Activities Prediction of Drug Molecules by Using Automated Model Building with Descriptor Selection

2021 ◽  
pp. 73-84
Author(s):  
Yue Liu ◽  
Wenjie Tian ◽  
Hao Zhang
Keyword(s):  
Model Building ◽  
Drug Molecules ◽  
Descriptor Selection ◽  
Automated Model Building

Towards the automatic crystal structure solution of nucleic acids: automated model building using the new CAB program

2021 ◽  
Vol 77 (12) ◽  
Author(s):  
Giovanni Luca Cascarano ◽  
Carmelo Giacovazzo
Keyword(s):  
Nucleic Acids ◽  
Prior Information ◽  
Model Building ◽  
Phase Error ◽  
Heavy Atoms ◽  
Density Maps ◽  
Structure Solution ◽  
Nucleic Acid Structures ◽  
Automated Model Building ◽  
Model Chain

CAB, a recently described automated model-building (AMB) program, has been modified to work effectively with nucleic acids. To this end, several new algorithms have been introduced and the libraries have been updated. To reduce the input average phase error, ligand heavy atoms are now located before starting the CAB interpretation of the electron-density maps. Furthermore, alternative approaches are used depending on whether the ligands belong to the target or to the model chain used in the molecular-replacement step. Robust criteria are then applied to decide whether the AMB model is acceptable or whether it must be modified to fit prior information on the target structure. In the latter case, the model chains are rearranged to fit prior information on the target chains. Here, the performance of the new AMB program CAB applied to various nucleic acid structures is discussed. Other well documented programs such as Nautilus, ARP/wARP and phenix.autobuild were also applied and the experimental results are described.

scholarly journals Variability Attribution for Automated Model Building

2019 ◽  
Vol 21 (3) ◽  
Author(s):  
Moustafa M. A. Ibrahim ◽  
Rikard Nordgren ◽  
Maria C. Kjellsson ◽  
Mats O. Karlsson
Keyword(s):  
Model Building ◽  
Automated Model Building

scholarly journals Map segmentation, automated model-building and their application to the Cryo-EM Model Challenge

2018 ◽  
Author(s):  
Thomas C. Terwilliger ◽  
Paul D. Adams ◽  
Pavel V. Afonine ◽  
Oleg V. Sobolev
Keyword(s):  
Model Building ◽  
Map Segmentation ◽  
Segmentation Approach ◽  
Density Map ◽  
Automated Model Building ◽  
Unique Part

AbstractA recently-developed method for identifying a compact, contiguous region representing the unique part of a density map was applied to 218 cryo-EM maps with resolutions of 4.5 Å or better. The key elements of the segmentation procedure are (1) identification of all regions of density above a threshold and (2) choice of a unique set of these regions, taking symmetry into consideration, that maximize connectivity and compactness. This segmentation approach was then combined with tools for automated map sharpening and model-building to generate models for the 12 maps in the 2016 cryo-EM model challenge in a fully automated manner. The resulting models have completeness from 24% to 82% and RMS distances from reference interpretations of 0.6 Å to 2.1 Å.

A fragmentation and reassembly method forab initiophasing

2015 ◽  
Vol 71 (2) ◽  
pp. 304-312 ◽  
Author(s):  
Rojan Shrestha ◽  
Kam Y. J. Zhang
Keyword(s):  
Ab Initio ◽  
Structure Determination ◽  
Model Building ◽  
De Novo ◽  
Sequence Information ◽  
Molecular Replacement ◽  
Protein Targets ◽  
Current State ◽  
Viable Approach ◽  
Automated Model Building

Ab initiophasing withde novomodels has become a viable approach for structural solution from protein crystallographic diffraction data. This approach takes advantage of the known protein sequence information, predictsde novomodels and uses them for structure determination by molecular replacement. However, even the current state-of-the-artde novomodelling method has a limit as to the accuracy of the model predicted, which is sometimes insufficient to be used as a template for successful molecular replacement. A fragment-assembly phasing method has been developed that starts from an ensemble of low-accuracyde novomodels, disassembles them into fragments, places them independently in the crystallographic unit cell by molecular replacement and then reassembles them into a whole structure that can provide sufficient phase information to enable complete structure determination by automated model building. Tests on ten protein targets showed that the method could solve structures for eight of these targets, although the predictedde novomodels cannot be used as templates for successful molecular replacement since the best model for each target is on average more than 4.0 Å away from the native structure. The method has extended the applicability of theab initiophasing byde novomodels approach. The method can be used to solve structures when the bestde novomodels are still of low accuracy.

scholarly journals How good are my data and what is the resolution?

2013 ◽  
Vol 69 (7) ◽  
pp. 1204-1214 ◽  
Author(s):  
Philip R. Evans ◽  
Garib N. Murshudov
Keyword(s):  
Model Building ◽  
Point Group ◽  
Simulated Data ◽  
Group Symmetry ◽  
Data Set ◽  
High Resolution Data ◽  
Common Scale ◽  
Scaling Models ◽  
Automated Model Building

Following integration of the observed diffraction spots, the process of `data reduction' initially aims to determine the point-group symmetry of the data and the likely space group. This can be performed with the programPOINTLESS. The scaling program then puts all the measurements on a common scale, averages measurements of symmetry-related reflections (using the symmetry determined previously) and produces many statistics that provide the first important measures of data quality. A new scaling program,AIMLESS, implements scaling models similar to those inSCALAbut adds some additional analyses. From the analyses, a number of decisions can be made about the quality of the data and whether some measurements should be discarded. The effective `resolution' of a data set is a difficult and possibly contentious question (particularly with referees of papers) and this is discussed in the light of tests comparing the data-processing statistics with trials of refinement against observed and simulated data, and automated model-building and comparison of maps calculated with different resolution limits. These trials show that adding weak high-resolution data beyond the commonly used limits may make some improvement and does no harm.

scholarly journals Automated nucleic acid chain tracing in real time

IUCrJ ◽  
2014 ◽  
Vol 1 (6) ◽  
pp. 387-392 ◽  
Author(s):  
Kevin Cowtan
Keyword(s):  
Electron Density ◽  
Model Building ◽  
Protein Structures ◽  
Current View ◽  
Crystallographic Structure ◽  
Large Structures ◽  
Density Maps ◽  
Structure Solution ◽  
Automated Model Building ◽  
Electron Density Map

The crystallographic structure solution of nucleotides and nucleotide complexes is now commonplace. The resulting electron-density maps are often poorer than for proteins, and as a result interpretation in terms of an atomic model can require significant effort, particularly in the case of large structures. While model building can be performed automatically, as with proteins, the process is time-consuming, taking minutes to days depending on the software and the size of the structure. A method is presented for the automatic building of nucleotide chains into electron density which is fast enough to be used in interactive model-building software, with extended chain fragments built around the current view position in a fraction of a second. The speed of the method arises from the determination of the `fingerprint' of the sugar and phosphate groups in terms of conserved high-density and low-density features, coupled with a highly efficient scoring algorithm. Use cases include the rapid evaluation of an initial electron-density map, addition of nucleotide fragments to prebuilt protein structures, and in favourable cases the completion of the structure while automated model-building software is still running. The method has been incorporated into theCootsoftware package.

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