New insights from structural biology into the druggability of G protein-coupled receptors

The recent availability of X-ray structures for diverse ligand-bound Family A G protein-coupled receptors (GPCRs) in multiple conformations (inactive form with an antagonist/inverse agonist bound and active form with an agonist bound) now enables rational drug design efforts that have historically been applied to soluble enzyme targets. Here, we review properties of these GPCR binding sites, using a unique combination of calculated physicochemical properties and water energetics (GRID, WaterMap and SZMAP) to provide a new perspective and rational assessment of druggability for each GPCR target binding site. Examples are described from several well-studied enzyme systems to support this advanced structure-based approach to assessing druggability and to contrast their properties with those of GPCRs. Changes in receptor conformations between the GPCR inactive and active forms evident from the protein structures are discussed, yielding important pointers for rational drug design of antagonists and agonists and a better understanding of GPCR activation.     

Chemical genetic approaches to kinase drug discovery

Chemical genetics is an important approach in biological research that utilizes small molecules to study protein function. In the context of kinase drug discovery, chemical genetics has broad applications in identifying and validating targets, demonstrating the druggability of a target and providing potential kinase inhibitor leads for further optimization. The successful application of this approach demands that the small-molecule kinase inhibitors used achieve a desired potency and selectivity. However, given the high number (> 518) and homology of kinases in the human genome, identifying potent and selective kinase inhibitors presents a major challenge. This article reviews recent advances in small-molecule kinase inhibitor design, with an emphasis on selectivity, and also discusses recent progress in the development of analog-sensitive kinase allele (ASKA)-based chemical genetics technology, which creates genetically engineered versions of protein kinases that are fully functional and can be selectively inhibited by a unique reference orthogonal inhibitor. Examples of how ASKA technology can be applied to kinase drug discovery is discussed.     

药物分子设计的策略:药理活性与成药性

化合物的内在活性和成药性是创新药物的两个基本要素,活性是药物的基础和核心,成药性是辅佐活性发挥药效的必要条件,两者互为依存。药物在体内的药剂相、药代动力相和药效相可概括为活性和成药性的展示过程。成药性是药物除活性外的其他所有性质,包括物理化学性质、生物化学性质、药代动力学性质和毒副作用,这是在不同层次上表征药物的性质和行为,但又相互关联与制约。活性与成药性由化学结构所决定,体现在微观结构与宏观性质的结合上,寓于分子的结构之中。先导物的优化是对活性、物化、生化、药代和安全性等性质的多维空间的分子操作,因而具有丰富的药物化学内涵。     

Multi‐target,ensemble‐based virtual screening yields novel allosteric KRAS inhibitors at high success rate

RAS mutations account for >15% of all human tumors, and of these ~85% are due to mutations in a particular RAS gene: KRAS. Recent studies revealed that KRAS harbors four druggable allosteric sites. Here, we have (a) used molecular simulations to generate ensembles of wild type and four major oncogenic KRAS mutants (G12V, G12D, G13D, and Q61H); (b) characterized the druggability of each allosteric pocket in each protein; (c) conducted extensive ensemble‐based virtual screening using pocket‐tailored ligand libraries; (d) prioritized hits through hierarchical postdocking analysis; and (e) validated predicted hits with NMR. Of the 785 diverse potential hits identified by our in silico analysis, we tested 90 for their ability to bind KRAS using NMR and found that nine cause backbone amide chemical shift perturbations of residues near the functionally responsive switch loops, suggesting potential binding. We conducted detailed biophysical analyses on a novel indole‐based compound to demonstrate the potential of our workflow to yield lead compounds. We believe the detailed information documented in this work regarding the druggability profile of each allosteric site and the chemical fingerprints of compounds that target them will serve as vital resources for future structure‐based drug design efforts against KRAS, a high‐value target for cancer therapy.     

天然产物的结构改造

药理活性和成药性是新药创制的两大要素, 药理活性自不待言, 成药性是由分子的物理化学性质、生物化学性质、药代动力学性质和安全性所支撑。天然产物作为动物、植物、微生物和海洋生物的次级代谢产物, 具有维持生理、自身防御和种群繁衍的功能, 许多药物来自天然产物。天然产物具有多样性和复杂性结构, 多含立体化学中心, 氮和卤素含量低。天然活性物质是良好的先导物, 但未必能满足成药性要求, 需要进行结构修饰和优化。结构改造的要旨是: 根据天然产物的分子大小和复杂程度, 采取不同的化学处置方式, 复杂和较大的分子作结构剖裂, 去除冗余原子; 研究构效关系, 提取药效团, 实现骨架迁越, 获得新结构类型分子; 消除不必要的手性中心, 保留与靶标结合的必须的构型与构象; 全合成实现工业化, 保护环境与资源。天然产物结构改造的方略是: 提高活性强度和选择性作用; 改善物理化学性质; 提高化学和代谢稳定性; 改善生物化学性质; 改善药代动力学性质; 消除或降低毒副作用和不良反应; 获得知识产权。本文以成功的实例解析以天然活性物质为先导物研制新药的内涵。     

Targeting enzyme inhibitors in drug discovery

Drugs that function as enzyme inhibitors constitute a significant portion of the orally bioavailable therapeutic agents that are in clinical use today. Likewise, much of drug discovery and development efforts at present are focused on identifying and optimizing drug candidates that act through inhibition of specific enzyme targets. The attractiveness of enzymes as targets for drug discovery stems from the high levels of disease association (target validation) and druggability (target tractability) that typically characterize this class of proteins. In this expert opinion the authors describe the existing practices and future directions in drug discovery enzymology, with emphasis on how a detailed understanding of the catalytic mechanism of specific targets can be used to identify and optimize small-molecule compounds that interact with conformationally distinct forms of the enzyme, thus resulting in high potency, high selectivity inhibitors.     

Targeting enzyme inhibitors in drug discovery

Drugs that function as enzyme inhibitors constitute a significant portion of the orally bioavailable therapeutic agents that are in clinical use today. Likewise, much of drug discovery and development efforts at present are focused on identifying and optimizing drug candidates that act through inhibition of specific enzyme targets. The attractiveness of enzymes as targets for drug discovery stems from the high levels of disease association (target validation) and druggability (target tractability) that typically characterize this class of proteins. In this expert opinion the authors describe the existing practices and future directions in drug discovery enzymology, with emphasis on how a detailed understanding of the catalytic mechanism of specific targets can be used to identify and optimize small-molecule compounds that interact with conformationally distinct forms of the enzyme, thus resulting in high potency, high selectivity inhibitors.     

Druggability indices for protein targets derived from NMR-based screening data

An analysis of heteronuclear-NMR-based screening data is used to derive relationships between the ability of small molecules to bind to a protein and various parameters that describe the protein binding site. It is found that a simple model including terms for polar and apolar surface area, surface complexity, and pocket dimensions accurately predicts the experimental screening hit rates with an R(2) of 0.72, an adjusted R(2) of 0.65, and a leave-one-out Q(2) of 0.56. Application of the model to predict the druggability of protein targets not used in the training set correctly classified 94% of the proteins for which high-affinity, noncovalent, druglike leads have been reported. In addition to understanding the pocket characteristics that contribute to high-affinity binding, the relationships that have been defined allow for quantitative comparative analyses of protein binding sites for use in target assessment and validation, virtual ligand screening, and structure-based drug design.     

Binding evaluation of fragment-based scaffolds for probing allosteric enzymes

Fragment-based drug discovery has become a powerful method for the generation of drug leads against therapeutic targets. Beyond the identification of novel and effective starting points for drug design, fragments have emerged as reliable tools for assessing protein druggability and identifying protein hot spots. Here, we have examined fragments resulting from the deconstruction of known inhibitors from the glycogen phosphorylase enzyme, a therapeutic target against type 2 diabetes, with two motivations. First, we have analyzed the fragment binding to the multiple binding sites of the glycogen phosphorylase, and then we have investigated the use of fragments to study allosteric enzymes. The work we report illustrates the power of fragmentlike ligands not only for probing the various binding pockets of proteins, but also for uncovering cooperativity between these various binding sites.     

What makes a good drug target?

Novel therapeutics in areas with a high unmet medical need are based on innovative drug targets. Although 'biologicals' have enlarged the space of druggable molecules, the number of appropriate drug targets is still limited. Discovering and assessing the potential therapeutic benefit of a drug target is based not only on experimental, mechanistic and pharmacological studies but also on a theoretical molecular druggability assessment, an early evaluation of potential side effects and considerations regarding opportunities for commercialization. This article defines key properties of a good drug target from the perspective of a pharmaceutical company.     

具有抗肿瘤活性的姜黄素衍生物研究进展

姜黄素是一种具有抗肿瘤、抗阿尔茨海默病、抗氧化、抗炎、抗病毒等多种生物活性的天然多酚,但其存在生物利用度低、水溶性较差等缺点,使其临床应用受到限制。基于此．研究人员对姜黄素展开了大量的结构改造工作,以期能改善其生物活性及成药性。综述了具有抗肿瘤活性的姜黄素衍生物的研究进展,旨在为相关药物的研发提供参考。     

Expanding the therapeutic options for Candida infections using novel inhibitors of secreted aspartyl proteases

For widening the therapeutic options for Candida management, the druggability of Candida proteome was systematically investigated using an innovative pipeline of high-throughput data mining algorithms, followed by in vitro validation of the observations. Through this exercise, HIV-1 protease was found to share structural similarity with secreted aspartyl protease-3 (SAP3), a virulence protein of Candida. Using the molecular fingerprint of HIV-1 protease inhibitor GRL-09510, we performed virtual screening of peptidomimetic library followed by high-precision docking and MD simulations for discovery of SAP inhibitors. Wet-lab validation of the four shortlisted peptidomimetics revealed that two molecules, when used in combination with fluconazole, could significantly reduce the dosage of fluconazole required for 50% inhibition of Candida albicans. The SAP inhibitory activity of these peptidomimetics was confirmed through SAP assays and found to be on par with pepstatin A, a known peptidomimetic inhibitor of aspartyl proteases.     

药物的杂泛性

成功的药物应具备两个要素:足够强度和选择性的药理作用,适宜的物理化学、药代、安全和结构新颖的成药性。药物的杂泛性涉及到这两个方面。杂泛性是指一种药物与多种靶标发生相互作用,而引起相同或不同的药理作用的现象,药物的杂泛性是多重药理学的基础,也是药物产生副作用和药代动力学不合理的原因。药物产生杂泛性的根源是蛋白的杂泛性,在进化过程中,为了结合、代谢和清除结构多样的内源和外源性物质,蛋白具有广泛和可变的结构容纳性,无需对每种化合物都准备特异的蛋白,体现了受体的杂泛性。靶标蛋白具有保守性和多样性。保守性体现在折叠成二级结构的结构域比较固定和保守,因而与配体分子的结合互有交盖,发生交叉反应性。多样性体现在精细的结构内涵,相似的结构域因为有不同的氨基酸序列,功能是不同的,体现了特异性,因而靶标多为一专多能的蛋白。利用杂泛性以设计(design in)治疗复杂疾病的多靶标药物,摒弃(design out)杂泛性的不利因素以完善成药性。所以认真分析和处置功过参半的杂泛性是提高药物设计成功率的重要保证,正确预测配体的杂泛性也是分子设计的终极目标。本文对受体、酶、离子通道和细胞色素CYP450等与配体的杂泛性和药物设计的关系进行...     

Protein models in drug discovery

Over the last few years, the utilization of protein structural information in drug discovery research has matured and is today applied throughout the process, ranging from genomics-derived target identification and selection to the final design of suitable drug candidates. An especially powerful methodology has arisen from the clear synergies of the combination of target structural information with combinatorial chemistry. Several structural genomics initiatives have recently been started and are now generating 3-D structures of target molecules at an unprecedented rate that will provide a wealth of novel information that can be utilized for rational drug design.     

DNA fragmentation-based combinatorial approaches to soluble protein expression Part I. Generating DNA fragment libraries

In addressing a new drug discovery target, the generation of tractable protein substrates for functional and structural analyses can represent a significant hurdle. Traditional approaches rely on protein expression trials of multiple variants in various systems, frequently with limited success. The increasing knowledge base derived from genomics and structural proteomics initiatives assists the bioinformatics-led design of these experiments. Nevertheless, for many eukaryotic polypeptides, particularly those with relatively few homologues, the generation of useful protein products can still be a major challenge. This review describes the basis of efforts to forge an alternative 'domain-hunting' paradigm, based upon combinatorial sampling of expression construct libraries derived by fragmentation of the encoding DNA template, namely the methods and considerations in generating fragment length DNA from target genes. An accompanying review focuses upon the expression screening of such combinatorial DNA libraries for the sampling of the corresponding set of protein fragments.     

Targetability of human disease genes

The availability of complete genome sequences and the wealth of large-scale biological datasets provide an unprecedented opportunity to elucidate the genetic basis of human diseases. Therapeutically relevant targets should be both 'druggable' and 'disease modifying'. In this review we examine the application of computational biology towards the exploration of druggability, targetability and evolutionary conservation of human disease genes. These analyses could have a tremendous potential for systematic in silico drug target identification in the post-genomic era.     

Differences between high- and low-affinity complexes of enzymes and nonenzymes

Physical differences in small molecule binding between enzymes and nonenzymes were found through mining the protein-ligand database, Binding MOAD (Mother of All Databases). The data suggest that divergent approaches may be more productive for improving the affinity of ligands for the two classes of proteins. High-affinity ligands of enzymes are much larger than those with low affinity, indicating that the addition of complementary functional groups is likely to improve the affinity of an enzyme inhibitor. However, this process may not be as fruitful for ligands of nonenzymes. High- and low-affinity ligands of nonenzymes are nearly the same size, so modest modifications and isosteric replacement might be most productive. The inherent differences between enzymes and nonenzymes have significant ramifications for scoring functions and structure-based drug design. In particular, nonenzymes were found to have greater ligand efficiencies than enzymes. Ligand efficiencies are often used to indicate druggability of a target, and this finding supports the feasibility of nonenzymes as drug targets. The differences in ligand efficiencies do not appear to come from the ligands; instead, the pockets yield different amino acid compositions despite very similar distributions of amino acids in the overall protein sequences.     

Expectations from Structural Genomics Revisited

Background: Current structural genomics projects are being driven by two main goals; to produce a representative set of protein folds that could be used as templates for comparative modeling purposes, and to provide insight into the function of the currently unannotated protein sequences. Such projects may reveal that a newly determined protein structure shares structural similarity with a previously observed structure or that it is a novel fold. The manner in which structure can be used to suggest the function of a protein will depend on the number and diversity of homologous sequences and the extent to which these sequences are functionally characterized. Method and results: Using sequence searching methods, we analyzed structural genomics target sequences to ascertain if they were members of functionally characterized protein families, protein families of unknown function, or orphan sequences. This analysis provided an indication of what could be expected to emerge from structural genomics projects. Matches were found to approximately 25% of the current functionally unannotated protein families in the PFAM database (protein families database of alignments and hidden Markov models). The 16% of strict orphan sequences will be the most problematic if their structures reveal novel folds. However, out of the remaining target sequences that match families whose members are largely of unknown function, 28% are particularly interesting in that they are part of protein families with considerable sequence diversity. Conclusion: The determination of a new structure of a member of these families is likely to offer considerable insight into possible functional roles of these proteins even if it is a new fold. Mapping the sequence conservation onto the structure may reveal functionally important residues for further study by experimental methods.