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How to characterize short protein sequences to make an effective connection to their functions is an unsolved problem. Here we propose to map the physicochemical properties of each amino acid onto unit spheres so that each protein sequence can be represented quantitatively. We demonstrate the usefulness of this representation by applying it to the prediction of cell penetrating peptides. We show that its combination with traditional composition features yields the best performance across different datasets, among several methods compared. For the convenience of users, a web server has been established for automatic calculations of the proposed features at http://biophy.dzu.edu.cn/SNumD/., (© 2019 Wiley Periodicals, Inc.)

Most natural protein sequences have resulted from millions or even billions of years of evolution. How they differ from random sequences is not fully understood. Previous computational and experimental studies of random proteins generated from noncoding regions yielded inclusive results due to species-dependent codon biases and GC contents. Here, we approach this problem by investigating 10,000 sequences randomized at the amino acid level. Using well-established predictors for protein intrinsic disorder, we found that natural sequences have more long disordered regions than random sequences, even when random and natural sequences have the same overall composition of amino acid residues. We also showed that random sequences are as structured as natural sequences according to contents and length distributions of predicted secondary structure, although the structures from random sequences may be in a molten globular-like state, according to molecular dynamics simulations. The bias of natural sequences toward more intrinsic disorder suggests that natural sequences are created and evolved to avoid protein aggregation and increase functional diversity.

The composition and sequence order of amino acid residues are the two most important characteristics to describe a protein sequence. Graphical representations facilitate visualization of biological sequences and produce biologically useful numerical descriptors. In this paper, we propose a novel cylindrical representation by placing the 20 amino acid residue types in a circle and sequence positions along the z axis. This representation allows visualization of the composition and sequence order of amino acids at the same time. Ten numerical descriptors and one weighted numerical descriptor have been developed to quantitatively describe intrinsic properties of protein sequences on the basis of the cylindrical model. Their applications to similarity/dissimilarity analysis of nine ND5 proteins indicated that these numerical descriptors are more effective than several classical numerical matrices. Thus, the cylindrical representation obtained here provides a new useful tool for visualizing and charactering protein sequences. An online server is available at http://biophy.dzu.edu.cn:8080/CNumD/input.jsp .

Over annotation of protein coding genes is common phenomenon in microbial genomes, the genome of Amsacta moorei entomopoxvirus (AmEPV) is a typical case, because more than 63% of its annotated ORFs are hypothetical. In this article, we propose an improved graphical representation titled I-TN (improved curve based on trinucleotides) curve, which allows direct inspection of composition and distribution of codons and asymmetric gene structure. This improved graphical representation can also provide convenient tools for genome analysis. From this presentation, 18 variables are exploited as numerical descriptors to represent the specific features of protein coding genes quantitatively, with which we reannotate the protein coding genes in several viral genomes. Using the parameters trained on the experimentally validated genes, all of the 30 experimentally validated genes and 63 putative genes in AmEPV genome are recognized correctly as protein coding, the accuracies of the present method for self-test and cross-validation are 100%, respectively. Twenty-eight annotated hypothetical genes are predicted as noncoding, and then the number of reannotated protein coding genes in AmEPV should be 266 instead of 294 reported in the original annotations. Extending the present method trained in AmEPV to other entomopoxvirus genomes directly, such as Melanoplus sanguinipes entomopoxvirus (MsEPV), all of the 123 annotated function-known and putative genes are recognized correctly as protein coding, and 17 hypothetical genes are recognized as noncoding. The present method could also be extended to other genomes with or without adaptation of training sets with high accuracy., ((c) 2010 Wiley Periodicals, Inc.)