Finite-state and reduced-parameter representations of protein backbone conformations

This article studies the representation of protein backbone conformations using a finite number of values for the backbone dihedral angles. We develop a combinatorial search algorithm that guarantees finding the global minima of functions over the configuration space of discrete protein conformations, and use this procedure to fit finite-state models to the backbones of globular proteins. It is demonstrated that a finite-state representation with a reasonably small number of states yields either a small root-mean-square error or a small dihedral angle deviation from the native structure, but not both at the same time. The problem can be resolved by introducing limited local optimization in each step of the combinatorial search. In addition, it is shown that acceptable approximation is achieved using a single dihedral angle as an independent variable in local optimization. Results for 11 proteins demonstrate the advantages and shortcomings of both the finite-state and reduced-parameter approximations of protein backbone conformations. © 1994 by John Wiley & Sons, Inc.     

Protein structure prediction aided by geometrical and probabilistic constraints

Database-assisted ab initio protein structure prediction methods have exhibited considerable promise in the recent past, with several implementations being successful in community-wide experiments (CASP). We have employed combinatorial optimization techniques toward solving the protein structure prediction problem. A Monte Carlo minimization algorithm has been employed on a constrained search space to identify minimum energy configurations. The search space is constrained by using radius of gyration cutoffs, the loop backbone dihedral probability distributions, and various secondary structure packing conformations. Simulations have been carried out on several sequences and 1000 conformations have been initially generated. Of these, 50 best candidates have then been selected as probable conformations. The search for the optimum has been simplified by incorporating various geometrical constraints on secondary structural elements using distance restraint potential functions. The advantages of the reported methodology are its simplicity, and modifiability to include other geometric and probabilistic restraints.     

Conformational analysis of nucleic acid molecules with flexible furanose rings in dihedral angle space

The computational algorithm that works in the coordinate space of dihedral angles (i.e., bond lengths and bond angles are kept fixed and only rotatable dihedral angles are treated as independent variables) is extended to deal with the pseudorotational m otion of furanose rings by introducing a variable of pseudorotation. Then, this algorithm is applied to a distance geometry calculation that generates three-dimensional (3D) structures that are consistent with given constraints of interatomic distances. This method efficiently generates 3D structures of an RNA hairpin loop which satisfy a set of experimental NMR data. © 1996 by John Wiley & Sons, Inc.     

Conformational sampling by a general linearized embedding algorithm

Linearized embedding is a variant on the usual distance geometry methods for finding atomic Cartesian coordinates given constraints on interatomic distances. Instead of dealing primarily with the matrix of interatomic distances, linearized embedding concentrates on properties of the metric matrix, the matrix of inner products between pairs of vectors defining local coordinate systems within the molecule. We developed a pair of general computer programs that first convert a given arbitrary conformation of any covalent molecule from atomic Cartesian coordinates representation to internal local coordinate systems enforcing rigid valence geometry and then generate a random sampling of conformers in terms of atomic Cartesian coordinates that satisfy the rigid local geometry and a given list of interatomic distance constraints. We studied the sampling properties of this linearized embedding algorithm vs. a standard metric matrix embedding program, DGEOM, on cyclohexane, cycloheptane, and a cyclic pentapeptide. Linearized embedding always produces exactly correct bond lengths, bond angles, planarities, and chiralities; it runs at least two times faster per structure generated, and is successful as much as four times as often at refining these structures to full agreement with the constraints. It samples the full range of allowed conformations broadly, although not perfectly uniformly. Because local geometry is rigid, linearized embedding's sampling in terms of torsion angles is more restricted than that of DGEOM, but it finds in some instances conformations missed by DGEOM. © 1992 by John Wiley & Sons, Inc.     

Enhanced sampling method in molecular simulations using genetic algorithm for biomolecular systems

We propose a molecular simulation method using genetic algorithm (GA) for biomolecular systems to obtain ensemble averages efficiently. In this method, we incorporate the genetic crossover, which is one of the operations of GA, to any simulation method such as conventional molecular dynamics (MD), Monte Carlo, and other simulation methods. The genetic crossover proposes candidate conformations by exchanging parts of conformations of a target molecule between a pair of conformations during the simulation. If the candidate conformations are accepted, the simulation resumes from the accepted ones. While conventional simulations are based on local update of conformations, the genetic crossover introduces global update of conformations. As an example of the present approach, we incorporated genetic crossover to MD simulations. We tested the validity of the method by calculating ensemble averages and the sampling efficiency by using two kinds of peptides, ALA3 and (AAQAA)₃. The results show that for ALA3 system, the distribution probabilities of backbone dihedral angles are in good agreement with those of the conventional MD and replica-exchange MD simulations. In the case of (AAQAA)₃ system, our method showed lower structural correlation of α-helix structures than the other two methods and more flexibility in the backbone ψ angles than the conventional MD simulation. These results suggest that our method gives more efficient conformational sampling than conventional simulation methods based on local update of conformations. © 2018 Wiley Periodicals, Inc.     

Linearized embedding: A new metric matrix algorithm for calculating molecular conformations subject to geometric constraints

There are many methods in the literature for calculating conformations of a molecule subject to geometric constraints, such as those derived from two-dimensional NMR experiments. One of the most general ones is the EMBED algorithm, based on distance geometry, where all constraints except chirality are converted into upper and lower bounds on interatomic distances. Here we propose a variation on this where the molecule is assumed to have fixed bond lengths, vicinal bond angles and chiral centers; and these holonomic constraints are enforced separately from the experimental constraints by being built into the mathematical structure of the problem. The advantages of this approach are: (1) for molecules having large rigid groups of atoms, there are substantially fewer variables in the problem than all the atomic coordinates; (2) rigid groups achieve in the end more accurate local geometry (e.g., planar aromatic rings are truly planar, chiral centers always have their correct absolute chirality); (3) it is easier to detect inconsistencies between the holonomic and the experimental constraints; and (4) when generating a random sampling of conformers consistent with all constraints, the probability of achieving satisfactory structures tends to be greater.     

Loop closure via bond scaling and relaxation

We describe, test, and apply a new computational algorithm for generating protein loop conformations subject to distance and secondary structure constraints. The algorithm is based upon initial scaling and subsequent relaxation of covalent bond lengths. The scaling-relaxation procedure needs no additional energy terms and can be readily incorporated into existing molecular modeling packages. The algorithm uses an all-atom energy function from the outset in a straightforward way so that about 60% of the generated loop conformations are free of severe distortions of covalent bond lengths and angles. An extensive application to the major loop conformations of TFIIIA-type zinc fingers (Zif268 and ADR1) is presented, as well as preliminary calculations on hypervariable loops of two immunoglobulins (MCPC603 and Bence-Jones). © 1993 John Wiley & Sons, Inc.     

简化的系统搜索法及其在构象分析中的应用

多肽的构象研究除了可以用X-射线晶体学及二维核磁共振等实验方法外,理论计算的方法有系统搜索法、蒙特卡洛方法、距离几何方法、分子动力学方法及能量极小化等.系统搜索法具有构象空间搜索彻底的特点,相对来说找到系统整体极小值的可能性较大,但由于其计算量较大,对于多肽及蛋白质很难实现.我们对系统搜索法进行了简化,不是同时旋转所有的二面角,而是成对地进行,以迭代的方法达到收敛,最后得到可能的构象.     

Conformational sampling by self-organization

A new stochastic algorithm for conformational sampling is described. The algorithm generates molecular conformations that are consistent with a set of geometric constraints, which include interatomic distance bounds and chiral volumes derived from the molecular connectivity table. The algorithm repeatedly selects individual geometric constraints at random and updates the respective atomic coordinates toward satisfying the chosen constraint. When compared to a conventional distance geometry algorithm based on the same set of geometric constraints, our method is faster and generates conformations that are more diverse and more energetically favorable.     

Conformational memories with variable bond angles

Conformational Memories (CM) is a simulated annealing/Monte Carlo method that explores peptide and protein dihedral conformational space completely and efficiently, independent of the original conformation. Here we extend the CM method to include the variation of a randomly chosen bond angle, in addition to the standard variation of two or three randomly chosen dihedral angles, in each Monte Carlo trial of the CM exploratory and biased phases. We test the hypothesis that the inclusion of variable bond angles in CM leads to an improved sampling of conformational space. We compare the results with variable bond angles to CM with no bond angle variation for the following systems: (1) the pentapeptide Met-enkephalin, which is a standard test case for conformational search methods; (2) the proline ring pucker in a 17mer model peptide, (Ala)(8)Pro(Ala)(8); and (3) the conformations of the Ser 7.39 chi(1) in transmembrane helix 7 (TMH7) of the cannabinoid CB1 receptor, a 25-residue system. In each case, analysis of the CM results shows that the inclusion of variable bond angles results in sampling of regions of conformational space that are inaccessible to CM calculations with only variable dihedral angles, and/or a shift in conformational populations from those calculated when variable bond angles are not included. The incorporation of variable bond angles leads to an improved sampling of conformational space without loss of efficiency. Our examples show that this improved sampling leads to better exploration of biologically relevant conformations that have been experimentally validated.     

Systematic stepsize variation: Efficient method for searching conformational space of polypeptides

A new and efficient method for overcoming the multiple minima problem of polypeptides, the systematic stepsize variation (SSV) method, is presented. The SSV is based on the assumption that energy barriers can be passed over by sufficiently large rotations about rotatable bonds: randomly chosen dihedral angles are updated starting with a small stepsize (i.e., magnitude of rotation). A new structure is accepted only if it possesses a lower energy than the precedent one. Local minima are passed over by increasing the stepsize systematically. When no new structures are found any longer, the simulation is continued with the starting structure, but other trajectories will be followed due to the random order in updating the torsional angles. First, the method is tested with Met-enkephalin, a peptide with a known global minimum structure; in all runs the latter is found at least once. The global minimum conformations obtained in the simulations show deviations of ±0.0004 kcal/mol from the reference structure and, consequently, are perfectly superposable. For comparison, Metropolis Monte Carlo simulated annealing (MMC-SA) is performed. To estimate the efficiency of the algorithm depending on the complexity of the optimization problem, homopolymers of Ala and Gly of different lengths are simulated, with both the SSV and the MMC-SA method. The comparative simulations clearly reveal the higher efficiency of SSV compared with MMC-SA. © 1998 John Wiley & Sons, Inc. J Comput Chem 19: 1470–1481, 1998     

Ferreting out correlations from trajectory data

Thermally driven materials characterized by complex energy landscapes, such as proteins, exhibit motions on a broad range of space and time scales. Principal component analysis (PCA) is often used to extract modes of motion from protein trajectory data that correspond to coherent, functional motions. In this work, two other methods, maximum covariance analysis (MCA) and canonical correlation analysis (CCA) are formulated in a way appropriate to analyze protein trajectory data. Both methods partition the coordinates used to describe the system into two sets (two measurement domains) and inquire as to the correlations that may exist between them. MCA and CCA provide rotations of the original coordinate system that successively maximize the covariance (MCA) or correlation (CCA) between modes of each measurement domain under suitable constraint conditions. We provide a common framework based on the singular value decomposition of appropriate matrices to derive MCA and CCA. The differences between and strengths and weaknesses of MCA and CCA are discussed and illustrated. The application presented here examines the correlation between the backbone and side chain of the peptide met-enkephalin as it fluctuates between open conformations, found in solution, to closed conformations appropriate to when it is bound to its receptor. Difficulties with PCA carried out in Cartesian coordinates are found and motivate a formulation in terms of dihedral angles for the backbone atoms and selected atom distances for the side chains. These internal coordinates are a more reliable basis for all the methods explored here. MCA uncovers a correlation between combinations of several backbone dihedral angles and selected side chain atom distances of met-enkephalin. It could be used to suggest residues and dihedral angles to focus on to favor specific side chain conformers. These methods could be applied to proteins with domains that, when they rearrange upon ligand binding, may have correlated functional motions or, for multi-subunit proteins, may exhibit correlated subunit motions.     

Knowledge-based potential for the polypeptide backbone

A knowledge-based potential for the polypeptide backbone as a function of the dihedral angles is developed and tested. The potential includes correlations due to the conformations and compositions of adjacent residues. Its purpose is to serve as a major component of a coarse-grained protein potential by including the most relevant local interactions while averaging out nonbonded ones. A probability density estimation algorithm and a multi-resolution probability combination procedure are developed to produce smooth probability distributions and dihedral angle potentials. The potential is described by a set of two-dimensional dihedral angle surfaces involving the various combinations of amino acid triplets and duplets. Several tests are carried out to evaluate the quality of the potential. Monte Carlo simulations, using only the dihedral angle potential and a coarse-grained excluded volume potential, show that the resulting dihedral angle distributions and correlations are consistent with those extracted from protein structures. Additional simulations of unfolded proteins are carried out to measure the NMR residual dipolar coupling (RDC). Significant correlations are obtained between the simulations and the corresponding experiments consistent with other simulations in the literature. Finally, the dihedral angle entropies are calculated for the 20 amino acids. In particular, the entropy difference between alanine and glycine agrees with the ones computed from molecular dynamics simulations ( approximately 0.4 kcal/mol).     

Energy minimization of rigid-geometry polypeptides with exactly closed disulfide loops

A method has been developed for minimizing the energy of a polypeptide with rigid geometry while keeping all disulfide loops closed exactly. Exact closure of disulfide loops implies that some dihedral angles become implicit functions of the remaining dihedral angles in the polypeptide; the efficacy of the method is related to the manner in which the implicitly defined dihedral angles are chosen. The method has been used to find minimum-energy conformations of bovine pancreatic trypsin inhibitor, ribonuclease A, crambin, the defensin HNP3 dimer, and ω-conotoxin. For the first two proteins, the starting conformations for energy minimization had been derived previously from crystal structures using pseudopotentials to keep the disulfide loops almost closed. Starting conformations for the remaining three proteins were derived from their crystal or NMR structures by similar procedures. In all cases, the energy-minimized structures had a significantly and, in some cases, substantially, lower energy than the starting structures. The RMS deviations between the exactly closed energy- minimized structures and the crystal or NMR structures from which they were derived ranged from 0.9 Å to 1.9 Å, suggesting that the computed structures can serve as “regularized” native structures for these proteins. The energy of a ribonuclease derivative lacking the 65–72 disulfide bridge was minimized using the procedure; the result showed that this derivative has a low-energy structure with a conformation very close to that of native ribonuclease, and is consistent with its postulated role in the folding of ribonuclease. These results offer strong support for the validity of the rigid-geometry model in the studies of the conformational energy of proteins. © 1997 by John Wiley & Sons, Inc.     

A method for constrained energy minimization of macromolecules

Two algorithms for the local energy minimization of the structure of macromolecules in the presence of constraints are proposed. They are a combination of the method of steepest descents and the method of conjugate gradients with the procedure SHAKE, by which distance constraints can be satisfied. The two algorithms are tested by applying them to a small protein, the bovine pancreatic trypsin inhibitor (BPTI), and compared with the penalty function method for conserving constraints. The efficiency of the proposed methods depends on the level of interdependence of the constraints. For bond-length constraints, the use of SHAKE is superior to the penalty function method. However, when bond-angle constraints are included, SHAKE is more efficient only if the curvature of the penalty function is considerably greater than that of the potential function being minimized. The results indicate that with bond-length constraints the minimization behavior is similar to that without constraints. However, the simultaneous application of bond-length and bond-angle constraints appears to confine the molecule to a very limited part of configuration space, very different from the part covered by an unconstrained minimization. This conclusion calls into question energy minimizations of protein systems in which only the dihedral angles are allowed to vary.     

Conformational studies of double-helical polynucleotides by the method of pair-wise potential functions

Double-helical polynucleotide conformations, poly(dA)·poly(dT), poly(d(A-T))·poly(d(T-A))·poly(dG)·poly(dC), and poly(d(G-C))·poly(d(C-G)) are analyzed by the atom–atom potential method. The energy optimization is carried out in the space of eight independent geometric parameters using analytical procedures for the constraints, taking into account the flexibility of the β-D -deoxyribose rings. At the first stage, the full screening of atomic partial charges was assumed. The structures of the calculated B and the A forms of DNA are characterized by low energy and absence of short contacts; the dihedral angles are near the average values in the monomers. With the typical energy difference of 3–5 kcal/mol nucleotide pairs in all cases, the B form is more preferable as compared to the A form. At the final step the effect of the Coulomb term is evaluated for poly(dA)·poly(dT) using various values of the effective dielectric constant (? = 28, 24, 20, 18, 14, 12, 10, 8, 6, 4, and 1). If ? ?24, the energy optimization leads A to B. We discuss the stereochemical details of the intermediate conformations on the A–B path and hypothesize the nature of stability of the A and the B forms and the mechanism of the A–B transition.     

DFT conformational studies of alpha-maltotriose

Recent DFT optimization studies on alpha-maltose improved our understanding of the preferred conformations of alpha-maltose. The present study extends these studies to alpha-maltotriose with three alpha-D-glucopyranose residues linked by two alpha-[1-->4] bridges, denoted herein as DP-3's. Combinations of gg, gt, and tg hydroxymethyl groups are included for both "c" and "r" hydroxyl rotamers. When the hydroxymethyl groups are for example, gg-gg-gg, and the hydroxyl groups are rotated from all clockwise, "c", to all counterclockwise, "r", the minimum energy positions of the bridging dihedral angles (phi(H) and psi(H)) move from the region of conformational space of (-, -), relative to (0 degrees , 0 degrees), to a new position defined by (+, +). Further, it was found previously that the relative energies of alpha-maltose gg-gg-c and "r" conformations were very close to one another; however, the DP-3's relative energies between hydroxyl "c" or "r" rotamers differ by more than one kcal/mol, in favor of the "c" form, even though the lowest energy DP-3 conformations have glycosidic dihedral angles similar to those found in the alpha-maltose study. Preliminary solvation studies using COSMO, a dielectric solvation method, point to important solvent contributions that reverse the energy profiles, showing an energy preference for the "r" forms. Only structures in which the rings are in the chair conformation are presented here.     

Protein structure determination by high-resolution solid-state NMR spectroscopy: application to microcrystalline ubiquitin

High-resolution solid-state NMR spectroscopy has become a promising method for the determination of three-dimensional protein structures for systems which are difficult to crystallize or exhibit low solubility. Here we describe the structure determination of microcrystalline ubiquitin using 2D (13)C-(13)C correlation spectroscopy under magic angle spinning conditions. High-resolution (13)C spectra have been acquired from hydrated microcrystals of site-directed (13)C-enriched ubiquitin. Inter-residue carbon-carbon distance constraints defining the global protein structure have been evaluated from 'dipolar-assisted rotational resonance' experiments recorded at various mixing times. Additional constraints on the backbone torsion angles have been derived from chemical shift analysis. Using both distance and dihedral angle constraints, the structure of microcrystalline ubiquitin has been refined to a root-mean-square deviation of about 1 A. The structure determination strategies for solid samples described herein are likely to be generally applicable to many proteins that cannot be studied by X-ray crystallography or solution NMR spectroscopy.     

Cluster distance geometry of polypeptide chains

Distance geometry has been a broadly useful tool for dealing with conformational calculations. Customarily each atom is represented as a point, constraints on the distances between some atoms are obtained from experimental or theoretical sources, and then a random sampling of conformations can be calculated that are consistent with the constraints. Although these methods can be applied to small proteins having on the order of 1000 atoms, for some purposes it is advantageous to view the problem at lower resolution. Here distance geometry is generalized to deal with distances between sets of points. In the end, much of the same techniques produce a sampling of different configurations of these sets of points subject to distance constraints, but now the radii of gyration of the different sets play an important role. A simple example is given of how the packing constraints for polypeptide chains combine with loose distance constraints to give good calculated protein conformers at a very low resolution.