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.     

蛋白质相互作用的生物信息学研究进展

生命过程的分子基础在于生物分子之间的相互作用,其中蛋白质分子之间的相互作用占有极其重要的地位。研究蛋白质相互作用对于理解生命的真谛、探讨致病微生物的致病机理,以及研究新药提高人们的健康水平具有重要的作用。用生物信息学的方法研究蛋白质的相互作用已经取得了许多重要的成果,但也有很多问题还需解决。本文从蛋白质相互作用的数据库、预测方法、可预测蛋白质相互作用的网上服务、蛋白质相互作用网络等几方面,对蛋白质相互作用的生物信息学研究成果及其存在的问题做了概述。     

基于分段氨基酸组成成分的蛋白质相互作用预测

蛋白质相互作用研究有助于揭示生命过程的许多本质问题,也有助于疾病预防、诊断,对药物研制具有重要的参考价值。文章首先构建出蛋白质作用数据库,提出分段氨基酸组成成分特征提取方法来预测蛋白质相互作用。10CV检验下,基于支持向量机的3段氨基酸组成成分特征提取方法的预测总精度为86.2%,比传统的氨基酸组成成分方法提高2.31个百分点;采用Guo的数据库和检验方法,3段氨基酸组成成分特征提取方法的预测总精度为90.11%,比Guo的自相关函数特征提取方法提高2.75个百分点,从而表明分段氨基酸组成成分特征提取方法可有效地应用于蛋白质相互作用预测。     

蛋白质相互作用研究进展

蛋白质行使功能时,需要与其他蛋白质或者其他分子相互作用才能完成.在蛋白质相互作用水平上研究蛋白质对理解蛋白质功能、疾病与进化具有重要的意义.就蛋白质相互作用的预测、常用的蛋白质相互作用数据库以及蛋白质相互作用网络的研究进行了介绍.     

生物信息学方法预测蛋白质相互作用网络中的功能模块

蛋白质相互作用是大多数生命过程的基础。随着高通量实验技术和计算机预测方法的发展,在各种生物中已获得了数目十分庞大的蛋白质相互作用数据,如何从中提取出具有生物学意义的数据是一项艰巨的挑战。从蛋白质相互作用数据出发获得相互作用网络进而预测出其中的功能模块,对于蛋白质功能预测、揭示各种生化反应过程的分子机理都有着极大的帮助。我们分类概括了用生物信息学预测蛋白质相互作用功能模块的方法,以及对这些方法的评价,并介绍了蛋白质相互作用网络比较的一些方法。     

蛋白质表面氨基酸特性研究进展

分布在蛋白质分子表面的暴露于溶剂的氨基酸所具有的一些特性对蛋白质的折叠和聚合过程、蛋白质一蛋白质相互作用以及蛋白质的功能具有重要影响。本文分析了蛋白质表面氨基酸在疏水性、保守性、静电势及结构方面的一些特性,阐述了近年来国际上利用这些特性对蛋白质一蛋白质相互作用界面进行预测的方法,最后介绍了几款预测蛋白质表面氨基酸的软件。     

蛋白质表面氨基酸特性研究进展

分布在蛋白质分子表面的暴露于溶剂的氨基酸所具有的一些特性对蛋白质的折叠和聚合过程、蛋白质-蛋白质相互作用以及蛋白质的功能具有重要影响。本文分析了蛋白质表面氨基酸在疏水性、保守性、静电势及结构方面的一些特性,阐述了近年来国际上利用这些特性对蛋白质-蛋白质相互作用界面进行预测的方法,最后介绍了几款预测蛋白质表面氨基酸的软件。     

基于Cytoscape下斑马鱼衰老基因-蛋白质的模块化分析

近20年来,斑马鱼逐渐成为研究人类基因功能的重要模型动物。同时,通过对斑马鱼参考基因组序列和10 000多个蛋白编码基因的鉴定,表明斑马鱼至少与人类基因有75%的同源性,进一步验证了斑马鱼基因组序列可以作为衰老的研究模型。此外,其良好保守的分子和细胞生理学的广泛特征使斑马鱼成为揭示衰老、疾病和修复的潜在机制的极好模型。但是斑马鱼衰老的分子机制很少发生分子间的相互作用,因此蛋白质-蛋白相互作用(PPI)网络是非常可取的。本实验描述了斑马鱼这种生物衰老机制的模型,其涵盖了与衰老相关的87种蛋白质之间的767种相互作用。这不仅包含准确预测的PPI,还包含从文献收集以及实验所得的那些分子相互作用。同时,将这些分子相互作用模块化,形成模块化,找到11个中心基因,分析预测其衰老过程。希望能帮助研究斑马鱼的学者研究其衰老过程,提供一些假说和帮助。     

蛋白质相互作用信息的文本挖掘研究进展

蛋白质相互作用是生命活动中一种极其重要的生物分子关系, 对此领域的研究不仅具有理论意义, 还具有较强的应用价值. 近年来, 随着研究的深入, 各种蛋白质相互作用的生物医学文献激增, 挖掘其中的蛋白质相互作用关系成为人们面临的一大挑战. 当前, 已提出了多种文本挖掘方法, 对分散于生物医学文献中的蛋白质相互作用信息进行结构化或半结构化处理. 对这些工作进行分析, 总结出基于生物文本挖掘蛋白质相互作用信息的一般流程, 从蛋白质命名实体的识别、蛋白质相互作用关系的提取和蛋白质相互作用注释信息的提取3个子任务进行阐述, 同时介绍了生物文本挖掘领域的评测会议和一些挖掘蛋白质相互作用相关信息的工具. 最后, 对该领域存在的一些重要问题进行分析, 并预测了未来可能的发展方向, 以期对该领域相关研究提供一定的参考.     

利用一种新的网上工具Scansite进行蛋白质磷酸化预测

Scansite分析软件是近两年建立的一种新的利用因特网,基于蛋白质分子中较短的模序进行蛋白质磷酸化和蛋白质蛋白质相互作用预测的工具。这里综述了Scansite的使用方法、功能介绍及与其他磷酸化分析软件的比较,并展望了Scansite在进行磷酸化预测中面临的问题和应用前景。     

Predicting specificity in bZIP coiled-coil protein interactions

We present a method for predicting protein-protein interactions mediated by the coiled-coil motif. When tested on interactions between nearly all human and yeast bZIP proteins, our method identifies 70% of strong interactions while maintaining that 92% of predictions are correct. Furthermore, cross-validation testing shows that including the bZIP experimental data significantly improves performance. Our method can be used to predict bZIP interactions in other genomes and is a promising approach for predicting coiled-coil interactions more generally.     

Inferring protein-protein interactions through high-throughput interaction data from diverse organisms

MOTIVATION: Identifying protein-protein interactions is critical for understanding cellular processes. Because protein domains represent binding modules and are responsible for the interactions between proteins, computational approaches have been proposed to predict protein interactions at the domain level. The fact that protein domains are likely evolutionarily conserved allows us to pool information from data across multiple organisms for the inference of domain-domain and protein-protein interaction probabilities. RESULTS: We use a likelihood approach to estimating domain-domain interaction probabilities by integrating large-scale protein interaction data from three organisms, Saccharomyces cerevisiae, Caenorhabditis elegans and Drosophila melanogaster. The estimated domain-domain interaction probabilities are then used to predict protein-protein interactions in S.cerevisiae. Based on a thorough comparison of sensitivity and specificity, Gene Ontology term enrichment and gene expression profiles, we have demonstrated that it may be far more informative to predict protein-protein interactions from diverse organisms than from a single organism. AVAILABILITY: The program for computing the protein-protein interaction probabilities and supplementary material are available at http://bioinformatics.med.yale.edu/interaction.     

Microarray image analysis: background estimation using quantile and morphological filters

Background

Identification of protein interaction networks has received considerable attention in the post-genomic era. The currently available biochemical approaches used to detect protein-protein interactions are all time and labour intensive. Consequently there is a growing need for the development of computational tools that are capable of effectively identifying such interactions.

Results

Here we explain the development and implementation of a novel Protein-Protein Interaction Prediction Engine termed PIPE. This tool is capable of predicting protein-protein interactions for any target pair of the yeast Saccharomyces cerevisiae proteins from their primary structure and without the need for any additional information or predictions about the proteins. PIPE showed a sensitivity of 61% for detecting any yeast protein interaction with 89% specificity and an overall accuracy of 75%. This rate of success is comparable to those associated with the most commonly used biochemical techniques. Using PIPE, we identified a novel interaction between YGL227W (vid30) and YMR135C (gid8) yeast proteins. This lead us to the identification of a novel yeast complex that here we term vid30 complex (vid30c). The observed interaction was confirmed by tandem affinity purification (TAP tag), verifying the ability of PIPE to predict novel protein-protein interactions. We then used PIPE analysis to investigate the internal architecture of vid30c. It appeared from PIPE analysis that vid30c may consist of a core and a secondary component. Generation of yeast gene deletion strains combined with TAP tagging analysis indicated that the deletion of a member of the core component interfered with the formation of vid30c, however, deletion of a member of the secondary component had little effect (if any) on the formation of vid30c. Also, PIPE can be used to analyse yeast proteins for which TAP tagging fails, thereby allowing us to predict protein interactions that are not included in genome-wide yeast TAP tagging projects.

Conclusion

PIPE analysis can predict yeast protein-protein interactions. Also, PIPE analysis can be used to study the internal architecture of yeast protein complexes. The data also suggests that a finite set of short polypeptide signals seem to be responsible for the majority of the yeast protein-protein interactions.     

Computational Prediction of Protein–Protein Interaction Networks: Algo-rithms and Resources

Protein interactions play an important role in the discovery of protein functions and pathways in biological processes. This is especially true in case of the diseases caused by the loss of specific protein-protein interactions in the organism. The accuracy of experimental results in finding protein-protein interactions, however, is rather dubious and high throughput experimental results have shown both high false positive beside false negative information for protein interaction. Computational methods have attracted tremendous attention among biologists because of the ability to predict protein-protein interactions and validate the obtained experimental results. In this study, we have reviewed several computational methods for protein-protein interaction prediction as well as describing major databases, which store both predicted and detected protein-protein interactions, and the tools used for analyzing protein interaction networks and improving protein-protein interaction reliability.     

Evaluation of the third solvent clusters fitting procedure for the prediction of protein-protein interactions based on the results at the CAPRI blind docking study

To predict protein-protein interactions, rough or coarse handling for the induced fit problem is proposed. Our method involves the overlap of two hydrophobic interactions as "third solvent clusters fitting." Predictions for binding sites and geometric centers were acceptable, but those of the binding axes were poor. In this study, only the largest benzene cluster was used for the third solvent clusters fitting. For the next CAPRI targets, we must perform protein-protein interaction analyses, which include other smaller benzene clusters.     

Co-evolutionary analysis of domains in interacting proteins reveals insights into domain-domain interactions mediating protein-protein interactions

Recent advances in functional genomics have helped generate large-scale high-throughput protein interaction data. Such networks, though extremely valuable towards molecular level understanding of cells, do not provide any direct information about the regions (domains) in the proteins that mediate the interaction. Here, we performed co-evolutionary analysis of domains in interacting proteins in order to understand the degree of co-evolution of interacting and non-interacting domains. Using a combination of sequence and structural analysis, we analyzed protein-protein interactions in F1-ATPase, Sec23p/Sec24p, DNA-directed RNA polymerase and nuclear pore complexes, and found that interacting domain pair(s) for a given interaction exhibits higher level of co-evolution than the non-interacting domain pairs. Motivated by this finding, we developed a computational method to test the generality of the observed trend, and to predict large-scale domain-domain interactions. Given a protein-protein interaction, the proposed method predicts the domain pair(s) that is most likely to mediate the protein interaction. We applied this method on the yeast interactome to predict domain-domain interactions, and used known domain-domain interactions found in PDB crystal structures to validate our predictions. Our results show that the prediction accuracy of the proposed method is statistically significant. Comparison of our prediction results with those from two other methods reveals that only a fraction of predictions are shared by all the three methods, indicating that the proposed method can detect known interactions missed by other methods. We believe that the proposed method can be used with other methods to help identify previously unrecognized domain-domain interactions on a genome scale, and could potentially help reduce the search space for identifying interaction sites.