The FSSP database of structurally aligned protein fold families.

By Liisa Holm, Christopher Sander. 1994

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1. Sheng Wang
2. Jianlin Cheng
3. Liam J. McGuffin
4. Jinbo Xu
5. Badri Adhikari
6. Yang Zhang
7. Jie Hou
8. David T. Jones

Citations in the corpus

• DeepSF: deep convolutional neural network for mapping protein sequences to folds (2017, methodology): “Several fold classification databases such as SCOP (Murzin et al., 1995), CATH (Greene et al., 2007), FSSP (Holm and Sander, 1994), ECOD (Cheng et al., 2014) have been developed to summarize the structural relationship between VC The Author 2017.”
• CATH--a hierarchic classification of protein domain structures. (1997, methodology): “By contrast Holm and Sander, use the structure comparison algorithm DALI to recognise structural neighbours, whether motif or fold based, without formally ordering proteins in the PDB into families [20].”
• HOMSTRAD: A database of protein structure alignments for homologous families (1998, methodology): “…HOMSTRAD is complementary to SCOP (Murzin et al., 1995) and CATH (Orengo et al., 1997), and our focus on homologous families, the JOY representation and manual intervention also distinguish HOMSTRAD from other databases including 3D-ali (Pasarella & Argos, 1992) and FSSP (Holm & Sander, 1994).”
• Improvement of the GenTHREADER Method for Genomic Fold Recognition (2003, methodology): “…architecture of the original neural network, however the protocol is modified so that PSI-BLAST is used to generate sequence profiles in the fold library and the back propagation neural network is trained to learn the FSSP Z -score (Holm and Sander, 1994) representing the relatedness of proteins.”
• ProbCons: Probabilistic consistency-based multiple sequence alignment. (2005, methodology): “The BAliBASE 2.01 benchmark alignment database is a collection of 141 reference protein alignments, consisting of structural alignments from the FSSP ( Holm and Sander 1994 ) and HOMSTRAD (Mizuguchi et al. 1998) databases and handconstructed alignments from the literature.”
• SP5: Improving Protein Fold Recognition by Using Torsion Angle Profiles and Profile-Based Gap Penalty Model (2008, methodology): “We use PREFAB because its reference alignment is made from the consensus of two separate structural alignment programs (CE [40] and FSSP [41]) rather than one in Prosup.”
• Fast Multiple Alignment of Protein Structures Using Conformational Letter Blocks (2009, methodology): “Distances used by DALI [3, 4] or CE [5] are the intrinsic property of a geometric object.”
• ClEPaps: Fast Pair Alignment of protein Structures Based on conformational Letters (2007, background): “For example, to lower the dimensionality of the problem, DALI identifies interaction patterns of fragment pairs,[1, 2] VAST describes secondary structure elements (SSEs) as a set of vectors,[3, 4] while CE designates short aligned fragment pairs (AFPs) of local structural similarities.”
• DALI and the persistence of protein shape (2019, background): “In the beginning of DALI, the non-redundant PDB contained a few hundred protein structures and it was possible to compare them all against all and store the results in a database called FSSP (Families of Structurally Similar Proteins 41 – 46 ).”