Comparative assessment of strategies to identify similar ligand-binding pockets in proteins

Govindaraj, Rajiv Gandhi; Bryliński, Michał

doi:10.1186/s12859-018-2109-2

Cited by 47 publications

(49 citation statements)

References 71 publications

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“…The APoc dataset (Gao and Skolnick, 2013) represents a step towards larger, more general datasets, comprising 34,970 positive and 20,744 negative pairs. Recently, Govindaraj and Brylinski (2018) proposed a large dataset, TOUGH-M1, of roughly one million pairs of protein-ligand binding sites curated from the PDB. Specifically, the authors considered a subset of the PDB including protein structures binding a single "druglike" ligand.…”

Section: Training Datasetmentioning

confidence: 99%

“…TOUGH-M1 (Govindaraj and Brylinski, 2018) is a dataset of 505,116 positive and 556,810 negative protein pocket pairs defined from 7,524 protein structures. Pockets are defined computationally with Fpocket 2.0 (Le Guilloux et al, 2009) and filtered to include only predicted cavities having the greatest overlap with known binding residues, see Section 2.1.…”

Section: Tough-m1 Datasetmentioning

confidence: 99%

“…A challenge for any machine learning-based method for pocket matching is presented by the available quantity of known protein pocket pairs and the quality of their annotations. Conveniently, Govindaraj and Brylinski (2018) have recently compiled TOUGH-M1, a large-scale dataset of roughly one million pairs of pockets differentiated by whether or not they bind structurally similar ligands. In this paper, we rely on this collection for training and expect that the scale will help the method overcome the noise inherently present in automated strategies for gathering data.…”

Section: Introductionmentioning

confidence: 99%

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DeeplyTough: Learning Structural Comparison of Protein Binding Sites

Simonovsky

Meyers

2019

Preprint

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Motivation: Protein binding site comparison (pocket matching) is of importance in drug discovery. Identification of similar binding sites can help guide efforts for hit finding, understanding polypharmacology and characterization of protein function. The design of pocket matching methods has traditionally involved much intuition, and has employed a broad variety of algorithms and representations of the input protein structures. We regard the high heterogeneity of past work and the recent availability of large-scale benchmarks as an indicator that a data-driven approach may provide a new perspective. Results: We propose DeeplyTough, a convolutional neural network that encodes a three-dimensional representation of protein binding sites into descriptor vectors that may be compared efficiently in an alignment-free manner by computing pairwise Euclidean distances. The network is trained with supervision: (i) to provide similar pockets with similar descriptors, (ii) to separate the descriptors of dissimilar pockets by a minimum margin, and (iii) to achieve robustness to nuisance variations. We evaluate our method using three large-scale benchmark datasets, on which it demonstrates excellent performance for held-out data coming from the training distribution and competitive performance when the trained network is required to generalize to datasets constructed independently.

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Section: Training Datasetmentioning

confidence: 99%

Section: Tough-m1 Datasetmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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DeeplyTough: Learning Structural Comparison of Protein Binding Sites

Simonovsky

Meyers

2019

Preprint

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“…In our previous study, we have developed an efficient local structure alignment tool, Graph‐based Local Structure Alignment (G‐LoSA; https://compbio.lehigh.edu/GLoSA). A recent comprehensive benchmark performance evaluation study reports that G‐LoSA offers a fairly robust overall performance over other widely used local structure alignment tools . Stalis is developed to design ligands by harnessing structure templates identified by G‐LoSA from the PDB structure libraries of small molecule ligands and their chemical fragments.…”

Section: Introductionmentioning

confidence: 99%

“…[22] A recent comprehensive benchmark performance evaluation study reports that G-LoSA offers a fairly robust overall performance over other widely used local structure alignment tools. [25] Stalis is developed to design ligands by harnessing structure templates identified by G-LoSA from the PDB structure libraries of small molecule ligands and their chemical fragments. We first describe the algorithm of Stalis.…”

Section: Introductionmentioning

confidence: 99%

Stalis: A Computational Method for Template‐Based Ab Initio Ligand Design

Lee

2019

J Comput Chem

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Proteins interact with small molecules through specific molecular recognition, which is central to essential biological functions in living systems. Therefore, understanding such interactions is crucial for basic sciences and drug discovery. Here, we present Structure template‐based ab initio ligand design solution (Stalis), a knowledge‐based approach that uses structure templates from the Protein Data Bank libraries of whole ligands and their fragments and generates a set of molecules (virtual ligands) whose structures represent the pocket shape and chemical features of a given target binding site. Our benchmark performance evaluation shows that ligand structure‐based virtual screening using virtual ligands from Stalis outperforms a receptor structure‐based virtual screening using AutoDock Vina, demonstrating reliable overall screening performance applicable to computational high‐throughput screening. However, virtual ligands from Stalis are worse in recognizing active compounds at the small fraction of a rank‐ordered list of screened library compounds than crystal ligands, due to the low resolution of the virtual ligand structures. In conclusion, Stalis can facilitate drug discovery research by designing virtual ligands that can be used for fast ligand structure‐based virtual screening. Moreover, Stalis provides actual three‐dimensional ligand structures that likely bind to a target protein, enabling to gain structural insight into potential ligands. Stalis can be an efficient computational platform for high‐throughput ligand design for fundamental biological study and drug discovery research at the proteomic level. © 2019 Wiley Periodicals, Inc.

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Ligand Binding Site Comparison — LiBiSCo — a web‐based tool for analyzing interactions between proteins and ligands to explore amino acid specificity within active sites

2021

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Interaction between protein and ligands are ubiquitous in a biological cell, and understanding these interactions at the atom level in protein–ligand complexes is crucial for structural bioinformatics and drug discovery. Here, we present a web‐based protein–ligand interaction application named Ligand Binding Site Comparison (LiBiSCo) for comparing the amino acid residues interacting with atoms of a ligand molecule between different protein–ligand complexes available in the Protein Data Bank (PDB) database. The comparison is performed at the ligand atom level irrespectively of having binding site similarity or not between the protein structures of interest. The input used in LiBiSCo is one or several PDB IDs of protein–ligand complex(es) and the tool returns a list of identified interactions at ligand atom level including both bonded and non‐bonded interactions. A sequence profile for the interaction for each ligand atoms is provided as a WebLogo. The LiBiSco is useful in understanding ligand binding specificity and structural promiscuity among families that are structurally unrelated. The LiBiSCo tool can be accessed through https://albiorix.bioenv.gu.se/LiBiSCo/HomePage.py.

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Comparative assessment of strategies to identify similar ligand-binding pockets in proteins

Cited by 47 publications

References 71 publications

DeeplyTough: Learning Structural Comparison of Protein Binding Sites

DeeplyTough: Learning Structural Comparison of Protein Binding Sites

Stalis: A Computational Method for Template‐Based Ab Initio Ligand Design

Ligand Binding Site Comparison — LiBiSCo — a web‐based tool for analyzing interactions between proteins and ligands to explore amino acid specificity within active sites

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