Structural insights into the membrane-extracted dimeric form of the ATPase TraB from the Escherichia coli pKM101 conjugation system

Background: Type IV secretion (T4S) systems are involved in secretion of virulence factors such as toxins or transforming molecules, or bacterial conjugation. T4S systems are composed of 12 proteins named VirB1-B11 and VirD4. Among them, three ATPases are involved in the assembly of the T4S system and/or provide energy for substrate transfer, VirB4, VirB11 and VirD4. The X-ray crystal structures of VirB11 and VirD4 have already been solved but VirB4 has proven to be reluctant to any structural investigation so far. Results: Here, we have used small-angle X-ray scattering to obtain the first structural models for the membrane-extracted, dimeric form of the TraB protein, the VirB4 homolog encoded by the E. coli pKM101 plasmid, and for the monomeric soluble form of the LvhB4 protein, the VirB4 homolog of the T4S system encoded by the Legionella pneumophila lvh operon. We have obtained the low resolution structures of the full-length TraB and of its N- and C-terminal halves. From these SAXS models, we derive the internal organisation of TraB. We also show that the two TraB N- and C-terminal domains are independently involved in the dimerisation of the full-length protein. Conclusions: These models provide the first structural insights into the architecture of VirB4 proteins. In particular, our results highlight the modular arrangement and functional relevance of the dimeric-membrane-bound form of TraB

Predicting mostly disordered proteins by using structure-unknown protein data

BACKGROUND: Predicting intrinsically disordered proteins is important in structural biology because they are thought to carry out various cellular functions even though they have no stable three-dimensional structure. We know the structures of far more ordered proteins than disordered proteins. The structural distribution of proteins in nature can therefore be inferred to differ from that of proteins whose structures have been determined experimentally. We know many more protein sequences than we do protein structures, and many of the known sequences can be expected to be those of disordered proteins. Thus it would be efficient to use the information of structure-unknown proteins in order to avoid training data sparseness. We propose a novel method for predicting which proteins are mostly disordered by using spectral graph transducer and training with a huge amount of structure-unknown sequences as well as structure-known sequences. RESULTS: When the proposed method was evaluated on data that included 82 disordered proteins and 526 ordered proteins, its sensitivity was 0.723 and its specificity was 0.977. It resulted in a Matthews correlation coefficient 0.202 points higher than that obtained using FoldIndex, 0.221 points higher than that obtained using the method based on plotting hydrophobicity against the number of contacts and 0.07 points higher than that obtained using support vector machines (SVMs). To examine robustness against training data sparseness, we investigated the correlation between two results obtained when the method was trained on different datasets and tested on the same dataset. The correlation coefficient for the proposed method is 0.14 higher than that for the method using SVMs. When the proposed SGT-based method was compared with four per-residue predictors (VL3, GlobPlot, DISOPRED2 and IUPred (long)), its sensitivity was 0.834 for disordered proteins, which is 0.052–0.523 higher than that of the per-residue predictors, and its specificity was 0.991 for ordered proteins, which is 0.036–0.153 higher than that of the per-residue predictors. The proposed method was also evaluated on data that included 417 partially disordered proteins. It predicted the frequency of disordered proteins to be 1.95% for the proteins with 5%–10% disordered sequences, 1.46% for the proteins with 10%–20% disordered sequences and 16.57% for proteins with 20%–40% disordered sequences. CONCLUSION: The proposed method, which utilizes the information of structure-unknown data, predicts disordered proteins more accurately than other methods and is less affected by training data sparseness

The N-Terminal residues 43 to 60 form the interface for dopamine mediated α-synuclein dimerisation

α-synuclein (α-syn) is a major component of the intracellular inclusions called Lewy bodies, which are a key pathological feature in the brains of Parkinson's disease patients. The neurotransmitter dopamine (DA) inhibits the fibrillisation of α-syn into amyloid, and promotes α-syn aggregation into SDS-stable soluble oligomers. While this inhibition of amyloid formation requires the oxidation of both DA and the methionines in α-syn, the molecular basis for these processes is still unclear. This study sought to define the protein sequences required for the generation of oligomers. We tested N- (α-syn residues 43-140) and C-terminally (1-95) truncated α-syn, and found that similar to full-length protein both truncated species formed soluble DA: α-syn oligomers, albeit 1-95 had a different profile. Using nuclear magnetic resonance (NMR), and the N-terminally truncated α-syn 43-140 protein, we analysed the structural characteristics of the DA:α-syn 43-140 dimer and α-syn 43-140 monomer and found the dimerisation interface encompassed residues 43 to 60. Narrowing the interface to this small region will help define the mechanism by which DA mediates the formation of SDS-stable soluble DA:α-syn oligomers

Structural studies of T4S systems by electron microscopy

Abstract: Type IV secretion (T4S) systems are large dynamic nanomachines that transport DNA and/or proteins through the membranes of bacteria. Analysis of T4S system architecture is an extremely challenging task taking into account their multi protein organisation and lack of overall global symmetry. Nonetheless the last decade demonstrated an amazing progress achieved by X-ray crystallography and cryo-electron microscopy. In this review we present a structural analysis of this dynamic complex based on recent advances in biochemical, biophysical and structural studies

Determining Rg of IDPs from SAXS Data

There is a great interest within the research community to understand the structure-function relationship for intrinsically disordered proteins (IDPs); however, the heterogeneous distribution of conformations that IDPs can adopt limits the applicability of conventional structural biology methods. Here, scattering techniques, such as small-angle X-ray scattering, can contribute. In this chapter, we will describe how to make a model-free determination of the radius of gyration by using two different approaches, the Guinier analysis and the pair distance distribution function. The ATSAS package (Franke et al., J Appl Crystallogr 50:1212-1225, 2017) has been used for the evaluation, and throughout the chapter, different examples will be given to illustrate the discussed phenomena, as well as the pros and cons of using the different approaches