scholarly journals A3D Database: Structure-based Protein Aggregation Predictions for the Human Proteome

2021 ◽  
Author(s):  
Aleksandra Elzbieta Badaczewska-Dawid ◽  
Javier Garcia-Pardo ◽  
Aleksander Kuriata ◽  
Jordi Pujols ◽  
Salvador Ventura ◽  
...  
Keyword(s):  
Protein Aggregation ◽  
Rational Design ◽  
Structural Information ◽  
Protein Solubility ◽  
Protein Structures ◽  
Human Proteome ◽  
Human Protein ◽  
Database Structure ◽  
Human Disorders ◽  
User Friendly

Motivation: Protein aggregation is associated with highly debilitating human disorders and constitutes a major bottleneck for producing therapeutic proteins. Our knowledge of the human protein structures repertoire has dramatically increased with the recent development of the AlphaFold (AF) deep-learning method. This structural information can be used to understand better protein aggregation properties and the rational design of protein solubility. This article uses the Aggrescan3D (A3D) tool to compute the structure-based aggregation predictions for the human proteome and make the predictions available in a database form. Results: Here, we present the A3D Database, in which we analyze the AF-predicted human protein structures (for over 17 thousand non-membrane proteins) in terms of their aggregation properties using the A3D tool. Each entry of the A3D Database provides a detailed analysis of the structure-based aggregation propensity computed with A3D. The A3D Database implements simple but useful graphical tools for visualizing and interpreting protein structure datasets. We discuss case studies illustrating how the database could be used to analyze physiologically relevant proteins. Furthermore, the database enables testing the influence of user-selected mutations on protein solubility and stability, all integrated into a user-friendly interface. Availability and implementation: A3D Database is freely available at: http://biocomp.chem.uw.edu.pl/A3D2/hproteome

The visualCMAT: A web-server to select and interpret correlated mutations/co-evolving residues in protein families

2018 ◽  
Vol 16 (02) ◽  
pp. 1840005 ◽  
Author(s):  
Dmitry Suplatov ◽  
Yana Sharapova ◽  
Daria Timonina ◽  
Kirill Kopylov ◽  
Vytas Švedas
Keyword(s):  
Rational Design ◽  
Visual Analysis ◽  
Structural Information ◽  
Protein Structures ◽  
Web Server ◽  
Physical Contact ◽  
Protein Families ◽  
Sequence Alignments ◽  
Homologous Proteins ◽  
Correlated Mutations

The visualCMAT web-server was designed to assist experimental research in the fields of protein/enzyme biochemistry, protein engineering, and drug discovery by providing an intuitive and easy-to-use interface to the analysis of correlated mutations/co-evolving residues. Sequence and structural information describing homologous proteins are used to predict correlated substitutions by the Mutual information-based CMAT approach, classify them into spatially close co-evolving pairs, which either form a direct physical contact or interact with the same ligand (e.g. a substrate or a crystallographic water molecule), and long-range correlations, annotate and rank binding sites on the protein surface by the presence of statistically significant co-evolving positions. The results of the visualCMAT are organized for a convenient visual analysis and can be downloaded to a local computer as a content-rich all-in-one PyMol session file with multiple layers of annotation corresponding to bioinformatic, statistical and structural analyses of the predicted co-evolution, or further studied online using the built-in interactive analysis tools. The online interactivity is implemented in HTML5 and therefore neither plugins nor Java are required. The visualCMAT web-server is integrated with the Mustguseal web-server capable of constructing large structure-guided sequence alignments of protein families and superfamilies using all available information about their structures and sequences in public databases. The visualCMAT web-server can be used to understand the relationship between structure and function in proteins, implemented at selecting hotspots and compensatory mutations for rational design and directed evolution experiments to produce novel enzymes with improved properties, and employed at studying the mechanism of selective ligand’s binding and allosteric communication between topologically independent sites in protein structures. The web-server is freely available at https://biokinet.belozersky.msu.ru/visualcmat and there are no login requirements.

Aggrescan3D standalone package for structure-based prediction of protein aggregation properties

2019 ◽  
Vol 35 (19) ◽  
pp. 3834-3835 ◽  
Author(s):  
Aleksander Kuriata ◽  
Valentin Iglesias ◽  
Mateusz Kurcinski ◽  
Salvador Ventura ◽  
Sebastian Kmiecik
Keyword(s):  
Protein Aggregation ◽  
Rational Design ◽  
Protein Solubility ◽  
Automated Analysis ◽  
Computational Framework ◽  
Computational Tools ◽  
Non Profit ◽  
Recent Experimental Study ◽  
The Impact ◽  
Python Package

Abstract Summary Aggrescan3D (A3D) standalone is a multiplatform Python package for structure-based prediction of protein aggregation properties and rational design of protein solubility. A3D allows the re-design of protein solubility by combining structural aggregation propensity and stability predictions, as demonstrated by a recent experimental study. It also enables predicting the impact of protein conformational fluctuations on the aggregation properties. The standalone A3D version is an upgrade of the original web server implementation—it introduces a number of customizable options, automated analysis of multiple mutations and offers a flexible computational framework for merging it with other computational tools. Availability and implementation A3D standalone is distributed under the MIT license, which is free for academic and non-profit users. It is implemented in Python. The A3D standalone source code, wiki with documentation and examples of use, and installation instructions for Linux, macOS and Windows are available in the A3D standalone repository at https://bitbucket.org/lcbio/aggrescan3d.

scholarly journals MutaFrame - an interpretative visualization framework for deleteriousness prediction of missense variants in the human exome

2021 ◽  
Author(s):  
François Ancien ◽  
Fabrizio Pucci ◽  
Wim Vranken ◽  
Marianne Rooman
Keyword(s):  
Protein Interactions ◽  
Structural Information ◽  
Protein Structures ◽  
Dimensional Structure ◽  
Visualization System ◽  
Monogenic Diseases ◽  
Human Disorders ◽  
Level Information ◽  
Protein Essentiality ◽  
The Impact

Motivation: High-throughput experiments are generating ever increasing amounts of various -omics data, so shedding new light on the link between human disorders, their genetic causes, and the related impact on protein behavior and structure. While numerous bioinformatics tools now exist that predict which variants in the human exome cause diseases, few tools predict the reasons why they might do so. Yet, understanding the impact of variants at the molecular level is a prerequisite for the rational development of targeted drugs or personalized therapies. Results: We present the updated MutaFrame webserver, which aims to meet this need. It offers two deleteriousness prediction softwares, DEOGEN2 and SNPMuSiC, and is designed for bioinformaticians and medical researchers who want to gain insights into the origins of monogenic diseases. It contains information at two levels for each human protein: its amino acid sequence and its 3-dimensional structure; we used the experimental structures whenever available, and modeled structures otherwise. MutaFrame also includes higher-level information, such as protein essentiality and protein-protein interactions. It has a user-friendly interface for the interpretation of results and a convenient visualization system for protein structures, in which the variant positions introduced by the user and other structural information are shown. In this way, MutaFrame aids our understanding of the pathogenic processes caused by single-site mutations and their molecular and contextual interpretation. Availability: Mutaframe webserver at http://mutaframe.com/

scholarly journals Protocols for rational design of protein solubility and aggregation properties using Aggrescan3D standalone

2020 ◽  
Author(s):  
Aleksander Kuriata ◽  
Aleksandra E. Badaczewska-Dawid ◽  
Jordi Pujols ◽  
Salvador Ventura ◽  
Sebastian Kmiecik
Keyword(s):  
Case Studies ◽  
Rational Design ◽  
Protein Solubility ◽  
Protein Structures ◽  
Three Dimensional ◽  
Computational Tools ◽  
Protein Surfaces ◽  
Amino Acid Mutations ◽  
Protein Variants ◽  
Therapeutics Development

SummaryProtein aggregation is a major hurdle in the development and manufacturing of protein-based therapeutics. Development of aggregation-resistant and stable protein variants can be guided by rational redesign using computational tools. Here, we describe the architecture and functionalities of the Aggrescan3D (A3D) standalone package for the rational design of protein solubility and aggregation properties based on three-dimensional protein structures. We present the case studies of the three therapeutic proteins, including antibodies, exploring the practical use of the A3D standalone tool. The case studies demonstrate that protein solubility can be easily improved by the A3D prediction of non-destabilizing amino acid mutations at the protein surfaces.

scholarly journals A graph-based algorithm for detecting rigid domains in protein structures

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Truong Khanh Linh Dang ◽  
Thach Nguyen ◽  
Michael Habeck ◽  
Mehmet Gültas ◽  
Stephan Waack
Keyword(s):  
Structural Changes ◽  
Viterbi Algorithm ◽  
Structural Information ◽  
Line Graph ◽  
Clustering Algorithms ◽  
Protein Structures ◽  
Alternative Methods ◽  
Structural Transitions ◽  
Tuning Parameters ◽  
Conformational Ensembles

Abstract Background Conformational transitions are implicated in the biological function of many proteins. Structural changes in proteins can be described approximately as the relative movement of rigid domains against each other. Despite previous efforts, there is a need to develop new domain segmentation algorithms that are capable of analysing the entire structure database efficiently and do not require the choice of protein-dependent tuning parameters such as the number of rigid domains. Results We develop a graph-based method for detecting rigid domains in proteins. Structural information from multiple conformational states is represented by a graph whose nodes correspond to amino acids. Graph clustering algorithms allow us to reduce the graph and run the Viterbi algorithm on the associated line graph to obtain a segmentation of the input structures into rigid domains. In contrast to many alternative methods, our approach does not require knowledge about the number of rigid domains. Moreover, we identified default values for the algorithmic parameters that are suitable for a large number of conformational ensembles. We test our algorithm on examples from the DynDom database and illustrate our method on various challenging systems whose structural transitions have been studied extensively. Conclusions The results strongly suggest that our graph-based algorithm forms a novel framework to characterize structural transitions in proteins via detecting their rigid domains. The web server is available at http://azifi.tz.agrar.uni-goettingen.de/webservice/.

scholarly journals Antibodies for profiling the human proteome-The Human Protein Atlas as a resource for cancer research

PROTEOMICS ◽  
2012 ◽  
Vol 12 (13) ◽  
pp. 2067-2077 ◽  
Author(s):  
Anna Asplund ◽  
Per-Henrik D. Edqvist ◽  
Jochen M. Schwenk ◽  
Fredrik Pontén
Keyword(s):  
Cancer Research ◽  
Human Proteome ◽  
Human Protein ◽  
Human Protein Atlas

SIR2004: an improved tool for crystal structure determination and refinement

2005 ◽  
Vol 38 (2) ◽  
pp. 381-388 ◽  
Author(s):  
Maria C. Burla ◽  
Rocco Caliandro ◽  
Mercedes Camalli ◽  
Benedetta Carrozzini ◽  
Giovanni L. Cascarano ◽  
...  
Keyword(s):  
Crystal Structure ◽  
Ab Initio ◽  
Structure Determination ◽  
Protein Structures ◽  
Medium Size ◽  
Graphical Interface ◽  
Asymmetric Unit ◽  
Density Maps ◽  
Data Resolution ◽  
User Friendly

SIR2004is the evolution of theSIR2002program [Burla, Camalli, Carrozzini, Cascarano, Giacovazzo, Polidori & Spagna (2003).J. Appl. Cryst.36, 1103]. It is devoted to the solution of crystal structures by direct and Patterson methods. Several new features implemented inSIR2004make this program efficient: it is able to solveab initioboth small/medium-size structures as well as macromolecules (up to 2000 atoms in the asymmetric unit). In favourable circumstances, the program is also able to solve protein structures with data resolution up to 1.4–1.5 Å, and to provide interpretable electron density maps. A powerful user-friendly graphical interface is provided.

Examining the structure of the mature amyloid fibril

2002 ◽  
Vol 30 (4) ◽  
pp. 521-525 ◽  
Author(s):  
O. S. Makin ◽  
L. C. Serpell
Keyword(s):  
Self Assembly ◽  
Rational Design ◽  
Amyloid Fibrils ◽  
Structural Information ◽  
X Ray ◽  
X Ray Crystallography ◽  
Cryo Electron Microscopy ◽  
Fibre Diffraction ◽  
Complementary Techniques ◽  
Mature Fibril

The pathogenesis of the group of diseases known collectively as the amyloidoses is characterized by the deposition of insoluble amyloid fibrils. These are straight, unbranching structures about 70–120 å (1 å = 0.1 nm) in diameter and of indeterminate length formed by the self-assembly of a diverse group of normally soluble proteins. Knowledge of the structure of these fibrils is necessary for the understanding of their abnormal assembly and deposition, possibly leading to the rational design of therapeutic agents for their prevention or disaggregation. Structural elucidation is impeded by fibril insolubility and inability to crystallize, thus preventing the use of X-ray crystallography and solution NMR. CD, Fourier-transform infrared spectroscopy and light scattering have been used in the study of the mechanism of fibril formation. This review concentrates on the structural information about the final, mature fibril and in particular the complementary techniques of cryo-electron microscopy, solid-state NMR and X-ray fibre diffraction.

scholarly journals Expanding our knowledge of the protein universe: Modelling of protein structures

2014 ◽  
Vol 70 (a1) ◽  
pp. C491-C491
Author(s):  
Jürgen Haas ◽  
Alessandro Barbato ◽  
Tobias Schmidt ◽  
Steven Roth ◽  
Andrew Waterhouse ◽  
...  
Keyword(s):  
Computational Modeling ◽  
Structure Prediction ◽  
Structural Information ◽  
Protein Structures ◽  
Model Organism ◽  
Data Bank ◽  
Continuous Model ◽  
Structure Modeling ◽  
Structure Comparison ◽  
Modeling And Prediction

Computational modeling and prediction of three-dimensional macromolecular structures and complexes from their sequence has been a long standing goal in structural biology. Over the last two decades, a paradigm shift has occurred: starting from a large "knowledge gap" between the huge number of protein sequences compared to a small number of experimentally known structures, today, some form of structural information – either experimental or computational – is available for the majority of amino acids encoded by common model organism genomes. Methods for structure modeling and prediction have made substantial progress of the last decades, and template based homology modeling techniques have matured to a point where they are now routinely used to complement experimental techniques. However, computational modeling and prediction techniques often fall short in accuracy compared to high-resolution experimental structures, and it is often difficult to convey the expected accuracy and structural variability of a specific model. Retrospectively assessing the quality of blind structure prediction in comparison to experimental reference structures allows benchmarking the state-of-the-art in structure prediction and identifying areas which need further development. The Critical Assessment of Structure Prediction (CASP) experiment has for the last 20 years assessed the progress in the field of protein structure modeling based on predictions for ca. 100 blind prediction targets per experiment which are carefully evaluated by human experts. The "Continuous Model EvaluatiOn" (CAMEO) project aims to provide a fully automated blind assessment for prediction servers based on weekly pre-released sequences of the Protein Data Bank PDB. CAMEO has been made possible by the development of novel scoring methods such as lDDT, which are robust against domain movements to allow for automated continuous structure comparison without human intervention.

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