Datamining protein structure databanks for crystallization patterns of proteins

Protein Crystallizability

Obtaining diffracting quality crystals remains a major challenge in protein structure research. We summarize and compare methods for selecting the best protein targets for crystallization, construct optimization and crystallization condition design. Target selection methods are divided into algorithms predicting the chance of successful progression through all stages of structural determination (from cloning to solving the structure) and those focusing only on the crystallization step. We tried to highlight pros and cons of different approaches examining the following aspects: data size, redundancy and representativeness, overfitting during model construction, and results evaluation. In summary, although in recent years progress was made and several sequence properties were reported to be relevant for crystallization, the successful prediction of protein crystallization behavior and selection of corresponding crystallization conditions continue to challenge structural researchers.

Protein crystallizability

Obtaining well-diffracting crystals remains a major challenge in protein structure research. In this chapter, we review currently available computational methods to estimate the crystallization potential of a protein, to optimize amino acid sequences toward improved crystallization likelihood, and to design optimal crystal screen conditions.

Will my protein crystallize? A sequence-based predictor

We propose a machine-learning approach to sequence-based prediction of protein crystallizability in which we exploit subtle differences between proteins whose structures were solved by X-ray analysis [or by both X-ray and nuclear magnetic resonance (NMR) spectroscopy] and those proteins whose structures were solved by NMR spectroscopy alone. Because the NMR technique is usually applied on relatively small proteins, sequence length distributions of the X-ray and NMR datasets were adjusted to avoid predictions biased by protein size. As feature space for classification, we used frequencies of mono-, di-, and tripeptides represented by the original 20-letter amino acid alphabet as well as by several reduced alphabets in which amino acids were grouped by their physicochemical and structural properties. The classification algorithm was constructed as a two-layered structure in which the output of primary support vector machine classifiers operating on peptide frequencies was combined by a second-level Naive Bayes classifier. Due to the application of metamethods for cost sensitivity, our method is able to handle real datasets with unbalanced class representation. An overall prediction accuracy of 67% [65% on the positive (crystallizable) and 69% on the negative (noncrystallizable) class] was achieved in a 10-fold cross-validation experiment, indicating that the proposed algorithm may be a valuable tool for more efficient target selection in structural genomics. A Web server for protein crystallizability prediction called SECRET is available at http://webclu.bio.wzw.tum.de:8080/secret.

Thermodynamic and dynamic factors involved in the stability of native protein structure in amorphous solids in relation to levels of hydration

The internal, dynamical fluctuations of protein molecules exhibit many of the features typical of polymeric and bulk small molecule glass forming systems. The response of a protein's internal molecular mobility to temperature changes is similar to that of other amorphous systems, in that different types of motions freeze out at different temperatures, suggesting they exhibit the alpha-beta-modes of motion typical of polymeric glass formers. These modes of motion are attributed to the dynamic regimes that afford proteins the flexibility for function but that also develop into the large-scale collective motions that lead to unfolding. The protein dynamical transition, T(d), which has the same meaning as the T(g) value of other amorphous systems, is attributed to the temperature where protein activity is lost and the unfolding process is inhibited. This review describes how modulation of T(d) by hydration and lyoprotectants can determine the stability of protein molecules that have been processed as bulk, amorphous materials. It also examines the thermodynamic, dynamic, and molecular factors involved in stabilizing folded proteins, and the effects typical pharmaceutical processes can have on native protein structure in going from the solution state to the solid state.