Ramón Garduño-Juárez

Exploration of human serum albumin binding sites by docking and molecular dynamics flexible ligand-protein interactions.

Five-nanosecond molecular dynamics (MD) simulations were performed on human serum albumin (HSA) to study the conformational features of its primary ligand binding sites (I and II). Additionally, 11 HSA snapshots were extracted every 0.5 ns to explore the binding affinity (K(d)) of 94 known HSA binding drugs using a blind docking procedure. MD simulations indicate that there is considerable flexibility for the protein, including the known sites I and II. Movements at HSA sites I and II were evidenced by structural analyses and docking simulations. The latter enabled the study and analysis of the HSA-ligand interactions of warfarin and ketoprofen (ligands binding to sites I and II, respectively) in greater detail. Our results indicate that the free energy values by docking (K(d) observed) depend upon the conformations of both HSA and the ligand. The 94 HSA-ligand binding K(d) values, obtained by the docking procedure, were subjected to a quantitative structure-activity relationship (QSAR) study by multiple regression analysis. The best correlation between the observed and QSAR theoretical (K(d) predicted) data was displayed at 2.5 ns. This study provides evidence that HSA binding sites I and II interact specifically with a variety of compounds through conformational adjustments of the protein structure in conjunction with ligand conformational adaptation to these sites. These results serve to explain the high ligand-promiscuity of HSA.

Applications of simulated annealing to the multiple-minima problem in small peptides.

A Simulated Annealing method has been implemented to overcome the multiple minima problem inherent in finding the global minimum of small peptides with 2, 3, 5, 10 and 24 dihedral angles. The algorithm works much better if one introduces the anticorrelations observed in Molecular Dynamics.

Effect of water molecules on the fluorescence enhancement of Aflatoxin B1 mediated by Aflatoxin B1:β-cyclodextrin complexes. A theoretical study

In order to explain the observed fluorescence enhancement of Aflatoxin B1 (AFB1) when forming AFB1:beta-cyclodextrin (AFB1:beta-CD) inclusion complexes, we have performed a theoretical (quantum chemistry calculations) study of AFB1 and AFB1:beta-CD in vacuum and in the presence of aqueous solvent. The AM1 method was used to calculate the absorption and emission wavelengths of these molecules. With the help of density functional theory (DFT) and time-dependent DFT (TDDFT) vibrational frequencies and related excitation energies of AFB1 and AFB1.(H2O)m = 4,5,6,11 were calculated. On the basis of these calculations we propose a plausible mechanism for the fluorescence enhancement of AFB1 in the presence of beta-CD: (1) before photoexcitation of AFB1 to its S1 excited state, there is a vibrational coupling between the vibrational modes involving the AFB1 carbonyl groups and the bending modes of the nearby water molecules (CG + WM); (2) these interactions allow a thermal relaxation of the excited AFB1 molecules that results in fluorescence quenching; (3) when the AFB1 molecules form inclusion complexes with beta-CD the CG + WM interaction decreases; and (4) this gives rise to a fluorescence enhancement.

The Multiple-Minima Problem in Small Peptides Revisited. The Threshold Accepting Approach

A recently reported optimization method, known as Threshold Accepting, was tested for the purpose of locating the structure of several peptide molecules with the lowest conformational energy. A comparison with previous results obtained with the Simulated Annealing technique was made. Our study indicate Threshold Accepting as a better technique in locating such structures.

Molecular Modelling of Protein-Nucleic Acid Interactions

Computer modeling techniques to study the interaction of proteins with nucleic acids are presented. The methods utilize information from genetic and chemical modification experiments and macromolecular structural constraints. These techniques, in addition to computer model building procedures and theoretical energy calculations, are illustrated for the study of the lac and cro repressor-operator systems. Our predicted interactions between lac and its operator agree with those recently reported for lac based upon sequence alignment with the cro repressor. Several molecular models of the putative helical segment of cro interacting with its OR3 operator are presented. These models are reflective of intermediate conformations experienced by the repressor in recognition of the operator sequence. The results of our studies are further discussed in terms of the design of short peptides interacting with nucleic acid sequences and the evolutionary requirements in establishing these repressor interactions.

The role of phosphorylation in dentin phosphoprotein peptide absorption to hydroxyapatite surfaces: a molecular dynamics study

Abstract Dentin phosphoprotein (DPP) is a protein expressed mainly in dentin and to a lesser extent in bone. DPP has a disordered structure, rich in glutamic acid, aspartic acid and phosphorylated serine/threonine residues. It has a high capacity for binding to calcium ions and to hydroxyapatite (HA) crystal surfaces. We used molecular dynamics (MD) simulations as a method for virtually screening interactions between DPP motifs and HA. The goal was to determine which motifs are absorbed to HA surfaces. For these simulations, we considered five peptides from the human DPP sequence. All-atom MD simulations were performed using GROMACS, the peptides were oriented parallel to the {100} HA crystal surface, the distance between the HA and the peptide was 3 nm. The system was simulated for 20 ns. Preliminary results show that for the unphosphorylated peptides, the acidic amino acids present an electrostatic attraction where their side chains are oriented towards HA. This attraction, however, is slow to facilitate bulk transport to the crystal surface. On the other hand, the phosphorylated (PP) peptides are rapidly absorbed on the surface of the HA with their centers of mass closer to the HA surface. More importantly, the root mean square fluctuation (RMSF) indicates that the average structures of the phosphorylated peptides are very inflexible and elongate, while that of the unphosphorylated peptides are flexible. Radius of gyration (Rg) analysis showed the compactness of un-phosphorylated peptides is lower than phosphorylated peptides. Phosphorylation of the DPP peptides is necessary for binding to HA surfaces.

A genetic algorithm with conformational memories for structure prediction of polypeptides.

Abstract We have developed an iterative hybrid algorithm (HA) to predict the 3D structure of peptides starting from their amino acid sequence. The HA is made of a modified genetic algorithm (GA) coupled to a local optimizer. Each HA iteration is carried out in two phases. In the first phase several GA runs are performed upon the entire peptide conformational space. In the second phase we used the manifestation of what we have called conformational memories, that arises at the end of the first phase, as a way of reducing the peptide conformational space in subsequent HA iterations. Use of conformational memories speeds up and refines the localization of the structure at the putative Global Energy Minimum (GEM) since conformational barriers are avoided. The algorithm has been used to predict successfully the putative GEM for Met- and Leu-enkephalin, and to obtain useful information regarding the 3D structure for the 8mer of polyglycine and the 16 residue (AAQAA)3Y peptide. The number of fitness function evaluations needed to locate the putative GEMs are fewer than those reported for other heuristic methods. This study opens the possibility of using Genetic Algorithms in high level predictions of secondary structure of polypeptides.

Exploring QSAR of antiamoebic agents of isolated natural products by MLR, ANN, and RTO

A QSAR study of antiamoebic agents isolated from natural products was performed by multi linear regression (MLR), artificial neuron network (ANN), and regression through origin (RTO). After several procedures to reduce the number of descriptors, 11 descriptors were selected from the descriptor pool by a complete MLR methodology. The best proportion between training:predicted:validation sets is 100:43:16 molecules. The Mor23m descriptor is a 3D-MoRSE descriptor and it is the main descriptor in the models studied. This result suggests that the three-dimensional structure and atomic properties like masses are very important in the models. The best quantitative structure–activity relationship model was proved to be independent of chance correlation.

QSAR Study on the Antinociceptive Activity of Some Morphinans

Quantitative structure–activity relationship studies were performed to describe and predict the antinociceptive activity of 31 morphinan derivatives reported by the US Drug Evaluation Committee in 2005 and 2006. From these, three data sets were constructed and several models were calculated following the multiple linear regression and Leave‐One‐Out Cross‐Validation (LOO‐CV) tests. In general, these models achieved good descriptive power (approximately 92%) as well as predictive power (approximately 76%), but were unable to predict an external validation set of morphinan derivatives. When artificial neural networks were applied to these models, an improvement of the predictive and external validation values was obtained. It was observed that the results of the NN models are significantly better that those obtained by multiple linear regression. In spite that the problem under investigation can be handled adequately by a linear model, a neural network does bring slight improvements in the predictive power.

QSAR/QSPR as an Application of Artificial Neural Networks

Quantitative Structure-Activity Relationships (QSARs) and Quantitative Structure-Property Relationships (QSPRs) are mathematical models used to describe and predict a particular activity/property of compounds. On the other hand, the Artificial Neural Network (ANN) is a tool that emulates the human brain to solve very complex problems. The exponential need for new compounds in the drug industry requires alternatives for experimental methods to decrease development time and costs. This is where chemical computational methods have a great relevance, especially QSAR/QSPR-ANN. This chapter shows the importance of QSAR/QSPR-ANN and provides examples of its use.