Blind docking method combining search of low‐resolution binding sites with ligand pose refinement by molecular dynamics‐based global optimization

This study describes the development of a new blind hierarchical docking method, bhDock, its implementation, and accuracy assessment. The bhDock method uses two‐step algorithm. First, a comprehensive set of low‐resolution binding sites is determined by analyzing entire protein surface and ranked by a simple score function. Second, ligand position is determined via a molecular dynamics‐based method of global optimization starting from a small set of high ranked low‐resolution binding sites. The refinement of the ligand binding pose starts from uniformly distributed multiple initial ligand orientations and uses simulated annealing molecular dynamics coupled with guided force‐field deformation of protein–ligand interactions to find the global minimum. Assessment of the bhDock method on the set of 37 protein–ligand complexes has shown the success rate of predictions of 78%, which is better than the rate reported for the most cited docking methods, such as AutoDock, DOCK, GOLD, and FlexX, on the same set of complexes. © 2009 Wiley Periodicals, Inc. J Comput Chem 2010     

A new method for the gradient‐based optimization of molecular complexes

We present a novel method for the local optimization of molecular complexes. This new approach is especially suited for usage in molecular docking. In molecular modeling, molecules are often described employing a compact representation to reduce the number of degrees of freedom. This compact representation is realized by fixing bond lengths and angles while permitting changes in translation, orientation, and selected dihedral angles. Gradient‐based energy minimization of molecular complexes using this representation suffers from well‐known singularities arising during the optimization process. We suggest an approach new in the field of structure optimization that allows to employ gradient‐based optimization algorithms for such a compact representation. We propose to use exponential mapping to define the molecular orientation which facilitates calculating the orientational gradient. To avoid singularities of this parametrization, the local minimization algorithm is modified to change efficiently the orientational parameters while preserving the molecular orientation, i.e. we perform well‐defined jumps on the objective function. Our approach is applicable to continuous, but not necessarily differentiable objective functions. We evaluated our new method by optimizing several ligands with an increasing number of internal degrees of freedom in the presence of large receptors. In comparison to the method of Solis and Wets in the challenging case of a non‐differentiable scoring function, our proposed method leads to substantially improved results in all test cases, i.e. we obtain better scores in fewer steps for all complexes. © 2008 Wiley Periodicals, Inc. J Comput Chem, 2009     

Importance of accurate charges in molecular docking: quantum mechanical/molecular mechanical (QM/MM) approach

The extent to which accuracy of electric charges plays a role in protein-ligand docking is investigated through development of a docking algorithm, which incorporates quantum mechanical/molecular mechanical (QM/MM) calculations. In this algorithm, fixed charges of ligands obtained from force field parameterization are replaced by QM/MM calculations in the protein environment, treating only the ligands as the quantum region. The algorithm is tested on a set of 40 cocrystallized structures taken from the Protein Data Bank (PDB) and provides strong evidence that use of nonfixed charges is important. An algorithm, dubbed "Survival of the Fittest" (SOF) algorithm, is implemented to incorporate QM/MM charge calculations without any prior knowledge of native structures of the complexes. Using an iterative protocol, this algorithm is able in many cases to converge to a nativelike structure in systems where redocking of the ligand using a standard fixed charge force field exhibits nontrivial errors. The results demonstrate that polarization effects can play a significant role in determining the structures of protein-ligand complexes, and provide a promising start towards the development of more accurate docking methods for lead optimization applications.     

Challenges in the determination of the binding modes of non-standard ligands in X-ray crystal complexes

Despite its central role in structure based drug design the determination of the binding mode (position, orientation and conformation in addition to protonation and tautomeric states) of small heteromolecular ligands in protein:ligand complexes based on medium resolution X-ray diffraction data is highly challenging. In this perspective we demonstrate how a combination of molecular dynamics simulations and free energy (FE) calculations can be used to correct and identify thermodynamically stable binding modes of ligands in X-ray crystal complexes. The consequences of inappropriate ligand structure, force field and the absence of electrostatics during X-ray refinement are highlighted. The implications of such uncertainties and errors for the validation of virtual screening and fragment-based drug design based on high throughput X-ray crystallography are discussed with possible solutions and guidelines.     

Chemical shift driven geometry optimization

A new method for refinement of 3D molecular structures by geometry optimization is presented. Prerequisites are a force field and a very fast procedure for the calculation of chemical shifts in every step of optimization. To the energy, provided by the force field (COSMOS force field), a pseudoenergy, depending on the difference between experimental and calculated chemical shifts, is added. In addition to the energy gradients, pseudoforces are computed. This requires the derivatives of the chemical shifts with respect to the coordinates. The pseudoforces are analytically derived from the integral expressions of the bond polarization theory. Single chemical shift values attributed to corresponding atoms are considered for structural correction. As a first example, this method is applied for proton position refinement of the D-mannitol X-ray structure. A crystal structure refinement with 13C chemical shift pseudoforces is carried out.     

Energy Landscape of Aptamer/Protein Complexes Studied by Single‐Molecule Force Spectroscopy

Aptamers are single‐stranded nucleic acid molecules selected in vitro to bind to a variety of target molecules. Aptamers bound to proteins are emerging as a new class of molecules that rival commonly used antibodies in both therapeutic and diagnostic applications. With the increasing application of aptamers as molecular probes for protein recognition, it is important to understand the molecular mechanism of aptamer–protein interaction. Recently, we developed a method of using atomic force microscopy (AFM) to study the single‐molecule rupture force of aptamer/protein complexes. In this work, we investigate further the unbinding dynamics of aptamer/protein complexes and their dissociation‐energy landscape by AFM. The dependence of single‐molecule force on the AFM loading rate was plotted for three aptamer/protein complexes and their dissociation rate constants, and other parameters characterizing their dissociation pathways were obtained. Furthermore, the single‐molecule force spectra of three aptamer/protein complexes were compared to those of the corresponding antibody/protein complexes in the same loading‐rate range. The results revealed two activation barriers and one intermediate state in the unbinding process of aptamer/protein complexes, which is different from the energy landscape of antibody/protein complexes. The results provide new information for the study of aptamer–protein interaction at the molecular level.     

Importance of ligand conformational energies in carbohydrate docking: Sorting the wheat from the chaff

Docking algorithms that aim to be applicable to a broad range of ligands suffer reduced accuracy because they are unable to incorporate ligand‐specific conformational energies. Here, we develop a set of Carbohydrate Intrinsic (CHI) energy functions that quantify the conformational properties of oligosaccharides, based on the values of their glycosidic torsion angles. The relative energies predicted by the CHI energy functions mirror the conformational distributions of glycosidic linkages determined from a survey of oligosaccharide‐protein complexes in the protein data bank. Addition of CHI energies to the standard docking scores in Autodock 3, 4.2, and Vina consistently improves pose ranking of oligosaccharides docked to a set of anticarbohydrate antibodies. The CHI energy functions are also independent of docking algorithm, and with minor modifications, may be incorporated into both theoretical modeling methods, and experimental NMR or X‐ray structure refinement programs. © 2013 Wiley Periodicals, Inc.     

Molecular dynamics simulations of isotope compounds of hydrogen atoms: prospects and progress

In this paper, we proceed with our project of generating an algorithm for molecular dynamics simulations of isotope complexes of hydrogen atoms (Chem. Phys. Lett. 320 (2000) 118). The isotope selection is carried out by forces derived from adiabatic potential energy curves that are obtained within a modified electron mass theory. Arguments are presented in favour of the use of the generalized valence bond electronic wavefunctions. We exemplify with simulations and geometry optimization of H₂ isotopomers and discuss the effects of long-range interactions and electron correlation on the forces.     

A machine learning technique toward generating minimum energy structures of small boron clusters

The search for a global minimum related to molecular electronic structure and chemical bonding has received wide attention based on some theoretical calculations at various levels of theory. Particle swarm optimization (PSO) algorithm and modified PSO have been used to predict the energetically stable/metastable states associated with a given chemical composition. Out of a variety of techniques such as genetic algorithm, basin hopping, simulated annealing, PSO, and so on, PSO is considered to be one of the most suitable methods due to its various advantages over others. We use a swarm‐intelligence based parallel code to improve a PSO algorithm in a multidimensional search space augmented by quantum chemical calculations on gas phase structures at 0 K without any symmetry constraint to obtain an optimal solution. Our currently employed code is interfaced with Gaussian software for single point energy calculations. The code developed here is shown to be efficient. Small population size (small cluster) in the multidimensional space is actually good enough to get better results with low computational cost than the typical larger population. But for larger systems also the analysis is possible. One can try with a large number of particles as well. We have also analyzed how arbitrary and random structures and the local minimum energy structures gravitate toward the target global minimum structure. At the same time, we compare our results with that obtained from other evolutionary techniques.     

Polarizable protein packing

To incorporate protein polarization effects within a protein combinatorial optimization framework, we decompose the polarizable force field AMOEBA into low order terms. Including terms up to the third-order provides a fair approximation to the full energy while maintaining tractability. We represent the polarizable packing problem for protein G as a hypergraph and solve for optimal rotamers with the FASTER combinatorial optimization algorithm. These approximate energy models can be improved to high accuracy [root mean square deviation (rmsd) < 1 kJ mol(-1)] via ridge regression. The resulting trained approximations are used to efficiently identify new, low-energy solutions. The approach is general and should allow combinatorial optimization of other many-body problems.     

An efficient molecular docking using conformational space annealing

Molecular docking falls into the general category of global optimization problems because its main purpose is to find the most stable complex consisting of a receptor and its ligand. Conformational space annealing (CSA), a powerful global optimization method, is incorporated with the Tinker molecular modeling package to perform molecular docking simulations of six receptor-ligand complexes (3PTB, 1ULB, 2CPP, 1STP, 3CPA, and 1PPH) from the Protein Data Bank. In parallel, Monte Carlo with the minimization (MCM) method is also incorporated into the Tinker package for comparison. The energy function, consisting of electrostatic interactions, van der Waals interactions, and torsional energy terms, is calculated using the AMBER94 all-atom empirical force field. Rigid docking simulations for all six complexes and flexible docking simulations for three complexes (1STP, 3CPA, and 1PPH) are carried out using the CSA and the MCM methods. The simulation results show that the docking procedures using the CSA method generally find the most stable complexes as well as the native-like complexes more efficiently and accurately than those using the MCM, demonstrating that CSA is a promising search method for molecular docking problems.     

GRID: A high‐resolution protein structure refinement algorithm

The energy‐based refinement of protein structures generated by fold prediction algorithms to atomic‐level accuracy remains a major challenge in structural biology. Energy‐based refinement is mainly dependent on two components: (1) sufficiently accurate force fields, and (2) efficient conformational space search algorithms. Focusing on the latter, we developed a high‐resolution refinement algorithm called GRID. It takes a three‐dimensional protein structure as input and, using an all‐atom force field, attempts to improve the energy of the structure by systematically perturbing backbone dihedrals and side‐chain rotamer conformations. We compare GRID to Backrub, a stochastic algorithm that has been shown to predict a significant fraction of the conformational changes that occur with point mutations. We applied GRID and Backrub to 10 high‐resolution (≤ 2.8 Å) crystal structures from the Protein Data Bank and measured the energy improvements obtained and the computation times required to achieve them. GRID resulted in energy improvements that were significantly better than those attained by Backrub while expending about the same amount of computational resources. GRID resulted in relaxed structures that had slightly higher backbone RMSDs compared to Backrub relative to the starting crystal structures. The average RMSD was 0.25 ± 0.02 Å for GRID versus 0.14 ± 0.04 Å for Backrub. These relatively minor deviations indicate that both algorithms generate structures that retain their original topologies, as expected given the nature of the algorithms. © 2012 Wiley Periodicals, Inc.     

Deciphering common failures in molecular docking of ligand-protein complexes

Common failures in predicting crystal structures of ligand-protein complexes are investigated for three ligand-protein systems by a combined thermodynamic and kinetic analysis of the binding energy landscapes. Misdocked predictions in ligand-protein docking are classified as `soft' and `hard' failures. While a soft failure arises when the search algorithm is unable to find the global energy minimum corresponding to the crystal structure, a hard failure results from a flaw of the energy function to qualify the crystal structure as the predicted lowest energy conformation in docking simulations. We find that neither the determination of a single structure with the lowest energy nor finding the most common binding mode is sufficient to predict crystal structures of the complexes, which belong to the category of hard failures. In a proposed hierarchical approach, structural similarity clustering of the conformations, generated from equilibrium simulations with the simplified energy function, is followed by energy refinement with the AMBER force field. This protocol, that involves a hierarchy of energy functions, resolves some common failures in ligand-protein docking and detects crystallographic binding modes that were not found during docking simulations.     

Reference energy extremal optimization: a stochastic search algorithm applied to computational protein design

We adapt a combinatorial optimization algorithm, extremal optimization (EO), for the search problem in computational protein design. This algorithm takes advantage of the knowledge of local energy information and systematically improves on the residues that have high local energies. Power-law probability distributions are used to select the backbone sites to be improved on and the rotamer choices to be changed to. We compare this method with simulated annealing (SA) and motivate and present an improved method, which we call reference energy extremal optimization (REEO). REEO uses reference energies to convert a problem with a structured local-energy profile to one with more random profile, and extremal optimization proves to be extremely efficient for the latter problem. We show in detail the large improvement we have achieved using REEO as compared to simulated annealing and discuss a number of other heuristics we have attempted to date.     

确定蛋白质-短肽复合物结构的新方法

大部分蛋白质 -蛋白质复合物的三维结构在接触表面都显示出很好的几何匹配 .由于蛋白质的表面几何形状和其它的一些物理化学性质在分子的专一性相互作用中起了主要作用 ,所以 ,接触表面几何形状的互补常常被认为是蛋白质分子识别的基础 .一般来说 ,蛋白质接触表面的几何匹配只涉及 5到 1 0几个紧密堆积的氨基酸残基 ,因此 ,蛋白质与蛋白质配体之间的识别计算可以通过蛋白质与突变周围的或与蛋白质表面紧密接触的配体肽段的识别计算来实现 . Stoddard等 [1] 已经利用从 MBP上选取的八肽成功地计算出接近晶体结构的 MBP-受体复合物 .许多研…     

Density-functional geometry optimization of the 150,000-atom photosystem-I trimer

We present a linear-scaling method based on the use of density-functional theory (DFT) for the system-wide optimization of x-ray structural coordinates and apply it to optimize the 150,000 atoms of the photosystem-I (PS-I) trimer. The method is based on repetitive applications of a multilevel ONIOM procedure using the PW916-31G(d) DFT calculations for the high level and PM3 for the lower level; this method treats all atoms in the structure equivalently, a structure in which the majority of the atoms can be considered as part of some internal "active site." To obtain a realistic single structure, some changes to the original protein model were necessary but these are kept to a minimum in order that the optimized structure most closely resembles the original x-ray one. Optimization has profound effects on the perceived electronic properties of the cofactors, with, e.g., optimization lowering the internal energy of the chlorophylls by on average 53 kcal mol(-1) and eliminates the enormous 115 kcal mol(-1) energy spread depicted by the original x-ray heavy-atom coordinates. A highly precise structure for PS-I results that is suitable for analysis of device function. Significant qualitative features of the structure are also improved such as correction of an error in the stereochemistry of one of the chlorophylls in the "special pair" of the reaction center, as well as the replacement of a water molecule with a metal cation in a critical region on the C3 axis. The method also reveals other unusual features of the structure, leading both to suggestions concerning device functionality and possible mutations between gene sequencing and x-ray structure determination. The optimization scheme is thus shown to augment the molecular modeling schemes that are currently used to add medium-resolution structural information to the raw scattering data in order to obtain atomically resolved structures. System-wide optimization is now a feasible process and its use within protein x-ray data refinement should be considered.     

An efficient newton-like method for molecular mechanics energy minimization of large molecules

Techniques from numerical analysis and crystallographic refinement have been combined to produce a variant of the Truncated Newton nonlinear optimization procedure. The new algorithm shows particular promise for potential energy minimization of large molecular systems. Usual implementations of Newton's method require storage space proportional to the number of atoms squared (i.e., O(N²)) and computer time of O(N³). Our suggested implementation of the Truncated Newton technique requires storage of less than O(N^1.5) and CPU time of less than O(N²) for structures containing several hundred to a few thousand atoms. The algorithm exhibits quadratic convergence near the minimum and is also very tolerant of poor initial structures. A comparison with existing optimization procedures is detailed for cyclohexane, arachidonic acid, and the small protein crambin. In particular, a structure for crambin (662 atoms) has been refined to an RMS gradient of 3.6 × 10^?6 kcal/mol/Å per atom on the MM2 potential energy surface. Several suggestions are made which may lead to further improvement of the new method.     

Low-spin versus high-spin ground state in pseudo-octahedral iron complexes

Pseudo-octahedral complexes of iron find applications as switches in molecular electronic devices, materials for data storage, and, more recently, as candidates for dye-sensitizers in dye-sensitized solar cells. Iron, as a first row transition metal, provides a weak ligand-field splitting in an octahedral environment. This results in the presence of low-lying (5)T excited states that, depending on the identity of iron ligands, can become the ground state of the complex. The small energy difference between the low-spin, (1)A, and high-spin, (5)T, states presents a challenge for accurate prediction of their ground state using density functional theory. In this work, we investigate the applicability of the B3LYP functional to the ground state determination of first row transition metal complexes, focusing mainly on Fe(II) polypyridine complexes with ligands of varying ligand field strength. It has been shown previously that B3LYP artificially favors the (5)T state as the ground state of Fe(II) complexes, and the error in the energy differences between the (1)A and (5)T states is systematic for a set of structurally related complexes. We demonstrate that structurally related complexes can be defined as pseudo-octahedral complexes that undergo similar distortion in the metal-ligand coordination environment between the high-spin and low-spin states. The systematic behavior of complexes with similar distortion can be exploited, and the ground state of an arbitrary Fe(II) complex can be determined by comparing the calculated energy differences between the singlet and quintet electronic states of a complex to the energy differences of structurally related complexes with a known, experimentally determined ground state.