Some remarks on protein attribute prediction and pseudo amino acid composition

By Kuo-Chen Chou. Journal of Theoretical Biology, 2010

1,216 citations recorded by the source corpus.

With the accomplishment of human genome sequencing, the number of sequence-known proteins has increased explosively. In contrast, the pace is much slower in determining their biological attributes. As a consequence, the gap between sequence-known proteins and attribute-known proteins has become increasingly large. The unbalanced situation, which has critically limited our ability to timely utilize the newly discovered proteins for basic research and drug development, has called for developing computational methods or high-throughput automated tools for fast and reliably identifying various attributes of uncharacterized proteins based on their sequence information alone. Actually, during the last two decades or so, many methods in this regard have been established in hope to bridge such a gap. In the course of developing these methods, the following things were often needed to consider: (1) benchmark dataset construction, (2) protein sample formulation, (3) operating algorithm (or engine), (4) anticipated accuracy, and (5) web-server establishment. In this review, we are to discuss each of the five procedures, with a special focus on the introduction of pseudo amino acid composition (PseAAC), its different modes and applications as well as its recent development, particularly in how to use the general formulation of PseAAC to reflect the core and essential features that are deeply hidden in complicated protein sequences.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/57f16f036921838771cb79b264d305f193c28fdf

How researchers see this paper

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In Molecular Biology

1. Kuo-Chen Chou
2. Xuan Xiao
3. Jianhua Jia
4. Bin Liu
5. Dongsheng Cao
6. Xin Gao
7. Zi Liu
8. Bingxiang Liu

Citations in the corpus

• DEEPre: sequence-based enzyme EC number prediction by deep learning (2017, background): “Pse-AAC (pseudo amino acid composition) and Pse-PSSM are typical examples of this category, which have been proved successful in a number of applications (Chou, 2009, 2011).”
• A comprehensive review and performance evaluation of bioinformatics tools for HLA class I peptide-binding prediction (2020, methodology): “Most tools constructed the prediction models in a five-step manner [64], which involves data collection and”
• PseKNC: a flexible web server for generating pseudo K-tuple nucleotide composition. (2014, background): “families [5], identifying submitochondrial localization of proteins [6], detecting remote homologous proteins [7], predicting G-protein-coupled receptor (GPCR) classes [8], predicting protein structural classes [9], identifying risk type of human papillomaviruses [10], predicting GABAA receptor prot”
• iSNO-PseAAC: Predict Cysteine S-Nitrosylation Sites in Proteins by Incorporating Position Specific Amino Acid Propensity into Pseudo Amino Acid Composition (2013, methodology): “One of the important procedures in developing a useful statistical predictor [19] is to objectively evaluate its performance or anticipated success rate.”