G‐LoSA: An efficient computational tool for local structure‐centric biological studies and drug design

Lee, Hui Sun; Im, Wonpil

doi:10.1002/pro.2890

Cited by 37 publications

(51 citation statements)

References 30 publications

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“…G‐LoSA is a computational tool to align protein local structures in a sequence order independent way and to provide a G‐LoSA Alignment score (GA‐score), a size‐independent quantity of structural similarity for a given structure pair . G‐LoSA generates possible structure alignments between two structures by iterative maximum clique search and fragment superposition based on the geometry of Cα atoms (i.e., single‐point representation of each residue).…”

Section: Methodsmentioning

confidence: 99%

“…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%

“…With a rapid increase in the number of high‐resolution protein structures in the Protein Data Bank (PDB, http://www.rcsb.org), structural information from known protein–ligand structures can be used to design small molecule ligands. Our previous study indicates that similar binding sites occur in unrelated protein structures, making it feasible to predict ligand structures from protein–ligand complex structures in the PDB . As an example of this knowledge‐based approach, a commercial tool, MED‐Portion/MED‐SuMo/MED‐Hybridize detects MED‐Portions, protein‐fragment binding sites that are derived from the PDB protein–ligand complex structures by MED‐SuMo and generate a pool of MED‐Portion hybrids by MED‐Hybridize .…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Lee

2019

J Comput Chem

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“…A more complete list of binding-site comparison methods is found in a review by Jalencas and Mestres. [45] There were five methods which were able to successfully accommodate calculations of large datasets, Apoc [38], G-LoSA [46], ProBis [47], FuzCav [48], and PocketMatch [49]. Apoc was chosen due to its efficiency, as it is prohibitive to utilize all four programs on the entire dataset simply to benchmark a best fit.…”

Section: New Features and Functionalitiesmentioning

confidence: 99%

Updates to Binding MOAD (Mother of All Databases): Polypharmacology Tools and Their Utility in Drug Repurposing

Smith

Clark

Ahmed

et al. 2019

Journal of Molecular Biology

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The goal of Binding MOAD is to provide users with a dataset focused on high-quality X-ray crystal structures that have been solved with biologically relevant ligands bound. Where available, experimental binding affinities (K a , K d , K i , IC 50) are provided from the primary literature of the crystal structure. The database has been updated regularly since 2005, and this most recent update has added nearly 7000 new structures (growth of 21%). MOAD currently contains 32,747 structures, composed of 9,117 protein families and 16,044 unique ligands. The data is freely available on www.BindingMOAD.org. This paper outlines updates to the data in Binding MOAD as well as improvements made to both the website and its contents. The NGL viewer has been added to improve visualization of the ligands and protein structures. MarvinJS has been implemented, over the outdated MarvinView, to work with JChem for small molecule searching in the database. To add tools for predicting polypharmacology, we have added information about sequence, binding-site, and ligand similarity between entries in the database. A main premise behind polypharmacology is that similar binding sites will bind similar ligands. The large amount of protein-ligand information available in Binding MOAD allows us to compute pairwise ligand and binding-site similarities. Lists of similar ligands and similar binding sites have been added to allow users to identify potential polypharmacology pairs. To show the utility of the polypharmacology data, we detail a few examples from Binding MOAD of drug repurposing targets with their respective similarities.

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“…Studies have shown that compounds derived from natural products are more active than compounds of other origins. After years of research and development in drug production, researchers have developed a variety of methods for predicting drug-likeness, such as virtual screening methods, pharmacophore model methods, and machine learning algorithms, which are widely used in various types of compounds [ 11 , 12 , 13 , 14 , 15 ]. The prediction of medicinal properties also has a good effect on the prediction of the drug-like properties of Chinese herbal medicine-derived compounds.…”

Section: Introductionmentioning

confidence: 99%

Prediction Methods of Herbal Compounds in Chinese Medicinal Herbs

et al. 2018

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Chinese herbal medicine has recently gained worldwide attention. The curative mechanism of Chinese herbal medicine is compared with that of western medicine at the molecular level. The treatment mechanism of most Chinese herbal medicines is still not clear. How do we integrate Chinese herbal medicine compounds with modern medicine? Chinese herbal medicine drug-like prediction method is particularly important. A growing number of Chinese herbal source compounds are now widely used as drug-like compound candidates. An important way for pharmaceutical companies to develop drugs is to discover potentially active compounds from related herbs in Chinese herbs. The methods for predicting the drug-like properties of Chinese herbal compounds include the virtual screening method, pharmacophore model method and machine learning method. In this paper, we focus on the prediction methods for the medicinal properties of Chinese herbal medicines. We analyze the advantages and disadvantages of the above three methods, and then introduce the specific steps of the virtual screening method. Finally, we present the prospect of the joint application of various methods.

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G‐LoSA: An efficient computational tool for local structure‐centric biological studies and drug design

Cited by 37 publications

References 30 publications

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

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

Updates to Binding MOAD (Mother of All Databases): Polypharmacology Tools and Their Utility in Drug Repurposing

Prediction Methods of Herbal Compounds in Chinese Medicinal Herbs

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