Integrative Structure Modeling: Overview and Assessment

2019 ◽  
Vol 88 (1) ◽  
pp. 113-135 ◽  
Author(s):  
Merav Braitbard ◽  
Dina Schneidman-Duhovny ◽  
Nir Kalisman
Keyword(s):  
Model Building ◽  
Structural Information ◽  
Protein Complexes ◽  
Structure Modeling ◽  
Sources Of Information ◽  
Multiple Sources ◽  
New Era ◽  
Integrative Modeling ◽  
Future Challenges ◽  
Computational Aspects

Integrative structure modeling computationally combines data from multiple sources of information with the aim of obtaining structural insights that are not revealed by any single approach alone. In the first part of this review, we survey the commonly used sources of structural information and the computational aspects of model building. Throughout the past decade, integrative modeling was applied to various biological systems, with a focus on large protein complexes. Recent progress in the field of cryo–electron microscopy (cryo-EM) has resolved many of these complexes to near-atomic resolution. In the second part of this review, we compare a range of published integrative models with their higher-resolution counterparts with the aim of critically assessing their accuracy. This comparison gives a favorable view of integrative modeling and demonstrates its ability to yield accurate and informative results. We discuss possible roles of integrative modeling in the new era of cryo-EM and highlight future challenges and directions.

scholarly journals Predicting protein model correctness in Coot using machine learning

2020 ◽  
Vol 76 (8) ◽  
pp. 713-723 ◽  
Author(s):  
Paul S. Bond ◽  
Keith S. Wilson ◽  
Kevin D. Cowtan
Keyword(s):  
Neural Networks ◽  
Test Performance ◽  
Model Building ◽  
Main Chain ◽  
Side Chains ◽  
Sources Of Information ◽  
Multiple Sources ◽  
Protein Model ◽  
Density Values ◽  
Automated Model Building

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.

scholarly journals DeepTracer: Fast Cryo-EM Protein Structure Modeling and Special Studies on CoV-related Complexes

2020 ◽  
Author(s):  
Jonas Pfab ◽  
Nhut Minh Phan ◽  
Dong Si
Keyword(s):  
Molecular Mechanisms ◽  
De Novo ◽  
Structural Information ◽  
Protein Complexes ◽  
Complex Structure ◽  
Structure Modeling ◽  
Density Maps ◽  
Average Residue ◽  
Development Processes ◽  
Special Studies

AbstractInformation about macromolecular structure of protein complexes such as SARS-CoV-2, and related cellular and molecular mechanisms can assist the search for vaccines and drug development processes. To obtain such structural information, we present DeepTracer, a fully automatic deep learning-based method for fast de novo multi-chain protein complex structure determination from high-resolution cryo-electron microscopy (cryo-EM) density maps. We applied DeepTracer on a previously published set of 476 raw experimental density maps and compared the results with a current state of the art method. The residue coverage increased by over 30% using DeepTracer and the RMSD value improved from 1.29Å to 1.18Å. Additionally, we applied DeepTracer on a set of 62 coronavirus-related density maps, among them 10 with no deposited structure available in EMDataResource. We observed an average residue match of 84% with the deposited structures and an average RMSD of 0.93Å. Additional tests with related methods further exemplify DeepTracer’s competitive accuracy and efficiency of structure modeling. DeepTracer allows for exceptionally fast computations, making it possible to trace around 60,000 residues in 350 chains within only two hours. The web service is globally accessible at https://deeptracer.uw.edu.

scholarly journals DeepTracer for fast de novo cryo-EM protein structure modeling and special studies on CoV-related complexes

2020 ◽  
Vol 118 (2) ◽  
pp. e2017525118
Author(s):  
Jonas Pfab ◽  
Nhut Minh Phan ◽  
Dong Si
Keyword(s):  
Molecular Mechanisms ◽  
De Novo ◽  
Structural Information ◽  
Protein Complexes ◽  
Complex Structure ◽  
Structure Modeling ◽  
Current State ◽  
Average Residue ◽  
Development Processes ◽  
Special Studies

Information about macromolecular structure of protein complexes and related cellular and molecular mechanisms can assist the search for vaccines and drug development processes. To obtain such structural information, we present DeepTracer, a fully automated deep learning-based method for fast de novo multichain protein complex structure determination from high-resolution cryoelectron microscopy (cryo-EM) maps. We applied DeepTracer on a previously published set of 476 raw experimental cryo-EM maps and compared the results with a current state of the art method. The residue coverage increased by over 30% using DeepTracer, and the rmsd value improved from 1.29 Å to 1.18 Å. Additionally, we applied DeepTracer on a set of 62 coronavirus-related cryo-EM maps, among them 10 with no deposited structure available in EMDataResource. We observed an average residue match of 84% with the deposited structures and an average rmsd of 0.93 Å. Additional tests with related methods further exemplify DeepTracer’s competitive accuracy and efficiency of structure modeling. DeepTracer allows for exceptionally fast computations, making it possible to trace around 60,000 residues in 350 chains within only 2 h. The web service is globally accessible at https://deeptracer.uw.edu.

Rapid Online Buffer Exchange: A Method for Screening of Proteins, Protein Complexes, and Cell Lysates by Native Mass Spectrometry

2019 ◽  
Author(s):  
Zachary VanAernum ◽  
Florian Busch ◽  
Benjamin J. Jones ◽  
Mengxuan Jia ◽  
Zibo Chen ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
High Speed ◽  
Structural Information ◽  
Protein Complexes ◽  
High Sensitivity ◽  
Native Mass Spectrometry ◽  
Structural Features ◽  
Consumer Products ◽  
Cell Lysates ◽  
Protein Expression And Purification

It is important to assess the identity and purity of proteins and protein complexes during and after protein purification to ensure that samples are of sufficient quality for further biochemical and structural characterization, as well as for use in consumer products, chemical processes, and therapeutics. Native mass spectrometry (nMS) has become an important tool in protein analysis due to its ability to retain non-covalent interactions during measurements, making it possible to obtain protein structural information with high sensitivity and at high speed. Interferences from the presence of non-volatiles are typically alleviated by offline buffer exchange, which is timeconsuming and difficult to automate. We provide a protocol for rapid online buffer exchange (OBE) nMS to directly screen structural features of pre-purified proteins, protein complexes, or clarified cell lysates. Information obtained by OBE nMS can be used for fast (<5 min) quality control and can further guide protein expression and purification optimization.

Protein Interaction Domains and Post-Translational Modifications: Structural Features and Drug Discovery Applications

2020 ◽  
Vol 27 (37) ◽  
pp. 6306-6355 ◽  
Author(s):  
Marian Vincenzi ◽  
Flavia Anna Mercurio ◽  
Marilisa Leone
Keyword(s):  
Drug Discovery ◽  
Protein Interaction ◽  
Protein Interactions ◽  
Structural Information ◽  
Protein Complexes ◽  
Structural Features ◽  
Protein Protein Interactions ◽  
Modular Architecture ◽  
Post Translational Modifications ◽  
Interaction Domains

Background:: Many pathways regarding healthy cells and/or linked to diseases onset and progression depend on large assemblies including multi-protein complexes. Protein-protein interactions may occur through a vast array of modules known as protein interaction domains (PIDs). Objective:: This review concerns with PIDs recognizing post-translationally modified peptide sequences and intends to provide the scientific community with state of art knowledge on their 3D structures, binding topologies and potential applications in the drug discovery field. Method:: Several databases, such as the Pfam (Protein family), the SMART (Simple Modular Architecture Research Tool) and the PDB (Protein Data Bank), were searched to look for different domain families and gain structural information on protein complexes in which particular PIDs are involved. Recent literature on PIDs and related drug discovery campaigns was retrieved through Pubmed and analyzed. Results and Conclusion:: PIDs are rather versatile as concerning their binding preferences. Many of them recognize specifically only determined amino acid stretches with post-translational modifications, a few others are able to interact with several post-translationally modified sequences or with unmodified ones. Many PIDs can be linked to different diseases including cancer. The tremendous amount of available structural data led to the structure-based design of several molecules targeting protein-protein interactions mediated by PIDs, including peptides, peptidomimetics and small compounds. More studies are needed to fully role out, among different families, PIDs that can be considered reliable therapeutic targets, however, attacking PIDs rather than catalytic domains of a particular protein may represent a route to obtain selective inhibitors.

Supporting the Development of Clinical Reasoning of Preprofessional Novices in Dysphagia Management

2019 ◽  
Vol 40 (03) ◽  
pp. 151-161 ◽  
Author(s):  
Sebastian Doeltgen ◽  
Stacie Attrill ◽  
Joanne Murray
Keyword(s):  
Clinical Reasoning ◽  
Clinical Decision Making ◽  
Management Practice ◽  
Clinical Decision ◽  
Assessment Process ◽  
Evidence Based ◽  
Sources Of Information ◽  
Multiple Sources ◽  
Implicit Processing ◽  
Reasoning Skills

AbstractProficient clinical reasoning is a critical skill in high-quality, evidence-based management of swallowing impairment (dysphagia). Clinical reasoning in this area of practice is a cognitively complex process, as it requires synthesis of multiple sources of information that are generated during a thorough, evidence-based assessment process and which are moderated by the patient's individual situations, including their social and demographic circumstances, comorbidities, or other health concerns. A growing body of health and medical literature demonstrates that clinical reasoning skills develop with increasing exposure to clinical cases and that the approaches to clinical reasoning differ between novices and experts. It appears that it is not the amount of knowledge held, but the way it is used, that distinguishes a novice from an experienced clinician. In this article, we review the roles of explicit and implicit processing as well as illness scripts in clinical decision making across the continuum of medical expertise and discuss how they relate to the clinical management of swallowing impairment. We also reflect on how this literature may inform educational curricula that support SLP students in developing preclinical reasoning skills that facilitate their transition to early clinical practice. Specifically, we discuss the role of case-based curricula to assist students to develop a meta-cognitive awareness of the different approaches to clinical reasoning, their own capabilities and preferences, and how and when to apply these in dysphagia management practice.

scholarly journals Structural catalog of core Atg proteins opens new era of autophagy research

2021 ◽  
Author(s):  
Kazuaki Matoba ◽  
Nobuo N Noda
Keyword(s):  
De Novo ◽  
Structural Information ◽  
Membrane Dynamics ◽  
Autophagosome Formation ◽  
New Era ◽  
Intracellular Degradation ◽  
Atg Proteins ◽  
Biological Studies ◽  
Evolutionarily Conserved ◽  
Biological Methods

Summary Autophagy, which is an evolutionarily conserved intracellular degradation system, involves de novo generation of autophagosomes that sequester and deliver diverse cytoplasmic materials to the lysosome for degradation. Autophagosome formation is mediated by approximately 20 core autophagy-related (Atg) proteins, which collaborate to mediate complicated membrane dynamics during autophagy. To elucidate the molecular functions of these Atg proteins in autophagosome formation, many researchers have tried to determine the structures of Atg proteins by using various structural biological methods. Although not sufficient, the basic structural catalog of all core Atg proteins was established. In this review article, we summarize structural biological studies of core Atg proteins, with an emphasis on recently unveiled structures, and describe the mechanistic breakthroughs in autophagy research that have derived from new structural information.

scholarly journals Mobility Trends before and after the Pandemic Outbreak: Analyzing the Metropolitan Area of Barcelona through the Lens of Equality and Sustainability

2021 ◽  
Vol 13 (14) ◽  
pp. 7908
Author(s):  
Lucía Mejía-Dorantes ◽  
Lídia Montero ◽  
Jaume Barceló
Keyword(s):  
Metropolitan Area ◽  
Spatial Arrangement ◽  
Individual Characteristics ◽  
Sources Of Information ◽  
Mobility Patterns ◽  
Multiple Sources ◽  
Temporal Dimension ◽  
Population Groups ◽  
Highly Educated ◽  
Before And After

The spatial arrangement of a metropolis is of utmost importance to carry out daily activities, which are constrained by space and time. Accessibility is not only shaped by the spatial and temporal dimension, but it is also defined by individual characteristics, such as gender, impairments, or socioeconomic characteristics of the citizens living or commuting in this area. This study analyzes mobility trends and patterns in the metropolitan area of Barcelona before and after the COVID-19 pandemic outbreak, with special emphasis on gender and equality. The study draws on multiple sources of information; however, two main datasets are analyzed: two traditional travel surveys from the transport metropolitan area of Barcelona and two coming from smartphone data. The results show that gender plays a relevant role when analyzing mobility patterns, as already highlighted in other studies, but, after the pandemic outbreak, some population groups were more likely to change their mobility patterns, for example, highly educated population groups and those with higher income. This study also highlights that e-activities may shape new mobility patterns and living conditions for some population segments, but some activities cannot be replaced by IT technologies. For all these reasons, city and transport planning should foster sustainable development policies, which will provide the maximum benefit for society.

Recent developments in dislocation pattern dynamics: Current opinions and perspectives

2018 ◽  
Vol 03 (03n04) ◽  
pp. 1840002 ◽  
Author(s):  
Dandan Lyu ◽  
Shaofan Li
Keyword(s):  
Crystal Plasticity ◽  
Materials Science ◽  
Pattern Dynamics ◽  
Future Challenges ◽  
Recent Developments ◽  
Computational Aspects ◽  
Stochastic Events ◽  
The Individual ◽  
Micro Structures ◽  
Dislocation Patterns

The development of crystal plasticity theory based on dislocation patterns dynamics has been an outstanding problem in materials science and condensed matter of physics. Dislocation is the origin of crystal plasticity, and it is both the individual dislocation behavior as well as the aggregated dislocations behaviors that govern the plastic flow. The interactions among dislocations are complex statistical and stochastic events, in which the spontaneous emergence of organized dislocation patterns formations is the most critical and intriguing events. Dislocation patterns consist of quasi-periodic dislocation-rich and dislocation poor regions, e.g. cells, veins, labyrinths, ladders structures, etc. during cyclic loadings. Dislocation patterns have prominent and decisive effects on work hardening and plastic strain localization, and thus these dislocation micro-structures are responsible to material properties at macroscale. This paper reviews the recent developments of experimental observation, physical modeling, and computer modeling on dislocation microstructure. In particular, we focus on examining the mechanism towards plastic deformation. The progress and limitations of different experiments and modeling approaches are discussed and compared. Finally, we share our perspectives on current issues and future challenges in both experimental, analytical modeling, and computational aspects of dislocation pattern dynamics.

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