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Data mining crystallization databases: Knowledge‐based approaches to optimize protein crystal screens
Author(s) -
Kimber Matthew S.,
Vallee François,
Houston Simon,
Nečakov Alexander,
Skarina Tatiana,
Evdokimova Elena,
Beasley Steven,
Christendat Dinesh,
Savchenko Alexei,
Arrowsmith Cheryl H.,
Vedadi Masoud,
Gerstein Mark,
Edwards Aled M.
Publication year - 2003
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.10340
Subject(s) - crystallization , protein crystallization , bottleneck , nucleation , database , proteomics , crystal (programming language) , computer science , data mining , computational biology , crystallography , chemistry , biology , biochemistry , programming language , organic chemistry , gene , embedded system
Abstract Protein crystallization is a major bottleneck in protein X‐ray crystallography, the workhorse of most structural proteomics projects. Because the principles that govern protein crystallization are too poorly understood to allow them to be used in a strongly predictive sense, the most common crystallization strategy entails screening a wide variety of solution conditions to identify the small subset that will support crystal nucleation and growth. We tested the hypothesis that more efficient crystallization strategies could be formulated by extracting useful patterns and correlations from the large data sets of crystallization trials created in structural proteomics projects. A database of crystallization conditions was constructed for 755 different proteins purified and crystallized under uniform conditions. Forty‐five percent of the proteins formed crystals. Data mining identified the conditions that crystallize the most proteins, revealed that many conditions are highly correlated in their behavior, and showed that the crystallization success rate is markedly dependent on the organism from which proteins derive. Of the proteins that crystallized in a 48‐condition experiment, 60% could be crystallized in as few as 6 conditions and 94% in 24 conditions. Consideration of the full range of information coming from crystal screening trials allows one to design screens that are maximally productive while consuming minimal resources, and also suggests further useful conditions for extending existing screens. Proteins 2003;51:562–568. © 2003 Wiley‐Liss, Inc.