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Hypotension is one of the main characteristics of the systemic inflammation, basically caused by endothelial dysfunction. Studies have shown that the amino acid L‐kynurenine (KYN) causes vasodilation in mammals, leading to hypotensive shock. In hypotensive shock, when activated by the KYN, the voltage‐gated potassium channel encoded by the family KCNQ (Kv7) gene can cause vasodilation. Fructose‐1,6‐bisphosphate (FBP) it is being considered in studies an anti‐inflammatory, antioxidant, immunomodulator, and a modulator of some ion channels (Ca2+, Na+, and K+). We analyzed the effects of KYN and FBP on mean blood pressure (MBP), systolic and diastolic (DBP) blood pressure, and heart rate variability (HRV) in Wistar rats. Results demonstrated that the administration of KYN significant decreased MBP, DBP, and increased HRV. Importantly, the FBP treatment reversed the KYN effects on MBP, DBP, and HRV. Molecular Docking Simulations suggested that KYN and FBP present a very close estimated free energy of binding and the same position into structure of KCNQ4. Our results did demonstrate that FBP blunted the decrease in BP, provoked by KYN. Results raise new hypotheses for future and studies in the treatment of hypotension resulting from inflammation. [ABSTRACT FROM AUTHOR]

Despite new therapies against malaria, this disease remains one of the main causes of death affecting humanity. The phenomenon of resistance has caused concern as the drugs no longer have the same efficacy, forcing scientific research to develop new methods that prospect new molecules. With the advancement of artificial intelligence, it becomes possible to apply machine learning (ML) techniques in the discovery and evaluation of new molecules, by employing the quantitative structure-activity relationship (QSAR), a classic method that uses regressions to create a model that allows identifying and evaluating new drug candidates. This work combined QSAR with ML and developed a supervised model that modeled a fourth degree polynomial equation capable of identifying new drug candidates derived from the triclosan compound-a classic inhibitor of Plasmodium falciparum growth, the cause of severe malaria. The model produces an R 2 greater than 80% for training and concurrent testing, as well as a correlation index greater than 80% between the calculated and experimental pEC 50 (negative logarithm of half maximal effective concentration) data. In addition, a web software (PlasmoQSAR) was created that allows researchers to calculate the EC 50 (half maximal effective concentration) of new molecules using the developed analytical method., Competing Interests: The authors declare no competing financial interest., (© 2024 The Authors. Published by American Chemical Society.)

Purine nucleoside phosphorylase (PNP) catalyzes the reversible phosphorolysis of N-ribosidic bonds of purine nucleosides and deoxynucleosides, except adenosine, to generate ribose 1-phosphate and the purine base. This work describes for the first time a structural model of PNP from Bacteroides fragilis (Bf). We modeled the complexes of BfPNP with six different ligands in order to determine the structural basis for specificity of these ligands against BfPNP. Comparative analysis of the model of BfPNP and the structure of HsPNP allowed identification of structural features responsible for differences in the computationally determined ligand affinities. The molecular dynamics (MD) simulation was assessed to evaluate the overall stability of the BfPNP model. The superposition of the final onto the initial minimized structure shows that there are no major conformational changes from the initial model, which is consistent with the relatively low root mean square deviation (RMSD). The results indicate that the structure of the model was stable during MD, and does not exhibit loosely structured loop regions or domain terminals.

The study of protein-drug interaction is of pivotal importance to understand the structural features essential for ligand affinity. The explosion of information about protein structures has paved the way to develop structure-based virtual screening approaches. Parasitic protein kinases have been pointed out as potential targets for antiparasitic development. The identification of protein kinases in the Plasmodium falciparum genome has opened the possibility to test new families of inhibitors as potential antimalarial drugs. In addition, other key enzymes which play roles in biosynthetic pathways, such as enoyl reductase and chorismate synthase, can be valuable targets for drug development. This review is focused on these protein targets that may help to materialize new generations of antimalarial drugs.

Drug development has become the Holy Grail of many structural bioinformatics groups. The explosion of information about protein structures, ligand-binding affinity, parasite genome projects, and biological activity of millions of molecules opened the possibility to correlate this scattered information in order to generate reliable computational models to predict the likelihood of being able to modulate a target with a small-molecule drug. Computational methods have shown their potential in drug discovery and development allied with in vitro and in vivo methodologies. The present review discusses the main bioinformatics tools available for drug discovery and development.

The development of databases devoted to biological information opened the possibility to integrate, query and analyze biological data obtained from several sources that otherwise would be scattered through the web. Several issues arise in the handling of biological information, mainly due to the diversity of biological subject matter and the complexity of biological approaches towards phenomena of the living world. The integration of genomic data, three-dimensional structures of proteins, biological activity, and drugs availability allows a system approach to the study of the biology. Here we review the current status of these research efforts to develop genomic databases for protozoan parasites, such as the apicomplexan parasites, Trypanosoma cruzi and Leishmania spp. These databases may help in the discovery and development of new drugs against parasite-mediated diseases.

Molecular docking simulations are of pivotal importance for analysis of protein-ligand interactions and also an essential resource for virtual-screening initiatives. In molecular docking simulations several possible docked structures are generated, which create an ensemble of structures representing binary complexes. Therefore, it is crucial to find the best solution for the simulation. One approach to this problem is to employ empirical scoring function to identify the best docked structure. It is expected that scoring functions show a descriptive funnel-shaped energy surface without many false minima to impair the efficiency of conformational sampling. We employed this methodology against a test set with 300 docked structures. Docking simulations of these ligands against enzyme binding pocket indicated a funnel-shaped behavior of the complexation for this system. This review compares a set of recently proposed polynomial empirical scoring functions, implemented in a program called POLSCORE, with two popular scoring function programs (XSCORE and DrugScore). Overall comparison indicated that POLSCORE works better to predict the correct docked position, for the ensemble of docked structures analyzed in the present work.

Drug development is a high cost and laborious process, requiring a number of tests until a drug is made available in the market. Therefore, the use of methods to screen large number of molecules with less cost is crucial for faster identification of hits and leads. One strategy to identify drug-like molecules is the search for molecules able to interfere with a protein function, since protein interactions control most biological processes. Ideally the use of in silico screenings would make drug development faster and less expensive. Currently, however, the confirmation of biological activity is still needed. Due to the complexity of the task of drug discovery, an integrated and multi-disciplinary approach is ultimately required. Here we discuss examples of drugs developed through a combination of in silico and in vitro strategies. The potential use of these methodologies for the identification of active compounds as well as for early toxicity and bioavailability is also reviewed.

With the progression of structural genomics projects, comparative modeling remains an increasingly important method of choice to obtain 3D structure of proteins. It helps to bridge the gap between the available sequence and structure information by providing reliable and accurate protein models. Comparative modeling based on more than 30% sequence identity is now approaching its natural template-based limits and further improvements require the development of effective refinement techniques capable of driving models toward native structure. For difficult targets, for which the most significant progress in recent years has been observed, optimal template selection and alignment accuracy are still the major problems. The past year has seen a maturation of molecular modeling, with an increasing number of comparative studies between established methods becoming possible, together with an explosion of new works especially in the areas of combinatorial chemistry and molecular diversity. To achieve this, knowledge about three-dimensional protein structures is crucial for the understanding of their functional mechanisms, and for a rational drug design. This review described recent progress in molecular modeling methodology.

Molecular recognition process describes the interaction involving two molecules. In the case of biomolecules, these pairs of molecules could be protein-protein, protein-ligand or protein-nucleic acid. The first model to capture the essential features, behind the molecular recognition problem, was the lock-and-key paradigm. The overall analysis protein-protein, protein-nucleic acid and protein-ligand interaction based on the three-dimensional structures and physicochemical parameters, such as binding affinity, opened the possibility to provide further insights in this basic phenomenon. The main ideas behind the molecular recognition are discussed in the present review.

Currently, in order to accelerate the process of drug development and also reduce costs, many of the experimental assays related to lead discovery and lead optimization processes are being replaced by computational, in silico, methods. In this context, the LIE (linear interaction energy) method has been used to calculate binding free energies for widely different compounds by averaging interaction energies obtained from molecular dynamics (MD) or Monte Carlo (MC) simulations. In particular, the combination of docking and affinity predictions with the LIE method can thus save valuable resources in lead discovery and optimization projects. This review presents a description of LIE methodology and some recent studies that illustrate the importance and utility of the method in the field of pharmaceutical research.

Recent developments in computer power and chemoinformatics methodology make possible that a huge amount of data become available through internet. These databases are devoted to a wide spectrum of scientific fields. Here we are concerned with databases related to protein-drug interactions. More specifically, databases where potential new molecules could be accessed to be used in virtual screening initiatives. In the past decade several databases have been developed where molecules to be used in the virtual screening could be easily identified, downloaded and even purchased. This review describes and summarizes the recent advances in the development of these databases, and also the main applications related to virtual screening projects.

The family of Cyclin-Dependent Kinases (CDKs) can be subdivided into two major functional groups based on their roles in cell cycle and/or transcriptional control. CDK9 is the catalytic subunit of positive transcription elongation factor b (P-TEFb). CDK9 is the kinase of the TAK complex (Tat-associated kinase complex), and binds to Tat protein of HIV, suggesting a possible role for CDK9 in AIDS progression. CDK9 complexed with its regulatory partner cyclin T1, serves as a cellular mediator of the transactivation function of the HIV Tat protein. P-TEFb is responsible for the phosphorylation of the carboxyl-terminal domain of RNA Pol II, resulting in stimulation of transcription. Furthermore, the complexes containing CDK9 induce the differentiation in distinct tissue. The CDK9/cyclin T1 complex is expressed at higher level in more differentiated primary neuroectodermal and neuroblastoma tumors, showing a correlation between the kinase expression and tumor differentiation grade. This may have clinical and therapeutical implications for these tumor types. Among the CDK inhibitors two have shown to be effective against CDK9: Roscovitine and Flavopiridol. These two inhibitors prevented the replication of human immunodeficiency virus (HIV) type 1 by blocking Tat transactivation of the HIV type 1 promoter. These compounds inhibit CDKs by binding to the catalytic domain in place of ATP, preventing transfer of a phosphate group to the substrate. More sensitive therapeutic agents of CDK9 can be designed, and structural studies can add information in the understanding of this kinase. The major features related to CDK9 inhibition will be reviewed in this article.