BioFolD -
                University of Bologna

Publications


2017

  1. Capriotti E, Fariselli P. (2017). PhD-SNPg: A webserver and lightweight tool for scoring single nucleotide variants. Nucleic Acids Research. Accepted.


  2. Carraro M, Minervini G, Giollo M, Bromberg Y, Capriotti E, Casadio R, Dunbrack R, Elefanti L, Fariselli P, Ferrari C, Gough J, Katsonis P, Leonardi E, Lichtarge O, Menin C, Martelli PL, Niroula A, Pal LR, Repo S, Scaini MC, Vihinen M, Wei Q, Xu Q, Yang Y, Yin Y, Zaucha J, Zhao H, Zhou Y, Brenner SE, Moult J, Tosatto SCE. (2017). Performance of in silico tools for the evaluation of p16INK4a (CDKN2A) variants in CAGI. Human Mutation. In press.


  3. Capriotti E, Martelli PL, Fariselli P, Casadio R. (2017). Blind prediction of deleterious amino acid variations with SNPs&GO. Human Mutation. In press.

2016

  1. Khass M, Blackburn T, Burrows PD, Walter MR, Capriotti E, Schroeder HW Jr. (2016). VpreB serves as an invariant surrogate antigen for selecting immunoglobulin antigen-binding sites. Science Immunology. 1: aaf6628.

2015

  1. Tian R, Basu MK, Capriotti E. (2015). Computational methods and resources for the interpretation of genomic variants in cancer. BMC Genomics. 16 (Suppl. 8): S7.


  2. Beerten J, Van Durme J, Gallardo R, Capriotti E, Serpell L, Rousseau F, Schymkowitz J. (2015). WALTZ-DB: a benchmark database of amyloidogenic hexapeptides. Bioinformatics. 31:1698-1700.

2014

  1. Tian R, Basu MK, Capriotti E. (2014). ContrastRank: a new method for ranking putative cancer driver genes and classification of tumor samples. Bioinformatics. 30: i572-i578.


  2. Di Tommaso P, Bussotti G, Kemena C, Capriotti E, Chatzou M, Prieto Barja P, Notredame C. (2014). SARA-Coffee web server, a tool for the computation of RNA sequence and structure multiple alignments. Nucleic Acids Research. 42: W356-360


  3. Li B, Seligman C, Thusberg J, Miller JL, Auer J, Whirl-Carrillo M, Capriotti E, Klein TE, Mooney SD. (2014). In silico comparative characterization of pharmacogenomic missense variants. BMC Genomics. 15 (Suppl. 4): S4.

2013

  1. Compiani M, Capriotti E. (2013). Computational and theoretical methods for protein folding. Biochemistry. 52: 8601-8624.


  2. Norambuena T, Cares JF, Capriotti E, Melo F (2013). WebRASP: a server for computing energy scores to assess the accuracy and stability of RNA 3D structures. Bioinformatics. 29:2649-2650.


  3. Capriotti E, Calabrese R, Fariselli P, Martelli PL, Altman RB, Casadio R (2013). WS-SNPs&GO: a web server for predicting the deleterious effect of human protein variants using functional annotation. BMC Genomics. 14 Suppl 3:S6.


  4. Capriotti E, Altman RB, Bromberg Y (2013). Collective judgment predicts disease-associated single nucleotide variants. BMC Genomics. 14 Suppl 3:S2.


  5. Kemena C, Bussotti G, Capriotti E, Marti-Renom MA, Notredame C. (2013). Using tertiary structure for the computation of highly accurate multiple RNA alignments with the SARA-Coffee package. Bioinformatics. 29:1112-1119.

2012

  1. Lahti JL, Tang GW, Capriotti E, Liu T, Altman RB. (2012). Bioinformatics and variability in drug response: a protein structural perspective. J R Soc Interface. 9; 1409-1437.


  2. Capriotti E, Nehrt NL, Kann MG, Bromberg Y. (2012). Bioinformatics for personal genome interpretation. Briefings in Bioinformatics. 13; 495-512.

2011

  1. Dewey FE, Chen R, Cordero SP, Ormond KE, Caleshu C, Karczewski KJ, Whirl-Carrillo M, Wheeler MT, Dudley JT, Byrnes JK, Cornejo OE, Knowles JW, Woon M, Sangkuhl K, Gong L, Thorn CF, Hebert JM, Capriotti E, David SP, Pavlovic A, West A, Thakuria JV, Ball MP, Zaranek AW, Rehm HL, Church GM, West JS, Bustamante CD, Snyder M, Altman RB, Klein TE, Butte AJ, Ashley EA. (2011). Phased whole-genome genetic risk in a family quartet using a major allele reference sequence. PLOS Genetics. 7; e1002280.


  2. Capriotti E, Altman RB. (2011). A new disease-specific machine learning approach for the prediction of cancer-causing missense variants. Genomics. 98; 310-317.


  3. Capriotti E, Altman RB. (2011). Improving the prediction of disease-related variants using protein three-dimensional structure. BMC Bioinformatics. 12 (Suppl 4); S3


  4. Fernald GH, Capriotti E, Daneshjou R, Karczewski KJ, Altman RB. (2011). Bioinformatics challenges for personalized medicine. Bioinformatics. 27; 1741-1748


  5. Liu T, Tang GW, Capriotti E. (2011). Comparative modeling: the state of the art and protein drug target structure prediction. Combinatorial Chemistry & High Throughput Screening. 14: 532-547


  6. Capriotti E, Norambuena T, Marti-Renom MA, Melo F. (2011). All atom knowledge-based potential for RNA structure prediction and assessment. Bioinformatics. 27; 1086-1093.


  7. Bau D, Sanyal A, Lajoie BR, Capriotti E, Byron M, Lawrence JB, Dekker J, Marti-Renom MA. (2011). The three-dimensional folding of the alpha-globin gene domain reveals formation of chromatin globules. Nat Struct Mol Biol. 18: 107-114.

2010

  1. Capriotti E, Marti-Renom MA. (2010). Quantifying the relationship between sequence and three-dimensional structure conservation in RNA. BMC Bioinformatics. 11: 322.

2009

  1. Calabrese R, Capriotti E, Fariselli P, Martelli PL, Casadio R. (2009). Functional annotations improve the predictive score of human disease-related mutations in proteins. Human Mutation. 30: 1237-1244.


  2. Capriotti E, Marti-Renom MA. (2009). SARA: a server for function annotation of RNA structures. Nucleic Acids Research. 37 (Web Server issue): W260-W265.

2008

  1. Capriotti E, Marti-Renom MA. (2008). RNA structure alignment by a unit-vector approach. Bioinformatics. 24: i112-i116.


  2. Capriotti E, Fariselli P, Rossi I, Casadio R. (2008). A three-state prediction of single point mutations on protein stability changes. BMC Bioinfomatics. 9 (Suppl 2): S6.


  3. Capriotti E, Marti-Renom MA. (2008). Computational RNA structure prediction. Current Bioinformatics. 3: 32-45.


  4. Capriotti E, Arbiza L, Casadio R, Dopazo J, Dopazo H, Marti-Renom MA. (2008). The use of estimated evolutionary strength at the codon level improves the prediction of disease related protein mutations in human. Human Mutation. 29: 198-204.

2007

  1. Capriotti E, Casadio R. (2007). K-Fold: a tool for the prediction of the protein folding kinetic order and rate. Bioinformatics. 23: 385-386.


  2. Fariselli P, Rossi I, Capriotti E, Casadio R. (2007) The WWWH of remote homolog detection: The state of the art. Brief. Bioinformatics. 8: 78-87.

2006

  1. Capriotti E, Compiani M. (2006). Diffusion-collision of foldons elucidates the kinetic effects of point mutations and suggests control strategies of the folding process of helical proteins. Proteins. 64: 198-209.


  2. Capriotti E, Calabrese R, Casadio R. (2006). Predicting the insurgence of human genetic diseases associated to single point protein mutations with support vector machines and evolutionary information. Bioinformatics. 22: 2729-2734.


  3. Grandi F, Sandal M, Guarguaglini G, Capriotti E, Casadio R, Samori B. Hierarchical Mechanochemical Switches in Angiostatin. Chembiochem. 7: 1774-1782.

2005

  1. Stizza A, Capriotti E, Compiani M. (2005). A Minimal Model of Three-State Folding Dynamics of Helical Proteins. J. Phys. Chem. B. 109: 4215-4226.


  2. Capriotti E, Fariselli P, Casadio R. (2005). I-Mutant2.0: predicting stability changes upon mutation from the protein sequence or structure. Nucleic Acids Research. 33(Web Server issue): W306-W310.


  3. Capriotti E, Fariselli P, Calabrese R, Casadio R. (2005) Predicting protein stability changes from sequences using support vector machines. Bioinformatics. 21: ii54-ii58.

2004

  1. Capriotti E, Fariselli, Casadio R (2004). A neural network-based method for predicting protein stability changes upon single point mutations. Bioinformatics. 20 (Suppl 1): I63-I68


  2. Compiani M, Capriotti E, Casadio R. (2004). The Dynamics of the Minimally Frustrated Helices Determine the Hierarchical Folding of Small Helical Proteins. Physical Review E. 69: 051905


  3. Capriotti E, Fariselli P, Rossi I, Casadio R (2004). A Shannon entropy-based filter detects high-quality profile-profile alignments in searches for remote homologues. Proteins. 54: 351-360.

Book chapters

  1. Dufour D, Capriotti E, Marti-Renom MA. (2014). Computational methods for RNA structure prediction and analysis. RNA Nanotechnology. CRC Press (Wang B Editor), 21-50.


  2. Capriotti E. (2013). Comparative modeling and structure prediction: application to drug discovery. In silico drug discovery and design. Future Science (Lill MA Editor), 34-48.


  3. Marti-Renom MA, Capriotti E, Shindyalov I, Bourne P. (2009). Structural Comparison and Alignment. Structural Bioinformatics II Edition. John Wiley & Sons, Inc. (Gu J and Bourne PE Editors), 397-418.


  4. Capriotti E, Marti-Renom, MA. (2007). Assessment of protein structure predictions. Computational Structural Biology: Methods and Applications. World Scientific Publishing Company (Schwede T and Peitsch MC Editors), 89-109.


  5. Bartoli L, Capriotti E, Fariselli P, Martelli PL, Casadio R. (2007). The pros and cons of predicting protein contact maps. Protein Structure Prediction Series: Methods in Molecular Biology. Springer, Humana Press, (Zaki M and Bystroff C Editors) 2nd ed., 413: 199-218.


  6. Casadio R, Capriotti E, Compiani M, Fariselli P, Jacoboni I, Martelli PL Rossi I, Tasco G. (2003). Neural Networks and the Prediction of Protein Structure. In: Artificial Intelligence and Heuristic Methods for Bioinformatics (Frasconi P and Shamir R Editors) NATO Series Books. IOS Press Amsterdam, 22-33.
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BioFolD -
   University of Bologna