Dataset Information

LibME-automatic extraction of 3D ligand-binding motifs for mechanistic analysis of protein-ligand recognition.

ABSTRACT: Identifying conserved binding motifs is an efficient way to study protein-ligand recognition. Most 3D binding motifs only contain information from the protein side, and so motifs that combine information from both protein and ligand sides are desired. Here, we propose an algorithm called LibME (Ligand-binding Motif Extractor), which automatically extracts 3D binding motifs composed of the target ligand and surrounding conserved residues. We show that the motifs extracted by LibME for ATP and its analogs are highly similar to well-known motifs reported by previous studies. The superiority of our method to handle flexible ligands was also demonstrated using isocitric acid as an example. Finally, we show that these motifs, together with their visual exhibition, permit better investigating and understanding of protein-ligand recognition process.

SUBMITTER: He W

PROVIDER: S-EPMC5324770 | biostudies-literature | 2016 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

LibME-automatic extraction of 3D ligand-binding motifs for mechanistic analysis of protein-ligand recognition.

He Wei W Liang Zhi Z Teng MaiKun M Niu LiWen L

FEBS open bio 20161130 12

Identifying conserved binding motifs is an efficient way to study protein-ligand recognition. Most 3D binding motifs only contain information from the protein side, and so motifs that combine information from both protein and ligand sides are desired. Here, we propose an algorithm called LibME (Ligand-binding Motif Extractor), which automatically extracts 3D binding motifs composed of the target ligand and surrounding conserved residues. We show that the motifs extracted by LibME for ATP and its ...[more]

PMID: 28255540

Similar Datasets

Project description:BACKGROUND: Since many of the new protein structures delivered by high-throughput processes do not have any known function, there is a need for structure-based prediction of protein function. Protein 3D structures can be clustered according to their fold or secondary structures to produce classes of some functional significance. A recent alternative has been to detect specific 3D motifs which are often associated to active sites. Unfortunately, there are very few known 3D motifs, which are usually the result of a manual process, compared to the number of sequential motifs already known. In this paper, we report a method to automatically generate 3D motifs of protein structure binding sites based on consensus atom positions and evaluate it on a set of adenine based ligands. RESULTS: Our new approach was validated by generating automatically 3D patterns for the main adenine based ligands, i.e. AMP, ADP and ATP. Out of the 18 detected patterns, only one, the ADP4 pattern, is not associated with well defined structural patterns. Moreover, most of the patterns could be classified as binding site 3D motifs. Literature research revealed that the ADP4 pattern actually corresponds to structural features which show complex evolutionary links between ligases and transferases. Therefore, all of the generated patterns prove to be meaningful. Each pattern was used to query all PDB proteins which bind either purine based or guanine based ligands, in order to evaluate the classification and annotation properties of the pattern. Overall, our 3D patterns matched 31% of proteins with adenine based ligands and 95.5% of them were classified correctly. CONCLUSION: A new metric has been introduced allowing the classification of proteins according to the similarity of atomic environment of binding sites, and a methodology has been developed to automatically produce 3D patterns from that classification. A study of proteins binding adenine based ligands showed that these 3D patterns are not only biochemically meaningful, but can be used for protein classification and annotation.

Project description:RNA-protein binding is critical to gene regulation, controlling fundamental processes including splicing, translation, localization and stability, and aberrant RNA-protein interactions are known to play a role in a wide variety of diseases. However, molecular understanding of RNA-protein interactions remains limited; in particular, identification of RNA motifs that bind proteins has long been challenging, especially when such motifs depend on both sequence and structure. Moreover, although RNA binding proteins (RBPs) often contain more than one binding domain, algorithms capable of identifying more than one binding motif simultaneously have not been developed. In this paper we present a novel pipeline to determine binding peaks in crosslinking immunoprecipitation (CLIP) data, to discover multiple possible RNA sequence/structure motifs among them, and to experimentally validate such motifs. At the core is a new semi-automatic algorithm SARNAclust, the first unsupervised method to identify and deconvolve multiple sequence/structure motifs simultaneously. SARNAclust computes similarity between sequence/structure objects using a graph kernel, providing the ability to isolate the impact of specific features through the bulge graph formalism. Application of SARNAclust to synthetic data shows its capability of clustering 5 motifs at once with a V-measure value of over 0.95, while GraphClust achieves only a V-measure of 0.083 and RNAcontext cannot detect any of the motifs. When applied to existing eCLIP sets, SARNAclust finds known motifs for SLBP and HNRNPC and novel motifs for several other RBPs such as AGGF1, AKAP8L and ILF3. We demonstrate an experimental validation protocol, a targeted Bind-n-Seq-like high-throughput sequencing approach that relies on RNA inverse folding for oligo pool design, that can validate the components within the SLBP motif. Finally, we use this protocol to experimentally interrogate the SARNAclust motif predictions for protein ILF3. Our results support a newly identified partially double-stranded UUUUUGAGA motif similar to that known for the splicing factor HNRNPC.

Dataset Information

LibME-automatic extraction of 3D ligand-binding motifs for mechanistic analysis of protein-ligand recognition.

Publications

LibME-automatic extraction of 3D ligand-binding motifs for mechanistic analysis of protein-ligand recognition.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets