<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Vyas, Renu</style></author><author><style face="normal" font="default" size="100%">Bapat, Sanket</style></author><author><style face="normal" font="default" size="100%">Goel, Purva</style></author><author><style face="normal" font="default" size="100%">Karthikeyan, Muthukumarasamy</style></author><author><style face="normal" font="default" size="100%">Tambe, Sanjeev S.</style></author><author><style face="normal" font="default" size="100%">Kulkarni, Bhaskar D.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Application of genetic programming (GP) formalism for building disease predictive models from protein-protein interactions (PPI) data</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE-ACM Transactions on Computational Biology and Bioinformatics</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Binding energy</style></keyword><keyword><style  face="normal" font="default" size="100%">cancer</style></keyword><keyword><style  face="normal" font="default" size="100%">Disease</style></keyword><keyword><style  face="normal" font="default" size="100%">genetic programming</style></keyword><keyword><style  face="normal" font="default" size="100%">machine learning</style></keyword><keyword><style  face="normal" font="default" size="100%">protein-protein interactions</style></keyword><keyword><style  face="normal" font="default" size="100%">symbolic regression</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2018</style></year><pub-dates><date><style  face="normal" font="default" size="100%">FEB</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">15</style></volume><pages><style face="normal" font="default" size="100%">27-37</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;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.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">1</style></issue><work-type><style face="normal" font="default" size="100%">Article</style></work-type><custom3><style face="normal" font="default" size="100%">Foreign</style></custom3><custom4><style face="normal" font="default" size="100%">1.955</style></custom4></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Sonawani, Archana</style></author><author><style face="normal" font="default" size="100%">Kharche, Shalmali</style></author><author><style face="normal" font="default" size="100%">Dasgupta, Debjani</style></author><author><style face="normal" font="default" size="100%">Sengupta, Durba</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Graphical Biology Insights into the dynamic interactions at chemokine-receptor interfaces and mechanistic models of chemokine binding</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Structural Biology</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Chemokine receptors</style></keyword><keyword><style  face="normal" font="default" size="100%">GPCRs</style></keyword><keyword><style  face="normal" font="default" size="100%">Intrinsically disordered protein domains</style></keyword><keyword><style  face="normal" font="default" size="100%">protein-protein interactions</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2022</style></year><pub-dates><date><style  face="normal" font="default" size="100%">SEP</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">214</style></volume><pages><style face="normal" font="default" size="100%">107877</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;
	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.&lt;/p&gt;
</style></abstract><issue><style face="normal" font="default" size="100%">3</style></issue><work-type><style face="normal" font="default" size="100%">Article</style></work-type><custom3><style face="normal" font="default" size="100%">&lt;p&gt;
	Foreign&lt;/p&gt;
</style></custom3><custom4><style face="normal" font="default" size="100%">&lt;p&gt;
	3.234&lt;/p&gt;
</style></custom4></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Shankar, S. Shiva</style></author><author><style face="normal" font="default" size="100%">Banarjee, Reema</style></author><author><style face="normal" font="default" size="100%">Jathar, Swaraj M.</style></author><author><style face="normal" font="default" size="100%">Rajesh, S.</style></author><author><style face="normal" font="default" size="100%">Ramasamy, Sureshkumar</style></author><author><style face="normal" font="default" size="100%">Kulkarni, Mahesh J.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">De novo structure prediction of meteorin and meteorin-like protein for identification of domains, functional receptor binding regions, and their high-risk missense variants</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Biomolecular Structure &amp; Dynamics</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">domains</style></keyword><keyword><style  face="normal" font="default" size="100%">Meteorin</style></keyword><keyword><style  face="normal" font="default" size="100%">meteorin-like</style></keyword><keyword><style  face="normal" font="default" size="100%">missense variants</style></keyword><keyword><style  face="normal" font="default" size="100%">protein-protein interactions</style></keyword><keyword><style  face="normal" font="default" size="100%">structure prediction</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2024</style></year><pub-dates><date><style  face="normal" font="default" size="100%">JUN </style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">42</style></volume><pages><style face="normal" font="default" size="100%">4522-4536</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;
	Meteorin (Metrn) and Meteorin-like (Metrnl) are homologous secreted proteins involved in neural development and metabolic regulation. In this study, we have performed de novo structure prediction and analysis of both Metrn and Metrnl using Alphafold2 (AF2) and RoseTTAfold (RF). Based on the domain and structural homology analysis of the predicted structures, we have identified that these proteins are composed of two functional domains, a CUB domain and an NTR domain, connected by a hinge/loop region. We have identified the receptor binding regions of Metrn and Metrnl using the machine-learning tools ScanNet and Masif. These were further validated by docking Metrnl with its reported KIT receptor, thus establishing the role of each domain in the receptor interaction. Also, we have studied the effect of non-synonymous SNPs on the structure and function of these proteins using an array of bioinformatics tools and selected 16 missense variants in Metrn and 10 in Metrnl that can affect the protein stability. This is the first study to comprehensively characterize the functional domains of Metrn and Metrnl at their structural level and identify the functional domains, and protein binding regions. This study also highlights the interaction mechanism of the KIT receptor and Metrnl. The predicted deleterious SNPs will allow further understanding of the role of these variants in modulating the plasma levels of these proteins in disease conditions such as diabetes.&lt;/p&gt;
</style></abstract><issue><style face="normal" font="default" size="100%">9</style></issue><work-type><style face="normal" font="default" size="100%">Article</style></work-type><custom3><style face="normal" font="default" size="100%">&lt;p&gt;
	Foreign&lt;/p&gt;
</style></custom3><custom4><style face="normal" font="default" size="100%">&lt;p&gt;4.4&lt;/p&gt;
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