Docking Search Research Articles

Re-Docking Scheme to Explore Docking Search Space by using Interaction Profiles

In protein-protein interaction predictions, there are various approaches to obtain near-native 3D structure of protein complexes. One of the most available methods is rigid-body docking process, generating many protein complexes (decoys) as candidates of the native complex.However, we sometime faced with one of the critical problems to solve, which is a situation of no near-native decoys including a decoy dataset. Even if the bound-state case, in 9 out of 44 protein pairs, we could not obtain near-native decoys. To overcome this situation, we applied interaction fingerprint (IFP) to this problem. IFP method in docking process is originally developed for cluster analysis by comparing among decoys in our previous work [Uchikoga & Hirokawa, (2010) BMC Bioinfor. 11:264]. This method can applied to proteins with large conformation changes, for example, calmodulin. IFP composed of frequencies of interaction between amino acid residues. Therefore, much more different structures can compare each other.The critical situation of no near-native decoys results from a fact that docking search space is not large enough to obtain near-native decoys. Therefore, we proposed re-docking scheme for exploring docking search spaces by restricting protein surfaces after assembling interaction surfaces of decoys using IFPs. We applied re-docking scheme to several docking cases and will discuss the results.

Abstract B101: Design and optimization of a new series of HSP70 inhibitors for use in the treatment of pancreatic cancer.

Abstract Through a combination of structure-based design and similarity methods, we have identified a novel series of HSP70 inhibitors that are directed towards the ATPase domain. The structural core was identified through a multi-stage docking and similarity search of screening compounds available from multiple commercial sources. Upon selection of the core, a virtual library was constructed, pre-filtered, and screened in two docking packages. By combining the resulting scores with those from a docking decoy library, workflow software was used to calculate a Z-score, which has recently been shown to assist with separating actives from in-actives. ADME predicted values were also calculated for each structure in the virtual library. Filters were applied in the workflow at several points to focus on the best compounds, thirty of which were ranked and considered for synthesis. The compounds were synthesized in five steps using commercially available starting materials. At present, six compounds from the series have been synthesized and four of those were tested alone and in combination with a known proteasome inhibitor, bortezomib. The third highest ranked compound showed the most potent effect on pancreatic cancer cell death in multiple cell lines. We will report on the design, synthesis, and biological characterization of this new class of HSP70 inhibitors. Citation Information: Mol Cancer Ther 2013;12(11 Suppl):B101. Citation Format: David S. Maxwell, Duoli Sun, Zhenghong Peng, Wei Qi, David McConkey, James L. Abbruzzese, William G. Bornmann. Design and optimization of a new series of HSP70 inhibitors for use in the treatment of pancreatic cancer. [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2013 Oct 19-23; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2013;12(11 Suppl):Abstract nr B101.

Protein-Protein Docking with F2Dock 2.0 and GB-Rerank

MotivationComputational simulation of protein-protein docking can expedite the process of molecular modeling and drug discovery. This paper reports on our new F2 Dock protocol which improves the state of the art in initial stage rigid body exhaustive docking search, scoring and ranking by introducing improvements in the shape-complementarity and electrostatics affinity functions, a new knowledge-based interface propensity term with FFT formulation, a set of novel knowledge-based filters and finally a solvation energy (GBSA) based reranking technique. Our algorithms are based on highly efficient data structures including the dynamic packing grids and octrees which significantly speed up the computations and also provide guaranteed bounds on approximation error.ResultsThe improved affinity functions show superior performance compared to their traditional counterparts in finding correct docking poses at higher ranks. We found that the new filters and the GBSA based reranking individually and in combination significantly improve the accuracy of docking predictions with only minor increase in computation time. We compared F2 Dock 2.0 with ZDock 3.0.2 and found improvements over it, specifically among 176 complexes in ZLab Benchmark 4.0, F2 Dock 2.0 finds a near-native solution as the top prediction for 22 complexes; where ZDock 3.0.2 does so for 13 complexes. F2 Dock 2.0 finds a near-native solution within the top 1000 predictions for 106 complexes as opposed to 104 complexes for ZDock 3.0.2. However, there are 17 and 15 complexes where F2 Dock 2.0 finds a solution but ZDock 3.0.2 does not and vice versa; which indicates that the two docking protocols can also complement each other.AvailabilityThe docking protocol has been implemented as a server with a graphical client (TexMol) which allows the user to manage multiple docking jobs, and visualize the docked poses and interfaces. Both the server and client are available for download. Server: http://www.cs.utexas.edu/~bajaj/cvc/software/f2dock.shtml. Client: http://www.cs.utexas.edu/~bajaj/cvc/software/f2dockclient.shtml.

Protein-Protein Interaction Prediction by using Amino Acids Interaction Pattern in Rigid-Body Docking Process

We approached to prediction of protein-protein interactions using rigid-body docking algorithm, generating many decoys including false positives. To search near-native decoys from a decoy set generated by a docking process, cluster analysis is useful with calculating similarities between decoys. However, this analysis goes well only in the case that a decoy set includes near-native decoys. Actually, there are some cases of protein pairs with no near-native decoys after an initial docking process.This problem is related with a size of docking search spaces. When a size of search space is small, near-native decoys cannot be obtained after a rigid-body docking process. We then applied a profile method to obtain a region including native interacting residue pairs for re-docking process by using Profile of Interaction FingerPrints (P-IFP).Interaction FingerPrints (IFP) has been developed for the post-docking analysis of protein-protein rigid-body docking. IFP is composed of binary states of interacting amino acid residues of each interacting proteins, as a scale for measuring unique similarities between the complex structures. Then, IFP can be used more easily to evaluate similarities between decoys than RMSD method, which depends on methods of superposing their structures. Additionally, IFP can be useful to assemble interaction profiles from various decoys.In this work, we examined re-docking process after generating P-IFP for rigid-body docking decoys. As results, we could obtain a set of decoys with higher similarities than that of decoys generated in the initial docking process. Re-docking method using the IFP method is expected to improve cases without near-native decoys in an initial docking process.

The Discovery of Phenylbenzamide Derivatives as Grb7‐Based Antitumor Agents

Grb7 is a non-catalytic protein, the overexpression of which has been associated with the proliferative and migratory potentials of cancer cells. Virtual screening strategies involving a shape-based similarity search, molecular docking, and 2D-similarity searches complemented by experimental binding studies (Thermofluor and isothermal titration calorimetry) resulted in the identification of nine novel phenylbenzamide-based antagonists of the Grb7 SH2 domain. Moderate binding affinities were observed, ranging from K(d)=32.3 μM for lead phenylbenzamide NSC 104999 (1) to K(d)=1.1 μM for a structurally related compound, NSC 57148 (2). Deconvolution of the affinity data into its components revealed differences in lead binding, from being entropy based (lead 1) to enthalpically driven (NSC 100874 (3), NSC 55158 (4), and compound 2). Finally, the lead compound 1 was found to decrease the growth of MDA-MB-468 breast cancer cells, with an IC(50) value of 39.9 μM. It is expected that these structures will serve as novel leads in the development of Grb7-based anticancer therapeutics.

In silico analysis of the two tandem somatomedin B domains of ENPP1 reveals hints on the homodimerization of the protein

The homodimerization of ENPP1 is mediated by the two somatomedin B (SMB) domains of the protein through a mechanism that is yet unknown at the atomistic level. The tandem arrangement of these domains without an intermediate spacer implies their possible packing into a functional assembly, which we explored by rigid docking. To exclude potential bias in the docking search we assessed the absence of flexible protein regions by evaluating the normalized B-factors calculated from the Cα atom displacements derived from molecular dynamics simulations. After filtering the docking results exploiting the criterion that residues located at the inter-domain interfaces are more conserved than non-interface residues, the resulting best model of the tandem SMB domains revealed the presence of two large conserved surface patches not engaged in the inter-domain contact. The largest patch is flat and contains all the invariant positively charged residues characterized by fully solvent-exposed side chains within the tandem SMB domains, suggesting as a possible role its interaction with the negative phospholipids on the cell surface. We envisage that an ENPP1 monomer bound to the cell membrane via the transmembrane segment can also interact with the cell surface through the largest conserved patch favoring a specific geometry of the tandem SMB module on the cell that optimally exposes the second conserved patch for the symmetric interaction with another membrane-bound ENPP1 monomer, finally promoting the homodimerization. Biological implications of this model and insights into the effects of the K173Q variant associated with insulin resistance and related abnormalities are presented.

Combining pharmacophore, docking and substructure search approaches to identify and optimize novel B-RafV600E inhibitors

In this study for searching novel B-RafV600E inhibitors, pharmacophore-based virtual screening identified 1 as a hit bearing 5-benzylidene-2-thioxodihydropyrimidine-4,6(1H,5H)-dione. Based on 1, scaffold hopping inspired by molecular docking discovered 5-(furan-2-ylmethylene)-2-thioxodihydropyrimidine-4,6(1H,5H)-dione as a new and better scaffold. Substructure search with the new scaffold identified 28 active compounds, among which 12 compounds (42.9%) showed IC50 less than 1μM. Especially, compound 3o, which is 10-fold more potent than the hit 1, is a potent inhibitor comparable to that of the marketed drug vemurafenib.

Structure-Based Inhibition of Norovirus RNA-Dependent RNA Polymerases

Caliciviridae are RNA viruses with a single-stranded, positively oriented polyadenylated genome, responsible for a broad spectrum of diseases such as acute gastroenteritis in humans. Recently, analyses on the structures and functionalities of the RNA-dependent RNA polymerase (RdRp) from several Caliciviruses have been reported. The RdRp is predicted to play a key role in genome replication, as well as in synthesis and amplification of additional subgenomic RNA.Starting from the crystal structures of human Norovirus (hNV) RdRp, we performed an in silico docking search to identify synthetic compounds with predicted high affinity for the enzyme active site. The best-ranked candidates were tested in vitro on murine Norovirus (MNV) and hNV RdRps to assay their inhibition of RNA polymerization. The results of such combined computational and experimental screening approach led to the identification of two high-potency inhibitors: Suramin and NF023, both symmetric divalent molecules hosting two naphthalene-trisulfonic acid heads. We report here the crystal structure of MNV RdRp alone and in the presence of the two identified inhibitors. Both inhibitory molecules occupy the same RdRp site, between the fingers and thumb domains, with one inhibitor head close to residue 42 and to the protein active site. To further validate the structural results, we mutated Trp42 to Ala in MNV RdRp and the corresponding residue (i.e., Tyr41 to Ala) in hNV RdRp. Both NF023 and Suramin displayed reduced inhibitory potency versus the mutated hNV RdRp, thus hinting at a conserved inhibitor binding mode in the two polymerases.

Atomic resolution model of the antibody Fc interaction with the complement C1q component

The globular C1q heterotrimer is a subunit of the C1 complement factor. Binding of the C1q subunit to the constant (Fc) part of antibody molecules is a first step and key event of complement activation. Although three-dimensional structures of C1q and antibody Fc subunits have been determined experimentally no atomic resolution structure of the C1q–Fc complex is known so far. Based on systematic protein–protein docking searches and Molecular Dynamics simulations a structural model of the C1q–IgG1–Fc-binding geometry has been obtained. The structural model is compatible with available experimental data on the interaction between the two partner proteins. It predicts a binding geometry that involves mainly the B-subunit of the C1q-trimer and both subunits of the IgG1–Fc-dimer with small conformational adjustments with respect to the unbound partners to achieve high surface complementarity. In addition to several charge–charge and polar contacts in the rim region of the interface it also involves nonpolar contacts between the two proteins and is compatible with the carbohydrate moiety of the Fc subunit. The model for the complex structure provides a working model for rationalizing available biochemical data on this important interaction and can form the basis for the design of Fc variants with a greater capacity to activate the complement system for example on binding to cancer cells or other target structures.

Structure Based Inhibition of the Calicivirus RNA-Dependent RNA-Polymerase

Caliciviridae are RNA viruses with single stranded positive-oriented genome causing a broad spectrum of diseases such as acute gastroenteritis in humans. The structures of the RNA-dependent RNA-polymerase (RdRp) of several Caliciviruses have been reported. The RdRp is predicted to play a key role in genome replication, as well as in the synthesis and amplification of subgenomic RNA.Starting from crystal structures of human (hNV) and Murine Norwalk virus (MNV) RdRp, we performed an in silico docking search to identify commercially available compounds with predicted high affinity for the enzyme active site. The best candidates were tested in vitro to assay their effective inhibition of MNV and hNV RdRp. The results of such combined computational and experimental screening approach led to the identification of two high-potency inhibitors: EM01 and EM02. The crystal structure of MNV in the presence of the two inhibitors showed a common binding site close to the protein active site where the addition of new nucleotides to the nascent RNA occurs. From such structures we could identify the main residues involved in inhibitor binding. We inserted a point mutation in one of these key residues showing the reduction of inhibitory potency in both MNV and hNV RdRps. Finally, we identified a common moiety present in the two inhibitors likely carrying most of the inhibitory power. Such fragment can be elongated/modified to enhance its druglikeness in order to develop novel anti-viral drugs.

Scoring optimisation of unbound protein–protein docking including protein binding site predictions

The prediction of the structure of the protein-protein complex is of great importance to better understand molecular recognition processes. During systematic protein-protein docking, the surface of a protein molecule is scanned for putative binding sites of a partner protein. The possibility to include external data based on either experiments or bioinformatic predictions on putative binding sites during docking has been systematically explored. The external data were included during docking with a coarse-grained protein model and on the basis of force field weights to bias the docking search towards a predicted or known binding region. The approach was tested on a large set of protein partners in unbound conformations. The significant improvement of the docking performance was found if reliable data on the native binding sites were available. This was possible even if data for single key amino acids at a binding interface are included. In case of binding site predictions with limited accuracy, only modest improvement compared with unbiased docking was found. The optimisation of the protocol to bias the search towards predicted binding sites was found to further improve the docking performance resulting in approximately 40% acceptable solutions within the top 10 docking predictions compared with 22% in case of unbiased docking of unbound protein structures.

A coarse-grained force field for Protein–RNA docking

The awareness of important biological role played by functional, non coding (nc) RNA has grown tremendously in recent years. To perform their tasks, ncRNA molecules typically unite with protein partners, forming ribonucleoprotein complexes. Structural insight into their architectures can be greatly supplemented by computational docking techniques, as they provide means for the integration and refinement of experimental data that is often limited to fragments of larger assemblies or represents multiple levels of spatial resolution. Here, we present a coarse-grained force field for protein-RNA docking, implemented within the framework of the ATTRACT program. Complex structure prediction is based on energy minimization in rotational and translational degrees of freedom of binding partners, with possible extension to include structural flexibility. The coarse-grained representation allows for fast and efficient systematic docking search without any prior knowledge about complex geometry.

PRUNE and PROBE--two modular web services for protein-protein docking

The protein–protein docking programs typically perform four major tasks: (i) generation of docking poses, (ii) selecting a subset of poses, (iii) their structural refinement and (iv) scoring, ranking for the final assessment of the true quaternary structure. Although the tasks can be integrated or performed in a serial order, they are by nature modular, allowing an opportunity to substitute one algorithm with another. We have implemented two modular web services, (i) PRUNE: to select a subset of docking poses generated during sampling search (http://pallab.serc.iisc.ernet.in/prune) and (ii) PROBE: to refine, score and rank them (http://pallab.serc.iisc.ernet.in/probe). The former uses a new interface area based edge-scoring function to eliminate >95% of the poses generated during docking search. In contrast to other multi-parameter-based screening functions, this single parameter based elimination reduces the computational time significantly, in addition to increasing the chances of selecting native-like models in the top rank list. The PROBE server performs ranking of pruned poses, after structure refinement and scoring using a regression model for geometric compatibility, and normalized interaction energy. While web-service similar to PROBE is infrequent, no web-service akin to PRUNE has been described before. Both the servers are publicly accessible and free for use.

Toward prediction of functional protein pockets using blind docking and pocket search algorithms

Location of functional binding pockets of bioactive ligands on protein molecules is essential in structural genomics and drug design projects. If the experimental determination of ligand-protein complex structures is complicated, blind docking (BD) and pocket search (PS) calculations can help in the prediction of atomic resolution binding mode and the location of the pocket of a ligand on the entire protein surface. Whereas the number of successful predictions by these methods is increasing even for the complicated cases of exosites or allosteric binding sites, their reliability has not been fully established. For a critical assessment of reliability, we use a set of ligand-protein complexes, which were found to be problematic in previous studies. The robustness of BD and PS methods is addressed in terms of success of the selection of truly functional pockets from among the many putative ones identified on the surfaces of ligand-bound and ligand-free (holo and apo) protein forms. Issues related to BD such as effect of hydration, existence of multiple pockets, and competition of subsidiary ligands are considered. Practical cases of PS are discussed, categorized and strategies are recommended for handling the different situations. PS can be used in conjunction with BD, as we find that a consensus approach combining the techniques improves predictive power.

Applying in silico tools to the discovery of novel CXCR4 inhibitors

AbstractThe process of HIV entry begins with the binding of the viral envelope glycoprotein gp120 to both the CD4 receptor and one of the CXCR4 or CCR5 chemokine co‐receptors. There is currently considerable interest in developing novel ligands that can bind to these co‐receptors and hence block virus‐cell fusion. This article reviews the use of different in silico structure‐based and ligand‐based virtual screening (VS) tools for the discovery of potential HIV entry inhibitors for the CXCR4 receptor. More specifically, it discusses homology modelling, de novo design, docking, QSAR analyses, pharmacophore modelling, and similarity searches. Results from retrospective VS of a library of known CXCR4 inhibitors taken from the literature and from prospective VS of a combinatorial virtual library are reviewed. The structures of active compounds found by these approaches, as well as CXCR4 inhibitors currently in development, are also discussed. Drug Dev Res 72: 95–111, 2011. © 2010 Wiley‐Liss, Inc.

Ultra-fast FFT protein docking on graphics processors

Modelling protein-protein interactions (PPIs) is an increasingly important aspect of structural bioinformatics. However, predicting PPIs using in silico docking techniques is computationally very expensive. Developing very fast protein docking tools will be useful for studying large-scale PPI networks, and could contribute to the rational design of new drugs. The Hex spherical polar Fourier protein docking algorithm has been implemented on Nvidia graphics processor units (GPUs). On a GTX 285 GPU, an exhaustive and densely sampled 6D docking search can be calculated in just 15 s using multiple 1D fast Fourier transforms (FFTs). This represents a 45-fold speed-up over the corresponding calculation on a single CPU, being at least two orders of magnitude times faster than a similar CPU calculation using ZDOCK 3.0.1, and estimated to be at least three orders of magnitude faster than the GPU-accelerated version of PIPER on comparable hardware. Hence, for the first time, exhaustive FFT-based protein docking calculations may now be performed in a matter of seconds on a contemporary GPU. Three-dimensional Hex FFT correlations are also accelerated by the GPU, but the speed-up factor of only 2.5 is much less than that obtained with 1D FFTs. Thus, the Hex algorithm appears to be especially well suited to exploit GPUs compared to conventional 3D FFT docking approaches. http://hex.loria.fr/ and http://hexserver.loria.fr/.

Binding site prediction and improved scoring during flexible protein–protein docking with ATTRACT

The ATTRACT protein-protein docking program combined with a coarse-grained protein model has been used to predict protein-protein complex structures in CAPRI rounds 13-19. For six targets acceptable or better quality solutions have been submitted (high quality predictions for targets 32, 40, 41, and 42). The improved performance compared to previous rounds can be attributed in part to the inclusion of conformational flexibility during systematic searches and an optimized scoring function. In addition, a recently developed method for the prediction of putative protein binding sites based on the electrostatic penalty to place neutral low dielectric probes on the protein surface was applied to the most recent targets. The approach resulted in useful predictions of putative binding sites that can help to limit the systematic docking searches. Possible improvements of the docking approach in particular at the scoring and refinement steps are discussed.

Energy minimum theorem based on AGA, Lyapunov and force field for CADD techniques

This paper employs the energy minimum to enhance drug docking efficiency in a computer aided drug design (CADD) system. The energy minimum application is used to enhance CADD docking performance. The proposed method is discussed in three aspects, adaptive genetic algorithms (AGA), Lyapunov stability theorem and molecular force field. As in previous researches, docking is the crucial component in drug development. The number of docking sites affects the drug docking speed. Reducing the scope of the geometric search is the key task. This paper proposes AGA to improve geometric molecular docking search efficiency. The Lyapunov stability theorem forwards the stability state identification. Protein folding intention generally finds the most appropriate stability state when the thermodynamic and molecular mechanical free energy has reached the lowest point. The AMBER force field simulation is used to discover the molecular statistical mechanics in a drug-ligand. AGA was found better in terms of processing the geometric graphic search operation. The AGA and Lyapunov algorithms were utilized to sieve out the global energy minimum approach from the numerous, raw docking sites.

Multiscale Modeling of PCNA - Ubiquitin Interactions

Covalent attachment of ubiquitin (Ub) to proliferating cell nuclear antigen (PCNA) plays a crucial role in translesion synthesis (TLS). However, structural knowledge of the complex between Ub and PCNA is currently lacking. The problem is important from a biological perspective since ubiquitinated PCNA is involved in the recruitment of specialized lesion bypass polymerases. A loss of regulation of TLS and other damage-avoidance pathways can lead to a variety of cell fates including apoptosis and uncontrolled cell growth. We have modeled the Ub-PCNA complex using a combination of tethered Brownian dynamics (TBD), protein-protein docking with RosettaDock, flexible loop modeling with ModLoop, and molecular dynamics(MD). The TBD simulations were used to generate a large ensemble of electrostatically and geometrically favorable configurations, subsequently used as a starting point for local docking searches. The final models were refined with all-atom explicit solvent MD simulations. Ubiquitin was found to bind in a groove on the PCNA surface directly above the PCNA subunit interface. A mutation originally identified in genetics screens (pol30-113) and known to interfere with TLS, is positioned directly beneath the bound ubiquitin in our models. Thus, the results provide unexpected insight into previously unexplained biological observations.View Large Image | View Hi-Res Image | Download PowerPoint Slide

Exploring novel target space: a need to partner high throughput docking and ligand-based similarity searches?

Recent advances in combinatorial chemistry (CC) and High throughput screening (HTS) approaches for use in drug discovery have made it possible to synthesize and/or screen large repositories of chemically diverse scaffolds in search of small molecules that disrupt or regulate macromolecular function. Although successful in the discovery of novel therapeutics this approach is both costly and time consuming. In silico computer aided drug discovery (CADD) approaches including; structure based virtual screening (SBVS) or high throughput docking (HTD) and/or ligand based virtual screening (LBVS) are areas experiencing renewed interest both in the pharmaceutical industry and academia. The emerging success of these approaches alone or partnered with HTS platforms in search of, and/or optimization of, novel therapeutic compounds represents a potential approach for the identification of therapies that target novel space. Here we will discuss how LBVS has been and continues to be partnered with HTS in early stage compound identification and/or triage. We will also provide a significant overview of how SBVS when partnered with LBVS can overcome the limitations inherent to each approach when used alone. We will discuss this partnered approach in the context of both traditional drug discovery targets and provide thoughts on its applicability to study novel chemical space including protein-protein and/or other historical intractable interfaces.