Prediction of the helix/strand content of globular proteins based on their primary sequences

Zhang, Chun‐Ting; Lin, Zheshuai; Zhang, Ziding; Yan, Ming

doi:10.1093/protein/11.11.971

Cited by 31 publications

(18 citation statements)

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“…The L1 52/55K protein, in association with a late protein, IVa2, plays a role in encapsidation of the viral DNA (42,83,84). Some of the mRNAs initiated from the major late promoter, particularly the L1 mRNAs, contain an additional leader (i-leader) spliced together between the second and third segments of the tripartite leader (17).…”

mentioning

confidence: 99%

Genetic Identification of Adenovirus Type 5 Genes That Influence Viral Spread

Subramanian

Vijayalingam

Chinnadurai

2006

J Virol

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The mechanisms that control cell-to-cell spread of human adenoviruses (Ad) are not well understood. Two early viral proteins, E1B-19K and E3-ADP, appear to have opposing effects since viral mutants that are individually deficient in E1B-19K produce large plaques (G. Chinnadurai, Cell 33:759-766, 1983), while mutants deficient in E3-ADP produce small plaques (A. E. Tollefson et al., J. Virol. 70:2296-2306, 1996) on infected cell monolayers. We have used a genetic strategy to identify different viral genes that influence adenovirus type 5 (Ad5) spread in an epithelial cancer cell line. An Ad5 mutant (dl327; lacking most of the E3 region) with the restricted-spread (small-plaque) phenotype was randomly mutagenized with UV, and 27 large-plaque (lp) mutants were isolated. A combination of analyses of viral proteins and genomic DNA sequences have indicated that 23 mutants contained lesions in the E1B region affecting either 19K or both 19K and 55K proteins. Four other lp mutants contained lesions in early regions E1A and E4, in the early L1 region that codes for the i-leader protein, and in late regions that code for the viral structural proteins, penton base, and fiber. Our results suggest that the requirement of E3-ADP for Ad spread could be readily compensated for by abrogation of the functions of E1B-19K and provide genetic evidence that these two viral proteins influence viral spread in opposing manners. In addition to E1B and E3 proteins, other early and late proteins that regulate viral replication and infectivity also influence lateral viral spread. Our studies have identified novel mutations that could be exploited in designing efficient oncolytic Ad vectors.The efficiency with which a virus spreads from an infected cell to the neighboring uninfected cells is an important determinant of viral pathogenesis. The mechanisms by which nonenveloped viruses, particularly the adenoviruses, spread laterally are not well understood. It is generally believed that subversion of the cellular macromolecular synthesis machineries and the increased intracellular viral load result in cellular destruction and release of virus particles. The human adenoviruses (Ads) replicate in differentiated epithelial cells. The group C Ads infect these cells in a two-step process. The Ads enter the cells by receptor-mediated endocytosis (12,70). The viral particles first bind to the primary receptor (CAR [coxsackie-adenovirus receptor]) (7, 67) through interaction with the knob structure of the fiber (73, 80). Subsequently, the viral capsid protein penton base interacts with the V type integrins (␣ V ␤ 3 and ␣ V ␤ 5 ) via the RGD motif (78). The interaction of the penton base with the integrins also contributes the characteristic Ad cytopathic effect (CPE) (5). After adsorption and internalization into the endosomes, acidification of the endosomes results in cytosolic penetration of the virus particles which is also aided by the penton base (50, 57). Virus particles are then dismantled in a stepwise manner in the cytosol (24). It is possibl...

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confidence: 99%

Genetic Identification of Adenovirus Type 5 Genes That Influence Viral Spread

Subramanian

Vijayalingam

Chinnadurai

2006

J Virol

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“…As one of the most efficient methods for analyzing the sequence of vector statistics, the Auto Covariance (AC) has been widely used in the prediction of secondary structure content [40,41], protein family classification by researchers [42,43], and protein interaction prediction [23]. AC variable indicates that in a given protein sequence of two residues average correlation, the expression is:…”

Section: Auto Covariancementioning

confidence: 99%

SIPEC: Systematic identification of self-interacting proteins with ensemble classifiers using evolutionary information

Wang¹,

You²,

Zhang³

et al. 2018

Oncotarget

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As the center of most biological processes, Protein-Protein Interactions (PPIs) constitute the basis of the formation of biological mechanisms. Deregulation of PPIs results in many diseases including cancer and pernicious anemia. As a special type of PPIs, the Self-interacting Proteins (SIPs) occupy an important position in them. Although a large number of SIPs data have been generated by experimental methods, currently-detected self-interacting proteins cover only a small part of the complete network. Therefore, there is a great need for computational methods to efficiently and accurately predict SIPs. In the present study, we introduce a novel computational method based on protein sequence information to predict SIPs. More specifically, each protein sequence is converted to Position-Specific Scoring Matrix (PSSM) containing the evolutionary information. And then an effective feature extraction approach, namely, Auto Covariance (AC) is employed to construct a feature set. Finally, the improved Rotation Forest (RF) model is used to remove the noise of the feature set and give prediction results. When performed on yeast and human SIPs data sets, the proposed method can achieve high accuracies of 80.50% and 93.70%, respectively. Our method also shows a good performance when compared with the SVM classifier and other existing methods. Consequently, the proposed method can be considered to be a promising model to predict SIPs. In addition, for the purpose of further research in the future, the user-friendly web server is freely available to academic use at http://www.proteininteraction.cn/sip/.

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“…Regardless of its simplicity, the amino acid composition contains much important information of proteins. It is found that the amino acid composition is relevant to the structural classes (Nakashima et al, 1986;Chou, 1995), the thermal stability (Luis & Argos, 1989), the secondary structure content (Krigbaum & Knutton, 1973;Muskal & Kim, 1992;Eisenhaber et al, 1996;Zhang et al, 1996Zhang et al, , 1998a and the cellular positions (Nakashima & Nishikawa, 1994;Cedano et al, 1997). However, the amino acid composition is insu$cient to re#ect the complete information of proteins.…”

Section: Databasementioning

confidence: 99%

“…the secondary structure content, we have used the auto-correlation functions (Zhang et al, 1998a). The concept of the auto-correlation functions of a protein was probably "rst introduced by Cornette et al (1987).…”

Section: Databasementioning

confidence: 99%

“…In addition to the amino acid composition, the structural class was also used as the necessary input information in these works (Zhang et al, 1996(Zhang et al, , 1998b. Meanwhile, in order to improve the prediction accuracy, instead of the amino acid composition, the primary sequence of a protein was used to predict the secondary structure content recently (Zhang et al, 1998a). This work is a continuous development of our previous work.…”

Section: Introductionmentioning

confidence: 99%

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