COMBREX-DB: an experiment centered database of protein function: knowledge, predictions and knowledge gaps

Chang, Yi-Chien; Hu, Zhenjun; Rachlin, John; Anton, Brian P.; Kasif, Simon; Roberts, Richard J.; Steffen, Martín

doi:10.1093/nar/gkv1324

Cited by 57 publications

(54 citation statements)

References 37 publications

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“…However, it is estimated that approximately one-third of all bacterial proteins have such low sequence similarity to do this accurately, meaning many databases annotate protein sequence with an incorrect function. 46,47 This is often the case with homologous proteins that have diverse substrate specificities, 48 such as the Gfo/Idh/MocA family. To investigate the function of the oxidoreductase YjhC and its predicted role in sialic acid catabolism, two approaches were taken.…”

Section: Yjhc Is Involved In Amino Sugar Catabolismmentioning

confidence: 99%

On the structure and function of Escherichia coli YjhC: An oxidoreductase involved in bacterial sialic acid metabolism

et al. 2019

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Human pathogenic and commensal bacteria have evolved the ability to scavenge host‐derived sialic acids and subsequently degrade them as a source of nutrition. Expression of the Escherichia coli yjhBC operon is controlled by the repressor protein nanR, which regulates the core machinery responsible for the import and catabolic processing of sialic acid. The role of the yjhBC encoded proteins is not known—here, we demonstrate that the enzyme YjhC is an oxidoreductase/dehydrogenase involved in bacterial sialic acid degradation. First, we demonstrate in vivo using knockout experiments that YjhC is broadly involved in carbohydrate metabolism, including that of N‐acetyl‐d‐glucosamine, N‐acetyl‐d‐galactosamine and N‐acetylneuraminic acid. Differential scanning fluorimetry demonstrates that YjhC binds N‐acetylneuraminic acid and its lactone variant, along with NAD(H), which is consistent with its role as an oxidoreductase. Next, we solved the crystal structure of YjhC in complex with the NAD(H) cofactor to 1.35 Å resolution. The protein fold belongs to the Gfo/Idh/MocA protein family. The dimeric assembly observed in the crystal form is confirmed through solution studies. Ensemble refinement reveals a flexible loop region that may play a key role during catalysis, providing essential contacts to stabilize the substrate—a unique feature to YjhC among closely related structures. Guided by the structure, in silico docking experiments support the binding of sialic acid and several common derivatives in the binding pocket, which has an overall positive charge distribution. Taken together, our results verify the role of YjhC as a bona fide oxidoreductase/dehydrogenase and provide the first evidence to support its involvement in sialic acid metabolism.

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Section: Yjhc Is Involved In Amino Sugar Catabolismmentioning

confidence: 99%

On the structure and function of Escherichia coli YjhC: An oxidoreductase involved in bacterial sialic acid metabolism

et al. 2019

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“…This precludes annotation by similarity and we still must annotate newly discovered genes from scratch (Iliopoulos et al, 2001). As a consequence, experimentally based evidence remains a critical issue (Chang et al, 2016).…”

Section: The Annotation Processmentioning

confidence: 99%

“…Emphasis on connection with properly annotated databases of experimental observations has repeatedly been highlighted but with not always with positive outcomes. Along this line, Roberts and co-workers have initiated an effort to construct a database of experimentally validated gene annotations, COMBREX (Chang et al, 2016). Others have proposed species-specific, not protein-centric (re-)annotation efforts (Ouzounis and Karp, 2002).…”

Section: Accurate Annotation: Against Majoritymentioning

confidence: 99%

No wisdom in the crowd: genome annotation in the era of big data – current status and future prospects

Danchin

Ouzounis

Tokuyasu

et al. 2018

Microbial Biotechnology

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SummaryScience and engineering rely on the accumulation and dissemination of knowledge to make discoveries and create new designs. Discovery‐driven genome research rests on knowledge passed on via gene annotations. In response to the deluge of sequencing big data, standard annotation practice employs automated procedures that rely on majority rules. We argue this hinders progress through the generation and propagation of errors, leading investigators into blind alleys. More subtly, this inductive process discourages the discovery of novelty, which remains essential in biological research and reflects the nature of biology itself. Annotation systems, rather than being repositories of facts, should be tools that support multiple modes of inference. By combining deduction, induction and abduction, investigators can generate hypotheses when accurate knowledge is extracted from model databases. A key stance is to depart from ‘the sequence tells the structure tells the function’ fallacy, placing function first. We illustrate our approach with examples of critical or unexpected pathways, using MicroScope to demonstrate how tools can be implemented following the principles we advocate. We end with a challenge to the reader.

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“…To address this gap, several computational methods have been developed to associate molecular functions and biological processes with genes lacking experimental annotations [4][5][6][7][8][9][10][11]. These predicted associations assist in prioritizing follow-up experiments to determine the biological functions performed by gene products [12]. Network-based techniques are among the most accurate and widely-used approaches for gene function prediction [4][5][6]9].…”

Section: Introductionmentioning

confidence: 99%

Accurate and Efficient Gene Function Prediction using a Multi-Bacterial Network

Law

Kale

Murali

2019

Preprint

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The rapid rise in newly sequenced genomes requires the development of computational methods to supplement experimental functional annotations. The challenge that arises is to develop methods for gene function prediction that integrate information for multiple species while also operating on a genomewide scale. We develop a label propagation algorithm called FastSinkSource and apply it to a sequence similarity network integrated with species-specific heterogeneous data for 19 pathogenic bacterial species. By using mathematically-provable bounds on the rate of progress of FastSinkSource during power iteration, we decrease the running time by a factor of 100 or more without sacrificing prediction accuracy. To demonstrate scalability, we expand to a 73-million edge network across 200 bacterial species while maintaining accuracy and efficiency improvements. Our results point to the feasibility and promise of multi-species, genomewide gene function prediction, especially as more experimental data and annotations become available for a diverse variety of organisms. * Corresponding author: murali@cs.vt.edu 1 iterations. This property enables us to decrease the running time of FastSinkSource by a factor of 100 or more without sacrificing prediction accuracy.We demonstrate the applicability of FastSinkSource using a leave-one-species-out (LOSO) validation framework that mimics the challenge of making large-scale functional predictions for the genome of a newlysequenced species where none of its genes have any annotations. We apply this evaluation framework to a sequence similarity network (SSN) of 19 bacterial species integrated with intra-species networks from the STRING database. FastSinkSource maintains accuracy and efficiency improvements even when applied to a large-scale 200-species network with 73 million edges.

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COMBREX-DB: an experiment centered database of protein function: knowledge, predictions and knowledge gaps

Cited by 57 publications

References 37 publications

On the structure and function of Escherichia coli YjhC: An oxidoreductase involved in bacterial sialic acid metabolism

On the structure and function of Escherichia coli YjhC: An oxidoreductase involved in bacterial sialic acid metabolism

No wisdom in the crowd: genome annotation in the era of big data – current status and future prospects

Accurate and Efficient Gene Function Prediction using a Multi-Bacterial Network

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