Diamond STING is a new version of the STING suite of programs for a comprehensive analysis of a relationship between protein sequence, structure, function and stability. We have added a number of new functionalities by both providing more structure parameters to the STING Database and by improving/expanding the interface for enhanced data handling. The integration among the STING components has also been improved. A new key feature is the ability of the STING server to handle local files containing protein structures (either modeled or not yet deposited to the Protein Data Bank) so that they can be used by the principal STING components: JavaProtein Dossier (JPD) and STING Report. The current capabilities of the new STING version and a couple of biologically relevant applications are described here. We have provided an example where Diamond STING identifies the active site amino acids and folding essential amino acids (both previously determined by experiments) by filtering out all but those residues by selecting the numerical values/ranges for a set of corresponding parameters. This is the fundamental step toward a more interesting endeavor—the prediction of such residues. Diamond STING is freely accessible at and .

No abstract

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 a...

Around 5.5 million people suffer from snakebites per year, with about 400,000 cases with some type of sequelae, such as amputation, and 20,000 to 125,000 cases with the fatal end. Usually, the victim outcome depends on correct, agile and many times in situ intervention based on the proper identification of the snake venom type and its potential effects, among other factors. Therefore, knowledge on the snake venom composition and a research on inhibitors of snake venom target components might ameliorate envenoming dangerous outcome. Herein, two thrombin-like serine proteases from the Crotalus simus snake venom - SVSP1 and SVSP2 - were isolated in two chromatographic steps, using gel filtration and then RP-HPLC. They showed molecular masses of around 31.3 and 24.6 kDa, respectively, and mostly β-sheet secondary structure features. The SVSP1 and SVSP2 were sequenced using tandem mass spectrometry (Q-TOF). Using the known serine protease structure (PDB entry: 4e7n), which was evaluated as homologous to the two target proteins, in silico docking results showed that hesperetin is its excellent inhibitor. Using in vitro tests with the commercial hesperetin, kinetic parameters were obtained for SVSPs against the synthetic substrate BApNA. Obtained results pointed that hesperetin might act as an uncompetitive (SVSP1) or mixed (SVSP2) inhibitor. Also, the fluorescence quenching upon inhibition was observed, as well as, red shift in maximums of around 20 nm, which indicate that the tryptophan residues in the target enzymes suffered conformational changes caused by hesperetin binding. Thus, a naturally occurring flavone that can easily be extracted from oranges might serve as low-cost inhibitor of the investigated snake venom proteases.