Genome Analysis of Pass2 a Semi-Automated Database of Protein Alignments Organised as Structural Superfamilies

Bhaduri, Anirban; Mallika, V.; Sowdhamini, R.

doi:10.1100/tsw.2002.5

Cited by 9 publications

(15 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Briefly, we started with the proteins of the HOMSTRAD database of structurally aligned homologous protein families (Mizuguchi et al, 1998;Stebbings and Mizuguchi, 2004), and we used the CAMPASS superfamily classification (Sowdhamini et al, 1998;Bhaduri et al, 2004) to group HOMSTRAD proteins into superfamilies. Then we obtained all the PDB files and removed all proteins that were unsuitable for the analysis of flexibility-profile conservation (e.g.…”

Section: Datasets Of Homologous Pairsmentioning

confidence: 99%

Evolutionary conservation of protein vibrational dynamics

Maguid

Fernández-Alberti

Echave

2008

Gene

View full text Add to dashboard Cite

Section: Datasets Of Homologous Pairsmentioning

confidence: 99%

Evolutionary conservation of protein vibrational dynamics

Maguid

Fernández-Alberti

Echave

2008

Gene

View full text Add to dashboard Cite

“…Proteins containing an experimentally-determined function (simply referred to as a known biochemical function here) and a known structure were selected from a number of databases. First, PASS2 [25] was used to determine an initial set of proteins containing a characterized function and structure from both superfamilies (6-HG superfamily SCOP code: 48208, CAL/G superfamily SCOP code: 49899). Next, the Dali server [9,10] was used to identify structurally similar proteins deposited in the PDB [1] using the initial set of proteins as a starting point.…”

Section: Identification Of Protein Structures Of Known Function In Thmentioning

confidence: 99%

Local structure based method for prediction of the biochemical function of proteins: Applications to glycoside hydrolases

Parasuram

Mills

Wang

et al. 2016

Methods

View full text Add to dashboard Cite

Thousands of protein structures of unknown or uncertain function have been reported as a result of high-throughput structure determination techniques developed by Structural Genomics (SG) projects. However, many of the putative functional assignments of these SG proteins in the Protein Data Bank (PDB) are incorrect. While high-throughput biochemical screening techniques have provided valuable functional information for limited sets of SG proteins, the biochemical functions for most SG proteins are still unknown or uncertain. Therefore, computational methods for the reliable prediction of protein function from structure can add tremendous value to the existing SG data. In this article, we show how computational methods may be used to predict the function of SG proteins, using examples from the six-hairpin glycosidase (6-HG) and the concanavalin A-like lectin/glucanase (CAL/G) superfamilies. Using a set of predicted functional residues, obtained from computed electrostatic and chemical properties for each protein structure, it is shown that these superfamilies may be sorted into functional families according to biochemical function. Within these superfamilies, a total of 18 SG proteins were analyzed according to their predicted, local functional sites: 13 from the 6-HG superfamily, five from the CAL/G superfamily. Within the 6-HG superfamily, an uncharacterized protein BACOVA_03626 from Bacteroides ovatus (PDB 3ON6) and a hypothetical protein BT3781 from Bacteroides thetaiotaomicron (PDB 2P0V) are shown to have very strong active site matches with exo-α-1,6-mannosidases, thus likely possessing this function. Also in this superfamily, it is shown that protein BH0842, a putative glycoside hydrolase from Bacillus halodurans (PDB 2RDY), has a predicted active site that matches well with a known α-L-galactosidase. In the CAL/G superfamily, an uncharacterized glycosyl hydrolase family 16 protein from Mycobacterium smegmatis (PDB 3RQ0) is shown to have local structural similarity at the predicted active site with the known members of the GH16 family, with the closest match to the endoglucanase subfamily. The method discussed herein can predict whether an SG protein is correctly or incorrectly annotated and can sometimes provide a reliable functional annotation. Examples of application of the method across folds, comparing active sites between two proteins of different structural folds, are also given.

show abstract

“…As a result, the identification of a homolog is a very useful means to infer the function and/or predict the structure of an uncharacterized protein. Many databases exist that classify proteins into families by their structures, including but not limited to SCOP,4 CATH,5 DaliDB,6 PASS2,7 MMDB,8 ASTRAL,9 HOMSTRAD,10 and LPFC 11. A review from Orengo and Thornton provides a very thorough discussion of protein evolution from a structural standpoint,12 and another recent review stresses that the classification in an evolutionary context is still an open problem 13…”

Section: Introductionmentioning

confidence: 99%

Overcoming sequence misalignments with weighted structural superposition

et al. 2012

View full text Add to dashboard Cite

An appropriate structural superposition identifies similarities and differences between homologous proteins that are not evident from sequence alignments alone. We have coupled our Gaussian-weighted RMSD (wRMSD) tool with a sequence aligner and seed extension (SE) algorithm to create a robust technique for overlaying structures and aligning sequences of homologous proteins (HwRMSD). HwRMSD overcomes errors in the initial sequence alignment that would normally propagate into a standard RMSD overlay. SE can generate a corrected sequence alignment from the improved structural superposition obtained by wRMSD. HwRMSD’s robust performance and its superiority over standard RMSD are demonstrated over a range of homologous proteins. Its better overlay results in corrected sequence alignments with good agreement to HOMSTRAD. Finally, HwRMSD is compared to established structural alignment methods: FATCAT, SSM, CE, and Dalilite. Most methods are comparable at placing residue pairs within 2 Å, but HwRMSD places many more residue pairs within 1 Å, providing a clear advantage. Such high accuracy is essential in drug design, where small distances can have a large impact on computational predictions. This level of accuracy is also needed to correct sequence alignments in an automated fashion, especially for omics-scale analysis. HwRMSD can align homologs with low sequence identity and large conformational differences, cases where both sequence-based and structural-based methods may fail. The HwRMSD pipeline overcomes the dependency of structural overlays on initial sequence pairing and removes the need to determine the best sequence-alignment method, substitution matrix, and gap parameters for each unique pair of homologs.

show abstract

Genome Analysis of Pass2 a Semi-Automated Database of Protein Alignments Organised as Structural Superfamilies

Cited by 9 publications

References 8 publications

Evolutionary conservation of protein vibrational dynamics

Evolutionary conservation of protein vibrational dynamics

Local structure based method for prediction of the biochemical function of proteins: Applications to glycoside hydrolases

Overcoming sequence misalignments with weighted structural superposition

Contact Info

Product

Resources

About