Fe(2)OG: an integrated HMM profile-based web server to predict and analyze putative non-haem iron(II)- and 2-oxoglutarate-dependent dioxygenase function in protein sequences

Modeling ligand-macromolecular interactions as eigenvalue-based transition-state dissociation constants may offer insights into biochemical function of the resulting complexes

A ligand when bound to a macromolecule (protein, DNA, RNA) will influence the biochemical function of that macromolecule. This observation is empirical and attributable to the association of the ligand with the amino acids/nucleotides that comprise the macromolecule. The binding affinity is a measure of the strength-of-association of a macromolecule for its ligand and is numerically characterized by the association/dissociation constant. However, despite being widely used, a mathematically rigorous explanation by which the association/dissociation constant can influence the biochemistry and molecular biology of the resulting complex is not available. Here, the ligand-macromolecular complex is modeled as a homo- or hetero-dimer with a finite and equal number of atoms/residues per monomer. The pairwise interactions are numeric, empirically motivated and are randomly chosen from a standard uniform distribution. The transition-state dissociation constants are the strictly positive real part of all complex eigenvalues of this interaction matrix, belong to the open interval (0,1), and form a sequence whose terms are finite, monotonic, non-increasing and convergent. The theoretical results are rigorous, presented as theorems, lemmas and corollaries and are complemented by numerical studies. An inferential analysis of the clinical outcomes of amino acid substitutions of selected enzyme homodimers is also presented. These findings are extendible to higher-order complexes such as those likely to occur in vivo. The study also presents a schema by which a ligand can be annotated and partitioned into high- and low-affinity variants. The influence of the transition-state dissociation constants on the biochemistry and molecular biology of non-haem iron (Ⅱ)- and 2-oxoglutarate-dependent dioxygenases (catalysis) and major histocompatibility complex (Ⅰ) mediated export of high-affinity peptides (non-enzymatic association/dissociation) are examined as special cases.

ProTG4: A Web Server to Approximate the Sequence of a Generic Protein From an in Silico Library of Translatable G-Quadruplex (TG4)-Mapped Peptides

An RNA G-quadruplex in the protein coding segment of mRNA is translatable (TG4) and may potentially impact protein translation. This can be consequent to staggered ribosomal synthesis and/or result in an increased frequency of missense translational events. A mathematical model of the peptides that encompass the substituted amino acids, ie, the TG4-mapped peptidome, has been previously studied. However, the significance and relevance to disease biology of this model remains to be established. ProTG4 computes a confidence-of-sequence-identity (γ)-score, which is the average weighted length of every matched TG4-mapped peptide in a generic protein sequence. The weighted length is the product of the length of the peptide and the probability of its non-random occurrence in a library of randomly generated sequences of equivalent lengths. This is then averaged over the entire length of the protein sequence. ProTG4 is simple to operate, has clear instructions, and is accompanied by a set of ready-to-use examples. The rationale of the study, algorithms deployed, and the computational pipeline deployed are also part of the web page. Analyses by ProTG4 of taxonomically diverse protein sequences suggest that there is significant homology to TG4-mapped peptides. These findings, especially in potentially infectious and infesting agents, offer plausible explanations into the aetiology and pathogenesis of certain proteopathies. ProTG4 can also provide a quantitative measure to identify and annotate the canonical form of a generic protein sequence from its known isoforms. The article presents several case studies and discusses the relevance of ProTG4-assisted peptide analysis in gaining insights into various mechanisms of disease biology (mistranslation, alternate splicing, amino acid substitutions).