Background: Current protocols yield crystals for <30% of known proteins, indicating that automatically identifying crystallizable proteins may improve high-throughput structural genomics efforts. We introduce CRYSTALP2, a kernel-based method that predicts the propensity of a given protein sequence to produce diffraction-quality crystals. This method utilizes the composition and collocation of amino acids, isoelectric point, and hydrophobicity, as estimated from the primary sequence, to generate predictions. CRYSTALP2 extends its predecessor, CRYSTALP, by enabling predictions for sequences of unrestricted size and provides improved prediction quality.

One of the most important issues for a fast fuzzycontrol system is to optimize the defuzzification module that involves computationally expensive multiplication and division operations. This paper introduces a logdomain approach that only requires addition/subtraction, max/min, and look-up table operations to implement a fuzzy controller. The control surfaces of the proposed system are compared to those of traditional fuzzy and PD controllers, and the results are almost the same when a small correction factor is included. For a second-order plant, the log-domain approach offers good stepresponse characteristics.

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