Classes of admissible exchange-correlation density functionals for pure spin and angular momentum states

We analyze the various approaches to construct exchange-correlation functionals which are able to describe states of definite spin multiplicity in the DFT realm and outline the characteristics of possible functionals consistent with the Kohn-Sham theory. To achieve this goal the unitary group technique is applied to label many-electron states of definite total spin and to calculate the corresponding analogs of the Roothaan coupling coefficients. The possibility of using range separated Coulomb potential of electron-electron interaction for constructing functionals discriminating multiplet states in the d-shells is explored and a tentative system of state-specific functionals, covering nontrivial correlations in d-shells of transition metal ions, is proposed for the Fe^{2+} ions.Comment: 7-th European Conference on Computational Chemistry, Venice, Italy, 12 - 15, Sept., 200

An in vivo study of the host response to starch-based polymers and composites subcutaneously implanted in rats

Implant failure is one of the major concerns in the biomaterials field. Several factors have been related to the fail but in general these biomaterials do not exhibit comparable physical, chemical or biological properties to natural tissues and ultimately, these devices can lead to chronic inflammation and foreign-body reactions. Starch-based biodegradable materials and composites have shown promising properties for a wide range of biomedical applications as well as a reduced capacity to elicit a strong reaction from immune system cells in vitro. In this work, blends of corn starch with ethylene vinyl alcohol (SEVA-C), cellulose acetate (SCA) and polycaprolactone (SPCL), as well as hydroxyapatite (HA) reinforced starch-based composites, were investigated in vivo. The aim of the work was to assess the host response evoked for starch-based biomaterials, identifying the presence of key cell types. The tissues surrounding the implant were harvested together with the material and processed histologically for evaluation using immunohistochemistry. At implant retrieval there was no cellular exudate around the implants and no macroscopic signs of an inflammatory reaction in any of the animals. The histological analysis of the sectioned interface tissue after immunohistochemical staining using ED1, ED2, CD54, MHC class II and a/b antibodies showed positively stained cells for all antibodies, except for a/b for all the implantation periods, where it was different for the various polymers and for the period of implantation. SPCL and SCA composites were the materials that stimulated the greatest cellular tissue responses, but generally biodegradable starch-based materials did not induce a severe reaction for the studied implantation times, which contrasts with other types of degradable polymeric biomaterials.Fundação para a Ciência e a Tecnologia (FCT

Using simple artificial intelligence methods for predicting amyloidogenesis in antibodies

<p>Abstract</p> <p>Background</p> <p>All polypeptide backbones have the potential to form amyloid fibrils, which are associated with a number of degenerative disorders. However, the likelihood that amyloidosis would actually occur under physiological conditions depends largely on the amino acid composition of a protein. We explore using a naive Bayesian classifier and a weighted decision tree for predicting the amyloidogenicity of immunoglobulin sequences.</p> <p>Results</p> <p>The average accuracy based on leave-one-out (LOO) cross validation of a Bayesian classifier generated from 143 amyloidogenic sequences is 60.84%. This is consistent with the average accuracy of 61.15% for a holdout test set comprised of 103 AM and 28 non-amyloidogenic sequences. The LOO cross validation accuracy increases to 81.08% when the training set is augmented by the holdout test set. In comparison, the average classification accuracy for the holdout test set obtained using a decision tree is 78.64%. Non-amyloidogenic sequences are predicted with average LOO cross validation accuracies between 74.05% and 77.24% using the Bayesian classifier, depending on the training set size. The accuracy for the holdout test set was 89%. For the decision tree, the non-amyloidogenic prediction accuracy is 75.00%.</p> <p>Conclusions</p> <p>This exploratory study indicates that both classification methods may be promising in providing straightforward predictions on the amyloidogenicity of a sequence. Nevertheless, the number of available sequences that satisfy the premises of this study are limited, and are consequently smaller than the ideal training set size. Increasing the size of the training set clearly increases the accuracy, and the expansion of the training set to include not only more derivatives, but more alignments, would make the method more sound. The accuracy of the classifiers may also be improved when additional factors, such as structural and physico-chemical data, are considered. The development of this type of classifier has significant applications in evaluating engineered antibodies, and may be adapted for evaluating engineered proteins in general.</p

Pathogen host interactions: proteomics of Influenza NEP during infection reveals an antagonistic role in the formation of tight junctions

Thesis (Ph.D.)--University of Washington, 2015-12Host-pathogen interaction networks are key to understanding the molecular mechanisms driving disease and can provide new targets for therapeutic intervention. We discovered new host factors interacting with the influenza nuclear export protein (NEP) using an engineered influenza virus expressing NEP with an N-terminal 3xFLAG tag. We collected immunopurification mass spectrometry data for 3xFLAG-NEP during an active infection and during plasmid expression in HEK293T cells. Network analysis of these complementary datasets revealed an enrichment of tight junction proteins in the NEP interactome. Expression of NEP in MDCK cells results in inhibition of tight junction formation as measured by transepithelial electrical resistance and inulin diffusion across the polarized cell monolayer. These findings reveal a new role for NEP as a tight junction antagonist