基於類神經網路之蛋白質金屬鍵結胺基酸預測

Abstract

傳統上，結構生物學家利用物理實驗的方式去了解金屬蛋白 (與金屬產生鍵結的蛋白質) 的特性，並藉由其立體結構與實驗上的觀察去推論酵素產生功能的原因及其反應機制。然而，大多數的蛋白質都已知其一級結構 (胺基酸序列組成) ，立體結構資訊卻不如序列資訊來的普遍。再者，目前由序列資訊預測蛋白質立體結構的技術，也仍尚未 達到絕對可靠的階段。

因此，本論文中提出，純粹以蛋白質序列的資訊，使用類神經網路為主要核心的預測器，搭配滑動框架式特徵擷取以及生物化學式特徵編碼的技術，對蛋白質金屬鍵結胺基酸進行預測。針對生命系統中的四種主要金屬 (鈣、鉀、鎂與鈉) 鍵結蛋白質中，在五等份的交叉驗證中，均可達到九成以上的鍵結偵測敏感度且兼具極佳的準確度。

Traditionally, structural biologists used to investigate properties of metalloproteins(proteins which bind with metal ions) by physical means and interpret the function formation and reaction mechanism of enzyme by their structures and observation from experiments in vitro. Most of proteins have primary structures (amino acid sequence information) only;however, the 3-dimension structures are not always available. Moreover, the prediction from protein sequence to structure is still not completely reliable so far.

Consequently, a direct analysis method to predict protein metal-binding amino acid residues only from its sequence information by neural network with sliding window-based feature extraction and biochemical feature encoding techniques in this thesis. In four major bulk elements (Calcium, Potassium, Magnesium, and Sodium) in life system, the metal-binding residues are identified by proposed method with a binding sensitivity > 90% and nearly 100% accuracy under five fold cross validation.