4 research outputs found
ProInflam: a webserver for the prediction of proinflammatory antigenicity of peptides and proteins
Additional file 2: Table S2. Dipeptide composition distribution between proinflammatory and non proinflammatory data
IL17eScan: A Tool for the Identification of Peptides Inducing IL-17 Response
IL-17 cytokines are pro-inflammatory cytokines and are crucial in host defense against various microbes. Induction of these cytokines by microbial antigens has been investigated in the case of ischemic brain injury, gingivitis, candidiasis, autoimmune myocarditis, etc. In this study, we have investigated the ability of amino acid sequence of antigens to induce IL-17 response using machine-learning approaches. A total of 338 IL-17-inducing and 984 IL-17 non-inducing peptides were retrieved from Immune Epitope Database. 80% of the data were randomly selected as training dataset and rest 20% as validation dataset. To predict the IL-17-inducing ability of peptides/protein antigens, different sequence-based machine-learning models were developed. The performance of support vector machine (SVM) and random forest (RF) was compared with different parameters to predict IL-17-inducing epitopes (IIEs). The dipeptide composition-based SVM-model displayed an accuracy of 82.4% with Matthews correlation coefficient = 0.62 at polynomial (t = 1) kernel on 10-fold cross-validation and outperformed RF. Amino acid residues Leu, Ser, Arg, Asn, and Phe and dipeptides LL, SL, LK, IL, LI, NL, LR, FK, SF, and LE are abundant in IIEs. The present tool helps in the identification of IIEs using machine-learning approaches. The induction of IL-17 plays an important role in several inflammatory diseases, and identification of such epitopes would be of great help to the immunologists. It is freely available at http://metagenomics.iiserb.ac.in/IL17eScan/ and http://metabiosys.iiserb.ac.in/IL17eScan/
Biased Signaling in Mutated Variants of β<sub>2</sub>‑Adrenergic Receptor: Insights from Molecular Dynamics Simulations
The molecular basis of receptor bias
in G protein-coupled
receptors
(GPCRs) caused by mutations that preferentially activate specific
intracellular transducers over others remains poorly understood. Two
experimentally identified biased variants of β2-adrenergic
receptors (β2AR), a prototypical GPCR, are a triple
mutant (T68F, Y132A, and Y219A) and a single mutant (Y219A); the former
bias the receptor toward the β-arrestin pathway by disfavoring
G protein engagement, while the latter induces G protein signaling
explicitly due to selection against GPCR kinases (GRKs) that phosphorylate
the receptor as a prerequisite of β-arrestin binding. Though
rigorous characterizations have revealed functional implications of
these mutations, the atomistic origin of the observed transducer selectivity
is not clear. In this study, we investigated the allosteric mechanism
of receptor bias in β2AR using microseconds of all-atom
Gaussian accelerated molecular dynamics (GaMD) simulations. Our observations
reveal distinct rearrangements in transmembrane helices, intracellular
loop 3, and critical residues R1313.50 and Y3267.53 in the conserved motifs D(E)RY and NPxxY for the mutant receptors,
leading to their specific transducer interactions. Moreover, partial
dissociation of G protein from the receptor core is observed in the
simulations of the triple mutant in contrast to the single mutant
and wild-type receptor. The reorganization of allosteric communications
from the extracellular agonist BI-167107 to the intracellular receptor–transducer
interfaces drives the conformational rearrangements responsible for
receptor bias in the single and triple mutants. The molecular insights
into receptor bias of β2AR presented here could improve
the understanding of biased signaling in GPCRs, potentially opening
new avenues for designing novel therapeutics with fewer side-effects
and superior efficacy