Shaun M Kandathil

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David JonesProfessor of Bioinformatics, University College LondonVerified email at ucl.ac.uk
Joe G GreenerGroup leader, MRC LMBVerified email at mrc-lmb.cam.ac.uk
Lewis MoffatPrev. The Francis Crick Institute & UCLVerified email at ucl.ac.uk
Andy LauApplied Research Scientist at InstaDeep, University College London, King's College LondonVerified email at instadeep.com
Simon LovellProfessor of Molecular Biology, University of ManchesterVerified email at manchester.ac.uk
Julia HandlProfessor in Decision Sciences, The University of ManchesterVerified email at manchester.ac.uk
Praveen K. VayalilUniversity of Alabama at birminghamVerified email at uab.edu
Mario Garza-FabreCinvestavVerified email at cinvestav.mx
Paul PopelierProfessor of Chemical Theory and ComputationVerified email at manchester.ac.uk
Joshua KnowlesSchlumberger | University of Birmingham | University of ManchesterVerified email at slb.com
Sam HayProfessor of Biophysical Chemistry, University of ManchesterVerified email at manchester.ac.uk
Nigel ScruttonProfessor of Enzymology, Manchester Institute of Biotechnology (MIB) and School of ChemistryVerified email at manchester.ac.uk
Abayomi OlabodeWestern UniversityVerified email at postgrad.manchester.ac.uk
David L RobertsonProfessor, CVR Bioinformatics, MRC-University of Glasgow Centre for Virus ResearchVerified email at glasgow.ac.uk
Alexandre HarléCNRS UMR 7039 CRAN, Université de Lorraine - Service de Biopathologie, ICLVerified email at nancy.unicancer.fr
Christine OrengoProfessor of Bioinformatics, University College LondonVerified email at biochem.ucl.ac.uk
Jude WellsPhD Student University College LondonVerified email at ucl.ac.uk
Vaishali Waman, PhDUniversity College LondonVerified email at ucl.ac.uk
Ian SillitoeUniversity College London (UK)Verified email at ucl.ac.uk
Nicola BordinUniversity College LondonVerified email at ucl.ac.uk

Shaun M Kandathil

University College London

Verified email at ucl.ac.uk

Protein structure prediction deep learning optimization


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A guide to machine learning for biologists JG Greener, SM Kandathil, L Moffat, DT Jones Nature reviews Molecular cell biology 23 (1), 40-55, 2022	1066	2022
A review of the chemistry and pharmacology of the date fruits (Phoenix dactylifera L.) MS Baliga, BRV Baliga, SM Kandathil, HP Bhat, PK Vayalil Food research international 44 (7), 1812-1822, 2011	747	2011
High precision in protein contact prediction using fully convolutional neural networks and minimal sequence features DT Jones, SM Kandathil Bioinformatics 34 (19), 3308-3315, 2018	203	2018
Deep learning extends de novo protein modelling coverage of genomes using iteratively predicted structural constraints JG Greener, SM Kandathil, DT Jones Nature communications 10 (1), 3977, 2019	184	2019
Prediction of interresidue contacts with DeepMetaPSICOV in CASP13 SM Kandathil, JG Greener, DT Jones Proteins: Structure, Function, and Bioinformatics 87 (12), 1092-1099, 2019	114	2019
Recent developments in deep learning applied to protein structure prediction SM Kandathil, JG Greener, DT Jones Proteins: Structure, Function, and Bioinformatics 87 (12), 1179-1189, 2019	66	2019
Accuracy and tractability of a kriging model of intramolecular polarizable multipolar electrostatics and its application to histidine SM Kandathil, TL Fletcher, Y Yuan, J Knowles, PLA Popelier Journal of computational chemistry 34 (21), 1850–1861, 2013	62	2013
Generating, maintaining, and exploiting diversity in a memetic algorithm for protein structure prediction M Garza-Fabre, SM Kandathil, J Handl, J Knowles, SC Lovell Evolutionary computation 24 (4), 577-607, 2016	42	2016
Ultrafast end-to-end protein structure prediction enables high-throughput exploration of uncharacterized proteins SM Kandathil, JG Greener, AM Lau, DT Jones Proceedings of the National Academy of Sciences 119 (4), e2113348119, 2022	41	2022
The prediction of atomic kinetic energies from coordinates of surrounding atoms using kriging machine learning TL Fletcher, SM Kandathil, PLA Popelier Theoretical Chemistry Accounts 133 (7), 1499, 2014	32	2014
Design in the DARK: learning deep generative models for De Novo protein design L Moffat, SM Kandathil, DT Jones bioRxiv, 2022.01. 27.478087, 2022	27	2022
Toward a detailed understanding of search trajectories in fragment assembly approaches to protein structure prediction SM Kandathil, J Handl, SC Lovell Proteins: Structure, Function, and Bioinformatics 84, 411–426, 2016	20	2016
Accurate prediction of polarised high order electrostatic interactions for hydrogen bonded complexes using the machine learning method kriging TJ Hughes, SM Kandathil, PLA Popelier Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 136, 32-41, 2015	20	2015
Improved fragment-based protein structure prediction by redesign of search heuristics SM Kandathil, M Garza-Fabre, J Handl, SC Lovell Scientific Reports 8 (1), 13694, 2018	19	2018
Deep learning-based prediction of protein structure using learned representations of multiple sequence alignments SM Kandathil, JG Greener, AM Lau, DT Jones Biorxiv, 2020.11. 27.401232, 2020	14	2020
Proton tunnelling and promoting vibrations during the oxidation of ascorbate by ferricyanide? SM Kandathil, MD Driscoll, RV Dunn, NS Scrutton, S Hay Physical Chemistry Chemical Physics 16 (6), 2256-2259, 2014	12	2014
Machine learning methods for predicting protein structure from single sequences SM Kandathil, AM Lau, DT Jones Current Opinion in Structural Biology 81, 102627, 2023	11	2023
Adaptive HIV-1 evolutionary trajectories are constrained by protein stability AS Olabode, SM Kandathil, SC Lovell, DL Robertson Virus Evolution 3 (2), vex019, 2017	11	2017
Merizo: a rapid and accurate protein domain segmentation method using invariant point attention AM Lau, SM Kandathil, DT Jones Nature Communications 14 (1), 8445, 2023	7	2023
Uncommon mutational profiles of metastatic colorectal cancer detected during routine genotyping using next generation sequencing C Franczak, SM Kandathil, P Gilson, M Husson, M Rouyer, J Demange, ... Scientific Reports 9 (1), 7083, 2019	6	2019

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