Protein Interaction Domains: structural features and drug discovery applications (part 2)

2020 ◽  
Vol 27 ◽  
Author(s):  
Marian Vincenzi ◽  
Flavia Anna Mercurio ◽  
Marilisa Leone
Keyword(s):  
Protein Interaction ◽  
Viral Infections ◽  
Structural Information ◽  
Data Bank ◽  
Structural Features ◽  
Modular Organization ◽  
Modular Architecture ◽  
Consensus Sequences ◽  
Interaction Domains ◽  
Catalytic Functions

Background: Proteins present a modular organization made up of several domains. Apart from domains playing catalytic functions, many others are crucial to recruit interactors. The latter domains can be defined "PIDs" (Protein Interaction Domains) and are responsible for pivotal outcomes in signal transduction and a certain array of normal physiological and disease-related pathways. Targeting such PIDs with small molecules and peptides able to modulate their interaction networks, may represent a valuable route to discover novel therapeutics. Objective: This work represents a continuation of a very recent review describing PIDs able to recognize post-translationally modified peptide segments. On the contrary, this second part concerns with PIDs that interact with simple peptide sequences provided with standard amino acids. Method: Crucial structural information on different domain subfamilies and their interactomes was gained by a wide search in different online available databases (including the PDB (Protein Data Bank), the Pfam (Protein family), and the SMART (Simple Modular Architecture Research Tool)). Pubmed was searched as well to explore the most recent literature related to the topic. Results and Conclusion: PIDs are multifaceted: they have all diverse structural features and can recognize several consensus sequences. PIDs can be linked to different diseases onset and progression, like cancer or viral infections and find applications in the personalized medicine field. Many efforts have been centered on peptide/peptidomimetic inhibitors of PIDs mediated interactions but much more work needs to be conducted to improve drug-likeness and interaction affinities of identified compounds.

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.

scholarly journals Lemon: a framework for rapidly mining structural information from the Protein Data Bank

2019 ◽  
Vol 35 (20) ◽  
pp. 4165-4167 ◽  
Author(s):  
Jonathan Fine ◽  
Gaurav Chopra
Keyword(s):  
Protein Data Bank ◽  
Structural Information ◽  
Computational Cost ◽  
Data Bank ◽  
Structural Features ◽  
Supplementary Information ◽  
Develop Software ◽  
Reading Text ◽  
Essential Resource ◽  
3D Descriptors

Abstract Motivation The Protein Data Bank (PDB) currently holds over 140 000 biomolecular structures and continues to release new structures on a weekly basis. The PDB is an essential resource to the structural bioinformatics community to develop software that mine, use, categorize and analyze such data. New computational biology methods are evaluated using custom benchmarking sets derived as subsets of 3D experimentally determined structures and structural features from the PDB. Currently, such benchmarking features are manually curated with custom scripts in a non-standardized manner that results in slow distribution and updates with new experimental structures. Finally, there is a scarcity of standardized tools to rapidly query 3D descriptors of the entire PDB. Results Our solution is the Lemon framework, a C++11 library with Python bindings, which provides a consistent workflow methodology for selecting biomolecular interactions based on user criterion and computing desired 3D structural features. This framework can parse and characterize the entire PDB in <10 min on modern, multithreaded hardware. The speed in parsing is obtained by using the recently developed MacroMolecule Transmission Format to reduce the computational cost of reading text-based PDB files. The use of C++ lambda functions and Python bindings provide extensive flexibility for analysis and categorization of the PDB by allowing the user to write custom functions to suite their objective. We think Lemon will become a one-stop-shop to quickly mine the entire PDB to generate desired structural biology features. Availability and implementation The Lemon software is available as a C++ header library along with a PyPI package and example functions at https://github.com/chopralab/lemon. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Lemon: a framework for rapidly mining structural information from the Protein Data Bank

2018 ◽  
Author(s):  
Jonathan Fine ◽  
Gaurav Chopra
Keyword(s):  
Protein Data Bank ◽  
Structural Information ◽  
Computational Cost ◽  
Data Bank ◽  
Structural Features ◽  
Develop Software ◽  
Reading Text ◽  
One Stop ◽  
Essential Resource ◽  
3D Descriptors

AbstractMotivationThe protein data bank (PDB) currently holds over 140,000 biomolecular structures and continues to release new structures on a weekly basis. The PDB is an essential resource to the structural bioinformatics community to develop software that mine, use, categorize, and analyze such data. New computational biology methods are evaluated using custom benchmarking sets derived as subsets of 3D experimentally determined structures and structural features from the PDB. Currently, such benchmarking features are manually curated with custom scripts in a non-standardized manner that results in slow distribution and updates with new experimental structures. Finally, there is a scarcity of standardized tools to rapidly query 3D descriptors of the entire PDB.ApproachOur solution is the Lemon framework, a C++11 library with Python bindings, which provides a consistent workflow methodology for selecting biomolecular interactions based on user criterion and computing desired 3D structural features. This framework can parse and characterize the entire PDB in less than ten minutes on modern, multithreaded hardware. The speed in parsing is obtained by using the recently developed MacroMolecule Transmission Format (MMTF) to reduce the computational cost of reading text-based PDB files. The use of C++ lambda functions and Python binds provide extensive flexibility for analysis and categorization of the PDB by allowing the user to write custom functions to suite their objective. We think Lemon will become a one-stop-shop to quickly mine the entire PDB to generate desired structural biology features. The Lemon software is available as a C++ header library along with example functions at https://github.com/chopralab/lemon.

scholarly journals High-Betweenness Proteins in the Yeast Protein Interaction Network

2005 ◽  
Vol 2005 (2) ◽  
pp. 96-103 ◽  
Author(s):  
Maliackal Poulo Joy ◽  
Amy Brock ◽  
Donald E. Ingber ◽  
Sui Huang
Keyword(s):  
Protein Interaction ◽  
Protein Interaction Network ◽  
Regulatory Networks ◽  
Interaction Network ◽  
Structural Features ◽  
Modular Organization ◽  
Yeast Protein ◽  
Scale Free ◽  
High Betweenness ◽  
Yeast Protein Interaction

Structural features found in biomolecular networks that are absent in random networks produced by simple algorithms can provide insight into the function and evolution of cell regulatory networks. Here we analyze “betweenness” of network nodes, a graph theoretical centrality measure, in the yeast protein interaction network. Proteins that have high betweenness, but low connectivity (degree), were found to be abundant in the yeast proteome. This finding is not explained by algorithms proposed to explain the scale-free property of protein interaction networks, where low-connectivity proteins also have low betweenness. These data suggest the existence of some modular organization of the network, and that the high-betweenness, low-connectivity proteins may act as important links between these modules. We found that proteins with high betweenness are more likely to be essential and that evolutionary age of proteins is positively correlated with betweenness. By comparing different models of genome evolution that generate scale-free networks, we show that rewiring of interactions via mutation is an important factor in the production of such proteins. The evolutionary and functional significance of these observations are discussed.

RING fingers and B-boxes: zinc-binding protein-protein interaction domains

1998 ◽  
Vol 76 (2-3) ◽  
pp. 351-358 ◽  
Author(s):  
Katherine LB Borden
Keyword(s):  
Conformational Changes ◽  
Protein Interaction ◽  
Protein Interactions ◽  
Structural Features ◽  
Zinc Binding ◽  
Protein Protein Interactions ◽  
Protein Protein Interaction ◽  
Biological Studies ◽  
Interaction Domains ◽  
Nmr Methods

The cysteine-rich zinc-binding motifs known as the RING and B-box are found in several unrelated proteins. Structural, biochemical, and biological studies of these motifs reveal that they mediate protein-protein interactions. Several RING-containing proteins are oncoproteins and recent data indicate that proapoptotic activities can be mediated through the RING. 1H NMR methods were used to determine the structures of RINGs and a B-box domain and to monitor the conformational changes these motifs undergo upon zinc ligation. This review discusses in detail the structural features of the RING and B-box domains. Further, possible structure function relationships for these motifs particularly in their role as protein interaction domains are discussed.Key words: RING, B-box, PML, NMR.

SH3 domains: complexity in moderation

2001 ◽  
Vol 114 (7) ◽  
pp. 1253-1263 ◽  
Author(s):  
B.J. Mayer
Keyword(s):  
Subcellular Localization ◽  
Protein Interaction ◽  
Protein Interactions ◽  
Binding Sites ◽  
Structural Information ◽  
Intramolecular Interactions ◽  
Local Concentration ◽  
Biological Processes ◽  
Sh3 Domains ◽  
Interaction Domains

The SH3 domain is perhaps the best-characterized member of the growing family of protein-interaction modules. By binding with moderate affinity and selectivity to proline-rich ligands, these domains play critical roles in a wide variety of biological processes ranging from regulation of enzymes by intramolecular interactions, increasing the local concentration or altering the subcellular localization of components of signaling pathways, and mediating the assembly of large multiprotein complexes. SH3 domains and their binding sites have cropped up in many hundreds of proteins in species from yeast to man, which suggests that they provide the cell with an especially handy and adaptable means of bringing proteins together. The wealth of genetic, biochemical and structural information available provides an intimate and detailed portrait of the domain, serving as a framework for understanding other modular protein-interaction domains. Processes regulated by SH3 domains also raise important questions about the nature of specificity and the overall logic governing networks of protein interactions.

Measurement and Reduction of Radiation Damage in Frozen Hydrated Crystalline Specimens

1978 ◽  
Vol 36 (3) ◽  
pp. 70-77 ◽  
Author(s):  
R.M. Glaeser ◽  
S.B. Hayward
Keyword(s):  
Diffraction Pattern ◽  
Structural Information ◽  
Structural Disorder ◽  
Structural Features ◽  
Biological Macromolecules ◽  
Diffraction Intensity ◽  
Object Structure ◽  
Specimen Material ◽  
Unit Cells ◽  
Identical Unit

Highly ordered or crystalline biological macromolecules become severely damaged and structurally disordered after a brief electron exposure. Evidence that damage and structural disorder are occurring is clearly given by the fading and eventual disappearance of the specimen's electron diffraction pattern. The fading and disappearance of sharp diffraction spots implies a corresponding disappearance of periodic structural features in the specimen. By the same token, there is a oneto- one correspondence between the disappearance of the crystalline diffraction pattern and the disappearance of reproducible structural information that can be observed in the images of identical unit cells of the object structure. The electron exposures that result in a significant decrease in the diffraction intensity will depend somewhat upon the resolution (Bragg spacing) involved, and can vary considerably with the chemical makeup and composition of the specimen material.

HAZARD: An Expert System for Risk Assessment of Environmental Chemicals

1987 ◽  
Vol 26 (01) ◽  
pp. 13-23 ◽  
Author(s):  
H. W. Gottinger
Keyword(s):  
Expert System ◽  
Structural Information ◽  
Chemical Carcinogenesis ◽  
Structural Features ◽  
Environmental Chemicals ◽  
Chemical Carcinogens ◽  
Carcinogenic Activity ◽  
Current State ◽  
Structure Activity ◽  
Carcinogenic Potential

AbstractThe purpose of this paper is to report on an expert system in design that screens for potential hazards from environmental chemicals on the basis of structure-activity relationships in the study of chemical carcinogenesis, particularly with respect to analyzing the current state of known structural information about chemical carcinogens and predicting the possible carcinogenicity of untested chemicals. The structure-activity tree serves as an index of known chemical structure features associated with carcinogenic activity. The basic units of the tree are the principal recognized classes of chemical carcinogens that are subdivided into subclasses known as nodes according to specific structural features that may reflect differences in carcinogenic potential among chemicals in the class. An analysis of a computerized data base of known carcinogens (knowledge base) is proposed using the structure-activity tree in order to test the validity of the tree as a classification scheme (inference engine).

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.

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.

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