Structure of the T. brucei kinetoplastid RNA editing substrate-binding complex core component, RESC5

Salinas, Raul; Cannistraci, Emily; Schumacher, Maria A.

doi:10.1371/journal.pone.0282155

Cited by 3 publications

(2 citation statements)

References 59 publications

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“…For RESC5 (UniProt ID: Q389F5), the prediction includes PF19420, identified as ‘ N , N -dimethylarginine dimethylhydrolase (DDAH) within eukaryotes’, a domain related to arginine metabolism. This prediction was bolstered by a recent study that succeeded in crystallizing the structure of RESC5, revealing its structural similarity to the DDAH fold ( 29 ). However, this same study also uncovered key differences.…”

Section: Resultsmentioning

confidence: 89%

“…The addition of the DDAH substrate and product to RESC5 had no discernible effect on the assay's results. This lack of effect serves as an indicator that there is no interaction between RESC5 and the DDAH substrate or product ( 29 ). The findings from this detailed analysis demonstrate that despite superficial similarities, RESC5 likely does not function in the same manner as DDAH.…”

Section: Resultsmentioning

confidence: 99%

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Functional domain annotation by structural similarity

Borujeni,

Salavati

2024

NAR Genomics and Bioinformatics

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Traditional automated in silico functional annotation uses tools like Pfam that rely on sequence similarities for domain annotation. However, structural conservation often exceeds sequence conservation, suggesting an untapped potential for improved annotation through structural similarity. This approach was previously overlooked before the AlphaFold2 introduction due to the need for more high-quality protein structures. Leveraging structural information especially holds significant promise to enhance accurate annotation in diverse proteins across phylogenetic distances. In our study, we evaluated the feasibility of annotating Pfam domains based on structural similarity. To this end, we created a database from segmented full-length protein structures at their domain boundaries, representing the structure of Pfam seeds. We used Trypanosoma brucei, a phylogenetically distant protozoan parasite as our model organism. Its structome was aligned with our database using Foldseek, the ultra-fast structural alignment tool, and the top non-overlapping hits were annotated as domains. Our method identified over 400 new domains in the T. brucei proteome, surpassing the benchmark set by sequence-based tools, Pfam and Pfam-N, with some predictions validated manually. We have also addressed limitations and suggested avenues for further enhancing structure-based domain annotation.

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Section: Resultsmentioning

confidence: 89%

Section: Resultsmentioning

confidence: 99%

Functional domain annotation by structural similarity

Borujeni,

Salavati

2024

NAR Genomics and Bioinformatics

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Functional domain annotation by structural similarity

Borujeni

Salavati

2023

Preprint

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The most widely used tools for electronic functional annotation rely on domain annotation. Pfam, one of the most used domain annotation tools, has a list of well-known protein domain sequences called seed sequences from which a domain's sequence signature is drawn and is used for annotating new domains in other proteins. From a biological point of view, structures are more conserved than sequences. Therefore, we expect to annotate more domains in phylogenetically remote proteins if we consider the structural similarity. However, it was not possible on a large scale until recently, as the structure of a limited number of proteins had been resolved experimentally, and computational approaches were not sufficiently accurate. Besides, there were no efficient tools for finding structurally similar proteins quickly. In this study, we took advantage of two state-of-the-art programs, Alphafold2, the pioneer program for predicting protein structures with an accuracy comparable to experiments, and Foldseek, the ultra-fast program for finding structurally similar proteins, to annotate new Pfam domains in the proteome of Trypanosoma brucei based on structural similarity to the Pfam Seed structures. We did this by training a model for estimating the confidence score based on alignment characteristics and greedily selecting the minimally overlapping most confident Pfam predictions. As a result, we predicted over 1100 new Pfam domains in T. brucei, which increases the number of annotated Pfam domains in T. brucei up to 20%. The scripts used in this work are freely available at https://github.com/Pooryamb/FDASS.

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Prioritization of Trypanosoma brucei editosome protein interactions interfaces at residue resolution through proteome-scale network analysis

Poorinmohammad,

Salavati

2024

BMC Mol and Cell Biol

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Background Trypanosoma brucei is the causative agent for trypanosomiasis in humans and livestock, which presents a growing challenge due to drug resistance. While identifying novel drug targets is vital, the process is delayed due to a lack of functional information on many of the pathogen’s proteins. Accordingly, this paper presents a computational framework for prioritizing drug targets within the editosome, a vital molecular machinery responsible for mitochondrial RNA processing in T. brucei. Importantly, this framework may eliminate the need for prior gene or protein characterization, potentially accelerating drug discovery efforts. Results By integrating protein-protein interaction (PPI) network analysis, PPI structural modeling, and residue interaction network (RIN) analysis, we quantitatively ranked and identified top hub editosome proteins, their key interaction interfaces, and hotspot residues. Our findings were cross-validated and further prioritized by incorporating them into gene set analysis and differential expression analysis of existing quantitative proteomics data across various life stages of T. brucei. In doing so, we highlighted PPIs such as KREL2-KREPA1, RESC2-RESC1, RESC12A-RESC13, and RESC10-RESC6 as top candidates for further investigation. This includes examining their interfaces and hotspot residues, which could guide drug candidate selection and functional studies. Conclusion RNA editing offers promise for target-based drug discovery, particularly with proteins and interfaces that play central roles in the pathogen’s life cycle. This study introduces an integrative drug target identification workflow combining information from the PPI network, PPI 3D structure, and reside-level information of their interface which can be applicable to diverse pathogens. In the case of T. brucei, via this pipeline, the present study suggested potential drug targets with residue-resolution from RNA editing machinery. However, experimental validation is needed to fully realize its potential in advancing urgently needed antiparasitic drug development.

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Structure of the T. brucei kinetoplastid RNA editing substrate-binding complex core component, RESC5

Cited by 3 publications

References 59 publications

Functional domain annotation by structural similarity

Functional domain annotation by structural similarity

Functional domain annotation by structural similarity

Prioritization of Trypanosoma brucei editosome protein interactions interfaces at residue resolution through proteome-scale network analysis

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