By Mr Thomas Litfin

Journal articles

Mishra BP; Loyo CL; Cai Y; Litfin T; Miraj G; Brillault L; Masic V; Mosaiab T; Rajaratnam P; Rudrawar S; Gu W; Kobe B; Gerdt JP; Grossman AD; Shi Y; Ve T, 2026, 'Molecular characterisation of the Bacillus subtilis SpbK antiphage defence system', Nature Communications, 17, http://dx.doi.org/10.1038/s41467-025-67810-5

Albers M; Bosse L; Schröter L; Junemann A-MT; Rossdam C; Hartmann M; Grove M; Litfin T; Egger A-S; Kwiatkowski M; Thedieck K; Zocher G; Buettner FFR; Malde AK; von Itzstein M; Mühlenhoff M, 2026, 'Interplay of SLC33A1-dependent and -independent Golgi sialic acid O-acetylation in CASD1 catalysis.', Nat Commun, 17, http://dx.doi.org/10.1038/s41467-026-71333-y

Mitic I; Rowell K; Litfin T; Michie KA; Jacques DA, 2026, 'Assessing the validity of leucine zipper constructs predicted by AlphaFold', Protein Science, 35, http://dx.doi.org/10.1002/pro.70438

Lang M; Litfin T; Chen K; Zhan J; Zhou Y, 2025, 'Benchmarking the methods for predicting base pairs in RNA-RNA interactions', Bioinformatics, 41, http://dx.doi.org/10.1093/bioinformatics/btaf289

Cumin C; Gee L; Litfin T; Muchabaiwa R; Martin G; Cooper O; Heinzelmann-Schwarz V; Lange T; von Itzstein M; Jacob F; Everest-Dass A, 2024, 'Highly Sensitive Spatial Glycomics at Near-Cellular Resolution by On-Slide Derivatization and Mass Spectrometry Imaging', Analytical Chemistry, 96, pp. 11163 - 11171, http://dx.doi.org/10.1021/acs.analchem.3c05984

Chen K; Litfin T; Singh J; Zhan J; Zhou Y, 2024, 'MARS and RNAcmap3: The Master Database of All Possible RNA Sequences Integrated with RNAcmap for RNA Homology Search', Genomics Proteomics and Bioinformatics, 22, http://dx.doi.org/10.1093/gpbjnl/qzae018

Zhang Y; Lang M; Jiang J; Gao Z; Xu F; Litfin T; Chen K; Singh J; Huang X; Song G; Tian Y; Zhan J; Chen J; Zhou Y, 2024, 'Multiple sequence alignment-based RNA language model and its application to structural inference', Nucleic Acids Research, 52, pp. E3, http://dx.doi.org/10.1093/nar/gkad1031

Zhou Y; Litfin T; Zhan J, 2023, '3 = 1 + 2: how the divide conquered de novo protein structure prediction and what is next?', National Science Review, 10, http://dx.doi.org/10.1093/nsr/nwad259

Del Conte A; Bouhraoua A; Mehdiabadi M; Clementel D; Monzon AM; Tosatto SCE; Piovesan D; Holehouse AS; Griffith D; Emenecker RJ; Patil A; Sharma R; Tsunoda T; Sharma A; Tang YJ; Liu B; Mirabello C; Wallner B; Rost B; Ilzhöfer D; Littmann M; Heinzinger M; Krautheimer LIM; Bernhofer M; McGuffin LJ; Callebaut I; Feildel TB; Liu J; Cheng J; Guo Z; Xu J; Wang S; Malhis N; Gsponer J; Kim CS; Han KS; Ma MC; Kurgan L; Ghadermarzi S; Katuwawala A; Zhao B; Peng Z; Wu Z; Hu G; Wang K; Hoque MT; Ul Kabir MW; Vendruscolo M; Sormanni P; Li M; Zhang F; Jia P; Wang Y; Lobanov MY; Galzitskaya OV; Vranken W; Díaz A; Litfin T; Zhou Y; Hanson J; Paliwal K; Dosztányi Z; Erdős G, 2023, 'CAID prediction portal: A comprehensive service for predicting intrinsic disorder and binding regions in proteins', Nucleic Acids Research, 51, pp. W62 - W69, http://dx.doi.org/10.1093/nar/gkad430

Singh J; Paliwal K; Litfin T; Singh J; Zhou Y, 2022, 'Reaching alignment-profile-based accuracy in predicting protein secondary and tertiary structural properties without alignment', Scientific Reports, 12, http://dx.doi.org/10.1038/s41598-022-11684-w

Singh J; Paliwal K; Litfin T; Singh J; Zhou Y, 2022, 'Predicting RNA distance-based contact maps by integrated deep learning on physics-inferred secondary structure and evolutionary-derived mutational coupling', Bioinformatics, 38, pp. 3900 - 3910, http://dx.doi.org/10.1093/bioinformatics/btac421

Solayman M; Litfin T; Singh J; Paliwal K; Zhou Y; Zhan J, 2022, 'Probing RNA structures and functions by solvent accessibility: an overview from experimental and computational perspectives', Briefings in Bioinformatics, 23, http://dx.doi.org/10.1093/bib/bbac112

Zhou K; Litfin T; Solayman M; Zhao H; Zhou Y; Zhan J, 2022, 'High-throughput split-protein profiling by combining transposon mutagenesis and regulated protein-protein interactions with deep sequencing', International Journal of Biological Macromolecules, 203, pp. 543 - 552, http://dx.doi.org/10.1016/j.ijbiomac.2022.01.173

Singh J; Litfin T; Singh J; Paliwal K; Zhou Y, 2022, 'SPOT-Contact-LM: Improving single-sequence-based prediction of protein contact map using a transformer language model', Bioinformatics, 38, pp. 1888 - 1894, http://dx.doi.org/10.1093/bioinformatics/btac053

Solayman M; Litfin T; Zhou Y; Zhan J, 2022, 'High-throughput mapping of RNA solvent accessibility at the single-nucleotide resolution by RtcB ligation between a fixed 5′-OH-end linker and unique 3′-P-end fragments from hydroxyl radical cleavage', RNA Biology, 19, pp. 1179 - 1189, http://dx.doi.org/10.1080/15476286.2022.2145098

Zhang T; Singh J; Litfin T; Zhan J; Paliwal K; Zhou Y, 2021, 'RNAcmap: a fully automatic pipeline for predicting contact maps of RNAs by evolutionary coupling analysis', Bioinformatics, 37, pp. 3494 - 3500, http://dx.doi.org/10.1093/bioinformatics/btab391

Singh J; Litfin T; Paliwal K; Singh J; Hanumanthappa AK; Zhou Y, 2021, 'SPOT-1D-Single: improving the single-sequence-based prediction of protein secondary structure, backbone angles, solvent accessibility and half-sphere exposures using a large training set and ensembled deep learning', Bioinformatics, 37, pp. 3464 - 3472, http://dx.doi.org/10.1093/bioinformatics/btab316

Singh J; Paliwal K; Zhang T; Singh J; Litfin T; Zhou Y, 2021, 'Improved RNA secondary structure and tertiary base-pairing prediction using evolutionary profile, mutational coupling and two-dimensional transfer learning', Bioinformatics, 37, pp. 2589 - 2600, http://dx.doi.org/10.1093/bioinformatics/btab165

Necci M; Piovesan D; Hoque MT; Walsh I; Iqbal S; Vendruscolo M; Sormanni P; Wang C; Raimondi D; Sharma R; Zhou Y; Litfin T; Galzitskaya OV; Lobanov MY; Vranken W; Wallner B; Mirabello C; Malhis N; Dosztányi Z; Erdős G; Mészáros B; Gao J; Wang K; Hu G; Wu Z; Sharma A; Hanson J; Paliwal K; Callebaut I; Bitard-Feildel T; Orlando G; Peng Z; Xu J; Wang S; Jones DT; Cozzetto D; Meng F; Yan J; Gsponer J; Cheng J; Wu T; Kurgan L; Promponas VJ; Tamana S; Marino-Buslje C; Martínez-Pérez E; Chasapi A; Ouzounis C; Dunker AK; Kajava AV; Leclercq JY; Aykac-Fas B; Lambrughi M; Maiani E; Papaleo E; Chemes LB; Álvarez L; González-Foutel NS; Iglesias V; Pujols J; Ventura S; Palopoli N; Benítez GI; Parisi G; Bassot C; Elofsson A; Govindarajan S; Lamb J; Salvatore M; Hatos A; Monzon AM; Bevilacqua M; Mičetić I; Minervini G; Paladin L; Quaglia F; Leonardi E; Davey N; Horvath T; Kovacs OP; Murvai N; Pancsa R; Schad E; Szabo B; Tantos A; Macedo-Ribeiro S; Manso JA; Pereira PJB; Davidović R; Veljkovic N; Hajdu-Soltész B; Pajkos M; Szaniszló T; Guharoy M; Lazar T; Macossay-Castillo M; Tompa P; Tosatto SCE, 2021, 'Critical assessment of protein intrinsic disorder prediction', Nature Methods, 18, pp. 472 - 481, http://dx.doi.org/10.1038/s41592-021-01117-3

Atack JM; Guo C; Litfin T; Yang L; Blackall PJ; Zhou Y; Jennings MP, 2020, 'Systematic analysis of REBASE identifies numerous Type I restriction-modification systems with duplicated, distinct hsdS specificity genes that can switch system specificity by recombination', Msystems, 5, http://dx.doi.org/10.1128/mSystems.00497-20

Hanson J; Paliwal KK; Litfin T; Yang Y; Zhou Y, 2020, 'Getting to Know Your Neighbor: Protein Structure Prediction Comes of Age with Contextual Machine Learning', Journal of Computational Biology, 27, pp. 796 - 814, http://dx.doi.org/10.1089/cmb.2019.0193

Cai Y; Li X; Sun Z; Lu Y; Zhao H; Hanson J; Paliwal K; Litfin T; Zhou Y; Yang Y, 2020, 'SPOT-Fold: Fragment-Free Protein Structure Prediction Guided by Predicted Backbone Structure and Contact Map', Journal of Computational Chemistry, 41, pp. 745 - 750, http://dx.doi.org/10.1002/jcc.26132

Hanson J; Litfin T; Paliwal K; Zhou Y, 2020, 'Identifying molecular recognition features in intrinsically disordered regions of proteins by transfer learning', Bioinformatics, 36, pp. 1107 - 1112, http://dx.doi.org/10.1093/bioinformatics/btz691

Hanson J; Paliwal KK; Litfin T; Zhou Y, 2019, 'SPOT-Disorder2: Improved Protein Intrinsic Disorder Prediction by Ensembled Deep Learning', Genomics Proteomics and Bioinformatics, 17, pp. 645 - 656, http://dx.doi.org/10.1016/j.gpb.2019.01.004

Hanson J; Paliwal K; Litfin T; Yang Y; Zhou Y, 2019, 'Improving prediction of protein secondary structure, backbone angles, solvent accessibility and contact numbers by using predicted contact maps and an ensemble of recurrent and residual convolutional neural networks', Bioinformatics, 35, pp. 2403 - 2410, http://dx.doi.org/10.1093/bioinformatics/bty1006

Litfin T; Yang Y; Zhou Y, 2019, 'SPOT-Peptide: Template-Based Prediction of Peptide-Binding Proteins and Peptide-Binding Sites', Journal of Chemical Information and Modeling, 59, pp. 924 - 930, http://dx.doi.org/10.1021/acs.jcim.8b00777

Brown P; Zhou Y; Tan AC; El-Esawi MA; Liehr T; Blanck O; Gladue DP; Almeida GMF; Cernava T; Sorzano CO; Yeung AWK; Engel MS; Chandrasekaran AR; Muth T; Staege MS; Daulatabad SV; Widera D; Zhang J; Meule A; Honjo K; Pourret O; Yin CC; Zhang Z; Cascella M; Flegel WA; Goodyear CS; van Raaij MJ; Bukowy-Bieryllo Z; Campana LG; Kurniawan NA; Lalaouna D; Hüttner FJ; Ammerman BA; Ehret F; Cobine PA; Tan EC; Han H; Xia W; McCrum C; Dings RPM; Marinello F; Nilsson H; Nixon B; Voskarides K; Yang L; Costa VD; Bengtsson-Palme J; Bradshaw W; Grimm DG; Kumar N; Martis E; Prieto D; Sabnis SC; Amer SEDR; Liew AWC; Perco P; Rahimi F; Riva G; Zhang C; Devkota HP; Ogami K; Basharat Z; Fierz W; Siebers R; Tan KH; Boehme KA; Brenneisen P; Brown JAL; Dalrymple BP; Harvey DJ; Ng G; Werten S; Bleackley M; Dai Z; Dhariwal R; Gelfer Y; Hartmann MD; Miotla P; Tamaian R; Govender P; Gurney-Champion OJ; Kauppila JH; Zhang X; Echeverría N; Subhash S; Sallmon H; Tofani M; Bae T; Bosch O; Cuív PO; Danchin A; Diouf B; Eerola T; Evangelou E; Filipp F; Klump H; Kurgan L; Smith SS; Terrier O; Tuttle N; Kohonen-Corish M; Lovell N; Webster R; Nakagawa-Lagisz M; Tahir N; Rouet R; Sharma S; Seib KL; Litfin T, 2019, 'Large expert-curated database for benchmarking document similarity detection in biomedical literature search', Database, 2019, pp. 1 - 67, http://dx.doi.org/10.1093/database/baz085

Hadley B; Litfin T; Day CJ; Haselhorst T; Zhou Y; Tiralongo J, 2019, 'Nucleotide Sugar Transporter SLC35 Family Structure and Function', Computational and Structural Biotechnology Journal, 17, pp. 1123 - 1134, http://dx.doi.org/10.1016/j.csbj.2019.08.002

Hanson J; Paliwal K; Litfin T; Yang Y; Zhou Y, 2018, 'Accurate prediction of protein contact maps by coupling residual two-dimensional bidirectional long short-term memory with convolutional neural networks', Bioinformatics, 34, pp. 4039 - 4045, http://dx.doi.org/10.1093/bioinformatics/bty481

Tiralongo J; Cooper O; Litfin T; Yang Y; King R; Zhan J; Zhao H; Bovin N; Day CJ; Zhou Y, 2018, 'YesU from Bacillus subtilis preferentially binds fucosylated glycans', Scientific Reports, 8, http://dx.doi.org/10.1038/s41598-018-31241-8

Litfin T; Zhou Y; Yang Y, 2017, 'SPOT-ligand 2: Improving structure-based virtual screening by binding-homology search on an expanded structural template library', Bioinformatics, 33, pp. 1238 - 1240, http://dx.doi.org/10.1093/bioinformatics/btw829

Yaxley J; Litfin T, 2016, 'Non-steroidal anti-inflammatories and the development of analgesic nephropathy: a systematic review', Renal Failure, 38, pp. 1328 - 1334, http://dx.doi.org/10.1080/0886022X.2016.1216708

Preprints

Litfin T; Zhou Y; von Itzstein M, 2025, Ultra-fast and highly sensitive protein structure alignment with segment-level representations and block-sparse optimization, http://dx.doi.org/10.1101/2025.03.14.643159

Litfin T; Caley JS; Michie K, 2025, LMI4Boltz: Optimising VRAM utilisation to predict large macromolecular complexes with consumer grade hardware, http://dx.doi.org/10.1101/2025.10.29.684571

Zhou Y; lang M; Litfin T; Chen K; Zhan J, 2023, Deep learning models of RNA base-pairing structures generalize to unseen folds and make accurate zero-shot predictions of base-base interactions of RNA complexes, http://dx.doi.org/10.21203/rs.3.rs-3387481/v1

lang M; Litfin T; Chen K; Zhan J; Zhou Y, 2023, Deep learning models of RNA base-pairing structures generalize to unseen folds and make accurate zero-shot predictions of base-base interactions of RNA complexes, http://dx.doi.org/10.1101/2023.09.26.559463

Zhang Y; Lang M; Jiang J; Gao Z; Xu F; Litfin T; Chen K; Singh J; Huang X; Song G; Tian Y; Zhan J; Chen J; Zhou Y, 2023, Multiple sequence-alignment-based RNA language model and its application to structural inference, http://dx.doi.org/10.1101/2023.03.15.532863

Chen K; Litfin T; Singh J; Zhan J; Zhou Y, 2023, The Master Database of All Possible RNA Sequences and Its Integration with RNAcmap for RNA Homology Search, http://dx.doi.org/10.1101/2023.02.01.526559

Singh J; Paliwal K; Singh J; Litfin T; Zhou Y, 2022, Improved RNA homology detection and alignment by automatic iterative search in an expanded database, http://dx.doi.org/10.1101/2022.10.03.510702

Singh J; Litfin T; Singh J; Paliwal K; Zhou Y, 2021, SPOT-Contact-Single: Improving Single-Sequence-Based Prediction of Protein Contact Map using a Transformer Language Model, http://dx.doi.org/10.1101/2021.06.19.449089

Zhang T; Singh J; Litfin T; Zhan J; Paliwal K; Zhou Y, 2020, RNAcmap: A Fully Automatic Method for Predicting Contact Maps of RNAs by Evolutionary Coupling Analysis, http://dx.doi.org/10.1101/2020.08.08.242636

Atack J; Guo C; Litfin T; Yang L; Blackall P; Zhou Y; Jennings M, 2020, Systematic analysis of REBASE identifies numerous Type I restriction-modification systems that contain duplicated, variablehsdSspecificity genes that randomly switch methyltransferase specificity by recombination, http://dx.doi.org/10.1101/2020.06.18.137471

Other

Gagalova K; Healy M; Magaña Gomez PG; Michie K; Gustafsson OJR; Liftin T, 2026, Foundations of protein structure, http://dx.doi.org/10.6019/tol.foundations-protein-structure-t.2026.00001.1

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