Application of an Interpretable Classification Model on Early Folding Residues during Protein Folding

Abstract

Background: Machine learning strategies are prominent tools for data analysis. Especially in life sciences, they have become increasingly important to handle the growing datasets collected by the scientific community. Meanwhile, algorithms improve in performance, but also gain complexity, and tend to neglect interpretability and comprehensiveness of the resulting models.Results: Generalized Matrix Learning Vector Quantization (GMLVQ) is a supervised, prototype-based machine learning method and provides comprehensive visualization capabilities not present in other classifiers which allow for a fine-grained interpretation of the data. In contrast to commonly used machine learning strategies, GMLVQ is well-suited for imbalanced classification problems which are frequent in life sciences. We present a Weka plug-in implementing GMLVQ. The feasibility of GMLVQ is demonstrated on a dataset of Early Folding Residues (EFR) that have been shown to initiate and guide the protein folding process. Using 27 features, an area under the receiver operating characteristic of 76.6% was achieved which is comparable to other state-of-the-art classifiers. Conclusions:The application on EFR prediction demonstrates how an easy interpretation of classification models can promote the comprehension of biological mechanisms. The results shed light on the special features of EFR which were reported as most influential for the classification: EFR are embedded in ordered secondary structure elements and they participate in networks of hydrophobic residues. Visualization capabilities of GMLVQ are presented as we demonstrate how to interpret the results.

The structural basis of the genetic code: amino acid recognition by aminoacyl-tRNA synthetases

“…It is conceivable that the discrimination between glutamic and aspartic acid was based on tertiary contacts between secondary structures elements and size selectivity rather than on specific side chain interactions 82 . The recent identification of a protein folding motif 83 strengthens this assumption. This is further supported by the observation that ancient proteins, based on a limited set of amino acids, were still capable to exhibit secondary structures 81 , 84 , 85 .…”

Detection of native and mirror protein structures based on Ramachandran plot analysis by interpretable machine learning models

“…Contact maps can be used as a set of constraints for ab initio structure prediction. They show predicted contacts based on co-evolving residues which imply their spatial proximity and are derived from a multiple sequence alignment [6].…”

“…GMLVQ is known to be robust and easy to interpret [46,58]. In biomedical context it was successfully applied to analyse flow cytometry data and to detect early folding residues during protein folding [4,7].…”