Predicting Important Residues and Interaction Pathways in Proteins Using Gaussian Network Model: Binding and Stability of HLA Proteins

A statistical thermodynamics approach is proposed to determine structurally and functionally important residues in native proteins that are involved in energy exchange with a ligand and other residues along an interaction pathway. The structure-function relationships, ligand binding and allosteric activities of ten structures of HLA Class I proteins of the immune system are studied by the Gaussian Network Model. Five of these models are associated with inflammatory rheumatic disease and the remaining five are properly functioning. In the Gaussian Network Model, the protein structures are modeled as an elastic network where the inter-residue interactions are harmonic. Important residues and the interaction pathways in the proteins are identified by focusing on the largest eigenvalue of the residue interaction matrix. Predicted important residues match those known from previous experimental and clinical work. Graph perturbation is used to determine the response of the important residues along the interaction pathway. Differences in response patterns of the two sets of proteins are identified and their relations to disease are discussed.     

Predicting Disease-Related Proteins Based on Clique Backbone in Protein-Protein Interaction Network

Network biology integrates different kinds of data, including physical or functional networks and disease gene sets, to interpret human disease. A clique (maximal complete subgraph) in a protein-protein interaction network is a topological module and possesses inherently biological significance. A disease-related clique possibly associates with complex diseases. Fully identifying disease components in a clique is conductive to uncovering disease mechanisms. This paper proposes an approach of predicting disease proteins based on cliques in a protein-protein interaction network. To tolerate false positive and negative interactions in protein networks, extending cliques and scoring predicted disease proteins with gene ontology terms are introduced to the clique-based method. Precisions of predicted disease proteins are verified by disease phenotypes and steadily keep to more than 95%. The predicted disease proteins associated with cliques can partly complement mapping between genotype and phenotype, and provide clues for understanding the pathogenesis of serious diseases.     

Identification of Small Molecules That Suppress Ricin-Induced Stress-Activated Signaling Pathways

Ricin is a member of the ribosome-inactivating protein (RIP) family of plant and bacterial toxins. In this study we used a high-throughput, cell-based assay to screen more than 118,000 compounds from diverse chemical libraries for molecules that reduced ricin-induced cell death. We describe three compounds, PW66, PW69, and PW72 that at micromolar concentrations significantly delayed ricin-induced cell death. None of the compounds had any demonstrable effect on ricin''s ability to arrest protein synthesis in cells or on ricin''s enzymatic activity as assessed in vitro. Instead, all three compounds appear to function by blocking downstream stress-induced signaling pathways associated with the toxin-mediated apoptosis. PW66 virtually eliminated ricin-induced TNF-α secretion by J774A.1 macrophages and concomitantly blocked activation of the p38 MAPK and JNK signaling pathways. PW72 suppressed ricin-induced TNF-α secretion, but not p38 MAPK and JNK signaling. PW69 suppressed activity of the executioner caspases 3/7 in ricin toxin- and Shiga toxin 2-treated cells. While the actual molecular targets of the three compounds have yet to be identified, these data nevertheless underscore the potential of small molecules to down-regulate inflammatory signaling pathways associated with exposure to the RIP family of toxins.     

应用平衡透析法分析药物小分子与蛋白质相互作用

蛋白质的相关信息一直是生命科学研究的重点,其中药物小分子与蛋白质的相互作用成为近年来的研究热点。平衡透析法是研究药物小分子与蛋白质相互作用的经典方法,通过该方法可以定量的讨论药物小分子与蛋白质结合的结合数量、结合常数。就平衡透析法用于分析药物小分子与蛋白质的作用方式、作用模型及国内外研究进展进行综述。     

MDM2-P53蛋白质相互作用的小分子抑制物

p53作为重要的抑癌基因已经成为一个治疗癌症重点的突破目标之一。直接调节p53基因或调节P53和MDM2蛋白质相互作用是再激活p53基因的两种重要机制。对于表达野生型P53的癌症设计小分子阻断剂阻断MDM2与P53蛋白相互作用是一个很有前景的治疗癌症的方向。文章主要总结了作为治疗癌症的新方法-MDM2-P53蛋白相互作用小分子抑制物的最新研究进展,其中最新的是人工合成化合物Nutlin-3和MI-219。     

Pathway-based Screening Strategy for Multitarget Inhibitors of Diverse Proteins in Metabolic Pathways

Many virtual screening methods have been developed for identifying single-target inhibitors based on the strategy of “one–disease, one–target, one–drug”. The hit rates of these methods are often low because they cannot capture the features that play key roles in the biological functions of the target protein. Furthermore, single-target inhibitors are often susceptible to drug resistance and are ineffective for complex diseases such as cancers. Therefore, a new strategy is required for enriching the hit rate and identifying multitarget inhibitors. To address these issues, we propose the pathway-based screening strategy (called PathSiMMap) to derive binding mechanisms for increasing the hit rate and discovering multitarget inhibitors using site-moiety maps. This strategy simultaneously screens multiple target proteins in the same pathway; these proteins bind intermediates with common substructures. These proteins possess similar conserved binding environments (pathway anchors) when the product of one protein is the substrate of the next protein in the pathway despite their low sequence identity and structure similarity. We successfully discovered two multitarget inhibitors with IC₅₀ of <10 µM for shikimate dehydrogenase and shikimate kinase in the shikimate pathway of Helicobacter pylori. Furthermore, we found two selective inhibitors (IC₅₀ of <10 µM) for shikimate dehydrogenase using the specific anchors derived by our method. Our experimental results reveal that this strategy can enhance the hit rates and the pathway anchors are highly conserved and important for biological functions. We believe that our strategy provides a great value for elucidating protein binding mechanisms and discovering multitarget inhibitors.     

Identification of New Candidate Genes and Chemicals Related to Esophageal Cancer Using a Hybrid Interaction Network of Chemicals and Proteins

Cancer is a serious disease responsible for many deaths every year in both developed and developing countries. One reason is that the mechanisms underlying most types of cancer are still mysterious, creating a great block for the design of effective treatments. In this study, we attempted to clarify the mechanism underlying esophageal cancer by searching for novel genes and chemicals. To this end, we constructed a hybrid network containing both proteins and chemicals, and generalized an existing computational method previously used to identify disease genes to identify new candidate genes and chemicals simultaneously. Based on jackknife test, our generalized method outperforms or at least performs at the same level as those obtained by a widely used method - the Random Walk with Restart (RWR). The analysis results of the final obtained genes and chemicals demonstrated that they highly shared gene ontology (GO) terms and KEGG pathways with direct and indirect associations with esophageal cancer. In addition, we also discussed the likelihood of selected candidate genes and chemicals being novel genes and chemicals related to esophageal cancer.     

Analysis and Prediction of the Metabolic Stability of Proteins Based on Their Sequential Features,Subcellular Locations and Interaction Networks

The metabolic stability is a very important idiosyncracy of proteins that is related to their global flexibility, intramolecular fluctuations, various internal dynamic processes, as well as many marvelous biological functions. Determination of protein''s metabolic stability would provide us with useful information for in-depth understanding of the dynamic action mechanisms of proteins. Although several experimental methods have been developed to measure protein''s metabolic stability, they are time-consuming and more expensive. Reported in this paper is a computational method, which is featured by (1) integrating various properties of proteins, such as biochemical and physicochemical properties, subcellular locations, network properties and protein complex property, (2) using the mRMR (Maximum Relevance & Minimum Redundancy) principle and the IFS (Incremental Feature Selection) procedure to optimize the prediction engine, and (3) being able to identify proteins among the four types: “short”, “medium”, “long”, and “extra-long” half-life spans. It was revealed through our analysis that the following seven characters played major roles in determining the stability of proteins: (1) KEGG enrichment scores of the protein and its neighbors in network, (2) subcellular locations, (3) polarity, (4) amino acids composition, (5) hydrophobicity, (6) secondary structure propensity, and (7) the number of protein complexes the protein involved. It was observed that there was an intriguing correlation between the predicted metabolic stability of some proteins and the real half-life of the drugs designed to target them. These findings might provide useful insights for designing protein-stability-relevant drugs. The computational method can also be used as a large-scale tool for annotating the metabolic stability for the avalanche of protein sequences generated in the post-genomic age.     

利用氨基酸结构倾向性预测固有不规则蛋白质

为了降低固有不规则蛋白质预测模型中特征矩阵的稀疏性,提高预测模型的性能,提出一种利用氨基酸结构倾向性预测固有不规则蛋白质的方法.利用氨基酸结构倾向性将20种氨基酸进行分类,构建氨基酸简化集合,从间接角度提取氨基酸序列中蕴含的不规则结构特征,利用新的简化集合重新描述氨基酸序列,构建固有不规则蛋白质预测模型.预测结果表明,基于氨基酸结构倾向性的预测模型能够有效地挖掘氨基酸结构倾向性中隐藏的不规则结构特征信息,提高固有不规则蛋白质预测模型的预测精度.     

细胞代谢过程分析方法及模型优化

细胞代谢是一个复杂的生物化学反应体系 ,可以在不同的水平上进行调控 ,如控制酶数量的翻译水平调控和调节酶活性的反应水平调控。细胞代谢为了一系列的特定目标而趋于最优化状态 ,如减少能量生产、减少ＮＡＤＰ的合成、增强氧气输送等等[1] 。在漫长的生物进化过程中 ,细胞已达到了这样的最优化状态。然而在一定的介质和条件下 ,生化反应中的微生物并不能充分发挥其潜在的全部催化活性 ,这是由于野生菌株还未能适应其新的目标———根据人类需要最大化生产或选择性生产特定物质。代谢工程通常被认为是“提高细胞活性的工程” ,是利用基因工…     

基于蛋白质网络功能模块的蛋白质功能预测

在破译了基因序列的后基因组时代,随着系统生物学实验的快速发展,产生了大量的蛋白质相互作用数据,利用这些数据寻找功能模块及预测蛋白质功能在功能基因组研究中具有重要意义.打破了传统的基于蛋白质间相似度的聚类模式,直接从蛋白质功能团的角度出发,考虑功能团间的一阶和二阶相互作用,提出了模块化聚类方法(MCM),对实验数据进行聚类分析,来预测模块内未知蛋白质的功能.通过超几何分布P值法和增、删、改相互作用的方法对聚类结果进行预测能力分析和稳定性分析.结果表明,模块化聚类方法具有较高的预测准确度和覆盖率,有很好的容错性和稳定性.此外,模块化聚类分析得到了一些具有高预测准确度的未知蛋白质的预测结果,将会对生物实验有指导意义,其算法对其他具有相似结构的网络也具有普遍意义.     

Fabrication of Electrochemical-DNA Biosensors for the Reagentless Detection of Nucleic Acids, Proteins and Small Molecules

As medicine is currently practiced, doctors send specimens to a central laboratory for testing and thus must wait hours or days to receive the results. Many patients would be better served by rapid, bedside tests. To this end our laboratory and others have developed a versatile, reagentless biosensor platform that supports the quantitative, reagentless, electrochemical detection of nucleic acids (DNA, RNA), proteins (including antibodies) and small molecules analytes directly in unprocessed clinical and environmental samples. In this video, we demonstrate the preparation and use of several biosensors in this "E-DNA" class. In particular, we fabricate and demonstrate sensors for the detection of a target DNA sequence in a polymerase chain reaction mixture, an HIV-specific antibody and the drug cocaine. The preparation procedure requires only three hours of hands-on effort followed by an overnight incubation, and their use requires only minutes.     

A Simple Method for Predicting Transmembrane Proteins Based on Wavelet Transform

The increasing protein sequences from the genome project require theoretical methods to predict transmembrane helical segments (TMHs). So far, several prediction methods have been reported, but there are some deficiencies in prediction accuracy and adaptability in these methods. In this paper, a method based on discrete wavelet transform (DWT) has been developed to predict the number and location of TMHs in membrane proteins. PDB coded as 1KQG is chosen as an example to describe the prediction process by this method. 80 proteins with known 3D structure from Mptopo database are chosen at random as data sets (including 325 TMHs) and 80 sequences are divided into 13 groups according to their function and type. TMHs prediction is carried out for each group of membrane protein sequences and obtain satisfactory result. To verify the feasibility of this method, 80 membrane protein sequences are treated as test sets, 308 TMHs can be predicted and the prediction accuracy is 96.3%. Compared with the main prediction results of seven popular prediction methods, the obtained results indicate that the proposed method in this paper has higher prediction accuracy.     

基于贝叶斯网络和相互作用可信度的蛋白质功能预测方法

蛋白质功能注释是后基因组时代研究的核心内容之一,基于蛋白质相互作用网络的蛋白质功能预测方法越来越受到研究者们的关注.提出了一种基于贝叶斯网络和蛋白质相互作用可信度的蛋白质功能预测方法.该方法在功能预测过程中为待注释的蛋白质建立贝叶斯网络预测模型,并充分考虑了蛋白质相互作用的可信度问题.在构建的芽殖酵母数据集上的三重交叉验证测试表明,在功能预测过程中考虑蛋白质可信度能够有效地提高功能预测的性能.与现有一些算法相比,该方法能够给出令人满意的预测效果.     

Predicting Drug-Target Interaction Networks Based on Functional Groups and Biological Features

Background

Study of drug-target interaction networks is an important topic for drug development. It is both time-consuming and costly to determine compound-protein interactions or potential drug-target interactions by experiments alone. As a complement, the in silico prediction methods can provide us with very useful information in a timely manner.

Methods/Principal Findings

To realize this, drug compounds are encoded with functional groups and proteins encoded by biological features including biochemical and physicochemical properties. The optimal feature selection procedures are adopted by means of the mRMR (Maximum Relevance Minimum Redundancy) method. Instead of classifying the proteins as a whole family, target proteins are divided into four groups: enzymes, ion channels, G-protein- coupled receptors and nuclear receptors. Thus, four independent predictors are established using the Nearest Neighbor algorithm as their operation engine, with each to predict the interactions between drugs and one of the four protein groups. As a result, the overall success rates by the jackknife cross-validation tests achieved with the four predictors are 85.48%, 80.78%, 78.49%, and 85.66%, respectively.

Conclusion/Significance

Our results indicate that the network prediction system thus established is quite promising and encouraging.     

Elucidating Turnover Pathways of Bioactive Small Molecules by Isotopomer Analysis: The Persistent Organic Pollutant DDT

The persistent organic pollutant DDT (1,1,1-trichloro-2,2-bis(4-chlorophenyl)ethane) is still indispensable in the fight against malaria, although DDT and related compounds pose toxicological hazards. Technical DDT contains the dichloro congener DDD (1-chloro-4-[2,2-dichloro-1-(4-chlorophenyl)ethyl]benzene) as by-product, but DDD is also formed by reductive degradation of DDT in the environment. To differentiate between DDD formation pathways, we applied deuterium NMR spectroscopy to measure intramolecular deuterium distributions (²H isotopomer abundances) of DDT and DDD. DDD formed in the technical DDT synthesis was strongly deuterium-enriched at one intramolecular position, which we traced back to ²H/¹H fractionation of a chlorination step in the technical synthesis. In contrast, DDD formed by reductive degradation was strongly depleted at the same position, which was due to the incorporation of ²H-depleted hydride equivalents during reductive degradation. Thus, intramolecular isotope distributions give mechanistic information on reaction pathways, and explain a puzzling difference in the whole-molecule ²H/¹H ratio between DDT and DDD. In general, our results highlight that intramolecular isotope distributions are essential to interpret whole-molecule isotope ratios. Intramolecular isotope information allows distinguishing pathways of DDD formation, which is important to identify polluters or to assess DDT turnover in the environment. Because intramolecular isotope data directly reflect isotope fractionation of individual chemical reactions, they are broadly applicable to elucidate transformation pathways of small bioactive molecules in chemistry, physiology and environmental science.     

Levels of Small Molecules and Enzymes in the Mother Cell Compartment and the Forespore of Sporulating Bacillus megaterium

We have determined the amounts of a number of small molecules and enzymes in the mother cell compartment and the developing forespore during sporulation of Bacillus megaterium. Significant amounts of adenosine 5'-triphosphate and reduced nicotinamide adenine dinucleotide were present in the forespore compartment before accumulation of dipicolinic acid (DPA), but these compounds disappeared as DPA was accumulated. 3-Phosphoglyceric acid (3-PGA) accumulated only within the developing forespore, beginning 1 to 2 h before DPA accumulation. Throughout its development the forespore contained constant levels of enzymes of both 3-PGA synthesis (phosphoglycerate kinase and glyceraldehyde-3-phosphate dehydrogenase) and 3-PGA utilization (phosphoglycerate mutase, enolase, and pyruvate kinase) at levels similar to those in the mother cell and the dormant spore. Despite the presence of enzymes for 3-PGA utilization, this compound was stable within isolated forespores. Two acid-soluble proteins (A and B proteins) also accumulated only in the forespore, beginning 1 to 2 h before DPA accumulation. At this time the specific protease involved in degradation of the A and B proteins during germination also appeared, but only in the forespore compartment. Nevertheless, the A and B proteins were stable within isolated forespores. Arginine and glutamic acid accumulated within the forespore in parallel with DPA accumulation. The forespore also contained the enzyme arginase at a level similar to that in the mother cell and a level of glutamic acid decarboxylase 2- to 25-fold higher than that in the mother cell, depending on when in sporulation the forespores were isolated. The specific activities of several other enzymes (protease active on hemoglobin, ornithine transcarbamylase, malate dehydrogenase, aconitase, and isocitrate dehydrogenase) in forespores were about 10% or less of the values in the mother cell. Aminopeptidase was present at similar levels in both compartments; threonine deaminase was not found in either compartment.     

Effects of Small Molecules on Chaperone-Mediated Autophagy

Autophagy, including macroautophagy (MA), chaperone-mediated autophagy (CMA), crinophagy, pexophagy and microautophagy, are processes by which cells select internal components such as proteins, secretory vesicles, organelles, or foreign bodies, and deliver them to lysosomes for degradation. MA and CMA are activated during conditions of serum withdrawal in cell culture and during short-term (MA) and prolonged (CMA) starvation in organisms. Although MA and CMA are activated under similar conditions, they are regulated by different mechanisms. We used pulse/chase analysis under conditions in which most intracellular proteolysis is due to CMA to test a variety of compounds for effects on CMA. We show that inhibitors of MA such as 3-methyladenine, wortmannin, and LY294002 have no effect on CMA. Protein degradation by MA is sensitive to microtubule inhibitors such as colcemide and vinblastine, but protein degradation by CMA is not. Activators of MA such as rapamycin also have no effect on CMA. We demonstrate that CMA, like MA, is inhibited by protein synthesis inhibitors anisomycin and cycloheximide. CMA is also partially inhibited when the P38 mitogen activated protein kinase is blocked. Finally we demonstrate that the glucose-6-phophate dehydrogenase inhibitor, 6-aminonicotinamide, and heat shock protein of 90 kilodaltons inhibitor, geldanamycin, have the ability to activate CMA.     

基于蛋白序列的IDQD算法预测蛋白质相互作用

基于蛋白质序列组分信息,提出一个离散增量结合二次判别分析法(IDQD)预测蛋白质相互作用的模型,对人类蛋白质相互作用进行预测.自洽检验的识别精度达到75.89%,3-fold交叉检验的敏感性和特异性分别为64.22%和64.68%.结果表明IDQD算法可以用于蛋白质相互作用的预测.