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A structural hierarchy matching approach for molecular similarity/substructure searching.

ABSTRACT: An approach for molecular similarity/substructure searching based on structural hierarchy matching is proposed. In this approach, small molecules are divided into two categories, acyclic and cyclic forms. The latter are further divided into three structural hierarchies, namely, framework, complicated-, and mono-rings. During searching, the similarity coefficients of a structural query and each retrieved molecule are calculated using the hierarchy of the query as the reference. A total of 13,911 chemicals were involved in this work, from which the minimal cyclic and acyclic substructures are extracted, and further processed into fuzzy structural fingerprints. Subsequently, the fingerprints are used as the searching indices for molecular similarity or substructure searching. The tests show that this approach can give user options to choose between one-substructure and multi-substructure searching with sorted results. Moreover, this algorithm has the potential to be developed for molecular similarity searching and substructure analysis.

SUBMITTER: Ji SS

PROVIDER: S-EPMC6272706 | biostudies-literature | 2015 May

REPOSITORIES: biostudies-literature

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A structural hierarchy matching approach for molecular similarity/substructure searching.

Ji Shu-Shen SS Dong Hong-Ju HJ Zhou Xin-Xin XX Liu Ya-Min YM Zhang Feng-Xue FX Wang Qi Q Huang Xin-An XA

Molecules (Basel, Switzerland) 20150515 5

An approach for molecular similarity/substructure searching based on structural hierarchy matching is proposed. In this approach, small molecules are divided into two categories, acyclic and cyclic forms. The latter are further divided into three structural hierarchies, namely, framework, complicated-, and mono-rings. During searching, the similarity coefficients of a structural query and each retrieved molecule are calculated using the hierarchy of the query as the reference. A total of 13,911 ...[more]

PMID: 25988610

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Project description:BackgroundVirtual screening methods are now well established as effective to identify hit and lead candidates and are fully integrated in most drug discovery programs. Ligand-based approaches make use of physico-chemical, structural and energetics properties of known active compounds to search large chemical libraries for related and novel chemotypes. While 2D-similarity search tools are known to be fast and efficient, the use of 3D-similarity search methods can be very valuable to many research projects as integration of "3D knowledge" can facilitate the identification of not only related molecules but also of chemicals possessing distant scaffolds as compared to the query and therefore be more inclined to scaffolds hopping. To date, very few methods performing this task are easily available to the scientific community.ResultsWe introduce a new approach (LigCSRre) to the 3D ligand similarity search of drug candidates. It combines a 3D maximum common substructure search algorithm independent on atom order with a tunable description of atomic compatibilities to prune the search and increase its physico-chemical relevance. We show, on 47 experimentally validated active compounds across five protein targets having different specificities, that for single compound search, the approach is able to recover on average 52% of the co-actives in the top 1% of the ranked list which is better than gold standards of the field. Moreover, the combination of several runs on a single protein target using different query active compounds shows a remarkable improvement in enrichment. Such Results demonstrate LigCSRre as a valuable tool for ligand-based screening.ConclusionLigCSRre constitutes a new efficient and generic approach to the 3D similarity screening of small compounds, whose flexible design opens the door to many enhancements. The program is freely available to the academics for non-profit research at: http://bioserv.rpbs.univ-paris-diderot.fr/LigCSRre.html.

Project description:BACKGROUND:There is an increasing number of proteins with known structure but unknown function. Determining their function would have a significant impact on understanding diseases and designing new therapeutics. However, experimental protein function determination is expensive and very time-consuming. Computational methods can facilitate function determination by identifying proteins that have high structural and chemical similarity. RESULTS:We present LabelHash, a novel algorithm for matching substructural motifs to large collections of protein structures. The algorithm consists of two phases. In the first phase the proteins are preprocessed in a fashion that allows for instant lookup of partial matches to any motif. In the second phase, partial matches for a given motif are expanded to complete matches. The general applicability of the algorithm is demonstrated with three different case studies. First, we show that we can accurately identify members of the enolase superfamily with a single motif. Next, we demonstrate how LabelHash can complement SOIPPA, an algorithm for motif identification and pairwise substructure alignment. Finally, a large collection of Catalytic Site Atlas motifs is used to benchmark the performance of the algorithm. LabelHash runs very efficiently in parallel; matching a motif against all proteins in the 95% sequence identity filtered non-redundant Protein Data Bank typically takes no more than a few minutes. The LabelHash algorithm is available through a web server and as a suite of standalone programs at http://labelhash.kavrakilab.org. The output of the LabelHash algorithm can be further analyzed with Chimera through a plugin that we developed for this purpose. CONCLUSIONS:LabelHash is an efficient, versatile algorithm for large-scale substructure matching. When LabelHash is running in parallel, motifs can typically be matched against the entire PDB on the order of minutes. The algorithm is able to identify functional homologs beyond the twilight zone of sequence identity and even beyond fold similarity. The three case studies presented in this paper illustrate the versatility of the algorithm.

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A structural hierarchy matching approach for molecular similarity/substructure searching.

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A structural hierarchy matching approach for molecular similarity/substructure searching.

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