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Register a new userEmbedding protein 3D-structures in a cubic lattice. I. The basic algorithms
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Added by
Jacques Gabarro-Arpa
Publication date
2010
fields
Physics
and research's language is
English
Authors
Jacques Gabarro-Arpa
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Realistic 3D-conformations of protein structures can be embedded in a cubic lattice using exclusively integer numbers, additions, subtractions and boolean operations.
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Sucralose is a commonly employed artificial sweetener that appears to destabilize protein native structures. This is in direct contrast to the bio-preservative nature of its natural counterpart, sucrose, which enhances the stability of biomolecules against environmental stress. We have further explored the molecular interactions of sucralose as compared to sucrose to illuminate the origin of the differences in their bio-preservative efficacy. We show that the mode of interactions of sucralose and sucrose in bulk solution differ subtly using hydration dynamics measurement and computational simulation. Sucralose does not appear to disturb the native state of proteins for moderate concentrations (<0.2 M) at room temperature. However, as the concentration increases, or in the thermally stressed state, sucralose appears to differ in its interactions with protein leading to the reduction of native state stability. This difference in interaction appears weak. We explored the difference in the preferential exclusion model using time-resolved spectroscopic techniques and observed that both molecules appear to be effective reducers of bulk hydration dynamics. However, the chlorination of sucralose appears to slightly enhance the hydrophobicity of the molecule, which reduces the preferential exclusion of sucralose from the protein-water interface. The weak interaction of sucralose with hydrophobic pockets on the protein surface differs from the behavior of sucrose. We experimentally followed up upon the extent of this weak interaction using isothermal titration calorimetry (ITC) measurements. We propose this as a possible origin for the difference in their bio-preservative properties.
A global optimization method called Greedy Neighborhood Search (GNS) and a novel conformational sampling method using a spherical distribution is proposed to find the minimum energy conformation of a protein-like heteropolymer model called AB model. The AB model consists of hydrophobic (A) and hydrophilic (B) monomers analogous to the real proteins. The AB model in three-dimensional space is represented by simple bead-rod chain system which is identical to the one-bead protein model. The minimum energy conformations of four different sequences consisting of 13, 21, 34, and 55 monomers are obtained by the GNS method. The minimum energies found are lower than those obtained by other methods. Also the minimum energy conformations found have a similarity with the real proteins forming a single hydrophobic core.
Computational drug discovery provides an efficient tool helping large scale lead molecules screening. One of the major tasks of lead discovery is identifying molecules with promising binding affinities towards a target, a protein in general. The accuracies of current scoring functions which are used to predict the binding affinity are not satisfactory enough. Thus, machine learning (ML) or deep learning (DL) based methods have been developed recently to improve the scoring functions. In this study, a deep convolutional neural network (CNN) model (called OnionNet) is introduced and the features are based on rotation-free element-pair specific contacts between ligands and protein atoms, and the contacts were further grouped in different distance ranges to cover both the local and non-local interaction information between the ligand and the protein. The prediction power of the model is evaluated and compared with other scoring functions using the comparative assessment of scoring functions (CASF-2013) benchmark and the v2016 core set of PDBbind database. When compared to a previous CNN-based scoring function, our model shows improvements of 0.08 and 0.16 in the correlations (R) and standard deviations (SD) of regression, respectively, between the predicted binding affinities and the experimental measured binding affinities. The robustness of the model is further explored by predicting the binding affinities of the complexes generated from docking simulations instead of experimentally determined PDB structures.
A combinatorial model of molecular conformational space that was previously developped (J. Gabarro-Arpa, Comp. Biol. and Chem. 27, (2003) 153-159), had the drawback that structures could not be properly embedded beacause it lacked explicit rotational symmetry. The problem can be circumvented by sorting the elementary 3D components of a molecular system into a finite set of classes that can be separately embedded. This also opens up the possibility of encoding the dynamical states into a graph structure.
Free energy landscapes decisively determine the progress of enzymatically catalyzed reactions[1]. Time-resolved macromolecular crystallography unifies transient-state kinetics with structure determination [2-4] because both can be determined from the same set of X-ray data. We demonstrate here how barriers of activation can be determined solely from five-dimensional crystallography [5]. Directly linking molecular structures with barriers of activation between them allows for gaining insight into the structural nature of the barrier. We analyze comprehensive time series of crystal-lographic data at 14 different temperature settings and determine entropy and enthalpy contributions to the barriers of activation. 100 years after the discovery of X-ray scattering, we advance X-ray structure determination to a new frontier, the determination of energy landscapes.
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