Prediction of RNA secondary structure by free energy minimization

RNA secondary structure is often predicted from sequence by free energy minimization. Over the past two years, advances have been made in the estimation of folding free energy change, the mapping of secondary structure and the implementation of computer programs for structure prediction. The trends in computer program development are: efficient use of experimental mapping of structures to constrain structure prediction; use of statistical mechanics to improve the fidelity of structure prediction; inclusion of pseudoknots in secondary structure prediction; and use of two or more homologous sequences to find a common structure.     

Docking of cyclohexanol-derivatives into the active site of liver alcohol dehydrogenase. Using computer graphics and energy minimization

Model building and energy minimization procedures have been used to determine a productive substrate binding mode in liver alcohol dehydrogenase for secondary alcohols. These docking results have been compared to some of the extensive amounts of kinetic data available for this enzyme. The indirect diamond lattice approach first suggested by Prelog (Prelog, V. (1964) Pure Appl. Chem. 9, 119-130) to describe the active site of an enzyme has been used to build a direct diamond lattice from the crystallographic model of the enzyme. This lattice was oriented and positioned into the active site using the productive binding mode of cyclohexanol derivatives obtained from model building. We then classified the positions as allowed, forbidden, or boundary depending on their distances to protein atoms. We found very good agreement between the classification of our direct diamond lattice points and those of the indirect lattice obtained by others from kinetic studies. Finally we have extended the lattice as an aid to predict the stereospecificity of the enzyme for molecules which cover other regions of the active site.     

Antiestrogenic piperidinediones designed prospectively using computer graphics and energy calculations of DNA-ligand complexes

Drug design technology based upon DNA stereochemistry and now supplemented by computer modeling was used to design a novel compound to inhibit estrogen-induced tumor cell growth. A known compound 3-phenylacetylamino-2,6-piperidinedione (PP) was accommodated in partially unwound DNA in a manner consistent with criteria for antiestrogens. Examination of the PP-DNA complex revealed that substitution of a hydroxyl group at the para position (p-OH-PP) would provide a stereospecific hydrogen bond and a substantial increase in fit as assessed by energy calculations. The antiestrogen tamoxifen could also be accommodated within the site; analogous substitution of a hydroxyl at the 4 position resulted in a better fitting molecule. 4-Hydroxytamoxifen is a more potent antiestrogen than tamoxifen. Synthesis and subsequent evaluation of p-OH-PP as an inhibitor of estrogen stimulated MCF-7 (E3) human breast cancer cell growth demonstrated that p-OH-PP was more active than both PP and its hydrolysis product phenylacetylglutamine. As predicted, the order of fit into DNA correlated with the relative ability to inhibit estrogen-induced growth of tumor cells suggesting that the evolving drug design technology will be valuable in developing new drugs for breast cancer.     

Computation of mixed phosphatidylcholine-cholesterol bilayer structures by energy minimization.

The energetically preferred structures of dimyristoylphosphatidylcholine (DMPC)-cholesterol bilayers were determined at a 1:1 mole ratio. Crystallographic symmetry operations were used to generate planar bilayers of cholesterol and DMPC. Energy minimization was carried out with respect to bond rotations, rigid body motions, and the two-dimensional lattice constants. The lowest energy structures had a hydrogen bond between the cholesterol hydroxyl and the carbonyl oxygen of the sn-2 acyl chain, but the largest contribution to the intermolecular energy was from the nonbonded interactions between the flat alpha surface of cholesterol and the acyl chains of DMPC. Two modes of packing in the bilayer were found; in structure A (the global minimum), unlike molecules are nearest neighbors, whereas in structure B (second lowest energy) like-like intermolecular interactions predominate. Crystallographic close packing of the molecules in the bilayer was achieved, as judged from the molecular areas and the bilayer thickness. These energy-minimized structures are consistent with the available experimental data on mixed bilayers of lecithin and cholesterol, and may be used as starting points for molecular dynamics or other calculations on bilayers.     

Accurate NMR structures through minimization of an extended hybrid energy

The use of generous distance bounds has been the hallmark of NMR structure determination. However, bounds necessitate the estimation of data quality before the calculation, reduce the information content, introduce human bias, and allow for major errors in the structures. Here, we propose a new rapid structure calculation scheme based on Bayesian analysis. The minimization of an extended energy function, including a new type of distance restraint and a term depending on the data quality, results in an estimation of the data quality in addition to coordinates. This allows for the determination of the optimal weight on the experimental information. The resulting structures are of better quality and closer to the X-ray crystal structure of the same molecule. With the new calculation approach, the analysis of discrepancies from the target distances becomes meaningful. The strategy may be useful in other applications-for example, in homology modeling.     

Evaluation of the suitability of free-energy minimization using nearest-neighbor energy parameters for RNA secondary structure prediction

Background

A detailed understanding of an RNA's correct secondary and tertiary structure is crucial to understanding its function and mechanism in the cell. Free energy minimization with energy parameters based on the nearest-neighbor model and comparative analysis are the primary methods for predicting an RNA's secondary structure from its sequence. Version 3.1 of Mfold has been available since 1999. This version contains an expanded sequence dependence of energy parameters and the ability to incorporate coaxial stacking into free energy calculations. We test Mfold 3.1 by performing the largest and most phylogenetically diverse comparison of rRNA and tRNA structures predicted by comparative analysis and Mfold, and we use the results of our tests on 16S and 23S rRNA sequences to assess the improvement between Mfold 2.3 and Mfold 3.1.

Results

The average prediction accuracy for a 16S or 23S rRNA sequence with Mfold 3.1 is 41%, while the prediction accuracies for the majority of 16S and 23S rRNA structures tested are between 20% and 60%, with some having less than 20% prediction accuracy. The average prediction accuracy was 71% for 5S rRNA and 69% for tRNA. The majority of the 5S rRNA and tRNA sequences have prediction accuracies greater than 60%. The prediction accuracy of 16S rRNA base-pairs decreases exponentially as the number of nucleotides intervening between the 5' and 3' halves of the base-pair increases.

Conclusion

Our analysis indicates that the current set of nearest-neighbor energy parameters in conjunction with the Mfold folding algorithm are unable to consistently and reliably predict an RNA's correct secondary structure. For 16S or 23S rRNA structure prediction, Mfold 3.1 offers little improvement over Mfold 2.3. However, the nearest-neighbor energy parameters do work well for shorter RNA sequences such as tRNA or 5S rRNA, or for larger rRNAs when the contact distance between the base-pairs is less than 100 nucleotides.     

X-ray structures of ferritins and related proteins

Ferritins are members of a much larger superfamily of proteins, which are characterised by a structural motif consisting of a bundle of four parallel and anti-parallel α helices. The ferritin superfamily itself is widely distributed across all three living kingdoms, in both aerobic and anaerobic organisms, and a considerable number of X-ray structures are available, some at extremely high resolution. We describe first of all the subunit structure of mammalian H and L chain ferritins and then discuss intersubunit interactions in the 24-subunit quaternary structure of these ferritins. Bacteria contain two types of ferritins, FTNs, which like mammalian ferritins do not contain haem, and the haem-containing BFRs. The characteristic carboxylate-bridged di-iron ferroxidase sites of H chain ferritins, FTNs and BFRs are compared, as are the potential entry sites for iron and the ‘nucleation’ site of L chain ferritins. Finally we discuss the three-dimensional structures of the 12-subunit bacterial Dps (DNA-binding protein from starved cells) proteins as well as their intersubunit di-iron ferroxidase site.     

Prediction of the binding energy for small molecules,peptides and proteins

A fast and reliable evaluation of the binding energy from a single conformation of a molecular complex is an important practical task. Knowledge‐based scoring schemes may not be sufficiently general and transferable, while molecular dynamics or Monte Carlo calculations with explicit solvent are too computationally expensive for many applications. Recently, several empirical schemes using finite difference Poisson–Boltzmann electrostatics to predict energies for particular types of complexes were proposed. Here, an improved empirical binding energy function has been derived and validated on three different types of complexes: protein–small ligand, protein–peptide and protein–protein. The function uses the boundary element algorithm to evaluate the electrostatic solvation energy. We show that a single set of parameters can predict the relative binding energies of the heterogeneous validation set of complexes with 2.5 kcal/mol accuracy. We also demonstrate that global optimization of the ligand and of the flexible side‐chains of the receptor improves the accuracy of the evaluation. Copyright © 1999 John Wiley & Sons, Ltd.     

Prediction of pi-turns in proteins using PSI-BLAST profiles and secondary structure information

Due to the structural and functional importance of tight turns, some methods have been proposed to predict gamma-turns, beta-turns, and alpha-turns in proteins. In the past, studies of pi-turns were made, but not a single prediction approach has been developed so far. It will be useful to develop a method for identifying pi-turns in a protein sequence. In this paper, the support vector machine (SVM) method has been introduced to predict pi-turns from the amino acid sequence. The training and testing of this approach is performed with a newly collected data set of 640 non-homologous protein chains containing 1931 pi-turns. Different sequence encoding schemes have been explored in order to investigate their effects on the prediction performance. With multiple sequence alignment and predicted secondary structure, the final SVM model yields a Matthews correlation coefficient (MCC) of 0.556 by a 7-fold cross-validation. A web server implementing the prediction method is available at the following URL: http://210.42.106.80/piturn/.     

Prediction of alpha-turns in proteins using PSI-BLAST profiles and secondary structure information

In this paper a systematic attempt has been made to develop a better method for predicting alpha-turns in proteins. Most of the commonly used approaches in the field of protein structure prediction have been tried in this study, which includes statistical approach "Sequence Coupled Model" and machine learning approaches; i) artificial neural network (ANN); ii) Weka (Waikato Environment for Knowledge Analysis) Classifiers and iii) Parallel Exemplar Based Learning (PEBLS). We have also used multiple sequence alignment obtained from PSIBLAST and secondary structure information predicted by PSIPRED. The training and testing of all methods has been performed on a data set of 193 non-homologous protein X-ray structures using five-fold cross-validation. It has been observed that ANN with multiple sequence alignment and predicted secondary structure information outperforms other methods. Based on our observations we have developed an ANN-based method for predicting alpha-turns in proteins. The main components of the method are two feed-forward back-propagation networks with a single hidden layer. The first sequence-structure network is trained with the multiple sequence alignment in the form of PSI-BLAST-generated position specific scoring matrices. The initial predictions obtained from the first network and PSIPRED predicted secondary structure are used as input to the second structure-structure network to refine the predictions obtained from the first net. The final network yields an overall prediction accuracy of 78.0% and MCC of 0.16. A web server AlphaPred (http://www.imtech.res.in/raghava/alphapred/) has been developed based on this approach.     

Prediction of structures of multidomain proteins from structures of the individual domains

We describe the development of a method for assembling structures of multidomain proteins from structures of isolated domains. The method consists of an initial low-resolution search in which the conformational space of the domain linker is explored using the Rosetta de novo structure prediction method, followed by a high-resolution search in which all atoms are treated explicitly and backbone and side chain degrees of freedom are simultaneously optimized. The method recapitulates, often with very high accuracy, the structures of existing multidomain proteins.     

Prediction of the three-dimensional structure of proteins using the electrostatic screening model and hierarchic condensation

We describe a method for predicting the three-dimensional (3-D) structure of proteins from their sequence alone. The method is based on the electrostatic screening model for the stability of the protein main-chain conformation. The free energy of a protein as a function of its conformation is obtained from the potentials of mean force analysis of high-resolution x-ray protein structures. The free energy function is simple and contains only 44 fitted coefficients. The minimization of the free energy is performed by the torsion space Monte Carlo procedure using the concept of hierarchic condensation. The Monte Carlo minimization procedure is applied to predict the secondary, super-secondary, and native 3-D structures of 12 proteins with 28–110 amino acids. The 3-D structures of the majority of local secondary and super-secondary structures are predicted accurately. This result suggests that control in forming the native-like local structure is distributed along the entire protein sequence. The native 3-D structure is predicted correctly for 3 of 12 proteins composed mainly from the α-helices. The method fails to predict the native 3-D structure of proteins with a predominantly β secondary structure. We suggest that the hierarchic condensation is not an appropriate procedure for simulating the folding of proteins made up primarily from β-strands. The method has been proved accurate in predicting the local secondary and super-secondary structures in the blind ab initio 3-D prediction experiment. Proteins 31:74–96, 1998. © 1998 Wiley-Liss, Inc.     

Fold helical proteins by energy minimization in dihedral space and a DFIRE-based statistical energy function

Statistical energy functions are discrete (or stepwise) energy functions that lack van der Waals repulsion. As a result, they are often applied directly to a given structure (native or decoy) without further energy minimization being performed to the structure. However, the full benefit (or hidden defect) of an energy function cannot be revealed without energy minimization. This paper tests a recently developed, all-atom statistical energy function by energy minimization with a fixed secondary helical structure in dihedral space. This is accomplished by combining the statistical energy function based on a distance-scaled finite ideal-gas reference (DFIRE) state with a simple repulsive interaction and an improper torsion energy function. The energy function was used to minimize 2000 random initial structures of 41 small and medium-sized helical proteins in a dihedral space with a fixed helical region. Results indicate that near-native structures for most studied proteins can be obtained by minimization alone. The average minimum root-mean-squared distance (rmsd) from the native structure for all 41 proteins is 4.1 A. The energy function (together with a simple clustering of similar structures) also makes a reasonable selection of near-native structures from minimized structures. The average rmsd value and the average rank for the best structure in the top five is 6.8 A and 2.4, respectively. The accuracy of the structures sampled and the structure selections can be improved significantly with the removal of flexible terminal regions in rmsd calculations and in minimization and with the increase in the number of minimizations. The minimized structures form an excellent decoy set for testing other energy functions because most structures are well-packed with minimum hard-core overlaps with correct hydrophobic/hydrophilic partitioning. They are available online at http://theory.med.buffalo.edu.