Fourier-filtered van der Waals contact surfaces: accurate ligand shapes from protein structures (original) (raw)
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A very simple, fast, and efficient scheme is proposed for performing preliminary protein-ligand docking as the first step of intensive high-throughput virtual screening. The procedure acts as a surface-complementarity filter that first calculates the 2D-contour maps of both the protein cavity and of the ligands using a spherical harmonics description of the associated molecular surfaces. Next, the obtained 2D-fingerprint images are compared to detect their complementarity. This scheme was tested on three typical cases of protein cavities, namely, a wellclosed pocket, a small open pocket, and a large open one. For that purpose, for each case, a sample of 101 ligand conformers was generated (the X-ray one and 100 different conformers generated using simulated annealing), and these conformational samples were ranked according to the complementarity with the protein cavity surface. Compared to traditional docking procedures such as FRED (considered as typical of a very fast rigid body docking algorithms) and GOLD (considered as typical of the more accurate flexible docking algorithms), our procedure was much faster and more successful in detecting the right Xray conformation. We did, however, identify a certain weakness in the case of the very large pocket where results were not as expected. In general, our method could be used for incorporating indirectly flexibility in protein-ligand docking calculations as such a scheme can easily handle several conformational states of both the protein and the ligand.
Accurate Detection of Protein:Ligand Binding Sites Using Molecular Dynamics Simulations
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Computational analyses of the surface properties of protein–protein interfaces
Acta Crystallographica Section D Biological Crystallography, 2007
Several potential applications of structural biology depend on discovering how one macromolecule might recognize a partner. Experiment remains the best way to answer this question, but computational tools can contribute where this fails. In such cases, structures may be studied to identify patches of exposed residues that have properties common to interaction surfaces and the locations of these patches can serve as the basis for further modelling or for further experimentation. To date, interaction surfaces have been proposed on the basis of unusual physical properties, unusual propensities for particular amino-acid types or an unusually high level of sequence conservation. Using the CXXSurface toolkit, developed as a part of the CCP4MG program, a suite of tools to analyse the properties of surfaces and their interfaces in complexes has been prepared and applied. These tools have enabled the rapid analysis of known complexes to evaluate the distribution of (i) hydrophobicity, (ii) electrostatic complementarity and (iii) sequence conservation in authentic complexes, so as to assess the extent to which these properties may be useful indicators of probable biological function.
Structure-based method for analyzing protein–protein interfaces
Journal of Molecular Modeling, 2004
Hydrogen bond, hydrophobic and vdW interactions are the three major non-covalent interactions at protein–protein interfaces. We have developed a method that uses only these properties to describe interactions between proteins, which can qualitatively estimate the individual contribution of each interfacial residue to the binding and gives the results in a graphic display way. This method has been applied to analyze alanine mutation data at protein–protein interfaces. A dataset containing 13 protein–protein complexes with 250 alanine mutations of interfacial residues has been tested. For the 75 hot-spot residues (ΔΔG≥1.5 kcal mol-1), 66 can be predicted correctly with a success rate of 88%. In order to test the tolerance of this method to conformational changes upon binding, we utilize a set of 26 complexes with one or both of their components available in the unbound form. The difference of key residues exported by the program is 11% between the results using complexed proteins and those from unbound ones. As this method gives the characteristics of the binding partner for a particular protein, in-depth studies on protein–protein recognition can be carried out. Furthermore, this method can be used to compare the difference between protein–protein interactions and look for correlated mutation. Figure Key interaction grids at the interface between barnase and barstar. Key interaction grid for barnase and barstar are presented in one figure according to their coordinates. In order to distinguish the two proteins, different icons were assigned. Crosses represent key grids for barstar and dots represent key grids for barnase. The four residues in ball and stick are Asp40 in barstar and Arg83, Arg87, His102 in barnase.
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Bioinformatics, 1999
Motivation: New software has been designed to assist the molecular biologist in understanding the structural consequences of modifying a ligand and/or protein. Results: Tools are described for the analysis of ligand-protein contacts (LPC software) and contacts of structural units (CSU software) such as helices, sheets, strands and residues. Our approach is based on a detailed analysis of interatomic contacts and interface complementarity. For any ligand or structural unit, these software automatically: (i) calculate the solvent-accessible surface of every atom; (ii) determine the contacting residues and type of interaction they undergo (hydrophobic-hydrophobic, aromatic-aromatic, etc.); (iii) indicate all putative hydrogen bonds. LPC software further predicts changes in binding strength following chemical modification of the ligand.
Crystallography and protein–protein interactions: biological interfaces and crystal contacts
Biochemical Society Transactions, 2008
Crystallography is commonly used for studying the structures of protein–protein complexes. However, a crystal structure does not define a unique protein–protein interface, and distinguishing a ‘biological interface’ from ‘crystal contacts’ is often not straightforward. A number of computational approaches exist for distinguishing them, but their error rate is high, emphasizing the need to obtain further data on the biological interface using complementary structural and functional approaches. In addition to reviewing the computational and experimental approaches for addressing this problem, we highlight two relevant examples. The first example from our laboratory involves the structure of acyl-CoA thioesterase 7, where each domain of this two-domain protein was crystallized separately, but both yielded a non-functional assembly. The structure of the full-length protein was uncovered using a combination of complementary approaches including chemical cross-linking, analytical ultracen...
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1999
This thesis describes the development and refinement of a number of techniques for molecular docking and ligand database screening, as well as the application of these techniques to predict the structures of several protein-ligand complexes and to discover novel ligands of an important receptor protein. Global energy optimisation by Monte-Carlo minimisation in internal co-ordinates was used to predict bound conformations of eight protein-ligand complexes. Experimental X-ray crystallography structures became available after the predictions were made. Comparison with the X-ray structures showed that the docking procedure placed 30 to 70% of the ligand molecule correctly within 1.5A from the native structure. The discrimination potential for identification of high-affinity ligands was derived and optimised using a large set of available protein-ligand complex structures. A fast boundary-element solvation electrostatic calculation algorithm was implemented to evaluate the solvation comp...
Characterisation of Protein-Ligand Interfaces: Separating Surfaces
Journal of Molecular Modeling, 1998
A new method for characterising protein-protein complexes is presented wherein the interface is modelled as a separating surface. This surface is defined by a set of points located halfway on the shortest distance vectors between surface points of the two molecular partners. The surface is generated using a grid-based algorithm. The distance to the nearest atom is stored on the grid points and an isosurface is generated forming the separating surface. Size and shape of the surface characterises the complex interface. Distances, forces, and other physicochemical properties can be mapped onto the surface and are used to study the intermolecular interactions. This is demonstrated with the systems lysozym-antibody, p53-DNA and trypsin-BPTI.
Proceedings of The National Academy of Sciences, 1992
A geometric recognition algorithm was developed to identify molecular surface complementarity. It is based on a purely geometric approach and takes advantage of techniques applied in the field ofpattern recognition. The algorithm involves an automated procedure including (i) a digital representation of the molecules (derived from atomic coordinates) by three-dimensional discrete functions that distinguishes between the surface and the interior; (ii) the calculation, using Fourier transformation, of a correlation function that assesses the degree of molecular surface overlap and penetration upon relative shifts of the molecules in three dimensions; and (iii) a scan of the relative orientations of the molecules in three dimensions. The algorithm provides a list of correlation values indicating the extent of geometric match between the surfaces of the molecules; each of these values is associated with six numbers describing the relative position (translation and rotation) of the molecules. The procedure is thus equivalent to a six-dimensional search but much faster by design, and the computation time is only moderately dependent on molecular size. The procedure was tested and validated by using five known complexes for which the correct relative position of the molecules in the respective adducts was successfully predicted. The molecular pairs were deoxyhemoglobin and methemoglobin, tRNA synthetase-tyrosinyl adenylate, aspartic proteinase-peptide inhibitor, and trypsin-trypsin inhibitor. A more realistic test was performed with the last two pairs by using the structures of uncomplexed aspartic proteinase and trypsin inhibitor, respectively. The results are indicative of the extent of conformational changes in the molecules tolerated by the algorithm.
Computational Analyses of Protein-Ligand Interactions
2010
Protein-ligand interactions have a central role in all processes in living systems. A comprehensive understanding of protein interactions with small molecules is of great interest as it provides opportunities for understanding protein function and therapeutic intervention. The major aims of this thesis were to characterise proteinligand interactions from databases of crystal structures and to apply molecular modelling techniques for accurate prediction of binding modes of molecular fragments in protein binding sites. Author's Declaration Chapter 1, 2, 3 in this thesis is my own work. Chapter 4 and 6 were done in collaboration with Dr. Hugues-Olivier Bertrand of Accelrys, who performed docking calculations with GOLD and validated my MCSS calculations by re-running them. Chapter 5 in this thesis is my own work.