image: The Focus Feature collection on 'Protein Function'" compiles related studies to encourage scientific discourse. view more
Credit: Lee et al.
A new "Focus Feature" collection on protein functions published by PLOS Computational Biology highlights new computational methods for exploring the molecular function of proteins. Launched in 2015, Focus Features aim to spur discussion on specific topics of interest to the computational biology community.
Much research into molecular protein function seeks to uncover how sequences of amino acids, the building blocks of proteins, determine the features and functions of "functional sites." These sites are the parts of proteins that are directly involved in activities such as catalyzing chemical reactions or binding to regulatory molecules within a cell.
Genome-sequencing initiatives continue to add to the overall number of known proteins at an exponential rate, and computational analysis helps researchers efficiently predict and explore the features of functional sites encoded by amino acid sequences. Recent work in this field is represented by key studies included in the new Focus Feature collection:
Novel Computational Protocols for Functionally Classifying and Characterising Serine Beta-Lactamases (Lee et al.):
Bacterial proteins known as beta-lactamases underlie antibiotic resistance and therefore pose a major medical challenge. Newly developed computational strategies enable improved classification of the many different types of beta-lactamases, as well as improved identification of the functional sites involved in antibiotic resistance, which could aid efforts to combat such resistance. http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1004926
Isofunctional Protein Subfamily Detection Using Data Integration and Spectral Clustering (Boari de Lima et al.):
A novel computational approach allows researchers to divide a large family of related proteins of unknown function into subtypes based on shared functional sites. This strategy aims to simplify the challenge of determining functions for an ever-increasing amount of known proteins, circumventing the need for costly, time-consuming experiments. http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005001
An Atlas of Peroxiredoxins Created Using an Active Site Profile-Based Approach to Functionally Relevant Clustering of Proteins (Harper et al.):
Another new method known as MISST takes a different computational approach to identify related proteins and classify them according to shared functional sites. In a validation test, MISST correctly identified six subgroups within a large superfamily of proteins known as peroxiredoxins, which perform a wide variety of biological functions. http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005284
Defining the Product Chemical Space of Monoterpenoid Synthases (Tian et al.):
The three studies described above address identification of functional sites in proteins, but do not explore what the proteins actually do. This Focus Feature study presents a variety of computational strategies, such as structural modeling and virtual chemical reactions, to systematically describe all possible products of terpenoid synthases, proteins that generate medically important carbon structures known as terpenoids. http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005053
The studies in this Focus Feature could help pave pathways for a wide variety of future research, including design of new drugs that inhibit specific functional sites more effectively and a better understanding of evolution.
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This collection, and other Focus Feature collections, can be found on the PLOS Collections website: http://collections.plos.org/s/focus-features
In your coverage please use this URL to provide access to the freely available article in PLOS Computational Biology: http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005756
Citation: Fetrow JS, Babbitt PC (2018) New computational approaches to understanding molecular protein function. PLoS Comput Biol 14 (4): e1005756. https://doi.org/10.1371/journal.pcbi.1005756
Funding: The authors received no specific funding for this article.
Competing Interests: The authors have declared that no competing interests exist.
Journal
PLoS Computational Biology