Showing papers on "Docking (molecular) published in 2007"

Optimizing fragment and scaffold docking by use of molecular interaction fingerprints.

Abstract: Protein−ligand interaction fingerprints have been used to postprocess docking poses of three ligand data sets: a set of 40 low-molecular-weight compounds from the Protein Data Bank, a collection of 40 scaffolds from pharmaceutically relevant protein ligands, and a database of 19 scaffolds extracted from true cdk2 inhibitors seeded in 2230 scaffold decoys. Four popular docking tools (FlexX, Glide, Gold, and Surflex) were used to generate poses for ligands of the three data sets. In all cases, scoring by the similarity of interaction fingerprints to a given reference was statistically superior to conventional scoring functions in posing low-molecular-weight fragments, predicting protein-bound scaffold coordinates according to the known binding mode of related ligands, and screening a scaffold library to enrich a hit list in true cdk2-targeted scaffolds.

pyDock: Electrostatics and desolvation for effective scoring of rigid‐body protein–protein docking

Abstract: The accurate scoring of rigid-body docking orientations represents one of the major difficulties in protein-protein docking prediction. Other challenges are the development of faster and more efficient sampling methods and the introduction of receptor and ligand flexibility during simulations. Overall, good discrimination of near-native docking poses from the very early stages of rigid-body protein docking is essential step before applying more costly interface refinement to the correct docking solutions. Here we explore a simple approach to scoring of rigid-body docking poses, which has been implemented in a program called pyDock. The scheme is based on Coulombic electrostatics with distance dependent dielectric constant, and implicit desolvation energy with atomic solvation parameters previously adjusted for rigid-body protein-protein docking. This scoring function is not highly dependent on specific geometry of the docking poses and therefore can be used in rigid-body docking sets generated by a variety of methods. We have tested the procedure in a large benchmark set of 80 unbound docking cases. The method is able to detect a near-native solution from 12,000 docking poses and place it within the 100 lowest-energy docking solutions in 56% of the cases, in a completely unrestricted manner and without any other additional information. More specifically, a near-native solution will lie within the top 20 solutions in 37% of the cases. The simplicity of the approach allows for a better understanding of the physical principles behind protein-protein association, and provides a fast tool for the evaluation of large sets of rigid-body docking poses in search of the near-native orientation.

Structure-based activity prediction for an enzyme of unknown function

Abstract: With many genomes sequenced, a pressing challenge in biology is predicting the function of the proteins that the genes encode. When proteins are unrelated to others of known activity, bioinformatics inference for function becomes problematic. It would thus be useful to interrogate protein structures for function directly. Here, we predict the function of an enzyme of unknown activity, Tm0936 from Thermotoga maritima, by docking high-energy intermediate forms of thousands of candidate metabolites. The docking hit list was dominated by adenine analogues, which appeared to undergo C6-deamination. Four of these, including 5-methylthioadenosine and S-adenosylhomocysteine (SAH), were tested as substrates, and three had substantial catalytic rate constants (10(5) M(-1 )s(-1)). The X-ray crystal structure of the complex between Tm0936 and the product resulting from the deamination of SAH, S-inosylhomocysteine, was determined, and it corresponded closely to the predicted structure. The deaminated products can be further metabolized by T. maritima in a previously uncharacterized SAH degradation pathway. Structure-based docking with high-energy forms of potential substrates may be a useful tool to annotate enzymes for function.

Validation studies of the site-directed docking program LibDock

Abstract: The performance of the site-features docking algorithm LibDock has been evaluated across eight GlaxoSmithKline targets as a follow-up to a broad validation study of docking and scoring software (Warren, G. L.; Andrews, W. C.; Capelli, A.; Clarke, B.; Lalonde, J.; Lambert, M. H.; Lindvall, M.; Nevins, N.; Semus, S. F.; Senger, S.; Tedesco, G.; Walls, I. D.; Woolven, J. M.; Peishoff, C. E.; Head, M. S. J. Med. Chem. 2006, 49, 5912-5931). Docking experiments were performed to assess both the accuracy in reproducing the binding mode of the ligand and the retrieval of active compounds in a virtual screening protocol using both the DJD (Diller, D. J.; Merz, K. M., Jr. Proteins 2001, 43, 113-124) and LigScore2 (Krammer, A. K.; Kirchoff, P. D.; Jiang, X.; Venkatachalam, C. M.; Waldman, M. J. Mol. Graphics Modell. 2005, 23, 395-407) scoring functions. This study was conducted using DJD scoring, and poses were rescored using all available scoring functions in the Accelrys LigandFit module, including LigScore2. For six out of eight targets at least 30% of the ligands were docked within a root-mean-square difference (RMSD) of 2.0 A for the crystallographic poses when the LigScore2 scoring function was used. LibDock retrieved at least 20% of active compounds in the top 10% of screened ligands for four of the eight targets in the virtual screening protocol. In both studies the LigScore2 scoring function enhanced the retrieval of crystallographic poses or active compounds in comparison with the results obtained using the DJD scoring function. The results for LibDock accuracy and ligand retrieval in virtual screening are compared to 10 other docking and scoring programs. These studies demonstrate the utility of the LigScore2 scoring function and that LibDock as a feature directed docking method performs as well as docking programs that use genetic/growing and Monte Carlo driven algorithms.

Docking ligands into flexible and solvated macromolecules. 1. Development and validation of FITTED 1.0.

Abstract: We report the development and validation of a novel suite of programs, FITTED 1.0, for the docking of flexible ligands into flexible proteins. This docking tool is unique in that it can deal with both the flexibility of macromolecules (side chains and main chains) and the presence of bridging water molecules while treating protein/ligand complexes as realistically dynamic systems. This software relies on a genetic algorithm to account for the flexibility of the two molecules as well as the location of bridging water molecules. In addition, FITTED 1.0 features a novel application of a switching function to retain or displace key water molecules from the protein-ligand complexes. Two independent modules, ProCESS and SMART, were developed to set up the proteins and the ligands prior to the docking stage. Validation of the accuracy of the software was achieved via the application of FITTED 1.0 to the docking of inhibitors of HIV-1 protease, thymidine kinase, trypsin, factor Xa, and MMP to their respective proteins.

EADock: docking of small molecules into protein active sites with a multiobjective evolutionary optimization.

Abstract: In recent years, protein-ligand docking has become a powerful tool for drug development. Although several approaches suitable for high throughput screening are available, there is a need for methods able to identify binding modes with high accuracy. This accuracy is essential to reliably compute the binding free energy of the ligand. Such methods are needed when the binding mode of lead compounds is not determined experimentally but is needed for structure-based lead optimization. We present here a new docking software, called EADock, that aims at this goal. It uses an hybrid evolutionary algorithm with two fitness functions, in combination with a sophisticated management of the diversity. EADock is interfaced with the CHARMM package for energy calculations and coordinate handling. A validation was carried out on 37 crystallized protein-ligand complexes featuring 11 different proteins. The search space was defined as a sphere of 15 A around the center of mass of the ligand position in the crystal structure, and on the contrary to other benchmarks, our algorithm was fed with optimized ligand positions up to 10 A root mean square deviation (RMSD) from the crystal structure, excluding the latter. This validation illustrates the efficiency of our sampling strategy, as correct binding modes, defined by a RMSD to the crystal structure lower than 2 A, were identified and ranked first for 68% of the complexes. The success rate increases to 78% when considering the five best ranked clusters, and 92% when all clusters present in the last generation are taken into account. Most failures could be explained by the presence of crystal contacts in the experimental structure. Finally, the ability of EADock to accurately predict binding modes on a real application was illustrated by the successful docking of the RGD cyclic pentapeptide on the alphaVbeta3 integrin, starting far away from the binding pocket.

A flexible approach to induced fit docking.

Abstract: We present Fleksy, a new approach to consider both ligand and receptor flexibility in small molecule docking. Pivotal to our method is the use of a receptor ensemble to describe protein flexibility. To construct these ensembles, we use a backbone-dependent rotamer library and implement the concept of interaction sampling. The latter allows the evaluation of different orientations of ambivalent interaction partners. The docking stage consists of an ensemble-based soft-docking experiment using FlexX-Ensemble, followed by an effective flexible receptor-ligand complex optimization using Yasara. Fleksy produces a set of receptor-ligand complexes ranked using a consensus scoring function combining docking scores and force field energies. Averaged over three cross-docking datasets, containing 35 different receptor-ligand complexes in total, Fleksy reproduces the observed binding mode within 2.0 A for 78% of the complexes. This compares favorably to the rigid receptor FlexX program, which on average reaches a success rate of 44% for these datasets.

Comparative Performance of Several Flexible Docking Programs and Scoring Functions: Enrichment Studies for a Diverse Set of Pharmaceutically Relevant Targets

Abstract: Virtual screening by molecular docking has become a widely used approach to lead discovery in the pharmaceutical industry when a high-resolution structure of the biological target of interest is available. The performance of three widely used docking programs (Glide, GOLD, and DOCK) for virtual database screening is studied when they are applied to the same protein target and ligand set. Comparisons of the docking programs and scoring functions using a large and diverse data set of pharmaceutically interesting targets and active compounds are carried out. We focus on the problem of docking and scoring flexible compounds which are sterically capable of docking into a rigid conformation of the receptor. The Glide XP methodology is shown to consistently yield enrichments superior to the two alternative methods, while GOLD outperforms DOCK on average. The study also shows that docking into multiple receptor structures can decrease the docking error in screening a diverse set of active compounds.

Flexible ligand docking with Glide.

Abstract: Glide is a ligand docking program for predicting protein-ligand binding modes and ranking ligands via high-throughput virtual screening. Glide utilizes two different scoring functions, SP and XP GlideScore, to rank-order compounds. Three modes of sampling ligand conformational and positional degrees of freedom are available to determine the optimal ligand orientation relative to a rigid protein receptor geometry. This unit presents protocols for flexible ligand docking with Glide, optionally including ligand constraints or ligand molecular similarities.

Highly Potent Triazole-Based Tubulin Polymerization Inhibitors

Abstract: We describe the synthesis and biological evaluation of a series of tubulin polymerization inhibitors that contain the 1,2,4-triazole ring to retain the bioactive configuration afforded by the cis double bond in combretastatin A-4 (CA-4). Several of the subject compounds exhibited potent tubulin polymerization inhibitory activity as well as cytotoxicity against a variety of cancer cells including multi-drug-resistant (MDR) cancer cell lines. Attachment of the N-methyl-5-indolyl moiety to the 1,2,4-triazole core, as exemplified by compound 7, conferred optimal properties among this series. Computer docking and molecular simulations of 7 inside the colchicine binding site of tubulin enabled identification of residues most likely to interact strongly with these inhibitors and explain their potent anti-tubulin activity and cytotoxicity. It is hoped that results presented here will stimulate further examination of these substituted 1,2,4-triazoles as potential anti-cancer therapeutic agents.

Small molecule inhibitors of the MDM2-p53 interaction discovered by ensemble-based receptor models.

Abstract: Five nonpeptide, small-molecule inhibitors of the human MDM2-p53 interaction are presented, and each inhibitor represents a new scaffold. The most potent compound exhibited a Ki of 110 +/- 30 nM. These compounds were identified using our multiple protein structure (MPS) method which incorporates protein flexibility into a receptor-based pharmacophore model that identifies appropriate hotspots of binding. Docking the inhibitors with an induced-fit docking protocol suggested that the inhibitors mimicked the three critical binding residues of p53 (Phe19, Trp23, and Leu26). Docking also predicted a new orientation of the scaffolds that more fully fills the binding cleft, enabling the inhibitors to take advantage of additional hydrogen-bonding possibilities not explored by other small molecule inhibitors. One inhibitor in particular was proposed to probe the hydrophobic core of the protein by taking advantage of the flexibility of the binding cleft floor. These results show that the MPS technique is a promising advance for structure-based drug discovery and that the method can truly explore broad chemical space efficiently in the quest to discover potent, small-molecule inhibitors of protein-protein interactions. Our MPS technique is one of very few ensemble-based techniques to be proven through experimental verification of the discovery of new inhibitors.

Protein protein interaction inhibition (2P2I) combining high throughput and virtual screening: Application to the HIV-1 Nef protein.

Abstract: Protein–protein recognition is the cornerstone of multiple cellular and pathological functions. Therefore, protein–protein interaction inhibition (2P2I) is endowed with great therapeutic potential despite the initial belief that 2P2I was refractory to small-molecule intervention. Improved knowledge of complex molecular binding surfaces has recently stimulated renewed interest for 2P2I, especially after identification of “hot spots” and first inhibitory compounds. However, the combination of target complexity and lack of starting compound has thwarted experimental results and created intellectual barriers. Here we combined virtual and experimental screening when no previously known inhibitors can be used as starting point in a structure-based research program that targets an SH3 binding surface of the HIV type I Nef protein. High-throughput docking and application of a pharmacophoric filter on one hand and search for analogy on the other hand identified drug-like compounds that were further confirmed to bind Nef in the micromolar range (isothermal titration calorimetry), to target the Nef SH3 binding surface (NMR experiments), and to efficiently compete for Nef–SH3 interactions (cell-based assay, GST pull-down). Initial identification of these compounds by virtual screening was validated by screening of the very same library of compounds in the cell-based assay, demonstrating that a significant enrichment factor was attained by the in silico screening. To our knowledge, our results identify the first set of drug-like compounds that functionally target the HIV-1 Nef SH3 binding surface and provide the basis for a powerful discovery process that should help to speed up 2P2I strategies and open avenues for new class of antiviral molecules.

Evaluations of molecular docking programs for virtual screening.

Abstract: Structure-based virtual screening is carried out using molecular docking programs. A number of such docking programs are currently available, and the selection of docking program is difficult without knowing the characteristics or performance of each program. In this study, the screening performances of three molecular docking programs, DOCK, AutoDock, and GOLD, were evaluated with 116 target proteins. The screening performances were validated using two novel standards, along with a traditional enrichment rate measurement. For the evaluations, each docking run was repeated 1000 times with three initial conformations of a ligand. While each docking program has some merit over the other docking programs in some aspects, DOCK showed an unexpectedly better screening performance in the enrichment rates. Finally, we made several recommendations based on the evaluation results to enhance the screening performances of the docking programs.

A computational model of the inhibition of Mycobacterium tuberculosis ATPase by a new drug candidate R207910

Abstract: Diarylquinolines (DARQs) are a new class of potent inhibitors of the ATPase of Mycobacterium tuberculosis. We have created a homology model of a binding site for this class of compounds located on the contact area of the a-subunit (gene atpB) and c-subunits (gene atpE) of Mycobacterium tuberculosis ATPase. The binding pocket that was identified from the analysis of the homology model is formed by 4 helices of three c-subunits and 2 helices of the a-subunit. The lead compound of the DARQ series, R207910, was docked into the pocket using a simulated annealing, multiple conformer, docking algorithm. Different stereoisomers were treated separately. The best docking pose for each stereoisomer was optimized by molecular dynamics simulation on the 5300 atoms of the binding region and ligand. The interaction energies in the computed complexes enable us to rank the different stereoisomers in order of interaction strength with the ATPase binding pockets. We propose that the activity of R207910 against Mycobacterium tuberculosis is based on interference of the compound with the escapement geometry of the proton transfer chain. Upon binding the compound mimicks the conserved Arg-186 residue of the a-subunit and interacts in its place with the conserved acidic residue Glu-61 of the c-subunit. This mode of action is corroborated by the good agreement between the computed interaction energies and the observed pattern of stereo-specificity in the model of the binding region. Proteins 2007. © 2007 Wiley-Liss, Inc.

Novel PPAR-gamma agonists identified from a natural product library: a virtual screening, induced-fit docking and biological assay study.

Abstract: Peroxisome proliferator-activated receptor-gamma (PPAR-gamma) plays an essential role in lipid and glucose homeostasis. It is recognized as the receptor of the thiazolidinediones-a synthetic class of anti-diabetic drugs-and is the target of many drug discovery efforts because of its role in disease states, such as type II diabetes mellitus. In this study, structure-based virtual screening of the PPAR-gamma ligand binding domain against a natural product library has revealed 29 potential agonists. In vitro testing of this list identified six flavonoids to have stimulated PPAR-gamma transcriptional activity in a transcriptional factor assay. Of these, flavonoid-psi-baptigenin-was classed as the most potent PPAR-gamma agonist, possessing low micromolar affinity (EC(50) = 2.9 microM). Further in vitro testing using quantitative RT-PCR and immunoblotting experiments demonstrated that psi-baptigenin activated PPAR-gamma mRNA (4.1 +/- 0.2-fold) and protein levels (2.9 +/- 0.4-fold) in THP-1 macrophages. Moreover, psi-baptigenin's-induced PPAR-gamma enhancement was abolished in the presence of a selective PPAR-gamma antagonist, GW9662. Induced-fit docking investigations provide a detailed understanding on the ligands' mechanism of action, suggesting five of the active flavonoids induce significant conformational change in the receptor upon binding. Overall, these results offer insight into various naturally derived flavonoids as leads/templates for development of novel PPAR-gamma ligands.

Identification of Nonpeptide CCR5 Receptor Agonists by Structure-based Virtual Screening

Abstract: A three-dimensional model of the chemokine receptor CCR5 has been built to fulfill structural peculiarities of its alpha-helix bundle and to distinguish known CCR5 antagonists from randomly chosen drug-like decoys. In silico screening of a library of 1.6 million commercially available compounds against the CCR5 model by sequential filters (drug-likeness, 2-D pharmacophore, 3-D docking, scaffold clustering) yielded a hit list of 59 compounds, out of which 10 exhibited a detectable binding affinity to the CCR5 receptor. Unexpectedly, most binders tested in a functional assay were shown to be agonists of the CCR5 receptor. A follow-up database query based on similarity to the most potent binders identified three new CCR5 agonists. Despite a moderate affinity of all nonpeptide ligands for the CCR5 receptor, one of the agonists was shown to promote efficient receptor internalization, which is a process therapeutically favorable for protection against HIV-1 infection.

Molecular lipophilicity in protein modeling and drug design

Abstract: Hydrophobic interactions play a key role in the folding and maintenance of the 3-dimensional structure of proteins, as well as in the binding of ligands (e.g. drugs) to protein targets. Therefore, quantitative assessment of spatial hydrophobic (lipophilic) properties of these molecules is indispensable for the development of efficient computational methods in drug design. One possible solution to the problem lies in application of a concept of the 3-dimensional molecular hydrophobicity potential (MHP). The formalism of MHP utilizes a set of atomic physicochemical parameters evaluated from octanol-water partition coefficients (log P) of numerous chemical compounds. It permits detailed assessment of the hydrophobic and/or hydrophilic properties of various parts of molecules and may be useful in analysis of protein-protein and protein-ligand interactions. This review surveys recent applications of MHP-based techniques to a number of biologically relevant tasks. Among them are: (i) Detailed assessment of hydrophobic/hydrophilic organization of proteins; (ii) Application of this data to the modeling of structure, dynamics, and function of globular and membrane proteins, membrane-active peptides, etc. (iii) Employment of the MHP-based criteria in docking simulations for ligands binding to receptors. It is demonstrated that the application of the MHP-based techniques in combination with other molecular modeling tools (e.g. Monte Carlo and molecular dynamics simulations, docking, etc.) permits significant improvement to the standard computational approaches, provides additional important insights into the intimate molecular mechanisms driving protein assembling in water and in biological membranes, and helps in the computer-aided drug discovery process.

New Pyrrole Inhibitors of Monoamine Oxidase: Synthesis, Biological Evaluation, and Structural Determinants of MAO-A and MAO-B Selectivity

Abstract: A series of new pyrrole derivatives have been synthesized and evaluated for their monoamine oxidase (MAO) A and B inhibitory activity and selectivity. N-Methyl,N-(benzyl),N-(pyrrol-2-ylmethyl)amine (7) and N-(2-benzyl),N-(1-methylpyrrol-2-ylmethyl)amine (18) were the most selective MAO-B (7, SI = 0.0057) and MAO-A (18, SI = 12500) inhibitors, respectively. Docking and molecular dynamics simulations gave structural insights into the MAO-A and MAO-B selectivity. Compound 18 forms an H-bond with Gln215 through its protonated amino group into the MAO-A binding site. This H-bond is absent in the 7/MAO-A complex. In contrast, compound 7 places its phenyl ring into an aromatic cage of the MAO-B binding pocket, where it forms charge-transfer interactions. The slightly different binding pose of 18 into the MAO-B active site seems to be forced by a bulkier Tyr residue, which replaces a smaller Ile residue present in MAO-A.

GPCR structure-based virtual screening approach for CB2 antagonist search.

Abstract: The potential for therapeutic specificity in regulating diseases has made cannabinoid (CB) receptors one of the most important G-protein-coupled receptor (GPCR) targets in search for new drugs. Considering the lack of related 3D experimental structures, we have established a structure-based virtual screening protocol to search for CB2 bioactive antagonists based on the 3D CB2 homology structure model. However, the existing homology-predicted 3D models often deviate from the native structure and therefore may incorrectly bias the in silico design. To overcome this problem, we have developed a 3D testing database query algorithm to examine the constructed 3D CB2 receptor structure model as well as the predicted binding pocket. In the present study, an antagonist-bound CB2 receptor complex model was initially generated using flexible docking simulation and then further optimized by molecular dynamic and mechanical (MD/MM) calculations. The refined 3D structural model of the CB2-ligand complex was then inspected by exploring the interactions between the receptor and ligands in order to predict the potential CB2 binding pocket for its antagonist. The ligand-receptor complex model and the predicted antagonist binding pockets were further processed and validated by FlexX-Pharm docking against a testing compound database that contains known antagonists. Furthermore, a consensus scoring (CScore) function algorithm was established to rank the binding interaction modes of a ligand on the CB2 receptor. Our results indicated that the known antagonists seeded in the testing database can be distinguished from a significant amount of randomly chosen molecules. Our studies demonstrated that the established GPCR structure-based virtual screening approach provided a new strategy with a high potential for in silico identifying novel CB2 antagonist leads based on the homology-generated 3D CB2 structure model.

Lessons in molecular recognition. 2. Assessing and improving cross-docking accuracy.

Abstract: Docking methods are used to predict the manner in which a ligand binds to a protein receptor. Many studies have assessed the success rate of programs in self-docking tests, whereby a ligand is docked into the protein structure from which it was extracted. Cross-docking, or using a protein structure from a complex containing a different ligand, provides a more realistic assessment of a docking program's ability to reproduce X-ray results. In this work, cross-docking was performed with CDocker, Fred, and Rocs using multiple X-ray structures for eight proteins (two kinases, one nuclear hormone receptor, one serine protease, two metalloproteases, and two phosphodiesterases). While average cross-docking accuracy is not encouraging, it is shown that using the protein structure from the complex that contains the bound ligand most similar to the docked ligand increases docking accuracy for all methods ("similarity selection"). Identifying the most successful protein conformer ("best selection") and similarity selection substantially reduce the difference between self-docking and average cross-docking accuracy. We identify universal predictors of docking accuracy (i.e., showing consistent behavior across most protein-method combinations), and show that models for predicting docking accuracy built using these parameters can be used to select the most appropriate docking method.

pso@autodock: a fast flexible molecular docking program based on Swarm intelligence.

Abstract: On the quest of novel therapeutics, molecular docking methods have proven to be valuable tools for screening large libraries of compounds determining the interactions of potential drugs with the target proteins. A widely used docking approach is the simulation of the docking process guided by a binding energy function. On the basis of the molecular docking program autodock, we present pso@autodock as a tool for fast flexible molecular docking. Our novel Particle Swarm Optimization (PSO) algorithms varCPSO and varCPSO-ls are suited for rapid docking of highly flexible ligands. Thus, a ligand with 23 rotatable bonds was successfully docked within as few as 100 000 computing steps (rmsd = 0.87 A), which corresponds to only 10% of the computing time demanded by autodock. In comparison to other docking techniques as gold 3.0, dock 6.0, flexx 2.2.0, autodock 3.05, and sodock, pso@autodock provides the smallest rmsd values for 12 in 37 protein-ligand complexes. The average rmsd value of 1.4 A is significantly lower then those obtained with the other docking programs, which are all above 2.0 A. Thus, pso@autodock is suggested as a highly efficient docking program in terms of speed and quality for flexible peptide-protein docking and virtual screening studies.

A de novo designed protein–protein interface

Abstract: As an approach to both explore the physical/chemical parameters that drive molecular self-assembly and to generate novel protein oligomers, we have developed a procedure to generate protein dimers from monomeric proteins using computational protein docking and amino acid sequence design. A fast Fourier transform-based docking algorithm was used to generate a model for a dimeric version of the 56-amino-acid beta1 domain of streptococcal protein G. Computational amino acid sequence design of 24 residues at the dimer interface resulted in a heterodimer comprised of 12-fold and eightfold variants of the wild-type protein. The designed proteins were expressed, purified, and characterized using analytical ultracentrifugation and heteronuclear NMR techniques. Although the measured dissociation constant was modest ( approximately 300 microM), 2D-[(1)H,(15)N]-HSQC NMR spectra of one of the designed proteins in the absence and presence of its binding partner showed clear evidence of specific dimer formation.

FLIPDock: docking flexible ligands into flexible receptors.

Abstract: Conformational changes of biological macromolecules when binding with ligands have long been observed and remain a challenge for automated docking methods. Here we present a novel protein-ligand docking software called FLIPDock (Flexible LIgand-Protein Docking) allowing the automated docking of flexible ligand molecules into active sites of flexible receptor molecules. In FLIPDock, conformational spaces of molecules are encoded using a data structure that we have developed recently called the Flexibility Tree (FT). While the FT can represent fully flexible ligands, it was initially designed as a hierarchical and multiresolution data structure for the selective encoding of conformational subspaces of large biological macromolecules. These conformational subspaces can be built to span a range of conformations important for the biological activity of a protein. A variety of motions can be combined, ranging from domains moving as rigid bodies or backbone atoms undergoing normal mode-based deformations, to side chains assuming rotameric conformations. In addition, these conformational subspaces are parameterized by a small number of variables which can be searched during the docking process, thus effectively modeling the conformational changes in a flexible receptor. FLIPDock searches the variables using genetic algorithm-based search techniques and evaluates putative docking complexes with a scoring function based on the AutoDock3.05 force-field. In this paper, we describe the concepts behind FLIPDock and the overall architecture of the program. We demonstrate FLIPDock's ability to solve docking problems in which the assumption of a rigid receptor previously prevented the successful docking of known ligands. In particular, we repeat an earlier cross docking experiment and demonstrate an increased success rate of 93.5%, compared to original 72% success rate achieved by AutoDock over the 400 cross-docking calculations. We also demonstrate FLIPDock's ability to handle conformational changes involving backbone motion by docking balanol to an adenosine-binding pocket of protein kinase A.

DrugScoreRNAKnowledge-Based Scoring Function To Predict RNA−Ligand Interactions

Abstract: There is growing interest in RNA as a drug target due to its widespread involvement in biological processes. To exploit the power of structure-based drug-design approaches, novel scoring and docking tools need to be developed that can efficiently and reliably predict binding modes and binding affinities of RNA ligands. We report for the first time the development of a knowledge-based scoring function to predict RNA−ligand interactions (DrugScoreRNA). Based on the formalism of the DrugScore approach, distance-dependent pair potentials are derived from 670 crystallographically determined nucleic acid−ligand and −protein complexes. These potentials display quantitative differences compared to those of DrugScore (derived from protein−ligand complexes) and DrugScoreCSD (derived from small-molecule crystal data). When used as an objective function for docking 31 RNA−ligand complexes, DrugScoreRNA generates “good” binding geometries (rmsd (root mean-square deviation) < 2 A) in 42% of all cases on the first scori...

Prediction and assignment of function for a divergent N -succinyl amino acid racemase

Abstract: The protein databases contain many proteins with unknown function. A computational approach for predicting ligand specificity that requires only the sequence of the unknown protein would be valuable for directing experiment-based assignment of function. We focused on a family of unknown proteins in the mechanistically diverse enolase superfamily and used two approaches to assign function: (i) enzymatic assays using libraries of potential substrates, and (ii) in silico docking of the same libraries using a homology model based on the most similar (35% sequence identity) characterized protein. The results matched closely; an experimentally determined structure confirmed the predicted structure of the substrate-liganded complex. We assigned the N-succinyl arginine/lysine racemase function to the family, correcting the annotation (L-Ala-D/L-Glu epimerase) based on the function of the most similar characterized homolog. These studies establish that ligand docking to a homology model can facilitate functional assignment of unknown proteins by restricting the identities of the possible substrates that must be experimentally tested.

Kinetics of DNA-mediated docking reactions between vesicles tethered to supported lipid bilayers

Abstract: Membrane–membrane recognition and binding are crucial in many biological processes. We report an approach to studying the dynamics of such reactions by using DNA-tethered vesicles as a general scaffold for displaying membrane components. This system was used to characterize the docking reaction between two populations of tethered vesicles that display complementary DNA. Deposition of vesicles onto a supported lipid bilayer was performed by using a microfluidic device to prevent mixing of the vesicles in bulk during sample preparation. Once tethered onto the surface, vesicles mixed via two-dimensional diffusion. DNA-mediated docking of two reacting vesicles results in their colocalization after collision and their subsequent tandem motion. Individual docking events and population kinetics were observed via epifluorescence microscopy. A lattice-diffusion simulation was implemented to extract from experimental data the probability, Pdock, that a collision leads to docking. For individual vesicles displaying small numbers of docking DNA, Pdock shows a first-order relationship with copy number as well as a strong dependence on the DNA sequence. Both trends are explained by a model that includes both tethered vesicle diffusion on the supported bilayer and docking DNA diffusion over each vesicle's surface. These results provide the basis for the application of tethered vesicles to study other membrane reactions including protein-mediated docking and fusion.