SPICKER: A clustering approach to identify near‐native protein folds

By Yang Zhang, Jeffrey Skolnick. 2004

430 citations recorded by the source corpus.

Subfields: Materials Chemistry, Molecular Biology, Computational Biology & Bioinformatics

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Materials Chemistry

In Molecular Biology

In Computational Biology & Bioinformatics

Citations in the corpus

I-TASSER server: new development for protein structure and function predictions (2015, background): “The structure trajectories are clustered by SPICKER (22) to identify low free-energy states.”
TM-align: a protein structure alignment algorithm based on the TM-score (2005, positive): “The ﬁve lowest free-energy models are selected by SPICKER clustering (31).”
EvoDesign: Designing protein-protein binding interactions using evolutionary interface profiles in conjunction with an optimized physical energy function (2019, methodology): “…from the multiple sequence alignment of monomer proteins that have a similar fold to the scaffold chain as identified by TM-align Following the REMC simulation, the generated sequence decoys are clustered by SPICKER [18] based on the distance matrix defined by their BLOSUM62 sequence similarity.”
I-TASSER-MTD: a deep-learning-based platform for multi-domain protein structure and function prediction (2022, methodology): “Five REMC simulations are performed in parallel for each protein, where the structural decoys from 8 (or 3 for hard targets) low-temperature replicas are clustered by SPICKER 65 .”
GPCR-I-TASSER: A hybrid approach to G protein-coupled receptor structure modeling and the application to the human genome (2015, methodology): “…> 30% (i.e., dropping off the PSI-BLAST E-value ﬁlter) as done in many previous benchmark experiments of structure prediction (Simons et al., 1999; Zhang and Skolnick, 2004a), the TM-score and RMSD of the threading templates will increase to 0.756 and 4.65 A˚, respectively, while the quality of…”
Template‐based modeling and free modeling by I‐TASSER in CASP7 (2007, methodology): “37 So it will be helpful to develop robust domain parser programs to split the domains correctly and fold the individual domain separately.”
Deep-learning contact-map guided protein structure prediction in CASP13 (2019, methodology): “39 Then the cluster centroids are aligned against the structures in the PDB library using TM-align.”
Detecting distant-homology protein structures by aligning deep neural-network based contact maps (2019, methodology): “Following the simulations, the decoys from the simulation trajectories are clustered by SPICKER [51], where the conformations that are closest to cluster centroids are selected and refined by FG-MD [52] to generate the final top 5 structure models.”
Improving the physical realism and structural accuracy of protein models by a two-step atomic-level energy minimization. (2011, methodology): “4 a is the cluster center obtained by SPICKER on the I-TASSER decoys.”
Using iterative fragment assembly and progressive sequence truncation to facilitate phasing and crystal structure determination of distantly related proteins. (2016, methodology): “Bibby et al. (2012) documented a correlation between conformational variation and modeling error, and proposed trimming off residues with large variation after structure clustering by SPICKER (Zhang & Skolnick, 2004a).”
Ab initio modeling of small proteins by iterative TASSER simulations (2007, positive): “The structure trajectories of the first-round TASSER simulations are clustered by SPICKER [38].”