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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., in press (2020). Direct Link

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 , in press (2020). Sequencing Data CODA Code

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. Comput. Biol. , in press (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 , in press (2020).TBA

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).PDF

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). Open Access Server/Programs/Datasets

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). Open Access

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, in press (2019). [TBA][Server/Datasets][On line link]

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, in press (2019). [DFIRE-RNA source code]

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). [Online link].[PDF]

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). [Online link].[PDF]

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, in press (2019).[TBA][Server].

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).[READ]News & Views

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, in press (2019). [TBA] [Online link]

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). [PDF]

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).[PDF]

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). [PDF] [Software].

197.

RELISH
P. Brown, RELISH Consortium, and Y. Zhou, “Large expert-curated database for benchmarking document similarity in biomedical literature search”, Database, in press (2019).[TBA] [Server & Datasets]

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 , in press (2019). [TBA] [Server & Datasets][Standalone Copy]

195.

SPOT-Disorder 2
J. Hanson, K. Paliwal, T. Litfin, and Y. Zhou, “Enhancing protein intrinsic disorder prediction by utilizing deep squeeze and excitation residual inception and long short-term memory networks.”, Genomics, Proteomics & Bioinformatics, in press (2019). [TBA] [Server][Datasets]

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). [PDF]

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). [PDF][Server & Datasets]

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). [PDF][Server][Datasets]

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). [PDF]

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). [PDF]

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). [PDF][Server][Datasets]

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.”, J. Chem. Info. Modelling , 58: 2033–2042 (2018). [PDF][Server & Datasets]

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). [Free Access]

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). [PDF][Server][9993 train][1000 validation][1199 test]

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). [PDF][Server]

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).[PDF]

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). [PDF][Server]

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). [PDF]

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). [PDF]

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). [PDF][Server]

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). [PDF]

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) [PDF][Server]

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) [PDF][Server]

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). [PDF]

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). [PDF][Server & Datasets]

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). [Direct Link][Database Website]

171

P. Brown and Y. Zhou, “Biomedical literature: Testers wanted for article search tool.”, Nature 549, 31 (2017) [Non-peer-reviewed correspondence]. [Direct Link]

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). [PDF]

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). [PDF] [Servers]

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). [PDF]

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) [PDF] [Server & Datasets]

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). [PDF]

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). [PDF]

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). [PDF]

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). [PDF]

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). [PDF][Server & Datasets]

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).[PDF] [Test115][Direct Link]

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).[PDF] [Server & Datasets]

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).[PDF][Server][Datasets]

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).[PDF]

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). [PDF]

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. [PDF]

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. [PDF]

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. [PDF]

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). [PDF] [Server & Datasets]

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). [PDF] [Server & Datasets]

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). [PDF]

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). [PDF]

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). [PDF]

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). [PDF] [Server & Datasets]

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). [PDF] [Server & Datasets]

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). [PDF]

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). [PDF][Dataset Pnat][Dataset Prnd][Dataset Peqp]

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). [PDF] [Server & Datasets]

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). [PDF]

142

Y. Yang and Y. Zhou, “Effective protein conformational sampling based on predicted torsion angles”, J. Comput. Chem. 37, 976-980 (2016) [PDF]

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) [PDF] [Server & Datasets]

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). [PDF][Server]

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). [PDF]

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).
[PDF][Server & Datasets]

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).
[PDF][Server & Datasets]

136


E. Faraggi, Y. Zhou, and A. Kloczkowski, “Accurate single-sequence prediction of solvent accessible surface area using local and global features.”, Proteins, 82 3170-3176 (2014)
[PDF]

135

SPOT-Glycan
H. Zhao, Y. Yang, M. von Itzstein, and Y. Zhou, “Carbohydrate-binding protein identification by coupling structural similarity search with binding affinity prediction.”, J. Comput. Chem., 35, 2177-2183 (2014) [PDF with cover image]

134

SPIN
Z. Li, Y. Yang, E. Faraggi, J. Zhan, and Y. Zhou, “Direct prediction of the profile of sequences compatible to a protein structure by neural networks with fragment-based local and energy-based nonlocal profiles.”, Proteins, 82, 2565-2573 (2014). [PDF] [Server]

133

SPIDER
J. Lyons, A. Dehzangi, R. Heffernan, A. Sharma, K. Paliwal, A. Sattar, Y. Zhou, and Y. Yang, “Predicting backbone Cα angles and dihedrals from protein sequences by stacked sparse auto-encoder deep neural network.”, J. Comp. Chem. 35, 2040-2046 (2014)[PDF] [Server]

132

SPOT-DNA (SEQ)
H. Zhao, J. Wang, Y. Zhou, and Y. Yang “Predicting DNA-binding proteins and binding residues by complex structure prediction and application to human proteome.”, PLOS one, 9, e96694 (2014). [PDF] [Server]

131


X. Zhang, H. Lin, H. Zhao, M. Mort, D. N. Cooper, Y. Zhou, and Y. Liu, “Impact of human pathogenic micro-insertions and micro-deletions on post-transcriptional regulation.”, Human Molecular Genetics, 23, 3024–3034 (2014). [PDF]

130

LEAP
S. Liang, C. Zhang, and Y. Zhou, “LEAP: Highly accurate prediction of protein loop conformations by integrating coarse-grained sampling and optimized energy scores with all- atom refinement of backbone and side chains”, J. Comput. Chem. 35, 335-341 (2014). [PDF] [Program Downloads] [Dataset/loop ID program Downloads] [200 Protein Test Set]

129

SPOT-RNA (SEQ)
H. Zhao, Y. Yang, S. C. Janga, C. Kao, and Y. Zhou, “Prediction and validation of the unexplored RNA-binding protein atlas of the human proteome”, Proteins, 82 640-647 (2014). [PDF][List of >2000 Novel RBPs] [Server]

128

Y. Yang, H. Zhao, J. Wang, and Y. Zhou, “SPOT-Seq: Predicting protein-RNA complex structure and RNA-binding function by fold recognition and binding affinity prediction”, Methods in Molecular Biology (Protein Structure Prediction, 3rd Edition), Edited by D. Kihara, 1137, 119-130 (2014).

127

B.-R. Zhou, H. Feng, H. Kato, L. Dai, Y. Yang, Y. Zhou and Y. Bai, “Structural insights into the histone H1-nucleosome complex”, Proc. Natl. Acad. Sci. (USA) 110, 19390-19395 (2013). [PDF]

126

J. Wang, Y. Yang, Z. Cao, Z. Li, H. Zhao and Y. Zhou, “The role of semi-disorder in temperature adaptation of bacterial FlgM proteins”, Biophys. J. 105, 2598-2605 (2013).[PDF]

125

H. Zhao, Y. Yang and Y. Zhou, “Prediction of RNA binding proteins comes of age from low resolution to high resolution”, Molecular Biosystems 9 , 2417-2425 (2013). [PDF] [RBP106] [RBP-S20] [RBP-N67]

124

T. Zhang, E. Faraggi, Z. Li, and Y. Zhou, “Intrinsically semi-disordered state and its role in induced folding and protein aggregation “, Cell Biochemistry & Biophys 67, 1193-1205 (2013). [PDF]

123

DDIG-in
H. Zhao, Y. Yang, H. Lin, X. Zhang, M. Mort, D. N. Cooper, Y. Liu and Y. Zhou, “DDIG-in: Discriminating between disease-associated and neutral non-frameshifting micro-indels”, Genome Biology 14 , R43 (2013). [PDF][DDIG-in Server] [Disease INDEL set][Neutral INDEL set]

122

Z. Li, Y. Yang, J. Zhan, L. Dai and Y. Zhou, “Energy Functions in De Novo Protein Design: Current Challenges and Future Prospects”, Ann. Rev. Biophysics 42, 315-335 (2013). [E-reprint PDF] [List of Proteins]

121

J. Gao, E. Faraggi, Y. Zhou, J. Ruan, and L. Kurgan, “BEST: Improved Prediction of B-Cell Epitopes from Antigen Sequences”, PLoS one 7, e40104 (2012). [PDF]

120

SPalign
Y. Yang, J. Zhan, H. Zhao, and Y. Zhou, “A new size-independent score for pairwise protein structure alignment and its assessment by structure classification and nucleic-acid binding prediction”, Proteins 80, 2080-2088 (2012). [PDF][SPalign Programs and Server ]

119

T. Lu, Y. Yang, B. Yao, S. Liu, Y. Zhou, and C. Zhang, “Template-based structure prediction and classification of transcription factors in arabidopsis thaliana.”, Protein Science 21, 828-838 (2012).[PDF]

118

SPINE-D
T. Zhang, E. Faraggi, B. Xue, A. K. Dunker, V. N. Uversky and Y. Zhou, “SPINE-D: Accurate prediction of short and long disordered regions by a single neural-network-based method”, J. Biomol. Struc. Dyn. 29 , 799-813 (2012). [PDF][Server][Datasets]

117

SPINE X
E. Faraggi, T. Zhang, Y. Yang, L. Kurgan and Y. Zhou, “SPINE X: Improving protein secondary structure prediction by multi-step learning coupled with prediction of solvent accessible surface area and backbone torsion angles, J. Comput. Chem.” 33 , 259-267 (2012). [PDF][SPINE X Server]

116

S. J. Fleishman, T. A. Whitehead, E.-M. Strauch, J. E. Corn, S. Qin, H.-X. Zhou, J. C. Mitchell, O. N. Demerdash, M. Takeda-Shitaka, G. Terashi, I. H. Moal, X. Li, P. A. Bates, M. Zacharias, H. Park, J. Ko, H. Lee, C. Seok, T. Bourquard, J. Bernauer, A. Poupon, J. Aze, S. Soner, S. K. Ovali, P. Ozbek, N. Ben Tal, T. Haliloglu, H. Hwang, T. Vreven, B. G. Pierce, Z. Weng, L. Perez-Cano, C. Pons, J. Fernadez-Recio, F. Jiang, F. Yang, X. Gong, L. Cao, X. Xu, B. Liu, P. Wang, C. Li, C. Wang, C. H. Robert, M. Guharoy, S. Liu, Y. Huang, L. Li , D. Guo, Y. Chen, Y. Xiao, N. London, Z. Itzhaki, O. Schueler-Furman, Y. Inbar, V. Patapov, M. Cohen, G. Schreiber, Y. Tsuchiya, E. Kanamori, D. M. Standley, H. Nakamura, K. Kinoshita, C. M. Driggers, R. G. Hall, J. L. Morgan, V. L. Hsu, J. Zhan, Y. Yang, Y. Zhou, P. L. Kastritis, A. M. J. J. Bonvin, W. Zhang, C. J. Camacho, K. P. Kilambi, A. Sircar, J. J. Gray, M, Ohue, N. Uchikoga, Y. Matsuzaki, T. Ishida, Y. Akiyama, R. Khashan, S. Bush, D. Fouches, A. Tropsha, J. Esquivel-Rodrigez, D. Kihara, P. B. Stranges, R. Jacak, B. Kuhlman, S. Huang, X. Zou, S. J. Wodak, J. Janin, and D. Baker, “Community-wide assessment of protein-interface modeling suggests improvements to design methodology”, J. Molec. Biol. 114, 289-302 (2011). [pdf]

115

SPOT
H. Zhao, Y. Yang, and Y. Zhou, “Highly accurate and high-resolution function prediction of RNA binding proteins by fold recognition and binding affinity prediction”, RNA Biology 8, 988-996 (2011). [pdf]SPOT server for RNA-binding prediction ][RB-C174][RB-C257]

114

SPARKS X
Y. Yang, E. Faraggi, H. Zhao and Y. Zhou, “Improving protein fold recognition and template-based modeling by employing probabilistic-based matching between predicted one-dimensional structural properties of the query and corresponding native properties of templates” Bioinformatics 27, 2076-2082 (2011). [PDF][Journal access] [Server]

113

M. Mizianty, T. Zhang, B. Xue, Y. Zhou, A. K. Dunker, V. N. Uversky and L. Kurgan, “In-silico prediction of disorder content using hybrid sequence representation”, BMC Bioinformatics 12, 245 (2011). [Open Access]

112

H. Cheng, W. S. Chan, Z. Li, D. Wang, S. Liu, Y. Zhou, “Small open reading frames: Current prediction techniques and future prospect” Current Protein and Peptide Science 12, 503-507 (2011). [PDF] [Positive] [Negative Datasets]

111

L. Dai and Y. Zhou, “Characterizing the existing and potential structural space of proteins by large-scale multiple loop permutations” J. Molec. Biol. 408, 585-595 (2011). [PDF] [MLP2843 database]

110

OSCAR
S. Liang, Y. Zhou, N. Grishin, and D. M. Standley, “Protein side chain modeling with orientation dependent atomic force fields derived by series expansions.”, J. Comput. Chem. 32, 1680-1686 (2011). [PDF]

109

SPOT
H. Zhao, Y. Yang, and Y. Zhou, “Structure-based prediction of RNA-binding domains and RNA-binding sites and application to structural genomics targets” Nucleic Acids Research 39, 3017-3025 (2011). [pdf][SPOT server for RNA-binding prediction ] [Dataset]

108

Y. Zhou, Y. Duan, Y. Yang, E. Faraggi, H. Lei, “Trends in template/fragment-free protein structure prediction” (Invited feature article) Theor. Chem. Accounts 128, 3-16 (2011). [Open Access PDF]

107

T. Zhang, E. Faraggi, and Y. Zhou, “Fluctuations of backbone torsion angles obtained from NMR-determined structures and their prediction.” Proteins 78, 3353-3362 (2010). (SPINE-Tau is a part of SPINE-D distribution) [PDF][Dataset]

106

SPOT
H. Zhao, Y. Yang, and Y. Zhou, “Structure-based prediction of DNA-binding proteins by structural alignment and a volume-fraction corrected DFIRE-based energy function.” Bioinformatics 26, 1857-1863 (2010). [PDF][SPOT server and DDNA3 energy function]

105

RosettaDesign-SR
L. Dai, Y. Yang, H. Kim and Y. Zhou, “Improving computational protein design by using structure-derived sequence profile.” Proteins 78, 2338-2348 (2010). [PDF]

104

Y. Zhou and E. Faraggi, “Prediction of one-dimensional structural properties of proteins by integrated neural network” in H. Rangwala and G. Karypis (Eds.) Protein Structure Prediction: Method and Algorithms (Chapter 4, pp. 45-74), Hoboken, NJ: Wiley (2010). [PDF]

103

SPINE XI
E. Faraggi, Y. Yang, S. Zhang and Y. Zhou, “Predicting continuous local structure and the effect of its substitution for secondary structure in fragment-free protein structure prediction”, Structure 17,1515-1527 (2009) [PDF] [SPINE X Server][SPINE XI Server]

102

DDNA2
B. Xu, Y. Yang, H. Liang and Y. Zhou, “An all-atom knowledge-based energy function for protein-DNA threading, decoy discrimination, and prediction of transcription-factor binding profiles”, Proteins 76, 718-730 (2009) [PDF] [Corrected Table 4] [DDNA 2 Download ] [List of Protein-DNA complexes]

101

DEMPIRE
S. Liang, G. Wang, and Y. Zhou, “Refining near-native protein-protein docking decoys by local re-sampling and energy minimization”, Proteins 76, 309-316 (2009) [PDF]

100

SPINE-2D
B. Xue, E. Faraggi, and Y. Zhou, “Predicting residue-residue contact maps by a two-layer, integrated neural-network method.”, Proteins 76, 176-183 (2009) SPINE-2D server[PDF] [Download] [500 Training] [500 Testing]

099

S. Liang, L. Li, W.L. Hsu, V. N. Uversky, Y. Zhou, A. K. Dunker and S. O. Meroueh, “Exploring the molecular design of protein interaction sites with molecular dynamics and free energy calculations”, Biochemistry 48, 399-414 (2009).[PDF]

098

ENDES
S. Liang, S. O. Meroueh, Guangce Wang, Chao Qiu and Y. Zhou, “Consensus scoring for enriching near-native structures from protein-protein docking decoys”, Proteins 75, 397-403 (2009) ENDES download ] [ENDES download] [PDF]

097

Real-SPINE 3.0
E. Faraggi, B. Xue, and Y. Zhou, “Improving the prediction accuracy of residue solvent accessibility and real-value backbone torsion angles of proteins by fast guided-learning through a two-layer neural network.”, Proteins 74, 857-871 (2009) Real-SPINE 3.0 server ][PDF] [Data Set]

096

H. Lei, C. Wu, Z. X. Wang, Y. Zhou, and Y. Duan, “Folding processes of the B domain of protein A to the native state observed in all-atom ab initio folding simulations”, J. Chem. Phys.128, 235105 (2008). [PDF]

095

Z. Luo, J. Ding and Y. Zhou, “Folding mechanisms of individual beta-hairpins in a Go model of Pin1 WW domain by all-atom molecular dynamics simulations”, J. Chem. Phys. 128, 225103 (2008)[PDF]

094

SP5
W. Zhang, S. Liu and Y. Zhou, “SP5: Improving protein fold recognition by using predicted torsion angles and profile-based gap penalty”, PLoS ONE 3, e2325 (2008)[PDF] [SP5 server] [List of Prefab pairs]

093

DFIRE2
Y. Yang and Y. Zhou, “Ab initio folding of terminal segments with secondary structures reveals the fine difference between two closely-related all-atom statistical energy functions.“, Protein Science 17 1212-1219, (2008)[PDF] [DFIRE2]

092

dDFIRE
Y. Yang and Y. Zhou, “Specific interactions for ab initio folding of protein terminal regions with secondary structures.“, Proteins 72, 793-803 (2008)[PDF] [dDFIRE]

091

Real-SPINE 2.0
B. Xue, O. Dor, E. Faraggi and Y. Zhou, “Real value prediction of backbone torsion angles.”, Proteins 72, 427-433 (2008) Real-SPINE 2.0 server ][PDF]

090

SKSP
W. Zhang, A. K. Dunker, and Y. Zhou, “Assessing secondary-structure assignment of protein structures by using pairwise sequence-alignment benchmarks.”, Proteins 71, 61-67 (2008) SKSP server ][PDF] List of Prefab pairs (Table IV)]

089

Z. Luo, J. Ding, and Y. Zhou, “Temperature-Dependent Folding Pathways of Pin1 WW Domain: An All-Atom Molecular Dynamics Simulation of a Gō Model.“, Biophys. J.93, 2152-2161 (2007) [PDF]

088

EMPIRE
S. Liang, S. Liu, C. Zhang and Y. Zhou, “A simple reference state makes a significant improvement in near-native selections from structurally refined docking decoys.”, Proteins 69, 244-253 (2007) [PDF] [Download] [Server [On the Cover]

087

Y. Chen, Y. Zhou and J. Ding, “The helix-coil transition revisited.”, Proteins 69, 58-68 (2007) [PDF]

086

DDOMAIN
H. Zhou, B. Xue, and Y. Zhou, “DDOMAIN: Dividing structures into domains using a normalized domain-domain interaction profile”, Protein Sci.16,947-955 (2007)[DDOMAIN server] [DDOMAIN download] [PDF]

085

SP4
S. Liu, C. Zhang, S. Liang, and Y. Zhou, “Fold Recognition by Concurrent Use of Solvent Accessibility and Residue Depth”, Proteins68, 636-645, (2007)SP4 Server] [PDF] SALIGN Benchmark] [On the Cover]

084

Real-SPINE
O. Dor and Y. Zhou, “Real-SPINE: An integrated system of neural networks for real-value prediction of protein structural properties, Proteins” 68, 76-81, (2007)SPINE/Real-SPINE Server] [PDF]

083

SPINE
O. Dor and Y. Zhou, “Achieving 80% ten-fold cross-validated accuracy for secondary structure prediction by large-scale training”, Proteins 66, 838-845, (2007)SPINE Server & Downloads] [PDF]

082.


Y. Zhou, H. Zhou, and M. Karplus, “Cooperativity in Scapharca dimeric hemoglobin: Simulation of binding intermediates and elucidation of the role of interfacial water”, Rend. Fis. Acc. Lincei, 17:191-211 (2006) [Reprinted from #54].

081

PINUP
S. Liang, C. Zhang, S. Liu and Y. Zhou, “Protein binding site prediction with an empirical scoring function, Nucl. Acids Res. “34, 3698-3707 (2006). PINUP Server] [PINUP 64 bits downloads] [PDF]

080

MC2
C. Zhang, S. Liu and Y. Zhou, “MC2: Identifying high-quality protein-interaction modules by clique merging”, J. Proteome Res. 5, 801-807 (2006). MC2 Server [PDF]

079

S. Liu, C. Zhang and Y. Zhou, “Uneven size distribution of mammalian genes in the number of tissues expressed and in the number of co-expressed genes”, Human Molec. Genetics 15, 1313-1318 (2006) Cover Article]. Abstract. [PDF]

078

Y. Zhou, H. Zhou, C. Zhang and S. Liu, “What is a desirable statistical energy function for proteins and how can it be obtained?”, Cell Biochem. Biophys. 46, 165-174 (2006) [Review].[PDF]

077

Z. Xu and C. Zhang and S. Liu and Y. Zhou, “QBES: Predicting real values of solvent accessibility from sequences by efficient, constrained energy optimization”, Proteins 63, 961-966 (2006). [PDF]

076

Z. Zhou, H. Feng, H. Zhou, Y. Zhou and Y. Bai, “Design and folding of a multi-domain protein”, Biochemistry 44, 12107-12112 (2005). [PDF]

075

SPEM
H. Zhou and Y. Zhou, “SPEM: Improving multiple-sequence alignment with sequence profiles and predicted secondary structures”, Bioinformatics 21, 3615-3621 (2005). [PDF] [SPEM download] [SPEM Server]

074

H. Zhou and Y. Zhou, “SPARKS 2 and SP3 servers in CASP 6.”, Proteins (Supplement CASP issue), Suppl 7 152-156 (2005). [PDF]

073

H. Li and Y. Zhou, “Fold helical proteins by energy minimization in dihedral space and a DFIRE-based statistical energy function”, J. Bioinfo. Comput. Biol. 3, 1151-1170 (2005). [PDF]

072

B. P. Pandey, C. Zhang, X. Yuan, J. Zi and Y. Zhou, “Protein flexibility prediction by an all-atom mean-field statistical theory”, Protein Science 14, 1772-1777 (2005). [PDF]

071

SCUD
H. Li and Y. Zhou, “SCUD: Fast structure clustering of decoys using reference state to remove overall rotation”, J. Comput. Chem. 26, 1189-1192 (2005). [PDF] [SCUD Server] [SCUD Download]

070

H. Zhou, C. Zhang, S. Liu, and Y. Zhou, “Web-based toolkits for topology prediction of transmembrane helical proteins, fold recognition, structure and binding scoring, folding-kinetics analysis, and comparative analysis of domain combinations”, Nucl. Acids Res. (Server issue) 33, W193-W197 (2005). [PDF]

069

C. Zhang, S. Liu, and Y. Zhou, “Docking prediction using biological information, ZDOCK sampling technique and clustering guided by the DFIRE statistical energy function”, Proteins (Special CAPRI issue) 60, 314-318 (2005) [Invited, Peer-reviewed Conference Paper]. [PDF]

068

DDNA
C. Zhang, S. Liu, Q. Zhu, and Y. Zhou, “A knowledge-based energy function for protein-ligand, protein-protein and protein-DNA complexes”, J. Med. Chem. 48, 2325-2335 (2005). [PDF] DDNA Server]

067

DOGMA
S. Liu, C. Zhang, and Y. Zhou, “Domain Graph of Arabidopsis thaliana Proteome by comparative analysis”, J. Proteome Res. 4, 435-444 (2005). DOGMA Server/Database] [PDF]

066

SP3
H. Zhou, and Y. Zhou, “Fold recognition by combining sequence profiles derived from evolution and from depth-dependent structural alignment of fragments.”, Proteins. 58, 321-328 (2005). [PDF] Server & Download] Independent LiveBench Performance CAFASP4 CASP6 Ranked #2 in CM (SPARKS 2 as #1) and #5 in FR/H targets in 49 servers assessed at CASP6

065

Y. Bai, H. Zhou, and Y. Zhou, “Critical nucleation size in the folding of small apparently two-state proteins.”, Protein Sci. 13, 1173-1181 (2004). [PDF]

064

C. Zhang, S. Liu, H. Zhou, and Y. Zhou, “The dependence of all-atom statistical potentials on training structural database.”, Biophys. J. 86, 3349-3358 (2004). [PDF] [397 alpha proteins] [438 beta proteins]

063

C. Zhang, S. Liu, H. Zhou, and Y. Zhou, “An accurate residue-level pair potential of mean force for folding and binding based on the distance-scaled ideal-gas reference state.”, Protein Sci. 13, 400-411 (2004). [PDF] SCM Download]

062

DLOOP
C. Zhang, S. Liu, and Y. Zhou, “Accurate and efficient loop selections using DFIRE-based all-atom statistical potential.”, Protein Sci. 13, 391-399 (2004). [PDF] [Supplement Materials] [ Server]

061

DCOMPLEX
S. Liu, C. Zhang, H. Zhou, and Y. Zhou, “A physical reference state unifies the structure-derived potential of mean force for protein folding and binding.”, Proteins 56, 93-101 (2004). [PDF] [ Server] DCOMPLEX Download ]

060

H. Jang, C. K. Hall, and Y. Zhou, “Thermodynamics and stability of a beta-sheet complex: Molecular dynamics simulations on simplified off-lattice protein models”, Protein Sci., 13, 40-53 (2004) [PDF]

059

SPARKS Method, Ranked #1 in CM targets in 49 servers assessed in CASP6
H. Zhou and Y. Zhou,``Single-body residue-level knowledge-based energy score combined with sequence-profile and secondary structure information for fold recognition”, Proteins, 55, 1005-1013 (2004).[ Subject Area:Bioinformatics][ Server & Download] Abstract [PDF] 20X25 contact energy function] Contact energy function usage] Independent LiveBench Performance CASP6]

058

H. Jang, C. K. Hall, and Y. Zhou, “Assembly and kinetic folding pathways of a tetrameric beta-sheet complex: Molecular dynamics simulations on simplified off-lattice protein models”, Biophys. J., 86, 31-49 (2004). [ Subject Area:Folding Kinetics/Thermodynamcs] [ PDF ]

057

H. Zhou and Y. Zhou, “Quantifying the effect of burial of amino acid residues on protein stability”, Proteins, 54, 315-322 (2004). [ Subject Area:Bioinformatics] [ PDF ] List of 200 PDB structures

056

THUMBUP/UMDHMM Methods
H. Zhou and Y. Zhou, “Predicting the topology of transmembrane helical proteins using mean burial propensity and a hidden-Markov-model based method”, Protein Sci., 12, 1547-1555 (2003). [ Subject Area:Bioinformatics] [ THUMBUP Server] [ UMBHMM Server] Abstract[PDF] 73 protein sequences topologies pdb list

055

Y. Zhou, C. Zhang, G. Stell, and J. Wang, “Temperature dependence of the distribution of the first passage time: Results from all-atom discontinuous molecular dynamics simulations of the second beta-hairpin fragment of protein G”, J. Am. Chem. Soc., 125, 6300-6305 (2003). [ Subject Area:Folding kinetics] [PDF]

054

Y. Zhou, H. Zhou, and M. Karplus, “Cooperativity in Scapharca dimeric hemoglobin: Simulation of binding intermediates and elucidation of the role of interfacial water”, J. Mol. Biol., 326 , 593-606 (2003). [ Subject Area: Binding Cooperativity, Protein Dynamics] Abstract [PDF]

053

A. Linhananta and Y. Zhou, “The role of sidechain packing and native contact interactions in folding: Discontinuous molecular dynamics folding simulations of an all-atom Go model of fragment B of Staphylococcal protein A, J. Chem. Phys.“, 117 , 8983-8995 (2002). [ Subject Area: Folding Mechanism, Folding Kinetics, Protein Dynamics] [PDF]

052

DFIRE Method (DMONOMER/DMUTANT)
H. Zhou and Y. Zhou, “Distance-scaled, finite ideal-gas reference state improves structure-derived potentials of mean force for structure selection and stability prediction, Protein Science, 11 , 2714-2726 (2002). [ Subject Area: Bioinformatics, Folding Stability, Structure Prediction] The 895 mutation list Abstract [PDF] [CORRECTIONS] Protein Science”, 12, 2121 (2003). [ DMONMER Server] [ DMUTANT Server]

051

H. Zhou and Y. Zhou, “The stability scale and atomic solvation parameters extracted from 1023 mutation experiments”, Proteins, 49 , 483-492 (2002). [ Subject Area: Bioinformatics, Folding Stability] The 1023 mutation list [PDF]

050

A. Linhananta, H. Zhou and Y. Zhou, “The dual role of a loop with low loop contact distance in folding and domain swapping”, Protein Science, 11 , 1695-1701 (2002). [ Subject Area: Folding Mechanism, Folding Kinetics, Bioinformatics] Abstract [PDF]

049

H. Jang, C. K. Hall, and Y. Zhou “Protein folding pathways and kinetics: Molecular dynamics simulations of beta-strand motifs”, Biophys. J. 83 , 819-835 (2002). [ Subject Area: Folding Mechanism, Folding Kinetics] [PDF]

048

Y. Zhou and A. Linhananta, “Role of hydrophilic and hydrophobic contacts in folding of the second beta-hairpin fragment of protein G: Molecular dynamics simulation studies of an all-atom model”, Proteins, 47 , 154-162 (2002). [ Subject Area: Folding Mechanism, Folding Kinetics] Abstract [PDF]

047

Y. Zhou and A. Linhananta, “Thermodynamics of an all-atom off-lattice model of the fragment B of Staphylococcal protein A: Implication for the origin of the cooperativity of protein folding”, J. Phys. Chem. B, 106 , 1481-1485 (2002). [ Subject Area: Folding Thermodynamics, Folding Cooperativity] Abstract [PDF]

046

H. Jang, C. K. Hall, and Y. Zhou “Folding thermodynamics of model four-strand antiparallel beta-sheet proteins, Biophys. J.” 82 , 646-659 (2002). [Subject Area: Folding Thermodynamics] [PDF]

045

Y. Zhou, M. Karplus, K. D. Ball and R. S. Berry, “The distance fluctuation criterion for melting: Comparison of square-well and Morse potential models for clusters and homopolymers”,J. Chem. Phys., 116 , 2323-2329 (2002). [ Subject Area:Polymer Thermodynamics, Statistical Mechanics] [PDF]

044

H. Zhou and Y. Zhou, “Folding rate prediction using total contact distance.”, Biophys. J. 82 , 458-463 (2002). [ Subject Area: Folding Kinetics] [ Server] Abstract Residue ranges for some proteins] [PDF]

043

Y. Zhou, “Fast and accurate thermodynamics of square-well systems from umbrella-sampling simulations of hard-sphere systems.”, J. Chem. Phys. 115 , 7550-7553 (2001). [ Subject Area:Polymer Thermodynamics, Statistical Mechanics] [PDF]

042

Y. Zhou, M. Cook, and M. Karplus, “Protein motions at zero-total angular momentum: The importance of long-range correlations.“, Biophys. J. 79 , 2902-2908 (2000). [PDF]

041

Y. Zhou and M. Karplus, “Folding of a model three-helix bundle protein: A thermodynamic and kinetic analysis.“, J. Molec. Biol. 293 , 917-951 (1999). [PDF]

040

Y. Zhou and M. Karplus, “Interpreting the folding kinetics of helical proteins.”, Nature 401 , 400-403 (1999). [PDF]

039

Y. Zhou, C. K. Hall, and M. Karplus, “The calorimetric criterion for a two-state process revisited.”, Protein Science 8 , 1064 (1999). [PDF]

038

Y. Zhou, D. Vitkup, and M. Karplus, “Native proteins are surface-molten solids: Application of the Lindemann criterion for the solid versus liquid state.“, J. Molec. Biol. 285 , 1371 (1999). [PDF]

037

Y. Zhou, “Salt effects on protein titration and binding.”, J. Phys. Chem. 102 , 10615 (1998). [PDF]

036

Y. Zhou and M. Karplus, “Folding thermodynamics of a model three-helix bundle protein.”, Proc. Natl. Acad. Sci. (USA) 94 , 14429 (1997). [PDF]

035

Y. Zhou, M. Karplus, J. M. Wichert, and C. K. Hall, “Equilibrium thermodynamics of homopolymers and clusters: Molecular dynamics and Monte Carlo simulation studies of systems with square-well interactions.“, J. Chem. Phys. 107 , 10691 (1997). [PDF]

034

Y. Zhou, C. K. Hall, and M. Karplus, “A first-order disorder-to-order transition in an isolated homopolymer model.”, Phys. Rev. Lett. 77 , 2822 (1996). [PDF]

033

Y. Zhou and M. Karplus, “Exact results for the effect of bond flexibility on the structure and the collapse transition of isolated square-well trimers.”, Molec. Phys. 89 , 1707 (1996).

032

Y. Zhou and C. K. Hall, “Solute excluded-volume effects on the stability of globular proteins: A statistical thermodynamic theory.“, Biopolymers 38 , 273 (1996).

031

S. Yeh, Y. Zhou, and G. Stell, “Phase separation of ionic fluids: An extended Ebeling-Grigo approach.“, J. Phys. Chem. (H. L. Friedman Issue) 100 , 1415 (1996). [PDF]

030

Y. Zhou, C. K. Hall, and G. Stell, “Exact results for isolated sticky chains.”, Molec. Phys. 86 , 1485 (1995).

029

Y. Zhou, S. W. Smith, and C. K. Hall, “Linear dependence of thermodynamic properties of tangent hard-sphere chains on chain length.”, Molec. Phys. 86 , 1157 (1995).

028

Y. Zhou, C. K. Hall, and G. Stell, “The thermodynamic perturbation theory for fused hard-sphere chain fluids.”, J. Chem. Phys. 103 , 2688 (1995).

027

L. A. Costa, Y. Zhou, C. K. Hall, and S. Carr, “Fused hard-sphere chain molecules: Comparison between monte carlo simulation for the bulk pressure and generalized Flory theories.“, J. Chem. Phys. 102 , 6212 (1995).

026

Y. Zhou and G. Stell, “Criticality of charged systems: II. The binary mixture of hard spheres and ions.“, J. Chem. Phys. 102 , 5796 (1995).

025

Y. Zhou, S. Yeh, and G. Stell, “Criticality of charged systems: I. The restricted primitive model.“, J. Chem. Phys. 102 , 5785 (1995).

024

Y. Zhou and G. Stell, “Chemical association in simple models of molecular and ionic fluids IV. New approximation for the cavity function and an application to the theory of weak electrolytes.”, J. Chem. Phys. 102 , 8089 (1995).

023

Y. Zhou and G. Stell, “Analytical approach to molecular liquids: V. Symmetric dissociative dipolar dumbbells with the bonding length, simga/2 and related systems.“, J. Chem. Phys. 98 , 5777 (1993).

022

G. Stell and Y. Zhou, “Microscopic modeling of association.”, Fluid Phase Equilibria 79 , 1 (1992).

021

Y. Zhang, Y. Zhou, Z. Luo, and D. Hanson, “Electron rearrangement and energy relaxation due to a core hole creation in molecules.”, J. Phys. Chem. 96 , 2949 (1992).

020

Y. Zhou and G. Stell, “Chemical association in simple models of molecular and ionic fluids III. The cavity functions.”, J. Chem. Phys. 96 , 1507 (1992).

019

Y. Zhou and G. Stell, “Chemical association in simple models of molecular and ionic fluids II. Thermodynamic properties.”, J. Chem. Phys. 96 , 1504 (1992).

018

F. O. Raineri, Y. Zhou, H. L. Friedman, and G. Stell, “Ion solvation dynamics in an interaction site model solvent.”, Chem. Phys. 152 , 201 (1991).

017

Y. Zhou, H. L. Friedman, and G. Stell, “Outer-sphere electron transfer reactions in model molecular solvents: The mean spherical approximation.“, Chem. Phys. 152 , 185 (1991).

016

Y. Zhou and G. Stell, “Nonlocal integral-equation approximations: II. The Lennard-Jones fluid.“, J. Chem. Phys. 92 , 5544 (1990).

015

Y. Zhou and G. Stell, “Nonlocal integral-equation approximations: I. The hydrostatic approximation with applications.“, J. Chem. Phys. 92 , 5533 (1990).

014

Y. Zhou and G. Stell, “Fluids inside a pore - an integral-equation approach III. Water-in-oil microemulsions.”, Molec. Phys. 68 , 1265 (1989).

013

Y. Zhou, H. L. Friedman, and G. Stell, “Analytical approach to molecular liquids: IV. Solvation dynamics and electron-transfer reactions., J. Chem. Phys.” 91 , 4885 (1989).

012

Y. Zhou, H. L. Friedman, and G. Stell, “Analytical approach to molecular liquids: III. The Born solvation free energy of two fixed ions in a dipolar solvent.“, J. Chem. Phys. 91 , 4879 (1989).

011

Y. Zhou and G. Stell, “Analytical approach to molecular liquids: II. Solvation of ions in molecular fluids.“, J. Chem. Phys. 91 , 4869 (1989).

010

G. Stell and Y. Zhou, “Analytical approach to molecular liquids: I. Site-site interaction model using an extended mean-spherical approximation.“, J. Chem. Phys. 91 , 4861 (1989).

009

G. Stell and Y. Zhou, “Chemical association in simple models of molecular and ionic fluids.”, J. Chem. Phys. 91 , 3618 (1989).

008

Y. Zhou and G. Stell, “The theory of semipermeable vesicles and membranes: An integral-equation approach. III. Vesicles with internal nonpermeating ions.“, J. Chem. Phys. 91 , 3208 (1989).

007

Y. Zhou and G. Stell, “Fluids inside a pore – an integral-equation approach II. Cylindrical pores.”, Molec. Phys. 66 , 791 (1989).

006

Y. Zhou and G. Stell, “Fluids inside a pore – an integral-equation approach I. General formalism and hard-spheres inside spherical and slit pores.”, Molec. Phys. 66 , 767 (1989).

005

Y. Zhou and G. Stell, “The theory of semipermeable vesicles and membranes: An integral-equation approach. II. Donnan equilibrium.“, J. Chem. Phys. 89 , 7020 (1988).

004

Y. Zhou and G. Stell, “The theory of semipermeable vesicles and membranes: An integral-equation approach. I. General formalism and application to a hard-sphere mixture.“, J. Chem. Phys. 89 , 7010 (1988).

003

Y. Zhou and G. Stell, “Equations of state for hard-sphere fluids.”, Int. J. Thermophys. 9 , 953 (1988).

002

Y. Zhou, G. Stell, and H. L. Friedman, “Note on standard free energy of transfer and partitioning of ionic species between two fluid phases.”, J. Chem. Phys. 89 , 3836 (1988).

001

Y. Zhou and G. Stell, “The hard-sphere fluid: New exact results with applications.“, J. Stat. Phys., Howard Reiss Issue 52 , 1389 (1988)