GRAFENE: Graphlet-based alignment-free network approach integrates 3D structural and sequence (residue order) data to improve protein structural comparison

Abstract

Initial protein structural comparisons were sequence-based. Since amino acids that are distant in the sequence can be close in the 3-dimensional (3D) structure, 3D contact approaches can complement sequence approaches. Traditional 3D contact approaches study 3D structures directly and are alignment-based. Instead, 3D structures can be modeled as protein structure networks (PSNs). Then, network approaches can compare proteins by comparing their PSNs. These can be alignment-based or alignment-free. We focus on the latter. Existing network alignment-free approaches have drawbacks: 1) They rely on naive measures of network topology. 2) They are not robust to PSN size. They cannot integrate 3) multiple PSN measures or 4) PSN data with sequence data, although this could improve comparison because the different data types capture complementary aspects of the protein structure. We address this by: 1) exploiting well-established graphlet measures via a new network alignment-free approach, 2) introducing normalized graphlet measures to remove the bias of PSN size, 3) allowing for integrating multiple PSN measures, and 4) using ordered graphlets to combine the complementary PSN data and sequence (specifically, residue order) data. We compare synthetic networks and real-world PSNs more accurately and faster than existing network (alignment-free and alignment-based), 3D contact, or sequence approaches.

Network-based protein structural classification

“…that measure different network properties. One such baseline PSN feature is Existing-all, which integrates seven network properties to represent a PSN [6]. Another popular PSN feature that counts different types of network patterns is the concept of graphlets; graphlets are subgraphs or small lego-like building blocks of complex networks [26].…”

“…PSNs. Also, this approach only used the concept of regular graphlets, while we also test a newer concept of ordered graphlets [30] (see Methods), which outperformed regular graphlets in the GRAFENE study [6].…”

“…First, we use a set of 17 036 proteins that was previously used in the large-scale unsupervised protein structural comparison GRAFENE study [6]. Per this study, this dataset contains all proteins from the Protein Data Bank (PDB) [37] that are at most 90% sequence similar.…”

Functional geometry of protein interactomes

“…Nodes 4, 5 , 6 and 7 are connected by a 3-dimensional simplex (tetrahedron, in blue). Pržulj, 2007;Yaveroglu et al, 2014), as well as between protein 3D structures represented by networks (Malod-Dognin and Pržulj, 2014;Faisal et al, 2017). In particular, graphlets have been used to characterize and compare the local wiring patterns around nodes in a PPI network (Milenković and Pržulj, 2008), which revealed that molecules involved in similar functions tend to be similarly wired (Davis et al, 2015).…”

DeeplyEssential: a deep neural network for predicting essential genes in microbes

“…These features can be broadly categorized into (i) sequence features, i.e., codon frequency, GC content, gene length [16][17][18], (ii) topological features, i.e., degree centrality, cluster coefficient [19][20][21][22], and (iii) functional features, i.e., homology, gene expression cellular localization, functional domain and other molecular properties [10,[23][24][25][26]. More recent studies about the 3D structure of proteins can also be incorporated in topological features set [27,28].…”