Charlotte M. Deane

发表

Predicting loop conformational ensembles

Jiye Shi, Charlotte M. Deane, Claire Marks, 2018, Bioinform..

Identifying networks with common organizational principles

Gesine Reinert, Charlotte M. Deane, Anatol E. Wegner, 2017, J. Complex Networks.

Protein Family-Specific Models Using Deep Neural Networks and Transfer Learning Improve Virtual Screening and Highlight the Need for More Data

Charlotte M. Deane, Mihaela van der Schaar, Fergus Imrie, 2018, J. Chem. Inf. Model..

Comparison of large networks with sub-sampling strategies

Gesine Reinert, Charlotte M. Deane, Anatol E. Wegner, 2016, Scientific Reports.

Structural diversity of B-cell receptor repertoires along the B-cell differentiation axis in humans and mice

Sebastian Kelm, Charlotte M. Deane, Claire Marks, 2019, bioRxiv.

In silico structural modeling of multiple epigenetic marks on DNA

Charlotte M. Deane, Konrad Krawczyk, Samuel Demharter, 2018, Bioinform..

Gro2mat: A package to efficiently read gromacs output in MATLAB

Charlotte M. Deane, Bernhard Knapp, Hung Dien, 2014, J. Comput. Chem..

SAbPred: a structure-based antibody prediction server

Sebastian Kelm, Charlotte M. Deane, Claire Marks, 2016, Nucleic Acids Res..

Ten Simple Rules for a Successful Cross-Disciplinary Collaboration

Alexander G. Fletcher, Charlotte M. Deane, David Gavaghan, 2015, PLoS Comput. Biol..

Predicting and Validating Protein Interactions Using Network Structure

Gesine Reinert, Charlotte M. Deane, Pao-Yang Chen, 2008, PLoS Comput. Biol..

MP-T: improving membrane protein alignment for structure prediction

Charlotte M. Deane, Jamie R. Hill, C. Deane, 2013, Bioinform..

Measuring rank robustness in scored protein interaction networks

Gesine Reinert, Charlotte M. Deane, Lyuba V. Bozhilova, 2018, BMC Bioinformatics.

Cotranslational protein folding - fact or fiction?

Charlotte M. Deane, Graham R. Wood, Mingqiang Dong, 2007, ISMB/ECCB.

Comparing co-evolution methods and their application to template-free protein structure prediction

Jiye Shi, Charlotte M. Deane, Saulo Henrique Pires de Oliveira, 2016, Bioinform..

Directionality in protein fold prediction

Charlotte M. Deane, Graham R. Wood, Fabien P. E. Huard, 2010, BMC Bioinformatics.

Tertiary Element Interaction in HIV-1 TAR

Charlotte M. Deane, Konrad Krawczyk, Bernhard Knapp, 2016, J. Chem. Inf. Model..

pyHVis3D: visualising molecular simulation deduced H-bond networks in 3D: application to T-cell receptor interactions

Charlotte M. Deane, Hao Zhang, Bernhard Knapp, 2018, Bioinform..

MHC binding affects the dynamics of different T-cell receptors in different ways

Charlotte M. Deane, Bernhard Knapp, Omer Dushek, 2019, PLoS Comput. Biol..

Crowdsourcing Yields a New Standard for Kinks in Protein Helices

Jiye Shi, Charlotte M. Deane, Bernhard Knapp, 2014, J. Chem. Inf. Model..

A statistical approach using network structure in the prediction of protein characteristics

Gesine Reinert, Charlotte M. Deane, Pao-Yang Chen, 2007, Bioinform..

Using the protein interaction network to predict protein folds without homology

Charlotte M. Deane, Ramazan Saeed, 2007, BMC Systems Biology.

Exploring the potential of template-based modelling

Charlotte M. Deane, Graham R. Wood, Braddon K. Lance, 2010, Bioinform..

Assessment of model fit via network comparison methods based on subgraph counts

Gesine Reinert, Charlotte M. Deane, Luis Ospina-Forero, 2018, J. Complex Networks.

Sphinx: merging knowledge‐based and ab initio approaches to improve protein loop prediction

Sebastian Kelm, Jiye Shi, Charlotte M. Deane, 2017, Bioinform..

The evolution of contact prediction: evidence that contact selection in statistical contact prediction is changing

Charlotte M. Deane, Saulo Henrique Pires de Oliveira, Konrad Krawczyk, 2020, Bioinform..

The function of communities in protein interaction networks at multiple scales

Charlotte M. Deane, Nick S. Jones, Nick S. Jones, 2009, BMC Systems Biology.

ABodyBuilder: Automated antibody structure prediction with data–driven accuracy estimation

Jiye Shi, Charlotte M. Deane, James Dunbar, 2016, mAbs.

Ten simple rules for surviving an interdisciplinary PhD

Charlotte M. Deane, David Gavaghan, Samuel Demharter, 2017, PLoS Comput. Biol..

How old is your fold?

Charlotte M. Deane, Sanne Abeln, Henry F. Winstanley, 2005, ISMB.

Browsing the SLoop database of structurally classified loops connecting elements of protein secondary structure

Charlotte M. Deane, David F. Burke, Tom L. Blundell, 2000, Bioinform..

ANARCI: antigen receptor numbering and receptor classification

Charlotte M. Deane, James Dunbar, C. Deane, 2015, Bioinform..

Improving B-cell epitope prediction and its application to global antibody-antigen docking

Jiye Shi, Charlotte M. Deane, Konrad Krawczyk, 2014, Bioinform..

An assessment of the uses of homologous interactions

Charlotte M. Deane, Ramazan Saeed, C. Deane, 2008, Bioinform..

Role of mutual information for predicting contact residues in proteins

Gesine Reinert, Charlotte M. Deane, 2012 .

Alignment-free protein interaction network comparison

Gesine Reinert, Charlotte M. Deane, Waqar Ali, 2014, Bioinform..

Freely Available Conformer Generation Methods: How Good Are They?

Charlotte M. Deane, Garrett M. Morris, Jean-Paul Ebejer, 2012, J. Chem. Inf. Model..

Structural Bridges through Fold Space

Charlotte M. Deane, Hannah Edwards, C. Deane, 2015, PLoS Comput. Biol..

STCRDab: the structural T-cell receptor database

Charlotte M. Deane, Konrad Krawczyk, Jinwoo Leem, 2017, Nucleic Acids Res..

Protein protein interactions, evolutionary rate, abundance and age

Charlotte M. Deane, Ramazan Saeed, C. Deane, 2006, BMC Bioinformatics.

Using Phylogeny to Improve Genome-Wide Distant Homology Recognition

Charlotte M. Deane, Sanne Abeln, Carlo Teubner, 2006, PLoS Comput. Biol..

Exploring peptide/MHC detachment processes using hierarchical natural move Monte Carlo

Charlotte M. Deane, Samuel Demharter, Bernhard Knapp, 2015, Bioinform..

Combining co‐evolution and secondary structure prediction to improve fragment library generation

Charlotte M. Deane, Saulo Henrique Pires de Oliveira, C. Deane, 2018, Bioinform..

Linking evolution of protein structures through fragments

Charlotte M. Deane, Sanne Abeln, 2007, BMC Systems Biology.

OOMMPPAA: A Tool To Aid Directed Synthesis by the Combined Analysis of Activity and Structural Data

Charlotte M. Deane, Darren V. S. Green, Anthony R. Bradley, 2014, J. Chem. Inf. Model..

RFQAmodel: Random Forest Quality Assessment to identify a predicted protein structure in the correct fold

Charlotte M. Deane, Clare E. West, Saulo H. P. de Oliveira, 2019, bioRxiv.

JOY: protein sequence-structure representation and analysis

Charlotte M. Deane, Kenji Mizuguchi, John P. Overington, 1998, Bioinform..

Functionally guided alignment of protein interaction networks for module detection

Charlotte M. Deane, Waqar Ali, C. Deane, 2009, Bioinform..

Additional file 1 of Measuring rank robustness in scored protein interaction networks

Gesine Reinert, Charlotte M. Deane, Lyuba V. Bozhilova, 2019 .

Data Set Augmentation Allows Deep Learning-Based Virtual Screening to Better Generalize to Unseen Target Classes and Highlight Important Binding Interactions

Charlotte M. Deane, Nathan Brown, Jack Scantlebury, 2020, J. Chem. Inf. Model..

CommWalker: correctly evaluating modules in molecular networks in light of annotation bias

Gesine Reinert, Charlotte M. Deane, S. Mason, 2017, Bioinform..

SAbDab: the structural antibody database

Jiye Shi, Charlotte M. Deane, Konrad Krawczyk, 2013, Nucleic Acids Res..

Memoir: template-based structure prediction for membrane proteins

Sebastian Kelm, Jiye Shi, Charlotte M. Deane, 2013, Nucleic Acids Res..

WONKA and OOMMPPAA: analysis of protein–ligand interaction data to direct structure-based drug design

Charlotte M. Deane, Darren V. S. Green, Anthony R. Bradley, 2017, Acta crystallographica. Section D, Structural biology.

The Importance of Age and High Degree, in Protein-Protein Interaction Networks

Gesine Reinert, Charlotte M. Deane, Tiago Rito, 2012, J. Comput. Biol..

Faculty Opinions recommendation of How precise are reported protein coordinate data

Charlotte M. Deane, Nicholas Pearce, C. Deane, 2014 .

Revisiting Date and Party Hubs: Novel Approaches to Role Assignment in Protein Interaction Networks

Charlotte M. Deane, Mason A. Porter, Nick S. Jones, 2009, PLoS Comput. Biol..

Examining Variable Domain Orientations in Antigen Receptors Gives Insight into TCR-Like Antibody Design

Jiye Shi, Charlotte M. Deane, Bernhard Knapp, 2014, PLoS Comput. Biol..

Deep Generative Models for 3D Compound Design

Charlotte M. Deane, Fergus Imrie, Mihaela van der Schaar, 2019, bioRxiv.

Faculty Opinions recommendation of Residues coevolution guides the systematic identification of alternative functional conformations in proteins.

Charlotte M. Deane, 2017 .

Data driven approaches to improve the drug discovery process: a virtual screening quest in drug discovery

Charlotte M. Deane, Garrett M. Morris, 2014 .

Environment specific substitution tables improve membrane protein alignment

Sebastian Kelm, Jiye Shi, Charlotte M. Deane, 2011, Bioinform..

Progress and challenges in predicting protein interfaces

Jean-Christophe Nebel, Charlotte M. Deane, Konrad Krawczyk, 2015, Briefings Bioinform..

Efficient Sampling for the Prediction of Long and Multidomain Protein Structures

Sebastian Kelm, Jiye Shi, Charlotte M. Deane, 2018 .

SCORE: predicting the core of protein models

Charlotte M. Deane, Tom L. Blundell, Quentin Kaas, 2001, Bioinform..

Deciphering chemotaxis pathways using cross species comparisons

Gesine Reinert, Charlotte M. Deane, Pao-Yang Chen, 2010, BMC Systems Biology.

Deep Generative Models for 3D Linker Design

Charlotte M. Deane, Mihaela van der Schaar, Fergus Imrie, 2020, J. Chem. Inf. Model..

How threshold behaviour affects the use of subgraphs for network comparison

Gesine Reinert, Charlotte M. Deane, Zi Wang, 2010, Bioinform..

T-Cell Receptor Binding Affects the Dynamics of the Peptide/MHC-I Complex

Charlotte M. Deane, Bernhard Knapp, C. Deane, 2016, J. Chem. Inf. Model..

Producing High-Accuracy Lattice Models from Protein Atomic Coordinates Including Side Chains

Charlotte M. Deane, Rolf Backofen, Martin Mann, 2012, Adv. Bioinformatics.

Large Scale Characterization of the LC13 TCR and HLA-B8 Structural Landscape in Reaction to 172 Altered Peptide Ligands: A Molecular Dynamics Simulation Study

Charlotte M. Deane, Bernhard Knapp, James Dunbar, 2014, PLoS Comput. Biol..

Modelling sequential protein folding under kinetic control

Charlotte M. Deane, Graham R. Wood, Fabien P. E. Huard, 2006, ISMB.

Exploring Fold Space Preferences of New-born and Ancient Protein Superfamilies

Charlotte M. Deane, Sanne Abeln, Hannah Edwards, 2013, PLoS Comput. Biol..

Chapter 8:Virtual Screening with Convolutional Neural Networks

Charlotte M. Deane, Fergus Imrie, Anthony R. Bradley, 2020 .

WONKA: objective novel complex analysis for ensembles of protein–ligand structures

Charlotte M. Deane, Darren V. S. Green, Anthony R. Bradley, 2015, Journal of Computer-Aided Molecular Design.

The evolution of contact prediction: Evidence that contact selection in statistical contact prediction is changing

Charlotte M. Deane, Konrad Krawczyk, Saulo H. P. de Oliveira, 2019, bioRxiv.

The prospects of quantum computing in computational molecular biology

Charlotte M. Deane, Garrett M. Morris, Carlos Outeiral, 2020, WIREs Computational Molecular Science.

MEDELLER: homology-based coordinate generation for membrane proteins

Sebastian Kelm, Jiye Shi, Charlotte M. Deane, 2010, Bioinform..

The function of communities in protein interaction networks at multiple scales

Charlotte M. Deane, Mason A. Porter, Nick S. Jones, 2009, BMC Systems Biology.

Ten Simple Rules for Effective Computational Research

Alexander G. Fletcher, Charlotte M. Deane, David Gavaghan, 2014, PLoS Comput. Biol..

Sequential search leads to faster, more efficient fragment-based de novo protein structure prediction

Jiye Shi, Charlotte M. Deane, Saulo Henrique Pires de Oliveira, 2018, Bioinform..

What Evidence Is There for the Homology of Protein-Protein Interactions?

Charlotte M. Deane, Mason A. Porter, Nick S. Jones, 2012, PLoS Comput. Biol..

Current status and future challenges in T-cell receptor/peptide/MHC molecular dynamics simulations

Charlotte M. Deane, Samuel Demharter, Bernhard Knapp, 2015, Briefings Bioinform..

Protein Interaction Networks and Their Statistical Analysis

Gesine Reinert, Charlotte M. Deane, Waqar Ali, 2011 .

Ribosome occupancy profiles are conserved between structurally and evolutionarily related yeast domains

Charlotte M. Deane, Mark Chonofsky, Daniel A. Nissley, 2021, Bioinform..

DLAB: deep learning methods for structure-based virtual screening of antibodies

Charlotte M. Deane, Bruck Taddese, Andrew Buchanan, 2022, Bioinform..

Humanization of antibodies using a machine learning approach on large-scale repertoire data

Charlotte M. Deane, Claire Marks, Alissa M. Hummer, 2021, Bioinform..

Deep generative design with 3D pharmacophoric constraints

Charlotte M. Deane, Fergus Imrie, Anthony R. Bradley, 2021, bioRxiv.

Robust gene coexpression networks using signed distance correlation

Gesine Reinert, Charlotte M. Deane, Mariano Beguerisse-Díaz, 2021, Bioinform..

Generating property-matched decoy molecules using deep learning

Charlotte M. Deane, Fergus Imrie, Anthony R. Bradley, 2021, Bioinform..

Co-evolutionary distance predictions contain flexibility information

Sebastian Kelm, Jiye Shi, Charlotte M. Deane, 2021, Bioinform..

Public Baseline and shared response structures support the theory of antibody repertoire functional commonality

Jiye Shi, Charlotte M. Deane, Claire Marks, 2021, PLoS Comput. Biol..

Learning from Docked Ligands: Ligand-Based Features Rescue Structure-Based Scoring Functions When Trained on Docked Poses

Charlotte M. Deane, Garrett M. Morris, F Boyles, 2021, J. Chem. Inf. Model..

Exploring Folding Features in Protein Structure Prediction

Charlotte M. Deane, Saulo H. P. de Oliveira, C. Deane, 2018 .

CoV-AbDab: the Coronavirus Antibody Database

Charlotte M. Deane, Claire Marks, Aleksandr Kovaltsuk, 2020, bioRxiv.

Building a Better Fragment Library for De Novo Protein Structure Prediction

Jiye Shi, Charlotte M. Deane, Saulo H. P. de Oliveira, 2015, PloS one.

Partial-occupancy binders identified by the Pan-Dataset Density Analysis method offer new chemical opportunities and reveal cryptic binding sites

Charlotte M. Deane, Brian D. Marsden, Frank von Delft, 2017, Structural dynamics.

Synonymous codon usage influences the local protein structure observed

Charlotte M. Deane, Rhodri Saunders, C. Deane, 2010, Nucleic acids research.

Thera-SAbDab: the Therapeutic Structural Antibody Database

Jiye Shi, Charlotte M. Deane, Claire Marks, 2020, Nucleic Acids Res..

How long is a piece of loop?

Charlotte M. Deane, Sumeet Agarwal, Yoonjoo Choi, 2013, PeerJ.

Local Network Patterns in Protein-Protein Interfaces

Gesine Reinert, Charlotte M. Deane, Rebecca Hamer, 2013, PloS one.

Protein Modeling and Structural Prediction

Sebastian Kelm, Charlotte M. Deane, Yoonjoo Choi, 2014 .

Rapid, Precise, and Reproducible Prediction of Peptide-MHC Binding Affinities from Molecular Dynamics That Correlate Well with Experiment.

Charlotte M. Deane, Bernhard Knapp, Peter V. Coveney, 2015, Journal of chemical theory and computation.

The contribution of major histocompatibility complex contacts to the affinity and kinetics of T cell receptor binding

Charlotte M. Deane, Omer Dushek, C. Deane, 2016, Scientific Reports.

Functional module detection through integration of single-cell RNA sequencing data with protein–protein interaction networks

Gesine Reinert, Charlotte M. Deane, Fang Zhang, 2019, BMC Genomics.