【233】B. Evert, V. Slesarenko, J. Punnasseril, T. T, J. Zhan, Y. Zhou, E. Semchenko, and K. Seib, “Self-inhibitory peptides targeting the Neisseria gonorrhoeae mtrcde efflux pump increase antibiotic susceptibility.”, Antimicrobial Agents and Chemotherapy , in press (2021).
【232】 Q. Yuan, J. Chen, H. Zhao, Y. Zhou, and Y. Yang, “Structure-aware protein-protein interaction site prediction using deep graph convolutional network.”, Bioinformatics , in press(2021).
【231】 S. Liang, Z. Li, J. Zhan, and Y. Zhou, “De novo protein design by an energy function based on series expansion in distance and orientation dependence.”, Bioinformatics , in press (2021).
【230】Y. Xu, K. Chen, J. Pan, Y. Lei, D. Zhang, L. Fang, J. Tang, X. Chen, Y. Ma, Y. Zheng,B. Zhang, Y. Zhou, J. Zhan, and W. Xu, “Repurposing clinically approved drugs for COVID-19 treatment targeting SARS-CoV-2 papain-like protease.”, International Journal of Biological Macromolecules 188, 137–146 (2021).
【229】S. Jin, J. Zhan, and Y. Zhou, Argonaute proteins: Structures and their endonuclease activity, Molecular Biology Reports 48, 4837–4849 (2021).
【228】 T. Zhang, J. Singh, T. Litfin, J. Zhan, K. Paliwal, and Y. Zhou, “RNAcmap: A fully automatic pipeline for predicting contact maps of RNAs by evolutionary coupling analysis.”, Bioinformatics , in press (2021).
【227】J. Singh, K. Paliwal, J. Singh, and Y. Zhou, “RNA backbone torsion and pseudotorsion angle prediction using dilated convolutional neural networks.”, J. Chem. Info. Modeling 61, 2610–2622 (2021).
【226】Q. Chen, K. Liu, R. Yu, B. Zhou, P. Huang, Z. Cao, Y. Zhou, and J. Wang, “From “dark matter” to “star”: Insight into the regulation mechanisms of plant functional long non-coding RNAs.”, Frontiers in Plant Science , 12:650926 (2021).
【225】 J. Singh, T. Litfin, K. Paliwal, J. Singh, A. K. Hanumanthappa, and Y. Zhou, “SPOT-1D-Single: Improving the single-sequence-based prediction of protein secondary structure, back-bone angles, solvent accessibility and half-sphere exposures using a large training set and ensembled deep learning.”, Bioinformatics , in press (2021).
【224】P. Xiong, R. Wu, J. Zhan, and Y. Zhou, “Pairing a high-resolution statistical potential with a nucleobase-centric sampling algorithm for improving RNA model refinement.”, Nature Communications , 12，2777 (2021).
【223】(SPOT-RNA2) J. Singh, K. Paliwal, T. Zhang, J. Singh, T. Litfin, and Y. Zhou, Improved RNA secondary structure and tertiary base-pairing prediction using evolutionary profile, mutational coupling and two-dimensional transfer learning, Bioinformatics, in press (2021).
【222】M. Necci, D. Piovesan, M. T. Hoque, I. Walsh, S. Iqbal, M. Vendruscolo, P. Sormanni, C. Wang, D. Raimondi, R. Sharma, Y. Zhou, T. Litfin, O. V. Galzitskaya, M. Y. Lobanov, W. Vranken, B. Wallner, C. Mirabello, N. Malhis, Z. Dosztnyi, G. Erds, B. Mszros, J. Gao, K. Wang, G. Hu, Z. Wu, A. Sharma, J. Hanson, K. Paliwal, I. Callebaut, T. Bitard-Feildel, G. Orlando, Z. Peng, J. Xu, S. Wang, D. T. Jones, D. Cozzetto, F. Meng, J. Yan, J. Gsponer, J. Cheng, T. Wu, L. Kurgan, V. J. Promponas, S. Tamana, C. Marino-Buslje, E. Martnez-Prez, A. Chasapi, C. Ouzounis, A. K. Dunker, A. V. Kajava, J. Y. Leclercq, B. Aykac-Fas, M. Lambrughi, E. Maiani, E. Papaleo, L. B. Chemes, L. lvarez, N. S. Gonzlez-Foutel, V. Iglesias, J. Pujols, S. Ventura, N. Palopoli, G. I. Bentez, G. Parisi, C. Bassot, A. Elofsson, S. Govindarajan, J. Lamb, M. Salvatore, A. Hatos, A. M. Monzon, M. Bevilacqua, I. Mieti, G. Minervini, L. Paladin, F. Quaglia, E. Leonardi, N. Davey, T. Horvath, O. P. Kovacs, N. Murvai, R. Pancsa, E. Schad, B. Szabo, A. Tantos, S. Macedo-Ribeiro, J. A. Manso, P. J. B. Pereira, R. Davidovi, N. Veljkovic, B. Hajdu-Soltsz, M. Pajkos, T. Szaniszl, M. Guharoy, T. Lazar, M. Macossay-Castillo, P. Tompa, and S. C. Tosatto, Critical assessment of protein intrinsic disorder prediction, Nature Methods, 18, 472481 (2021).
【221】B. Zhou, B. Ji, K. Liu, G. Hu, F. Wang, Q. Chen, R. Yu, P. Huang, J. Ren, C. Guo, H. Zhao, H. Zhang, D. Zhao, Z. Li, Q. Zeng, J. Yu, Y. Bian, Z. Cao, S. Xu, Y. Yang, Y. Zhou, and J. Wang, EVLncRNAs 2.0: an updated database of manually curated functional long non-coding RNAs validated by low-throughput experiments, Nucleic Acids Research (Database Issue) 49, D86–D91 (2021).
【220】B. Zhao, A. Katuwawala, C. J. Oldfield, K. Dunker, E. Faraggi, J. Gsponer, A. Kloczkowski, N. Malhis, M. Mirdita, Z. Obradovic, J. Sding, M. Steinegger, Y. Zhou, and L. Kurgan, DescribePROT: Database of amino acid-level protein structure and function predictions, Nucleic Acids Research (Database Issue) 49, D298–D308 (2021).
【219】J. Atack, C. Guo, T. Litfin, L. Yang, P. Blackall, Y. Zhou, and M. Jennings, Systematic analysis of REBASE identifies numerous Type I restriction-modification systems that contain duplicated, variable hsds specificity genes that randomly switch methyltransferase specificity by recombination, mSystems 5, e00497–20 (2020).
【218】（RNAsnap2）A. Kumar, J. Singh, K. Paliwal, J. Singh, Y. Zhou, Single-sequence and profile-based Prediction of RNA solvent accessibility using dilated convolution neural network, Bioinformatics, 36, 5169–5176 (2020).
【217】Z. Cao, L. Liu, G. Hu, Y. Bian, H. Li, J. Wang, and Y. Zhou, Interplay of hydrophobic and hydrophilic interactions in sequence-dependent cell penetration of spontaneous membrane-translocating peptides revealed by bias-exchange metadynamics simulations, Biochimica et Biophysica Acta (BBA) - Biomembranes, 1862, 183402 (2020).
【216】A. Tan, L. V. Blakeway, Taha, Y. Yang, Y. Zhou, J. M. Atack, I. R. Peak, and K. L. Seib, Moraxella catarrhalis phase-variable loci show differences in expression during conditions relevant to disease, Scientific Reports, 15, e0234306 (2020).
【215】K. Wang, N. Lyu, H. Diao, S. Jin, T. Zeng, Y. Zhou, and R. Wu, GM-DockZn: A geometry matching based docking algorithm for zinc proteins, Bioinformatics, 36, 4004–4011 (2020).
【214】A. Barik, A. Katuwawala, J. Hanson, K. Paliwal, Y. Zhou, and L. Kurgan, DEPICTER: intrinsic disorder and disorder function prediction server, J. Molec. Biol., 48: 1451-1465 (2020).
【213】Z. Zhang, P. Xiong, T. Zhang, J. Wang, J. Zhan, and Y. Zhou, Accurate inference of the full base-pairing structure of RNA by deep mutational scanning and covariation-induced deviation of activity, Nucleic Acids Research, 48:1451-1465 (2020).
【212】Y. Cai, X. Li, Z. Sun, Y. Lu, H. Zhao, J. Hanson, K. Paliwal, T. Litfin, Y. Zhou, and Y. Yang, SPOT-Fold: Fragment-free protein structure prediction guided by predicted backbone structure and contact map, J. Computational Chemistry, 41: 745-750 (2020)
【211】J. M. Atack, C. Guo, L. Yang, Y. Zhou, and M. P. Jennings, DNA sequence repeats identify numerous Type I restriction-modification systems that are potential epigenetic regulators controlling phase variable regulons; phasevarions, FASEB J , 34: 1038-1051 (2020).
【210】J. L. Abrahams, G. Taherzadeh, G. Jarvas, A. Guttman, Y. Zhou, and M. P. Campbell, Recent advances in glycoinformatic platforms for glycomics and glycoproteomics, Current Opinion in Structural Biology, 62, 56-69 (2020).
【209】（SPOT-RNA）J. Singh, J. Hanson, K. Paliwal, and Y. Zhou, RNA secondary structure prediction using an ensemble of two-dimensional deep neural networks and transfer learning, Nature Communications 10, 5407 (2019).
【208】H. Lin, K. A. Hargreaves, R. Li, J. L. Reiter, Y. Wang, M. Mort, D. N. Cooper, Y. Zhou, C. Zhang, M. T. Eadon, M. E. Dolan, J. Ipe, T. Skaar, and Y. Liu, RegSNPs-intron: a computational framework for predicting pathogenic impact of intronic single nucleotide variants, Genome Biology, 20: 254 (2019).
【207】（SPOT-MoRF） J. Hanson, T. Litfin, K. Paliwal, and Y. Zhou, Identifying molecular recognition features in intrinsically disordered regions of proteins by transfer learning, Bioinformatics, 36: 1106-1107 (2020).
【206】T. Zhang, G. Hu, Y. Yang, J. Wang, and Y. Zhou, All-atom knowledge-based potential for RNA structure discrimination based on the distance-scaled finite ideal-gas reference state, J. Computational Biology, 27, 856-867 (2020).
【205】B. Hadley, T. Litfin, C. J. Day, T. Haselhorst, Y. Zhou, and J. Tiralongo, Nucleotide sugar transporter SLC35 family structure and function, Computational and Structural Biotechnology Journal, 17: 1123-1134 (2019).
【204】W. T. Clark, L. K. L, C. B. C, Z. Hu, G. Andreoletti, G. Babbi, Y. Bromberg, R. Casadio, R. Dunbrack, L. Folkman, C. T. Ford, D. Jones, P. Katsonis, K. Kundu, O. Lichtarge, P. L. Martelli, S. D. Mooney, C. Nodzak, L. R. Pal, P. Radivojac, C. Savojardo, X. Shi, Y. Zhou, A. Uppal, Q. Xu, Y. Yin, V. Pejaver, M. Wang, L. Wei, J. Moult, G. K. Yu, S. E. Brenner, and J. H. LeBowitz, Assessment of predicted enzymatic activity of alpha-n-acetylglucosaminidase (NAGLU) variants of unknown significance for CAGI 2016, Hum Mutation, 40: 1519–1529.(2019).
【203】（EVlncRNApred）B. Zhou, Y. Yang, J. Zhan, X. Dou, J. Wang, and Y. Zhou, Predicting functional long non-coding RNAs validated by low throughput experiments, RNA Biology, 16: 1555-1564 (2019).
【202】T. Wang, Y. Qiao, W. Ding, W. Mao, Y. Zhou, and H. Gong, Improved fragment sampling for ab initio protein structure prediction using deep neural networks, Nature Machine Intelligence 1, 347–355 (2019).
【201】J. Hanson, K. Paliwal, T. Lifin, Y. Yang, and Y. Zhou, Getting to know your neighbor: Protein structure prediction comes of age with contextual machine learning, J. Computational Biology, 27: 796–814 (2020).
【200】C. Savojardo, M. Petrosino, G. Babbi, S. Bovo, C. Corbi-Verge, R. Casadio, P. Fariselli, L. Folkman, A. Garg, M. Karimi, P. Katsonis, P. M. Kim, O. Lichtarge, P. L. Martelli, A. Pasquo, D. Pal, Y. Shen, A. V. Strokach, P. Turina, Y. Zhou, G. Andreoletti, R. Chiaraluce, V. Consalvi, and E. Capriotti, Evaluating the predictions of the protein stability change upon single amino acid substitutions for the FXN CAGI5 challenge, Human Mutation , 40: 1392–1399 (2019).
【199】V. Pejaver, G. Babbi, R. Casadio, L. Folkman, P. Katsonis, K. Kundu, O. Lichtarge, P. L. Martelli, M. Miller, J. Moult, L. R. Pal, C. Savojardo, Y. Yin, Y. Zhou, P. Radivojac, and Y. Bromberg, Assessment of methods for predicting the effects of PTEN and TPMT protein variants, Human Mutation, 40: 1495–1506. (2019).
【198】（DLIGAND2）P. Chen, Y. Ke, H. Zhao, Y. Lu, Y. Du, J. Li, H. Yan, Y. Zhou, and Y. Yang, DLIGAND2: An improved knowledge-based energy function for protein-ligand interactions using the distance-scaled, finite, ideal-gas reference state, J. Cheminformatics ,11:52 (2019).
【197】（RELISH）P. Brown, RELISH Consortium, and Y. Zhou, Large expert-curated database for benchmarking document similarity in biomedical literature search, Database, 2019, baz085 (2019).
【196】（SPRINT-Gly）G. Taherzadeh, A. Dehzangi, M. Golchin, Y. Zhou, and M. P. Campbell, SPRINT-Gly: Predicting N- and O-linked glycosylation sites of human and mouse proteins by using sequence and predicted structural properties, Bioinformatics, 35: 4140-4146 (2019).
【195】（SPOT-Disorder 2）J. Hanson, K. Paliwal, T. Litfin, and Y. Zhou, SPOT-Disorder2: Improved Protein Intrinsic Disorder Prediction by Ensembled Deep Learning, Genomics, Proteomics & Bioinformatics, 17: 645-656 (2019).
【194】Z. Cao, X. Zhang, C. Wang, L. Liu, L. Zhao, J. Wang, and Y. Zhou, Different effects of cholesterol on membrane permeation of arginine and tryptophan revealed by bias-exchange metadynamics simulation, J. Chem. Phys. 150, 084106 (2019).
【193】（SPOT-Peptide）T. Litfin, Y. Yang, and Y. Zhou, SPOT-peptide: Template-based prediction of peptide-binding proteins and peptide-binding sites, Journal of Chemical Information and Modeling, 59: 924-930 (2019).
【192】（SPOT-1D）J. Hanson, K. Paliwal, T. Litfin, Y. Yang, and Y. Zhou, 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: 2403–2410 (2019).
【191】M. Bajzikova, J. Kovarova, A. R. Coelho, S. Boukalova, S. Oh, K. Rohlenova, D. Svec, S. Hubackova, B. Endaya, K. Judasova, A. Bezawork-Geleta, K. Kluckova, L. Chatre, R. Zobalova, A. Novakova, K. Vanova, Z. Ezrova, G. J. Maghzal, S. M. Novais, M. Olsinova, L. Krobova, Y. J. An, E. Davidova, Z. Nahacka, M. Sobol, T. Cunha-Oliveira, C. S.-A. Sandoval-Acuna, H. Strnad, T. Zhang, T. Huynh, T. L. Serafim, P. Hozak, V. A. Sardao, W. J. H. Koopman, M. Ricchetti, P. J. Oliveira, F. Kolar, M. Kubista, J. Truksa, K. Dvorakova-Hortova, K. Pacak, R. Gurlich, R. Stocker, Y. Zhou, M. V. Berridge, S. Park, L. Dong, J. Rohlena, and J. Neuzil, Reactivation of dihydroorotate dehydrogenase by respiration restores tumor growth of mitochondrial DNA-depleted cancer cells, Cell Metabolism, 29, 399–416 (2019).
【190】J. Zhan, H. Jia, E. A. Semchenko, Y. Bian, A. M. Zhou, Z. Li, Y. Yang, J. Wang, S. Sarkar, M. Totsika, H. Blanchard, F. E.-C. Jen, Q. Ye, T. Haselhorst, M. P. Jennings, K. L. Seib, and Y. Zhou, Self-derived structure-disrupting peptides targeting methionine aminopeptidase in pathogenic bacteria; a new strategy to generate antimicrobial peptides, FASEB J. , 33: 2095–2104 (2019).
【189】（SPOT-Disorder-Single）J. Hanson, K. Paliwal, and Y. Zhou, Accurate single-sequence prediction of protein intrinsic disorder by an ensemble of deep recurrent and convolutional architectures, Journal of Chemical Information and Modeling, 58: 2369–2376 (2018).
【188】（SPOT-Omega）J. Singh, J. Hanson, R. Heffernan, K. Paliwal, Y. Yang, and Y. Zhou, Detecting proline and non-proline cis-isomers in protein structures from sequences using deep residual ensemble learning, Journal of Chemical Information and Modeling, 58: 2033–2042 (2018).
【187】J. Tiralongo, O. Cooper, T. Litfin, Y. Yang, R. King, J. Zhan, H. Zhao, N. Bovin, C. Day, and Y. Zhou, YesU from Bacillus subtilis preferentially binds fucosylated glycans, Scientific Reports, 8, 13139 (2018).
【186】（SPIDER3-Single）R. Heffernan, K. Paliwal, J. Lyons, J. Singh, Y. Yang, and Y. Zhou, Single-sequence-based prediction of protein secondary structures and solvent accessibility by deep whole-sequence learning, J. Computational Chemistry, 39, 2210-2216 (2018).
【185】（SPOT-Contact）J. Hanson, K. Paliwal, T. Litfin, Y. Yang, and Y. Zhou, Accurate prediction of protein contact maps by coupling residual two-dimensional bidirectional long short-term memory with convolutional neural networks, Bioinformatics, 34: 4039–4045 (2018).
【184】L. Zhao, Z. Cao, Y. Bian, G. Hu, J. Wang, and Y. Zhou, Molecular dynamics simulations of human antimicrobial peptide LL-37 in model POPC and POPG lipid bilayers, Int. J. Mole. Sciences 19, 1186 (2018).
【183】（SPRINT-Mal）G. Taherzadeh, Y. Yang, H. Xu, Y. Xue, A. W.-C. Liew, and Y. Zhou, Predicting lysine-malonylation sites of proteins using sequence and predicted structural features, J. Computational Chemistry, 39, 1757–1763 (2018).
【182】Z. Cao, Y. Bian, G. Hu, L. Zhao, Z. Kong, Y. Yang, J. Wang, and Y. Zhou, Bias-exchange metadynamics simulation of membrane permeation of 20 amino acids, International Journal of Molecular Sciences, 19, 885 (2018).
【181】(Book Chapter) B. Zhou, H. Zhao, J. Yu, C. Guo, X. Dou, F. Song, G. Hu, Z. Cao, Y. Qu, Y. Yang, Y. Zhou, and J. Wang, Experimentally validated plant lncRNAs in EVLncRNAs database, in Methods in Molecular Biology: Plant long Non-coding RNAs: Methods and Protocols, 1933, 431-437, 2019.
【180】J. M. Atack, Y. Yang, K. L. Seib, Y. Zhou, and M. P. Jennings, A survey of Type III restriction-modification systems reveals numerous, novel epigenetic regulators controlling phase-variable regulons; phasevarions, Nucleic Acids Research, 46, 3532–3542 (2018).
【179】（SPIN 2）J. O’Connell, Z. Li, J. Hanson, R. Heffernan, J. Lyons, K. Paliwal, A. Dehzangi, Y. Yang, and Y. Zhou, SPIN2: Predicting sequence profiles from protein structures using deep neural networks Proteins, 86: 629-633 (2018).
【178】M. Khorramdelazad, I. Bar, P. Whatmore, G. Smetham, V. Bhaaskaria, Y. Yang, S. H. Bai, N. Mantri, Y. Zhou, and R. Ford, Transcriptome profiling of lentil (Lens culinaris) through the first 24 hours of Ascochyta lentis infection reveals key defence response genes, BMC Genomics 19, 108 (2018).
【177】（SPIDER 2 -Grid）J. Gao, Y. Yang and Y. Zhou, Grid-based prediction of torsion angle probabilities of protein backbone and its application to discrimination of protein intrinsic disorder regions and selection of model structures, BMC Bioinformatics, 19, 29 (2018)
【176】（RNAflex）I. Guruge, G. Taherzadeh, J. Zhan, Y. Zhou, and Y. Yang, B-factor profile prediction for RNA flexibility using support vector machines, J. Comput. Chem. 39, 407-411 (2018)
【175】H. Zhao, Y. Yang, Y. Lu, M. Mort, D. N. Cooper, and Y. Zhou, Quantitative mapping of genetic similarity in human heritable diseases by shared mutations, Human Mutation, 39, 292-301 (2018).
【174】（SPRINT）G. Taherzadeh, Y. Zhou, A. W.-C. Liew, and Y. Yang, Structure-based prediction of protein-peptide binding regions using random forest, Bioinformatics, 34, 477-484 (2018).
【173】(Book Chapter) H. Zhao, G. Taherzadeh, Y. Zhou, and Y. Yang, Computational prediction of carbohydrate-binding proteins and binding sites, in Current Protocols in Protein Science, edited by G. Taylor, 94, e75 (2018).
【172】B. Zhou, H. Zhao, J. Yu, C. Guo, X. Dou, F. Song, G. Hu, Z. Cao, Y. Qu, Y. Yang, Y. Zhou, and J. Wang, EVLncRNAs: a manually curated database for long non-coding RNAs validated by low throughput experiments, Nucleic Acids Research, 46, D100-D105 (2018).
【171】P. Brown and Y. Zhou, Biomedical literature: Testers wanted for article search tool, Nature 549, 31 (2017).
【170】C. Jegousse, Y. Yang, J. Zhan, J. Wang, and Y. Zhou, Structural signatures of thermal adaptation of bacterial ribosomal RNA, transfer RNA, and messenger RNA, PLOS One 12, e0184722 (2017).
【169】（DDIG）M. Livingstone, L. Folkman, Y. Yang, P. Zhang, M. Mort, D. N. Cooper, Y. Liu, B. Stantic, and Y. Zhou, Investigating DNA, RNA and protein-based features as a means to discriminate pathogenic synonymous variants, Human Mutation 38: 1336-1347 (2017).
【168】M. Carraro, G. Minervini, M. Giollo, Y. Bromberg, E. Capriotti, R. Casadio, R. L. Dunbrack, L. Elefanti, P. Fariselli, C. Ferrari, J. Gough, P. Katsonis, E. Leonardi, O. Lichtarge, C. Menin, P. L. Martelli, A. Niroula, L. Pal, S. Repo, M. C. Scaini, M. Vihinen, Q. Wei, Q. Xu, Y. Yang, Y. Yin, J. Zaucha, H. Zhao, Y. Zhou, S. Brenner, J. Moult, and S. C. Tosatto, Performance of in silico tools for the evaluation of p16INK4a (CDKN2A) variants in CAGI, Human Mutation, 38: 1042-1050 (2017).
【167】（SPIDER 3）R. Heffernan, Y. Yang, K. Paliwal, and Y. Zhou, Capturing non-local interactions by long short term memory bidirectional recurrent neural networks for improving prediction of protein secondary structure, backbone angles, contact numbers, and solvent accessibility, Bioinformatics, 33: 2842-2849 (2017)
【166】X. Zhang, M. Li, H. Lin, X. Rao, W. Feng, Y. Yang, M. Mort, D. N. Cooper, Y. Wang, Y. Wang, C. Wells, Y. Zhou, and Y. Liu, regSNPs-splicing: a tool for prioritizing synonymous single-nucleotide substitution, Human Genetics,136: 1279–1289 (2017).
【165】J.-F. Yu, X.-H. Dou, Y.-J. Sha, C.-L. Wang, B.-H. Wang, Y.-T. Chen, F. Zhang, Y. Zhou, and J.-H. Wang, DisBind: A database of classified functional binding sites in disordered and structured regions of intrinsically disordered proteins, BMC Bioinformatics 18, 206 (2017).
【164】S. Xu, J. Zhan, B. Man, S. Jiang, W. Yue, S. Gao, C. Guo, H. Liu, Z. Li, J. Wang, and Y. Zhou, Real-time reliable determination of binding kinetics of DNA hybridization using a multi-channel graphene biosensor, Nature Communications 8, 14902 (2017).
【163】H. Cao, W. Du, Y. Yang, Y. Shang, G. Li, Y. Zhou, Q. Ma, and Y. Xu, Systems-level understanding of ethanol-induced stresses and adaptation in E. coli, Scientific Reports 7, 44150 (2017).
【162】（SPOT-Ligand 2）T. Litfin, Y. Zhou and Y. Yang, SPOT-Ligand 2: Improving structure-based virtual screening by binding-homology search on an expanded structural template library, Bioinformatics, 33 1238–1240 (2017).
【161】Y. Yang, J. Gao, J. Wang, R. Heffernan, J. Hanson, K. Paliwal and Y. Zhou, Sixty-five years of the long march in protein secondary structure prediction: the final stretch?, Briefings in Bioinformatics, 19, 482–494 (2018).
【160】（RNAsnap）Y. Yang, X. Li, H. Zhao, J. Zhan, J. Wang and Y. Zhou, Genome-scale characterization of RNA tertiary structures and their functional impact by RNA solvent accessibility prediction, RNA, 23: 14-22 (2017).
【159】（SPOT-Disorder）J. Hanson, Y. Yang, K. Paliwal, and Y. Zhou, Improving protein disorder prediction by deep bidirectional long short-term memory recurrent neural networks, Bioinformatics, 33: 685–692 (2017).
【158】T. Wang, Y. Yang, Y. Zhou, and H. Gong, LRFragLib: an effective algorithm to identify fragments for de novo protein structure prediction, Bioinformatics, 33: 677-684 (2017).
【157】M. Li, W. Feng, X. Zhang, Y. Yang, K.Wang, M. Mort, D. Cooper, Y.Wang, Y. Zhou, and Y. Liu, Exonimpact: Prioritizing pathogenic alternative splicing events, Human Mutation, 38: 16-24 (2017).
【156】(Book Chapter) T. Zhang, E. Faraggi, Z. Li, and Y. Zhou, Intrinsic disorder and semi-disorder prediction by SPINE-D, Methods Mol Biol. 1484:159-174 Prediction of Protein Secondary Structure, Methods in Molecular Biology, edited by Y. Zhou, A. Kloczkowski, E. Faraggi, and Y. Yang, Springer Science+Business Media, Humana Press, New York.
【155】(Book Chapter) E. Faraggi, M. Kouza, Y. Zhou, and A. Kloczkowski, Fast and accurate accessible surface area prediction without a sequence profile, Methods Mol Biol. 1484:127-136. Prediction of Protein Secondary Structure, Methods in Molecular Biology, edited by Y. Zhou, A. Kloczkowski, E. Faraggi, and Y. Yang, Springer Science+Business Media, Humana Press, New York.
【154】(Book Chapter) Y. Yang, R. Heffernan, K. Paliwal, J. Lyons, A. Dehzangi, A. Sharma, J. Wang, A. Sattar, and Y. Zhou, SPIDER2: A package to predict secondary structure, accessible surface area, and main-chain torsional angles by deep neural networks, Methods in Mol Biol. 1484: 55-63. Prediction of Protein Secondary Structure, Methods in Molecular Biology, edited by Y. Zhou, A. Kloczkowski, E. Faraggi, and Y. Yang, Springer Science+Business Media, Humana Press, New York.
【153】（SPRINT-CBH）G. Taherzadeh, Y. Zhou, A. W. Liew, and Y. Yang, Sequence-based Prediction of Protein-Carbohydrate Binding Sites Using Support Vector Machines , Journal of Chemical Information and Modeling, 56, 2115–2122 (2016).
【152】（SPIDER-delta）J. Gao, Y. Yang and Y. Zhou, Predicting the errors of predicted local backbone angles and nonlocal solvent-accessibilities of proteins by deep neural networks, Bioinformatics, 32: 3768-3773 (2016).
【151】D. Stanisic, J. Gerrard, J. Fink, P. Griffin, X. Liu, L. Sundac, S. Sekuloski, I. Rodriguez, J. Pingnet, Y. Yang, Y. Zhou, K. Trenholme, C. Wang, H. Hackett, J.-A. Chan, C. Langer, E. Hanssen, S. Hoffman, J. Beeson, J. McCarthy, and M. Good, Infectivity of Plasmodium falciparum in malaria-naïve individuals is related to knob expression and cytoadherence of the parasite, Infection and Immunity, 84, 2689-2696 (2016).
【150】W. Zhang, M. Yang, Y. Yang, J. Zhan, Y. Zhou and X. Zhao, Optimal Secretion of Alkali-tolerant Xylanase in Bacillus subtilis by Signal Peptide Screening, Applied Microbiology and Biotechnology, 100, 8745-8756 (2016).
【149】G. Ni, S. Chen, Y. Yang, S. F. Cummins, J. Zhan, Z. Li, B. Zhu, K. Mounsey, S. Walton, M. Q. Wei, Y. Wang, Y. Zhou, T. Wang, and X. Liu, Investigation of the Possibility of Using Peptides with a Helical Repeating Pattern of Hydrophobic and Hydrophilic Residues to Inhibit IL-10, PLoS ONE, 11, e0153939 (2016).
【148】（SPOT-ligand）Y. Yang, J. Zhan and Y. Zhou, SPOT-Ligand: Fast and effective structure-based virtual screening by binding homology search according to ligand and receptor similarity, Journal of Computational Chemistry, 37, 1734-1739, (2016).
【147】（EASE-MM）L. Folkman, B. Stantic, A. Sattar, and Y. Zhou, EASE-MM: Sequence-Based Prediction of Mutation-Induced Stability Changes with Feature-Based Multiple Models, Journal of Molecular Biology, 428, 1394-1405 (2016).
【146】M. T. Hoque, Y. Yang, A. Mishra and Yaoqi Zhou, sDFIRE: Sequence‐specific statistical energy function for protein structure prediction by decoy selections, Journal of Computational Chemistry, 37, 1119-1124 (2016).
【145】J. Yu, Z. Cao, Y. Yang, C. Wang, Z. Su, Y. Zhao, J. Wang and Y. Zhou, Natural protein sequences are more intrinsically disordered than random sequences, Cellular and Molecular Life Sciences, 73, 2949-2957 (2016).
【144】（SPRINT）G. Taherzadeh, Y. Yang, T. Zhang, A. W. Liew and Y. Zhou, Sequence-based prediction of protein-peptide binding sites using Support Vector Machine, Journal of Computational Chemistry, 37, 1223-1229 (2016).
【143】P. Brown, Y. Yang, Y. Zhou, and W. Pullan, A Heuristic for the Time Constrained Asymmetric Linear Sum Assignment Problem, Journal of Combinatorial Optimization, 33, 551-566 (2017).
【142】Y. Yang and Y. Zhou, Effective protein conformational sampling based on predicted torsion angles, J. Comput. Chem. 37, 976-980 (2016)
【141】（SPIDER2）R. Heffernan, A. Dehzangi, J. Lyons, K. Paliwal, A. Sharma, J. Wang, A. Sattar, Y. Zhou and Yuedong Yang, Highly Accurate Sequence-based Prediction of Half-Sphere Exposures of Amino Acid Residues in Proteins, Bioinformatics, 32, 843-849 (2016)
【140】（SPalign-NS）P. Brown, W. Pullan, Y. Yang and Y. Zhou, Fast and accurate non-sequential protein structure alignment using a new asymmetric linear sum assignment heuristic, Bioinformatics, 32, 370-377 (2016).
【139】J. Lyons, A. Dehzangi, R. Heffernan, Y. Yang, Y. Zhou, A. Sharma and K. Paliwal, Advancing the Accuracy of Protein Fold Recognition by Utilizing Profiles from Hidden Markov Models, IEEE Transactions on NanoBioscience, 14, 761–772 (2015).
【138】（SPIDER2）R. Heffernan, K. Paliwal, J. Lyons, A. Dehzangi, A. Sharma, J. Wang, A. Sattar, Y. Yang and Y. Zhou, Improving prediction of secondary structure, local backbone angles, and solvent accessible surface area of proteins by iterative deep learning, Scientific Reports, 5 11476 (2015).
【137】（DDIG-in FS/NS）L. Folkman, Y. Yang, Z. Li, B. Stantic, A. Sattar, M. Mort, D. N. Cooper, Y. Liu, and Y. Zhou, DDIG-in: detecting disease-causing genetic variations due to frameshifting indels and nonsense mutations employing sequence and structural properties at nucleotide and protein levels, Bioinformatics, 31 1599–1606 (2015).