Editorial [Hot Topic: Workshop on the Definition of Protein Domains and their Likelihood of Crystallization (Guest Editors: Oliviero Carugo, Kristina Djinovic, Sasha Gorbalenya and Paul Tucker)]

This issue of Current Protein and Peptide Science is devoted to the emerging field of likelihood of protein crystallization and is related to the seminars and lectures presented recently at the Workshop on the definition of protein domains and their likelihood of crystallization, held in Vienna at the end of June 2006 (http://www.emblhamburg. de/workshops/2006/domains/), where a number of scientists addressed these questions by presenting and debating both experimental and computational approaches. Likelihood of crystallization must be predicted computationally and/or determined experimentally in order to avoid time expensive experiments on samples, the three-dimensional structure of which cannot be determined experimentally, because of a series of possible obstacles. For example, if a protein is natively disordered, in the sense that it is not characterized by a unique, well defined conformation, its three-dimensional structure cannot be determined experimentally, since it does not exist. Moreover, a sequence construct that does not correspond to a protein domain might be difficult to express because of its misfolding or its reduced solubility. This is particularly important in the structural genomics era, in which high throughput approaches are applied to the determination of three-dimensional structures of proteins, the biochemical, biophysical, and biological features of which were not previously studied. However, the preliminary analysis and estimation of the likelihood of crystallization is not relegated to proteomics studies only, but it is important also for traditional hypothesis driven projects, in which the optimization of the protein sample is equally important, allowing one to generate samples suitable for structural studies and/or improve diffraction quality of crystals and obtain, as a consequence, more reliable final results. The first review, written by Dmitrij Frishman and co-workers (Technische Universitat Munchen, Germany), deals with the general problem of predicting, with computational and bioinformatics methods, experimental success in cloning, expression, soluble expression, purification and crystallization of proteins. On the basis of publicly available resources, sophisticated machine learning algorithms allow one to make reasonable predictions. For example, solubility predictions are reaching the accuracy of over 70%. The successive four reviews are devoted to prediction, determination, and analysis of conformational disorder. Sonia Longhi and co-workers (CNRS and Universites Aix-Marseille I et II, France) presents an overview of several methods currently employed for predicting protein conformational disorder and present some practical examples of how they can be combined in order to achieve more reliable predictions. Anne Poupon and co-workers (Universite Paris-Sud, France) report the high throughput application of disorder predictions in a structural genomics project on soluble yeast proteins and focus their attention on strategies for tailoring proteins into crystallizable domains. Predictions of conformational disorder are analyzed also by Zsuzsanna Dosztanyi and co-workers (Hungarian Academy of Sciences, Hungary), though from a different perspective. The primary focus of this review is the systematic interpretation of the scores of different predictors. Experimental approaches for the detection of protein disorder are reviewed by Peter Tompa and co-workers (Hungarian Academy of Sciences, Hungary), with special emphasis on proteomic-scale methods, like heat- or acid treatments with a subsequent two-dimensional electrophoresis/mass spectrometry characterization. Furthermore, the problem of defining domain boundaries on the basis of the amino acidic sequences is analyzed in the next two reviews. David Jones and co-workers (University College London, United Kingdom) compare completely automatic and computer-assisted methods and discuss the problem of benchmarking different predictors. Furthermore, the DomPred server, which includes predictors based on sequence comparisons and on secondary structure predictors, is critically analyzed in order to allow its optimal use.......