Binding similarity network of ligand

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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“…Branchand-bound algorithm for multiple alignments Sum of similarities for the matched pseudocenters Park and Kim [128,129] 2006…”

Section: Binding Site Comparisonmentioning

confidence: 99%

Identification of Similar Binding Sites to Detect Distant Polypharmacology

Jalencas

Mestres

2013

Molecular Informatics

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“…Positions 2-7 in the alignment belong to the Met20 loop (loop 1) of DHFR (E. coli DHFR: 9-24, human DHFR: [11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27]). This loop lies over the active site and regulates entry and exit of the substrate, product and Note: Only first author and year are provided for each reference.…”

Section: M20 Loopmentioning

confidence: 99%

Structural analysis of dihydrofolate reductases enables rationalization of antifolate binding affinities and suggests repurposing possibilities

Bhosle

Chandra

2016

The FEBS Journal

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Antifolates are competitive inhibitors of dihydrofolate reductase (DHFR), a conserved enzyme that is central to metabolism and widely targeted in pathogenic diseases, cancer and autoimmune disorders. Although most clinically used antifolates are known to be target specific, some display a fair degree of cross-reactivity with DHFRs from other species. A method that enables identification of determinants of affinity and specificity in target DHFRs from different species and provides guidelines for the design of antifolates is currently lacking. To address this, we first captured the potential druggable space of a DHFR in a substructure called the 'supersite' and classified supersites of DHFRs from 56 species into 16 'site-types' based on pairwise structural similarity. Analysis of supersites across these site-types revealed that DHFRs exhibit varying extents of dissimilarity at structurally equivalent positions in and around the binding site. We were able to explain the pattern of affinities towards chemically diverse antifolates exhibited by DHFRs of different site-types based on these structural differences. We then generated an antifolate-DHFR network by mapping known high-affinity antifolates to their respective supersites and used this to identify antifolates that can be repurposed based on similarity between supersites or antifolates. Thus, we identified 177 human-specific and 458 pathogen-specific antifolates, a large number of which are supported by available experimental data. Thus, in the light of the clinical importance of DHFR, we present a novel approach to identifying differences in the druggable space of DHFRs that can be utilized for rational design of antifolates.

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“…Further, Zhang et al [7] constructed similarity networks of binding sites. Liu et al [8] constructed a pocket (protein surface) similarity network and Park et al [9] constructed a binding similarity network of ligands. All these networks are useful in predicting protein function.…”

Section: Introductionmentioning

confidence: 99%

Recognition of Structure Similarities in Proteins

et al. 2008

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Protein fold structure is more conserved than its amino acid sequence and closely associated with biological function, so calculating the similarity of protein structures is a fundamental problem in structural biology and plays a key role in protein fold classification, fold function inference, and protein structure prediction. Large progress has been made in recent years in this field and many methods for considering structural similarity have been proposed, including methods for protein structure comparison, retrieval of protein structures from databases, and ligand binding site comparison. Most of those methods can be available on the World Wide Web, but evaluation of all the methods is still a hard problem. This paper summarizes some popular methods and latest methods for structure similarities, including structure alignment, protein structure retrieval, and ligand binding site alignment.

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Binding similarity network of ligand

Cited by 35 publications

References 38 publications

Identification of Similar Binding Sites to Detect Distant Polypharmacology

Identification of Similar Binding Sites to Detect Distant Polypharmacology

Structural analysis of dihydrofolate reductases enables rationalization of antifolate binding affinities and suggests repurposing possibilities

Recognition of Structure Similarities in Proteins

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