Computational Methods ◽  
Data Analytics ◽  
Drug Targets ◽  
Structural Data ◽  
Target Protein ◽  
Structure Based Drug Design

Introduction: Structure-based drug design is a wide area of identification of selective inhibitors of a target of interest. From the time of the availability of three dimensional structure of the drug targets, mostly the proteins, many computational methods had emerged to address the challenges associated with drug design process. Particularly, drug-likeness, druggability of the target protein, specificity, off-target binding, etc., are the important factors to determine the efficacy of new chemical inhibitors. Objective: The aim of the present research was to improve the drug design strategies in field of design of novel inhibitors with respect to specific target protein in disease pathology. Recent statistical machine learning methods applied for structural and chemical data analysis had been elaborated in current drug design field. Methods: As the size of the biological data shows a continuous growth, new computational algorithms and analytical methods are being developed with different objectives. It covers a wide area, from protein structure prediction to drug toxicity prediction. Moreover, the computational methods are available to analyze the structural data of varying types and sizes of which, most of the semi-empirical force field and quantum mechanics based molecular modeling methods showed a proven accuracy towards analysing small structural data sets while statistics based methods such as machine learning, QSAR and other specific data analytics methods are robust for large scale data analysis. Results: In this present study, the background has been reviewed for new drug lead development with respect specific drug targets of interest. Overall approach of both the extreme methods were also used to demonstrate with the plausible outcome. Conclusion: In this chapter, we focus on the recent developments in the structure-based drug design using advanced molecular modeling techniques in conjunction with machine learning and other data analytics methods. Natural products based drug discovery is also discussed.

scholarly journals Abstract P-25: High-Resolution Cryo-Electron Microscopy Structure of the Staphylococcus Aureus Ribosome Brings to Light New Possible Drug Targets

2021 ◽  
Vol 11 (Suppl_1) ◽  
pp. S22-S23
Author(s):  
Alexander Golubev ◽  
Bulat Fatkhullin ◽  
Iskander Khusainov ◽  
Shamil Validov ◽  
Marat Yusupov ◽  
...  
Keyword(s):  
Staphylococcus Aureus ◽  
Protein Synthesis ◽  
Drug Discovery ◽  
Drug Design ◽  
Messenger Rna ◽  
Drug Targets ◽  
Structural Data ◽  
New Drugs ◽  
Fine Tuning ◽  
Structure Based Drug Design

Background: Antibiotic resistance is a growing worldwide problem. One of the major resistant bacterial pathogens is Staphylococcus aureus, which became a burden of healthcare systems around the world. To overcome the issue, more drug discovery studies are needed. One of the main antibiotic targets is a ribosome – the central hub of protein synthesis. Structural data of the ribosome and its features are a crucial milestone for the effective development of new drugs, especially using structure-based drug design approaches. Apart from many small structural features, ribosome possesses rRNA modifications that play a role in the fine-tuning of protein synthesis. Detailed species-specific structural data of the S. aureus ribosome is also a useful model for understanding the resistance mechanisms. This information could help with the design of new antibiotics and the upgrading of old ones. The data on S. aureus ribosomal RNA modifications and corresponding modification enzymes are very limited. Our aim was to improve the current models of the S. aureus ribosome by determining its structure with functional ligands at a much higher resolution - thereby creating a foundation for structure-based drug design experiments and research of new drug targets. Methods: The S. aureus ribosome complex consists of three components: ribosome, fMet-tRNAfMet, mRNA and 70S ribosome. The complex from purified components was formed in vitro and applied to cryo-EM grids. Data was collected at Titan Krios with Gatan K2 detector (IGBMC, France). The data was processed and modeled in Relion 2.1, Chimera, Coot, and Phenix. Results: We determined the cryo-EM reconstruction at 3.2 Å resolution of the S. aureus ribosome with P-site tRNA, messenger RNA. Based on the experimental map and existing bioinformatic data, we at the first time identified and assigned 10 modifications of S. aureus rRNA. We analyzed the positions of rRNA modifications and their possible functions. Conclusion: In this study, we describe our structure of S. aureus ribosome with functional ligands. The present model is the highest resolution and most precise that is available at the moment. We propose a set of methyltransferases as targets for future drug discovery studies. The proposed methyltransferases and corresponding modifications may play an important role in protein synthesis and its regulation.

scholarly journals Three-Dimensional Structures of Carbohydrates and Where to Find Them

2020 ◽  
Vol 21 (20) ◽  
pp. 7702 ◽  
Author(s):  
Sofya I. Scherbinina ◽  
Philip V. Toukach
Keyword(s):  
Experimental Data ◽  
Molecular Modeling ◽  
Computational Methods ◽  
Three Dimensional ◽  
Structural Diversity ◽  
Structural Data ◽  
Structural Features ◽  
Data Validation ◽  
Data Generation ◽  
Efficient Treatment

Analysis and systematization of accumulated data on carbohydrate structural diversity is a subject of great interest for structural glycobiology. Despite being a challenging task, development of computational methods for efficient treatment and management of spatial (3D) structural features of carbohydrates breaks new ground in modern glycoscience. This review is dedicated to approaches of chemo- and glyco-informatics towards 3D structural data generation, deposition and processing in regard to carbohydrates and their derivatives. Databases, molecular modeling and experimental data validation services, and structure visualization facilities developed for last five years are reviewed.

Techniques and Methods That Help to Make Big Data the Simplest Recipe for Success

2019 ◽  
pp. 161-194
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Data Analysis ◽  
Statistical Analysis ◽  
Data Analytics ◽  
Big Data Analysis ◽  
Customer Segmentation ◽  
Learning Context ◽  
Feature Vectors

Data analytics has grown in a machine learning context. Whatever the reason data is used or exploited, customer segmentation or marketing targeting, it must be processed first and represented on feature vectors. Many algorithms, such as clustering, regression, classification, and others, need to be represented and clarified in order to facilitate processing and statistical analysis. If we have seen, through the previous chapters, the importance of big data analysis (the Why?), as with every major innovation, the biggest confusion lies in the exact scope (What?) and its implementation (How?). In this chapter, we will take a look at the different algorithms and techniques analytics that we can use in order to exploit the large amounts of data.

scholarly journals Iron superoxide dismutases in eukaryotic pathogens: new insights from Apicomplexa andTrypanosomastructures

2015 ◽  
Vol 71 (5) ◽  
pp. 615-621 ◽  
Author(s):  
Isabelle Q. H. Phan ◽  
Douglas R. Davies ◽  
Nilmar Silvio Moretti ◽  
Dhanasekaran Shanmugam ◽  
Igor Cestari ◽  
...  
Keyword(s):  
Drug Targets ◽  
Leishmania Major ◽  
Structural Data ◽  
Babesia Bovis ◽  
Superoxide Dismutases ◽  
Intramolecular Electron Transfer ◽  
Structure Based Drug Design ◽  
Electron Transfer Pathway ◽  
Existing Structures ◽  
Isoform B

Prior studies have highlighted the potential of superoxide dismutases as drug targets in eukaryotic pathogens. This report presents the structures of three iron-dependent superoxide dismutases (FeSODs) fromTrypanosoma cruzi,Leishmania majorandBabesia bovis. Comparison with existing structures fromPlasmodiumand other trypanosome isoforms shows a very conserved overall fold with subtle differences. In particular, structural data suggest thatB. bovisFeSOD may display similar resistance to peroxynitrite-mediated inactivationviaan intramolecular electron-transfer pathway as previously described inT. cruziFeSOD isoform B, thus providing valuable information for structure-based drug design. Furthermore, lysine-acetylation results inT. cruziindicate that acetylation occurs at a position close to that responsible for the regulation of acetylation-mediated activity in the human enzyme.

scholarly journals Advances toward structure-based drug discovery for inflammasome targets

2021 ◽  
Vol 219 (1) ◽  
Author(s):  
Li Wang ◽  
Michael A. Crackower ◽  
Hao Wu
Keyword(s):  
Drug Discovery ◽  
High Resolution ◽  
Drug Design ◽  
Structural Biology ◽  
Drug Targets ◽  
Unmet Need ◽  
Structure Based Drug Design ◽  
Important Family ◽  
Recent Advances

Inflammasome proteins play an important role in many diseases of high unmet need, making them attractive drug targets. However, drug discovery for inflammasome proteins has been challenging in part due to the difficulty in solving high-resolution structures using cryo-EM or crystallography. Recent advances in the structural biology of NLRP3 and NLRP1 have provided the first set of data that proves a promise for structure-based drug design for this important family of targets.

scholarly journals Recent Trends and Applications of Molecular Modeling in GPCR–Ligand Recognition and Structure-Based Drug Design

2018 ◽  
Vol 19 (7) ◽  
pp. 2105 ◽  
Author(s):  
Xiaojing Yuan ◽  
Yechun Xu
Keyword(s):  
Molecular Modeling ◽  
Drug Design ◽  
Ligand Recognition ◽  
Energy Calculation ◽  
Enhanced Sampling ◽  
Structure Based Drug Design ◽  
Recent Trends ◽  
Indispensable Tool ◽  
G Protein Coupled

G protein-coupled receptors represent the largest family of human membrane proteins and are modulated by a variety of drugs and endogenous ligands. Molecular modeling techniques, especially enhanced sampling methods, have provided significant insight into the mechanism of GPCR–ligand recognition. Notably, the crucial role of the membrane in the ligand-receptor association process has earned much attention. Additionally, docking, together with more accurate free energy calculation methods, is playing an important role in the design of novel compounds targeting GPCRs. Here, we summarize the recent progress in the computational studies focusing on the above issues. In the future, with continuous improvement in both computational hardware and algorithms, molecular modeling would serve as an indispensable tool in a wider scope of the research concerning GPCR–ligand recognition as well as drug design targeting GPCRs.

A Review of Recent Advances and Research on Drug Target Identification Methods

2019 ◽  
Vol 20 (3) ◽  
pp. 209-216 ◽  
Author(s):  
Yang Hu ◽  
Tianyi Zhao ◽  
Ningyi Zhang ◽  
Ying Zhang ◽  
Liang Cheng
Keyword(s):  
Machine Learning ◽  
Computational Methods ◽  
Drug Target ◽  
Drug Targets ◽  
Target Identification ◽  
Machine Learning Algorithms ◽  
Topological Features ◽  
Drug Target Identification ◽  
Incomplete Datasets ◽  
Optimal Set

Background:From a therapeutic viewpoint, understanding how drugs bind and regulate the functions of their target proteins to protect against disease is crucial. The identification of drug targets plays a significant role in drug discovery and studying the mechanisms of diseases. Therefore the development of methods to identify drug targets has become a popular issue.Methods:We systematically review the recent work on identifying drug targets from the view of data and method. We compiled several databases that collect data more comprehensively and introduced several commonly used databases. Then divided the methods into two categories: biological experiments and machine learning, each of which is subdivided into different subclasses and described in detail.Results:Machine learning algorithms are the majority of new methods. Generally, an optimal set of features is chosen to predict successful new drug targets with similar properties. The most widely used features include sequence properties, network topological features, structural properties, and subcellular locations. Since various machine learning methods exist, improving their performance requires combining a better subset of features and choosing the appropriate model for the various datasets involved.Conclusion:The application of experimental and computational methods in protein drug target identification has become increasingly popular in recent years. Current biological and computational methods still have many limitations due to unbalanced and incomplete datasets or imperfect feature selection methods

Applications of Machine Learning in Drug Target Discovery

2020 ◽  
Vol 21 (10) ◽  
pp. 790-803 ◽  
Author(s):  
Dongrui Gao ◽  
Qingyuan Chen ◽  
Yuanqi Zeng ◽  
Meng Jiang ◽  
Yongqing Zhang
Keyword(s):  
Machine Learning ◽  
Drug Development ◽  
Computational Methods ◽  
Drug Target ◽  
Drug Targets ◽  
Drug Target Discovery ◽  
Target Discovery ◽  
Modern Drug ◽  
Applications Of Machine Learning

Drug target discovery is a critical step in drug development. It is the basis of modern drug development because it determines the target molecules related to specific diseases in advance. Predicting drug targets by computational methods saves a great deal of financial and material resources compared to in vitro experiments. Therefore, several computational methods for drug target discovery have been designed. Recently, machine learning (ML) methods in biomedicine have developed rapidly. In this paper, we present an overview of drug target discovery methods based on machine learning. Considering that some machine learning methods integrate network analysis to predict drug targets, network-based methods are also introduced in this article. Finally, the challenges and future outlook of drug target discovery are discussed.

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