scholarly journals Discrete analysis of camelid variable domains: sequences, structures, and in-silico structure prediction

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e8408
Author(s):  
Akhila Melarkode Vattekatte ◽  
Nicolas Ken Shinada ◽  
Tarun J. Narwani ◽  
Floriane Noël ◽  
Olivier Bertrand ◽  
...  
Keyword(s):  
Heavy Chain ◽  
Structure Prediction ◽  
Structural Models ◽  
Structural Features ◽  
Variable Domain ◽  
Antigen Binding ◽  
Structural Alphabet ◽  
Thermo Stability ◽  
Immunoglobulin Gamma

Antigen binding by antibodies requires precise orientation of the complementarity- determining region (CDR) loops in the variable domain to establish the correct contact surface. Members of the family Camelidae have a modified form of immunoglobulin gamma (IgG) with only heavy chains, called Heavy Chain only Antibodies (HCAb). Antigen binding in HCAbs is mediated by only three CDR loops from the single variable domain (VHH) at the N-terminus of each heavy chain. This feature of the VHH, along with their other important features, e.g., easy expression, small size, thermo-stability and hydrophilicity, made them promising candidates for therapeutics and diagnostics. Thus, to design better VHH domains, it is important to thoroughly understand their sequence and structure characteristics and relationship. In this study, sequence characteristics of VHH domains have been analysed in depth, along with their structural features using innovative approaches, namely a structural alphabet. An elaborate summary of various studies proposing structural models of VHH domains showed diversity in the algorithms used. Finally, a case study to elucidate the differences in structural models from single and multiple templates is presented. In this case study, along with the above-mentioned aspects of VHH, an exciting view of various factors in structure prediction of VHH, like template framework selection, is also discussed.

Differential binding of heavy chain variable domain 3 antigen binding fragments to protein a chromatography resins

2015 ◽  
Vol 1409 ◽  
pp. 60-69 ◽  
Author(s):  
Julia Bach ◽  
Nathaniel Lewis ◽  
Kathy Maggiora ◽  
Alison J. Gillespie ◽  
Lisa Connell-Crowley
Keyword(s):  
Heavy Chain ◽  
Protein A ◽  
Variable Domain ◽  
Antigen Binding ◽  
Differential Binding ◽  
Domain 3

scholarly journals Deep convolutional neural networks for predicting the quality of single protein structural models

2019 ◽  
Author(s):  
Jie Hou ◽  
Renzhi Cao ◽  
Jianlin Cheng
Keyword(s):  
Structure Prediction ◽  
Structural Model ◽  
Structural Models ◽  
Structural Features ◽  
Fixed Number ◽  
Deep Convolutional Neural Networks ◽  
Global Quality ◽  
Protein Model ◽  
Single Protein

AbstractPredicting the global quality and local (residual-specific) quality of a single protein structural model is important for protein structure prediction and application. In this work, we developed a deep one-dimensional convolutional neural network (1DCNN) that predicts the absolute local quality of a single protein model as well as two 1DCNNs to predict both local and global quality simultaneously through a novel multi-task learning framework. The networks accept sequential and structural features (i.e. amino acid sequence, agreement of secondary structure and solvent accessibilities, residual disorder properties and Rosetta energies) of a protein model of any size as input to predict its quality, which is different from existing methods using a fixed number of hand-crafted features as input. Our three methods (InteractQA-net, JointQA-net and LocalQA-net) were trained on the structural models of the single-domain protein targets of CASP8, 9, 10 and evaluated on the models of CASP11 and CASP12 targets. The results show that the performance of our deep learning methods is comparable to the state-of-the-art quality assessment methods. Our study also demonstrates that combining local and global quality predictions together improves the global quality prediction accuracy. The source code and executable of our methods are available at:https://github.com/multicom-toolbox/DeepCovQA

A Systematic Review on Popularity, Application and Characteristics of Protein Secondary Structure Prediction Tools

2019 ◽  
Vol 16 (2) ◽  
pp. 159-172 ◽  
Author(s):  
Elaheh Kashani-Amin ◽  
Ozra Tabatabaei-Malazy ◽  
Amirhossein Sakhteman ◽  
Bagher Larijani ◽  
Azadeh Ebrahim-Habibi
Keyword(s):  
Systematic Review ◽  
Secondary Structure ◽  
Structure Prediction ◽  
Web Of Science ◽  
Secondary Structure Prediction ◽  
Structural Information ◽  
Protein Secondary Structure ◽  
Structural Features ◽  
Prediction Tools ◽  
Insight Into

Background: Prediction of proteins’ secondary structure is one of the major steps in the generation of homology models. These models provide structural information which is used to design suitable ligands for potential medicinal targets. However, selecting a proper tool between multiple Secondary Structure Prediction (SSP) options is challenging. The current study is an insight into currently favored methods and tools, within various contexts. Objective: A systematic review was performed for a comprehensive access to recent (2013-2016) studies which used or recommended protein SSP tools. Methods: Three databases, Web of Science, PubMed and Scopus were systematically searched and 99 out of the 209 studies were finally found eligible to extract data. Results: Four categories of applications for 59 retrieved SSP tools were: (I) prediction of structural features of a given sequence, (II) evaluation of a method, (III) providing input for a new SSP method and (IV) integrating an SSP tool as a component for a program. PSIPRED was found to be the most popular tool in all four categories. JPred and tools utilizing PHD (Profile network from HeiDelberg) method occupied second and third places of popularity in categories I and II. JPred was only found in the two first categories, while PHD was present in three fields. Conclusion: This study provides a comprehensive insight into the recent usage of SSP tools which could be helpful for selecting a proper tool.

Introduction to Part I

2018 ◽  
Author(s):  
Andrew Berg ◽  
Rafael Portillo
Keyword(s):  
Monetary Policy ◽  
Structural Models ◽  
Stylized Facts ◽  
Policy Regimes ◽  
Detailed Case ◽  
Inaccurate Data ◽  
Cross Country

Developing an understanding of monetary policy in LICs must start with the evidence. This chapter briefly reviews the challenges facing the empirical researcher in SSA, including scarce and inaccurate data, short policy regimes that make powerful inference difficult, and the lack of structural models to help interpret the data. It provides an overview of Chapters 4–6, which take three very different approaches to looking at these data: a broad search for cross-country stylized facts (Chapter 4), a detailed case study of a major monetary policy event (Chapter 5), and an examination of whether vector auto-regressions (VARs)—the workhorse empirical tool in this area—are likely to yield useful results in the SSA context (Chapter 6).

Health system characteristics of quality care delivery: A comparative case study examination of palliative care for cancer patients in four regions in Ontario, Canada

2011 ◽  
Vol 26 (4) ◽  
pp. 322-335 ◽  
Author(s):  
Jonathan Sussman ◽  
Lisa Barbera ◽  
Daryl Bainbridge ◽  
Doris Howell ◽  
Jinghao Yang ◽  
...  
Keyword(s):  
Palliative Care ◽  
Health System ◽  
Acute Care ◽  
Pattern Analysis ◽  
Care Delivery ◽  
Quality Care ◽  
Structural Features ◽  
Comparative Case Study ◽  
Care Utilization

Background: A number of palliative care delivery models have been proposed to address the structural and process gaps in this care. However, the specific elements required to form competent systems are often vaguely described. Aim: The purpose of this study was to explore whether a set of modifiable health system factors could be identified that are associated with population palliative care outcomes, including less acute care use and more home deaths. Design: A comparative case study evaluation was conducted of ‘palliative care’ in four health regions in Ontario, Canada. Regions were selected as exemplars of high and low acute care utilization patterns, representing both urban and rural settings. A theory-based approach to data collection was taken using the System Competency Model, comprised of structural features known to be essential indicators of palliative care system performance. Key informants in each region completed study instruments. Data were summarized using qualitative techniques and an exploratory factor pattern analysis was completed. Results: 43 participants (10+ from each region) were recruited, representing clinical and administrative perspectives. Pattern analysis revealed six factors that discriminated between regions: overall palliative care planning and needs assessment; a common chart; standardized patient assessments; 24/7 palliative care team access; advanced practice nursing presence; and designated roles for the provision of palliative care services. Conclusions: The four palliative care regional ‘systems’ examined using our model were found to be in different stages of development. This research further informs health system planners on important features to incorporate into evolving palliative care systems.

scholarly journals Zoomqa: Residue-Level Single-Model QA Support Vector Machine Utilizing Sequential and 3D Structural Features

2021 ◽  
Author(s):  
Kyle Hippe ◽  
Cade Lilley ◽  
William Berkenpas ◽  
Kiyomi Kishaba ◽  
Renzhi Cao
Keyword(s):  
Protein Complex ◽  
Structure Prediction ◽  
Tertiary Structure ◽  
State Of The Art ◽  
Chemical Properties ◽  
Structural Features ◽  
Residue Level ◽  
Support Vector ◽  
Single Model

ABSTRACTMotivationThe Estimation of Model Accuracy problem is a cornerstone problem in the field of Bioinformatics. When predictions are made for proteins of which we do not know the native structure, we run into an issue to tell how good a tertiary structure prediction is, especially the protein binding regions, which are useful for drug discovery. Currently, most methods only evaluate the overall quality of a protein decoy, and few can work on residue level and protein complex. Here we introduce ZoomQA, a novel, single-model method for assessing the accuracy of a tertiary protein structure / complex prediction at residue level. ZoomQA differs from others by considering the change in chemical and physical features of a fragment structure (a portion of a protein within a radius r of the target amino acid) as the radius of contact increases. Fourteen physical and chemical properties of amino acids are used to build a comprehensive representation of every residue within a protein and grades their placement within the protein as a whole. Moreover, ZoomQA can evaluate the quality of protein complex, which is unique.ResultsWe benchmark ZoomQA on CASP14, it outperforms other state of the art local QA methods and rivals state of the art QA methods in global prediction metrics. Our experiment shows the efficacy of these new features, and shows our method is able to match the performance of other state-of-the-art methods without the use of homology searching against database or PSSM matrix.Availabilityhttp://[email protected]

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