[Post-translational modification (PTM) bioinformatics in China: progresses and perspectives].

Post-translational modifications (PTMs) are essential for regulating conformational changes, activities and functions of proteins, and are involved in almost all cellular pathways and processes. Identification of protein PTMs is the basis for understanding cellular and molecular mechanisms. In contrast with labor-intensive and time-consuming experiments, the PTM prediction using various bioinformatics approaches can provide accurate, convenient, and efficient strategies and generate valuable information for further experimental consideration. In this review, we summarize the current progresses made by Chineses bioinformaticians in the field of PTM Bioinformatics, including the design and improvement of computational algorithms for predicting PTM substrates and sites, design and maintenance of online and offline tools, establishment of PTM-related databases and resources, and bioinformatics analysis of PTM proteomics data. Through comparing similar studies in China and other countries, we demonstrate both advantages and limitations of current PTM bioinformatics as well as perspectives for future studies in China.

Improving the accuracy of predicting disulfide connectivity by feature selection.

Disulfide bonds are primary covalent cross-links formed between two cysteine residues in the same or different protein polypeptide chains, which play important roles in the folding and stability of proteins. However, computational prediction of disulfide connectivity directly from protein primary sequences is challenging due to the nonlocal nature of disulfide bonds in the context of sequences, and the number of possible disulfide patterns grows exponentially when the number of cysteine residues increases. In the previous studies, disulfide connectivity prediction was usually performed in high-dimensional feature space, which can cause a variety of problems in statistical learning, such as the dimension disaster, overfitting, and feature redundancy. In this study, we propose an efficient feature selection technique for analyzing the importance of each feature component. On the basis of this approach, we selected the most important features for predicting the connectivity pattern of intra-chain disulfide bonds. Our results have shown that the high-dimensional features contain redundant information, and the prediction performance can be further improved when these high-dimensional features are reduced to a lower but more compact dimensional space. Our results also indicate that the global protein features contribute little to the formation and prediction of disulfide bonds, while the local sequential and structural information play important roles. All these findings provide important insights for structural studies of disulfide-rich proteins.

A novel method of analyzing proline synonymous codons in E. coli.

Proline is a special imino acid in protein and the isomerization of the prolyl peptide bond has notable biological significance and influences the final structure of protein greatly, so the correlation between proline synonymous codon usage and local amino acid, the correlation between proline synonymous codon usage and the isomerization of the prolyl peptide bond were both investigated in the Escherichia coli genome by using a novel method based on information theory. The results show that in peptide chain, the residue at the first position C-terminal influences the usage of proline synonymous codon greatly and proline synonymous codons contain some factors influencing the isomerization of the prolyl peptide bond.

Prediction of the disulfide-bonding state of cysteines in proteins based on dipeptide composition.

In this paper, a novel approach has been introduced to predict the disulfide-bonding state of cysteines in proteins by means of a linear discriminator based on their dipeptide composition. The prediction is performed with a newly enlarged dataset with 8114 cysteine-containing segments extracted from 1856 non-homologous proteins of well-resolved three-dimensional structures. The oxidation of cysteines exhibits obvious cooperativity: almost all cysteines in disulfide-bond-containing proteins are in the oxidized form. This cooperativity can be well described by protein's dipeptide composition, based on which the prediction accuracy of the oxidation form of cysteines scores as high as 89.1% and 85.2%, when measured on cysteine and protein basis using the rigorous jack-knife procedure, respectively. The result demonstrates the applicability of this new relatively simple method and provides superior prediction performance compared with existing methods for the prediction of the oxidation states of cysteines in proteins.