MOTIVATION: Protein activity is a significant characteristic for recombinant proteins which can be used as biocatalysts. High activity of proteins reduces the cost of biocatalysts. A model that can predict protein activity from amino acid sequence is highly desired, as it aids experimental improvement of proteins. However, only limited data for protein activity are currently available, which prevents the development of such models. Since protein activity and solubility are correlated for some proteins, the publicly available solubility dataset may be adopted to develop models that can predict protein solubility from sequence. The models could serve as a tool to indirectly predict protein activity from sequence. In literature, predicting protein solubility from sequence has been intensively explored, but the predicted solubility represented in binary values from all the developed models was not suitable for guiding experimental designs to improve protein solubility. Here we propose new machine learning (ML) models for improving protein solubility in vivo. RESULTS: We first implemented a novel approach that predicted protein solubility in continuous numerical values instead of binary ones. After combining it with various ML algorithms, we achieved a R2 of 0.4115 when support vector machine algorithm was used. Continuous values of solubility are more meaningful in protein engineering, as they enable researchers to choose proteins with higher predicted solubility for experimental validation, while binary values fail to distinguish proteins with the same value-there are only two possible values so many proteins have the same one. AVAILABILITY AND IMPLEMENTATION: We present the ML workflow as a series of IPython notebooks hosted on GitHub (https://github.com/xiaomizhou616/protein_solubility). The workflow can be used as a template for analysis of other expression and solubility datasets. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The use of frequent itemsets has been limited by the high computational cost as well as the large number of resulting itemsets. In many real-world scenarios, however, it is often sufficient to mine a small representative subset of frequent itemsets with low computational cost. To that end, in this paper, we define a new problem of finding the frequent itemsets with a maximum length and present a novel algorithm to solve this problem. Indeed, maximum length frequent itemsets can be efficiently identified in very large data sets and are useful in many application domains. Our algorithm generates the maximum length frequent itemsets by adapting a pattern fragment growth methodology based on the FP-tree structure. Also, a number of optimization techniques have been exploited to prune the search space. Our experimental results show that our algorithm is very efficient and scalable