Ti trovi qui: Home » References » Modelling & Nano

Modelling & Nano

Mignogna M. et al, The intellectual disability protein RAB39B selectively regulates GluA2 trafficking to determine synaptic AMPAR composition. Nature Communications 2015, 6, 6504. [Link]

Mariani S et al, Network and atomistic simulations unveil the structural determinants of mutations linked to retinal diseases. PLoS Comput Biol 2013, 9: e1003207. [Link]

Fanelli F, et al. Update 1 of Computational Modeling Approaches to Structure-Function Analysis of G Protein-Coupled Receptors. Chem. Rev. 2011, 111:PR438-535. [Link]

Matteini P, et al. Site-Selective Surface-Enhanced Raman Detection of Proteins - ACS NANO, 2017, 11, 918-926. [Link]

Tavanti F, et al. Competitive Binding of Proteins to Gold Nanoparticles Disclosed by Molecular Dynamics Simulations, J. Phys. Chem. C, 2015, 119, 22172-22180. [Link]

Affinito A, A simulative model for the analysis of conduction properties of ion channels based on first-principle approaches” J. Comput. Elect,  2005, 4 171-174 . [Link]

Yi H et al, High adaptability of the omega loop underlies the substrate-spectrum-extension evolution of the class A β-lactamase, PenL Scientific Reports – 2016, 36527. [Link]

Brunetti, R et al., Shot noise in single open ion channels: a computational approach based on atomistic simulations, J. Comp. Electr. 2007, 6, 391-394. [Link]

Piccinini E, et al.,: Exploring  free-energy profiles through ion channels: comparison on a test case, J. Comp. Electr. 2007, 6, 373-376. [Link]

Piccinini E, et al. Computational Analysis of current and noise properties of a single open ion channel,  J. Chem. Theory Comput. 2007,3, 248-255. [Link]

Piccinini E, eta l. Biased Molecular Simulations for Free-Energy Mapping: A Comparison on the KcsA Channel as a Test Case,  J. Chem. Theory Comput. 2008, 4, 173-183. [Link]

Forcato M, et al, Comparison of computational methods for Hi-C data analysis. Nat Methods. 2017, 14, 679-685. [Link]

Panciera T, et al. Induction of Expandable Tissue-Specific Stem/Progenitor Cells through Transient Expression of YAP/TAZ. Cell Stem Cell. 2016 19, 725-737. [Link]

Caroli J, et al. APTANI: a computational tool to select aptamers through sequence-structure motif analysis of HT-SELEX data. Bioinformatics. 2016, 32, 161-4. [Link]

Taccioli C, et al, MDP, a database linking drug response data to genomic information, identifies dasatinib and statins as a combinatorial strategy to inhibit YAP/TAZ in cancer cells. Oncotarget. 2015 6, 38854-65. [Link]

Elia A, et al, Multivariate data analysis to assess dry powder inhalers performance from powder properties - POWDER TECHNOLOGY – 2016, 301 830-838. [Link]

Prats-Montalbán, JM et al. N-way modeling for wavelet filter determination in multivariate image analysis – J Chemomet 2015, 29, 379-388. [Link]

Silvestri M, et al. A mid level data fusion strategy for the Varietal Classification of Lambrusco PDO wines – Chemomet and Intel Lab Systems, 2014, 137., 181-189. [Link]

Favilla S et al, Ranking brain areas encoding the perceived level of pain from fMRI data, Neuroimage, 2014, 90, 153-162 . [Link]

Favilla S, et al ( 2013 ) - Assessing feature relevance in models by VIP , Chemomet and Intel Lab Systems, 2013, 129, 76-86. [Link]

Pota M, et al. Molecular dynamics simulations of sodium silicate glasses:Optimization and limits of the computational procedure, Comput Mater Sci, 2010, 47, 739–751. [Link]