RESUMO
Abstract In recent years, the development of high-throughput technologies for obtaining sequence data leveraged the possibility of analysis of protein data in silico. However, when it comes to viral polyprotein interaction studies, there is a gap in the representation of those proteins, given their size and length. The prepare for studies using state-of-the-art techniques such as Machine Learning, a good representation of such proteins is a must. We present an alternative to this problem, implementing a fragmentation and modeling protocol to prepare those polyproteins in the form of peptide fragments. Such procedure is made by several scripts, implemented together on the workflow we call PolyPRep, a tool written in Python script and available in GitHub. This software is freely available only for noncommercial users.
Resumo Nos últimos anos, o desenvolvimento de tecnologias de alto rendimento para obtenção de dados sequenciais potencializou a possibilidade de análise de dados proteicos in silico. No entanto, quando se trata de estudos de interação de poliproteínas virais, existe uma lacuna na representação dessas proteínas, devido ao seu tamanho e comprimento. Para estudos utilizando técnicas de ponta como o Aprendizado de Máquina, uma boa representação dessas proteínas é imprescindível. Apresentamos uma alternativa para este problema, implementando um protocolo de fragmentação e modelagem para preparar essas poliproteínas na forma de fragmentos de peptídeos. Tal procedimento é feito por diversos scripts, implementados em conjunto no workflow que chamamos de PolyPRep, uma ferramenta escrita em script Python e disponível no GitHub. Este software está disponível gratuitamente apenas para usuários não comerciais.
RESUMO
Abstract In recent years, the development of high-throughput technologies for obtaining sequence data leveraged the possibility of analysis of protein data in silico. However, when it comes to viral polyprotein interaction studies, there is a gap in the representation of those proteins, given their size and length. The prepare for studies using state-of-the-art techniques such as Machine Learning, a good representation of such proteins is a must. We present an alternative to this problem, implementing a fragmentation and modeling protocol to prepare those polyproteins in the form of peptide fragments. Such procedure is made by several scripts, implemented together on the workflow we call PolyPRep, a tool written in Python script and available in GitHub. This software is freely available only for noncommercial users.
Resumo Nos últimos anos, o desenvolvimento de tecnologias de alto rendimento para obtenção de dados sequenciais potencializou a possibilidade de análise de dados proteicos in silico. No entanto, quando se trata de estudos de interação de poliproteínas virais, existe uma lacuna na representação dessas proteínas, devido ao seu tamanho e comprimento. Para estudos utilizando técnicas de ponta como o Aprendizado de Máquina, uma boa representação dessas proteínas é imprescindível. Apresentamos uma alternativa para este problema, implementando um protocolo de fragmentação e modelagem para preparar essas poliproteínas na forma de fragmentos de peptídeos. Tal procedimento é feito por diversos scripts, implementados em conjunto no workflow que chamamos de PolyPRep, uma ferramenta escrita em script Python e disponível no GitHub. Este software está disponível gratuitamente apenas para usuários não comerciais.
Assuntos
Protease de HIV , Poliproteínas , Software , Simulação de Acoplamento MolecularRESUMO
Supramolecular structures are of great interest due to their applicability in various scientific and industrial fields. The sensible definition of supramolecular molecules is being set by investigators who, because of the different sensitivities of their methods and observational timescales, may have different views on as to what constitutes these supramolecular structures. Furthermore, diverse polymers have been found to offer unique avenues for multifunctional systems with properties in industrial medicine applications. Aspects of this review provide different conceptual strategies to address the molecular design, properties, and potential applications of self-assembly materials and the use of metal coordination as a feasible and useful strategy for constructing complex supramolecular structures. This review also addresses systems that are based on hydrogel chemistry and the enormous opportunities to design specific structures for applications that demand enormous specificity. According to the current research status on supramolecular hydrogels, the central ideas in the present review are classic topics that, however, are and will be of great importance, especially the hydrogels that have substantial potential applications in drug delivery systems, ophthalmic products, adhesive hydrogels, and electrically conductive hydrogels. The potential interest shown in the technology involving supramolecular hydrogels is clear from what we can retrieve from the Web of Science.
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The coinage-metal clusters possess a natural complexity in their theoretical treatment that may be accompanied by inherent shortcomings in the methodological approach. Herein, we performed a scalar-relativistic density functional theory study, considering Perdew, Burke, and Ernzerhof (PBE) with (empirical and semi empirical) van der Waals (vdW), spin-orbit coupling (SOC), +U (Hubbard term), and their combinations, to treat the Cu 13 , Ag 13 , and Au 13 clusters in different structural motifs. The energetic scenario is given by the confirmation of the 3D lowest energy configurations for Cu 13 and Ag 13 within all approaches, while for Au 13 there is a 2D/3D competition, depending on the applied correction. The 2D geometry is 0.43 eV more stable with plain PBE than the 3D one, the SOC, +U, and/or vdW inclusion decreases the overestimated stability of the planar configurations, where the most surprising result is found by the D3 and D3BJ vdW corrections, for which the 3D configuration is 0.29 and 0.11 eV, respectively, more stable than the 2D geometry (with even higher values when SOC and/or +U are added). The D3 dispersion correction represents 7.9% (4.4%) of the total binding energy for the 3D (2D) configuration, (not) being enough to change the sd hybridization and the position of the occupied d -states. Our predictions are in agreement with experimental results and in line with the best results obtained for bulk systems, as well as with hybrid functionals within D3 corrections. The properties description undergoes small corrections with the different approaches, where general trends are maintained, that is, the average bond length is smaller (larger) for lower (higher)-coordinated structures, since a same number of electrons are shared by a smaller (larger) number of bonds, consequently, the bonds are stronger (weaker) and shorter (longer) and the sd hybridization index is larger (smaller). Thus, Au has a distinct behavior in relation to its lighter congeners, with a complex potential energy surface, where in addition to the relevant relativistic effects, correlation and dispersion effects must also be considered.
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Plants synthesize a large number of natural products, many of which are bioactive and have practical values as well as commercial potential. To explore this vast structural diversity, we present PSC-db, a unique plant metabolite database aimed to categorize the diverse phytochemical space by providing 3D-structural information along with physicochemical and pharmaceutical properties of the most relevant natural products. PSC-db may be utilized, for example, in qualitative estimation of biological activities (Quantitative Structure-Activity Relationship, QSAR) or massive docking campaigns to identify new bioactive compounds, as well as potential binding sites in target proteins. PSC-db has been implemented using the open-source PostgreSQL database platform where all compounds with their complementary and calculated information (classification, redundant names, unique IDs, physicochemical properties, etc.) were hierarchically organized. The source organism for each compound, as well as its biological activities against protein targets, cell lines and different organism were also included. PSC-db is freely available for public use and is hosted at the Universidad de Talca.
Assuntos
Bases de Dados de Compostos Químicos , Compostos Fitoquímicos/química , Plantas/química , Simulação de Acoplamento Molecular , Compostos Fitoquímicos/metabolismo , Plantas/metabolismo , Relação Quantitativa Estrutura-AtividadeRESUMO
The emergence of the COVID-19 has caused public health problems worldwide and there is no effective pharmacological treatment for this disease. Research on 3D models of proteins and the search for active molecular sites are important tools to assist in the discovery of effective antiviral drugs to combat COVID-19. To address this problem, the 3D protein structures of SARS-CoV 2 were analyzed and submitted to cavities research, evaluation of their druggabillity and liganbility, and applied to molecular docking studies with potential ligand candidates actually assayed against COVID-19. Eight druggable potential cavity sites were determined in model structures' PDB code, 6W4B, 6VWW, 6W01, 6M3M, and 6VYO, and these are the good alternatives to be characterized as targets for antiviral compounds. The good cavity model of the protease 3D structure was used in molecular docking, and this allowed verifying the theoric interactions of this protein and lopinavir and ritonavir antiviral drugs. These results may assist in the use of 3D protein models in drug design studies aiming to develop drugs against the COVID-19 pandemic.