Bacterial Protein Interaction Networks: Connectivity is Ruled by Gene Conservation, Essentiality and Function

Dilucca, Maddalena; Cimini, Giulio; Giansanti, Andrea

doi:10.2174/1389202922666210219110831

Cited by 2 publications

(3 citation statements)

References 49 publications

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“…The connectivity of proteins within a PPI network is another factor that can inform protein function as highly connected proteins are believed to perform essential biological functions and form a tightly connected core. In bacteria, these essential functions include processes related to translation, metabolism, transcription and replication [80].…”

Section: Guilt-by-associationmentioning

confidence: 99%

What the protein!? Computational methods for predicting microbial protein functions

Grigson¹,

Edwards²

2023

Preprint

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The identification of protein functions is crucial for understanding microbial life at a molecular scale. While computational methods for annotating protein sequences have greatly advanced in recent years, 30% of all bacterial and 65% of all viral protein sequences cannot be attributed a known biological function. As a result, protein function inference remains a fundamental challenge in computational biology. This paper reviews various bioinformatics methods for annotating microbial and viral proteins, categorised into homology-based and homology-free approaches. Widely used homology-based methods encompass sequence similarity searches such as BLAST and profile hidden Markov models, both of which compare novel protein sequences to databases of protein sequences with known functions. These homology-based methods have limitations, particularly for viral sequences which are severely underrepresented in protein sequence databases. As a result, homology-free methods, including numerical feature extraction, language-based models, guilt-by-association, and protein structure prediction software, offer potential alternatives. In addition, it is also important to critically consider the functional labels used to describe protein functions, and the hierarchical organisation of functional labels, regardless of the annotation method implemented. This review highlights that a combination of multiple functional prediction strategies, including machine learning, may provide the best improvements for microbial protein annotation and alleviate the ever-expanding sequence-function gap affecting microbial proteins. Overall, we provide experimental biologists with a comprehensive overview of annotation methods and inform computational scientists of open challenges and future research avenues.

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Section: Guilt-by-associationmentioning

confidence: 99%

What the protein!? Computational methods for predicting microbial protein functions

Grigson¹,

Edwards²

2023

Preprint

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“…In the meantime, intensive work continues to attempt to use the capabilities of modern computer tools for the analysis of available interactomes, especially bacterial ones. As illustrative examples, one can cite the studies of Wuchty et al [ 82 ] and Dilucca et al [ 83 ].…”

Section: Complete Interactomes—an (Unreachable?) Dream Of Systems Bio...mentioning

confidence: 99%

“…In the second study [ 83 ], the authors related the evolutionary conservation, essentiality, and functional repertoire of a gene to the connectivity k (the number of interprotein links) in the PPI network of 42 bacteria with genomes of different sizes, and reasonably separated evolutionary trajectories. In particular, they demonstrated that highly connected proteins (with connectivity a k ≥ 40) were encoded by genes that were conserved and essential among the species considered [ 84 ].…”

Section: Complete Interactomes—an (Unreachable?) Dream Of Systems Bio...mentioning

confidence: 99%

Data Incompleteness May form a Hard-to-Overcome Barrier to Decoding Life’s Mechanism

Kondratyeva

Alekseenko

Чернов

2022

Biology

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In this brief review, we attempt to demonstrate that the incompleteness of data, as well as the intrinsic heterogeneity of biological systems, may form very strong and possibly insurmountable barriers for researchers trying to decipher the mechanisms of the functioning of live systems. We illustrate this challenge using the two most studied organisms: E. coli, with 34.6% genes lacking experimental evidence of function, and C. elegans, with identified proteins for approximately 50% of its genes. Another striking example is an artificial unicellular entity named JCVI-syn3.0, with a minimal set of genes. A total of 31.5% of the genes of JCVI-syn3.0 cannot be ascribed a specific biological function. The human interactome mapping project identified only 5–10% of all protein interactions in humans. In addition, most of the available data are static snapshots, and it is barely possible to generate realistic models of the dynamic processes within cells. Moreover, the existing interactomes reflect the de facto interaction but not its functional result, which is an unpredictable emerging property. Perhaps the completeness of molecular data on any living organism is beyond our reach and represents an unsolvable problem in biology.

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Bacterial Protein Interaction Networks: Connectivity is Ruled by Gene Conservation, Essentiality and Function

Cited by 2 publications

References 49 publications

What the protein!? Computational methods for predicting microbial protein functions

What the protein!? Computational methods for predicting microbial protein functions

Data Incompleteness May form a Hard-to-Overcome Barrier to Decoding Life’s Mechanism

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