Template-based prediction of protein function

Petrey, Donald; Chen, T Scott; Deng, Lei; Garzón, José Ignacio; Hwang, Howook; Lasso, Gorka; Lee, Hunjoong; Silkov, Antonina; Honig, Barry

doi:10.1016/j.sbi.2015.01.007

Cited by 42 publications

(22 citation statements)

References 55 publications

(71 reference statements)

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“…Structure determination lags far behind DNA sequencing, but high-throughput computational modeling (2,3) can leverage the PDB to provide accurate structural predictions for a large fraction of protein sequences. Thus, structural data now scale with sequencing, providing a wealth of detail about molecular functions.…”

mentioning

confidence: 99%

Unexpected features of the dark proteome

Perdigão

Heinrich

Stolte

et al. 2015

Proc. Natl. Acad. Sci. U.S.A.

170

211

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We surveyed the "dark" proteome-that is, regions of proteins never observed by experimental structure determination and inaccessible to homology modeling. For 546,000 Swiss-Prot proteins, we found that 44-54% of the proteome in eukaryotes and viruses was dark, compared with only ∼14% in archaea and bacteria. Surprisingly, most of the dark proteome could not be accounted for by conventional explanations, such as intrinsic disorder or transmembrane regions. Nearly half of the dark proteome comprised dark proteins, in which the entire sequence lacked similarity to any known structure. Dark proteins fulfill a wide variety of functions, but a subset showed distinct and largely unexpected features, such as association with secretion, specific tissues, the endoplasmic reticulum, disulfide bonding, and proteolytic cleavage. Dark proteins also had short sequence length, low evolutionary reuse, and few known interactions with other proteins. These results suggest new research directions in structural and computational biology. structure prediction | protein disorder | transmembrane proteins | secreted proteins | unknown unknowns T he Protein Data Bank (PDB) (1) of experimentally determined macromolecular structures recently surpassed 110,000 entries-a landmark in understanding the molecular machinery of life. Structure determination lags far behind DNA sequencing, but high-throughput computational modeling (2, 3) can leverage the PDB to provide accurate structural predictions for a large fraction of protein sequences. Thus, structural data now scale with se-

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mentioning

confidence: 99%

Unexpected features of the dark proteome

Perdigão

Heinrich

Stolte

et al. 2015

Proc. Natl. Acad. Sci. U.S.A.

170

211

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“…Homology modeling has been used in various stages of drug discovery including the study of protein function and mechanisms [74], analysis of the effects of mutations in binding sites of receptor proteins [75], identification of druggable pockets [76], and various virtual screening studies [77–80]. …”

Section: Role Of Structural Bioinformatics – Snp Analysis In Drug Dismentioning

confidence: 99%

Role of Structural Bioinformatics in Drug Discovery by Computational SNP Analysis

Brown¹,

Bishop

2017

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With the completion of the human genome project at the beginning of the 21st century, the biological sciences entered an unprecedented age of data generation, and made its first steps towards an era of personalized medicine. This abundance of sequence data has led to the proliferation of numerous sequence-based techniques for associating variation with disease, such as Genome-Wide Association Studies (GWAS) and Candidate Gene Association Studies (CGAS). However, these statistical methods do not provide an understanding of the functional effects of variation. Structure-based drug discovery and design is increasingly incorporating structural bioinformatics techniques to model and analyze protein targets, perform large scale virtual screening to identify hit to lead compounds, and simulate molecular interactions. These techniques are fast, cost-effective, and complement existing experimental techniques such as High Throughput Sequencing (HTS). In this paper, we will discuss the contributions of structural bioinformatics to drug discovery, focusing particularly on the analysis of non-synonymous Single Nucleotide Polymorphisms (SNPs). We conclude by suggesting a protocol for future analyses of the structural effects of non-synonymous SNPs on proteins and protein complexes.

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“…Greater confidence in function prediction comes from multiple lines of evidence as demonstrated in Figure 4, which illustrates the use of genomic context and remote homology detection to predict novel CAZymes. Data integration continues to increase in part because of the increased coverage of protein structure space, which enables a greater fraction of predicted protein sequences to be homology modelled and analyzed using structural bioinformatics [72].…”

Section: Recent Applications Of Integrative Approaches To Function Prmentioning

confidence: 99%