Protein complex prediction: A survey

Protein complexes are one of the most important functional units for deriving biological processes within the cell. Experimental methods have provided valuable data to infer protein complexes. However, these methods have inherent limitations. Considering these limitations, many computational methods have been proposed to predict protein complexes, in the last decade. Almost all of these in-silico methods predict protein complexes from the ever-increasing protein–protein interaction (PPI) data. These computational approaches usually use the PPI data in the format of a huge protein–protein interaction network (PPIN) as input and output various sub-networks of the given PPIN as the predicted protein complexes. Some of these methods have already reached a promising efficiency in protein complex detection. Nonetheless, there are challenges in prediction of other types of protein complexes, specially sparse and small ones. New methods should further incorporate the knowledge of biological properties of proteins to improve the performance. Additionally, there are several challenges that should be considered more effectively in designing the new complex prediction algorithms in the future. This article not only reviews the history of computational protein complex prediction but also provides new insight for improvement of new methodologies. In this article, most important computational methods for protein complex prediction are evaluated and compared. In addition, some of the challenges in the reconstruction of the protein complexes are discussed. Finally, various tools for protein complex prediction and PPIN analysis as well as the current high-throughput databases are reviewed.     

Structure- and sequence-based function prediction for non-homologous proteins

The structural genomics projects have been accumulating an increasing number of protein structures, many of which remain functionally unknown. In parallel effort to experimental methods, computational methods are expected to make a significant contribution for functional elucidation of such proteins. However, conventional computational methods that transfer functions from homologous proteins do not help much for these uncharacterized protein structures because they do not have apparent structural or sequence similarity with the known proteins. Here, we briefly review two avenues of computational function prediction methods, i.e. structure-based methods and sequence-based methods. The focus is on our recent developments of local structure-based and sequence-based methods, which can effectively extract function information from distantly related proteins. Two structure-based methods, Pocket-Surfer and Patch-Surfer, identify similar known ligand binding sites for pocket regions in a query protein without using global protein fold similarity information. Two sequence-based methods, protein function prediction and extended similarity group, make use of weakly similar sequences that are conventionally discarded in homology based function annotation. Combined together with experimental methods we hope that computational methods will make leading contribution in functional elucidation of the protein structures.     

检测聚丙烯酰胺凝胶中蛋白质的3种染色方法比较

用考马斯亮蓝染色、银染色、铜染色等 3种方法对同一种蛋白质染色的灵敏度、快速性进行比较 ,得出 3种蛋白质染色方法的优缺点 ,为蛋白质电泳染色合理选用不同方法提供依据     

蛋白质间相互作用技术的研究近况

蛋白质间相互作用技术的研究近况黄翠芬叶棋浓（军事医学科学院生物工程研究所,北京１００８５０关键词：蛋白质,相互作用,技术ＲｅｃｅｎｔＡｄｖａｎｃｅｓｉｎｔｈｅＴｅｃｈｎｉｑｕｅｓｏｆＰｒｏｔｅｉｎ┐ＰｒｏｔｅｉｎＩｎｔｅｒａｃｔｉｏｎｓＨｕａｎｇＣｕ...     

仙人掌多糖提取过程中脱蛋白方法的研究

用水提取的仙人掌多糖常含有蛋白质C,实验以多糖损失率和脱蛋白率为衡量指标,选用了Sevag法、三氯乙酸法和酶法对仙人掌多糖提取物进行脱蛋白处理。实验结果表明,酶法、三氯乙酸法和Sevag法的脱蛋白率分别为89.67%、56.20%和45.65%,多糖损失率分别为7.19%、9.49%和13.75%。因此,酶提取法是替代常规方法的一种有效方法。     

猪水疱病病毒外壳蛋白的二级结构和抗原表位预测

以SVDV外壳蛋白基因序列为基础,采用Chou-Fasman法、Garnier-Robson 法和Karplus-Schulz法预测蛋白质的二级结构;按Kyte-Doolittle方案、Emini方案和Jameson-Wolf方案预测SVDV外壳蛋白的B细胞表位。预测结果表明,SVDV外壳蛋白的二级结构较为复杂,含有较多的转角和无规则卷曲等柔性区域以及α-螺旋和β-折叠区段;SVDV外壳蛋白的VP1、VP2和VP3上均有多个区域为B细胞优势表位,其中,VP1蛋白的B细胞表位优势区域比VP2和VP3蛋白的多,与已鉴定的B细胞表位相比较,该方法预测的结果有较高的准确度。为实验确定SVDV外壳蛋白的B细胞表位和反向疫苗学设计提供理论基础。     

高效液相色谱检测聚乙二醇化天花粉蛋白方法比较

目的：建立聚乙二醇化天花粉蛋白纯度的检测方法。方法：使用分子筛色谱法和反相高效液相色谱法测定了聚乙二醇天花粉蛋白。结果：聚乙二醇天花粉蛋白纯度分别为100％和98．14％。结论：高效液相色谱法简单易行、准确、灵敏度高,适用于聚乙二醇化天花粉蛋白纯度检测。     

Secondary structure prediction and determination of proteins--a review

The rapid increase in sequence data in combination with a greater understanding of the forces regulating protein structure has been the impetus for an upsurge in the development of theoretical prediction methods. These methods have afforded protein chemists the ability to identify and quantify the various secondary structures along the protein chain. Concurrently, various physico-chemical techniques have been developed such as nuclear Overhauser enhancement n.m.r. and laser Raman spectroscopy. In addition, traditional methods such as infrared and circular dichroism spectroscopy have been refined. Although both predictive and physico-chemical techniques are limited in the types of secondary structure they are capable of determining, they have provided valuable information with regards to protein folding and topology in the absence of X-ray data, and have formed the basis for the development of improved methods for secondary structure determination. This paper reviews some of the predictive and physico-chemical methods presently used to determine protein secondary structure.     

Deciphering protein-protein interactions. Part II. Computational methods to predict protein and domain interaction partners

Recent advances in high-throughput experimental methods for the identification of protein interactions have resulted in a large amount of diverse data that are somewhat incomplete and contradictory. As valuable as they are, such experimental approaches studying protein interactomes have certain limitations that can be complemented by the computational methods for predicting protein interactions. In this review we describe different approaches to predict protein interaction partners as well as highlight recent achievements in the prediction of specific domains mediating protein-protein interactions. We discuss the applicability of computational methods to different types of prediction problems and point out limitations common to all of them.     

Enzyme-mediated methodologies for protein modification and bioconjugate synthesis

Bioconjugates are valuable tools in many fields, including protein engineering and environmental and therapeutic research. Chemical methods are commonly used to synthesize protein-protein and protein-functional molecule bioconjugates because they permit easy tethering through covalent bonds. However, chemical methods often produce heterogeneous products and lead to degradation of protein activity due to random modifications. Recently, a number of techniques for modifying proteins or synthesizing bioconjugates have been reported, including more sophisticated chemical modification methods, utilization of noncovalent affinity, and protein splicing. Enzymatic methods in particular have attracted much attention due to the substrate specificity of enzymes, which enables site-specific tethering of proteins to other proteins or functional molecules. Here, we discuss newly developed methods for protein modification and bioconjugate synthesis that exploit the properties of acyltransferases, ligases, and other enzymes.     

A topological algorithm for identification of structural domains of proteins

Background  

Identification of the structural domains of proteins is important for our understanding of the organizational principles and mechanisms of protein folding, and for insights into protein function and evolution. Algorithmic methods of dissecting protein of known structure into domains developed so far are based on an examination of multiple geometrical, physical and topological features. Successful as many of these approaches are, they employ a lot of heuristics, and it is not clear whether they illuminate any deep underlying principles of protein domain organization. Other well-performing domain dissection methods rely on comparative sequence analysis. These methods are applicable to sequences with known and unknown structure alike, and their success highlights a fundamental principle of protein modularity, but this does not directly improve our understanding of protein spatial structure.     

Conserved network motifs allow protein-protein interaction prediction

MOTIVATION: High-throughput protein interaction detection methods are strongly affected by false positive and false negative results. Focused experiments are needed to complement the large-scale methods by validating previously detected interactions but it is often difficult to decide which proteins to probe as interaction partners. Developing reliable computational methods assisting this decision process is a pressing need in bioinformatics. RESULTS: We show that we can use the conserved properties of the protein network to identify and validate interaction candidates. We apply a number of machine learning algorithms to the protein connectivity information and achieve a surprisingly good overall performance in predicting interacting proteins. Using a 'leave-one-out' approach we find average success rates between 20 and 40% for predicting the correct interaction partner of a protein. We demonstrate that the success of these methods is based on the presence of conserved interaction motifs within the network. AVAILABILITY: A reference implementation and a table with candidate interacting partners for each yeast protein are available at http://www.protsuggest.org.     

Implementation of high-content assay for inhibitors of mitogen-activated protein kinase phosphatases

Small molecule inhibitors of protein tyrosine kinases have become both powerful chemical probes of biological processes and clinically effective therapeutics. In contrast, few small molecule inhibitors of protein tyrosine phosphatases have been identified and none are currently approved for clinical use. New cell-based high-content methods have been developed that should enable investigators to probe for selective inhibitors of diseases-relevant protein phosphatases. Details of these methods are described herein.     

GeneSilico protein structure prediction meta-server

Rigorous assessments of protein structure prediction have demonstrated that fold recognition methods can identify remote similarities between proteins when standard sequence search methods fail. It has been shown that the accuracy of predictions is improved when refined multiple sequence alignments are used instead of single sequences and if different methods are combined to generate a consensus model. There are several meta-servers available that integrate protein structure predictions performed by various methods, but they do not allow for submission of user-defined multiple sequence alignments and they seldom offer confidentiality of the results. We developed a novel WWW gateway for protein structure prediction, which combines the useful features of other meta-servers available, but with much greater flexibility of the input. The user may submit an amino acid sequence or a multiple sequence alignment to a set of methods for primary, secondary and tertiary structure prediction. Fold-recognition results (target-template alignments) are converted into full-atom 3D models and the quality of these models is uniformly assessed. A consensus between different FR methods is also inferred. The results are conveniently presented on-line on a single web page over a secure, password-protected connection. The GeneSilico protein structure prediction meta-server is freely available for academic users at http://genesilico.pl/meta.     

Progressive Clustering Based Method for Protein Function Prediction

In recent years, significant effort has been given to predicting protein functions from protein interaction data generated from high throughput techniques. However, predicting protein functions correctly and reliably still remains a challenge. Recently, many computational methods have been proposed for predicting protein functions. Among these methods, clustering based methods are the most promising. The existing methods, however, mainly focus on protein relationship modeling and the prediction algorithms that statically predict functions from the clusters that are related to the unannotated proteins. In fact, the clustering itself is a dynamic process and the function prediction should take this dynamic feature of clustering into consideration. Unfortunately, this dynamic feature of clustering is ignored in the existing prediction methods. In this paper, we propose an innovative progressive clustering based prediction method to trace the functions of relevant annotated proteins across all clusters that are generated through the progressive clustering of proteins. A set of prediction criteria is proposed to predict functions of unannotated proteins from all relevant clusters and traced functions. The method was evaluated on real protein interaction datasets and the results demonstrated the effectiveness of the proposed method compared with representative existing methods.     

indel-Seq-Gen: a new protein family simulator incorporating domains, motifs, and indels

Reconstructing the evolutionary history of protein sequences will provide a better understanding of divergence mechanisms of protein superfamilies and their functions. Long-term protein evolution often includes dynamic changes such as insertion, deletion, and domain shuffling. Such dynamic changes make reconstructing protein sequence evolution difficult and affect the accuracy of molecular evolutionary methods, such as multiple alignments and phylogenetic methods. Unfortunately, currently available simulation methods are not sufficiently flexible and do not allow biologically realistic dynamic protein sequence evolution. We introduce a new method, indel-Seq-Gen (iSG), that can simulate realistic evolutionary processes of protein sequences with insertions and deletions (indels). Unlike other simulation methods, iSG allows the user to simulate multiple subsequences according to different evolutionary parameters, which is necessary for generating realistic protein families with multiple domains. iSG tracks all evolutionary events including indels and outputs the "true" multiple alignment of the simulated sequences. iSG can also generate a larger sequence space by allowing the use of multiple related root sequences. With all these functions, iSG can be used to test the accuracy of, for example, multiple alignment methods, phylogenetic methods, evolutionary hypotheses, ancestral protein reconstruction methods, and protein family classification methods. We empirically evaluated the performance of iSG against currently available methods by simulating the evolution of the G protein-coupled receptor and lipocalin protein families. We examined their true multiple alignments, reconstruction of the transmembrane regions and beta-strands, and the results of similarity search against a protein database using the simulated sequences. We also presented an example of using iSG for examining how phylogenetic reconstruction is affected by high indel rates.     

Exploiting the co-evolution of interacting proteins to discover interaction specificity

Protein interactions are fundamental to the functioning of cells, and high throughput experimental and computational strategies are sought to map interactions. Predicting interaction specificity, such as matching members of a ligand family to specific members of a receptor family, is largely an unsolved problem. Here we show that by using evolutionary relationships within such families, it is possible to predict their physical interaction specificities. We introduce the computational method of matrix alignment for finding the optimal alignment between protein family similarity matrices. A second method, 3D embedding, allows visualization of interacting partners via spatial representation of the protein families. These methods essentially align phylogenetic trees of interacting protein families to define specific interaction partners. Prediction accuracy depends strongly on phylogenetic tree complexity, as measured with information theoretic methods. These results, along with simulations of protein evolution, suggest a model for the evolution of interacting protein families in which interaction partners are duplicated in coupled processes. Using these methods, it is possible to successfully find protein interaction specificities, as demonstrated for >18 protein families.     

Engineered protein function by selective amino acid diversification

Almost all protein engineering methods rely upon making changes to naturally occurring proteins that already possess some of the desired properties. This will probably remain the case as long as we lack a complete understanding of the way that an amino acid sequence gives rise to a protein with a precisely defined biological function. Common to all methods for altering an existing protein is the selection of a subset of amino acids in the protein for variation and a choice of which substitutions to make at each position. Variants are then tested empirically and further variants are created based upon their performance. Differences between protein engineering methods are the ways in which amino acids are chosen for variation, the protocols followed for creating the variants, and how information regarding variant properties is used in creating subsequent variants. In this article, we describe these differences and provide examples of how the experimental parameters of specific projects determine which method is most suitable.     

EVA: continuous automatic evaluation of protein structure prediction servers.

Evaluation of protein structure prediction methods is difficult and time-consuming. Here, we describe EVA, a web server for assessing protein structure prediction methods, in an automated, continuous and large-scale fashion. Currently, EVA evaluates the performance of a variety of prediction methods available through the internet. Every week, the sequences of the latest experimentally determined protein structures are sent to prediction servers, results are collected, performance is evaluated, and a summary is published on the web. EVA has so far collected data for more than 3000 protein chains. These results may provide valuable insight to both developers and users of prediction methods. AVAILABILITY: http://cubic.bioc.columbia.edu/eva. CONTACT: eva@cubic.bioc.columbia.edu     

The folding of an enzyme. V. H/2H exchange-nuclear magnetic resonance studies on the folding pathway of barnase: complementarity to and agreement with protein engineering studies.

Two major methods are currently being used to characterize transient intermediates during protein folding at the level of individual residues. Nuclear magnetic resonance (n.m.r.) measurements on the protection of peptide NH hydrogens against exchange with solvent during refolding can provide information about secondary structure formation. Protein engineering and kinetics can provide direct information about intramolecular interactions of protein side-chains and indirect evidence on secondary structure. These procedures have provided the most complete pictures so far about protein folding intermediates. Both methods have been applied to the characterization of an intermediate in the refolding of barnase. Although the two methods give complementary information, there are some regions of the protein where the methods overlap well. We show that, with one possible exception that is obscure, n.m.r. and protein engineering give identical results for those interactions that can be analysed by both methods. This suggests that these are valid approaches for the study of protein folding intermediates in the case of barnase and that the combination of the methods is a powerful analytical procedure. Information provided by n.m.r. data that is complementary to the protein engineering experiments is: (1) early formation of the C terminus of helix2; (2) early formation of helix3; (3) early formation of several beta-turns (46-49, 101-104 in loop5); and (5) partial formation of loop5. Confirmatory evidence of protein engineering data on the intermediate is: (1) helix1 is complete from residues 10 to 18; (2) the interactions between all beta-strands are present; (3) part of loop2 is not formed; (4) part of loop3 is formed; and (5) some specific tertiary interactions are not made. For some interactions the protein engineering and H/2H exchange methods overlap directly. The information obtained for direct overlap is self consistent.