Calcium is a noncompetitive inhibitor of DMT1 on the intestinal iron absorption process: empirical evidence and mathematical modeling analysis.

Iron absorption is a complex and highly controlled process where DMT1 transports nonheme iron through the brush-border membrane of enterocytes to the cytoplasm but does not transport alkaline-earth metals such as calcium. However, it has been proposed that high concentrations of calcium in the diet could reduce iron bioavailability. In this work, we investigate the effect of intracellular and extracellular calcium on iron uptake by Caco-2 cells, as determined by calcein fluorescence quenching. We found that extracellular calcium inhibits iron uptake by Caco-2 cells in a concentration-dependent manner. Chelation of intracellular calcium with BAPTA did not affect iron uptake, which indicates that the inhibitory effect of calcium is not exerted through intracellular calcium signaling. Kinetic studies performed, provided evidence that calcium acts as a reversible noncompetitive inhibitor of the iron transport activity of DMT1. Based on these experimental results, a mathematical model was developed that considers the dynamics of noncompetitive inhibition using a four-state mechanism to describe the inhibitory effect of calcium on the DMT1 iron transport process in intestinal cells. The model accurately predicts the calcein fluorescence quenching dynamics observed experimentally after an iron challenge. Therefore, the proposed model structure is capable of representing the inhibitory effect of extracellular calcium on DMT1-mediated iron entry into the cLIP of Caco-2 cells. Considering the range of calcium concentrations that can inhibit iron uptake, the possible inhibition of dietary calcium on intestinal iron uptake is discussed.

Novel Symbiotic Genome-Scale Model Reveals Wolbachia's Arboviral Pathogen Blocking Mechanism in Aedes aegypti.

Wolbachia are endosymbiont bacteria known to infect arthropods causing different effects, such as cytoplasmic incompatibility and pathogen blocking in Aedes aegypti. Although several Wolbachia strains have been studied, there is little knowledge regarding the relationship between this bacterium and their hosts, particularly on their obligate endosymbiont nature and its pathogen blocking ability. Motivated by the potential applications on disease control, we developed a genome-scale model of two Wolbachia strains: wMel and the strongest Dengue blocking strain known to date: wMelPop. The obtained metabolic reconstructions exhibit an energy metabolism relying mainly on amino acids and lipid transport to support cell growth that is consistent with altered lipid and cholesterol metabolism in Wolbachia-infected mosquitoes. The obtained metabolic reconstruction was then coupled with a reconstructed mosquito model to retrieve a symbiotic genome-scale model accounting for 1,636 genes and 6,408 reactions of the Aedes aegypti-Wolbachia interaction system. Simulation of an arboviral infection in the obtained novel symbiotic model represents a metabolic scenario characterized by pathogen blocking in higher titer Wolbachia strains, showing that pathogen blocking by Wolbachia infection is consistent with competition for lipid and amino acid resources between arbovirus and this endosymbiotic bacteria. IMPORTANCE Arboviral diseases such as Zika and Dengue have been on the rise mainly due to climate change, and the development of new treatments and strategies to limit their spreading is needed. The use of Wolbachia as an approach for disease control has motivated new research related to the characterization of the mechanisms that underlie its pathogen-blocking properties. In this work, we propose a new approach for studying the metabolic interactions between Aedes aegypti and Wolbachia using genome-scale models, finding that pathogen blocking is mainly influenced by competition for the resources required for Wolbachia and viral replication.

A systems biology approach for studying Wolbachia metabolism reveals points of interaction with its host in the context of arboviral infection.

Wolbachia are alpha-proteobacteria known to infect arthropods, which are of interest for disease control since they have been associated with improved resistance to viral infection. Although several genomes for different strains have been sequenced, there is little knowledge regarding the relationship between this bacterium and their hosts, particularly on their dependency for survival. Motivated by the potential applications on disease control, we developed genome-scale models of four Wolbachia strains known to infect arthropods: wAlbB (Aedes albopictus), wVitA (Nasonia vitripennis), wMel and wMelPop (Drosophila melanogaster). The obtained metabolic reconstructions exhibit a metabolism relying mainly on amino acids for energy production and biomass synthesis. A gap analysis was performed to detect metabolic candidates which could explain the endosymbiotic nature of this bacterium, finding that amino acids, requirements for ubiquinone precursors and provisioning of metabolites such as riboflavin could play a crucial role in this relationship. This work provides a systems biology perspective for studying the relationship of Wolbachia with its host and the development of new approaches for control of the spread of arboviral diseases. This approach, where metabolic gaps are key objects of study instead of just additions to complete a model, could be applied to other endosymbiotic bacteria of interest.

Mathematical modeling of the relocation of the divalent metal transporter DMT1 in the intestinal iron absorption process.

Iron is essential for the normal development of cellular processes. This metal has a high redox potential that can damage cells and its overload or deficiency is related to several diseases, therefore it is crucial for its absorption to be highly regulated. A fast-response regulatory mechanism has been reported known as mucosal block, which allows to regulate iron absorption after an initial iron challenge. In this mechanism, the internalization of the DMT1 transporters in enterocytes would be a key factor. Two phenomenological models are proposed for the iron absorption process: DMT1's binary switching mechanism model and DMT1's swinging-mechanism model, which represent the absorption mechanism for iron uptake in intestinal cells. The first model considers mutually excluding processes for endocytosis and exocytosis of DMT1. The second model considers a Ball's oscillator to represent the oscillatory behavior of DMT1's internalization. Both models are capable of capturing the kinetics of iron absorption and represent empirical observations, but the DMT1's swinging-mechanism model exhibits a better correlation with experimental data and is able to capture the regulatory phenomenon of mucosal block. The DMT1 swinging-mechanism model is the first phenomenological model reported to effectively represent the complexity of the iron absorption process, as it can predict the behavior of iron absorption fluxes after challenging cells with an initial dose of iron, and the reduction in iron uptake observed as a result of mucosal block after a second iron dose.

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.

Iron-induced reactive oxygen species mediate transporter DMT1 endocytosis and iron uptake in intestinal epithelial cells.

Recent evidence shows that iron induces the endocytosis of the iron transporter dimetal transporter 1 (DMT1) during intestinal absorption. We, and others, have proposed that iron-induced DMT1 internalization underlies the mucosal block phenomena, a regulatory response that downregulates intestinal iron uptake after a large oral dose of iron. In this work, we investigated the participation of reactive oxygen species (ROS) in the establishment of this response. By means of selective surface protein biotinylation of polarized Caco-2 cells, we determined the kinetics of DMT1 internalization from the apical membrane after an iron challenge. The initial decrease in DMT1 levels in the apical membrane induced by iron was followed at later times by increased levels of DMT1. Addition of Fe(2+), but not of Cd(2+), Zn(2+), Cu(2+), or Cu(1+), induced the production of intracellular ROS, as detected by 2',7'-dichlorofluorescein (DCF) fluorescence. Preincubation with the antioxidant N-acetyl-l-cysteine (NAC) resulted in increased DMT1 at the apical membrane before and after addition of iron. Similarly, preincubation with the hydroxyl radical scavenger dimethyl sulfoxide (DMSO) resulted in the enhanced presence of DMT1 at the apical membrane. The decrease of DMT1 levels at the apical membrane induced by iron was associated with decreased iron uptake rates. A kinetic mathematical model based on operational rate constants of DMT1 endocytosis and exocytosis is proposed. The model qualitatively captures the experimental observations and accurately describes the effect of iron, NAC, and DMSO on the apical distribution of DMT1. Taken together, our data suggest that iron uptake induces the production of ROS, which modify DMT1 endocytic cycling, thus changing the iron transport activity at the apical membrane.

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 substrate's 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.

Molecular dynamics simulation of halogen bonding mimics experimental data for cathepsin L inhibition.

A MD simulation protocol was developed to model halogen bonding in protein-ligand complexes by inclusion of a charged extra point to represent the anisotropic distribution of charge on the halogen atom. This protocol was then used to simulate the interactions of cathepsin L with a series of halogenated and non-halogenated inhibitors. Our results show that chloro, bromo and iodo derivatives have progressively narrower distributions of calculated geometries, which reflects the order of affinity I > Br > Cl, in agreement with the IC50 values. Graphs for the Cl, Br and I analogs show stable interactions between the halogen atom and the Gly61 carbonyl oxygen of the enzyme. The halogen-oxygen distance is close to or less than the sum of the van der Waals radii; the C-X···O angle is about 170°; and the X···O=C angle approaches 120°, as expected for halogen bond formation. In the case of the iodo-substituted analogs, these effects are enhanced by introduction of a fluorine atom on the inhibitors' halogen-bonding phenyl ring, indicating that the electron withdrawing group enlarges the σ-hole, resulting in improved halogen bonding properties.

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.

Mathematical modeling of the dynamic storage of iron in ferritin.

BACKGROUND: Iron 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. RESULTS: Simulation 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. CONCLUSIONS: The 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.

Modeling heterocyst pattern formation in cyanobacteria.

BACKGROUND: To 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. METHODS: A 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 system's behavior such as transcript profiles for ntcA, hetR and patS between consecutive heterocysts were studied. RESULTS: The 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 system's 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. CONCLUSION: A 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.

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.

Prediction of the partitioning behaviour of proteins in aqueous two-phase systems using only their amino acid composition.

The prediction of the partition behaviour of proteins in aqueous two-phase systems (ATPS) using mathematical models based on their amino acid composition was investigated. The predictive models are based on the average surface hydrophobicity (ASH). The ASH was estimated by means of models that use the three-dimensional structure of proteins and by models that use only the amino acid composition of proteins. These models were evaluated for a set of 11 proteins with known experimental partition coefficient in four-phase systems: polyethylene glycol (PEG) 4000/phosphate, sulfate, citrate and dextran and considering three levels of NaCl concentration (0.0% w/w, 0.6% w/w and 8.8% w/w). The results indicate that such prediction is feasible even though the quality of the prediction depends strongly on the ATPS and its operational conditions such as the NaCl concentration. The ATPS 0 model which use the three-dimensional structure obtains similar results to those given by previous models based on variables measured in the laboratory. In addition it maintains the main characteristics of the hydrophobic resolution and intrinsic hydrophobicity reported before. Three mathematical models, ATPS I-III, based only on the amino acid composition were evaluated. The best results were obtained by the ATPS I model which assumes that all of the amino acids are completely exposed. The performance of the ATPS I model follows the behaviour reported previously, i.e. its correlation coefficients improve as the NaCl concentration increases in the system and, therefore, the effect of the protein hydrophobicity prevails over other effects such as charge or size. Its best predictive performance was obtained for the PEG/dextran system at high NaCl concentration. An increase in the predictive capacity of at least 54.4% with respect to the models which use the three-dimensional structure of the protein was obtained for that system. In addition, the ATPS I model exhibits high correlation coefficients in that system being higher than 0.88 on average. The ATPS I model exhibited correlation coefficients higher than 0.67 for the rest of the ATPS at high NaCl concentration. Finally, we tested our best model, the ATPS I model, on the prediction of the partition coefficient of the protein invertase. We found that the predictive capacities of the ATPS I model are better in PEG/dextran systems, where the relative error of the prediction with respect to the experimental value is 15.6%.

Current insights on protein behaviour in hydrophobic interaction chromatography.

This paper gives a summary of different aspects for predicting protein behaviour in hydrophobic interaction chromatography (HIC). First, a brief description of HIC, hydrophobic interactions, amino acid and protein hydrophobicity is presented. After that, several factors affecting protein chromatographic behaviour in HIC are described. Finally, different approaches for predicting protein retention time in HIC are shown. Using all this information, it could be possible to carry out computational experiments by varying the different operating conditions for the purification of a target protein; and then selecting the best conditions in silico and designing a rational protein purification process involving an HIC step.

Predicting the behaviour of proteins in hydrophobic interaction chromatography. 1: Using the hydrophobic imbalance (HI) to describe their surface amino acid distribution.

This paper focuses on the prediction of the dimensionless retention time of proteins (DRT) in hydrophobic interaction chromatography (HIC) by means of mathematical models based on characteristics of the surface hydrophobicity distribution. We introduce a new parameter, called hydrophobic imbalance (HI), obtained from the three-dimensional structure of proteins. This parameter quantifies the displacement of the superficial geometric centre of the protein when the effect of the hydrophobicity of each amino acid is considered. This parameter is simpler and less expensive than those reported previously. We use HI as a way to incorporate information about the surface hydrophobicity distribution in order to improve the prediction of DRT. We tested the performance of our DRT predictive models in a set of 15 proteins. This set includes four proteins whose DRTs are known as very difficult to predict. By means of the variable HI, it was possible to improve the predictive characteristics obtained by models based on the average surface hydrophobicity (ASH) by 9.1%. Also, we studied linear multivariable models based on characteristics determined from the HI. By using this multivariable model, a correlation coefficient of 0.899 was obtained. With this model, we managed to improve the predictive characteristics shown by previous models based on ASH by 31.8%.

Predicting the behaviour of proteins in hydrophobic interaction chromatography. 2. Using a statistical description of their surface amino acid distribution.

This paper focuses on the prediction of the dimensionless retention time (DRT) of proteins in hydrophobic interaction chromatography (HIC) by means of mathematical models based on the statistical description of the amino acid surface distribution. Previous models characterises the protein surface as a whole. However, most of the time it is not the whole protein but some of its specific regions that interact with the environment. It seems much more natural to use local measurements of the characteristics of the surface. Therefore, the statistical characterisation of the distribution of an amino acid property on the protein surface was carried out from the systematic calculation of the local average of this property in a neighbourhood placed sequentially on each of the amino acids on the protein surface. This process allowed us to characterise the distribution of this property quantitatively using three main statistics: average, standard deviation and maximum. In particular, if the property considered is a hydrophobicity scale, these statistics allowed us to characterise the average hydrophobicity and the hydrophobic content of the most hydrophobic cluster or hotspot, as well as the heterogeneity of the hydrophobicity distribution on the protein surface. We tested the performance of the DRT predictive models based on these statistics on a set of 15 proteins. We obtained better predictive results with respect to the models previously reported. The best predictive model was a linear model based on the maximum. This statistic was calculated using an index of the mobilities of amino acids in chromatography. The predictive performance of this model (measured as the Jack Knife MSE) was 26.9% better than those obtained by the best model which does not consider the amino acid distribution and 19.5% better than the model based on the hydrophobic imbalance (HI). In addition, the best performance was obtained by a linear multivariable model based on the HI and the maximum. The difference between the experimental data and the prediction carried out by this model was smaller than those observed previously. In fact, this model obtained better predictive capacities than a previous linear multivariable model decreasing the Jack Knife MSE in 8.7%. In addition, this model allowed us to diminish the number of variables required, increasing, in this way, the degrees of freedom of the model.

Prediction of retention times of proteins in hydrophobic interaction chromatography using only their amino acid composition.

This paper focuses on the prediction of the dimensionless retention time of proteins (DRT) in hydrophobic interaction chromatography (HIC) by means of mathematical models based, essentially, only on aminoacidic composition. The results show that such prediction is indeed possible. Our main contribution was the design of models that predict the DRT using the minimal information concerning a protein: its aminoacidic composition. The performance is similar to that observed in models that use much more sophisticated information such as the three-dimensional structure of proteins. Three models that, in addition to the amino acid composition, use different assumptions about the amino acids tendency to be exposed to the solvent, were evaluated in 12 proteins with known experimental DRT. In all the cases analyzed, the model that obtained the best results was the one based on a linear estimation of the aminoacidic surface composition. The models were adjusted using a collection of 74 vectors of aminoacidic properties plus a set of 6388 vectors derived from these using two mathematical tools: k-means and self-organizing maps (SOM) algorithms. The best vector was generated by the SOM algorithm and was interpreted as a hydrophobicity scale based partly on the tendency of the amino acids to be hidden in proteins. The prediction error (MSE(JK)) obtained by this model was almost 35% smaller than that obtained by the model that supposes that all the amino acids are completely exposed and 40% smaller than that obtained by the model that uses a simple correction factor considering the general tendency of each amino acid to be exposed to the solvent. In fact, the performance of the best model based on the aminoacidic composition was 5% better than that observed in the model based on the three-dimensional structure of proteins.

Is it possible to predict the average surface hydrophobicity of a protein using only its amino acid composition?

Hydrophobicity is one of the most important physicochemical properties of proteins. Moreover, it plays a fundamental role in hydrophobic interaction chromatography, a separation technique that, at present time, is used in most industrial processes for protein purification as well as in laboratory scale applications. Although there are many ways of assessing the hydrophobicity value of a protein, recently, it has been shown that the average surface hydrophobicity (ASH) is an important tool in the area of protein separation and purification particularly in protein chromatography. The ASH is calculated based on the hydrophobic characteristics of each class of amino acid present on the protein surface. The hydrophobic characteristics of the amino acids are determined by a scale of aminoacidic hydrophobicity. In this work, the scales of Cowan-Whittaker and Berggren were studied. However, to calculate the ASH, it is necessary to have the three-dimensional protein structure. Frequently this data does not exist, and the only information available is the amino acid sequence. In these cases it would be desirable to estimate the ASH based only on properties extracted from the protein sequence. It was found that it is possible to predict the ASH from a protein to an acceptable level for many practical applications (correlation coefficient > 0.8) using only the aminoacidic composition. Two predictive tools were built: one based on a simple linear model and the other on a neural network. Both tools were constructed starting from the analysis of a set of 1982 non-redundant proteins. The linear model was able to predict the ASH for an independent subset with a correlation coefficient of 0.769 for the case of Cowan-Whittaker and 0.803 for the case of Berggren. On the other hand, the neural model improved the results shown by the linear model obtaining correlation coefficients of 0.831 and 0.836, respectively. The neural model was somewhat more robust than the linear model particularly as it gave similar correlation coefficients for both hydrophobicity scales tested, moreover, the observed variabilities did not overcome 6.1% of the mean square error. Finally, we tested our models in a set of nine proteins with known retention time in hydrophobic interaction chromatography. We found that both models can predict this retention time with correlation coefficients only slightly inferior (11.5% and 5.5% for the linear and the neural network models, respectively) than models that use the information about the three-dimensional structure of proteins.