Quantitative description and classification of protein structures by a novel robust amino acid network: interaction selective network (ISN)

Abstract

To quantitatively categorize protein structures, we developed a quantitative coarse-grained model of protein structures with a novel amino acid network, the interaction selective network (ISN), characterized by the links based on interactions in both the main and side chains. We found that the ISN is a novel robust network model to show the higher classification probability in the plots of average vertex degree (k) versus average clustering coefficient (C), both of which are typical network parameters for protein structures, and successfully distinguished between “all-α” and “all-β” proteins. On the other hand, one of the typical conventional networks, the α-carbon network (CAN), was found to be less robust than the ISN, and another typical network, atomic distance network (ADN), failed to distinguish between these two protein structures. Considering that the links in the CAN and ADN are defined by the interactions only between the main chain atoms and by the distance of the closest atom pair between the two amino acid residues, respectively, we can conclude that reflecting structural information from both secondary and tertiary structures in the network parameters improves the quantitative evaluation and robustness in network models, resulting in a quantitative and more robust description of three-dimensional protein structures in the ISN.

Visualizing the residue interaction landscape of proteins by temporal network embedding

“…Due to its modularity, it can incorporate any type of protein graph that allows for the calculation of node centralities. There are many different tools and formalisms to translate proteins into graphs, 12,13,15 making it possible to adjust the information content of the description, for example by including details on the interaction types 43 or energies, 16 or by describing the protein at higher (atomistic) 44 or lower (coarsegrained) 15,18 resolution. Nevertheless, transforming the Cartesian coordinates of the protein into a relational data set of its interactions is bound to reduce the information content in the resulting description.…”

Predicting Protein-Ligand Binding Site for Drug Design Using Context Relevant Self Organizing Maps (CRSOM)

“…In (10), (11), and (12), 2 is the learning rate for reference vectors. is the backpropagated error information from the output layer.…”

“…Basically, drug design is carried out on information from the structure of the protein to look for suitable ligands [9]. Information on protein structure is the result of the analysis of the geometry, sequences, and energy of the protein obtained from the three-dimensional structure of the target, and the binding site of proteinligands found is the basis for the search for cavities (binding site) [10].…”

“…So that, CRSOM is an optimal internal representation [16]. Binary classification can be used to formulate problems in binding site prediction, namely as a differentiator of the binding site area and not the binding site [10]. Based on research by Hartono (2016) [16], Context Relevant Self Organizing Maps (CRSOM) can be used in classification problems and have given small training error results and large Semantic Relevance Index (SRI) ability to separate data.…”

Development of a TSR-Based Method for Protein 3-D Structural Comparison With Its Applications to Protein Classification and Motif Discovery

“…The last step is to employ statistical analysis, e.g., Z-score and p -value, to provide confidence for the structural similarity. A number of algorithms have been developed and/or employed for structural comparisons: Maximal common subgraph detection (Bron and Kerbosch, 1973 ), Ullmann subgraph isomorphism algorithm (Ullmann, 1976 ), and geometric hashing (Nussinov and Wolfson, 1991 ) in geometry-based; Monte Carlo (Holm and Sander, 1993 ), Combinatorial Extension (CE) (Shindyalov and Bourne, 1998 ), and Comparative Structural Alignment (CSA) (Wohlers et al, 2012 ) algorithms in distance-based, a genetic algorithm (Szustakowski and Weng, 2000 ) and Dictionary of Secondary Structure of Proteins (DSSP) (Kabsch and Sander, 1983 ) in secondary structure-based comparisons, and amino acid network (AAN) (Alves and Martinez, 2007 ; Bartoli et al, 2008 ) including C α network (CAN) and atom distance network (ADN) and interaction selective network (ISN) (Konno et al, 2019 ) in network-based comparisons. Dynamic programming algorithms have been used in both distance- (Blundell et al, 1988 ; Taylor and Orengo, 1989 ; Lackner et al, 2000 ) and secondary structure-based (Taylor and Orengo, 1989 ; Yang and Honig, 2000 ) comparisons.…”