SMAP-WS: a parallel web service for structural proteome-wide ligand-binding site comparison
SMAP-WS: a parallel web service for structural proteome-wide ligand-binding site comparison is a research paper published in Nucleic Acids Research (2010). On theSindex it has a DataRank of 2.6. It has been cited 63 times, with 58 citing works in its 1-hop citation network.
Abstract
The proteome-wide characterization and analysis of protein ligand-binding sites and their interactions with ligands can provide pivotal information in understanding the structure, function and evolution of proteins and for designing safe and efficient therapeutics. The SMAP web service (SMAP-WS) meets this need through parallel computations designed for 3D ligand-binding site comparison and similarity searching on a structural proteome scale. SMAP-WS implements a shape descriptor (the Geometric Potential) that characterizes both local and global topological properties of the protein structure and which can be used to predict the likely ligand-binding pocket [Xie,L. and Bourne,P.E. (2007) A robust and efficient algorithm for the shape description of protein structures and its application in predicting ligand-binding sites. BMC bioinformatics, 8 (Suppl. 4.), S9.]. Subsequently a sequence order independent profile-profile alignment (SOIPPA) algorithm is used to detect and align similar pockets thereby finding protein functional and evolutionary relationships across fold space [Xie, L. and Bourne, P.E. (2008) Detecting evolutionary relationships across existing fold space, using sequence order-independent profile-profile alignments. Proc. Natl Acad. Sci. USA, 105, 5441-5446]. An extreme value distribution model estimates the statistical significance of the match [Xie, L., Xie, L. and Bourne, P.E. (2009) A unified statistical model to support local sequence order independent similarity searching for ligand-binding sites and its application to genome-based drug discovery. Bioinformatics, 25, i305-i312.]. These algorithms have been extensively benchmarked and shown to outperform most existing algorithms. Moreover, several predictions resulting from SMAP-WS have been validated experimentally. Thus far SMAP-WS has been applied to predict drug side effects, and to repurpose existing drugs for new indications. SMAP-WS provides both a user-friendly web interface and programming API for scientists to address a wide range of compute intense questions in biology and drug discovery. SMAP-WS is available from the URL http://smap.nbcr.net.
›Data sources & pipeline
FAIR Checklist
Context only (not used in score)- Has DOI
- Open Access
FAIR checklist signals are shown for context only and do not affect DataRank scoring.
DataRank Breakdown
Base Score Contribution
0.624
From this paper's citation signal
Citation Network Contribution
1.9
From 48 citing papers with measurable signal
Top 5 citers driving the network score
Ranked by citation count — the same ordering the engine uses when summing log1p(Cq) over citers.
- The Protein Data BankNucleic Acids Research200039,606 citationsDataRank 32.3Top 1%
- Detecting evolutionary relationships across existing fold space, using sequence order-independent profile–profile alignmentsProceedings of the National Academy of Sciences2008251 citationsDataRank 0.829
- A unified statistical model to support local sequence order independent similarity searching for ligand-binding sites and its application to genome-based drug discoveryBioinformatics200996 citationsDataRank 3.4
- In Silico Elucidation of the Molecular Mechanism Defining the Adverse Effect of Selective Estrogen Receptor ModulatorsPLoS Computational Biology200783 citationsDataRank 4.0
- A Multidimensional Strategy to Detect Polypharmacological Targets in the Absence of Structural and Sequence HomologyPLoS Computational Biology201076 citationsDataRank 2.7
Why this DataRank?
DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 24% comes from its base citations and 76% from the citation network (48 citing papers contributed measurable signal).
- Base score B(p)
- log1p(citation_count) — grows sub-linearly, so a paper with 1,000 citations is not 10× a paper with 100.
- Network N(p)
- Σ over citers of log1p(Cq) ÷ max(outdegreeq, 1). Being cited by a highly-cited paper with few references counts most.
- Damping factor d = 0.85
- DataRank = (1−d)·B(p) + d·N(p) — the two cards above are each already multiplied by their share.
- Self-citations excluded
- Citers sharing any OpenAlex author ID with this paper are filtered out before the network sum.
Citers are pulled from OpenAlex sorted by cited_by_count:descand capped per paper, so when the cap binds we keep the highest-signal references and the score is reproducible across reruns.
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