Ensemble docking into flexible active sites. Critical evaluation of FlexE against JNK-3 and beta-secretase

One of the main complicating factors in structure-based drug design is the conformational rearrangement of the receptor upon ligand binding implicating protein flexibility as a crucial component in virtual screening. The FlexE approach allows flexibility through discrete alternative conformations of varying parts of the protein taken from structures having similar backbone traces. Here the performance of FlexE was tested against that of FlexX and FlexX-Pharm, by carrying out virtual screening experiments on two sets of structurally distinct complexes, for the enzymes beta-secretase (BACE), and c-jun N-terminal kinase 3 (JNK-3). A large number of incompatible instances occurred between structural elements of the proteins thus loop movements could not be studied in JNK-3 as well as in BACE. The investigation of the side-chain flexibility revealed that at the most FlexE could achieve the enrichment yielded by FlexX in JNK-3 but not in BACE. Although limited side-chain variations (e.g. different protonation states) can be treated by FlexE, docking into protein ensembles remains a practical tool that decreases the average run time for a ligand.     

FIPSDock: A new molecular docking technique driven by fully informed swarm optimization algorithm

The accurate prediction of protein–ligand binding is of great importance for rational drug design. We present herein a novel docking algorithm called as FIPSDock, which implements a variant of the Fully Informed Particle Swarm (FIPS) optimization method and adopts the newly developed energy function of AutoDock 4.20 suite for solving flexible protein–ligand docking problems. The search ability and docking accuracy of FIPSDock were first evaluated by multiple cognate docking experiments. In a benchmarking test for 77 protein/ligand complex structures derived from GOLD benchmark set, FIPSDock has obtained a successful predicting rate of 93.5% and outperformed a few docking programs including particle swarm optimization (PSO)@AutoDock, SODOCK, AutoDock, DOCK, Glide, GOLD, FlexX, Surflex, and MolDock. More importantly, FIPSDock was evaluated against PSO@AutoDock, SODOCK, and AutoDock 4.20 suite by cross‐docking experiments of 74 protein–ligand complexes among eight protein targets (CDK2, ESR1, F2, MAPK14, MMP8, MMP13, PDE4B, and PDE5A) derived from Sutherland‐crossdock‐set. Remarkably, FIPSDock is superior to PSO@AutoDock, SODOCK, and AutoDock in seven out of eight cross‐docking experiments. The results reveal that FIPS algorithm might be more suitable than the conventional genetic algorithm‐based algorithms in dealing with highly flexible docking problems. © 2012 Wiley Periodicals, Inc.     

Sensitivity of molecular docking to induced fit effects in influenza virus neuraminidase

Many proteins undergo small side chain or even backbone movements on binding of different ligands into the same protein structure. This is known as induced fit and is potentially problematic for virtual screening of databases against protein targets. In this report we investigate the limits of the rigid protein approximation used by the docking program, GOLD, through cross-docking using protein structures of influenza neuraminidase. Neuraminidase is known to exhibit small but significant induced fit effects on ligand binding. Some neuraminidase crystal structures caused concern due to the bound ligand conformation and GOLD performed poorly on these complexes. A `clean' set, which contained unique, unambiguous complexes, was defined. For this set, the lowest energy structure was correctly docked (i.e. RMSD < 1.5 Å away from the crystal reference structure) in 84% of proteins, and the most promiscuous protein (1mwe) was able to dock all 15 ligands accurately including those that normally required an induced fit movement. This is considerably better than the 70% success rate seen with GOLD against general validation sets. Inclusion of specific water molecules involved in water-mediated hydrogen bonds did not significantly improve the docking performance for ligands that formed water-mediated contacts but it did prevent docking of ligands that displaced these waters. Our data supports the use of a single protein structure for virtual screening with GOLD in some applications involving induced fit effects, although care must be taken to identify the protein structure that performs best against a wide variety of ligands. The performance of GOLD was significantly better than the GOLD implementation of ChemScore and the reasons for this are discussed. Overall, GOLD has shown itself to be an extremely good, robust docking program for this system.     

Flexible docking under pharmacophore type constraints

FlexX-Pharm, an extended version of the flexible docking tool FlexX, allows the incorporation of information about important characteristics of protein-ligand binding modes into a docking calculation. This information is introduced as a simple set of constraints derived from receptor-based type pharmacophore features.The constraints are determined by selected FlexX interactions and inclusion volumes in the receptor active site. They guide the docking process to produce a set of docking solutions with particular properties. By applying a series of look-ahead checks during the flexible construction of ligand fragments within the active site, FlexX-Pharm determines which partially built docking solutions can potentially obey the constraints. Solutions that will not obey the constraints are deleted as early as possible, often decreasing the calculation time and enabling new docking solutions to emerge. FlexX-Pharm was evaluated on various individual protein-ligand complexes where the top docking solutions generated by FlexX had high root mean square deviations (RMSD) from the experimentally observed binding modes. FlexX-Pharm showed an improvement in the RMSD of the top solutions in most cases, along with a reduction in run time. We also tested FlexX-Pharm as a database screening tool on a small dataset of molecules for three target proteins. In two cases, FlexX-Pharm missed one or two of the active molecules due to the constraints selected. However, in general FlexX-Pharm maintained or improved the enrichment shown with FlexX, while completing the screen in considerably less run time.     

A_1腺苷受体的同源模建及其结构验证

采用同源模建的方法构建了A1腺苷受体的三维结构,并与拮抗剂分子DPCPX对接,将得到的复合物结构进行5 ns的分子动力学模拟,以最后2 ns的平均结构和平衡后抽取的11帧构象共12个蛋白结构为研究对象,用包含52个活性分子和1000个诱饵分子的测试库,分别通过DOCK、VINA和GOLD三种对接软件进行评价,最终得出合理的蛋白质模型.根据top10%的富集因子(EF)和ROC曲线下面积(AU-ROC)的计算结果,我们认为GOLD是最适合A1腺苷受体的对接软件,而12个蛋白质结构中F5和Favg的三维结构模型比较合理,可以作为进一步大规模虚拟筛选的模型.     

Molecular docking of intercalators and groove-binders to nucleic acids using Autodock and Surflex

The molecular docking tools Autodock and Surflex accurately reproduce the crystallographic structures of a collection of small molecule ligands that have been shown to bind nucleic acids. Docking studies were performed with the intercalators daunorubicin and ellipticine and the minor groove binders distamycin and pentamidine. Autodock and Surflex dock daunorubicin and distamycin to their nucleic acid targets within a resolution of approximately 2 A, which is similar to the limit of the crystal structure resolution. However, for the top ranked poses, Autodock and Surflex both dock ellipticine into the correct site but in a different orientation compared to the crystal structure. This appears not only to be partly related to the symmetry of the target nucleic acid, as ellipticine is able to dock from either side of the intercalation site, but also due to the shape of the ligand and docking accuracy. Surflex docks pentamidine in a symmetrically equivalent orientation relative to the crystal structure, while Autodock was able to dock this molecule in the original orientation. In the case of the Surflex docking of pentamidine, the initial rmsd is misleading, given the symmetrical structure of pentamidine. Importantly, the ranking functions of both of these programs are able to return a top pose within approximately 2 A rmsd for daunorubicin, distamycin, and pentamidine and approximately 3 A rmsd for ellipticine compared to their respective crystal structures. Some docking challenges and potential pitfalls are explored, such as the importance of hydrogen treatment on ligands as well as the scoring functions of Autodock and Surflex. Overall for this set of complexes, Surflex is preferred over Autodock for virtual screening, as although the results are comparable, Surflex has significantly faster performance and ease of use under the optimal software conditions tested. These experiments show that molecular docking techniques can be successfully extended to include nucleic acid targets, a finding which has important implications for virtual screening applications and in the design of new small molecules to target therapeutically relevant morphologies of nucleic acids.     

Ranking targets in structure-based virtual screening of three-dimensional protein libraries: methods and problems

Structure-based virtual screening is a promising tool to identify putative targets for a specific ligand. Instead of docking multiple ligands into a single protein cavity, a single ligand is docked in a collection of binding sites. In inverse screening, hits are in fact targets which have been prioritized within the pool of best ranked proteins. The target rate depends on specificity and promiscuity in protein-ligand interactions and, to a considerable extent, on the effectiveness of the scoring function, which still is the Achilles' heel of molecular docking. In the present retrospective study, virtual screening of the sc-PDB target library by GOLD docking was carried out for four compounds (biotin, 4-hydroxy-tamoxifen, 6-hydroxy-1,6-dihydropurine ribonucleoside, and methotrexate) of known sc-PDB targets and, several ranking protocols based on GOLD fitness score and topological molecular interaction fingerprint (IFP) comparison were evaluated. For the four investigated ligands, the fusion of GOLD fitness and two IFP scores allowed the recovery of most targets, including the rare proteins which are not readily suitable for statistical analysis, while significantly filtering out most false positive entries. The current survey suggests that selecting a small number of targets (<20) for experimental evaluation is achievable with a pure structure-based approach.     

Comparative study of several algorithms for flexible ligand docking

We have performed a comparative assessment of several programs for flexible molecular docking: DOCK 4.0, FlexX 1.8, AutoDock 3.0, GOLD 1.2 and ICM 2.8. This was accomplished using two different studies: docking experiments on a data set of 37 protein-ligand complexes and screening a library containing 10,037 entries against 11 different proteins. The docking accuracy of the methods was judged based on the corresponding rank-one solutions. We have found that the fraction of molecules docked with acceptable accuracy is 0.47, 0.31, 0.35, 0.52 and 0.93 for, respectively, AutoDock, DOCK, FlexX, GOLD and ICM. Thus ICM provided the highest accuracy in ligand docking against these receptors. The results from the other programs are found to be less accurate and of approximately the same quality. A speed comparison demonstrated that FlexX was the fastest and AutoDock was the slowest among the tested docking programs. The database screening was performed using DOCK, FlexX and ICM. ICM was able to identify the original ligands within the top 1% of the total library in 17 cases. The corresponding number for DOCK and FlexX was 7 and 8, respectively. We have estimated that in virtual database screening, 50% of the potentially active compounds will be found among approximately 1.5% of the top scoring solutions found with ICM and among approximately 9% of the top scoring solutions produced by DOCK and FlexX.     

Evaluation of the performance of four molecular docking programs on a diverse set of protein‐ligand complexes

Many molecular docking programs are available nowadays, and thus it is of great practical value to evaluate and compare their performance. We have conducted an extensive evaluation of four popular commercial molecular docking programs, including Glide, GOLD, LigandFit, and Surflex. Our test set consists of 195 protein‐ligand complexes with high‐resolution crystal structures (resolution ≤2.5 Å) and reliable binding data [dissociation constant (K_d) or inhibition constant (K_i)], which are selected from the PDBbind database with an emphasis on diversity. The top‐ranked solutions produced by these programs are compared to the native ligand binding poses observed in crystal structures. Glide and GOLD demonstrate better accuracy than the other two on the entire test set. Their results are also less sensitive to the starting structures for docking. Comparison of the results produced by these programs at three different computation levels reveal that their accuracy are not always proportional to CPU cost as one may expect. The binding scores of the top‐ranked solutions produced by these programs are in low to moderate correlations with experimentally measured binding data. Further analyses on the outcomes of these programs on three suites of subsets of protein‐ligand complexes indicate that these programs are less capable to handle really flexible ligands and relatively flat binding sites, and they have different preferences to hydrophilic/hydrophobic binding sites. Our evaluation can help other researchers to make reasonable choices among available molecular docking programs. It is also valuable for program developers to improve their methods further. © 2010 Wiley Periodicals, Inc. J Comput Chem, 2010     

An ultrafast time-resolved fluorescence spectroscopy system for metal ion complexation studies with organic ligands

A dedicated spectrofluorimeter using ultrashort laser pulses as an excitation source was developed to measure the fluorescence properties of organic ligands for metal ion complexation with organic ligands. The laser system consists of an oscillator system for generation of femtosecond laser pulses, an amplifier system to increase the pulse energy of the generated pulses to about 2 mJ and an optical parametrical amplifier system to provide tunable laser pulses over a wide wavelength range (280 nm-10 microm). The laser pulses were applied to the sample and the emitted fluorescence was detected using a fast-gating intensified CCD camera-based spectrometer. To verify the performance of the laser, the well-known protonation constant [Pure Appl. Chem. 69 (1997) 329] of 2,3-dihydroxybenzoic acid was determined. The fluorescence lifetime of the excited species was determined as 375+/-32 ps in the pH range from 1.0 to 6.0, having a fluorescence emission maximum at 438 nm. The first protonation constant was determined from fluorescence data as log K(3)=3.17+/-0.05 at an ionic strength of 0.1 M and at 294 K exploiting the Stern-Volmer mechanism. The agreement of the protonation constant with literature data (log K(3)=3.10+/-0.20, I=0.1 M, T=298 K [Bull. Soc. Jpn. 44 (1971) 3459]) demonstrates the excellent performance of our system. Furthermore, we determined the complex formation constant log K(1)=-3.11+/-0.16 by measuring the fluorescence properties of the ligand for the 1:1 uranyldihydroxobenzoate complex in the pH range from 3.0 to 4.5 at ionic strength of 0.1 M and at 294 K. We also determined the complex formation constant via the fluorescence emission of the metal ion uranium(VI). The fluorescence of the uranyl ion is influenced by dynamic quenching of the non-dissociated ligand and by static quenching due to the complex formation. After correction of these effects using the determined fluorescence lifetime, the complex formation constant was calculated to be log K(1)=-3.99+/-0.44. A 1:1 metal:ligand stoichiometry was determined with both measurement methods. However, the difference of the obtained formation constants and the derived standard deviations indicate a superimposition of effects with the excited-state reactions of the ligand.     

Binding of polyanions by biogenic amines. II. Formation and stability of protonated putrescine and cadaverine complexes with carboxylic ligands

The formation and stability of protonated diamines-carboxylic ligand complexes was studied potentiometrically (H(+)-glass electrode). Species formed are ALH(r) (A=cadaverine, putrescine, L=acetate, malate, tartrate, malonate, citrate, 1,2,3-propanetricarboxylate, 1,2,3,4-butanetetracarboxylate and glutamate; r=1...m+1, where m is the maximum degree of protonation of the carboxylic ligand), and their stability is a function of charges involved in the formation reaction. For the equilibrium H(i)A(i+)+H(j)L((j-z))=ALH((i+j-z))(i+j) the following linear relationships can be written: logK(1j)=-0.25+0.75 |j-z|, logK(2j)=0.50+0.90 |j-z| (by also considering some ethylenediamine and 1,2-diaminopropane complexes). Medium effects were considered. Comparison was made with analogous inorganic polyanion complexes. The simplest relationships -DeltaG(0)=6.5+/-0.3 and -DeltaG(0)=7.9+/-0.6 kJ mol(-1)n(-1) (n=number of possible salt bridges) were found for carboxylic and inorganic anions, respectively.     

The effect of pyridinecarboxylate chelating groups on the stability and electronic relaxation of gadolinium complexes

The ligand N,N'-bis[(6-carboxy-2-pyridylmethyl]ethylenediamine-N,N'-diacetic acid (H(4)bpeda) was synthesised using an improved procedure which requires a reduced number of steps and leads to a higher yield with respect to the published procedure. It was obtained in three steps from diethylpyridine-2,6-dicarboxylate and commercially available ethylenediamine-N,N[prime or minute]-diacetic acid with a total yield of approximately 20%. The crystal structure of the hexa-protonated form of the ligand which was determined by X-ray diffraction shows that the four carboxylates and the two amines are protonated. The crystal structure of the polynuclear complex [Gd(bpeda)(H(2)O)(2)](3)[Gd(H(2)O)(6)](2)Cl(3)(2), isolated by slow evaporation of a 1:1 mixture of GdCl(3) and H(4)bpeda at pH approximately 1, was determined by X-ray diffraction. In complex three [Gd(bpeda)(H(2)O)(2)] units, containing a Gd(III) ion ten-coordinated by the octadentate bpeda and two water molecules, are connected in a pentametallic structure by two hexa-aquo Gd(3+) cations through four carboxylato bridges. The protonation constants (pK(a1)= 2.9(1), pK(a2)= 3.5(1), pK(a3)= 5.2(2), and pK(a4)= 8.5(1)) and the stability constants of the complexes formed between Gd(III) and Ca(II) ions and H(4)bpeda (log beta(GdL)= 15.1(3); log beta(CaL)= 9.4(1)) were determined by potentiometric titration. The unexpected decrease in the stability of the gadolinium complex and of the calcium complex of the octadentate ligand bpeda(4-) with respect to the hexadentate ligand edta(4-) has been interpreted in terms of an overall lower contribution to stability of the metal-nitrogen interactions. The EPR spectra display very broad lines (apparent DeltaH(pp) approximately 800-1200 G at X-band and 90-110 G at Q-band depending on the temperature), indicating a rapid transverse electron spin relaxation. At X-band, Gd(bpeda) is among the fastest relaxing Gd(3+) complexes to date suggesting that the presence of pyridinecarboxylate chelating groups in itself does not lead to slow electron relaxation.     

Comparative performance of several flexible docking programs and scoring functions: enrichment studies for a diverse set of pharmaceutically relevant targets

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.     

Receptor-based virtual ligand screening for the identification of novel CDC25 phosphatase inhibitors

CDC25 phosphatases play critical roles in cell cycle regulation and are attractive targets for anticancer therapies. Several small non-peptide molecules are known to inhibit CDC25, but many of them appear to form a covalent bond with the enzyme or act through oxidation of the thiolate group of the catalytic cysteine. Structure-based virtual ligand screening computations were performed with FRED, Surflex, and LigandFit, a compound collection of over 310,000 druglike molecules and the crystal structure of CDC25B in order to identify novel classes of ligands. In vitro experiments carried out on a selected list of 1500 molecules led to the discovery of 99 compounds able to inhibit CDC25B activity at 100 microM. Further docking computations were applied, allowing us to propose a binding mode for the most potent molecule (IC50 = 13 microM). Our best compounds represent promising new classes of CDC25 inhibitors that also exhibit antiproliferative properties.     

Ligand versus metal protonation of an iron hydrogenase active site mimic

The protonation behavior of the iron hydrogenase active-site mimic [Fe2(mu-adt)(CO)4(PMe3)2] (1; adt=N-benzyl-azadithiolate) has been investigated by spectroscopic, electrochemical, and computational methods. The combination of an adt bridge and electron-donating phosphine ligands allows protonation of either the adt nitrogen to give [Fe2(mu-Hadt)(CO)4(PMe3)2]+ ([1 H]+), the Fe-Fe bond to give [Fe2(mu-adt)(mu-H)(CO)4(PMe3)2]+ ([1 Hy]+), or both sites simultaneously to give [Fe2(mu-Hadt)(mu-H)(CO)4(PMe3)2]2+ ([1 HHy]2 +). Complex 1 and its protonation products have been characterized in acetonitrile solution by IR, (1)H, and (31)P NMR spectroscopy. The solution structures of all protonation states feature a basal/basal orientation of the phosphine ligands, which contrasts with the basal/apical structure of 1 in the solid state. Density functional calculations have been performed on all protonation states and a comparison between calculated and experimental spectra confirms the structural assignments. The ligand protonated complex [1 H]+ (pKa=12) is the initial, metastable protonation product while the hydride [1 Hy]+ (pKa=15) is the thermodynamically stable singly protonated form. Tautomerization of cation [1 H]+ to [1 Hy]+ does not occur spontaneously. However, it can be catalyzed by HCl (k=2.2 m(-1) s(-1)), which results in the selective formation of cation [1 Hy]+. The protonations of the two basic sites have strong mutual effects on their basicities such that the hydride (pK(a)=8) and the ammonium proton (pK(a)=5) of the doubly protonated cationic complex [1 HHy]2+ are considerably more acidic than in the singly protonated analogues. The formation of dication [1 HHy]2+ from cation [1 H]+ is exceptionally slow with perchloric or trifluoromethanesulfonic acid (k=0.15 m(-1) s(-1)), while the dication is formed substantially faster (k>10(2) m(-1) s(-1)) with hydrobromic acid. Electrochemically, 1 undergoes irreversible reduction at -2.2 V versus ferrocene, and this potential shifts to -1.6, -1.1, and -1.0 V for the cationic complexes [1 H]+, [1 Hy]+, and [1 HHy]2+, respectively, upon protonation. The doubly protonated form [1 HHy]2+ is reduced at less negative potential than all previously reported hydrogenase models, although catalytic proton reduction at this potential is characterized by slow turnover.     

VoteDock: Consensus docking method for prediction of protein–ligand interactions

Molecular recognition plays a fundamental role in all biological processes, and that is why great efforts have been made to understand and predict protein–ligand interactions. Finding a molecule that can potentially bind to a target protein is particularly essential in drug discovery and still remains an expensive and time‐consuming task. In silico, tools are frequently used to screen molecular libraries to identify new lead compounds, and if protein structure is known, various protein–ligand docking programs can be used. The aim of docking procedure is to predict correct poses of ligand in the binding site of the protein as well as to score them according to the strength of interaction in a reasonable time frame. The purpose of our studies was to present the novel consensus approach to predict both protein–ligand complex structure and its corresponding binding affinity. Our method used as the input the results from seven docking programs (Surflex, LigandFit, Glide, GOLD, FlexX, eHiTS, and AutoDock) that are widely used for docking of ligands. We evaluated it on the extensive benchmark dataset of 1300 protein–ligands pairs from refined PDBbind database for which the structural and affinity data was available. We compared independently its ability of proper scoring and posing to the previously proposed methods. In most cases, our method is able to dock properly approximately 20% of pairs more than docking methods on average, and over 10% of pairs more than the best single program. The RMSD value of the predicted complex conformation versus its native one is reduced by a factor of 0.5 Å. Finally, we were able to increase the Pearson correlation of the predicted binding affinity in comparison with the experimental value up to 0.5. © 2010 Wiley Periodicals, Inc. J Comput Chem 32: 568–581, 2011     

Synthesis, structure, and acid-base and redox properties of a family of new Ru(II) isomeric complexes containing the trpy and the dinucleating Hbpp ligands

Three pairs of mononuclear geometrical isomers containing the ligand 3,5-bis(2-pyridyl)pyrazole (Hbpp) of general formula in- and out-[RuII(Hbpp)(trpy)X](n+) (trpy=2,2':6',2' '-terpyridine; X=Cl, n=1, 2a,b; X=H2O, n=2, 3a,b; X=py (pyridine), n=2, 4a,b) have been prepared through two different synthetic routes, isolated, and structurally characterized. The solid state structural characterization was performed by X-ray diffraction analysis of four complexes: 2a-4a and 4b. The structural characterization in solution was performed by means of 1D and 2D NMR spectroscopy for complexes 2a,b and 4a,b and coincides with the structures found in the solid state. All complexes were also spectroscopically characterized by UV-vis which also allowed us to carry out spectrophotometric acid-base titrations. Thus, a number of species were spectroscopically characterized with the same oxidation state but with a different degree of protonation. As an example, for 3a three pKa values were obtained: pKa1(RuII)=2.13, pKa2(RuII)=6.88, and pKa3(RuII)=11.09. The redox properties were also studied, giving in all cases a number of electron transfers coupled to proton transfers. The pH dependency of the redox potentials allowed us to calculate the pKa of the complexes in the Ru(III) oxidation state. For complex 3a, these were found to be pKa1(RuIII)=0.01, pKa2(RuIII)=2.78, and pKa3(RuIII)=5.43. The oxidation state Ru(IV) was only reached from the Ru-OH2 type of complexes 3a or 3b. It has also been shown that the RuIV=O species derived from 3a is capable of electrocatalytically oxidizing benzyl alcohol with a second-order rate constant of kcat=17.1 M(-1) s(-1).     

Can we trust docking results? Evaluation of seven commonly used programs on PDBbind database

Docking is one of the most commonly used techniques in drug design. It is used for both identifying correct poses of a ligand in the binding site of a protein as well as for the estimation of the strength of protein–ligand interaction. Because millions of compounds must be screened, before a suitable target for biological testing can be identified, all calculations should be done in a reasonable time frame. Thus, all programs currently in use exploit empirically based algorithms, avoiding systematic search of the conformational space. Similarly, the scoring is done using simple equations, which makes it possible to speed up the entire process. Therefore, docking results have to be verified by subsequent in vitro studies. The purpose of our work was to evaluate seven popular docking programs (Surflex, LigandFit, Glide, GOLD, FlexX, eHiTS, and AutoDock) on the extensive dataset composed of 1300 protein–ligands complexes from PDBbind 2007 database, where experimentally measured binding affinity values were also available. We compared independently the ability of proper posing [according to Root mean square deviation (or Root mean square distance) of predicted conformations versus the corresponding native one] and scoring (by calculating the correlation between docking score and ligand binding strength). To our knowledge, it is the first large‐scale docking evaluation that covers both aspects of docking programs, that is, predicting ligand conformation and calculating the strength of its binding. More than 1000 protein–ligand pairs cover a wide range of different protein families and inhibitor classes. Our results clearly showed that the ligand binding conformation could be identified in most cases by using the existing software, yet we still observed the lack of universal scoring function for all types of molecules and protein families. © 2010 Wiley Periodicals, Inc. J Comput Chem, 2011     

Surflex-Dock 2.1: Robust performance from ligand energetic modeling,ring flexibility,and knowledge-based search

The Surflex flexible molecular docking method has been generalized and extended in two primary areas related to the search component of docking. First, incorporation of a small-molecule force-field extends the search into Cartesian coordinates constrained by internal ligand energetics. Whereas previous versions searched only the alignment and acyclic torsional space of the ligand, the new approach supports dynamic ring flexibility and all-atom optimization of docked ligand poses. Second, knowledge of well established molecular interactions between ligand fragments and a target protein can be directly exploited to guide the search process. This offers advantages in some cases over the search strategy where ligand alignment is guided solely by a “protomol” (a pre-computed molecular representation of an idealized ligand). Results are presented on both docking accuracy and screening utility using multiple publicly available benchmark data sets that place Surflex’s performance in the context of other molecular docking methods. In terms of docking accuracy, Surflex-Dock 2.1 performs as well as the best available methods. In the area of screening utility, Surflex’s performance is extremely robust, and it is clearly superior to other methods within the set of cases for which comparative data are available, with roughly double the screening enrichment performance.     

Sequestering ability of polycarboxylic ligands towards dioxouranium(VI)

In this paper we report a comparison on the sequestering ability of some polycarboxylic ligands towards dioxouranium(VI) (UO(2)(2+), uranyl). Ligands taken into account are mono- (acetate), di- (oxalate, malonate, succinate and azelate), tri- (1,2,3-propanetricarboxylate) and hexa-carboxylate (1,2,3,4,5,6-benzenehexacarboxylate). The sequestering ability of polycarboxylic ligands towards UO(2)(2+) was quantified by a new approach expressed by means of a sigmoid Boltzman type equation and of a empirical parameters (pL(50)) which defines the amount of ligand necessary to sequester 50% of the total UO(2)(2+) concentration. A fairly linear correlation was obtained between pL(50) or log K(110) (log K(110) refers to the equilibrium: UO(2)(2+)+L(z-)=UO(2)L((2-z)); L=generic ligand) and the polyanion charges. In order to complete the picture, a tetra-carboxylate ligand (1,2,3,4-butanetetracarboxylate) was studied in NaCl aqueous solutions at 0相似文献