J. Cristian Salgado

Methods of calculating protein hydrophobicity and their application in developing correlations to predict hydrophobic interaction chromatography retention

Hydrophobic interaction chromatography (HIC) is a key technique for protein separation and purification. Different methodologies to estimate the hydrophobicity of a protein are reviewed, which have been related to the chromatographic behavior of proteins in HIC. These methodologies consider either knowledge of the three-dimensional structure or the amino acid composition of proteins. Despite some restrictions; they have proven to be useful in predicting protein retention time in HIC.

Mathematical modeling of the dynamic storage of iron in ferritin

BackgroundIron is essential for the maintenance of basic cellular processes. In the regulation of its cellular levels, ferritin acts as the main intracellular iron storage protein. In this work we present a mathematical model for the dynamics of iron storage in ferritin during the process of intestinal iron absorption. A set of differential equations were established considering kinetic expressions for the main reactions and mass balances for ferritin, iron and a discrete population of ferritin species defined by their respective iron content.ResultsSimulation results showing the evolution of ferritin iron content following a pulse of iron were compared with experimental data for ferritin iron distribution obtained with purified ferritin incubated in vitro with different iron levels. Distinctive features observed experimentally were successfully captured by the model, namely the distribution pattern of iron into ferritin protein nanocages with different iron content and the role of ferritin as a controller of the cytosolic labile iron pool (cLIP). Ferritin stabilizes the cLIP for a wide range of total intracellular iron concentrations, but the model predicts an exponential increment of the cLIP at an iron content > 2,500 Fe/ferritin protein cage, when the storage capacity of ferritin is exceeded.ConclusionsThe results presented support the role of ferritin as an iron buffer in a cellular system. Moreover, the model predicts desirable characteristics for a buffer protein such as effective removal of excess iron, which keeps intracellular cLIP levels approximately constant even when large perturbations are introduced, and a freely available source of iron under iron starvation. In addition, the simulated dynamics of the iron removal process are extremely fast, with ferritin acting as a first defense against dangerous iron fluctuations and providing the time required by the cell to activate slower transcriptional regulation mechanisms and adapt to iron stress conditions. In summary, the model captures the complexity of the iron-ferritin equilibrium, and can be used for further theoretical exploration of the role of ferritin in the regulation of intracellular labile iron levels and, in particular, as a relevant regulator of transepithelial iron transport during the process of intestinal iron absorption.

An Expert System for selection of protein purification processes : experimental validation

A hybrid Expert System has been implemented for selecting the optimum sequence of operations for purification of proteins. Two criteria were implemented to select the optimum sequence of purification. One of these uses the selection separation coefficient (SSC criterion) and the other uses the final level of purity (Purity criterion). The sequences suggested by the Expert System have been tested experimentally in two cases: with a model protein mixture and with the purification of β-1,3-glucanase from recombinant Bacillus subtilis culture. In both cases the sequences suggested were tested experimentally and gave very good agreement with the predicted purification. With the model protein mixture it was shown that the sequences suggested by the Purity criterion have fewer steps than those suggested by the SSC criterion.

Revealing Monoamine Oxidase B Catalytic Mechanisms by Means of the Quantum Chemical Cluster Approach.

Two of the possible catalytic mechanisms for neurotransmitter oxidative deamination by monoamine oxidase B (MAO B), namely, polar nucleophilic and hydride transfer, were addressed in order to comprehend the nature of their rate-determining step. The Quantum Chemical Cluster Approach was used to obtain transition states of MAO B complexed with phenylethylamine (PEA), benzylamine (BA), and p-nitrobenzylamine (NBA). The choice of these amines relies on their importance to address MAO B catalytic mechanisms so as to help us to answer questions such as why BA is a better substrate than NBA or how para-substitution affects substrates reactivity. Transition states were later validated by comparison with the experimental free energy barriers. From a theoretical point of view, and according to the our reported transition states, their calculated barriers and structural and orbital differences obtained by us among these compounds, we propose that good substrates such as BA and PEA might follow the hydride transfer pathway while poor substrates such as NBA prefer the polar nucleophilic mechanism, which might suggest that MAO B can act by both mechanisms. The low free energy barriers for BA and PEA reflect the preference that MAO B has for hydride transfer over the polar nucleophilic mechanism when catalyzing the oxidative deamination of neurotransmitters.

Modeling heterocyst pattern formation in cyanobacteria

BackgroundTo allow the survival of the population in the absence of nitrogen, some cyanobacteria strains have developed the capability of differentiating into nitrogen fixing cells, forming a characteristic pattern. In this paper, the process by which cyanobacteria differentiates from vegetative cells into heterocysts in the absence of nitrogen and the elements of the gene network involved that allow the formation of such a pattern are investigated.MethodsA simple gene network model, which represents the complexity of the differentiation process, and the role of all variables involved in this cellular process is proposed. Specific characteristics and details of the systems behavior such as transcript profiles for ntcA, hetR and patS between consecutive heterocysts were studied.ResultsThe proposed model is able to capture one of the most distinctive features of this system: a characteristic distance of 10 cells between two heterocysts, with a small standard deviation according to experimental variability. The systems response to knock-out and over-expression of patS and hetR was simulated in order to validate the proposed model against experimental observations. In all cases, simulations show good agreement with reported experimental results.ConclusionA simple evolution mathematical model based on the gene network involved in heterocyst differentiation was proposed. The behavior of the biological system naturally emerges from the network and the model is able to capture the spacing pattern observed in heterocyst differentiation, as well as the effect of external perturbations such as nitrogen deprivation, gene knock-out and over-expression without specific parameter fitting.

Influence of protonation on substrate and inhibitor interactions at the active site of human monoamine oxidase-A

Although substrate conversion mediated by human monoaminooxidase (hMAO) has been associated with the deprotonated state of their amine moiety, data regarding the influence of protonation on substrate binding at the active site are scarce. Thus, in order to assess protonation influence, steered molecular dynamics (SMD) runs were carried out. These simulations revealed that the protonated form of the substrate serotonin (5-HT) exhibited stronger interactions at the protein surface compared to the neutral form. The latter displayed stronger interactions in the active site cavity. These observations support the possible role of the deprotonated form in substrate conversion. Multigrid docking studies carried out to rationalize the role of 5-HT protonation in other sites besides the active site indicated two energetically favored docking sites for the protonated form of 5-HT on the enzyme surface. These sites seem to be interconnected with the substrate/inhibitor cavity, as revealed by the tunnels observed by means of CAVER program. pK(a) calculations in the surface loci pointed to Glu³²⁷, Asp³²⁸, His⁴⁸⁸, and Asp¹³² as candidates for a possible in situ deprotonation step. Docking analysis of a group of inhibitors (structurally related to substrates) showed further interactions with the same two docking access sites. Interestingly, the protonated/deprotonated amine moiety of almost all compounds attained different docking poses in the active site, none of them oriented to the flavin moiety, thus producing a more variable and less productive orientations to act as substrates. Our results highlight the role of deprotonation in facilitating substrate conversion and also might reflect the necessity of inhibitor molecules to adopt specific orientations to achieve enzyme inhibition.

Effect of the electrostatic potential on the internalization mechanism of cell penetrating peptides derived from TIRAP

In order to develop future therapeutic applications for cell penetrating peptides (CPPs), it is essential to characterize their internalization mechanisms, as they might affect the stability and the accessibility of the carried drug. Several internalization mechanisms have been described in literature, such as endocytosis and transduction. In this work we study the internalization mechanism in HeLa cells of two TIRAP derived peptides: pepTIRAP and pepTIRAPALA, where some of the cationic amino acids were replaced with alanines. Detailed analysis of internalization and the peptides electrostatic potential was carried out, to shed light on the internalization mechanism involved. Molecular modeling studies showed that the main difference identified between pepTIRAP and pepTIRAPALA is the distribution of their electrostatic potential field. The structure of pepTIRAP displays a predominantly positive potential when compared to pepTIRAPALA, which has a more balanced potential distribution. In addition, docking experiments show that interactions between pepTIRAP and negatively charged molecules on the cellular surface such as heparan sulfate are stronger than the ones exhibited by pepTIRAPALA. A mathematical model was proposed to quantify the amount of peptide internalized or non-specifically bound to the membrane. The model indicates a stronger interaction of pepTIRAP with the plasma membrane, compared to pepTIRAPALA. We propose these discrepancies are related to the differences in the electrostatic potential characteristics of each peptide. In the case of pepTIRAP, these interactions lead to the formation of nucleation zones, which are the first stage of the transduction internalization mechanism. These results should be considered for effective design of a cell penetrating peptide.

Mathematical Modeling of Intestinal Iron Absorption Using Genetic Programming

Iron is a trace metal, key for the development of living organisms. Its absorption process is complex and highly regulated at the transcriptional, translational and systemic levels. Recently, the internalization of the DMT1 transporter has been proposed as an additional regulatory mechanism at the intestinal level, associated to the mucosal block phenomenon. The short-term effect of iron exposure in apical uptake and initial absorption rates was studied in Caco-2 cells at different apical iron concentrations, using both an experimental approach and a mathematical modeling framework. This is the first report of short-term studies for this system. A non-linear behavior in the apical uptake dynamics was observed, which does not follow the classic saturation dynamics of traditional biochemical models. We propose a method for developing mathematical models for complex systems, based on a genetic programming algorithm. The algorithm is aimed at obtaining models with a high predictive capacity, and considers an additional parameter fitting stage and an additional Jackknife stage for estimating the generalization error. We developed a model for the iron uptake system with a higher predictive capacity than classic biochemical models. This was observed both with the apical uptake dataset used for generating the model and with an independent initial rates dataset used to test the predictive capacity of the model. The model obtained is a function of time and the initial apical iron concentration, with a linear component that captures the global tendency of the system, and a non-linear component that can be associated to the movement of DMT1 transporters. The model presented in this paper allows the detailed analysis, interpretation of experimental data, and identification of key relevant components for this complex biological process. This general method holds great potential for application to the elucidation of biological mechanisms and their key components in other complex systems.