Using genetic algorithms to select most predictive protein features

Abstract

Many important characteristics of proteins such as biochemical activity and subcellular localization present a challenge to machine-learning methods: it is often difficult to encode the appropriate input features at the residue level for the purpose of making a prediction for the entire protein. The problem is usually that the biophysics of the connection between a machine-learning method's input (sequence feature) and its output (observed phenomenon to be predicted) remains unknown; in other words, we may only know that a certain protein is an enzyme (output) without knowing which region may contain the active site residues (input). The goal then becomes to dissect a protein into a vast set of sequence-derived features and to correlate those features with the desired output. We introduce a framework that begins with a set of global sequence features and then vastly expands the feature space by generically encoding the coexistence of residue-based features. It is this combination of individual features, that is the step from the fractions of serine and buried (input space 20 + 2) to the fraction of buried serine (input space 20 * 2) that implicitly shifts the search space from global feature inputs to features that can capture very local evidence such as a the individual residues of a catalytic triad. The vast feature space created is explored by a genetic algorithm (GA) paired with neural networks and support vector machines. We find that the GA is critical for selecting combinations of features that are neither too general resulting in poor performance, nor too specific, leading to overtraining. The final framework manages to effectively sample a feature space that is far too large for exhaustive enumeration. We demonstrate the power of the concept by applying it to prediction of protein enzymatic activity.

An Evolutionary Algorithm Approach for Feature Generation from Sequence Data and Its Application to DNA Splice Site Prediction

“…The superiority of EAs was recognized early [43]. Since then, many studies have demonstrated the advantages of EAs for feature generation in different domains [2], [27], [38], [35], [14], [29], [20], [18], [17].…”

“…Some of our recent work has shown significant improvements in classification accuracies when genetic algorithms (GA) replace k-mer feature enumeration techniques in predicting DNA hypersensitive and splice sites [18], [19], [17]. Work on predicting enzymatic activity in proteins additionally shows the power of EAs in feature generation [20].…”

“…P REDICTING information such as protein crystallizability, enzymatic activity, subcellular localization, and DNA splice sites continues to spur research in machine learning [36], [51], [34], [15], [16], [20], [18], [19], [17]. Inferring that a biological sequence exhibits a certain property is difficult when no a priori information is available on what gives rise to the sought property.…”

“…Kamath [34], [15], [16], [20], [18], [19], [17]. For instance, work in [15] shows that different types of features are needed to obtain high accuracy and precision in DNA splice site prediction.…”

“…. , 6}, and positional information in the form of an integer in the range [20,50]. A complex feature, like the one illustrated in Fig.…”