蛋白质相互作用数据库及其应用

对蛋白质相互作用及其网络的了解不仅有助于深入理解生命活动的本质和疾病发生的机制,而且可以为药物研发提供靶点.目前,通过高通量筛选、计算方法预测和文献挖掘等方法,获得了大批量的蛋白质相互作用数据,并由此构建了很多内容丰富并日益更新的蛋白质相互作用数据库.本文首先简要阐述了大规模蛋白质相互作用数据产生的3种方法,然后重点介绍了几个人类相关的蛋白质相互作用公共数据库,包括HPRD、BIND、 IntAct、MINT、 DIP 和MIPS,并概述了蛋白质相互作用数据库的整合情况以及这些数据库在蛋白质相互作用网络构建上的应用.     

Predicting selective drug targets in cancer through metabolic networks

The interest in studying metabolic alterations in cancer and their potential role as novel targets for therapy has been rejuvenated in recent years. Here, we report the development of the first genome‐scale network model of cancer metabolism, validated by correctly identifying genes essential for cellular proliferation in cancer cell lines. The model predicts 52 cytostatic drug targets, of which 40% are targeted by known, approved or experimental anticancer drugs, and the rest are new. It further predicts combinations of synthetic lethal drug targets, whose synergy is validated using available drug efficacy and gene expression measurements across the NCI‐60 cancer cell line collection. Finally, potential selective treatments for specific cancers that depend on cancer type‐specific downregulation of gene expression and somatic mutations are compiled.     

Trends in ion channel drug discovery: advances in screening technologies

Ion channels mediate and regulate crucial electrical functions throughout the body. They are therapeutic drug targets for a variety of disorders and, in some cases, the direct cause of unwanted side-effects. Advances in medical genetics have increased our knowledge of ion channel structure–function relationships and identified disease-causing mutations in ion channel genes. The recognized importance of these proteins in health and disease has led to an active search for ion channel targets in the multi-billion-dollar worldwide drug discovery market. Trends in ion channel screening technologies have focused on increasing throughput and enhancing information content of assays through electrophysiological approaches. The ability to study ion channels by voltage clamp and their time-, voltage- and state-dependent drug interactions with enhanced throughput will ultimately play a key role in the development of novel, safe ion channel-targeted drugs.     

The integration of genomic and structural information in the development of high affinity plasmepsin inhibitors

The plasmepsins are key enzymes in the life cycle of the Plasmodium parasites responsible for malaria. Since plasmepsin inhibition leads to parasite death, these enzymes have been acknowledged to be important targets for the development of new antimalarial drugs. The development of effective plasmepsin inhibitors, however, is compounded by their genomic diversity which gives rise not to a unique target for drug development but to a family of closely related targets. Successful drugs will have to inhibit not one but several related enzymes with high affinity. Structure-based drug design against heterogeneous targets requires a departure from the classic 'lock-and-key' paradigm that leads to the development of conformationally constrained molecules aimed at a single target. Drug molecules designed along those principles are usually rigid and unable to adapt to target variations arising from naturally occurring genetic polymorphisms or drug-induced resistant mutations. Heterogeneous targets need adaptive drug molecules, characterised by the presence of flexible elements at specific locations that sustain a viable binding affinity against existing or expected polymorphisms. Adaptive ligands have characteristic thermodynamic signatures that distinguish them from their rigid counterparts. This realisation has led to the development of rigorous thermodynamic design guidelines that take advantage of correlations between the structure of lead compounds and the enthalpic and entropic components of the binding affinity. In this paper, we discuss the application of the thermodynamic approach to the development of high affinity (K(i) - pM) plasmepsin inhibitors. In particular, a family of allophenylnorstatine-based compounds is evaluated for their potential to inhibit a wide spectrum of plasmepsins.     

Yeast "N"-hybrid systems for protein-protein and drug-protein interaction discovery

The majority of small molecule drugs act on protein targets to exert a therapeutic function. It has become apparent in recent years that many small molecule drugs act on more than one particular target and consequently, approaches which profile drugs to uncover their target binding spectrum have become increasingly important. Classical yeast two-hybrid systems have mainly been used to discover and characterize protein-protein interactions, but recent modifications and improvements have opened up new routes towards screening for small molecule-protein interactions. Such yeast "n"-hybrid systems hold great promise for the development of drugs which interfere with protein-protein interactions and for the discovery of drug-target interactions. In this review, we discuss several yeast two-hybrid based approaches with applications in drug discovery and describe a protocol for yeast three-hybrid screening of small molecules to identify their direct targets.     

Comparative Proteomics Among Cytochrome P450 Family 1 for Differential Substrate Specificity

Apart from playing key roles in drug metabolism and adverse drug–drug interactions, CYPs are potential drug targets to treat a variety of diseases. The intervention of over expression of P450 1A1 (CYP1A1) in tumor cells is identified as a novel strategy for anticancer therapy. We investigated three isoforms of CYP1 family (CYP1A1, CYP1A2, and CYP1B1) for their substrate specificity. The understanding of macromolecular features that govern substrate specificity is required to understand the interplay between the protein function and dynamics. This can help in design of new antitumor molecule specifically metabolized by CYP1A1 to mediate their antitumor activity. In the present study, we carried out the comparative protein structure analysis of the three isoforms. Sequence alignment, root mean square deviation (RMSD) analysis, B-factor analysis was performed to give a better understanding of the macromolecular features involved in substrate specificity and to understand the interplay between protein dynamics and functions which will have important implications on rational design of anticancer drugs. We identified the differences in amino acid residues among the three isoforms of CYP1 family, which may account for differential substrate specificity. Six putative substrate recognition sequences are characterized along with the regions they form in the protein structure. Further the RMSD and B-factor analysis provides the information about the identified residues having the maximum RMSD and B-factor deviations.     

靶向共价键药物的研究进展

药物与靶标的结合是启动药理作用的本源,共价键药物是以共享电子的方式来实现与靶标的结合,其中大多为抗感染、抗肿瘤以及心脑血管、神经系统和代谢类药物。简介共价键药物与非共价键药物的区别以及既往的重磅级共价键药物与靶标的结合特点,分类综述靶向共价键药物的理性设计及与靶标的结合反应。     

Network Neighbors of Drug Targets Contribute to Drug Side-Effect Similarity

In pharmacology, it is essential to identify the molecular mechanisms of drug action in order to understand adverse side effects. These adverse side effects have been used to infer whether two drugs share a target protein. However, side-effect similarity of drugs could also be caused by their target proteins being close in a molecular network, which as such could cause similar downstream effects. In this study, we investigated the proportion of side-effect similarities that is due to targets that are close in the network compared to shared drug targets. We found that only a minor fraction of side-effect similarities (5.8 %) are caused by drugs targeting proteins close in the network, compared to side-effect similarities caused by overlapping drug targets (64%). Moreover, these targets that cause similar side effects are more often in a linear part of the network, having two or less interactions, than drug targets in general. Based on the examples, we gained novel insight into the molecular mechanisms of side effects associated with several drug targets. Looking forward, such analyses will be extremely useful in the process of drug development to better understand adverse side effects.     

A systematic analysis of FDA-approved anticancer drugs

Background

The discovery of novel anticancer drugs is critical for the pharmaceutical research and development, and patient treatment. Repurposing existing drugs that may have unanticipated effects as potential candidates is one way to meet this important goal. Systematic investigation of efficient anticancer drugs could provide valuable insights into trends in the discovery of anticancer drugs, which may contribute to the systematic discovery of new anticancer drugs.

Results

In this study, we collected and analyzed 150 anticancer drugs approved by the US Food and Drug Administration (FDA). Based on drug mechanism of action, these agents are divided into two groups: 61 cytotoxic-based drugs and 89 target-based drugs. We found that in the recent years, the proportion of targeted agents tended to be increasing, and the targeted drugs tended to be delivered as signal drugs. For 89 target-based drugs, we collected 102 effect-mediating drug targets in the human genome and found that most targets located on the plasma membrane and most of them belonged to the enzyme, especially tyrosine kinase. From above 150 drugs, we built a drug-cancer network, which contained 183 nodes (150 drugs and 33 cancer types) and 248 drug-cancer associations. The network indicated that the cytotoxic drugs tended to be used to treat more cancer types than targeted drugs. From 89 targeted drugs, we built a cancer-drug-target network, which contained 214 nodes (23 cancer types, 89 drugs, and 102 targets) and 313 edges (118 drug-cancer associations and 195 drug-target associations). Starting from the network, we discovered 133 novel drug-cancer associations among 52 drugs and 16 cancer types by applying the common target-based approach. Most novel drug-cancer associations (116, 87%) are supported by at least one clinical trial study.

Conclusions

In this study, we provided a comprehensive data source, including anticancer drugs and their targets and performed a detailed analysis in term of historical tendency and networks. Its application to identify novel drug-cancer associations demonstrated that the data collected in this study is promising to serve as a fundamental for anticancer drug repurposing and development.

     

A Network Inference Method for Large-Scale Unsupervised Identification of Novel Drug-Drug Interactions

Characterizing interactions between drugs is important to avoid potentially harmful combinations, to reduce off-target effects of treatments and to fight antibiotic resistant pathogens, among others. Here we present a network inference algorithm to predict uncharacterized drug-drug interactions. Our algorithm takes, as its only input, sets of previously reported interactions, and does not require any pharmacological or biochemical information about the drugs, their targets or their mechanisms of action. Because the models we use are abstract, our approach can deal with adverse interactions, synergistic/antagonistic/suppressing interactions, or any other type of drug interaction. We show that our method is able to accurately predict interactions, both in exhaustive pairwise interaction data between small sets of drugs, and in large-scale databases. We also demonstrate that our algorithm can be used efficiently to discover interactions of new drugs as part of the drug discovery process.     

Chemogenomic Approaches for Revealing Drug Target Interactions in Drug Discovery

The drug discovery process has been a crucial and cost-intensive process. This cost is not only monetary but also involves risks, time, and labour that are incurred while introducing a drug in the market. In order to reduce this cost and the risks associated with the drugs that may result in severe side effects, the in silico methods have gained popularity in recent years. These methods have had a significant impact on not only drug discovery but also the related areas such as drug repositioning, drug-target interaction prediction, drug side effect prediction, personalised medicine, etc. Amongst these research areas predicting interactions between drugs and targets forms the basis for drug discovery. The availability of big data in the form of bioinformatics, genetic databases, along with computational methods, have further supported data-driven decision-making. The results obtained through these methods may be further validated using in vitro or in vivo experiments. This validation step can further justify the predictions resulting from in silico approaches, further increasing the accuracy of the overall result in subsequent stages. A variety of approaches are used in predicting drug-target interactions, including ligand-based, molecular docking based and chemogenomic-based approaches. This paper discusses the chemogenomic methods, considering drug target interaction as a classification problem on whether or not an interaction between a particular drug and target would serve as a basis for understanding drug discovery/drug repositioning. We present the advantages and disadvantages associated with their application.     

Targeting protein-protein interactions for parasite control

Finding new drug targets for pathogenic infections would be of great utility for humanity, as there is a large need to develop new drugs to fight infections due to the developing resistance and side effects of current treatments. Current drug targets for pathogen infections involve only a single protein. However, proteins rarely act in isolation, and the majority of biological processes occur via interactions with other proteins, so protein-protein interactions (PPIs) offer a realm of unexplored potential drug targets and are thought to be the next-generation of drug targets. Parasitic worms were chosen for this study because they have deleterious effects on human health, livestock, and plants, costing society billions of dollars annually and many sequenced genomes are available. In this study, we present a computational approach that utilizes whole genomes of 6 parasitic and 1 free-living worm species and 2 hosts. The species were placed in orthologous groups, then binned in species-specific orthologous groups. Proteins that are essential and conserved among species that span a phyla are of greatest value, as they provide foundations for developing broad-control strategies. Two PPI databases were used to find PPIs within the species specific bins. PPIs with unique helminth proteins and helminth proteins with unique features relative to the host, such as indels, were prioritized as drug targets. The PPIs were scored based on RNAi phenotype and homology to the PDB (Protein DataBank). EST data for the various life stages, GO annotation, and druggability were also taken into consideration. Several PPIs emerged from this study as potential drug targets. A few interactions were supported by co-localization of expression in M. incognita (plant parasite) and B. malayi (H. sapiens parasite), which have extremely different modes of parasitism. As more genomes of pathogens are sequenced and PPI databases expanded, this methodology will become increasingly applicable.     

Structural genomics and drug discovery

Structure determination has already proven useful for lead optimization and direct drug design. The number of high-resolution structures available in public databases today exceeds 30,000 and will definitely aid in structure-based drug design. Structural genomics approaches covering whole genomes, topologically similar proteins or gene families are great assets for further progress in the development of new drugs. However, membrane proteins representing 70% of current drug targets are poorly characterized structurally. The problems have been related to difficulties in obtaining large amount of recombinant membrane proteins as well as their purification and structure determination. Structural genomics has proven successful in developing new methods in areas from expression to structure determination by studying a large number of target proteins in parallel.     

Mechanistic regulation of epithelial-to-mesenchymal transition through RAS signaling pathway and therapeutic implications in human cancer

RAS effector signaling instead of being simple, unidirectional and linear cascade, is actually recognized as highly complex and dynamic signaling network. RAF-MEK-ERK cascade, being at the center of complex signaling network, links to multiple scaffold proteins through feed forward and feedback mechanisms and dynamically regulate tumor initiation and progression. Three isoforms of Ras harbor mutations in a cell and tissue specific manner. Besides mutations, their epigenetic silencing also attributes them to exhibit oncogenic activities. Recent evidences support the functions of RAS oncoproteins in the acquisition of tumor cells with Epithelial-to-mesenchymal transition (EMT) features/ epithelial plasticity, enhanced metastatic potential and poor patient survival. Google Scholar electronic databases and PubMed were searched for original papers and reviews available till date to collect information on stimulation of EMT core inducers in a Ras driven cancer and their regulation in metastatic spread. Improved understanding of the mechanistic basis of regulatory interactions of microRNAs (miRs) and EMT by reprogramming the expression of targets in Ras activated cancer, may help in designing effective anticancer therapies. Apparent lack of adverse events associated with the delivery of miRs and tissue response make ‘drug target miRNA’ an ideal therapeutic tool to achieve progression free clinical response.     

An insight into major signaling pathways and protein–protein interaction networks involved in the pathogenesis of gestational diabetes mellitus

Gestational diabetes mellitus (GDM) is associated with the increase of glucose in the blood rather than being absorbed by the cells. A better understanding of the signaling pathways is necessary to understand the pathophysiology of GDM. This study provides details about a series of signaling pathways and protein–protein interactions involved in the pathogenesis of GDM and their evaluations in GDM development. Protein–protein interactions were found between proteins of several signaling pathways that suggest interlink between these signaling pathways. Protein–protein interactions were generated with high confidence interaction scores based on textmining, cooccurrence, coexpression, neighborhood, gene fusion, experiments, and databases. The dysregulation of signaling pathways may also contribute to the increased risk of complications associated with GDM in the mother and child. Further, studies on signaling pathways involved in the pathogenesis of GDM would help in the development of an effective intervention to prevent GDM along with the identification of key targets for effective therapies in the future.     

DaiCee: A database for anti-cancer compounds with targets and side effect profiles

Identification of the toxicity of compounds is more crucial before entering clinical trials. Awareness of physiochemical properties, possible targets and side effects has become a major public health issue to reduce risks. Experimental determination of analyzing the physiochemical properties of a drug, their interaction with specific receptors and identifying their side-effects remain challenging is time consuming and costly. We describe a manually compiled database named DaiCee database, which contains 2100 anticancer drugs with information on their physiochemical properties, targets of action and side effects. It includes both synthetic and herbal anti-cancer compounds. It allows the search for SMILES notation, Lipinski''s and ADME/T properties, targets and side effect profiles of the drugs. This helps to identify drugs with effective anticancer properties, their toxic nature, drug-likeness for in-vitro and in-vivo experiments. It also used for comparative analysis and screening of effective anticancer drugs using available data for compounds in the database. The database will be updated regularly to provide the users with latest information. The database is available at the URL http://www.hccbif.org/usersearch.php     

miRNAs modulate the drug response of tumor cells

Chemotherapy is one of the major treatments of malignant carcinomas. However,its efficiency is affected by both intrinsic and acquired resistance to anticancer drugs. The cellular mechanisms of drug resistance include the overexpression of energy-dependent transporters that eject anticancer drugs from cells such as p-glycoprotein and multidrug resistance related protein (MRP),the mutation of drug targets,the activation of DNA repair pathways,the defects in cellular death pathways and so on. The genetic and ...     

Structure-based de novo design and biochemical evaluation of novel Cdc25 phosphatase inhibitors

Cdc25 phosphatases have been considered as attractive drug targets for anticancer therapy due to the correlation of their overexpression with a wide variety of cancers. We have been able to identify 32 novel Cdc25 phosphatase inhibitors with micromolar activity by means of a structure-based de novo design method with the two known inhibitor scaffolds. Because the newly discovered inhibitors are structurally diverse and have desirable physicochemical properties as a drug candidate, they deserve further investigation as anticancer drugs. The differences in binding modes of the identified inhibitors in the active sites of Cdc25A and B are addressed in detail.     

Novel putative drugs and key initiating genes for neurodegenerative disease determined using network-based genetic integrative analysis

Understanding the genetic causes of neurodegenerative disease (ND) can be useful for their prevention and treatment. Among the genetic variations responsible for ND, heritable germline variants have been discovered in genome-wide association studies (GWAS), and nonheritable somatic mutations have been discovered in sequencing projects. Distinguishing the important initiating genes in ND and comparing the importance of heritable and nonheritable genetic variants for treating ND are important challenges. In this study, we analysed GWAS results, somatic mutations and drug targets of ND from large databanks by performing directed network-based analysis considering a randomised network hypothesis testing procedure. A disease-associated biological network was created in the context of the functional interactome, and the nonrandom topological characteristics of directed-edge classes were interpreted. Hierarchical network analysis indicated that drug targets tend to lie upstream of somatic mutations and germline variants. Furthermore, using directed path length information and biological explanations, we provide information on the most important genes in these created node classes and their associated drugs. Finally, we identified nine germline variants overlapping with drug targets for ND, seven somatic mutations close to drug targets from the hierarchical network analysis and six crucial genes in controlling other genes from the network analysis. Based on these findings, some drugs have been proposed for treating ND via drug repurposing. Our results provide new insights into the therapeutic actionability of GWAS results and somatic mutations for ND. The interesting properties of each node class and the existing relationships between them can broaden our knowledge of ND.