172 research outputs found
MSJ768433_supplementary_material – Supplemental material for Comparative analysis of natalizumab versus fingolimod as second-line treatment in relapsing–remitting multiple sclerosis
No full textSupplemental material, MSJ768433_supplementary_material for Comparative analysis of natalizumab versus fingolimod as second-line treatment in relapsing–remitting multiple sclerosis by Johannes Lorscheider, Pascal Benkert, Carmen Lienert, Peter Hänni, Tobias Derfuss, Jens Kuhle, Ludwig Kappos and Özgür Yaldizli in Multiple Sclerosis Journal</p
QMEANclust : estimation of protein model quality by combining a composite scoring function with structural density information
No full textABSTRACT: BACKGROUND: The selection of the most accurate protein model from a set of alternatives is a crucial step in protein structure prediction both in template-based and ab initio approaches. Scoring functions have been developed which can either return a quality estimate for a single model or derive a score from the information contained in the ensemble of models for a given sequence. Local structural features occurring more frequently in the ensemble have a greater probability of being correct. Within the context of the CASP experiment, these so called consensus methods have been shown to perform considerably better in selecting good candidate models, but tend to fail if the best models are far from the dominant structural cluster. In this paper we show that model selection can be improved if both approaches are combined by pre-filtering the models used during the calculation of the structural consensus. RESULTS: Our recently published QMEAN composite scoring function has been improved by including an all-atom interaction potential term. The preliminary model ranking based on the new QMEAN score is used to select a subset of reliable models against which the structural consensus score is calculated. This scoring function called QMEANclust achieves a correlation coefficient of predicted quality score and GDT_TS of 0.9 averaged over the 98 CASP7 targets and perform significantly better in selecting good models from the ensemble of server models than any other groups participating in the quality estimation category of CASP7. Both scoring functions are also benchmarked on the MOULDER test set consisting of 20 target proteins each with 300 alternatives models generated by MODELLER. QMEAN outperforms all other tested scoring functions operating on individual models, while the consensus method QMEANclust only works properly on decoy sets containing a certain fraction of near-native conformations. We also present a local version of QMEAN for the per-residue estimation of model quality (QMEANlocal) and compare it to a new local consensus-based approach. CONCLUSION: Improved model selection is obtained by using a composite scoring function operating on single models in order to enrich higher quality models which are subsequently used to calculate the structural consensus. The performance of consensus-based methods such as QMEANclust highly depends on the composition and quality of the model ensemble to be analysed. Therefore, performance estimates for consensus methods based on large meta-datasets (e.g. CASP) might overrate their applicability in more realistic modelling situations with smaller sets of models based on individual methods
sj-tiff-1-msj-10.1177_13524585211047977 – Supplemental material for Measurement of neurofilaments improves stratification of future disease activity in early multiple sclerosis
No full textSupplemental material, sj-tiff-1-msj-10.1177_13524585211047977 for Measurement of neurofilaments improves stratification of future disease activity in early multiple sclerosis by Tomas Uher, Eva Kubala Havrdova, Pascal Benkert, Niels Bergsland, Jan Krasensky, Barbora Srpova, Michael Dwyer, Michaela Tyblova, Stephanie Meier, Manuela Vaneckova, Dana Horakova, Robert Zivadinov, David Leppert, Tomas Kalincik and Jens Kuhle in Multiple Sclerosis Journal</p
sj-docx-3-msj-10.1177_13524585211047977 – Supplemental material for Measurement of neurofilaments improves stratification of future disease activity in early multiple sclerosis
No full textSupplemental material, sj-docx-3-msj-10.1177_13524585211047977 for Measurement of neurofilaments improves stratification of future disease activity in early multiple sclerosis by Tomas Uher, Eva Kubala Havrdova, Pascal Benkert, Niels Bergsland, Jan Krasensky, Barbora Srpova, Michael Dwyer, Michaela Tyblova, Stephanie Meier, Manuela Vaneckova, Dana Horakova, Robert Zivadinov, David Leppert, Tomas Kalincik and Jens Kuhle in Multiple Sclerosis Journal</p
sj-docx-1-msj-10.1177_13524585211047977 – Supplemental material for Measurement of neurofilaments improves stratification of future disease activity in early multiple sclerosis
No full textSupplemental material, sj-docx-1-msj-10.1177_13524585211047977 for Measurement of neurofilaments improves stratification of future disease activity in early multiple sclerosis by Tomas Uher, Eva Kubala Havrdova, Pascal Benkert, Niels Bergsland, Jan Krasensky, Barbora Srpova, Michael Dwyer, Michaela Tyblova, Stephanie Meier, Manuela Vaneckova, Dana Horakova, Robert Zivadinov, David Leppert, Tomas Kalincik and Jens Kuhle in Multiple Sclerosis Journal</p
sj-docx-2-msj-10.1177_13524585211047977 – Supplemental material for Measurement of neurofilaments improves stratification of future disease activity in early multiple sclerosis
No full textSupplemental material, sj-docx-2-msj-10.1177_13524585211047977 for Measurement of neurofilaments improves stratification of future disease activity in early multiple sclerosis by Tomas Uher, Eva Kubala Havrdova, Pascal Benkert, Niels Bergsland, Jan Krasensky, Barbora Srpova, Michael Dwyer, Michaela Tyblova, Stephanie Meier, Manuela Vaneckova, Dana Horakova, Robert Zivadinov, David Leppert, Tomas Kalincik and Jens Kuhle in Multiple Sclerosis Journal</p
sj-docx-1-msj-10.1177_13524585211032348 – Supplemental material for Sustained reduction of serum neurofilament light chain over 7 years by alemtuzumab in early relapsing–remitting MS
No full textSupplemental material, sj-docx-1-msj-10.1177_13524585211032348 for Sustained reduction of serum neurofilament light chain over 7 years by alemtuzumab in early relapsing–remitting MS by Jens Kuhle, Nadia Daizadeh, Pascal Benkert, Aleksandra Maceski, Christian Barro, Zuzanna Michalak, Maria Pia Sormani, Jean Godin, Srinivas Shankara, Tarek A Samad, Alan Jacobs, Luke Chung, Nora Rӧsch, Carina Kaiser, Colin P Mitchell, David Leppert, Evis Havari and Ludwig Kappos in Multiple Sclerosis Journal</p
sj-docx-1-msj-10.1177_13524585231198760 – Supplemental material for Optical coherence tomography versus other biomarkers: Associations with physical and cognitive disability in multiple sclerosis
No full textSupplemental material, sj-docx-1-msj-10.1177_13524585231198760 for Optical coherence tomography versus other biomarkers: Associations with physical and cognitive disability in multiple sclerosis by Nuria Cerdá-Fuertes, Marc Stoessel, Gintaras Mickeliunas, Silvan Pless, Alessandro Cagol, Muhamed Barakovic, Aleksandra Maleska Maceski, Cesar Álvarez González, Marcus D’ Souza, Sabine Schaedlin, Pascal Benkert, Pasquale Calabrese, Konstantin Gugleta, Tobias Derfuss, Till Sprenger, Cristina Granziera, Yvonne Naegelin, Ludwig Kappos, Jens Kuhle and Athina Papadopoulou in Multiple Sclerosis Journal</p
Global and local model quality estimation at CASP8 using the scoring functions QMEAN and QMEANclust
No full textIdentifying the best candidate model among an ensemble of alternatives is crucial in protein structure prediction. For this purpose, scoring functions have been developed which either calculate a quality estimate on the basis of a single model or derive a score from the information contained in the ensemble of models generated for a given sequence (i.e., consensus methods). At CASP7, consensus methods have performed considerably better than scoring functions operating on single models. However, consensus methods tend to fail if the best models are far from the center of the dominant structural cluster. At CASP8, we investigated whether our hybrid method QMEANclust may overcome this limitation by combining the QMEAN composite scoring function operating on single models with consensus information. We participated with four different scoring functions in the quality assessment category. The QMEANclust consensus scoring function turned out to be a successful method both for the ranking of entire models but especially for the estimation of the per-residue model quality. In this article, we briefly describe the two scoring functions QMEAN and QMEANclust and discuss their performance in the context of what went right and wrong at CASP8. Both scoring functions are publicly available at http://swissmodel.expasy.org/qmean/. Proteins 2009. (c) 2009 Wiley-Liss, Inc
sj-docx-2-msj-10.1177_13524585231198760 – Supplemental material for Optical coherence tomography versus other biomarkers: Associations with physical and cognitive disability in multiple sclerosis
No full textSupplemental material, sj-docx-2-msj-10.1177_13524585231198760 for Optical coherence tomography versus other biomarkers: Associations with physical and cognitive disability in multiple sclerosis by Nuria Cerdá-Fuertes, Marc Stoessel, Gintaras Mickeliunas, Silvan Pless, Alessandro Cagol, Muhamed Barakovic, Aleksandra Maleska Maceski, Cesar Álvarez González, Marcus D’ Souza, Sabine Schaedlin, Pascal Benkert, Pasquale Calabrese, Konstantin Gugleta, Tobias Derfuss, Till Sprenger, Cristina Granziera, Yvonne Naegelin, Ludwig Kappos, Jens Kuhle and Athina Papadopoulou in Multiple Sclerosis Journal</p
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