Exploring NMR ensembles of calcium binding proteins: perspectives to design inhibitors of protein-protein interactions

Calcium ion binding to calmodulin: binding free energy calculation using the molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) method by incorporating implicit polarization

Binding of calcium ion to calcium-binding proteins (CBP) triggers a large number of biological processes in a cell. CBP are known to play important roles in various diseases, such as cancer, alzheimer, and neuronal problems. However, the calculation of the binding affinity of calcium ion to CBP still possesses a significant challenge to the computational investigators. One of the main reasons for this difficulty is the polarization of CBP due to the binding of calcium. In the current work, we have used the implicit polarization method of Leontyev et al. (PCCP, 13.7 (2011): 2613-2626) to calculate the binding free energy of calcium ion binding to calmodulin, an important CBP. We have used the widely used MM-PBSA method to find a good protocol of calculation with implicit polarization. We have also optimized the best value of the calcium radius to match the experimental results. Our results show incorporation of polarization improves the agreement between the calculated and experimental results, although still, some discrepancy remains. On the whole, this work shows implicit polarization when combined with the MM-PBSA method can give results better than calculation without any polarization, and further improvement is necessary to get a quantitative match with experiments.Communicated by Ramaswamy H. Sarma.

Enhancing Virtual Screening Performance of Protein Kinases with Molecular Dynamics Simulations

In silico virtual screening (VS) is a powerful hit identification technique used in drug discovery projects that aims to effectively distinguish true actives from inactive or decoy molecules. To better capture the dynamic behavior of protein drug targets, compound databases may be screened against an ensemble of protein conformations, which may be experimentally determined or generated computationally, i.e. via molecular dynamics (MD) simulations. Several studies have shown that conformations generated by MD are useful in identifying novel hit compounds, in part because structural rearrangements sampled during MD can provide novel targetable areas. However, it remains difficult to predict a priori when an MD conformation will outperform a VS against the crystal structure alone. Here, we assess whether MD conformations result in improved VS performance for six protein kinases. MD conformations are selected using three different methods, and their VS performances are compared to the corresponding crystal structures. Additionally, these conformations are used to train ensembles, and their VS performance is compared to the individual MD conformations and the corresponding crystal structures using receiver operating characteristic curve (ROC) metrics. We show that performing MD results in at least one conformation that offers better VS performance than the crystal structure, and that, while it is possible to train ensembles to outperform the crystal structure alone, the extent of this enhancement is target dependent. Lastly, we show that the optimal structural selection method is also target dependent and recommend optimizing virtual screens on a kinase-by-kinase basis to improve the likelihood of success.

Sampling of conformational ensemble for virtual screening using molecular dynamics simulations and normal mode analysis

Aim: Molecular dynamics simulations and normal mode analysis are well-established approaches to generate receptor conformational ensembles (RCEs) for ligand docking and virtual screening. Here, we report new fast molecular dynamics-based and normal mode analysis-based protocols combined with conformational pocket classifications to efficiently generate RCEs. Materials & Methods: We assessed our protocols on two well-characterized protein targets showing local active site flexibility, dihydrofolate reductase and large collective movements, CDK2. The performance of the RCEs was validated by distinguishing known ligands of dihydrofolate reductase and CDK2 among a dataset of diverse chemical decoys. Results & discussion: Our results show that different simulation protocols can be efficient for generation of RCEs depending on different kind of protein flexibility.[Formula: see text]

Surfing the Protein-Protein Interaction Surface Using Docking Methods: Application to the Design of PPI Inhibitors

Blocking protein-protein interactions (PPI) using small molecules or peptides modulates biochemical pathways and has therapeutic significance. PPI inhibition for designing drug-like molecules is a new area that has been explored extensively during the last decade. Considering the number of available PPI inhibitor databases and the limited number of 3D structures available for proteins, docking and scoring methods play a major role in designing PPI inhibitors as well as stabilizers. Docking methods are used in the design of PPI inhibitors at several stages of finding a lead compound, including modeling the protein complex, screening for hot spots on the protein-protein interaction interface and screening small molecules or peptides that bind to the PPI interface. There are three major challenges to the use of docking on the relatively flat surfaces of PPI. In this review we will provide some examples of the use of docking in PPI inhibitor design as well as its limitations. The combination of experimental and docking methods with improved scoring function has thus far resulted in few success stories of PPI inhibitors for therapeutic purposes. Docking algorithms used for PPI are in the early stages, however, and as more data are available docking will become a highly promising area in the design of PPI inhibitors or stabilizers.

Structure-based computational approaches for small-molecule modulation of protein-protein interactions

Three-dimensional structures of proteins offer an opportunity for the rational design of small molecules to modulate protein-protein interactions. The presence of a well-defined binding pocket on the surface of protein complexes, particularly at their interface, can be used for docking-based virtual screening of chemical libraries. Several approaches have been developed to identify binding pockets that are implemented in programs such as SiteMap, fpocket, and FTSite. These programs enable the scoring of these pockets to determine whether they are suitable to accommodate high-affinity small molecules. Virtual screening of commercial or combinatorial libraries can be carried out to enrich these libraries and select compounds for further experimental validation. In virtual screening, a compound library is docked to the target protein. The resulting structures are scored and ranked for the selection and experimental validation of top candidates. Molecular docking has been implemented in a number of computer programs such as AutoDock Vina. We select a set of protein-protein interactions that have been successfully inhibited with small molecules in the past. Several computer programs are applied to identify pockets on the surface, and molecular docking is conducted in an attempt to reproduce the binding pose of the inhibitors. The results highlight the strengths and limitations of computational methods for the design of PPI inhibitors.

Enrichment of chemical libraries docked to protein conformational ensembles and application to aldehyde dehydrogenase 2

Molecular recognition is a complex process that involves a large ensemble of structures of the receptor and ligand. Yet, most structure-based virtual screening is carried out on a single structure typically from X-ray crystallography. Explicit-solvent molecular dynamics (MD) simulations offer an opportunity to sample multiple conformational states of a protein. Here we evaluate our recently developed scoring method SVMSP in its ability to enrich chemical libraries docked to MD structures of seven proteins from the Directory of Useful Decoys (DUD). SVMSP is a target-specific rescoring method that combines machine learning with statistical potentials. We find that enrichment power as measured by the area under the ROC curve (ROC-AUC) is not affected by increasing the number of MD structures. Among individual MD snapshots, many exhibited enrichment that was significantly better than the crystal structure, but no correlation between enrichment and structural deviation from crystal structure was found. We followed an innovative approach by training SVMSP scoring models using MD structures (SVMSPMD). The resulting models were applied to two difficult cases (p38 and CDK2) for which enrichment was not better than random. We found remarkable increase in enrichment power, particularly for p38, where the ROC-AUC increased by 0.30 to 0.85. Finally, we explored approaches for a priori identification of MD snapshots with high enrichment power from an MD simulation in the absence of active compounds. We found that the use of randomly selected compounds docked to the target of interest using SVMSP led to notable enrichment for EGFR and Src MD snapshots. SVMSP rescoring of protein-compound MD structures was applied for the search of small-molecule inhibitors of the mitochondrial enzyme aldehyde dehydrogenase 2 (ALDH2). Rank-ordering of a commercial library of 50 000 compounds docked to MD structures of ALDH2 led to five small-molecule inhibitors. Four compounds had IC50s below 5 μM. These compounds serve as leads for the design and synthesis of more potent and selective ALDH2 inhibitors.

CABS-flex predictions of protein flexibility compared with NMR ensembles

Motivation: Identification of flexible regions of protein structures is important for understanding of their biological functions. Recently, we have developed a fast approach for predicting protein structure fluctuations from a single protein model: the CABS-flex. CABS-flex was shown to be an efficient alternative to conventional all-atom molecular dynamics (MD). In this work, we evaluate CABS-flex and MD predictions by comparison with protein structural variations within NMR ensembles.

Results: Based on a benchmark set of 140 proteins, we show that the relative fluctuations of protein residues obtained from CABS-flex are well correlated to those of NMR ensembles. On average, this correlation is stronger than that between MD and NMR ensembles. In conclusion, CABS-flex is useful and complementary to MD in predicting protein regions that undergo conformational changes as well as the extent of such changes.

Availability and implementation: The CABS-flex is freely available to all users at http://biocomp.chem.uw.edu.pl/CABSflex.

Contact: sekmi@chem.uw.edu.pl

Supplementary information:Supplementary data are available at Bioinformatics online.

In silico mechanistic profiling to probe small molecule binding to sulfotransferases

Drug metabolizing enzymes play a key role in the metabolism, elimination and detoxification of xenobiotics, drugs and endogenous molecules. While their principal role is to detoxify organisms by modifying compounds, such as pollutants or drugs, for a rapid excretion, in some cases they render their substrates more toxic thereby inducing severe side effects and adverse drug reactions, or their inhibition can lead to drug–drug interactions. We focus on sulfotransferases (SULTs), a family of phase II metabolizing enzymes, acting on a large number of drugs and hormones and showing important structural flexibility. Here we report a novel in silico structure-based approach to probe ligand binding to SULTs. We explored the flexibility of SULTs by molecular dynamics (MD) simulations in order to identify the most suitable multiple receptor conformations for ligand binding prediction. Then, we employed structure-based docking-scoring approach to predict ligand binding and finally we combined the predicted interaction energies by using a QSAR methodology. The results showed that our protocol successfully prioritizes potent binders for the studied here SULT1 isoforms, and give new insights on specific molecular mechanisms for diverse ligands’ binding related to their binding sites plasticity. Our best QSAR models, introducing predicted protein-ligand interaction energy by using docking, showed accuracy of 67.28%, 78.00% and 75.46%, for the isoforms SULT1A1, SULT1A3 and SULT1E1, respectively. To the best of our knowledge our protocol is the first in silico structure-based approach consisting of a protein-ligand interaction analysis at atomic level that considers both ligand and enzyme flexibility, along with a QSAR approach, to identify small molecules that can interact with II phase dug metabolizing enzymes.

Computational analysis of protein-protein interfaces involving an alpha helix: insights for terphenyl-like molecules binding

Background

Protein-Protein Interactions (PPIs) are key for many cellular processes. The characterization of PPI interfaces and the prediction of putative ligand binding sites and hot spot residues are essential to design efficient small-molecule modulators of PPI. Terphenyl and its derivatives are small organic molecules known to mimic one face of protein-binding alpha-helical peptides. In this work we focus on several PPIs mediated by alpha-helical peptides.

Method

We performed computational sequence- and structure-based analyses in order to evaluate several key physicochemical and surface properties of proteins known to interact with alpha-helical peptides and/or terphenyl and its derivatives.

Results

Sequence-based analysis revealed low sequence identity between some of the analyzed proteins binding alpha-helical peptides. Structure-based analysis was performed to calculate the volume, the fractal dimension roughness and the hydrophobicity of the binding regions. Besides the overall hydrophobic character of the binding pockets, some specificities were detected. We showed that the hydrophobicity is not uniformly distributed in different alpha-helix binding pockets that can help to identify key hydrophobic hot spots.

Conclusions

The presence of hydrophobic cavities at the protein surface with a more complex shape than the entire protein surface seems to be an important property related to the ability of proteins to bind alpha-helical peptides and low molecular weight mimetics. Characterization of similarities and specificities of PPI binding sites can be helpful for further development of small molecules targeting alpha-helix binding proteins.

Tranilast binds to aβ monomers and promotes aβ fibrillation

The antiallergy and potential anticancer drug tranilast has been patented for treating Alzheimer's disease (AD), in which amyloid β-protein (Aβ) plays a key pathogenic role. We used solution NMR to determine that tranilast binds to Aβ40 monomers with ∼300 μM affinity. Remarkably, tranilast increases Aβ40 fibrillation more than 20-fold in the thioflavin T assay at a 1:1 molar ratio, as well as significantly reducing the lag time. Tranilast likely promotes fibrillation by shifting Aβ monomer conformations to those capable of seed formation and fibril elongation. Molecular docking results qualitatively agree with NMR chemical shift perturbation, which together indicate that hydrophobic interactions are the major driving force of the Aβ-tranilast interaction. These data suggest that AD may be a potential complication for tranilast usage in elderly patients.

Toward in silico structure-based ADMET prediction in drug discovery

Quantitative structure-activity relationship (QSAR) methods and related approaches have been used to investigate the molecular features that influence the absorption, distribution, metabolism, excretion and toxicity (ADMET) of drugs. As the three-dimensional structures of several major ADMET proteins become available, structure-based (docking-scoring) computations can be carried out to complement or to go beyond QSAR studies. Applying docking-scoring methods to ADMET proteins is a challenging process because they usually have a large and flexible binding cavity; however, promising results relating to metabolizing enzymes have been reported. After reviewing current trends in the field we applied structure-based methods in the context of receptor flexibility in a case study involving the phase II metabolizing sulfotransferases. Overall, the explored concepts and results suggested that structure-based ADMET profiling will probably join the mainstream during the coming years.