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On the development of protein p K a calculation algorithms
Author(s) -
Carstensen Tommy,
Farrell Damien,
Huang Yong,
Baker Nathan A.,
Nielsen Jens Erik
Publication year - 2011
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.23091
Subject(s) - computer science , set (abstract data type) , algorithm , basis (linear algebra) , noise (video) , energy (signal processing) , space (punctuation) , experimental data , artificial intelligence , mathematics , statistics , geometry , programming language , image (mathematics) , operating system
Protein p K a calculation methods are developed partly to provide fast non‐experimental estimates of the ionization constants of protein side chains. However, the most significant reason for developing such methods is that a good p K a calculation method is presumed to provide an accurate physical model of protein electrostatics, which can be applied in methods for drug design, protein design, and other structure‐based energy calculation methods. We explore the validity of this presumption by simulating the development of a p K a calculation method using artificial experimental data derived from a human‐defined physical reality. We examine the ability of an RMSD‐guided development protocol to retrieve the correct (artificial) physical reality and find that a rugged optimization landscape and a huge parameter space prevent the identification of the correct physical reality. We examine the importance of the training set in developing p K a calculation methods and investigate the effect of experimental noise on our ability to identify the correct physical reality, and find that both effects have a significant and detrimental impact on the physical reality of the optimal model identified. Our findings are of relevance to all structure‐based methods for protein energy calculations and simulation, and have large implications for all types of current p K a calculation methods. Our analysis furthermore suggests that careful and extensive validation on many types of experimental data can go some way in making current models more realistic. Proteins 2011; © 2011 Wiley‐Liss, Inc.