Molecular replacement

2013 ◽  
Author(s):  
Carmelo Giacovazzo
Keyword(s):  
Experimental Data ◽  
Ab Initio ◽  
Prior Information ◽  
Direct Methods ◽  
Molecular Geometry ◽  
Anomalous Dispersion ◽  
Supplementary Information ◽  
Molecular Replacement ◽  
Ab Initio Methods ◽  
Dispersion Effects

Modern phasing methods may be subdivided into: (a) ab initio approaches, which include direct methods, Patterson techniques, charge flipping, and VLD (vive la difference). These approaches do not use (but, suitably modified, some of them can) any prior information on the molecular geometry. (b) non-ab initio methods. In this category, we include molecular replacement (MR), isomorphous derivatives (SIR-MIR) and anomalous dispersion (SAD-MAD) approaches. MR exploits information on the molecular geometry (i.e. the target molecule is known to be similar to that present in another previously solved structure), SIR-MIR uses the supplementary information contained in the experimental data from one or more isomorphous structures, and SAD-MAD exploits anomalous dispersion effects (we will see that such effects simulate isomorphism). It is immediately clear that classification into ab initio and non-ab initio categories may be questionable, because it hides substantial diversities in the prior information. For example, SAD-MAD, unlike SIR-MIR, may use the native protein data only, and no prior information on the molecular geometry is necessary; apparently, this may be considered to belong to the ab initio category. MR does not use supplementary experimental data, and therefore seems not to be similar to SAD-MAD and SIR-MIR. The latter two techniques are often referred to as experimental phasing approaches, but also this appellation is questionable; indeed, the experiment does not provide phases, these are derived by treating the experimental data, as in any other phasing approach. The above considerations suggest that a more precise, even if conventional, definition for ab initio methods is necessary; in this book, they are identified as those techniques which do not use the molecular geometry as prior information and exploit only native data, without anomalous dispersion effects. We have seen in Section 12.8 that some approaches use low-level prior information, not specific to the current structure, but valid for a large range of compounds (e.g. the coordination of some heavy atoms and corresponding bond angles and distances). Also such procedures may be considered as ab initio approaches; to this category we add ARCIMBOLDO, which combines the ‘trivial’ information that a protein consists of smaller molecular fragments of known geometry (among which are α-helices) with MR. ARCIMBOLDO is summarized in Section 13.9.

Calculations of the Spontaneous Polarizations and Dielectric Constants for AlN, GaN, InN, and SiC

2010 ◽  
Vol 645-648 ◽  
pp. 1203-1206
Author(s):  
Sergey Y. Davydov ◽  
Alexander A. Lebedev
Keyword(s):  
Experimental Data ◽  
Ab Initio ◽  
Spontaneous Polarization ◽  
Reasonable Agreement ◽  
Low Frequency ◽  
Analytical Form ◽  
Dielectric Constants ◽  
Numerical Calculations ◽  
Ab Initio Methods ◽  
Theoretical Results

Within the scope of the Harrison’s bond orbital model the spontaneous polarization, high- and low frequency dielectric constants are obtained in an analytical form. Theoretical results are in a reasonable agreement with the experimental data available and the numerical calculations based on the ab initio methods.

THE DETERMINATION OF ENERGY BY AB INITIO METHODS: ESTIMATION OF ERRORS AND SOME IMPROVEMENTS

2007 ◽  
Vol 06 (02) ◽  
pp. 269-279 ◽  
Author(s):  
ABRAHAM F. JALBOUT
Keyword(s):  
Experimental Data ◽  
Statistical Analysis ◽  
Ab Initio ◽  
Random Error ◽  
Theoretical Calculations ◽  
Ab Initio Methods ◽  
Accurate Data ◽  
Standard Procedures ◽  
Random Components

This work presents a statistical analysis of errors in the ab initio determination of molecular energy. These sets of analyses have allowed us to separate the errors in systematic and random components and also to realize that differences between experimental data and theoretical calculations are larger than those initially suspected. Although there is a limit to how small this difference can be analyzed by our methods, procedures to improve ab initio molecular energies are proposed. This has been achieved by reducing the systematic error obtained by correlating the calculated results to the most accurate data (in this case CCSD (T)), as well as by reducing the random error by mixing the results of different standard procedures.

EXPO2011: A new package for powder crystallography

2011 ◽  
Vol 26 (S1) ◽  
pp. S2-S12 ◽  
Author(s):  
Angela Altomare ◽  
Corrado Cuocci ◽  
Carmelo Giacovazzo ◽  
Anna Moliterni ◽  
Rosanna Rizzi
Keyword(s):  
Ab Initio ◽  
Simulated Annealing Algorithm ◽  
Rietveld Method ◽  
Structure Refinement ◽  
A Priori ◽  
Direct Methods ◽  
Molecular Geometry ◽  
Real Space ◽  
Basic Algorithm ◽  
Structure Solution

EXPO2011 is a new package for phasing crystal structures from powder diffraction diagrams. It is able to carry out all the steps necessary for crystal structure solution, from pattern indexation up to Rietveld method for structure refinement: for each step, the basic algorithm is described. Phasing is performed viaab initio (e.g., Direct Methods, integrated by real space refinement) and non ab initio techniques (e.g., simulated annealing algorithm, when molecular geometry is a priori known). Some emphasis is given to running procedures: the main commands and directives are described, to allow the user to run default and non-default phasing attempts.

CH2 revisited

2004 ◽  
Vol 82 (6) ◽  
pp. 684-693 ◽  
Author(s):  
Apostolos Kalemos ◽  
Thom H Dunning Jr. ◽  
Aristides Mavridis ◽  
James F Harrison
Keyword(s):  
Experimental Data ◽  
Electronic Structure ◽  
Potential Energy ◽  
Ab Initio ◽  
Key Words ◽  
State Of The Art ◽  
Basis Sets ◽  
Ab Initio Methods ◽  
H Channels ◽  
Potential Curves

The first four states of the CH2 molecule ([Formula: see text]3B1, ã1 A1, [Formula: see text]1A1, and [Formula: see text]1A1) are examined using state-of-the-art ab initio methods and basis sets. The construction of potential energy curves with respect to the C + H2 and CH + H channels provides significant clues to understanding the geometric and electronic structure of the above states. All of our numerical findings are in excellent agreement with the existing experimental data. Key words: CH2, MRCI, potential curves, vbL icons.

scholarly journals Ab initio solution of macromolecular crystal structures without direct methods

2017 ◽  
Vol 114 (14) ◽  
pp. 3637-3641 ◽  
Author(s):  
Airlie J. McCoy ◽  
Robert D. Oeffner ◽  
Antoni G. Wrobel ◽  
Juha R. M. Ojala ◽  
Karl Tryggvason ◽  
...  
Keyword(s):  
Ab Initio ◽  
Crystal Structures ◽  
Signal To Noise Ratio ◽  
Direct Methods ◽  
Molecular Replacement ◽  
Signal To Noise ◽  
Model Quality ◽  
Theoretical Understanding ◽  
Unknown Structure ◽  
Noise Ratio

The majority of macromolecular crystal structures are determined using the method of molecular replacement, in which known related structures are rotated and translated to provide an initial atomic model for the new structure. A theoretical understanding of the signal-to-noise ratio in likelihood-based molecular replacement searches has been developed to account for the influence of model quality and completeness, as well as the resolution of the diffraction data. Here we show that, contrary to current belief, molecular replacement need not be restricted to the use of models comprising a substantial fraction of the unknown structure. Instead, likelihood-based methods allow a continuum of applications depending predictably on the quality of the model and the resolution of the data. Unexpectedly, our understanding of the signal-to-noise ratio in molecular replacement leads to the finding that, with data to sufficiently high resolution, fragments as small as single atoms of elements usually found in proteins can yield ab initio solutions of macromolecular structures, including some that elude traditional direct methods.

Assignment of Raman-active vibrational modes of tetragonal mackinawite: Raman investigations and ab initio calculations

RSC Advances ◽  
2014 ◽  
Vol 4 (49) ◽  
pp. 25827-25834 ◽  
Author(s):  
Y. El Mendili ◽  
B. Minisini ◽  
A. Abdelouas ◽  
J.-F. Bardeau
Keyword(s):  
Experimental Data ◽  
Perturbation Theory ◽  
Ab Initio Calculations ◽  
Ab Initio ◽  
Density Functional ◽  
Direct Methods ◽  
Vibrational Modes ◽  
Dispersion Correction ◽  
Density Functional Perturbation Theory ◽  
Calculation Results

We report on the first assignment of the Raman-active vibrational modes of mackinawite using Density Functional Perturbation Theory and direct methods with BLYP + dispersion correction. Based on experimental data and calculation results, the Raman bands were assigned as 236 cm−1 (B1g), 256 cm−1 (Eg), 376 cm−1 (A1g) and 395 cm−1 (Eg).

Crystal structure determination and refinementviaSIR2014

2015 ◽  
Vol 48 (1) ◽  
pp. 306-309 ◽  
Author(s):  
Maria Cristina Burla ◽  
Rocco Caliandro ◽  
Benedetta Carrozzini ◽  
Giovanni Luca Cascarano ◽  
Corrado Cuocci ◽  
...  
Keyword(s):  
Crystal Structure ◽  
Ab Initio ◽  
Structure Refinement ◽  
Three Dimensional ◽  
Direct Methods ◽  
Molecular Replacement ◽  
Dimensional Electron ◽  
Large Structures ◽  
Density Maps ◽  
Structure Solution

SIR2014is the latest program of theSIRsuite for crystal structure solution of small, medium and large structures. A variety of phasing algorithms have been implemented, bothab initio(standard or modern direct methods, Patterson techniques,Vive la Différence) and non-ab initio(simulated annealing, molecular replacement). The program contains tools for crystal structure refinement and for the study of three-dimensional electron-density mapsviasuitable viewers.

Automatic structure determination from powder data withEXPO2004

2004 ◽  
Vol 37 (6) ◽  
pp. 1025-1028 ◽  
Author(s):  
Angela Altomare ◽  
Rocco Caliandro ◽  
Mercedes Camalli ◽  
Corrado Cuocci ◽  
Carmelo Giacovazzo ◽  
...  
Keyword(s):  
Structure Factor ◽  
Solution Process ◽  
Direct Methods ◽  
Molecular Geometry ◽  
Supplementary Information ◽  
Structure Model ◽  
Graphical Interface ◽  
Automatic Structure ◽  
Structure Solution

EXPO2004is the updated version of theEXPOprogram [Altomareet al.(1999).J. Appl. Cryst.32, 339–340]. The traditional steps of theab initiopowder solution process are performed automatically: indexing, space-group determination, decomposition of the pattern for extracting the observed structure-factor moduli, structure solution by direct methods, model refinement by Rietveld technique. Special strategies may be applied to improve both the estimates of the extracted structure-factor moduli and the quality of the structure model. In addition, the use of special procedures exploiting available supplementary information on molecular geometry can be successfully adopted. The graphical interface has also been improved.

aPRBind: protein–RNA interface prediction by combining sequence and I-TASSER model-based structural features learned with convolutional neural networks

2020 ◽  
Author(s):  
Yang Liu ◽  
Weikang Gong ◽  
Yanpeng Zhao ◽  
Xueqing Deng ◽  
Shan Zhang ◽  
...  
Keyword(s):  
Ab Initio ◽  
Binding Site ◽  
Rna Binding ◽  
Critical Role ◽  
Structural Features ◽  
Supplementary Information ◽  
Ab Initio Methods ◽  
Site Prediction ◽  
Sequence Features ◽  
Binding Site Prediction

Abstract Motivation Protein–RNA interactions play a critical role in various biological processes. The accurate prediction of RNA-binding residues in proteins has been one of the most challenging and intriguing problems in the field of computational biology. The existing methods still have a relatively low accuracy especially for the sequence-based ab-initio methods. Results In this work, we propose an approach aPRBind, a convolutional neural network-based ab-initio method for RNA-binding residue prediction. aPRBind is trained with sequence features and structural ones (particularly including residue dynamics information and residue–nucleotide propensity developed by us) that are extracted from the predicted structures by I-TASSER. The analysis of feature contributions indicates the sequence features are most important, followed by dynamics information, and the sequence and structural features are complementary in binding site prediction. The performance comparison of our method with other peer ones on benchmark dataset shows that aPRBind outperforms some state-of-the-art ab-initio methods. Additionally, aPRBind can give a better prediction for the modeled structures with TM-score≥0.5, and meanwhile since the structural features are not very sensitive to the refined 3D structures, aPRBind has only a marginal dependence on the accuracy of the structure model, which allows aPRBind to be applied to the RNA-binding site prediction for the modeled or unbound structures. Availability and implementation The source code is available at https://github.com/ChunhuaLiLab/aPRbind. Supplementary information Supplementary data are available at Bioinformatics online.

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