Alexandre L. Magalhães

CONTRIBUTION OF UNUSUAL ARGININE-ARGININE SHORT-RANGE INTERACTIONS TO STABILIZATION AND RECOGNITION IN PROTEINS

Although the majority of the ion pairs found in proteins consists of two charges of opposite sign, the observation of some unusual arrangements of two arginines led us to a search of such occurrences in the Brookhaven Protein Data Bank. We have found 41 Arginine-Arginine interactions with a Cζ...Cζ distance less than 5 å. Computer graphics analysis of these structures shows that most of the Arg-Arg pairs are found in the vicinity of the surface of the proteins, in an easily hydrated region. In order to determine which factors could stabilize such arrangements of species of similar charge, we have carried out AM1 semi-empirical calculations on a model of two guanidinium ions surrounded by several water molecules. The results show the existence of stable clusters with six or more water molecules, with distances between Cζ atoms around 3 å. The bridging role of the water molecules is an important structural and energetic feature and we find bridges of two and three molecules between the guanidinium ions. These results are in good agreement with the structures found in our search of the experimental data. Enhancement of the electrostatic potential around these clusters, when compared to one of the guanidinium ions alone, is also demonstrated.

Multi-target QPDR classification model for human breast and colon cancer-related proteins using star graph topological indices

Abstract The cancer diagnostic is a complex process and, sometimes, the specific markers can interfere or produce negative results. Thus, new simple and fast theoretical models are required. One option is the complex network graphs theory that permits us to describe any real system, from the small molecules to the complex genetic, neural or social networks by transforming real properties in topological indices. This work converts the protein primary structure data in specific Randics star networks topological indices using the new sequence to star networks (S2SNet) application. A set of 1054 proteins were selected from previous works and contains proteins related or not with two types of cancer, human breast cancer (HBC) and human colon cancer (HCC). The general discriminant analysis method generates an input-coded multi-target classification model with the training/predicting set accuracies of 90.0% for the forward stepwise model type. In addition, a protein subset was modified by single amino acid mutations with higher log-odds PAM250 values and tested with the new classification if can be related with HBC or HCC. In conclusion, we shown that, using simple input data such is the primary protein sequence and the simples linear analysis, it is possible to obtain accurate classification models that can predict if a new protein related with two types of cancer. These results promote the use of the S2SNet in clinical proteomics.

Enzymes/non-enzymes classification model complexity based on composition, sequence, 3D and topological indices

The huge amount of new proteins that need a fast enzymatic activity characterization creates demands of protein QSAR theoretical models. The protein parameters that can be used for an enzyme/non-enzyme classification includes the simpler indices such as composition, sequence and connectivity, also called topological indices (TIs) and the computationally expensive 3D descriptors. A comparison of the 3D versus lower dimension indices has not been reported with respect to the power of discrimination of proteins according to enzyme action. A set of 966 proteins (enzymes and non-enzymes) whose structural characteristics are provided by PDB/DSSP files was analyzed with Python/Biopython scripts, STATISTICA and Weka. The list of indices includes, but it is not restricted to pure composition indices (residue fractions), DSSP secondary structure protein composition and 3D indices (surface and access). We also used mixed indices such as composition-sequence indices (Chous pseudo-amino acid compositions or coupling numbers), 3D-composition (surface fractions) and DSSP secondary structure amino acid composition/propensities (obtained with our Prot-2S Web tool). In addition, we extend and test for the first time several classic TIs for the Randics protein sequence Star graphs using our Sequence to Star Graph (S2SG) Python application. All the indices were processed with general discriminant analysis models (GDA), neural networks (NN) and machine learning (ML) methods and the results are presented versus complexity, average of Shannons information entropy (Sh) and data/method type. This study compares for the first time all these classes of indices to assess the ratios between model accuracy and indices/model complexity in enzyme/non-enzyme discrimination. The use of different methods and complexity of data shows that one cannot establish a direct relation between the complexity and the accuracy of the model.

Natural/random protein classification models based on star network topological indices

Abstract The development of the complex network graphs permits us to describe any real system such as social, neural, computer or genetic networks by transforming real properties in topological indices (TIs). This work uses Randics star networks in order to convert the protein primary structure data in specific topological indices that are used to construct a natural/random protein classification model. The set of natural proteins contains 1046 protein chains selected from the pre-compiled CulledPDB list from PISCES Dunbracks Web Lab. This set is characterized by a protein homology of 20%, a structure resolution of 1.6Å and R-factor lower than 25%. The set of random amino acid chains contains 1046 sequences which were generated by Python script according to the same type of residues and average chain length found in the natural set. A new Sequence to Star Networks (S2SNet) wxPython GUI application (with a Graphviz graphics back-end) was designed by our group in order to transform any character sequence in the following star network topological indices: Shannon entropy of Markov matrices, trace of connectivity matrices, Harary number, Wiener index, Gutman index, Schultz index, Moreau–Broto indices, Balaban distance connectivity index, Kier–Hall connectivity indices and Randic connectivity index. The model was constructed with the General Discriminant Analysis methods from STATISTICA package and gave training/predicting set accuracies of 90.77% for the forward stepwise model type. In conclusion, this study extends for the first time the classical TIs to protein star network TIs by proposing a model that can predict if a protein/fragment of protein is natural or random using only the amino acid sequence data. This classification can be used in the studies of the protein functions by changing some fragments with random amino acid sequences or to detect the fake amino acid sequences or the errors in proteins. These results promote the use of the S2SNet application not only for protein structure analysis but also for mass spectroscopy, clinical proteomics and imaging, or DNA/RNA structure analysis.

Alignment-free prediction of mycobacterial DNA promoters based on pseudo-folding lattice network or star-graph topological indices

Abstract The importance of the promoter sequences in the function regulation of several important mycobacterial pathogens creates the necessity to design simple and fast theoretical models that can predict them. This work proposes two DNA promoter QSAR models based on pseudo-folding lattice network (LN) and star-graphs (SG) topological indices. In addition, a comparative study with the previous RNA electrostatic parameters of thermodynamically-driven secondary structure folding representations has been carried out. The best model of this work was obtained with only two LN stochastic electrostatic potentials and it is characterized by accuracy, selectivity and specificity of 90.87%, 82.96% and 92.95%, respectively. In addition, we pointed out the SG result dependence on the DNA sequence codification and we proposed a QSAR model based on codons and only three SG spectral moments.

Theoretical study of arginine-carboxylate interactions

The importance of the guanidinium‐carboxylate interactions has sprung from the observed salt bridges often present in biological systems involving the arginine‐glutamate or arginine‐aspartate side chains. The strength of these interactions has been explained on the basis of a great coulombic energy gain, due to the closeness of two charges of opposite sign and the occurrence of H-bond interactions. However, in some environments proton transfer, from guanidinium to carboxylate, can occur with the consequent annihilation of charge. In this work, both ab-initio (6-31G** and MP2/6-31G**) and semi-empirical (AM1) calculations were performed in vacuo on appropriate models, methylguanidinium ‐acetate and methylguanidine‐acetic acid to simulate the zwitterionic and the neutral forms, respectively. The results obtained indicate that, in solvent-free hydrophobic environments, the neutral form should be more stable than the zwitterionic one. q 1999 Elsevier Science B.V. All rights reserved.

Amino acid pairing at the N‐ and C‐termini of helical segments in proteins

A systematic survey was carried out in an unbiased sample of 815 protein chains with a maximum of 20% homology selected from the Protein Data Bank, whose structures were solved at a resolution higher than 1.6 Å and with a R‐factor lower than 25%. A set of 5556 subsequences with α‐helix or 310‐helix motifs was extracted from the protein chains considered. Global and local propensities were then calculated for all possible amino acid pairs of the type (i, i + 1), (i, i + 2), (i, i + 3), and (i, i + 4), starting at the relevant helical positions N1, N2, N3, C3, C2, C1, and N‐int (interior positions), and also at the first nonhelical positions in both termini of the helices, namely, N‐cap and C‐cap. The statistical analysis of the propensity values has shown that pairing is significantly dependent on the type of the amino acids and on the position of the pair. A few sequences of three and four amino acids were selected and their high prevalence in helices is outlined in this work. The Glu‐Lys‐Tyr‐Pro sequence shows a peculiar distribution in proteins, which may suggest a relevant structural role in α‐helices when Pro is located at the C‐cap position. A bioinformatics tool was developed, which updates automatically and periodically the results and makes them available in a web site. Proteins 2008.

The molecular dissociation of formaldehyde at medium photoexcitation energies: A quantum chemistry and direct quantum dynamics study

The mechanisms of radiationless decay involved in the photodissociation of formaldehyde into H(2) and CO have been investigated using complete active space self-consistent field (CASSCF) calculations and direct dynamics variational multiconfiguration Gaussian (DD-vMCG) quantum dynamics in the S(1), T(1), and S(0) states. A commonly accepted scheme involves Fermi Golden Rule internal conversion from S(1) followed by dissociation of vibrationally hot H(2)CO in S(0). We recently proposed a novel mechanism [M. Araujo et al., J. Phys. Chem. A 112, 7489 (2008)] whereby internal conversion and dissociation take place in concert through a seam of conical intersection between S(1) and S(0) after the system has passed through an S(1) transition barrier. The relevance of this mechanism depends on the efficiency of tunneling in S(1). At lower energy, an alternative scheme to internal conversion involves intersystem crossing via T(1) to regenerate the reactant before the S(0) barrier to dissociation. We propose here a previously unidentified mechanism leading directly to H(2) and CO products via T(1). This channel opens at medium energies, near or above the T(1) barrier to dissociation and still lower than the S(1) barrier, thus making T(1) a possible shortcut to molecular dissociation.

Controlling product selection in the photodissociation of formaldehyde: direct quantum dynamics from the S1 barrier.

Controlling the selectivity between H(2)+CO and H+HCO in the S(1)/S(0) nonadiabatic photodissociation of formaldehyde has been investigated using direct quantum dynamics. Simulations started from the S(1) transition state have suggested that a key feature for controlling the branching ratio of ground-state products is the size of the momentum given to the wavepacket along the transition vector. Our results show that letting the wavepacket fall down from the barrier to the conical intersection with no initial momentum leads to H(2)+CO, while extra momentum toward products favors the formation of H+HCO through the same conical intersection. Quantum dynamics results are interpreted in semiclassical terms with the aid of a Mulliken-like analysis of the final population distribution among both products and the reactant on each electronic state.

Unusual coordination environment for barium cations in ion recognition conducting poly[Ni(salen)(receptor)] films.

[Ni( salen)] complexes bearing different crown ether receptors were electropolymerized to give films whose voltammetric signatures responded to Ba2+. In line with DFT calculations, X-ray absorption spectroscopy (XAS) near the Ni K-edge showed the nickel local environment in the monomers and their corresponding polymers (in the presence or absence of barium) to be identical. However, the expectation of crown size-dependent barium local environment (based on geometry and donor atom availability) was not found. XAS near the Ba K-edge showed that Ba2+ in the films coordinated to only two oxygen donors, irrespective of crown size. This surprisingly low coordination number (compared to solution species) is accompanied by a higher barium/crown ratio than the anticipated 1:1 stoichiometry. The implications of these effects for design and performance of sensors based on metal ion recognition chemistry are discussed.