Structure-based drug design of catechol-O-methyltransferase inhibitors for CNS disorders

Catechol-O-methyltransferase (COMT) is of great importance in pharmacology because it catalyzes the metabolism (methylation) of endogenous and xenobiotic catechols. Moreover, inhibition of COMT is the drug target in the management of central nervous system (CNS) disorders such as Parkinson''s disease due to its role in regulation of the dopamine level in the brain. The X-ray crystal structures for COMT have been available since 1994. The active sites for cofactor and substrate/inhibitor binding are well resolved to an atomic level, providing valuable insights into the catalytic mechanisms as well as the role of magnesium ions in catalysis. Determination of how the substrates/inhibitors bind to the protein leads to a structure-based approach that has resulted in potent and selective inhibitors. This review focuses on the design of two types of inhibitors (nitrocatechol-type and bisubstrate inhibitors) for COMT using the protein structures.     

A Protein Relational Database and Protein Family Knowledge Bases to Facilitate Structure-Based Design Analyses

The Protein Data Bank is the most comprehensive source of experimental macromolecular structures. It can, however, be difficult at times to locate relevant structures with the Protein Data Bank search interface. This is particularly true when searching for complexes containing specific interactions between protein and ligand atoms. Moreover, searching within a family of proteins can be tedious. For example, one cannot search for some conserved residue as residue numbers vary across structures. We describe herein three databases, Protein Relational Database, Kinase Knowledge Base, and Matrix Metalloproteinase Knowledge Base, containing protein structures from the Protein Data Bank. In Protein Relational Database, atom–atom distances between protein and ligand have been precalculated allowing for millisecond retrieval based on atom identity and distance constraints. Ring centroids, centroid–centroid and centroid–atom distances and angles have also been included permitting queries for π-stacking interactions and other structural motifs involving rings. Other geometric features can be searched through the inclusion of residue pair and triplet distances. In Kinase Knowledge Base and Matrix Metalloproteinase Knowledge Base, the catalytic domains have been aligned into common residue numbering schemes. Thus, by searching across Protein Relational Database and Kinase Knowledge Base, one can easily retrieve structures wherein, for example, a ligand of interest is making contact with the gatekeeper residue.     

化学蛋白质组学与药物靶点的发现

小分子药物靶点的发现对于生物和医学的研究者而言,是一项既重要又艰巨的任务,医学和药学界研究工作者急切需要发现和确认新的靶点。为了克服药物靶点确认的瓶颈,已经发展了许多新技术用以研究小分子化合物与蛋白质分子间的相互作用,其中包括化学蛋白质组学方法。化学蛋白质组是全蛋白质组学研究的一个亚类,化学蛋白质组学是利用能够与靶蛋白质发生特异性相互作用的化学小分子来干扰和探测蛋白质组,在分子水平上系统揭示特定蛋白质的功能以及蛋白质与化学小分子的相互作用,从而准确找到药物作用靶点的组学研究方法。化学蛋白质组学技术和方法不断成熟,在药物作用靶点的发现、确认和药物多靶点研究等方面都将起到重要的作用,并将大大提高药物发现的效率。     

Structure-based drug design meets the ribosome

The high-resolution structures of the bacterial ribosomal subunits and those of their complexes with antibiotics have advanced significantly our understanding of small-molecule interactions with RNA. The wealth of RNA structural data generated by these structures has allowed computational chemists to employ a drug discovery paradigm focused on RNA-based targets. The structures also show how target-based resistance affects antibiotics acting at the level of the ribosome. Not only are the sites pinpointed where different classes of antibiotics inhibit protein synthesis, but their orientations, relative dispositions, and unique mechanisms of action are also revealed at the atomic level. Both the 30S and the 50S ribosomal subunits have been shown to be "targets of targets", offering several adjacent, functionally relevant binding pockets for antibiotics. It is the detailed knowledge of these validated locations, or ribofunctional loci, plus the mapping of the resistance hot-spots that allow the rational design of next-generation antibacterials. When the structural information is combined with a data-driven computational toolkit able to describe and predict molecular properties appropriate for bacterial cell penetration and drug-likeness, a structure-based drug design approach for novel antibacterials shows great promise.     

Identifying targets for drug discovery using bioinformatics

Background: Drug discovery is the process of discovering and designing drugs, which includes target identification, target validation, lead identification, lead optimization and introduction of the new drugs to the public. This process is very important, involving analyzing the causes of the diseases and finding ways to tackle them. Objective: The problems we must face include: i) that this process is so long and expensive that it might cost millions of dollars and take a dozen years; and ii) the accuracy of identification of targets is not good enough, which in turn delays the process. Introducing bioinformatics into the drug discovery process could contribute much to it. Bioinformatics is a booming subject combining biology with computer science. It can explore the causes of diseases at the molecular level, explain the phenomena of the diseases from the angle of the gene and make use of computer techniques, such as data mining, machine learning and so on, to decrease the scope of analysis and enhance the accuracy of the results so as to reduce the cost and time. Methods: Here we describe recent studies about how to apply bioinformatics techniques in the four phases of drug discovery, how these techniques improve the drug discovery process and some possible difficulties that should be dealt with. Results: We conclude that combining bioinformatics with drug discovery is a very promising method although it faces many problems currently.     

Matrix metalloproteases: Structure-based drug discovery targets

Summary Matrix metalloproteases (MMPs) are a large family of mammalian zinc-dependent proteases that have garnered much attention as targets for drug discovery. In part, this interest is spurred by the central role these enzymes may play in diseases such as arthritis and cancer. One consequence of this attention has been the rapid accumulation of structure information. The structures of inhibitor-MMP complexes have provided a focus for drug discovery efforts in defining features of the MMP catalytic domain that will be critical in developing potent and selective inhibitors. Inhibitor interactions at the active-site zinc are clearly important in defining the binding mode and relative inhibitor potency. Selective inhibitors will also, most likely, take advantage of the S₁ substrate binding pocket, as there are relatively obvious differences at this site between the various members of the MMP family.     

Exploitation of silicon medicinal chemistry in drug discovery

There remains considerable pressure on the pharmaceutical industry to increase productivity and reduce the attrition of drug candidates. Genomics, parallel chemistry and high-throughput biology have not yielded the anticipated benefits, resulting in a renewed focus on validated targets and an aim to generate drugs directed towards such targets, which have a clear advantage. One strategy to identify and develop best-in-class drugs is to apply a high degree of innovation in chemistry and apply this to targets from gene families that have been clinically validated as tractable and drugable. The application of organosilicon medicinal chemistry in the context of privileged structures to aid drug design and development is one such innovative approach that is reviewed in this paper.     

Structure-based Discovery of Cell-Potent Peptidomimetic Inhibitors for Protein N-Terminal Methyltransferase 1

Protein N-terminal methyltransferases (NTMTs) catalyze the methylation of the α-N-terminal amines of proteins starting with an X–P–K/R motif. NTMT1 has been implicated in various cancers and in aging, implying its role as a potential therapeutic target. Through structural modifications of a lead NTMT1 inhibitor, BM30, we designed and synthesized a diverse set of inhibitors to probe the NTMT1 active site. The incorporation of a naphthyl group at the N-terminal region and an ortho-aminobenzoic amide at the C-terminal region of BM30 generates the top cell-potent inhibitor DC541, demonstrating increased activity on both purified NTMT1 (IC₅₀ of 0.34 ± 0.02 μM) and the cellular α-N-terminal methylation level of regulator of chromosome condensation 1 (RCC1, IC₅₀ value of 30 μM) in human colorectal cancer HT29 cells. Furthermore, DC541 exhibits over 300-fold selectivity to several methyltransferases. This study points out the direction for the development of more cell-potent inhibitors for NTMT1.     

基于靶点的抗新型冠状病毒病COVID-19药物发现

新型冠状病毒(SARS-CoV-2)引发的新冠病毒病(COVID-19),采取对症治疗不失为可行有效的治疗方案,但治疗药物大多缺乏针对性。基于病毒复制过程中的关键蛋白和病毒引发的病理机制,研制有针对性的治疗药物,将为临床提供更加有效的治疗方案。此外,由于新型冠状病毒是RNA病毒,而RNA病毒基因易于变异,因此针对新冠病毒病的新药研发将是一项长期而艰巨的任务。本文基于新型冠状病毒从吸附、进入宿主细胞到病毒复制过程中的关键蛋白及病毒感染引发的致病因素等多个环节的潜在靶点,利用分子模拟和机器学习等算法,探讨防治COVID-19新药发现的研究思路,并简述本课题组所开展的相关工作,为促进不同作用机制的新药发现提供可行性研究方法和策略。     

Hot-spot analysis for drug discovery targeting protein-protein interactions

Introduction: Protein-protein interactions are important for biological processes and pathological situations, and are attractive targets for drug discovery. However, rational drug design targeting protein-protein interactions is still highly challenging. Hot-spot residues are seen as the best option to target such interactions, but their identification requires detailed structural and energetic characterization, which is only available for a tiny fraction of protein interactions.

Areas covered: In this review, the authors cover a variety of computational methods that have been reported for the energetic analysis of protein-protein interfaces in search of hot-spots, and the structural modeling of protein-protein complexes by docking. This can help to rationalize the discovery of small-molecule inhibitors of protein-protein interfaces of therapeutic interest. Computational analysis and docking can help to locate the interface, molecular dynamics can be used to find suitable cavities, and hot-spot predictions can focus the search for inhibitors of protein-protein interactions.

Expert opinion: A major difficulty for applying rational drug design methods to protein-protein interactions is that in the majority of cases the complex structure is not available. Fortunately, computational docking can complement experimental data. An interesting aspect to explore in the future is the integration of these strategies for targeting PPIs with large-scale mutational analysis.     

Meeting the challenge of cardiovascular new drug discovery

The general challenge of new drug discovery is to translate a new idea in science or medicine into a useful therapeutic agent. The specific challenge of cardiovascular new drug discovery (CVNDD) is to translate a new idea in science or medicine into an agent for the treatment of cardiovascular disease such as hypertension, congestive heart failure, or ischemic heart disease. CVNDD has a fundamental and increasingly important role in providing a maturing world population with medicaments designed to assist the human organism adjust to aging and to changes in its environment. The economic impact of CVNDD is critical to the world pharmaceutical industry. The economic impact of CVNDD is insignificant, however, compared to the potential world health impact of successful CVNDD that generates effective new cardiovascular drugs. There can be no doubt that present cardiovascular drugs have enhanced the quality of life of millions of people worldwide and have contributed to a reduction in the morbidity and mortality of cardiovascular disease. Although the gains have been modest in some areas such as sudden cardiac death, they have been dramatic in other areas, such as stroke. The length and quality of life have been enhanced by the rational discovery, development, and therapeutic use of cardiovascular drugs. The future holds the promise of even more impressive gains through new generations of drugs—more efficacious, safer drugs [Fisher, 1980; Pletscher, 1980; Kaplan and Smith, 1981; Smith, 1983].     

The clinical pharmacologist in drug discovery and development

1 Clinical pharmacology is a key activity in drug discovery and drug development with much to contribute to drug innovation.
2 However, very few clinical pharmacologists choose the pharmaceutical industry as their ultimate career.
3 Medical alumni of the RPMS clinical pharmacology department illustrate this; only four industrial careers vs thirty professors of clinical pharmacology or medicine.     

GPCR heterodimers: asymmetries in ligand binding and signalling output offer new targets for drug discovery

Dimers of GPCRs have held the imagination of researchers for almost 20 years. However, only recently has their value as potentially novel drug targets been increased significantly, and primarily, in the context of GPCR heterodimers. The view of receptor heterodimers as allosteric machines has transformed the way we understand structural and functional asymmetries inherent in their organization. These asymmetries alter both signalling output and how they might be targeted pharmacologically. The paper in this issue of BJP by Siddiquee and colleagues (2013) highlights our growing understanding of such asymmetries and their implications. They show that heterodimers of the angiotensin II AT1 receptor and the apelin receptor recognize and respond to their respective ligands in distinct ways from the parent receptors expressed alone. Further, they demonstrate asymmetric allosteric effects in the context of the heterodimer that may have significant implications for our understanding of such receptor complexes.

Linked Article

This article is a commentary on the research paper by Siddiquee et al., pp. 1104–1117 of this issue. To view this paper visit http://dx.doi.org/10.1111/j.1476-5381.2012.02192.x     

绿色荧光蛋白在药物发现研究中的应用进展

目的药物的设计与筛选是药物研究的重要环节,绿色荧光蛋白(green fluorescent protein,GFP)在药物发现研究中有着重要的意义和价值。方法通过综述22篇中、英文文献,在化学药物基因药物等方面介绍了绿色荧光蛋白及其在药物发现研究中的应用。结果绿色荧光蛋白最早发现于美国西北海岸的水母中,在紫外照射下可以产生明亮的绿色荧光。它具有很多理想性的特征,如对酸、碱、氧化还原剂等许多化学试剂有极强的稳定性,因此常被于活体细胞或组织的跟踪、标记中,被喻为"活的"分子探针。通过监测绿色荧光蛋白可以对体内基因表达、细胞内蛋白质原位定位,观测肿瘤发生、生长、转移等过程,提供重要生物学靶标有效信息。结论绿色荧光蛋白在药物设计和筛选等领域展示了广阔前景,它与药物设计、药物筛选的结合将为新药研究和开发注入新的活力。     

Biomarkers in pharmacology and drug discovery

Biomarkers, quantitatively measurable indicators of biological or pathogenic processes, once validated play a critical role in disease diagnostics, the prediction of disease progression, and/or monitoring of the response to treatment. They may also represent drug targets. A number of different methods can be used for biomarker discovery and validation, including proteomics methods, metabolomics, imaging, and genome wide association studies (GWASs) and can be analysed using receiver operating characteristic (ROC) plots. The relative utility of single biomarkers compared to biomarker panels is discussed, along with paradigms for biomarker development, the latter in the context of three large-scale biomarker consortia, the Critical Path Predictive Safety Testing Consortium (PSTC), the NCI Early Detection Research Network (EDRN) and the Alzheimer's Disease Neuroimaging Initiative (ADNI). The importance of systematic optimization of many parameters in biomarker analysis, including validation, reproducibility, study design, statistical analysis and avoidance of bias are critical features used by these consortia. Problems including introduction of bias into study designs, data reporting or data analysis are also reviewed.     

Ayurvedic drug discovery

Ayurveda is a major traditional system of Indian medicine that is still being successfully used in many countries. Recapitulation and adaptation of the older science to modern drug discovery processes can bring renewed interest to the pharmaceutical world and offer unique therapeutic solutions for a wide range of human disorders. Eventhough time-tested evidences vouch immense therapeutic benefits for ayurvedic herbs and formulations, several important issues are required to be resolved for successful implementation of ayurvedic principles to present drug discovery methodologies. Additionally, clinical examination in the extent of efficacy, safety and drug interactions of newly developed ayurvedic drugs and formulations are required to be carefully evaluated. Ayurvedic experts suggest a reverse-pharmacology approach focusing on the potential targets for which ayurvedic herbs and herbal products could bring tremendous leads to ayurvedic drug discovery. Although several novel leads and drug molecules have already been discovered from ayurvedic medicinal herbs, further scientific explorations in this arena along with customization of present technologies to ayurvedic drug manufacturing principles would greatly facilitate a standardized ayurvedic drug discovery.     

Glycobiology of the Leishmania parasite and emerging targets for antileishmanial drug discovery

Importance of the field: Parasitic diseases that pose a threat to human life include leishmaniasis – caused by protozoa of Leishmania species. Existing drugs have limitations due to deleterious side effects like teratogenicity and factors like cost and drug resistance, thus furthering the need to develop this area of research.

Areas covered in this review: We came across drug targets, very recently characterised, cloned and validated by genomics and bioinformatics. We bring these promising drug targets into focus so that they can be explored to their fullest.

What the reader will gain: In an effort to bridge the gaps between existing knowledge and future prospects of drug discovery, we found interesting studies validating drug targets and paving the way for better experiments to be designed. In a few cases, novel pathways have been characterized, while in others, well established pathways when probed further, led to the discovery of new drug targets.

Take home message: The review constitutes a comprehensive report on upcoming drug targets, with emphasis on glycosylphosphatidylinositol (GPI)-anchored glycoconjugates along with related biochemistry of enolase, glycosome and purine salvage pathways, as we strive to bring ourselves a step closer to being able to combat this deadly disease.     

The compromise of virtual screening and its impact on drug discovery

Introduction: Docking and structure-based virtual screening (VS) have been standard approaches in structure-based design for over two decades. However, our understanding of the limitations, potential, and strength of these techniques has enhanced, raising expectations.

Areas covered: Based on a survey of reports in the past five years, we assess whether VS: (1) predicts binding poses in agreement with crystallographic data (when available); (2) is a superior screening tool, as often claimed; (3) is successful in identifying chemical scaffolds that can be starting points for subsequent lead optimization cycles. Data shows that knowledge of the target and its chemotypes in postprocessing lead to viable hits in early drug discovery endeavors.

Expert opinion: VS is capable of accurate placements in the pocket for the most part, but does not consistently score screening collections accurately. What matters is capitalization on available resources to get closer to a viable lead or optimizable series. Integration of approaches, subjective hit selection guided by knowledge of the receptor or endogenous ligand, libraries driven by experimental guides, validation studies to identify the best docking/scoring that reproduces experimental findings, constraints regarding receptor–ligand interactions, thoroughly designed methodologies, and predefined cutoff scoring criteria strengthen VS’s position in pharmaceutical research.