ProtDCal‐Suite: A web server for the numerical codification and functional analysis of proteins

Abstract

Computational tools for the analysis of protein data and the prediction of biological properties are essential in life sciences and biomedical research. Here, we introduce ProtDCal‐Suite, a web server comprising a set of machine learning‐based methods for studying proteins. The main module of ProtDCal‐Suite is the ProtDCal software. ProtDCal translates the structural information of proteins into numerical descriptors that serve as input to machine‐learning techniques. The ProtDCal‐Suite server also incorporates a post‐processing optional stage that allows ranking and filtering the obtained descriptors by computing their Shannon entropy values across the input set of proteins. ProtDCal's codification was used in the development of models for the prediction of specific protein properties. Thus, the other modules of ProtDCal‐Suite are protein analysis tools implemented using ProtDCal's descriptors. Among them are PPI‐Detect, for predicting the interaction likelihood of protein–protein and protein–peptide pairs, Enzyme Identifier, for identifying enzymes from amino acid sequences or 3D structures, and Pred‐NGlyco, for predicting N‐glycosylation sites. ProtDCal‐Suite is freely accessible at https://protdcal.zmb.uni‐due.de.

Automatic construction of molecular similarity networks for visual graph mining in chemical space of bioactive peptides: an unsupervised learning approach

“…Unlike the legacy descriptors, new descriptors (those in Table SI1-2) are implemented by www.nature.com/scientificreports/ applying statistical and aggregation operators on amino acid property vectors, e.g., measures of central tendency, statistical dispersion, OWA operators 54,55 , and fuzzy Choquet integral operators 56,57 . Further, it should be noted that the reasons for using these operators in the calculation of MDs have been demonstrated elsewhere [58][59][60][61][62] . Let D = [x ij ] n×m be a descriptor matrix whose rows and columns represent peptide instances and calculated features, respectively, i.e., x ij encodes the numerical value for the jth descriptor of the ith peptide sequence.…”

Respiratory ß-2-Microglobulin exerts pH dependent antimicrobial activity

“…To localize the region in B2M responsible for its antibacterial activity, the amino acid sequence of B2M was subjected to a massive virtual screening of all possible fragments with a sequence length between 10 and 30 residues (2078 peptides in total). Our in-house machine-learning-based predictor, ABP-Finder ( https://protdcal.zmb.uni-due.de/ABP-Finder/index.php ) [ 28 ], was used to first identify putative antibacterial peptides (ABP), and to predict whether the bacterial targets for each of these ABP belong to the classes Gram-positive, Gram-negative, or to both types of the Gram staining assay. ABP-Finder was used to score the 2078 peptides derived from B2M.…”

Protein–Protein Interactions Efficiently Modeled by Residue Cluster Classes

“…For every one of the proteins in these two sets, the corresponding three-dimensional structure was obtained from the Protein Data Bank [41]. The RCCs for each protein were calculated as previously described [21], but we varied the distance criterion from 4 to 15 Å (4,5,6,7,8,9,10,11,12,13,14, and 15 Å) and either included or did not include the atoms of the sidechains. Then, the resulting RCCs for every pair of proteins (positive and negative sets of PPI) were added or concatenated to produce a single numeric representation for every protein pair, with 26 or 52 features (RCC1, RCC2, RCC3, .…”