How to Measure the Similarity Between Protein Ligand-Binding Sites?

Kellenberger, Esther; Schalon, Claire; Rognan, Didier

doi:10.2174/157340908785747401

Cited by 67 publications

(69 citation statements)

References 29 publications

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“…[59] For each of these tasks a rich variety of approaches exist. [59][60][61][62] The following sections contain a detailed survey of those methods, many of which are compiled in Table 1, including concrete examples of identification of similar binding sites and how these gave rise, in some cases, to detection of distant drug polypharmacology.…”

Section: Identification Of Similar Binding Sitesmentioning

confidence: 99%

Identification of Similar Binding Sites to Detect Distant Polypharmacology

Jalencas

Mestres

2013

Molecular Informatics

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The ability of small molecules to interact with multiple proteins is referred to as polypharmacology. This property is often linked to the therapeutic action of drugs but it is known also to be responsible for many of their side effects. Because of its importance, the development of computational methods that can predict drug polypharmacology has become an important line of research that led recently to the identification of many novel targets for known drugs. Nowadays, the majority of these methods are based on measuring the similarity of a query molecule against the hundreds of thousands of molecules for which pharmacological data on thousands of proteins are available in public sources. However, similarity-based methods are inherently biased by the chemical coverage offered by the active molecules present in those public repositories, which limits significantly their capacity to predict interactions with proteins structurally and functionally unrelated to any of the already known targets for drugs. It is in this respect that structure-based methods aiming at identifying similar binding sites may offer an alternative complementary means to ligand-based methods for detecting distant polypharmacology. The different existing approaches to binding site detection, representation, comparison, and fragmentation are reviewed and recent successful applications presented.

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Section: Identification Of Similar Binding Sitesmentioning

confidence: 99%

Identification of Similar Binding Sites to Detect Distant Polypharmacology

Jalencas

Mestres

2013

Molecular Informatics

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“…But in the case of compound 5, this negative EPS could have attracted the tertiary amine functional group ( Figure 5, right and top). Generally, compounds 4 and 5 bound to the same pocket, indicating similar core of EPS that could possibly explain their similar values for E bind (Kellenberger et al 2008). However, as discussed, each compound had its own favourable binding interactions, which when combined with the binding pocket shape complementary, may lead to the different levels of inhibition activity.…”

Section: Binding Pocket and Electrostatic Potential Surface (Eps)mentioning

confidence: 76%

Molecular Docking Studies of Selected Medicinal Drugs as Dengue Virus-2 Protease Inhibitors

Othman¹,

Othman²,

Baharuddin³

et al. 2017

JSM

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Dengue is a potentially deadly disease with no effective drug. An in silico molecular docking was performed using Autodock 4.2.6 to investigate the molecular interactions between protease inhibitors, comprising antibiotic derivatives namely doxycycline (3), rolitetracycline (5) and a non-steroidal anti-inflammatory drug (NSAID), meclofenamic acid (4), against the NS2B-NS3 protease from dengue virus-2 (DENV-2). The non-competitive inhibitor (3) showed lower binding energy (-5.15 kcal/mol) than the predicted competitive inhibitors 4 and 5 (-3.64 and -3.21 kcal/mol, respectively (-5.15 kcal/mol) berbanding sebatian 4 dan 5 (masing-masing -3.64 dan -3.21 kcal/mol). Analisis struktur menunjukkan sebatian 3 yang terikat pada kawasan alosterik, berinteraksi dengan Lys74, iaitu residu asid amino yang terikat dengan salah satu daripada residu triad pemangkinan, Asp75. Sebatian 4 dan 5 pula menunjukkan ikatan langsung dengan dua triad pemangkinan iaitu His51 dan Ser135, justeru diramalkan sebagai perencat kompetitif. Kata kunci: Mengedok; perencat; Denggi adalah sejenis penyakit yang boleh membawa maut dan sehingga kini tiada sebarang ubat untuk merawat penyakit tersebut. Mengedok molekul secara in silico menggunakan Autodock 4.2.6 telah dijalankan untuk mengkaji interaksi molekul antara perencat protease yang terdiri daripada derivatif antibiotik iaitu doxycycline (3) dan rolitetracycline (5) dan dadah anti-radang bukan steroid (NSAID), asid meklofenamik (4), terhadap NS2B-NS3 daripada virus denggi-2 (DENV-2). Perencat tidak-kompetitif (3) menunjukkan tenaga ikatan yang lebih rendah

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“…CavBase takes advantage of clique detection algorithms to align binding sites by representing patterns as graphs with pattern elements as nodes and element proximities as edges [231]. Other factors impacting the performance of ligand binding site comparison tools include the geometric pattern resolution and incorporation of binding site dynamics [232].…”

Section: Binding Site Structural Comparisonmentioning

confidence: 99%

Bioinformatics and variability in drug response: a protein structural perspective

Lahti

Tang

Capriotti

et al. 2012

J. R. Soc. Interface.

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Marketed drugs frequently perform worse in clinical practice than in the clinical trials on which their approval is based. Many therapeutic compounds are ineffective for a large subpopulation of patients to whom they are prescribed; worse, a significant fraction of patients experience adverse effects more severe than anticipated. The unacceptable risk -benefit profile for many drugs mandates a paradigm shift towards personalized medicine. However, prior to adoption of patient-specific approaches, it is useful to understand the molecular details underlying variable drug response among diverse patient populations. Over the past decade, progress in structural genomics led to an explosion of available three-dimensional structures of drug target proteins while efforts in pharmacogenetics offered insights into polymorphisms correlated with differential therapeutic outcomes. Together these advances provide the opportunity to examine how altered protein structures arising from genetic differences affect protein -drug interactions and, ultimately, drug response. In this review, we first summarize structural characteristics of protein targets and common mechanisms of drug interactions. Next, we describe the impact of coding mutations on protein structures and drug response. Finally, we highlight tools for analysing protein structures and protein -drug interactions and discuss their application for understanding altered drug responses associated with protein structural variants.

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How to Measure the Similarity Between Protein Ligand-Binding Sites?

Cited by 67 publications

References 29 publications

Identification of Similar Binding Sites to Detect Distant Polypharmacology

Identification of Similar Binding Sites to Detect Distant Polypharmacology

Molecular Docking Studies of Selected Medicinal Drugs as Dengue Virus-2 Protease Inhibitors

Bioinformatics and variability in drug response: a protein structural perspective

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