Prediction of Membrane Protein Structures using a Projection based Meta-cognitive Radial Basis Function Network

The membrane proteins are an important group of molecules whose 3-D structure is difficult to obtain experimentally. Membrane proteins are implicated as drug targets and play an important role in disease pathways. The computational structure prediction from membrane protein sequences aids understanding of the structures. The prediction of structural preferences of individual residues within a protein sequence is often used as a starting point by higher order structure prediction algorithms that predict atomic coordinates. The low number of membrane proteins relative to globular proteins is a motivation for classifiers employing a meta-cognitive framework, as they have been shown in the machine learning literature to learn from a smaller number of samples and to generalize well to new datasets. In this paper, the recently developed Projection based, Metacognitive Radial Basis Function Network (PBL-McRBFN) was used in the membrane protein structure prediction problem. The PBL-McRBFN consists of a cognitive component that employs a projection-based learning algorithm. The meta-cognitive component controls the architecture and learning strategies of the cognitive component. The prediction of residue preferences with respect to the membrane (inside, outside, membrane) is considered as a threecategory classification problem. A dataset of transmembrane (TM) helix sequences was encoded as Position Specific Scoring Matrices (PSSM) and the performance compared with an SVM classifier. The results of the study indicate that the PBL-McRBFN classifier performs better than the SVM for the overall accuracy and is able to generalize better to the test residues than the SVM classifier.