<i>CAB</i>: a cyclic automatic model-building procedure

Burla, Maria Cristina; Carrozzini, Benedetta; Cascarano, G.; Polidori, Giampiero; Giacovazzo, Carmelo

doi:10.1107/s2059798318013438

Cited by 5 publications

(6 citation statements)

References 17 publications

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“…CAB (Burla et al, 2018) is a model-building pipeline in the SIR suite (Burla et al, 2015) that uses Buccaneer. It starts with a phase-refinement procedure that combines classical density modification using DM (Cowtan et al, 2001) with other phaserefinement techniques (Burla et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

ModelCraft: an advanced automated model-building pipeline using Buccaneer

Bond¹,

Cowtan²

2022

Acta Crystallogr D Struct Biol

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Interactive model building can be a difficult and time-consuming step in the structure-solution process. Automated model-building programs such as Buccaneer often make it quicker and easier by completing most of the model in advance. However, they may fail to do so with low-resolution data or a poor initial model or map. The Buccaneer pipeline is a relatively simple program that iterates Buccaneer with REFMAC to refine the model and update the map. A new pipeline called ModelCraft has been developed that expands on this to include shift-field refinement, machine-learned pruning of incorrect residues, classical density modification, addition of water and dummy atoms, building of nucleic acids and final rebuilding of side chains. Testing was performed on 1180 structures solved by experimental phasing, 1338 structures solved by molecular replacement using homologues and 2030 structures solved by molecular replacement using predicted AlphaFold models. Compared with the previous Buccaneer pipeline, ModelCraft increased the mean completeness of the protein models in the experimental phasing cases from 91% to 95%, the molecular-replacement cases from 50% to 78% and the AlphaFold cases from 82% to 91%.

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Section: Introductionmentioning

confidence: 99%

ModelCraft: an advanced automated model-building pipeline using Buccaneer

Bond¹,

Cowtan²

2022

Acta Crystallogr D Struct Biol

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“…The last refinement step involves the application of AMB programs: among the most popular AMB tools for proteins, we refer to Buccaneer (Cowtan, 2006), ARP/wARP (Langer et al, 2008) and phenix.autobuild (Terwilliger et al, 2008). Recently, the CAB program has been described (Burla et al, 2018), which is based on the cyclic application of Buccaneer. The basic Buccaneer algorithms are not modified.…”

Section: Introductionmentioning

confidence: 99%

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

Cascarano

Giacovazzo

2021

Acta Cryst Sect D Struct Biol

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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.

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“…There are Buccaneer pipelines available in CCP4i (Potterton et al, 2003) and CCP4i2 (Potterton et al, 2018). Buccaneer is also used in other pipelines such as CRANK (Ness et al, 2004;Pannu et al, 2011), CAB (Burla et al, 2018) and CCP4Build, which is a new model-building pipeline available in CCP4Cloud (Krissinel et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Predicting protein model correctness in Coot using machine learning

Bond

Wilson

Cowtan

2020

Acta Crystallogr D Struct Biol

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Manually identifying and correcting errors in protein models can be a slow process, but improvements in validation tools and automated model-building software can contribute to reducing this burden. This article presents a new correctness score that is produced by combining multiple sources of information using a neural network. The residues in 639 automatically built models were marked as correct or incorrect by comparing them with the coordinates deposited in the PDB. A number of features were also calculated for each residue using Coot, including map-to-model correlation, density values, B factors, clashes, Ramachandran scores, rotamer scores and resolution. Two neural networks were created using these features as inputs: one to predict the correctness of main-chain atoms and the other for side chains. The 639 structures were split into 511 that were used to train the neural networks and 128 that were used to test performance. The predicted correctness scores could correctly categorize 92.3% of the main-chain atoms and 87.6% of the side chains. A Coot ML Correctness script was written to display the scores in a graphical user interface as well as for the automatic pruning of chains, residues and side chains with low scores. The automatic pruning function was added to the CCP4i2 Buccaneer automated model-building pipeline, leading to significant improvements, especially for high-resolution structures.

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CAB: a cyclic automatic model-building procedure

Cited by 5 publications

References 17 publications

ModelCraft: an advanced automated model-building pipeline using Buccaneer

ModelCraft: an advanced automated model-building pipeline using Buccaneer

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

Predicting protein model correctness in Coot using machine learning

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