Solution Structure of the Tandem Acyl Carrier Protein Domains from a Polyunsaturated Fatty Acid Synthase Reveals Beads-on-a-String Configuration
Stagg, Loren J.
Vassallo, David A.
Vega, Irving E.
Arold, Stefan T.
KAUST DepartmentBiological and Environmental Sciences and Engineering (BESE) Division
Computational Bioscience Research Center (CBRC)
Structural Biology and Engineering
Permanent link to this recordhttp://hdl.handle.net/10754/325312
MetadataShow full item record
AbstractThe polyunsaturated fatty acid (PUFA) synthases from deep-sea bacteria invariably contain multiple acyl carrier protein (ACP) domains in tandem. This conserved tandem arrangement has been implicated in both amplification of fatty acid production (additive effect) and in structural stabilization of the multidomain protein (synergistic effect). While the more accepted model is one in which domains act independently, recent reports suggest that ACP domains may form higher oligomers. Elucidating the three-dimensional structure of tandem arrangements may therefore give important insights into the functional relevance of these structures, and hence guide bioengineering strategies. In an effort to elucidate the three-dimensional structure of tandem repeats from deep-sea anaerobic bacteria, we have expressed and purified a fragment consisting of five tandem ACP domains from the PUFA synthase from Photobacterium profundum. Analysis of the tandem ACP fragment by analytical gel filtration chromatography showed a retention time suggestive of a multimeric protein. However, small angle X-ray scattering (SAXS) revealed that the multi-ACP fragment is an elongated monomer which does not form a globular unit. Stokes radii calculated from atomic monomeric SAXS models were comparable to those measured by analytical gel filtration chromatography, showing that in the gel filtration experiment, the molecular weight was overestimated due to the elongated protein shape. Thermal denaturation monitored by circular dichroism showed that unfolding of the tandem construct was not cooperative, and that the tandem arrangement did not stabilize the protein. Taken together, these data are consistent with an elongated beads-on-a-string arrangement of the tandem ACP domains in PUFA synthases, and speak against synergistic biocatalytic effects promoted by quaternary structuring. Thus, it is possible to envision bioengineering strategies which simply involve the artificial linking of multiple ACP domains for increasing the yield of fatty acids in bacterial cultures. 2013 Trujillo et al.
CitationTrujillo U, Vázquez-Rosa E, Oyola-Robles D, Stagg LJ, Vassallo DA, et al. (2013) Solution Structure of the Tandem Acyl Carrier Protein Domains from a Polyunsaturated Fatty Acid Synthase Reveals Beads-on-a-String Configuration. PLoS ONE 8: e57859. doi:10.1371/journal.pone.0057859.
PublisherPublic Library of Science (PLoS)
PubMed Central IDPMC3585217
- Expression of dehydratase domains from a polyunsaturated fatty acid synthase increases the production of fatty acids in Escherichia coli.
- Authors: Oyola-Robles D, Rullán-Lind C, Carballeira NM, Baerga-Ortiz A
- Issue date: 2014 Feb 5
- Artificial covalent linkage of bacterial acyl carrier proteins for fatty acid production.
- Authors: Rullán-Lind C, Ortiz-Rosario M, García-González A, Stojanoff V, Chorna NE, Pietri RB, Baerga-Ortiz A
- Issue date: 2019 Nov 5
- Tandem acyl carrier protein domains in polyunsaturated fatty acid synthases.
- Authors: Jiang H, Rajski SR, Shen B
- Issue date: 2009
- Loading of malonyl-CoA onto tandem acyl carrier protein domains of polyunsaturated fatty acid synthases.
- Authors: Santín O, Moncalián G
- Issue date: 2018 Aug 10
- Identification of novel protein domains required for the expression of an active dehydratase fragment from a polyunsaturated fatty acid synthase.
- Authors: Oyola-Robles D, Gay DC, Trujillo U, Sánchez-Parés JM, Bermúdez ML, Rivera-Díaz M, Carballeira NM, Baerga-Ortiz A
- Issue date: 2013 Jul
Showing items related by title, author, creator and subject.
CMsearch: simultaneous exploration of protein sequence space and structure space improves not only protein homology detection but also protein structure predictionCui, Xuefeng; Lu, Zhiwu; wang, sheng; Wang, Jim Jing-Yan; Gao, Xin (Bioinformatics, Oxford University Press (OUP), 2016-06-15) [Article]Motivation: Protein homology detection, a fundamental problem in computational biology, is an indispensable step toward predicting protein structures and understanding protein functions. Despite the advances in recent decades on sequence alignment, threading and alignment-free methods, protein homology detection remains a challenging open problem. Recently, network methods that try to find transitive paths in the protein structure space demonstrate the importance of incorporating network information of the structure space. Yet, current methods merge the sequence space and the structure space into a single space, and thus introduce inconsistency in combining different sources of information. Method: We present a novel network-based protein homology detection method, CMsearch, based on cross-modal learning. Instead of exploring a single network built from the mixture of sequence and structure space information, CMsearch builds two separate networks to represent the sequence space and the structure space. It then learns sequence–structure correlation by simultaneously taking sequence information, structure information, sequence space information and structure space information into consideration. Results: We tested CMsearch on two challenging tasks, protein homology detection and protein structure prediction, by querying all 8332 PDB40 proteins. Our results demonstrate that CMsearch is insensitive to the similarity metrics used to define the sequence and the structure spaces. By using HMM–HMM alignment as the sequence similarity metric, CMsearch clearly outperforms state-of-the-art homology detection methods and the CASP-winning template-based protein structure prediction methods.
Prediction of Novel Virus–Host Protein Protein Interactions From Sequences and Infectious Disease PhenotypesWang, Liu-Wei (2020-11-11) [Thesis]
Advisor: Tegner, Jesper
Committee members: Hoehndorf, Robert; Ombao, HernandoInfectious diseases from novel viruses have become a major public health concern. Rapid identification of virus–host interactions can reveal mechanistic insights into infectious diseases and shed light on potential treatments. Current computational prediction methods for novel viruses are based mainly on protein sequences. However, it is not clear to what extent other important features, such as the symptoms caused by the viruses, could contribute to a predictor. Disease phenotypes (i.e., signs and symptoms) are readily accessible from clinical diagnosis and we hypothesize that they may act as a potential proxy and an additional source of information for the underlying molecular interactions between the pathogens and hosts. We developed DeepViral, a deep learning based method that predicts protein– protein interactions (PPI) between humans and viruses. Motivated by the potential utility of infectious disease phenotypes, we first embedded human proteins and viruses in a shared space using their associated phenotypes and functions, supported by formalized background knowledge from biomedical ontologies. By jointly learning from protein sequences and phenotype features, DeepViral significantly improves over existing sequence-based methods for intra- and inter-species PPI prediction. Lastly, we propose a novel experimental setup to realistically evaluate prediction methods for novel viruses.
QAUST: protein function prediction using structure similarity search, protein interaction and functional sequence motifsSmaili, Fatima Z.; Tian, Shuye; Roy, Ambrish; Alazmi, Meshari; Arold, Stefan T.; Mukherjee, Srayanta; Hefty, P. Scott; Chen, Wei; Gao, Xin (Accepted by Genomics, Proteomics, and Bioinformatics, Elsevier BV, 2020) [Article]The number of available protein sequences in public databases is increasing exponentially. However, a significant percentage of these sequences lack functional annotation, which is essential for the understanding of how biological systems operate. We propose a novel method, QAUST, to infer protein functions, specifically Gene Ontology (GO) terms and Enzyme Commission (EC) numbers. Our method uses three sources of information: structure information encoded by global and local structure similarity search, biological network information inferred by protein-protein interaction data, and sequence information extracted from functionally discriminative sequence motifs. The three pieces of information are combined by consensus averaging to make the final prediction. Our approach has been tested on 500 protein targets from the CAFA benchmark set. The results show that our method provides accurate functional annotation and outperforms other prediction methods based on sequence similarity search or threading. We further demonstrate that a previously unknown function of TRIM22 protein predicted by QAUST can be experimentally validated. Availability: http://www.cbrc.kaust.edu.sa/qaust/submit/.