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  • Open access
  • 123 Reads
Free energy theoretical calculations of PKA–Kemptide complex formation, and effect of mutation of Kemptide arginines to homoarginines.
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Protein kinases (PKs) constitute a large and diverse family of enzymes that modify other proteins (substrates) by phosphorylation, which is crucial for cell-division regulation, metabolic routes regulation, and many other cellular functions.1 A malfunction in such regulation processes can lead to several diseases like inflammatory disorders, endocrine disorders, and cancer. It is well known that PKs are highly selective during the substrate recognition process, and this selectivity is mainly associated to the chemical forces between the binding site of the protein and the sequence in the surroundings of the local phosphorylation site of the protein substrate.

In this work, in order to understand the selectivity of PKs for their substrates, we studied the interactions between PKA, a PK whose enzymatic activity is dependent on cellular levels of cyclic AMP, and the substrate Kemptide.2 This substrate is the short synthetic heptapeptide Leu-Arg-Arg-Ala-Ser-Leu-Gly which has good selectivity for PKA. The substitution of either of the two arginine residues by other residues lead to higher apparent KM values than the parent peptide. For instance, when arginines are replaced by the non-natural amino acid homoarginine, which is a very similar residue, the affinity of Kemptide for PKA decreases significantly.

Our purpose was to replicate experimental evidences by using a theoretical atomistic protocol. Molecular dynamic (MD) simulations of PKA (Protein Kinase A) forming complexes with the substrate Kemptide and mutants containing homoarginine instead of typical arginines were carried out. The models used to develop this study contains PKA, Kemptide substrate or its mutants, the solvent media, and ionic environment. After MD simulations, computational free energy calculations were developed by using the free energy perturbation (FEP) method implemented in NAMD software. The free energies differences (less than1 kcal/mol) between our calculations and the experimental data indicate that this computational protocol could replicate the experimental thermodynamic differences at atomistic level.

  • Open access
  • 191 Reads
Insights into the inhibitory effect of Ca2+ on protein kinase A from molecular dynamics simulations.
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Protein kinases are an important family of enzymes that govern many signaling processes within cells by transferring a phosphoryl group from an ATP molecule onto a substrate protein.  cAMP-dependent protein kinase, also called protein kinase A (PKA), is one of the most well-studied protein kinases, and because of the high conservation of the protein kinase family, it serves as a model for all protein kinases. Mg2+, as the most abundant divalent metal ion in the cell, is believed to be the favored coordinating ion for kinases, however it has been proven experimentally than other divalent metals such as Ca2+ can also promote the phosphoryl transfer but at much lower rates.

We observed, through preliminary molecular dynamics simulations (MDs), that Ca2+ tends to increase the mobility of the protein substrate and reduce the mobility of the phosphorylated substrate in PKA; thereby corroborating experimental observations about a “trapping effect”  and consequently an inhibitory effect produced by Ca2+. This information is expected to be valuable for the understanding of the catalytic mechanisms in protein kinases which could lead to the design of more potent inhibitors as well as to understand a possible regulation mechanism exerted by Ca2+ on kinases.

  • Open access
  • 127 Reads
A bioinformatic approach to search for active transposases in genomes.

Eukaryotic transposons are DNA sequences able to move inside a genome. They are characterized by a sequence that encodes a transposase protein of ~300 aminoacids and flanking it, short terminal inverted repeats of ~30bp. Active DNA transposons are very difficult to predict computationally because: 1. Due to their activity, there are many copies, or paralogous, of the transposons of a family in a genome; 2. Due to mutation, a high diversity of sequences has resulted, and as consequence; 3. Many transposons are incomplete or mutated enough to render the element inactive. In order to circumvent these issues, we generated Hidden Markov Models (HMMs) for 12 families of eukaryotic transposases because HMMs are an appropriate technique for searching evolutionary divergent sequences.

In animals, during their development, transposons activity is regulated by piRNAs. This regulation occurs via Watson-Crick base pairing between the piRNA and the transposase transcript. In order to test the ability of our models to predict active transposases, we used as reference the mapping of known piRNAs sequences of an organism on its own genome, and compared it to our transposase predictions, and to those made by RepeatMasker, the current gold standard software for prediction of mobile elements. We found that, while RepeatMasker has a higher absolute number of predictions, its sensitivity and selectivity as classifier of active transposases is lower than our HMMs for all tested organisms. Although, there is a lot of room for improvement, these results are a step towards the improvement of the accuracy of prediction of active transposases.

  • Open access
  • 162 Reads
Aplications of mass spectrometry to medical imagin

Dr. Ruiz-Romero was invited to give the talk at IWMEDIC 2016, IV International Workshop on Medical Imaging and Mass Spectroscopy. Please, follow the link to see the video presentation uploaded (or to be uploaded soon) at the workshop official site:

  • Open access
  • 118 Reads
Virtual Colonoscopy: State of art

Dr. Martínez Sapiña was invited to give the talk at IWMEDIC 2016, IV International Workshop on Virtual Colonoscopy. Please, follow the link to see the video presentation uploaded (or to be uploaded soon) at the workshop official site:

  • Open access
  • 98 Reads
Drug repositioning for the treatment of obsessive-compulsive disorder.

The Obsessive-compulsive disorder (OCD) is a common psychiatric disorder characterized by obsessions and compulsions. Obsessions are repetitive thoughts, intrusive unwanted, images or impulses that cause, fear or anxiety in the minds of oneself. The Compulsion is a repetitive ritual behavior and it is defined as inappropriate actions at the situation. However, these persist and often result in undesirable consequences.1 The fears and concerns of the patients difficult them to carry out daily activities. About 3% of the world population has OCD. Children can also suffer OCD.2

ODC is a clinically heterogeneous disorder. Although some structural brain abnormalities have been consistently reported in the OCD, its interaction with certain clinical subtypes deserves further examination. Studies of twins, families, and segregation analysis provided convincing evidence that OCD has a strong genetic component.3 Treatment is available for people with anxiety disorders. In addition, researchers are looking for new treatments that help to relieve the symptoms. Currently there are drugs to help in the treatment of this disorder, but none of them attacks only this syndrome, and they have a number of side effects or may even present levels of toxicity.

That is why it has been tried to reuse or to reposition drugs already approved by the FDA for treating OCD. The repositioning of drugs is the process of drug development based on the identification and development of new uses for existing drugs. These medicines may be in the market, or they have been discarded due to errors in the final stages of clinical trials. The traditional drug development has duration of 10-17 years, with costs and very high failure rates; while repositioned drug development takes about eight years, with lower costs of R & D (research and development) and a higher rate of success.4 Finally, bioinformatics studies have been of great help for the research and development of new drugs, and also for research repositioning of these as they can minimize the time spent searching for the active sites of target proteins; and to make molecular dynamic simulations to test new drugs, among other useful applications.

  • Open access
  • 104 Reads
Development of a method for inferring regulatory networks of genes time and specific location: application and comparative studies in D. melanogaster.
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The regulation of gene expression is one of the determining factors in the development and maintenance of life in all organisms. This regulation is carried out mainly through the action of Transcription Factors (TFs), although other elements are also involved. Notably, any new knowledge on the regulation of the expression is key to unravel the functioning of the various organisms at the molecular level. This knowledge also has direct application in the understanding of the processes that trigger different diseases, allowing the development of new therapeutic strategies. Gene regulation is usually represented in the form of Gene Regulatory Networks (GRNs). These networks are a simplified representation of how genes are controlled allowing the characterization and study of the different interdependencies of the various factors that are involved in the regulation of the expression of genes.

Given the abundance of experimental data on the various factors involved in the regulation of gene expression and the little specific knowledge of this regulation in different tissues and cell types forming organisms in certain stages of development, the creation of new computational methods to integrate all this information into site and time specific networks is a key element for future studies. In this work, time and condition specific GRNs will be conducted to study the development of the embryo of Drosophila melanogaster. D. melanogaster, is a model organism widely studied, given its short generation time and easy culture. In this way, different networks of each stage of development will be created by integrating experimental data from various databases. Finally, GRNs obtained will be characterized and studied employing graphlets based techniques to identify specific elements whose relationship with the rest of the network vary over time during the development of the fly embryo.