G-Protein coupled receptors (GPCRs) are involved in a myriad of pathways key for human physiology through the formation of complexes with intracellular partners such as G-proteins and arrestins (Arrs). However, the structural and dynamical determinants of these complexes are still largely unknown. Herein, we developed a computational big-data pipeline that enables the structural characterization of GPCR complexes with no available structure. This pipeline was used to study a well-known group of catecholamine receptors, the human dopamine receptor (DXR) family and its complexes, producing novel insights into the physiological properties of these important drug targets. A detailed description of the protein interfaces of all members of the DXR family (D1R, D2R, D3R, D4R, and D5R) and the corresponding protein interfaces of their binding partners (Arrs: Arr2 and Arr3; G-proteins: Gi1, Gi2, Gi3, Go, Gob, Gq, Gslo, Gssh, Gt2, and Gz) was generated. To produce reliable structures of the DXR family in complex with either G-proteins or Arrs, we performed homology modeling using as templates the structures of the beta 2-adrenergic receptor (beta 2AR) bound to Gs, the rhodopsin bound to Gi, and the recently acquired neurotensin receptor-1 (NTSR1) and muscarinic 2 receptor (M2R) bound to arrestin (Arr). Among others, the work demonstrated that the three partner groups, Arrs and Gs- and Gi-proteins, are all structurally and dynamically distinct. Additionally, it was revealed the involvement of different structural motifs in G-protein selective coupling between D1- and D2-like receptors. Having constructed and analyzed 50 models involving DXR, this work represents an unprecedented large-scale analysis of GPCR-intracellular partner interface determinants.

Neurodegenerative diseases (ND), a major worldwide health problem, present a multifactorial nature. This implies that a multitargeted therapy approach can be considered more effective in such cases when comparing with “one drug-one target” based therapies. Multitarget drugs interact simultaneously with two or more therapeutic targets, thus acting synergistically to improve the disease conditions. This chapter discusses the recent advances in chemometric techniques in multitarget anti-ND drug design. After a brief introduction to the most relevant pathophysiological aspects of some common neurodegenerative diseases, it analyses not only pathophysiology versus therapeutic targets but also conventional versus novel chemometric techniques within such context. The emergence of novel and various chemometric techniques undoubtedly contributed to the design of multitarget-directed ligands (MTDLs) over the last decade, laying emphasis on the sound prospective for future therapeutics regarding diseases such as Alzheimer’s and Parkinson’s disease. © 2018, Springer Science+Business Media, LLC, part of Springer Nature.

The Enzyme Classification (EC) number is a numerical classification scheme for enzymes, established using the chemical reactions they catalyze. This classification is based on the recommendation of the Nomenclature Committee of the International Union of Biochemistry and Molecular Biology. Six enzyme classes were recognised in the first Enzyme Classification and Nomenclature List, reported by the International Union of Biochemistry in 1961. However, a new enzyme group was recently added as the six existing EC classes could not describe enzymes involved in the movement of ions or molecules across membranes. Such enzymes are now classified in the new EC class of translocases (EC 7). Several computational methods have been developed in order to predict the EC number. However, due to this new change, all such methods are now outdated and need updating. In this work, we developed a new multi-task quantitative structure-activity relationship (QSAR) method aimed at predicting all 7 EC classes and subclasses. In so doing, we developed an alignment-free model based on artificial neural networks that proved to be very successful.

<jats:p>Resumo No final do século XIX, a química ganhou notoriedade como uma das principais “ciências ao serviço” da nação. O surgimento de novos tópicos, métodos e práticas úteis contribuíram para a valorização da química e para a definição de medidas governamentais em temas como saúde pública, educação e proteção ambiental. Lente na Academia Politécnica do Porto entre 1877 e 1910, António Ferreira da Silva (1853–1923) desempenhou um papel central na modernização do ensino e da investigação em química em Portugal. Ferreira da Silva foi responsável pela introdução de cursos suplementares de química, pela reformulação do ensino prático, e pela elaboração de novos procedimentos e regulamentos de ensino “que em muito engrandeceram a educação científica” em Portugal. Enquanto lente da Academia Politécnica do Porto, Ferreira da Silva privilegiou ainda a articulação entre o Laboratório da Academia e as indústrias nacionais, contribuindo, em larga medida, para emergência da Química Analítica como uma nova disciplina.Palavras-chave: António Ferreira da Silva; Academia Politécnica do Porto; Química Analítica. Abstract By the turn of the nineteenth century, chemistry had become a “science at the service” of the nation. The emergence of useful topics, methods, and practices contributed to the valorization of chemistry and to the definition of new governmental directives on issues such as public health, education and environment. Lecturer at the Academia Politécnica do Porto between 1877 and 1911, António Ferreira da Silva (1853–1923) played a crucial role in the modernization of the teaching and practice of chemistry in Portugal. Ferreira da Silva created new supplementary chemistry courses, reformed the practical teaching of chemistry, and drafted new proceedings and syllabi “that glorified scientific education” in Portugal. As lecturer of the Academia Politécnica do Porto, he made important steps in the establishment of collaborations between the Academia’s Laboratory and national industries, which largely contributed to the emergence of Analytical Chemistry as an autonomous discipline. Keywords: António Ferreira da Silva; Academia Politécnica do Porto; Analytical Chemistry.</jats:p>

Consensus scoring has become a commonly used strategy within structure-based virtual screening (VS) workflows with improved performance compared to those based in a single scoring function. However, no research has been devoted to analyze the worth of docking scoring functions components in consensus scoring. We implemented and tested a method that incorporates docking scoring functions components into the setting of high performance VS workflows. This method uses genetic algorithms for finding the combination of scoring components that maximizes the VS enrichment for any target. Our methodology was validated using a data set including ligands and decoys for 102 targets that have been widely used in VS validation studies. Results show that our approach outperforms other methods for all targets. It also boosts the initial enrichment performance of the traditional use of whole scoring functions in consensus scoring by an average of 45%. Our methodology showed to be outstandingly predictive when challenged to restore external (previously unseen) data. Remarkably, CompScore was able not only to retain its performance after redocking with a different software, but also proved that the enrichment obtained was not artificial.

<jats:title>Abstract</jats:title><jats:p>Consensus scoring has become a commonly used strategy within structure-based virtual screening (VS) workflows with improved performance compared to those based in a single scoring function. However, no research has been devoted to analyze the worth of docking scoring functions components in consensus scoring. We implemented and tested a method that incorporates docking scoring functions components into the setting of high performance VS workflows. This method uses genetic algorithms for finding the combination of scoring components that maximizes the VS enrichment for any target. Our methodology was validated using a dataset that contains ligands and decoys for 102 targets that has been widely used in VS validation studies. Results show that our approach outperforms other methods for all targets. It also boosts the initial enrichment performance of the traditional use of whole scoring functions in consensus scoring by an average of 45%. CompScore is freely available at: <jats:ext-link xmlns:xlink="http://www.w3.org/1999/xlink" ext-link-type="uri" xlink:href="http://bioquimio.udla.edu.ec/compscore/">http://bioquimio.udla.edu.ec/compscore/</jats:ext-link></jats:p>

We present an in silico approach for modeling the noncovalent interactions between the human mitochondrial voltage-dependent anion channel (hVDAC1) and a family of single-walled carbon nanotubes (SWCNTs) with a defined pattern of topological vacancies (v = 1-16), obtained by removing atoms from the SWCNT surface. The general results showed more stable docking interaction complexes (SWCNT-hVDAC1), with more negative Gibbs free energy of binding affinity values, and a strong dependence on the vacancy number (R-2 = 0.93) and vacancy formation energy (R-2 = 0.96). In addition, for most of the SWCNT vacancies that were analyzed, the interatomic distances for the interactions of the SWCNT-hVDAC1 complex with the functional catalytic residues (i.e., Pro7, Gln199, Gln182, Phe181, Val20, Asp19, Lys15, Gly14, Asp12, Ala11, and Arg18) that form the hVDAC1 active site (i.e., the voltage-sensing N-terminal alpha-helix segment) were very similar to or shorter than the interatomic distances of these residues for ATP-hVDAC1 interactions. In particular, the hVDAC1 residues that can be phosphorylated like Tyr10, Tyr198, and Se16 were significantly perturbed by the interactions with SWCNT with at least nine vacancies. In addition, the SWCNT vacancy family members can affect the flexibility properties of the hVDAC1 N-terminal alpha-helix segment inducing different patterns of local perturbations in inter-residue communication. Finally, vacancy quantitative structure-binding relationships (V-QSBRs) were unveiled for setting up a robust model that can predict the strength of docking interactions between SWCNTs with a specific topological vacancy and hVDAC1. The developed V-QSBR model classified properly all of the SWCNTs with a different number of SWCNT vacancies with exceptional sensitivity and specificity (both equal to 100%), indicating a strong potential to unequivocally predict the influence of SWCNT vacancies on the mitochondrial channel interactions.

The present work aims at establishing multi-target chemometric models using the recently launched quantitative structure-activity relationship (QSAR)-Co tool for predicting the activity of inhibitor compounds against different isoforms of phosphoinositide 3-kinase (PI3K) under various experimental conditions. The inhibitors of class I phosphoinositide 3-kinase (PI3K) isoforms have emerged as potential therapeutic agents for the treatment of various disorders, especially cancer. The cell-based enzyme inhibition assay results of PI3K inhibitors were curated from the CHEMBL database. Factors such as the nature and mutation of cell lines that may significantly alter the assay outcomes were considered as important experimental elements for mt-QSAR model development. The models, in turn, were developed using two machine learning techniques as implemented in QSAR-Co: linear discriminant analysis (LDA) and random forest (RF). Both techniques led to models with high accuracy (ca. 90%). Several molecular fragments were extracted from the current dataset, and their quantitative contributions to the inhibitory activity against all the proteins and experimental conditions under study were calculated. This case study also demonstrates the utility of QSAR-Co tool in solving multi-factorial and complex chemometric problems. Additionally, the combination of different in silico methods employed in this work can serve as a valuable guideline to speed up early discovery of PI3K inhibitors.