Entropy-driven translocation of disordered proteins through the Gram-positive bacterial cell wall.

In Gram-positive bacteria, a thick cross-linked cell wall separates the membrane from the extracellular space. Some surface-exposed proteins, such as the Listeria monocytogenes actin nucleation-promoting factor ActA, remain associated with the bacterial membrane but somehow thread through tens of nanometres of cell wall to expose their amino terminus to the exterior. Here, we report that entropy enables the translocation of disordered transmembrane proteins through the Gram-positive cell wall. We build a physical model, which predicts that the entropic constraint imposed by a thin periplasm is sufficient to drive the translocation of an intrinsically disordered protein such as ActA across a porous barrier similar to a peptidoglycan cell wall. We experimentally validate our model and show that ActA translocation depends on the cell-envelope dimensions and disordered-protein length, and that translocation is reversible. We also show that disordered regions of eukaryotic proteins can translocate Gram-positive cell walls via entropy. We propose that entropic forces are sufficient to drive the translocation of specific proteins to the outer surface.

Structural disorder of plasmid-encoded proteins in Bacteria and Archaea.

BACKGROUND: In the last decade and a half it has been firmly established that a large number of proteins do not adopt a well-defined (ordered) structure under physiological conditions. Such intrinsically disordered proteins (IDPs) and intrinsically disordered (protein) regions (IDRs) are involved in essential cell processes through two basic mechanisms: the entropic chain mechanism which is responsible for rapid fluctuations among many alternative conformations, and molecular recognition via short recognition elements that bind to other molecules. IDPs possess a high adaptive potential and there is special interest in investigating their involvement in organism evolution. RESULTS: We analyzed 2554 Bacterial and 139 Archaeal proteomes, with a total of 8,455,194 proteins for disorder content and its implications for adaptation of organisms, using three disorder predictors and three measures. Along with other findings, we revealed that for all three predictors and all three measures (1) Bacteria exhibit significantly more disorder than Archaea; (2) plasmid-encoded proteins contain considerably more IDRs than proteins encoded on chromosomes (or whole genomes) in both prokaryote superkingdoms; (3) plasmid proteins are significantly more disordered than chromosomal proteins only in the group of proteins with no COG category assigned; (4) antitoxin proteins in comparison to other proteins, are the most disordered (almost double) in both Bacterial and Archaeal proteomes; (5) plasmidal proteins are more disordered than chromosomal proteins in Bacterial antitoxins and toxin-unclassified proteins, but have almost the same disorder content in toxin proteins. CONCLUSION: Our results suggest that while disorder content depends on genome and proteome characteristics, it is more influenced by functional engagements than by gene location (on chromosome or plasmid).

What are preferred water-aromatic interactions in proteins and crystal structures of small molecules?

The distribution of water molecules around aromatic rings in the protein structures and crystal structures of small molecules shows quite a small number of the strongest OH-π interactions, a larger number of parallel interactions, and the largest number of the weakest CH-O interactions.

What are the preferred horizontal displacements of aromatic-aromatic interactions in proteins? Comparison with the calculated benzene-benzene potential energy surface.

The data from protein structures from the Protein Data Bank and quantum chemical calculations indicate the importance of aromatic-aromatic interactions at large horizontal displacements (offsets). The protein stacking interactions of the phenylalanine residue show preference for large offsets (3.5-5.0 Å), while the calculations show substantially strong interactions, of about -2.0 kcal mol(-1).

Bioinformatics analysis of disordered proteins in prokaryotes.

BACKGROUND: A significant number of proteins have been shown to be intrinsically disordered, meaning that they lack a fixed 3 D structure or contain regions that do not posses a well defined 3 D structure. It has also been proven that a protein's disorder content is related to its function. We have performed an exhaustive analysis and comparison of the disorder content of proteins from prokaryotic organisms (i.e., superkingdoms Archaea and Bacteria) with respect to functional categories they belong to, i.e., Clusters of Orthologous Groups of proteins (COGs) and groups of COGs-Cellular processes (Cp), Information storage and processing (Isp), Metabolism (Me) and Poorly characterized (Pc). We also analyzed the disorder content of proteins with respect to various genomic, metabolic and ecological characteristics of the organism they belong to. We used correlations and association rule mining in order to identify the most confident associations between specific modalities of the characteristics considered and disorder content. RESULTS: Bacteria are shown to have a somewhat higher level of protein disorder than archaea, except for proteins in the Me functional group. It is demonstrated that the Isp and Cp functional groups in particular (L-repair function and N-cell motility and secretion COGs of proteins in specific) possess the highest disorder content, while Me proteins, in general, posses the lowest. Disorder fractions have been confirmed to have the lowest level for the so-called order-promoting amino acids and the highest level for the so-called disorder promoters. For each pair of organism characteristics, specific modalities are identified with the maximum disorder proteins in the corresponding organisms, e.g., high genome size-high GC content organisms, facultative anaerobic-low GC content organisms, aerobic-high genome size organisms, etc. Maximum disorder in archaea is observed for high GC content-low genome size organisms, high GC content-facultative anaerobic or aquatic or mesophilic organisms, etc. Maximum disorder in bacteria is observed for high GC content-high genome size organisms, high genome size-aerobic organisms, etc. Some of the most reliable association rules mined establish relationships between high GC content and high protein disorder, medium GC content and both medium and low protein disorder, anaerobic organisms and medium protein disorder, Gammaproteobacteria and low protein disorder, etc. A web site Prokaryote Disorder Database has been designed and implemented at the address http://bioinfo.matf.bg.ac.rs/disorder, which contains complete results of the analysis of protein disorder performed for 296 prokaryotic completely sequenced genomes. CONCLUSIONS: Exhaustive disorder analysis has been performed by functional classes of proteins, for a larger dataset of prokaryotic organisms than previously done. Results obtained are well correlated to those previously published, with some extension in the range of disorder level and clear distinction between functional classes of proteins. Wide correlation and association analysis between protein disorder and genomic and ecological characteristics has been performed for the first time. The results obtained give insight into multi-relationships among the characteristics and protein disorder. Such analysis provides for better understanding of the evolutionary process and may be useful for taxon determination. The main drawback of the approach is the fact that the disorder considered has been predicted and not experimentally established.

A reexamination of correlations of amino acids with particular secondary structures.

Using the data from Protein Data Bank the correlations of primary and secondary structures of proteins were analyzed. The correlation values of the amino acids and the eight secondary structure types were calculated, where the position of the amino acid and the position in sequence with the particular secondary structure differ at most 25. The diagrams describing these results indicate that correlations are significant at distances between -9 and 10. The results show that the substituents on Cbeta or Cgamma atoms of amino acid play major role in their preference for particular secondary structure at the same position in the sequence, while the polarity of amino acid has significant influence on alpha-helices and strands at some distance in the sequence. The diagrams corresponding to polar amino acids are noticeably asymmetric. The diagrams point out the exchangeability of residues in the proteins; the amino acids with similar diagrams have similar local folding requirements.

A reexamination of the propensities of amino acids towards a particular secondary structure: classification of amino acids based on their chemical structure.

The correlation between the primary and secondary structures of proteins was analysed using a large data set from the Protein Data Bank. Clear preferences of amino acids towards certain secondary structures classify amino acids into four groups: alpha-helix preferrers, strand preferrers, turn and bend preferrers, and His and Cys (the latter two amino acids show no clear preference for any secondary structure). Amino acids in the same group have similar structural characteristics at their Cbeta and Cgamma atoms that predicts their preference for a particular secondary structure. All alpha-helix preferrers have neither polar heteroatoms on Cbeta and Cgamma atoms, nor branching or aromatic group on the Cbeta atom. All strand preferrers have aromatic groups or branching groups on the Cbeta atom. All turn and bend preferrers have a polar heteroatom on the Cbeta or Cgamma atoms or do not have a Cbeta atom at all. These new rules could be helpful in making predictions about non-natural amino acids.