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Predicting protein p K a by environment similarity
Author(s) -
Milletti Francesca,
Storchi Loriano,
Cruciani Gabriele
Publication year - 2009
Publication title -
proteins: structure, function, and bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.699
H-Index - 191
eISSN - 1097-0134
pISSN - 0887-3585
DOI - 10.1002/prot.22363
Subject(s) - similarity (geometry) , mean squared error , chemistry , metric (unit) , residue (chemistry) , root mean square , biological system , protein structure prediction , training set , fingerprint (computing) , data mining , protein structure , computer science , mathematics , artificial intelligence , statistics , biochemistry , engineering , biology , operations management , image (mathematics) , electrical engineering
A statistical method to predict protein p K a has been developed by using the 3D structure of a protein and a database of 434 experimental protein p K a values. Each p K a in the database is associated with a fingerprint that describes the chemical environment around an ionizable residue. A computational tool, MoKaBio, has been developed to identify automatically ionizable residues in a protein, generate fingerprints that describe the chemical environment around such residues, and predict p K a from the experimental p K a values in the database by using a similarity metric. The method, which retrieved the p K a of 429 of the 434 ionizable sites in the database correctly, was crossvalidated by leave‐one‐out and yielded root mean square error (RMSE) = 0.95, a result that is superior to that obtained by using the Null Model (RMSE 1.07) and other well‐established protein p K a prediction tools. This novel approach is suitable to rationalize protein p K a by comparing the region around the ionizable site with similar regions whose ionizable site p K a is known. The p K a of residues that have a unique environment not represented in the training set cannot be predicted accurately, however, the method offers the advantage of being trainable to increase its predictive power. Proteins 2009. © 2009 Wiley‐Liss, Inc.