Reduced alphabet for protein folding prediction

Abstract

What are the key building blocks that would have been needed to construct complex protein folds? This is an important issue for understanding protein folding mechanism and guiding de novo protein design. Twenty naturally occurring amino acids and eight secondary structures consist of a 28-letter alphabet to determine folding kinetics and mechanism. Here we predict folding kinetic rates of proteins from many reduced alphabets. We find that a reduced alphabet of 10 letters achieves good correlation with folding rates, close to the one achieved by full 28-letter alphabet. Many other reduced alphabets are not significantly correlated to folding rates. The finding suggests that not all amino acids and secondary structures are equally important for protein folding. The foldable sequence of a protein could be designed using at least 10 folding units, which can either promote or inhibit protein folding. Reducing alphabet cardinality without losing key folding kinetic information opens the door to potentially faster machine learning and data mining applications in protein structure prediction, sequence alignment and protein design.

Protein folding prediction

“…后来, 又出现了诸多从蛋白质序列出发预测蛋白折叠速率 的 算 法和 软件 (或 网络 服务 ), 详 见文献 [65]. 最 近, 又出现了用精简的氨基酸和二级结构元件集来预测 蛋白折叠速率的方法, 结果表明, 折叠速率只与20种 氨基酸之中的8种、7种二级结构类型中的2种密切相 关 [66] . 上的准确率 [22] .…”

dagLogo: An R/Bioconductor package for identifying and visualizing differential amino acid group usage in proteomics data

“…To date, there are no tools that have the functionalities to group AAs based on their physicochemical or other properties and test the differential usage of AA groups. Historically, twenty naturally occurring AAs have been classified into two to 19 groups based on some measures of their relative similarity in physicochemical, structural, and functional properties; this has resulted in a variety of reduced (degenerate or simplified) alphabets [24][25][26][27]. With reduced alphabets, several AAs of a similar property are lumped together and represented by one letter.…”

dagLogo: an R/Bioconductor Package for Identifying and Visualizing Differential Amino Acid Group Usage in Proteomics data

“…Historically, twenty naturally occurring AAs has been classified into two to 19 groups based on some measures of their relative similarity in physicochemical, structural, and functional properties; this has resulted in a variety of reduced (degenerate or simplified) alphabets [24][25][26][27]. With reduced alphabets, several AAs of a similar property are lumped together and represented by one letter.…”