@article {44229,
	title = {Application of genetic programming (GP) formalism for building disease predictive models from protein-protein interactions (PPI) data},
	journal = {IEEE-ACM Transactions on Computational Biology and Bioinformatics},
	volume = {15},
	year = {2018},
	month = {FEB},
	pages = {27-37},
	type = {Article},
	abstract = {<p>Protein-protein interactions (PPIs) play a vital role in the biological processes involved in the cell functions and disease pathways. The experimental methods known to predict PPIs require tremendous efforts and the results are often hindered by the presence of a large number of false positives. Herein, we demonstrate the use of a new Genetic Programming (GP) based Symbolic Regression (SR) approach for predicting PPIs related to a disease. In this case study, a dataset consisting of 135 PPI complexes related to cancer was used to construct a generic PPI predicting model with good PPI prediction accuracy and generalization ability. A high correlation coefficient (CC) magnitude of 0.893, and low root mean square error (RMSE), and mean absolute percentage error (MAPE) values of 478.221 and 0.239, respectively, were achieved for both the training and test set outputs. To validate the discriminatory nature of the model, it was applied on a dataset of diabetes complexes where it yielded significantly low CC values. Thus, the GP model developed here serves a dual purpose: (a) a predictor of the binding energy of cancer related PPI complexes, and (b) a classifier for discriminating PPI complexes related to cancer from those of other diseases.</p>},
	keywords = {Binding energy, cancer, Disease, genetic programming, machine learning, protein-protein interactions, symbolic regression},
	issn = {1545-5963},
	doi = {10.1109/TCBB.2016.2621042},
	author = {Vyas, Renu and Bapat, Sanket and Goel, Purva and Karthikeyan, Muthukumarasamy and Tambe, Sanjeev S. and Kulkarni, Bhaskar D.}
}
@article {48363,
	title = {Graphical Biology Insights into the dynamic interactions at chemokine-receptor interfaces and mechanistic models of chemokine binding},
	journal = {Journal of Structural Biology},
	volume = {214},
	year = {2022},
	month = {SEP},
	pages = {107877},
	type = {Article},
	abstract = {<p>
	Chemokine receptors are the central signaling hubs of several processes such as cell migration, chemotaxis and cell positioning. In this graphical review, we provide an overview of the structural and mechanistic principles governing chemokine recognition that are currently emerging. Structural models of chemokine-receptor cocomplexes with endogenous chemokines, viral chemokines and therapeutics have been resolved that highlight multiple interaction sites, termed as CRS1, CRS1.5 etc. The first site of interaction has been shown to be the Nterminal domain of the receptors (CRS1 site). A large structural flexibility of the N-terminal domain has been reported that was supported by both experimental and simulation studies. Upon chemokine binding, the Nterminal domain appears to show constricted dynamics and opens up to interact with the chemokine via a large interface. The subsequent sites such as CRS1.5 and CRS2 sites have been structurally well resolved although differences arise such as the localization of the N-terminus of the ligand to a major or minor pocket of the orthosteric binding site. Several computational studies have highlighted the dynamic protein-protein interface at the CRS1 site that seemingly appears to resolve the differences in NMR and mutagenesis studies. Interestingly, the differential dynamics at the CRS1 site suggests a mixed model of binding with complex signatures of both conformational selection and induced fit models. Integrative experimental and computational approaches could help unravel the structural basis of promiscuity and specificity in chemokine-receptor binding and open up new avenues of therapeutic design.</p>
},
	keywords = {Chemokine receptors, GPCRs, Intrinsically disordered protein domains, protein-protein interactions},
	issn = {1047-8477},
	doi = {10.1016/j.jsb.2022.107877},
	author = {Sonawani, Archana and Kharche, Shalmali and Dasgupta, Debjani and Sengupta, Durba}
}
