Search Results - "Kamisetty, Hetunandan"
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Robust and accurate prediction of residue-residue interactions across protein interfaces using evolutionary information
Published in eLife (01-05-2014)“…Do the amino acid sequence identities of residues that make contact across protein interfaces covary during evolution? If so, such covariance could be used to…”
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Assessing the utility of coevolution-based residue–residue contact predictions in a sequence- and structure-rich era
Published in Proceedings of the National Academy of Sciences - PNAS (24-09-2013)“…Recently developed methods have shown considerable promise in predicting residue–residue contacts in protein 3D structures using evolutionary covariance…”
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Protein structure determination using metagenome sequence data
Published in Science (American Association for the Advancement of Science) (20-01-2017)“…Despite decades of work by structural biologists, there are still ~5200 protein families with unknown structure outside the range of comparative modeling. We…”
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Learning generative models for protein fold families
Published in Proteins, structure, function, and bioinformatics (01-04-2011)“…We introduce a new approach to learning statistical models from multiple sequence alignments (MSA) of proteins. Our method, called GREMLIN (Generative…”
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Optimization of affinity, specificity and function of designed influenza inhibitors using deep sequencing
Published in Nature biotechnology (01-06-2012)“…To increase the affinity of designed protein inhibitors for influenza hemagglutinin, Whitehead et al . use yeast display and deep sequencing to measure the…”
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Computation and Functional Studies Provide a Model for the Structure of the Zinc Transporter hZIP4
Published in The Journal of biological chemistry (17-07-2015)“…Members of the Zrt and Irt protein (ZIP) family are a central participant in transition metal homeostasis as they function to increase the cytosolic…”
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Large-scale determination of previously unsolved protein structures using evolutionary information
Published in eLife (03-09-2015)“…The prediction of the structures of proteins without detectable sequence similarity to any protein of known structure remains an outstanding scientific…”
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An efficient randomized algorithm for contact-based NMR backbone resonance assignment
Published in Bioinformatics (15-01-2006)“…Motivation: Backbone resonance assignment is a critical bottleneck in studies of protein structure, dynamics and interactions by nuclear magnetic resonance…”
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Origins of coevolution between residues distant in protein 3D structures
Published in Proceedings of the National Academy of Sciences - PNAS (22-08-2017)“…Residue pairs that directly coevolve in protein families are generally close in protein 3D structures. Here we study the exceptions to this general…”
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Accounting for conformational entropy in predicting binding free energies of protein-protein interactions
Published in Proteins, structure, function, and bioinformatics (01-02-2011)“…Protein‐protein interactions are governed by the change in free energy upon binding, ΔG = ΔH − TΔS. These interactions are often marginally stable, so one must…”
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Learning sequence determinants of protein:protein interaction specificity with sparse graphical models
Published in Journal of computational biology (01-06-2015)“…In studying the strength and specificity of interaction between members of two protein families, key questions center on which pairs of possible partners…”
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Learning generative models of molecular dynamics
Published in BMC genomics (17-01-2012)“…We introduce three algorithms for learning generative models of molecular structures from molecular dynamics simulations. The first algorithm learns a…”
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Free energy estimates of all-atom protein structures using generalized belief propagation
Published in Journal of computational biology (01-09-2008)“…We present a technique for approximating the free energy of protein structures using generalized belief propagation (GBP). The accuracy and utility of these…”
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Match-Tensor: a Deep Relevance Model for Search
Published 26-01-2017“…The application of Deep Neural Networks for ranking in search engines may obviate the need for the extensive feature engineering common to current…”
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