Molecular docking analysis of antimicrobial peptides with the CXCL1 protein target for colorectal cancer

Antimicrobial peptides (AMPs) play a prominent role in drug discovery due to the rapid increase in drug resistant infections. Hence, we report the molecular docking analysis of antimicrobial peptides MREEKKERKRD and MVQGAKRGGRLHRV with the target protein CXCL1 in the context of colorectal cancer for further consideration in drug discovery.     

Natural product-like synthetic libraries

There is a paucity of chemical matter suitably poised for effective drug development. Improving the quality and efficiency of research early on in the drug discovery process has been a long standing objective for the drug industry and improvements to the accessibility and quality of compound screening decks might have a significant and positive impact. In the absence of specific molecular information that can be modeled and used predicatively we are far from identifying which small molecules are most relevant to emerging biological targets such as protein-protein interactions. Natural products have been historically successful as an entry point for drug discovery and recently screening libraries are being synthesized to emulate natural product like features.     

Novel computational biology methods and their applications to drug discovery

Computational biology methods are now firmly entrenched in the drug discovery process. These methods focus on modeling and simulations of biological systems to complement and direct conventional experimental approaches. Two important branches of computational biology include protein homology modeling and the computational biophysics method of molecular dynamics. Protein modeling methods attempt to accurately predict three-dimensional (3D) structures of uncrystallized proteins for subsequent structure-based drug design applications. Molecular dynamics methods aim to elucidate the molecular motions of the static representations of crystallized protein structures. In this review we highlight recent novel methodologies in the field of homology modeling and molecular dynamics. Selected drug discovery applications using these methods conclude the review.     

AlphaFold2 protein structure prediction: Implications for drug discovery

The drug discovery process involves designing compounds to selectively interact with their targets. The majority of therapeutic targets for low molecular weight (small molecule) drugs are proteins. The outstanding accuracy with which recent artificial intelligence methods compile the three-dimensional structure of proteins has made protein targets more accessible to the drug design process. Here, we present our perspective of the significance of accurate protein structure prediction on various stages of the small molecule drug discovery life cycle focusing on current capabilities and assessing how further evolution of such predictive procedures can have a more decisive impact in the discovery of new medicines.     

Dictyostelium discoideum to human cells: pharmacogenetic studies demonstrate a role for sphingolipids in chemoresistance

Resistance to chemotherapy is a major obstacle for the treatment of cancer and a subject of extensive research. Numerous mechanisms of drug resistance have been proposed, and they differ for different drugs. Nevertheless, it is clear that our understanding of this important problem is still incomplete, and that new targets for modulating therapy still await discovery. The attractive biology and the availability of powerful molecular techniques have made the cellular slime mold Dictyostelium discoideum, a powerful non-mammalian model for drug target discovery, and the problem of drug resistance. To understand the molecular basis of chemoresistance to the widely used drug cisplatin, both genetic and pharmacological approaches have been applied to this versatile experimental system. These studies have resulted in the identification of novel molecular pathways which can be used to increase the efficacy of cisplatin, and brought attention to the role of sphingolipids in mediating the cellular response to chemotherapeutic drugs. In the following review, we will describe the history and utility of D. discoideum in pharmacogenetics, and discuss recent studies which focus attention on the role of sphingolipids in chemotherapy and chemoresistance.     

CNS Drug Design Based on Principles of Blood-Brain Barrier Transport

Abstract: Lipid-soluble small molecules with a molecular mass under a 400–600-Da threshold are transported readily through the blood-brain barrier in vivo owing to lipid-mediated transport. However, other small molecules lacking these particular molecular properties, antisense drugs, and peptide-based pharmaceuticals generally undergo negligible transport through the blood-brain barrier in pharmacologically significant amounts. Therefore, if present day CNS drug discovery programs are to avoid termination caused by negligible blood-brain barrier transport, it is important to merge CNS drug discovery and CNS drug delivery as early as possible in the overall CNS drug development process. Strategies for special formulation that enable drug transport through the blood-brain barrier arise from knowledge of the molecular and cellular biology of blood-brain barrier transport processes.     

Genomic filtering: an approach to discovering novel antiparasitics

Genomic filtering is a rapid approach to identifying and prioritizing molecular targets for drug discovery. For infectious disease applications, comparative genomics filters allow the selection of pathogen-specific gene products, whereas functional genomics filters, such as RNA interference (RNAi), allow the selection of gene products essential for pathogen survival. The approach is especially applicable to antiparasitic drug discovery where the phylogenetic distance between parasite and host make the likelihood of drug cross-toxicity due to conservation of molecular targets greater than for more distantly related pathogens such as prokaryotes. This article discusses some of the inherent challenges of applying genomics to the early steps of drug discovery and describes one successful comparative and functional genomics filtering strategy that has been implemented to prioritize molecular targets and identify chemical leads for nematode control.     

Opportunities for pharmacoproteomics in biomarker discovery

Proteomic data are a uniquely valuable resource for drug response prediction and biomarker discovery because most drugs interact directly with proteins in target cells rather than with DNA or RNA. Recent advances in mass spectrometry and associated processing methods have enabled the generation of large-scale proteomic datasets. Here we review the significant opportunities that currently exist to combine large-scale proteomic data with drug-related research, a field termed pharmacoproteomics. We describe successful applications of drug response prediction using molecular data, with an emphasis on oncology. We focus on technical advances in data-independent acquisition mass spectrometry (DIA-MS) that can facilitate the discovery of protein biomarkers for drug responses, alongside the increased availability of big biomedical data. We spotlight new opportunities for machine learning in pharmacoproteomics, driven by the combination of these large datasets and improved high-performance computing. Finally, we explore the value of pre-clinical models for pharmacoproteomic studies and the accompanying challenges of clinical validation. We propose that pharmacoproteomics offers the potential for novel discovery and innovation within the cancer landscape.     

Validation of ion channel targets

A prerequisite for a successful target-based drug discovery program is a robust data set that increases confidence in the validation of the molecular target and the therapeutic approach. Given the significant time and resource investment required to carry a drug to market, early selection of targets that can be modulated safely and effectively forms the basis for a strong portfolio and pipeline. In this article we present some of the more useful scientific approaches that can be applied toward the validation of ion channel targets, a molecular family with a history of clinical success in therapeutic areas such as cardiovascular, respiratory, pain and neuroscience.     

New approaches to molecular cancer therapeutics

Cancer drug development is leading the way in exploiting molecular biological and genetic information to develop "personalized" medicine. The new paradigm is to develop agents that target the precise molecular pathology driving the progression of individual cancers. Drug developers have benefited from decades of academic cancer research and from investment in genomics, genetics and automation; their success is exemplified by high-profile drugs such as Herceptin (trastuzumab), Gleevec (imatinib), Tarceva (erlotinib) and Avastin (bevacizumab). However, only 5% of cancer drugs entering clinical trials reach marketing approval. Cancer remains a high unmet medical need, and many potential cancer targets remain undrugged. In this review we assess the status of the discovery and development of small-molecule cancer therapeutics. We show how chemical biology approaches offer techniques for interconnecting elements of the traditional linear progression from gene to drug, thereby providing a basis for increasing speed and success in cancer drug discovery.     

High-Performance Drug Discovery: Computational Screening by Combining Docking and Molecular Dynamics Simulations

Virtual compound screening using molecular docking is widely used in the discovery of new lead compounds for drug design. However, this method is not completely reliable and therefore unsatisfactory. In this study, we used massive molecular dynamics simulations of protein-ligand conformations obtained by molecular docking in order to improve the enrichment performance of molecular docking. Our screening approach employed the molecular mechanics/Poisson-Boltzmann and surface area method to estimate the binding free energies. For the top-ranking 1,000 compounds obtained by docking to a target protein, approximately 6,000 molecular dynamics simulations were performed using multiple docking poses in about a week. As a result, the enrichment performance of the top 100 compounds by our approach was improved by 1.6–4.0 times that of the enrichment performance of molecular dockings. This result indicates that the application of molecular dynamics simulations to virtual screening for lead discovery is both effective and practical. However, further optimization of the computational protocols is required for screening various target proteins.     

中药网络药理学研究中的生物信息学方法

为了更有效地治疗癌症、心血管疾病、免疫系统疾病等复杂疾病,基于分子网络的多靶点药物发现理念逐渐成为一种新的趋势,而中药整体、辨证、协同的用药观再一次引起了药物发现领域的极大兴趣。中药在治疗复杂慢性疾病方面有确切的疗效和较小的毒副作用。中药网络药理学从分子网络调控的水平上阐明中药的作用机制,为多靶点药物发现提供有益的启示和借鉴,并有可能从临床有效的中药反向开发现代多组分、多靶点新药。针对基于生物分子网络的中药药理学研究路线中的4 个步骤,介绍近年来中药网络药理学研究中相关的生物信息学方法。     

Ensemble Docking in Drug Discovery

Ensemble docking corresponds to the generation of an “ensemble” of drug target conformations in computational structure-based drug discovery, often obtained by using molecular dynamics simulation, that is used in docking candidate ligands. This approach is now well established in the field of early-stage drug discovery. This review gives a historical account of the development of ensemble docking and discusses some pertinent methodological advances in conformational sampling.     

Caenorhabditis elegans: a versatile platform for drug discovery

Drug discovery and drug target identification are two intimately linked facets of intervention strategies aimed at effectively combating pathological conditions in humans. Simple model organisms provide attractive platforms for devising and streamlining efficient drug discovery and drug target identification methodologies. The nematode worm Caenorhabditis elegans has emerged as a particularly convenient and versatile tool that can be exploited to achieve these goals. Although C. elegans is a relatively modern addition to the arsenal of model organisms, its biology has already been investigated to an exceptional level. This, coupled with effortless handling and a notable low cost of cultivation and maintenance, allows seamless implementation of high-throughput drug screening approaches as well as in-depth genetic and biochemical studies of the molecular pathways targeted by specific drugs. In this review, we introduce C. elegans as a model organism with significant advantages toward the identification of molecular drug targets. In addition, we discuss the value of the worm in the development of drug screening and drug evaluation protocols. The unique features of C. elegans, which greatly facilitate drug studies, hold promise for both deciphering disease pathogenesis and formulating educated and effective therapeutic interventions.     

Illuminating the complexity of GPCR pathway selectivity – advances in biosensor development

It should come as no surprise that G protein-coupled receptors (GPCRs) continue to occupy the focus of drug discovery efforts. Their widespread expression and broad role in signal transduction underline their importance in human physiology. Despite more than 800 GPCRs sharing a common architecture, unique differences govern ligand specificity and pathway selectivity. From the relatively simplified view offered by classical radioligand binding assays and contractility responses in organ baths, the road from ligand binding to biological action has become more and more complex as we learn about the molecular mediators that underly GPCR activation and translate it to physiological outcomes. In particular, the development of biosensors has evolved over the years to dissect the capacity of a given receptor to activate individual pathways. Here, we discuss how recent biosensor development has reinforced the idea that biased signaling may become mainstream in drug discovery programs.     

Molecular complexity and fragment-based drug discovery: ten years on

We review the concept of molecular complexity in the context of the very simple model of molecular interactions that we introduced over ten years ago. A summary is presented of efforts to validate this simple model using screening data. The relationship between the complexity model and the problem of sampling chemical space is discussed, together with the relevance of these theoretical concepts to fragment-based drug discovery.     

Exploiting drug-disease relationships for computational drug repositioning

Finding new uses for existing drugs, or drug repositioning, has been used as a strategy for decades to get drugs to more patients. As the ability to measure molecules in high-throughput ways has improved over the past decade, it is logical that such data might be useful for enabling drug repositioning through computational methods. Many computational predictions for new indications have been borne out in cellular model systems, though extensive animal model and clinical trial-based validation are still pending. In this review, we show that computational methods for drug repositioning can be classified in two axes: drug based, where discovery initiates from the chemical perspective, or disease based, where discovery initiates from the clinical perspective of disease or its pathology. Newer algorithms for computational drug repositioning will likely span these two axes, will take advantage of newer types of molecular measurements, and will certainly play a role in reducing the global burden of disease.     

Venomics: a new paradigm for natural products-based drug discovery

The remarkable potency and pharmacological diversity of animal venoms has made them an increasingly valuable source of lead molecules for drug and insecticide discovery. Nevertheless, most of the chemical diversity encoded within these venoms remains uncharacterized, despite decades of research, in part because of the small quantities of venom available. However, recent advances in the miniaturization of bioassays and improvements in the sensitivity of mass spectrometry and NMR spectroscopy have allowed unprecedented access to the molecular diversity of animal venoms. Here, we discuss these technological developments in the context of establishing a high-throughput pipeline for venoms-based drug discovery.     

药物靶点的选择和验证

药物靶点的选择和验证是药物开发研究中一个重要的环节.随着现代分子生物学技术的发展和人类基因组计划的完成,出现了大量可供治疗干预的新型分子靶点,对这些新型分子靶点进行验证成为药物开发科学家所面临的重要任务.为此,就药物靶点及其选择、验证所需的分子技术基础作一简要综述.     

Molecular simulation of drug-binding kinetics

Early drug discovery often focuses on improving drug–receptor binding thermodynamics without considering drug-binding kinetics. This article first reviews some experiments and pathway simulations that point to the significance of considering drug-binding kinetics in drug discovery. It then describes our development and application of a molecular dynamics-based mining-minima approach to studying drug-binding kinetics, with the goal of aiding the design of drug candidates with certain desired binding kinetics. Discussions on further refinement of this approach with the Feynman path integral formalism then follow.