Organisms with SMase D-like proteins.

<p>Metazoa (A), bacteria (B) and fungi (C). The species in gray have been described as possessing SMase D activities in previous works and, in blue, are the new species with SMase D-like proteins found in this work. </p

Fluorescence intensity (proportional to the SMase D activity) using 0.5 µg of <i>Loxosceles</i><i>laeta</i> crude venom and 50 µg of <i>Aspergillus flavus</i> extract.

<p>H₂O₂ was used as a positive control, whereas the negative controls were SMase C from <i>Bacillus cereus</i> (control) and reaction buffer alone (blank).</p

Multiple sequence alignment of one SMase D sequence for each genus identified.

<p>Consensus sequences and secondary structure of <i>L. laeta</i> SMase D (pdb number:1xx1) are shown underneath the alignment. Conserved catalytic histidines residues are marked with blue boxes (Label H12 and H47 in secondary structure of 1xx1). Residues responsible for Mg⁺² coordination are marked with red boxes (Label Mg in secondary structure of 1xx1) and amino acids involved in network of hydrogen bonds are marked with black boxes (label HB in secondary structure of 1xx1). The following sequences were used to build this alignment and were obtained from the NCBI nr protein database: <i>Ajellomyces</i> (GI: 225556466), <i>Arthroderma</i> (GI: 302499957), <i>Trichophyton</i> (GI: 327309460), <i>Uncinocarpus</i> (GI: 258566630), <i>Coccidioides</i> (GI: 119182286), <i>Paracoccidioides</i> (GI: 225681750), <i>Aspergillus</i> (GI: 220691453), <i>Metaseiulus</i> (GI: 391334832), <i>Loxosceles</i> (GI: 60594084), <i>Sicarius</i> (GI: 292630576), <i>Ixodes</i> (GI: 121962650), <i>Amblyomma</i> (GI: 346467405), <i>Rhipicephalus</i> (GI: 427782159), <i>Burkholderia</i> (GI: 116687281), <i>Arcanobacterium</i> (GI: 297571658), <i>Corynebacterium</i> (GI: 300857446), Streptomyces (GI: 254384004) and <i>Austwickia</i> (GI:403190737). Six genera have not entries in NCBI nr protein database, but their protein sequences were translated from in NCBI WGS or dbEST entries:, <i>Fusarium</i> (GI: 144921672, position: 31242-32224), <i>Gibberella</i> (GI: 116139506, position: 93710-94513), <i>Passalora</i> (GI: 407486978, position: 4015-4803) , <i>Metarhizium</i> (GI: 322696462), <i>Stegodyphus</i> (GI: 374247203) and <i>Acanthoscurria</i> (GIs: 68762186 and 68761727). The genera <i>Dermatophagoides</i>, <i>Psoroptes</i>, <i>Varroa</i> and <i>Tetranycus</i> have incomplete sequences and were not used in this analysis.</p

Structural alignment of SMases D.

<p><i>L. laeta</i> SMase D (1xx1) (purple) and the models for the SMase D-like proteins from the fungus <i>A. flavus</i> (yellow) and from <i>C. pseudotuberculosis</i> (blue), indicating the overall alignment (A) and the active site residues superposed in sticks (B), the Mg⁺² ion as a green sphere and the co-crystalized SO^4- ion in sticks. </p

Study of specific nanoenvironments containing α-helices in all-α and (α+β)+(α/β) proteins

<div><p>Protein secondary structure elements (PSSEs) such as α-helices, β-strands, and turns are the primary building blocks of the tertiary protein structure. Our primary interest here is to reveal the characteristics of the nanoenvironment formed by both PSSEs and their surrounding amino acid residues (AARs), which might contribute to the general understanding of how proteins fold. The characteristics of such nanoenvironments must be specific to each secondary structure element, and we have set our goal here to gather the fullest possible description of the α-helical nanoenvironment. In general, this postulate (the existence of specific nanoenvironments for specific protein substructures/neighbourhoods/regions with distinct functionality) was already successfully explored and confirmed for some protein regions, such as protein-protein interfaces and enzyme catalytic sites. Consequently, PSSEs were the obvious next choice for additional work for further evidence showing that specific nanoenvironments (having characteristics fully describable by means of structural and physical chemical descriptors) do exist for the corresponding and determined intraprotein regions. The nanoenvironment of α-helices (nEoαH) is defined as any region of the protein where this secondary structure element type is detected. The nEoαH, therefore, includes not only the α-helix amino acid residues but also the residues immediately around the α-helix. The hypothesis that motivated this work is that it might in fact be possible to detect a postulated “signal” or “signature” that distinguishes the specific location of α-helices. This “signal” must be discernible by tracking differences in the values of physical, chemical, physicochemical, structural and geometric descriptors immediately before (or after) the PSSE from those in the region along the α-helices. The search for this specific nanoenvironment “signal” was made possible by aligning previously selected α-helices of equal length. Afterward, we calculated the average value, standard deviation and mean square error at each aligned residue position for each selected descriptor. We applied Student’s t-test, the Kolmogorov-Smirnov test and MANOVA statistical tests to the dataset constructed as described above, and the results confirmed that the hypothesized “signal”/“signature” is both existing/identifiable and capable of distinguishing the presence of an α-helix inside the specific nanoenvironment, contextualized as a specific region within the whole protein. However, such conclusion might rarely be reached if only one descriptor is considered at a time. A more accurate signal with broader coverage is achieved only if one applies multivariate analysis, which means that several descriptors (usually approximately 10 descriptors) should be considered at the same time. To a limited extent (up to a maximum of 15% of cases), such conclusion is also possible with only a single descriptor, and the conclusion is also possible in general for up to 50–80% of cases when no less than 5 nonlinear descriptors are selected and considered. Using all the descriptors considered in this work, provided all assumptions about data characteristics for this analysis are met, multivariate analysis regularly reached a coverage and accuracy above 90%. Understanding how secondary structure elements are formed and maintained within a protein structure could enable a more detailed understanding of how proteins reach their final 3D structure and consequently, their function. Likewise, this knowledge may also improve the tools used to determine how good a structure is by means of comparing the “signal” around a selected PSSE with the one obtained from the best (resolution and quality wise) protein structures available.</p></div

The KS test applied for sliding windows in all size helices using single parameter analysis.

<p>The KS test applied for sliding windows in all size helices using single parameter analysis.</p

Variation in the number of descriptors that passed both the normal distribution test and the no mutual correlation test for different helix sizes.

<p>Variation in the number of descriptors that passed both the normal distribution test and the no mutual correlation test for different helix sizes.</p

Composite graphs showing the following: Descriptor variation along the regions before, at and after the analysed PSSE; the reliability value (or % of helical structure at each loci) and the p-value for the descriptor: Number of contacts, type “HBMM”. Data are drawn from the datamart containing PSSEs of length = 12 AARs; the consensus definition of a helix element is from “PDB-DSSP-Stride”, and the redundancy is 70% similarity at the sequence level.

<p>Composite graphs showing the following: Descriptor variation along the regions before, at and after the analysed PSSE; the reliability value (or % of helical structure at each loci) and the p-value for the descriptor: Number of contacts, type “HBMM”. Data are drawn from the datamart containing PSSEs of length = 12 AARs; the consensus definition of a helix element is from “PDB-DSSP-Stride”, and the redundancy is 70% similarity at the sequence level.</p