Allosteric regulation is essential for modulating protein function and represents a promising target for therapeutic intervention, yet the complex dynamics of the protein nanoenvironment hinder the reliable identification of allosteric sites. Traditional pocket-based predictors miss $\sim $18% of experimentally confirmed sites that lie outside surface invaginations. To overcome this limitation, we developed STINGAllo, an interactive web server that introduces a residue-centric machine-learning model. Using 54 optimized internal protein nanoenvironment descriptors, STINGAllo predicts allosteric site-forming residues at single-residue resolution. By integrating hydrophobic interaction networks, local density, graph connectivity, and a unique "sponge effect" metric, STINGAllo detects allosteric sites independently of surface geometry, including concave pockets, flat surfaces, or even cryptic regions. It achieves a success rate of $\sim $78% on benchmark datasets, substantially outperforming existing methods with a 60.2% overall success rate compared with 21.1%-24.2% for contemporary pocket-based predictors. Our analysis further reveals that nearly 52.7% of unique proteins in the Protein Data Bank [(PDB); 119 851 entries, 14 November 2024] contain at least one chain with a predicted allosteric site. STINGAllo accepts protein structures via PDB identifiers or custom uploads, provides interactive 3D visualization of predicted pockets, and supports integration into computational pipelines through a RESTful application programming interface. Overall, STINGAllo bridges advanced computational prediction with user-friendly design, offering a robust tool expected to deepen understanding of protein regulation and accelerate allosteric drug discovery. The server is freely accessible at https://www.stingallo.cbi.cnptia.embrapa.br/.

MOTIVATION: A graphical representation of physicochemical and structural descriptors attributed to amino acid residues occupying the same topological position in different, structurally aligned proteins can provide a more intuitive way to associate possible functional implications to identified variations in structural characteristics. This could be achieved by observing selected characteristics of amino acids and of their corresponding nano environments, described by the numerical value of matching descriptor. For this purpose, a web-based tool called multiple structure single parameter (MSSP) was developed and here presented. RESULTS: MSSP produces a two-dimensional plot of a single protein descriptor for a number of structurally aligned protein chains. From a total of 150 protein descriptors available in MSSP, selected of >1500 parameters stored in the STING database, it is possible to create easily readable and highly informative XY-plots, where X-axis contains the amino acid position in the multiple structural alignment, and Y-axis contains the descriptor's numerical values for each aligned structure. To illustrate one of possible MSSP contributions to the investigation of changes in physicochemical and structural properties of mutants, comparing them with the cognate wild-type structure, the oncogenic mutation of M918T in RET kinase is presented. The comparative analysis of wild-type and mutant structures shows great changes in their electrostatic potential. These variations are easily depicted at the MSSP-generated XY-plot. AVAILABILITY AND IMPLEMENTATION: The web server is freely available at http://www.cbi.cnptia.embrapa.br/SMS/STINGm/MPA/index.html Web server implemented in Perl, Java and JavaScript and JMol or Protein Viewer as structure visualizers. CONTACT: [email protected] or [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.