scholarly journals Sequence assignment validation in cryo-EM models with checkMySequence

2022 ◽  
Author(s):  
Grzegorz Chojnowski
Keyword(s):  
Structural Biology ◽  
Structure Prediction ◽  
Model Building ◽  
Visual Interpretation ◽  
Interactive Tools ◽  
Map Interpretation ◽  
Map Resolution ◽  
Protein Models ◽  
Register Error ◽  
Sequence Assignment

The availability of new AI-based protein structure prediction tools radically changed the way cryo-EM maps are interpreted, but it has not eliminated the challenges of map interpretation faced by a microscopist. Models will continue to be locally rebuilt and refined using interactive tools. This inevitably results in occasional errors, among which register-shifts remain one of the most difficult to identify and correct. Here we introduce checkMySequence; a fast, fully automated and parameter-free method for detecting register-shifts in protein models built into cryo-EM maps. We show that the method can assist model building in cases where poorer map resolution hinders visual interpretation. We also show that checkMySequence could have helped avoid a widely discussed sequence register error in a model of SARS-CoV-2 RNA-dependent RNA polymerase that was originally detected thanks to a visual residue-by-residue inspection by members of the structural biology community.

scholarly journals Sequence assignment for low-resolution modelling of protein crystal structures

2019 ◽  
Vol 75 (8) ◽  
pp. 753-763 ◽  
Author(s):  
Grzegorz Chojnowski ◽  
Joana Pereira ◽  
Victor S. Lamzin
Keyword(s):  
Model Building ◽  
Protein Crystal ◽  
Side Chain ◽  
Sequence Coverage ◽  
Protein Fragments ◽  
New Methods ◽  
Combined Use ◽  
Protein Models ◽  
Automated Model Building ◽  
Sequence Assignment

The performance of automated model building in crystal structure determination usually decreases with the resolution of the experimental data, and may result in fragmented models and incorrect side-chain assignment. Presented here are new methods for machine-learning-based docking of main-chain fragments to the sequence and for their sequence-independent connection using a dedicated library of protein fragments. The combined use of these new methods noticeably increases sequence coverage and reduces fragmentation of the protein models automatically built with ARP/wARP.

scholarly journals Cryo‐EM map interpretation and protein model‐building using iterative map segmentation

2019 ◽  
Vol 29 (1) ◽  
pp. 87-99 ◽  
Author(s):  
Thomas C. Terwilliger ◽  
Paul D. Adams ◽  
Pavel V. Afonine ◽  
Oleg V. Sobolev
Keyword(s):  
Model Building ◽  
Map Interpretation ◽  
Protein Model ◽  
Map Segmentation

Exact methods for lattice protein models

2014 ◽  
Vol 10 (4) ◽  
Author(s):  
Martin Mann ◽  
Rolf Backofen
Keyword(s):  
Protein Structure ◽  
Structure Prediction ◽  
Prediction Method ◽  
Hydrophobic Core ◽  
Exact Methods ◽  
Optimal Structures ◽  
Energy Models ◽  
Formation And Evolution ◽  
Protein Models ◽  
Lattice Protein Models

AbstractLattice protein models are well-studied abstractions of globular proteins. By discretizing the structure space and simplifying the energy model over regular proteins, they enable detailed studies of protein structure formation and evolution. However, even in the simplest lattice protein models, the prediction of optimal structures is computationally difficult. Therefore, often, heuristic approaches are applied to find such conformations. Commonly, heuristic methods find only locally optimal solutions. Nevertheless, there exist methods that guarantee to predict globally optimal structures. Currently, only one such exact approach is publicly available, namely the constraint-based protein structure prediction method and variants. Here, we review exact approaches and derived methods. We discuss fundamental concepts like hydrophobic core construction and their use in optimal structure prediction, as well as possible applications like combinations of different energy models.

scholarly journals The accuracy of protein models automatically built into cryo-EM maps with ARP/wARP

2021 ◽  
Vol 77 (2) ◽  
pp. 142-150
Author(s):  
Grzegorz Chojnowski ◽  
Egor Sobolev ◽  
Philipp Heuser ◽  
Victor S. Lamzin
Keyword(s):  
Main Chain ◽  
De Novo ◽  
Atomic Displacement ◽  
Test Cases ◽  
Model Accuracy ◽  
X Ray ◽  
Map Interpretation ◽  
Recent Developments ◽  
Atomic Displacement Parameters ◽  
Protein Models

Recent developments in cryogenic electron microscopy (cryo-EM) have enabled structural studies of large macromolecular complexes at resolutions previously only attainable using macromolecular crystallography. Although a number of methods can already assist in de novo building of models into high-resolution cryo-EM maps, automated and reliable map interpretation remains a challenge. Presented here is a systematic study of the accuracy of models built into cryo-EM maps using ARP/wARP. It is demonstrated that the local resolution is a good indicator of map interpretability, and for the majority of the test cases ARP/wARP correctly builds 90% of main-chain fragments in regions where the local resolution is 4.0 Å or better. It is also demonstrated that the coordinate accuracy for models built into cryo-EM maps is comparable to that of X-ray crystallographic models at similar local cryo-EM and crystallographic resolutions. The model accuracy also correlates with the refined atomic displacement parameters.

scholarly journals MELD-DNA: A new tool for capturing protein-DNA binding

2021 ◽  
Author(s):  
Antonio Bauza ◽  
Alberto Perez
Keyword(s):  
Structural Biology ◽  
Conformational Changes ◽  
Dna Sequences ◽  
Structure Prediction ◽  
Binding Mode ◽  
Binding Modes ◽  
Dna Interactions ◽  
Protein Dna Interactions ◽  
Multiple Binding ◽  
New Software Development

Herein we present MELD-DNA, a novel computational approach to address the problem of protein-DNA structure prediction. This method addresses well-known issues hampering current computational approaches to bridge the gap between structural and sequence knowledge, such as large conformational changes in DNA and highly charged electrostatic interaction during binding. MELD- DNA is able to: i) sample multiple binding modes, ii) identify the preferred binding mode from the ensembles, and iii) provide qualitative binding preferences between DNA sequences. We expect the results presented herein will have impact in the field of biophysics (through new software development), structural biology (by complementing DNA structural databases) and supramolecular chemistry (by bringing new insights into protein-DNA interactions).

scholarly journals Decoding of novel missense TSC2 gene variants using in-silico methods

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Shruthi Sudarshan ◽  
Manoj Kumar ◽  
Punit Kaur ◽  
Atin Kumar ◽  
Sethuraman G. ◽  
...  
Keyword(s):  
Homology Modeling ◽  
Structure Prediction ◽  
Model Building ◽  
Catalytic Domain ◽  
Hydrophobic Interactions ◽  
Core Protein ◽  
Dimensional Structure ◽  
Specific Cell ◽  
Autosomal Dominant Disorder ◽  
The Impact

Abstract Background Mutations in TSC1 or TSC2 gene cause tuberous sclerosis complex (TSC), an autosomal dominant disorder characterized by the formation of non-malignant hamartomas in multiple vital organs. TSC1 and TSC2 gene products form TSC heterodimer that senses specific cell growth conditions to control mTORC1 signalling. Methods In the present study 98 TSC patients were tested for variants in TSC1 and TSC2 genes and 14 novel missense variations were identified. The pathogenecity of these novel variations was determined by applying different bioinformatics tools involving computer aided protein modeling. Results Protein modelling could be done only for ten variants which were within the functional part of the protein. Homology modeling is the most reliable method for structure prediction of a protein. Since no sequence homology structure was available for the tuberin protein, three dimensional structure was modeled by a combination of homology modeling and the predictive fold recognition and threading method using Phyre2 threading server. The best template structures for model building of the TSC1 interacting domain, tuberin domain and GAP domain are the crystal structures of clathrin adaptor core protein, Rap1GAP catalytic domain and Ser/Thr kinase Tor protein respectively. Conclusions In this study, an attempt has been made to assess the impact of each novel missense variant based on their TSC1-TSC2 hydrophobic interactions and its effect on protein function.

Increasing the accuracy of protein loop structure prediction with evolutionary constraints

2018 ◽  
Vol 35 (15) ◽  
pp. 2585-2592 ◽  
Author(s):  
Claire Marks ◽  
Charlotte M Deane
Keyword(s):  
Structure Prediction ◽  
De Novo ◽  
Conformational Space ◽  
Supplementary Information ◽  
Evolutionary Constraints ◽  
Final Model ◽  
Loop Conformations ◽  
Loop Regions ◽  
Accuracy Of Prediction ◽  
Protein Models

Abstract Motivation Accurate prediction of loop structures remains challenging. This is especially true for long loops where the large conformational space and limited coverage of experimentally determined structures often leads to low accuracy. Co-evolutionary contact predictors, which provide information about the proximity of pairs of residues, have been used to improve whole-protein models generated through de novo techniques. Here we investigate whether these evolutionary constraints can enhance the prediction of long loop structures. Results As a first stage, we assess the accuracy of predicted contacts that involve loop regions. We find that these are less accurate than contacts in general. We also observe that some incorrectly predicted contacts can be identified as they are never satisfied in any of our generated loop conformations. We examined two different strategies for incorporating contacts, and on a test set of long loops (10 residues or more), both approaches improve the accuracy of prediction. For a set of 135 loops, contacts were predicted and hence our methods were applicable in 97 cases. Both strategies result in an increase in the proportion of near-native decoys in the ensemble, leading to more accurate predictions and in some cases improving the root-mean-square deviation of the final model by more than 3 Å. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Synchrotron-based macromolecular crystallography module for an undergraduate biochemistry laboratory course

2016 ◽  
Vol 49 (6) ◽  
pp. 2235-2243 ◽  
Author(s):  
Kyle M. Stiers ◽  
Christopher B. Lee ◽  
Jay C. Nix ◽  
John J. Tanner ◽  
Lesa J. Beamer
Keyword(s):  
Diffraction Data ◽  
Model Building ◽  
Data Sets ◽  
Macromolecular Crystallography ◽  
X Ray Diffraction ◽  
Synchrotron Source ◽  
Undergraduate Laboratory ◽  
Laboratory Component ◽  
Set Up ◽  
Protein Models

This paper describes the introduction of synchrotron-based macromolecular crystallography (MX) into an undergraduate laboratory class. An introductory 2 week experimental module on MX, consisting of four laboratory sessions and two classroom lectures, was incorporated into a senior-level biochemistry class focused on a survey of biochemical techniques, including the experimental characterization of proteins. Students purified recombinant protein samples, set up crystallization plates and flash-cooled crystals for shipping to a synchrotron. Students then collected X-ray diffraction data sets from their crystalsviathe remote interface of the Molecular Biology Consortium beamline (4.2.2) at the Advanced Light Source in Berkeley, CA, USA. Processed diffraction data sets were transferred back to the laboratory and used in conjunction with partial protein models provided to the students for refinement and model building. The laboratory component was supplemented by up to 2 h of lectures by faculty with expertise in MX. This module can be easily adapted for implementation into other similar undergraduate classes, assuming the availability of local crystallographic expertise and access to remote data collection at a synchrotron source.

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