19,717 research outputs found
Binding Ligand Prediction for Proteins Using Partial Matching of Local Surface Patches
Functional elucidation of uncharacterized protein structures is an important task in bioinformatics. We report our new approach for structure-based function prediction which captures local surface features of ligand binding pockets. Function of proteins, specifically, binding ligands of proteins, can be predicted by finding similar local surface regions of known proteins. To enable partial comparison of binding sites in proteins, a weighted bipartite matching algorithm is used to match pairs of surface patches. The surface patches are encoded with the 3D Zernike descriptors. Unlike the existing methods which compare global characteristics of the protein fold or the global pocket shape, the local surface patch method can find functional similarity between non-homologous proteins and binding pockets for flexible ligand molecules. The proposed method improves prediction results over global pocket shape-based method which was previously developed by our group
Structure-based prediction of protein allostery
Allostery is the functional change at one site on a protein caused by a change at a distant site. In order for the benefits of allostery to be taken advantage of, both for basic understanding of proteins and to develop new classes of drugs, the structure-based prediction of allosteric binding sites, modulators and communication pathways is necessary. Here we review the recently emerging field of allosteric prediction, focusing mainly on computational methods. We also describe the search for cryptic binding pockets and attempts to design allostery into proteins. The development and adoption of such methods is essential or the long-preached potential of allostery will remain elusive
Hot-spot analysis for drug discovery targeting protein-protein interactions
Introduction: Protein-protein interactions are important for biological processes and pathological situations, and are attractive targets for drug discovery. However, rational drug design targeting protein-protein interactions is still highly challenging. Hot-spot residues are seen as the best option to target such interactions, but their identification requires detailed structural and energetic characterization, which is only available for a tiny fraction of protein interactions.
Areas covered: In this review, the authors cover a variety of computational methods that have been reported for the energetic analysis of protein-protein interfaces in search of hot-spots, and the structural modeling of protein-protein complexes by docking. This can help to rationalize the discovery of small-molecule inhibitors of protein-protein interfaces of therapeutic interest. Computational analysis and docking can help to locate the interface, molecular dynamics can be used to find suitable cavities, and hot-spot predictions can focus the search for inhibitors of protein-protein interactions.
Expert opinion: A major difficulty for applying rational drug design methods to protein-protein interactions is that in the majority of cases the complex structure is not available. Fortunately, computational docking can complement experimental data. An interesting aspect to explore in the future is the integration of these strategies for targeting PPIs with large-scale mutational analysis.This work has been funded by grants BIO2016-79930-R and SEV-2015-0493 from the Spanish Ministry of Economy, Industry and Competitiveness, and grant EFA086/15 from EU Interreg V POCTEFA. M Rosell is supported by an FPI fellowship from the Severo Ochoa program. The authors are grateful for the support of the the Joint BSC-CRG-IRB Programme in Computational Biology.Peer ReviewedPostprint (author's final draft
Computation of protein geometry and its applications: Packing and function prediction
This chapter discusses geometric models of biomolecules and geometric
constructs, including the union of ball model, the weigthed Voronoi diagram,
the weighted Delaunay triangulation, and the alpha shapes. These geometric
constructs enable fast and analytical computaton of shapes of biomoleculres
(including features such as voids and pockets) and metric properties (such as
area and volume). The algorithms of Delaunay triangulation, computation of
voids and pockets, as well volume/area computation are also described. In
addition, applications in packing analysis of protein structures and protein
function prediction are also discussed.Comment: 32 pages, 9 figure
PocketPicker: analysis of ligand binding-sites with shape descriptors
Background Identification and evaluation of surface binding-pockets and occluded cavities are initial steps in protein structure-based drug design. Characterizing the active site's shape as well as the distribution of surrounding residues plays an important role for a variety of applications such as automated ligand docking or in situ modeling. Comparing the shape similarity of binding site geometries of related proteins provides further insights into the mechanisms of ligand binding. Results We present PocketPicker, an automated grid-based technique for the prediction of protein binding pockets that specifies the shape of a potential binding-site with regard to its buriedness. The method was applied to a representative set of protein-ligand complexes and their corresponding apo-protein structures to evaluate the quality of binding-site predictions. The performance of the pocket detection routine was compared to results achieved with the existing methods CAST, LIGSITE, LIGSITEcs, PASS and SURFNET. Success rates PocketPicker were comparable to those of LIGSITEcs and outperformed the other tools. We introduce a descriptor that translates the arrangement of grid points delineating a detected binding-site into a correlation vector. We show that this shape descriptor is suited for comparative analyses of similar binding-site geometry by examining induced-fit phenomena in aldose reductase. This new method uses information derived from calculations of the buriedness of potential binding-sites. Conclusions The pocket prediction routine of PocketPicker is a useful tool for identification of potential protein binding-pockets. It produces a convenient representation of binding-site shapes including an intuitive description of their accessibility. The shape-descriptor for automated classification of binding-site geometries can be used as an additional tool complementing elaborate manual inspections
Molecular modeling of an antigenic complex between a viral peptide and a class I major histocompatibility glycoprotein
Computer simulation of the
conformations of short antigenic peptides (&lo
residues) either free or bound to their receptor,
the major histocompatibility complex (MHC)-
encoded glycoprotein H-2 Ld, was employed to
explain experimentally determined differences
in the antigenic activities within a set of related
peptides. Starting for each sequence from the
most probable conformations disclosed by a
pattern-recognition technique, several energyminimized
structures were subjected to molecular
dynamics simulations (MD) either in vacuo
or solvated by water molecules. Notably, antigenic
potencies were found to correlate to the
peptides propensity to form and maintain an
overall a-helical conformation through regular
i,i + 4 hydrogen bonds. Accordingly, less active
or inactive peptides showed a strong tendency
to form i,i+3 hydrogen bonds at their Nterminal
end. Experimental data documented
that the C-terminal residue is critical for interaction
of the peptide with H-2 Ld. This finding
could be satisfactorily explained by a 3-D
Q.S.A.R. analysis postulating interactions between
ligand and receptor by hydrophobic
forces. A 3-D model is proposed for the complex
between a high-affinity nonapeptide and the H-
2 Ld receptor. First, the H-2 Ld molecule was
built from X-ray coordinates of two homologous
proteins: HLA-A2 and HLA-Aw68, energyminimized
and studied by MD simulations. With
HLA-A2 as template, the only realistic simulation
was achieved for a solvated model with minor
deviations of the MD mean structure from
the X-ray conformation. Water simulation of the
H-2 Ld protein in complex with the antigenic
nonapeptide was then achieved with the template-
derived optimal parameters. The bound
peptide retains mainly its a-helical conformation
and binds to hydrophobic residues of H-2
Ld that correspond to highly polymorphic positions
of MHC proteins. The orientation of the
nonapeptide in the binding cleft is in accordance
with the experimentally determined distribution
of its MHC receptor-binding residues
(agretope residues). Thus, computer simulation was successfully employed to explain functional
data and predicts a-helical conformation
for the bound peptid
How interface geometry dictates water's thermodynamic signature in hydrophobic association
As a common view the hydrophobic association between molecular-scale binding
partners is supposed to be dominantly driven by entropy. Recent calorimetric
experiments and computer simulations heavily challenge this established
paradigm by reporting that water's thermodynamic signature in the binding of
small hydrophobic ligands to similar-sized apolar pockets is enthalpy-driven.
Here we show with purely geometric considerations that this controversy can be
resolved if the antagonistic effects of concave and convex bending on water
interface thermodynamics are properly taken into account. A key prediction of
this continuum view is that for fully complementary binding of the convex
ligand to the concave counterpart, water shows a thermodynamic signature very
similar to planar (large-scale) hydrophobic association, that is,
enthalpy-dominated, and hardly depends on the particular pocket/ligand
geometry. A detailed comparison to recent simulation data qualitatively
supports the validity of our perspective down to subnanometer scales. Our
findings have important implications for the interpretation of thermodynamic
signatures found in molecular recognition and association processes.
Furthermore, traditional implicit solvent models may benefit from our view with
respect to their ability to predict binding free energies and entropies.Comment: accepted for publication in J. Stat. Phys., special issue on
water&associated liquid
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