肽鍵（英语：Peptide bond，「肽」，拼音：tài）是一分子胺基酸的α－羧基(−COOHdisplaystyle ce -COOH)和另一分子胺基酸的α－胺基(−NH2displaystyle ce -NH2)脱水缩合形成的酰胺键，即−CO−NH−displaystyle ce -CO-NH -，為連結兩單體胺基酸之共價鍵，氨基酸借肽键联结成多肽链。由於共振而無法自由旋轉，具部分雙鍵特性。
肽鍵
目录
1生成
2分解
3光譜
4化學反應
5參見
6參考文獻
生成
兩個胺基酸透過肽鍵生成合為一個二肽，此稱縮合反應。在縮合反應中，兩個胺基酸靠近對方，羧基與氨基相互接近，一個會失去在羧基(COOHdisplaystyle ce COOH)上的一氫一氧，另一個則會失去氨基(NH2displaystyle ce NH2)上的一氫。此反應生成一分子的水(H2Odisplaystyle ce H2O) 及被肽鍵(−CO−NH−displaystyle ce -CO-NH -)連結的胺基酸，其連結的氨基酸被稱為二肽。
在肽鍵生成時，一胺基酸的羧基會與另一個胺基酸的氨基反應，失去一分子的水(H2Odisplaystyle ce H2O)，故過程為脫水反應(也名為脫水縮合)。
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1 I having trouble getting my ResourceDictionary.MergedDictionaries to load from app.xaml. My WPF app has a static class with a Main defined and startup object set to it. Within Main I created an instance of App and run it. The override OnStartup fires and the mainwindow.cs InitializeComponent gives the error "Message "Cannot find resource named 'MaterialDesignFloatingActionMiniAccentButton'. If I put the resources in the mainwindow.xaml everything is fine, but I wanted them to load at the app level so I they are not in each page. Any help appreciated. public partial class App protected override void OnStartup(StartupEventArgs e) base.OnStartup(e); var app = new MainWindow(); var context = new MainWindowViewModel(); app.DataContext = context; app.Show(); from the Main.. var app = new App(); app.Run(); app.xaml.. <Application x:Class="GS.Server.App" xmlns="http://schemas.microsoft.com/winfx/2006/xaml/presentation" xmlns:...
up vote 2 down vote favorite There is a clear pattern that show for two separate subsets (set of columns); If one value is missing in a column, values of other columns in the same subset are missing for any row. Here is a visualization of missing data My tries up until now, I used ycimpute library to learn from other values, and applied Iterforest. I noted, score of Logistic regression is so weak (0.6) and thought Iterforest might not learn enough or anyway, except from outer subset which might not be enough? for example the subset with 11 columns might learn from the other columns but not from within it's members, and the same goes for the subset with four columns. This bar plot show better quantity of missings So of course, dealing with missings is better than dropping rows because It would affect my prediction which does contain the same missings quantity relatively. Any better way to deal with these ? [EDIT] The nullity pattern is confirmed: machine-learning cor...
