Abstract
Identification of protein domains is a key step for understanding protein function. Hidden Markov Models (HMMs) have proved to be a powerful tool for this task. The Pfam database notably provides a large collection of HMMs which are widely used for the annotation of proteins in sequenced organisms. This is done via sequence/HMM comparisons. However, this approach may lack sensitivity when searching for domains in divergent species. Recently, methods for HMM/HMM comparisons have been proposed and proved to be more sensitive than sequence/HMM approaches in certain cases. However, these approaches are usually not used for protein domain discovery at a genome scale, and the benefit that could be expected from their utilization for this problem has not been investigated. Using proteins of P. falciparum and L. major as examples, we investigate the extent to which HMM/HMM comparisons can identify new domain occurrences not already identified by sequence/HMM approaches. We show that although HMM/HMM comparisons are much more sensitive than sequence/HMM comparisons, they are not sufficiently accurate to be used as a standalone complement of sequence/HMM approaches at the genome scale. Hence, we propose to use domain co-occurrence — the general domain tendency to preferentially appear along with some favorite domains in the proteins — to improve the accuracy of the approach. We show that the combination of HMM/HMM comparisons and co-occurrence domain detection boosts protein annotations. At an estimated False Discovery Rate of 5%, it revealed 901 and 1098 new domains in Plasmodium and Leishmania proteins, respectively. Manual inspection of part of these predictions shows that it contains several domain families that were missing in the two organisms. All new domain occurrences have been integrated in the EuPathDomains database, along with the GO annotations that can be deduced.
Highlights
With the continuous improvement of genome sequencing technologies, an increasing number of new genomes are emerging everyday, enhancing basic knowledge on the diversity of organisms and providing valuable data to understand their biology and evolutionary relationships
The aim of this work is to boost Pfam domain predictions using profile/profile comparison in order to enrich our knowledge on the protein domain catalogue of the two major pathogens L. major and P. falciparum
All Pfam domains that can be identified by HMMER with the recommended score thresholds are considered as known in the following, and our aim is to identify new domain occurrences
Summary
With the continuous improvement of genome sequencing technologies, an increasing number of new genomes are emerging everyday, enhancing basic knowledge on the diversity of organisms and providing valuable data to understand their biology and evolutionary relationships. Since functional annotation tools have been developed based on this wealth of unbalanced data, they show limits when applied to the exploration of divergent genomes [1,2]. Two thirds of mono-domain proteins having the same domain have the same function. 35% of multi-domain proteins having one common domain present similar functions, while this rate increases to 80% when they share two common domains [4]. Protein domains provide meaningful information for comparative genomics [5,6] as well as for studying protein-protein interactions [7]
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
