GReMLIN: A Graph Mining Strategy to Infer Protein-Ligand Interaction Patterns

Santana, César Costapinto; Cerqueira, Fábio Ribeiro; Silveira, Carlos H. da; Fassio, Alexandre Victor; Melo‐Minardi, Raquel Cardoso de; Silveira, Simonton Andrade

doi:10.1109/bibe.2016.48

Cited by 5 publications

(6 citation statements)

References 31 publications

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“…For each chain of a particular PDB id, we constructed an atomic level contact graph where nodes represent atoms and edges represent interactions among them. Nodes are labeled according to their physicochemical properties as positive, negative, hydrogen bond donor, hydrogen bond acceptor, aromatic, hydrophobic and cysteine based on our previous works [26,27], which were, in turn, derived from [28]. Edges are labeled according to interatomic interactions and distance criteria such as hydrogen bond, repulsive, salt bridge, aromatic, hydrophobic and disulfide bridge based on [29].…”

Section: Visualized Attributes Computation Methodsmentioning

confidence: 99%

Vermont: a multi-perspective visual interactive platform for mutational analysis

Fassio

Martins

Guimarães

et al. 2017

Preprint

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Background: A huge amount of data about genomes and sequence variation is available and continues to grow on a large scale, which makes experimentally characterizing these mutations infeasible regarding disease association and effects on protein structure and function. Therefore, reliable computational approaches are needed to support the understanding of mutations and their impacts. Here, we present VERMONT 2.0, a visual interactive platform that combines sequence and structural parameters with interactive visualizations to make the impact of protein point mutations more understandable. Results: We aimed to contribute a novel visual analytics oriented method to analyze and gain insight on the impact of protein point mutations.To assess the ability of VERMONT to do this, we visually examined a set of mutations that were experimentally characterized to determine if VERMONT could identify damaging mutations and why they can be considered so. Conclusions: VERMONT allowed us to understand mutations by interpreting position-specific structural and physicochemical properties. Additionally, we note some specific positions we believe have an impact on protein function/structure in the case of mutation.

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Section: Visualized Attributes Computation Methodsmentioning

confidence: 99%

Vermont: a multi-perspective visual interactive platform for mutational analysis

Fassio

Martins

Guimarães

et al. 2017

Preprint

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“…Nodes were labeled according to their physicochemical properties as acceptor (ACP), aromatic (ARM), donor (DON), hydrophobic (HPB), negative (NEG) or positive (POS) as in [26,[43][44][45][46]. Edges were labeled as aromatic, hydrogen bond, hydrophobic, repulsive and salt bridge, based on the physicochemical properties of their atoms and on a distance criteria.…”

Section: Modeling Ppis As Graphsmentioning

confidence: 99%

“…In this work, being able to perform this mapping is relevant due to the biological semantic of patterns, so that the domain specialist knows which are the relevant atoms and interactions to allow protein-protein complexes to be formed. For details, see [26,58].…”

Section: Subgraph Miningmentioning

confidence: 99%

“…The resulting set of clusters go through a frequent subgraph mining (FSM) to reveal substructures that are conserved across the whole dataset of protein-protein interactions. ppiGReMLIN requires a set of protein-protein complexes involving proteins of interests and is derived from GReMLIN (GRaph Mining strategy to infer proten-Ligand INteraction patterns) [26], which is a graph-based strategy successfully used to infer patterns in proteinligand interactions. Our method does not rely on sequence alignment nor structural superimposition, and can be used in large-scale datasets of protein interactions.…”

Section: Introductionmentioning

confidence: 99%

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ppiGReMLIN: a graph mining based detection of conserved structural arrangements in protein-protein interfaces

et al. 2020

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Background: Protein-protein interactions (PPIs) are fundamental in many biological processes and understanding these interactions is key for a myriad of applications including drug development, peptide design and identification of drug targets. The biological data deluge demands efficient and scalable methods to characterize and understand protein-protein interfaces. In this paper, we present ppiGReMLIN, a graph based strategy to infer interaction patterns in a set of protein-protein complexes. Our method combines an unsupervised learning strategy with frequent subgraph mining in order to detect conserved structural arrangements (patterns) based on the physicochemical properties of atoms on protein interfaces. To assess the ability of ppiGReMLIN to point out relevant conserved substructures on protein-protein interfaces, we compared our results to experimentally determined patterns that are key for protein-protein interactions in 2 datasets of complexes, Serine-protease and BCL-2. Results: ppiGReMLIN was able to detect, in an automatic fashion, conserved structural arrangements that represent highly conserved interactions at the specificity binding pocket of trypsin and trypsin-like proteins from Serine-protease dataset. Also, for the BCL-2 dataset, our method pointed out conserved arrangements that include critical residue interactions within the conserved motif LXXXXD, pivotal to the binding specificity of BH3 domains of pro-apoptotic BCL-2 proteins towards apoptotic suppressors. Quantitatively, ppiGReMLIN was able to find all of the most relevant residues described in literature for our datasets, showing precision of at least 69% up to 100% and recall of 100%. Conclusions: ppiGReMLIN was able to find highly conserved structures on the interfaces of protein-protein complexes, with minimum support value of 60%, in datasets of similar proteins. We showed that the patterns automatically detected on protein interfaces by our method are in agreement with interaction patterns described in the literature.

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“…In this paper, we propose visGReMLIN (visual Graph Mining strategy to infer protein-Ligand INteraction patterns), a user-friendly web server implementation of our GReMLIN method [20], which uses a graph mining-based strategy to detect conserved structural motifs in largescale datasets of protein-ligand interactions. visGReMLIN is a visual interactive platform to support the detection of trends and exceptions in protein-ligand interactions by domain specialists, allowing them to explore and make sense of the motifs.…”

Section: Introductionmentioning

confidence: 99%

visGReMLIN: graph mining-based detection and visualization of conserved motifs at 3D protein-ligand interface at the atomic level

et al. 2020

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Background: Interactions between proteins and non-proteic small molecule ligands play important roles in the biological processes of living systems. Thus, the development of computational methods to support our understanding of the ligand-receptor recognition process is of fundamental importance since these methods are a major step towards ligand prediction, target identification, lead discovery, and more. This article presents visGReMLIN, a web server that couples a graph mining-based strategy to detect motifs at the protein-ligand interface with an interactive platform to visually explore and interpret these motifs in the context of protein-ligand interfaces. Results: To illustrate the potential of visGReMLIN, we conducted two cases in which our strategy was compared with previous experimentally and computationally determined results. visGReMLIN allowed us to detect patterns previously documented in the literature in a totally visual manner. In addition, we found some motifs that we believe are relevant to protein-ligand interactions in the analyzed datasets. Conclusions: We aimed to build a visual analytics-oriented web server to detect and visualize common motifs at the protein-ligand interface. visGReMLIN motifs can support users in gaining insights on the key atoms/residues responsible for protein-ligand interactions in a dataset of complexes.

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GReMLIN: A Graph Mining Strategy to Infer Protein-Ligand Interaction Patterns

Cited by 5 publications

References 31 publications

Vermont: a multi-perspective visual interactive platform for mutational analysis

Vermont: a multi-perspective visual interactive platform for mutational analysis

ppiGReMLIN: a graph mining based detection of conserved structural arrangements in protein-protein interfaces

visGReMLIN: graph mining-based detection and visualization of conserved motifs at 3D protein-ligand interface at the atomic level

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