SOFTDOCK: understanding of molecular recognition through a systematic docking study

Two New Aristolochic Acid Analogues from the Roots of Aristolochia contorta with Significant Cytotoxic Activity

Twelve compounds, including two new aristolochic acid analogues with a formyloxy moiety (9–10) and 10 known aristolochic acid derivates (1–8 and 11–12), were obtained from the roots of Aristolochiacontorta. Their structures were elucidated using extensive spectroscopic methods. Their cytotoxic activity in human proximal tubular cells HK-2 was evaluated by the MTT method, which has been widely used to assess cell viability. Among these molecules, compounds 3 and 9 were found to be more cytotoxic. Furthermore, molecular modeling was used to evaluate, for the first time, the interactions of compounds 3 and 9 with the target protein organic anionic transporter 1 (OAT1) that plays a key role in mediating aristolochic acid nephropathy. Structure–activity relationships are briefly discussed.

Novel K-Ras G12C Switch-II Covalent Binders Destabilize Ras and Accelerate Nucleotide Exchange

The success of targeted covalent inhibitors in the global pharmaceutical industry has led to a resurgence of covalent drug discovery. However, covalent inhibitor design for flexible binding sites remains a difficult task due to a lack of methodological development. Here, we compared covalent docking to empirical electrophile screening against the highly dynamic target K-Ras^G12C. While the overall hit rate of both methods was comparable, we were able to rapidly progress a docking hit to a potent irreversible covalent binder that modifies the inactive, GDP-bound state of K-Ras^G12C. Hydrogen-deuterium exchange mass spectrometry was used to probe the protein dynamics of compound binding to the switch-II pocket and subsequent destabilization of the nucleotide-binding region. SOS-mediated nucleotide exchange assays showed that, contrary to prior switch-II pocket inhibitors, these new compounds appear to accelerate nucleotide exchange. This study highlights the efficiency of covalent docking as a tool for the discovery of chemically novel hits against challenging targets.

Common Hits Approach: Combining Pharmacophore Modeling and Molecular Dynamics Simulations

We present a new approach that incorporates flexibility based on extensive MD simulations of protein-ligand complexes into structure-based pharmacophore modeling and virtual screening. The approach uses the multiple coordinate sets saved during the MD simulations and generates for each frame a pharmacophore model. Pharmacophore models with the same pharmacophore features are pooled. In this way the high number of pharmacophore models that results from the MD simulation is reduced to only a few hundred representative pharmacophore models. Virtual screening runs are performed with every representative pharmacophore model; the screening results are combined and rescored to generate a single hit-list. The score for a particular molecule is calculated based on the number of representative pharmacophore models which classified it as active. Hence, the method is called common hits approach (CHA). The steps between the MD simulation and the final hit-list are performed automatically and without user interaction. We test the performance of CHA for virtual screening using screening databases with active and inactive compounds for 40 protein-ligand systems. The results of the CHA are compared to the (i) median screening performance of all representative pharmacophore models of protein-ligand systems, as well as to the virtual screening performance of (ii) a random classifier, (iii) the pharmacophore model derived from the experimental structure in the PDB, and (iv) the representative pharmacophore model appearing most frequently during the MD simulation. For the 34 (out of 40) protein-ligand complexes, for which at least one of the approaches was able to perform better than a random classifier, the highest enrichment was achieved using CHA in 68% of the cases, compared to 12% for the PDB pharmacophore model and 20% for the representative pharmacophore model appearing most frequently. The availabilithy of diverse sets of different pharmacophore models is utilized to analyze some additional questions of interest in 3D pharmacophore-based virtual screening.

Incorporation of protein flexibility and conformational energy penalties in docking screens to improve ligand discovery

Proteins fluctuate between alternative conformations, which presents a challenge for ligand discovery because such flexibility is difficult to treat computationally owing to problems with conformational sampling and energy weighting. Here we describe a flexible docking method that samples and weights protein conformations using experimentally derived conformations as a guide. The crystallographically refined occupancies of these conformations, which are observable in an apo receptor structure, define energy penalties for docking. In a large prospective library screen, we identified new ligands that target specific receptor conformations of a cavity in cytochrome c peroxidase, and we confirm both ligand pose and associated receptor conformation predictions by crystallography. The inclusion of receptor flexibility led to ligands with new chemotypes and physical properties. By exploiting experimental measures of loop and side-chain flexibility, this method can be extended to the discovery of new ligands for hundreds of targets in the Protein Data Bank for which similar experimental information is available.

Optimized grid-based protein-protein docking as a global search tool followed by incorporating experimentally derivable restraints

Unbound protein docking, or the computational prediction of the structure of a protein complex from the structures of its separated components, is of importance but still challenging. A practical approach toward reliable results for unbound docking is to incorporate experimentally derived information with computation. To this end, truly systematic search of the global docking space is desirable. The fast Fourier transform (FFT) docking is a systematic search method with high computational efficiency. However, by using FFT to perform unbound docking, possible conformational changes upon binding must be treated implicitly. To better accommodate the implicit treatment of conformational flexibility, we develop a rational approach to optimize "softened" parameters for FFT docking. In connection with the increased "softness" of the parameters in this global search step, we use a revised rule to select candidate models from the search results. For complexes designated as of low and medium difficulty for unbound docking, these adaptations of the original FTDOCK program lead to substantial improvements of the global search results. Finally, we show that models resulted from FFT-based global search can be further filtered with restraints derivable from nuclear magnetic resonance (NMR) chemical shift perturbation or mutagenesis experiments, leading to a small set of models that can be feasibly refined and evaluated using computationally more expensive methods and that still include high-ranking near-native conformations.

Sampling the conformation of protein surface residues for flexible protein docking

BACKGROUND

The problem of determining the physical conformation of a protein dimer, given the structures of the two interacting proteins in their unbound state, is a difficult one. The location of the docking interface is determined largely by geometric complementarity, but finding complementary geometry is complicated by the flexibility of the backbone and side-chains of both proteins. We seek to generate candidates for docking that approximate the bound state well, even in cases where there is backbone and/or side-chain difference from unbound to bound states.

RESULTS

We divide the surfaces of each protein into local patches and describe the effect of side-chain flexibility on each patch by sampling the space of conformations of its side-chains. Likely positions of individual side-chains are given by a rotamer library; this library is used to derive a sample of possible mutual conformations within the patch. We enforce broad coverage of torsion space. We control the size of the sample by using energy criteria to eliminate unlikely configurations, and by clustering similar configurations, resulting in 50 candidates for a patch, a manageable number for docking.

CONCLUSIONS

Using a database of protein dimers for which the bound and unbound structures of the monomers are known, we show that from the unbound patch we are able to generate candidates for docking that approximate the bound structure. In patches where backbone change is small (within 1 Å RMSD of bound), we are able to account for flexibility and generate candidates that are good approximations of the bound state (82% are within 1 Å and 98% are within 1.4 Å RMSD of the bound conformation). We also find that even in cases of moderate backbone flexibility our candidates are able to capture some of the overall shape change. Overall, in 650 of 700 test patches we produce a candidate that is either within 1 Å RMSD of the bound conformation or is closer to the bound state than the unbound is.

SOFTDOCK application to protein-protein interaction benchmark and CAPRI

The success of molecular docking requires cooperation of sampling and scoring of various conformations. The SOFTDOCK package uses a coarse-grained docking method to sample all possible conformations of complexes. SOFTDOCK uses a new Voronoi molecular surface and calculates several grid-based scores. It is shown by the leave-one-out test that three geometry scores and an FTDOCK-like electrostatics score contribute the most to the discrimination of near-native conformations. However, an atom-based solvation score is shown to be ineffective. It is also found that an increased Voronoi surface thickness greatly increases the accuracy of docking results. Finally, the clustering procedure is shown to improve the overall ranking, but leads to less accurate docking results. The application of SOFTDOCK in Critical Assessment of PRedicted Interactions involves four steps: (i) sampling with INTELEF; (ii) clustering; (iii) AMBER energy minimization; and (iv) manual inspection. Biological information from literature is used as filters in some of the sampling and manual inspection according to different targets. Two of our submissions have L_rmsd around 10 A. Although they are not classified as acceptable solutions, they are considered successful because they are comparable to the accuracy of our method.

AVAILABILITY

SOFTDOCK is open source code and can be downloaded at http://bio.iphy.ac.cn

Protein conformational transitions coupled to binding in molecular recognition of unstructured proteins: deciphering the effect of intermolecular interactions on computational structure prediction of the p27Kip1 protein bound to the cyclin A-cyclin-dependent kinase 2 complex

The relationship between folding mechanism coupled to binding and structure prediction of the tertiary complexes is studied for the p27(Kip) (1) protein which has an intrinsically disordered unbound form and undergoes a functional folding transition during complex formation with the phosphorylated cyclin A-cyclin-dependent kinase 2 (Cdk2) binary complex. Hierarchy of p27(Kip1) structural loss determined in our earlier studies from temperature-induced Monte Carlo simulations and subsequent characterization of the transition state ensemble (TSE) for the folding reaction have shown that simultaneous ordering of the p27(Kip1) native intermolecular interface for the beta-hairpin and beta-strand secondary structure elements is critical for nucleating a rapid kinetic transition to the native tertiary complex. In the present study, we investigate the effect of forming specific intermolecular interactions on structure prediction of the p27(Kip1) tertiary complex. By constraining different secondary structure elements of p27(Kip1) in their native bound conformations and conducting multiple simulated annealing simulations, we analyze differences in the success rate of predicting the native structure of p27(Kip1) in the tertiary complex. In accordance with the nucleation-condensation mechanism, we have found that further stabilization of the native intermolecular interface for the beta-hairpin and beta-strand elements of p27(Kip1), that become ordered in the TSE, but are hardly populated in the unbound state, results in a consistent acquisition of the native bound structure. Conversely, the excessive stablization of the local secondary structure elements, which are rarely detected in the TSE, has a detrimental effect on convergence to the native bound structure.

Protein-protein docking with simultaneous optimization of rigid-body displacement and side-chain conformations

Protein-protein docking algorithms provide a means to elucidate structural details for presently unknown complexes. Here, we present and evaluate a new method to predict protein-protein complexes from the coordinates of the unbound monomer components. The method employs a low-resolution, rigid-body, Monte Carlo search followed by simultaneous optimization of backbone displacement and side-chain conformations using Monte Carlo minimization. Up to 10(5) independent simulations are carried out, and the resulting "decoys" are ranked using an energy function dominated by van der Waals interactions, an implicit solvation model, and an orientation-dependent hydrogen bonding potential. Top-ranking decoys are clustered to select the final predictions. Small-perturbation studies reveal the formation of binding funnels in 42 of 54 cases using coordinates derived from the bound complexes and in 32 of 54 cases using independently determined coordinates of one or both monomers. Experimental binding affinities correlate with the calculated score function and explain the predictive success or failure of many targets. Global searches using one or both unbound components predict at least 25% of the native residue-residue contacts in 28 of the 32 cases where binding funnels exist. The results suggest that the method may soon be useful for generating models of biologically important complexes from the structures of the isolated components, but they also highlight the challenges that must be met to achieve consistent and accurate prediction of protein-protein interactions.