A scoring function for docking ligands to low-resolution protein structures

We present a docking method that uses a scoring function for protein-ligand docking that is designed to maximize the docking success rate for low-resolution protein structures. We find that the resulting scoring function parameters are very different depending on whether they were optimized for high- or low-resolution protein structures. We show that this docking method can be successfully applied to predict the ligand-binding site of low-resolution structures. For a set of 25 protein-ligand complexes, in 76% of the cases, more than 50% of ligand-contacting residues are correctly predicted (using receptor crystal structures where the binding site is unspecified). Using decoys of the receptor structures having a 4 A RMSD from the native structure, for the same set of complexes, in 72% of the cases, we obtain at least one correctly predicted ligand-contacting residue. Furthermore, using an 81-protein-ligand set described by Jain, in 76 (93.8%) cases, the algorithm correctly predicts more than 50% of the ligand-contacting residues when native protein structures are used. Using 3 A RMSD from native decoys, in all but two cases (97.5%), the algorithm predicts at least one ligand-binding residue correctly. Finally, compared to the previously published Dolores method, for 298 protein-ligand pairs, the number of cases in which at least half of the specific contacts are correctly predicted is more than four times greater.     

RMSD and Symmetry

A common approach for comparing the structures of biomolecules or solid bodies is to translate and rotate one structure with respect to the other to minimize the pointwise root-mean-square deviation (RMSD). We present a new, robust numerical algorithm that computes the RMSD between two molecules or all the mutual RMSDs of a list of molecules and, if desired, the corresponding rotation matrix in a minimal number of operations as compared to previous algorithms. The RMSD gradient can also be computed. We address the problem of symmetry, both in alignment (possible alternative alignments due to indistinguishable atoms) as well as geometry. In the latter case, it is possible to have degenerate superposition. A necessary condition is optimal superimposability to one's mirror image. Double (respectively, triple) degeneracy results in a one- (respectively, two)-parameter family of rotations leaving the superposition invariant. The software, frmsd , is freely available at http://www.ams.stonybrook.edu/~coutsias/codes/frmsd.tgz . © 2019 Wiley Periodicals, Inc.     

Replica exchange Monte-Carlo simulations of helix bundle membrane proteins: rotational parameters of helices

We propose a united-residue model of membrane proteins to investigate the structures of helix bundle membrane proteins (HBMPs) using coarse-grained (CG) replica exchange Monte-Carlo (REMC) simulations. To demonstrate the method, it is used to identify the ground state of HBMPs in a CG model, including bacteriorhodopsin (BR), halorhodopsin (HR), and their subdomains. The rotational parameters of transmembrane helices (TMHs) are extracted directly from the simulations, which can be compared with their experimental measurements from site-directed dichroism. In particular, the effects of amphiphilic interaction among the surfaces of TMHs on the rotational angles of helices are discussed. The proposed CG model gives a reasonably good structure prediction of HBMPs, as well as a clear physical picture for the packing, tilting, orientation, and rotation of TMHs. The root mean square deviation (RMSD) in coordinates of C_α atoms of the ground state CG structure from the X-ray structure is 5.03 Å for BR and 6.70 Å for HR. The final structure of HBMPs is obtained from the all-atom molecular dynamics simulations by refining the predicted CG structure, whose RMSD is 4.38 Å for BR and 5.70 Å for HR.     

Protein side chain modeling with orientation-dependent atomic force fields derived by series expansions

We describe the development of new force fields for protein side chain modeling called optimized side chain atomic energy (OSCAR). The distance‐dependent energy functions (OSCAR‐d) and side‐chain dihedral angle potential energy functions were represented as power and Fourier series, respectively. The resulting 802 adjustable parameters were optimized by discriminating the native side chain conformations from non‐native conformations, using a training set of 12,000 side chains for each residue type. In the course of optimization, for every residue, its side chain was replaced by varying rotamers, whereas conformations for all other residues were kept as they appeared in the crystal structure. Then, the OSCAR‐d were multiplied by an orientation‐dependent function to yield OSCAR‐o. A total of 1087 parameters of the orientation‐dependent energy functions (OSCAR‐o) were optimized by maximizing the energy gap between the native conformation and subrotamers calculated as low energy by OSCAR‐d. When OSCAR‐o with optimized parameters were used to model side chain conformations simultaneously for 218 recently released protein structures, the prediction accuracies were 88.8% for χ₁, 79.7% for χ_{1 + 2}, 1.24 Å overall root mean square deviation (RMSD), and 0.62 Å RMSD for core residues, respectively, compared with the next‐best performing side‐chain modeling program which achieved 86.6% for χ₁, 75.7% for χ_{1 + 2}, 1.40 Å overall RMSD, and 0.86 Å RMSD for core residues, respectively. The continuous energy functions obtained in this study are suitable for gradient‐based optimization techniques for protein structure refinement. A program with built‐in OSCAR for protein side chain prediction is available for download at http://sysimm.ifrec.osaka‐u.ac.jp/OSCAR/ . © 2011 Wiley Periodicals, Inc. J Comput Chem 2011     

ICFF: a new method to incorporate implicit flexibility into an internal coordinate force field

We introduce a new method to accurately "project" a Cartesian force field onto an internal coordinate molecular model with fixed-bond geometry. The algorithm automatically generates the Internal Coordinate Force Field (ICFF), which is a close approximation of the "source" Cartesian force field. The ICFF method reduces the number of free variables in a model by at least 10-fold and facilitates the fast convergence of geometry optimizations, an advantage that is critical for many applications such as the docking of flexible ligands or conformational modeling of macromolecules. Although covalent geometry is fixed in an ICFF model, implicit flexibility is incorporated into the force field parameters in the following two ways. First, we formulate an empirical torsion energy term in ICFF as a sixfold Fourier series and develop a procedure to calculate the Fourier coefficients from the conformational energy profiles of the fully flexible Cartesian model. The ICFF torsion parameters thus represent not only torsion component of the source force field, but also bond bending, bond stretching, and "1-4" van der Waals interactions. Second, we use a soft polynomial repulsion function for "1-5" and "1-6" interactions to mimic the flexibility of bonds, connecting these atoms. Also, we suggest a way to use a local part of the Cartesian force field to automatically generate fixed covalent geometries, compatible with the ICFF energy function. Here, we present an implementation of the ICFF algorithm, which employs the MMFF94s Cartesian force field as a "source." Extensive benchmarking of ICFF with a representative set of organic molecules demonstrates that the implicit flexibility model accurately reproduces MMFF94s equilibrium conformational energy differences (RMSD approximately 0.64 kcal) and, most importantly, detailed torsion energy profiles (RMSD approximately 0.37 kcal). This accuracy is characteristic of the method, because all the ICFF parameters (except one scaling factor in the "1-5,1-6" repulsion term) are derived directly from the source Cartesian force field and do not depend on any particular molecular set. In contrast, the rigid geometry model with the MMFF94s energy function yields highly biased estimations in this test with the RMSD exceeding 1.2 kcal for the equilibrium energy comparisons and approximately 3.4 kcal for the torsion energy profiles.     

Development and evaluation of a generic evolutionary method for protein-ligand docking

We have developed a generic evolutionary method with an empirical scoring function for the protein-ligand docking, which is a problem of paramount importance in structure-based drug design. This approach, referred to as the GEMDOCK (Generic Evolutionary Method for molecular DOCKing), combines both continuous and discrete search mechanisms. We tested our approach on seven protein-ligand complexes, and the docked lowest energy structures have root-mean-square derivations ranging from 0.32 to 0.99 A with respect to the corresponding crystal ligand structures. In addition, we evaluated GEMDOCK on crossdocking experiments, in which some complexes with an identical protein used for docking all crystallized ligands of these complexes. GEMDOCK yielded 98% docked structures with RMSD below 2.0 A when the ligands were docked into foreign protein structures. We have reported the validation and analysis of our approach on various search spaces and scoring functions. Experimental results show that our approach is robust, and the empirical scoring function is simple and fast to recognize compounds. We found that if GEMDOCK used the RMSD scoring function, then the prediction accuracy was 100% and the docked structures had RMSD below 0.1 A for each test system. These results suggest that GEMDOCK is a useful tool, and may systematically improve the forms and parameters of a scoring function, which is one of major bottlenecks for molecular recognition.     

蛋白质折叠类型的分类建模与识别

蛋白质的氨基酸序列如何决定空间结构是当今生命科学研究中的核心问题之一. 折叠类型反映了蛋白质核心结构的拓扑模式, 折叠识别是蛋白质序列-结构研究的重要内容. 我们以占Astral 1.65序列数据库中α, β和α/β三类蛋白质总量41.8%的36个无法独立建模的折叠类型为研究对象, 选取其中序列一致性小于25%的样本作为训练集, 以均方根偏差(RMSD)为指标分别进行系统聚类, 生成若干折叠子类, 并对各子类建立基于多结构比对算法(MUSTANG)结构比对的概形隐马尔科夫模型(profile-HMM). 将Astral 1.65中序列一致性小于95%的9505个样本作为检验集, 36个折叠类型的平均识别敏感性为90%, 特异性为99%, 马修斯相关系数(MCC)为0.95. 结果表明: 对于成员较多, 无法建立统一模型的折叠类型, 基于RMSD的系统分类建模均可实现较高准确率的识别, 为蛋白质折叠识别拓展了新的方法和思路, 为进一步研究奠定了基础.     

Accurate and efficient generalized born model based on solvent accessibility: derivation and application for LogP octanol/water prediction and flexible peptide docking

A novel method for fast and accurate evaluation of the generalized Born radii in macromolecular solvation electrostatics calculations is proposed, based on the solvent accessibility of the first two solvation layers around an atom. The reverse generalized Born radii calculated by the method have correlation coefficient of 98.7% and RMSD of 0.031 A(-1) with the values obtained using a precise but significantly slower numerical boundary element solution. The method is applied to derive an estimate of the free solvation energy difference between octanol and water and to predict LogP octanol-water. A nine-parameter model is optimized on an 81 compound training set and applied to predict LogP(ow) for an external evaluation set of 19 drug molecules with RMSD of 0.9. The new GB approximation is also tested in Monte Carlo docking simulations of the fully flexible p53 peptide fragment to MDM2. The best energy solution found in the simulations has RMSD of 2.8 A to the X-ray structure.     

Polymorph specific RMSD local order parameters for molecular crystals and nuclei: α-, β-, and γ-glycine

Crystal nucleation is important for many processes including pharmaceutical crystallization, biomineralization, and material synthesis. The progression of structural changes which occur during crystal nucleation are often described using order parameters. Polymorph specific order parameters have been developed for crystallization of spherically symmetric particles; however, polymorph specific order parameters for molecular crystals remain a challenge. We introduce template based polymorph specific order parameters for molecular crystals. For each molecule in a simulation, we compute the root mean squared deviation (RMSD) between the local environment around the molecule and a template of the perfect crystal structure for each polymorph. The RMSD order parameters can clearly distinguish the α-, β-, and γ-glycine polymorph crystal structures in the bulk crystal and also in solvated crystallites. Surface melting of glycine crystallites in supersaturated aqueous solution is explored using the newly developed order parameters. The solvated α-glycine crystallite has a thinner surface melted layer than the γ-glycine crystallite. α-glycine forms first out of aqueous solution, so surface melted layer thickness may provide insight into interfacial energy and polymorph selection.     

Cooperative hydrogen bonding effects are key determinants of backbone amide proton chemical shifts in proteins

A computational methodology for backbone amide proton chemical shift (delta(H)) predictions based on ab initio quantum mechanical treatment of part of the protein is presented. The method is used to predict and interpret 13 delta(H) values in protein G and ubiquitin. The predicted amide-amide delta(H) values are within 0.6 ppm of experiment, with a root-mean-square deviation (RMSD) of 0.3 ppm. We show that while the hydrogen bond geometry is the most important delta(H)-determinant, longer-range cooperative effects of extended hydrogen networks make significant contributions to delta(H). We present a simple model that accurately relates the protein structure to delta(H).     

An alternative method for the evaluation of docking performance: RSR vs RMSD

A new assessment criterion for docking poses is proposed in which experimental electron density is taken into account when evaluating the ability of docking programs to reproduce experimentally observed binding modes. Three docking programs (Gold, Glide, and Fred) were used to generate poses for a set of 88 protein-ligand complexes for which the crystal structure is known. The new criterion is based on the real space R-factor (RSR), which measures how well a group of atoms-in our case the ligand-fits the experimental electron density by comparing that density to the expected density, calculated from the model (i.e., the predicted ligand pose). The RSR-based measure is compared to the traditional criterion, the root-mean-square distance (RMSD) between the docking pose and the binding configuration in the crystallographic model. The results highlight several shortcomings of the RMSD criterion that do not affect the RSR-based measure. Examples illustrate that the RSR-derived approach allows a more meaningful a posteriori assessment of docking methods and results. Practical implications for docking evaluations and for methodological development work in this field are discussed.     

HIV-1 protease flaps spontaneously close to the correct structure in simulations following manual placement of an inhibitor into the open state

We report unrestrained, all-atom molecular dynamics simulations of HIV-1 protease (HIV-PR) with a continuum solvent model that reproducibly sample closing of the active site flaps following manual placement of a cyclic urea inhibitor into the substrate binding site of the open protease. The open form was obtained from the unbound, semi-open HIV-PR crystal structure, which we recently reported (Hornak, V.; et al. Proc. Natl. Acad. Sci. U.S.A. 2006, 103, 915-920.) to have spontaneously opened during unrestrained dynamics. In those simulations, the transiently open flaps always returned to the semi-open form that is observed in all crystal structures of the free protease. Here, we show that manual docking of the inhibitor reproducibly induces spontaneous conversion to the closed form as seen in all inhibitor-bound HIV-PR crystal structures. These simulations reproduced not only the greater degree of flap closure, but also the striking difference in flap "handedness" between bound and free enzyme. In most of the simulations, the final structures were highly accurate. Root-mean-square deviations (RMSD) from the crystal structure of the complex were approximately 1.5 A (averaged over the last 100 ps) for the inhibitor and each flap despite initial RMSD of 2-5 A for the inhibitors and 6-11 A for the flaps. Key hydrogen bonds were formed between the flap tips and between flaps and inhibitor that match those seen in the crystal structure. The results demonstrate that all-atom simulations have the ability to significantly improve poorly docked ligand conformations and reproduce large-scale receptor conformational changes that occur upon binding.     

Solvent Effect on The Equilibrium and Rate Constant of the Tautomeric Reaction in Nexium,Skelaxin, Aldara and Efavirenz Drugs: A Dft Study

Some drugs have two tautomeric structures in the liquid state and the tautomeric equilibrium is formed between these structures. The reaction rate and equilibrium constant of this reaction varies in different solvents. Thus, the determination of the appropriate solvent to separate tautomers is important. In this research, we used the DFT level of B3LYP and the 6-311++G** basis set to obtain the proper solvent for Nexium, Skelaxin, Aldara and Efavirenz drugs. All calculations were made above the melting point of the mentioned drugs, because these drugs are solid at room temperature. The solvent effect is included in the calculation utilizing the polarizable continuum model PCM. The transition state of the tautomeric reaction is determined using the quadratic synchronous transit (QST2) method. The geometry, energy, and dipole moment of transition states are analyzed in different solvent. The root mean square deviation (RMSD) analysis is performed to determine the degree of a structure deviation of tautomer 1 and tautomer 2 from the transition state in different solvents. It is found that the RMSD value for tatumer1 is higher than that for tautomer 2 in all studied drugs. The proper solvent for the separation of tautomers is determined from the analysis of thermodynamics and kinetics.     

Comparison between self-guided Langevin dynamics and molecular dynamics simulations for structure refinement of protein loop conformations

This article presents a comparative analysis of two replica‐exchange simulation methods for the structure refinement of protein loop conformations, starting from low‐resolution predictions. The methods are self‐guided Langevin dynamics (SGLD) and molecular dynamics (MD) with a Nosé–Hoover thermostat. We investigated a small dataset of 8‐ and 12‐residue loops, with the shorter loops placed initially from a coarse‐grained lattice model and the longer loops from an enumeration assembly method (the Loopy program). The CHARMM22 + CMAP force field with a generalized Born implicit solvent model (molecular‐surface parameterized GBSW2) was used to explore conformational space. We also assessed two empirical scoring methods to detect nativelike conformations from decoys: the all‐atom distance‐scaled ideal‐gas reference state (DFIRE‐AA) statistical potential and the Rosetta energy function. Among the eight‐residue loop targets, SGLD out performed MD in all cases, with a median of 0.48 Å reduction in global root‐mean‐square deviation (RMSD) of the loop backbone coordinates from the native structure. Among the more challenging 12‐residue loop targets, SGLD improved the prediction accuracy over MD by a median of 1.31 Å, representing a substantial improvement. The overall median RMSD for SGLD simulations of 12‐residue loops was 0.91 Å, yielding refinement of a median 2.70 Å from initial loop placement. Results from DFIRE‐AA and the Rosetta model applied to rescoring conformations failed to improve the overall detection calculated from the CHARMM force field. We illustrate the advantage of SGLD over the MD simulation model by presenting potential‐energy landscapes for several loop predictions. Our results demonstrate that SGLD significantly outperforms traditional MD in the generation and populating of nativelike loop conformations and that the CHARMM force field performs comparably to other empirical force fields in identifying these conformations from the resulting ensembles. Published 2011 Wiley Periodicals, Inc. J Comput Chem, 2011     

SODOCK: swarm optimization for highly flexible protein-ligand docking

Protein-ligand docking can be formulated as a parameter optimization problem associated with an accurate scoring function, which aims to identify the translation, orientation, and conformation of a docked ligand with the lowest energy. The parameter optimization problem for highly flexible ligands with many rotatable bonds is more difficult than that for less flexible ligands using genetic algorithm (GA)-based approaches, due to the large numbers of parameters and high correlations among these parameters. This investigation presents a novel optimization algorithm SODOCK based on particle swarm optimization (PSO) for solving flexible protein-ligand docking problems. To improve efficiency and robustness of PSO, an efficient local search strategy is incorporated into SODOCK. The implementation of SODOCK adopts the environment and energy function of AutoDock 3.05. Computer simulation results reveal that SODOCK is superior to the Lamarckian genetic algorithm (LGA) of AutoDock, in terms of convergence performance, robustness, and obtained energy, especially for highly flexible ligands. The results also reveal that PSO is more suitable than the conventional GA in dealing with flexible docking problems with high correlations among parameters. This investigation also compared SODOCK with four state-of-the-art docking methods, namely GOLD 1.2, DOCK 4.0, FlexX 1.8, and LGA of AutoDock 3.05. SODOCK obtained the smallest RMSD in 19 of 37 cases. The average 2.29 A of the 37 RMSD values of SODOCK was better than those of other docking programs, which were all above 3.0 A.     

The relationship between crystal structure and methyl and t-butyl group dynamics in van der Waals organic solids

We report x-ray diffractometry in a single crystal of 2-t-butyl-4-methylphenol (TMP) and low-frequency solid state nuclear magnetic resonance (NMR) proton relaxometry in a polycrystalline sample of TMP. The x-ray data show TMP to have a monoclinic, P2(1)/c, structure with eight molecules per unit cell and two crystallographically inequivalent t-butyl group (C(CH(3))(3)) sites. The proton spin-lattice relaxation rates were measured between 90 and 310 K at NMR frequencies of 8.50, 22.5, and 53.0 MHz. The relaxometry data is fitted with two models characterizing the dynamics of the t-butyl groups and their constituent methyl groups, both of which are consistent with the determined x-ray structure. In addition to presenting results for TMP, we review previously reported x-ray diffractometry and low-frequency NMR relaxometry in two other van der Waals solids which have a simpler structure. In both cases, a unique model for the reorientational dynamics was found. Finally, we review a similar previously reported analysis in a van der Waals solid with a very complex structure in which case fitting the NMR relaxometry requires very many parameters and serves mainly as a flag for a careful x-ray diffraction study.     

Some aspects of AFM nanomechanical probing of surface polymer films

We analyzed how the approach developed for the microindentation of non-uniform elastic solids can be adapted to analyze the atomic force microscopy (AFM) probing of ultrathin (1-100 nm thick) polymer films on a solid substrate, as well as polymer films with a multilayered structure. We suggested that recent Johnson's modification of the contact mechanics model that included a viscoelastic contribution could also be utilized to analyze rate-dependent loading data for polymer surfaces. The graded model proposed for microindentation experiments was modified allowing to account not only for variable elastic moduli within different layers but also for the gradient of properties between layers within a transition zone. Two examples of a recent application of this model for molecularly thick hyperbranched polymer monolayers (<3 nm thick) and tri-layered polymer films (20-40 nm thick) tethered to a solid substrate were presented and discussed. In both cases, complex shapes of both loading curves and elastic modulus depth profiles obtained from experimental AFM data were successfully fitted by the graded model with realistic structural parameters.     

SPICKER: a clustering approach to identify near-native protein folds

We have developed SPICKER, a simple and efficient strategy to identify near-native folds by clustering protein structures generated during computer simulations. In general, the most populated clusters tend to be closer to the native conformation than the lowest energy structures. To assess the generality of the approach, we applied SPICKER to 1489 representative benchmark proteins 相似文献