Protein-ligand interaction is one of the fundamental molecular interactions of living systems. Proteins are the building blocks of functions in life at the molecular level. Ligands are small molecules that interact with proteins at specific regions on the surface of proteins called binding sites. Understanding the physicochemical properties of ligand-binding sites is very important in the field of drug discovery as well as understanding biological systems. Protein-ligand binding site plays an essential role in the interaction between protein and ligand that is necessary for any living system to survive. Comparing similarities between binding siteshas been one of the main focus areas since the last decade in bioinformatics and drug discovery . In this regard, several computational methods have been developed to compare binding sites so far. Binding site comparison requires fast and efficient method as the amount of three-dimensional protein structural information is increasing rapidly nowadays . We report in this study, development of Site2Vec , a novel machine learning-based method for reference frame invariant ligand-independent vector embedding of the 3D structure of a protein-ligand binding site. Each binding site is represented in a d-dimensional vector form. The 3D structures of binding sites are mapped to vector form such that similar binding sites hash into proximal localities, and dissimilar sitesfall across diverse regions. A sensitivity analysis of rotation and perturbation and validation study is performed to understand the behavior of the method . Benchmarking exercises have been carried out against state of the art binding site comparison methods on state of the art datasets. The exercises validate our proposed method and demonstrate that the proposed method is rotationally invariant and can handle natural perturbations expected in the biological system.
Protein-ligand interactions are one of the fundamental types of molecular interactions in living systems. Ligands are small molecules that interact with protein molecules at specific regions on their surfaces called binding sites. Tasks such as assessment of protein functional similarity and detection of side effects of drugs need identification of similar binding sites of disparate proteins across diverse pathways. Machine learning methods for similarity assessment require feature descriptors of binding sites. Traditional methods based on hand engineered motifs and atomic configurations are not scalable across several thousands of sites . In this regard, deep neural network algorithms are now deployed which can capture very complex input feature space. However, one fundamental challenge in applying deep learning to structures of binding sites is the input representation and the reference frame . We report here a novel algorithm Site2Vec that derives reference frame invariant vector embedding of a protein-ligand binding site. The method is based on pairwise distances between representative points and chemical compositions in terms of constituent amino acids of a site. The vector embedding serves as a locality sensitive hash function for proximity queries and determining similar sites. The method has been the top performer with more than 95\% quality scores in extensive benchmarking studies carried over 10 datasets and against 23 other site comparison methods . The algorithm serves for high throughput processing and has been evaluated for stability with respect to reference frame shifts, coordinate perturbations and residue mutations. We provide Site2Vec as a stand alone executable and a web service hosted at URL
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Understanding the physicochemical properties of ligand-binding sites is very important in the field of drug discovery as well as understanding biological systems. Protein-ligand binding site plays an essential role in the interaction between protein and ligand that is necessary for any living system to survive. Comparing similarities between binding siteshas been one of the main focus areas since the last decade in bioinformatics and drug discovery
Tasks such as assessment of protein functional similarity and detection of side effects of drugs need identification of similar binding sites of disparate proteins across diverse pathways. Machine learning methods for similarity assessment require feature descriptors of binding sites. Traditional methods based on hand engineered motifs and atomic configurations are not scalable across several thousands of sites
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several computational methods have been developed to compare binding sites so far. Binding site comparison requires fast and efficient method as the amount of three-dimensional protein structural information is increasing rapidly nowadays
deep neural network algorithms are now deployed which can capture very complex input feature space. However, one fundamental challenge in applying deep learning to structures of binding sites is the input representation and the reference frame
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Each binding site is represented in a d-dimensional vector form. The 3D structures of binding sites are mapped to vector form such that similar binding sites hash into proximal localities, and dissimilar sitesfall across diverse regions. A sensitivity analysis of rotation and perturbation and validation study is performed to understand the behavior of the method . Benchmarking exercises have been carried out against state of the art binding
The method is based on pairwise distances between representative points and chemical compositions in terms of constituent amino acids of a site. The vector embedding serves as a locality sensitive hash function for proximity queries and determining similar sites. The method has been the top performer with more than 95\% quality scores in extensive benchmarking studies carried over 10 datasets and against 23 other
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on state of the art datasets. The exercises validate our proposed method and demonstrate that the proposed method is rotationally invariant and can handle natural perturbations expected in the biological system.
. The algorithm serves for high throughput processing and has been evaluated for stability with respect to reference frame shifts, coordinate perturbations and residue mutations. We provide Site2Vec as a stand alone executable and a web service hosted at URL