A topological approach for protein classification

Abstract

Protein function and dynamics are closely related to its sequence and structure. However prediction of protein function and dynamics from its sequence and structure is still a fundamental challenge in molecular biology. Protein classification, which is typically done through measuring the similarity between proteins based on protein sequence or physical information, serves as a crucial step toward the understanding of protein function and dynamics. Persistent homology is a new branch of algebraic topology that has found its success in the topological data analysis in a variety of disciplines, including molecular biology. The present work explores the potential of using persistent homology as an independent tool for protein classification. To this end, we propose a molecular topological fingerprint based support vector machine (MTF-SVM) classifier. Specifically, we construct machine learning feature vectors solely from protein topological fingerprints, which are topological invariants generated during the filtration process. To validate the present MTF-SVM approach, we consider four types of problems. First, we study protein-drug binding by using the M2 channel protein of influenza A virus. We achieve 96% accuracy in discriminating drug bound and unbound M2 channels. Additionally, we examine the use of MTF-SVM for the classification of hemoglobin molecules in their relaxed and taut forms and obtain about 80% accuracy. The identification of all alpha, all beta, and alpha-beta protein domains is carried out in our next study using 900 proteins. We have found a 85% success in this identification. Finally, we apply the present technique to 55 classification tasks of protein superfamilies over 1357 samples. An average accuracy of 82% is attained. The present study establishes computational topology as an independent and effective alternative for protein classification. I IntroductionProteins are essential building blocks of living organisms. They function as catalyst, structural elements, chemical signals, receptors, etc. The molecular mechanism of protein functions are closely related to their structures. The study of structure-function relationship is the holy grail of biophysics and has attracted enormous effort in the past few decades. The understanding of such a relationship enables us to predict protein functions from structure or amino acid sequence or both, which remains major challenge in molecular biology. Intensive experimental investigation has been carried out to explore the interactions among proteins or proteins with other biomolecules, e.g., DNAs and/or RNAs. In particular, the understanding of protein-drug interactions is of premier importance to human health.A wide variety of theoretical and computational approaches has been proposed to understand the protein structure-function relationship. One class of approaches is biophysical. From the point of view of biophysics, protein structure, function, dynamics and transport are, in general, dictated by protein interactions. Quantum mechanics (QM) is based ...

A review of mathematical representations of biomolecular data

“…90,140,158,[162][163][164][165][166] In particular, we introduced one the first topology-based machine learning algorithms for protein classification in 2015. 22 We further introduced element specific persistent homology, i.e., element-induced topology, to deal with massive and diverse bimolecular datasets. [22][23][24][25]90 Moreover, we introduced multi-level persistent homology to extract non-covalent-bond interactions.…”

“…22 We further introduced element specific persistent homology, i.e., element-induced topology, to deal with massive and diverse bimolecular datasets. [22][23][24][25]90 Moreover, we introduced multi-level persistent homology to extract non-covalent-bond interactions. 21 Furthermore, physics-embedded persistent homology was proposed to incorporate physical laws into topological invariants.…”

Adaptive Template Systems: Data-Driven Feature Selection for Learning with Persistence Diagrams

“…To compare our results with the ones in [4], we report the average classification accuracy over the 55 classification tasks in the data set PCB00019. -0.82 ± -- Table 4 shows the average classification accuracy and standard deviation for each of the classification tasks from PCB00019.…”

“…Here we used a regularized kernel ridge regression with polynomial, RBF and sigmoid kernels. The last row in Table 4 displays the average classification accuracy for the testing set reported in [4]. We Figure 6.…”

“…This data set contains spatial information for 1, 357 proteins as well as 55 distinct supervised classification tasks. The results on [4] are used as a benchmark, since they also use persistence diagrams, but the extracted features are hand-crafted to reflect chemical/physical properties of interest. In this experiment, adaptive template functions improve the average classification accuracy reported in [4] from 82% to around 98%.…”