气流床粉煤气化的Gibbs自由能最小化模拟

用Gibbs自由能最小化方法对粉煤气化过程进行了热力学平衡分析。对一混合煤种,在3.0 MPa和气化温度限制在1 200 ℃～1 450 ℃时,研究了氧-煤比、蒸气-煤比对气化炉出口气体组成、温度和有效气产率的影响,并由此确定了可行的操作域是氧-煤比545m3/t～605 m3/t、蒸气-煤比为152.64 kg/t～313.92 kg/t及其对应的工艺指标。从操作域中选择有代表性的工艺条件为氧-煤比578 m3/t、蒸气-煤比为187 kg/t,对应的气化炉出口温度1 358 ℃,CO+H2干基体积分数为91.5%,有效气产率为2.123(CO+H2)m3/kg。同时,研究了碳转化率和热损失对气化工艺指标的影响,其影响是显著的。     

Global energy minimization by rotational energy embedding

Given a sufficiently good empirical potential function for the internal energy of molecules, prediction of the preferred conformations is nearly impossible for large molecules because of the enormous number of local energy minima. Energy embedding has been a promising method for locating extremely good local minima, if not always the global minimum. The algorithm starts by locating a very good local minimum when the molecule is in a high-dimensional Euclidean space, and then it gradually projects down to three dimensions while allowing the molecule to relax its energy throughout the process. Now we present a variation on the method, called rotational energy embedding, where the descent into three dimensions is carried out by a sequence of internal rotations that are the multidimensional generalization of varying torsion angles in three dimensions. The new method avoids certain kinds of difficulties experienced by ordinary energy embedding and enables us to locate conformations very near the native for avian pancreatic polypeptide and apamin, given only their amino acid sequences and a suitable potential function.     

Variance minimization of free energy estimates from optimized expanded ensembles

We explored the possibility of improving the accuracy and precision of free-energy differences estimated via expanded ensembles by manipulation of the biasing weights. Three different weighing approaches were compared: the flat histogram (FH) method, the optimized ensemble (OE) method, and a method introduced in this work, denoted MinVar, which aims to explicitly minimize the expected variance. The performance of these three methods was tested for the simulation of chemical potentials in systems of symmetric diblock copolymers with chain lengths of either 10 or 4 beads, and a system of one large hard sphere of diameter 10 d immersed in a fluid of hard spheres of diameter d. In addition, the effect of the weighing method on the observed accuracy was investigated for different choices of macrostate staging and for both optimized and nonoptimized acceptance ratio methods for calculating free-energy differences. In the diblock copolymer systems, we found that the maximum attainable accuracy can be limited by correlations between the samples, causing the "real" observed variances to be much larger than the expected "ideal" ones. Hence, if the formal minimization of the variance, as aimed by the MinVar method, occurs at the expense of increasing the correlations in the data, the accuracy may actually decrease. Although maximizing the number of round trips between initial and final macrostates (as aimed by the OE method) was found to be directly related to data decorrelation, this only translates into increased accuracy if the correlations are the major source of errors in the free energy estimates. Finally, for the hard sphere system, we found that the MinVar method performs better than both the OE and FH methods even though the MinVar method in this case never completes a round trip, illustrating that maximizing the number of round trips for fixed computational cost does not necessarily lead to increased precision.     

Beyond energy minimization: approaches to the kinetic folding of RNA

The term RNA folding is often used synonymously with the prediction of equilibrium structures. Yet many RNAs function thanks to their ability to undergo structural changes. In this contribution we present a systematic overview of existing approaches to the prediction of RNA folding kinetics, and in particular discuss the strengths and limitations of each method.     

Optimization of equilibrium carbon dioxide methane reforming parameters by the Gibbs free energy minimization method

The thermodynamic equilibrium in the carbon dioxide conversion of methane is studied by Gibbs energy minimization. The curves that represent the dependences of the degree of coke formation, the content of methane and carbon dioxide in syngas, and the syngas module on the CO₂/CH₄ mole ratio in the initial mixture and on temperature at various pressures, are plotted. The regions in which the CO₂/CH₄ mole ratio is optimal for carbon dioxide conversion and no coke formation occurs, and which are characterized by a minimal content of methane and carbon dioxide in syngas, are revealed.     

Expected degree for RNA secondary structure networks

Consider the network of all secondary structures of a given RNA sequence, where nodes are connected when the corresponding structures have base pair distance one. The expected degree of the network is the average number of neighbors, where average may be computed with respect to the either the uniform or Boltzmann probability. Here, we describe the first algorithm, RNAexpNumNbors , that can compute the expected number of neighbors, or expected network degree, of an input sequence. For RNA sequences from the Rfam database, the expected degree is significantly less than the constrained minimum free energy structure, defined to have minimum free energy (MFE) over all structures consistent with the Rfam consensus structure. The expected degree of structural RNAs, such as purine riboswitches, paradoxically appears to be smaller than that of random RNA, yet the difference between the degree of the MFE structure and the expected degree is larger than that of random RNA. Expected degree does not seem to correlate with standard structural diversity measures of RNA, such as positional entropy and ensemble defect. The program RNAexpNumNbors is written in C, runs in cubic time and quadratic space, and is publicly available at http://bioinformatics.bc.edu/clotelab/RNAexpNumNbors . © 2014 Wiley Periodicals, Inc.     

Protein structure prediction using a combination of sequence homology and global energy minimization I. Global energy minimization of surface loops

A procedure has been developed for global energy minimization of surface loops of proteins in the presence of a fixed core. The ECEPP potential function has been modified to allow more accurate representations of hydrogen bond interactions and intrinsic torsional energies. A computationally efficient representation of hydration free energy has been introduced. A local minimization procedure has been developed that uses a cutoff distance, minimization with respect to subsets of degrees of freedom, analytical second derivatives, and distance constraints between rigid segments to achieve efficiency in applications to surface loops. Efficient procedures have been developed for deforming segments of the initial backbone structure and for removing overlaps. Global energy minimization of a surface loop is accomplished by generating a sequence (or a trajectory) of local minima, the component steps of which are generated by searching collections of local minima obtained by deforming seven-residue segments of the surface loop. The search at each component step consists of the following calculations: (1) A large collection of backbone structures is generated by deforming a seven-residue segment of the initial backbone structure. (2) A collection of low-energy backbone structures is generated by applying local energy minimization to the resulting collection of backbone structures (interactions involving side chains that will be searched in this component step are not included in the energy). (3) One low-energy side-chain structure is generated for each of the resulting low-energy backbone structures. (4) A collection of low-energy local minima is generated by applying local energy minimization to the resulting collection of structures. (5) The local minimum with the lowest energy is retained as the next point of the trajectory. Applications of our global search procedure to surface segments of bovine pancreatic trypsin inhibitor (BPTI) and bovine trypsin suggest that component-step searches are reasonably complete. The computational efficiency of component-step searches is such that trajectories consisting of about 10 component steps are feasible using an FPS-5200 array processor. Our procedure for global energy minimization of surface loops is being used to identify and correct problems with the potential function and to calculate protein structure using a combination of sequence homology and global energy minimization.     

Docking to RNA via root-mean-square-deviation-driven energy minimization with flexible ligands and flexible targets

Structure-based drug design is now well-established for proteins as a key first step in the lengthy process of developing new drugs. In many ways, RNA may be a better target to treat disease than a protein because it is upstream in the translation pathway, so inhibiting a single mRNA molecule could prevent the production of thousands of protein gene products. Virtual screening is often the starting point for structure-based drug design. However, computational docking of a small molecule to RNA seems to be more challenging than that to protein due to the higher intrinsic flexibility and highly charged structure of RNA. Previous attempts at docking to RNA showed the need for a new approach. We present here a novel algorithm using molecular simulation techniques to account for both nucleic acid and ligand flexibility. In this approach, with both the ligand and the receptor permitted some flexibility, they can bind one another via an induced fit, as the flexible ligand probes the surface of the receptor. A possible ligand can explore a low-energy path at the surface of the receptor by carrying out energy minimization with root-mean-square-distance constraints. Our procedure was tested on 57 RNA complexes (33 crystal and 24 NMR structures); this is the largest data set to date to reproduce experimental RNA binding poses. With our procedure, the lowest-energy conformations reproduced the experimental binding poses within an atomic root-mean-square deviation of 2.5 A for 74% of tested complexes.     

Evolutionary rate variation and RNA secondary structure prediction

Predicting RNA secondary structure using evolutionary history can be carried out by using an alignment of related RNA sequences with conserved structure. Accurately determining evolutionary substitution rates for base pairs and single stranded nucleotides is a concern for methods based on this type of approach. Determining these rates can be hard to do reliably without a large and accurate initial alignment, which ideally also has structural annotation. Hence, one must often apply rates extracted from other RNA families with trusted alignments and structures. Here, we investigate this problem by applying rates derived from tRNA and rRNA to the prediction of the much more rapidly evolving 5'-region of HIV-1. We find that the HIV-1 prediction is in agreement with experimental data, even though the relative evolutionary rate between A and G is significantly increased, both in stem and loop regions. In addition we obtained an alignment of the 5' HIV-1 region that is more consistent with the structure than that currently in the database. We added randomized noise to the original values of the rates to investigate the stability of predictions to rate matrix deviations. We find that changes within a fairly large range still produce reliable predictions and conclude that using rates from a limited set of RNA sequences is valid over a broader range of sequences.     

A comparison of accelerators for direct energy minimization in electronic structure calculations

We compare three different methods for direct energy minimization in electronic structure calculations where the gradient of the energy functional with respect to the molecular orbitals is available. These methods make use of the preconditioned gradient to increase robustness. An orbital transformation is used to ensure that the orthogonality constraint on the orbitals remains satisfied when using standard minimization methods. In addition, we propose an adaptive scheme for estimating the curvature of the energy functional to increase the performance of a line search free quasi-Newton method. We show that the performance of all methods is similar when robustness of the methods is ensured.     

A fast genetic algorithm for RNA secondary structure analysis

A fast genetic algorithm GArna for mass calculations of RNA secondary structures through the Internet is proposed. The algorithm GArna was used to study the effects of nucleotide composition on characteristics of the secondary structure of random RNA sequences. A contextual characteristics for evaluation of the stability was proposed and the application of standard statistical tests for heterogeneous RNA samplings was justified. The structure-contextual characteristics by which the 5"-untranslated regions of high- and low-expression genes of dicot plants and mammals differ were found, and the results were interpreted in terms of secondary structure influence on translation initiation and on the general scheme of expression regulation. The application of the results obtained for the development of computer methods for RNA structural genomics, in particular, for RNA search in genome sequences, is discussed.     

RNA secondary structure 2D graphical representation without degeneracy

A two‐dimensional graphical representation (2DGRR) of RNA secondary structures using a two Cartesian coordinates system has been derived for mathematical denotation of RNA structure. The 2DGRR resolves structure degeneracy and avoids loss of information and the limitation that different structures correspond to the same curve. The RNA pseudo‐knots also can be represented as 2D graphical representations. © 2006 Wiley Periodicals, Inc. Int J Quantum Chem, 2006     

A semi-supervised learning approach for RNA secondary structure prediction

RNA secondary structure prediction is a key technology in RNA bioinformatics. Most algorithms for RNA secondary structure prediction use probabilistic models, in which the model parameters are trained with reliable RNA secondary structures. Because of the difficulty of determining RNA secondary structures by experimental procedures, such as NMR or X-ray crystal structural analyses, there are still many RNA sequences that could be useful for training whose secondary structures have not been experimentally determined. In this paper, we introduce a novel semi-supervised learning approach for training parameters in a probabilistic model of RNA secondary structures in which we employ not only RNA sequences with annotated secondary structures but also ones with unknown secondary structures. Our model is based on a hybrid of generative (stochastic context-free grammars) and discriminative models (conditional random fields) that has been successfully applied to natural language processing. Computational experiments indicate that the accuracy of secondary structure prediction is improved by incorporating RNA sequences with unknown secondary structures into training. To our knowledge, this is the first study of a semi-supervised learning approach for RNA secondary structure prediction. This technique will be useful when the number of reliable structures is limited.     

Prospects for tertiary structure prediction of RNA based on secondary structure information

We developed a method, called RNA Assembler using Secondary Structure Information Effectively (RASSIE), for predicting RNA tertiary structures using known secondary structure information. We attempted a fragment assembly-based method that uses a secondary structure-based fragment library. For several typical target structures such as stem-loops, bulge-loops, and 2-way junctions, our method provided numerous good quality candidate structures in less computational time than previously proposed methods. By using a high-resolution potential energy function, we were able to select good predicted structures from candidate structures. This method of efficient conformational search and detailed structure evaluation using high-resolution potential is potentially useful for the tertiary structure prediction of RNA.     

Optimization of quantum Monte Carlo wave functions by energy minimization

We study three wave function optimization methods based on energy minimization in a variational Monte Carlo framework: the Newton, linear, and perturbative methods. In the Newton method, the parameter variations are calculated from the energy gradient and Hessian, using a reduced variance statistical estimator for the latter. In the linear method, the parameter variations are found by diagonalizing a nonsymmetric estimator of the Hamiltonian matrix in the space spanned by the wave function and its derivatives with respect to the parameters, making use of a strong zero-variance principle. In the less computationally expensive perturbative method, the parameter variations are calculated by approximately solving the generalized eigenvalue equation of the linear method by a nonorthogonal perturbation theory. These general methods are illustrated here by the optimization of wave functions consisting of a Jastrow factor multiplied by an expansion in configuration state functions (CSFs) for the C2 molecule, including both valence and core electrons in the calculation. The Newton and linear methods are very efficient for the optimization of the Jastrow, CSF, and orbital parameters. The perturbative method is a good alternative for the optimization of just the CSF and orbital parameters. Although the optimization is performed at the variational Monte Carlo level, we observe for the C2 molecule studied here, and for other systems we have studied, that as more parameters in the trial wave functions are optimized, the diffusion Monte Carlo total energy improves monotonically, implying that the nodal hypersurface also improves monotonically.     

Monte Carlo free energy calculations using electronic structure methods

The molecular mechanics-based importance sampling function (MMBIF) algorithm [R. Iftimie, D. Salahub, D. Wei, and J. Schofield, J. Chem. Phys. 113, 4852 (2000)] is extended to incorporate semiempirical electronic structure methods in the secondary Markov chain, creating a fully quantum mechanical Monte Carlo sampling method for simulations of reactive chemical systems which, unlike the MMBIF algorithm, does not require the generation of a system-specific force field. The algorithm is applied to calculating the potential of mean force for the isomerization reaction of HCN using thermodynamic integration. Constraints are implemented in the sampling using a modification of the SHAKE algorithm, including that of a fixed, arbitrary reaction coordinate. Simulation results show that sampling efficiency with the semiempirical secondary potential is often comparable in quality to force fields constructed using the methods suggested in the original MMBIF work. The semiempirical based importance sampling method presented here is a useful alternative to MMBIF sampling as it can be applied to systems for which no suitable MM force field can be constructed.     

A visual representation for RNA secondary structure and its application

The graphical representation of biological sequences is an important subject in the area of genome studies. We propose a novel visual representation for RNA secondary structures. Some symmetric properties and information on the base distribution and compositions can be intuitively reflected by the projection graphs of the points corresponding to the RNA secondary structures. Then our method is applied to compute the similarity of 12 classical samples and 11 real RNA secondary structures. The results indicate that our method can not only effectively analyze the similarity between RNA secondary structures but also show a high consistency with other literatures. Moreover, our method only needs the geometrical center of the characteristic curve of the RNA secondary structure to compute the similarity matrix, which means a low computational complexity. © 2011 Wiley Periodicals, Inc. Int J Quantum Chem, 2011     

An algorithm for packing regular multistrand polypeptide structures by energy minimization

An algorithm has been developed for packing polypeptide chains by energy minimization subject to regularity conditions, in which regularity is maintained without the addition of pseudoenergy terms by defining the energy as a function of appropriately chosen independent variables. The gradient of the energy with respect to the independent variables is calculated analytically. The speed and efficiency of convergence of the algorithm to a local energy minimum are comparable to those of existing algorithms for minimizing the energy of a single polypeptide chain. The algorithm has been used to reinvestigate the minimum-energy regular structures of three-stranded (L -Ala)₈, three-stranded (L -Val)₆, five-stranded (L -Ile)₆, and the regular and truncated three-stranded (Gly-L -Pro-L -Pro)₄ triple helices. Local minima with improved packing energies, but with essentially unchanged geometrical properties, were obtained in all cases. The algorithm was also used to reinvestigate the structures proposed previously for the I and II forms of crystalline silk fibroin. The silk II structure was reproduced with slightly improved packing and little other change. The orthorhombic silk I structure showed more change and considerably improved packing energy, but the new regular monoclinic silk I structure had considerably higher energy. The results support the structure proposed previously for silk II and the orthorhombic structure, but not the monoclinic structure proposed for silk I. © 1994 by John Wiley & Sons, Inc.     

Predicting relative binding affinities of non-peptide HIV protease inhibitors with free energy perturbation calculations

The relative binding free energies in HIV protease of haloperidol thioketal (THK) and three of its derivatives were examined with free energy calculations. THK is a weak inhibitor (IC50 = 15 M) for which two cocrystal structures with HIV type 1 proteases have been solved [Rutenber, E. et al., J. Biol. Chem., 268 (1993) 15343]. A THK derivative with a phenyl group on C2 of the piperidine ring was expected to be a poor inhibitor based on experiments with haloperidol ketal and its 2- phenyl derivative (Caldera, P., personal communication). Our calculations predict that a 5-phenyl THK derivative, suggested based on examination of the crystal structure, will bind significantly better than THK. Although there are large error bars as estimated from hysteresis, the calculations predict that the 5-phenyl substituent is clearly favored over the 2-phenyl derivative as well as the parent compound. The unfavorable free energies of solvation of both phenyl THK derivatives relative to the parent compound contributed to their predicted binding free energies. In a third simulation, the change in binding free energy for 5-benzyl THK relative to THK was calculated. Although this derivative has a lower free energy in the protein, its decreased free energy of solvation increases the predicted G(bind) to the same range as that of the 2-phenyl derivative.