scholarly journals Predicting glycosaminoglycan surface protein interactions and implications for studying axonal growth

2017 ◽  
Vol 114 (52) ◽  
pp. 13697-13702 ◽  
Author(s):  
Adam R. Griffith ◽  
Claude J. Rogers ◽  
Gregory M. Miller ◽  
Ravinder Abrol ◽  
Linda C. Hsieh-Wilson ◽  
...  
Keyword(s):  
Cell Surface ◽  
Chondroitin Sulfate ◽  
Protein Interactions ◽  
Neural Development ◽  
Structural Information ◽  
Protein Structures ◽  
Computational Method ◽  
Cns Injury ◽  
Gag Protein ◽  
Cord Injury

Cell-surface carbohydrates play important roles in numerous biological processes through their interactions with various protein-binding partners. These interactions are made possible by the vast structural diversity of carbohydrates and the diverse array of carbohydrate presentations on the cell surface. Among the most complex and important carbohydrates are glycosaminoglycans (GAGs), which display varied stereochemistry, chain lengths, and patterns of sulfation. GAG–protein interactions participate in neuronal development, angiogenesis, spinal cord injury, viral invasion, and immune response. Unfortunately, little structural information is available for these complexes; indeed, for the highly sulfated chondroitin sulfate motifs, CS-E and CS-D, there are no structural data. We describe here the development and validation of the GAG-Dock computational method to predict accurately the binding poses of protein-bound GAGs. We validate that GAG-Dock reproduces accurately (<1-Å rmsd) the crystal structure poses for four known heparin–protein structures. Further, we predict the pose of heparin and chondroitin sulfate derivatives bound to the axon guidance proteins, protein tyrosine phosphatase σ (RPTPσ), and Nogo receptors 1–3 (NgR1-3). Such predictions should be useful in understanding and interpreting the role of GAGs in neural development and axonal regeneration after CNS injury.

scholarly journals Chondroitin Sulfate/Dermatan Sulfate-Protein Interactions and Their Biological Functions in Human Diseases: Implications and Analytical Tools

2021 ◽  
Vol 9 ◽  
Author(s):  
Bin Zhang ◽  
Lianli Chi
Keyword(s):  
Cell Surface ◽  
Chondroitin Sulfate ◽  
Protein Interactions ◽  
Dermatan Sulfate ◽  
Interaction Analysis ◽  
Structural Complexity ◽  
Protein Characterization ◽  
Biological Functions ◽  
Gag Protein ◽  
Core Proteins

Chondroitin sulfate (CS) and dermatan sulfate (DS) are linear anionic polysaccharides that are widely present on the cell surface and in the cell matrix and connective tissue. CS and DS chains are usually attached to core proteins and are present in the form of proteoglycans (PGs). They not only are important structural substances but also bind to a variety of cytokines, growth factors, cell surface receptors, adhesion molecules, enzymes and fibrillary glycoproteins to execute series of important biological functions. CS and DS exhibit variable sulfation patterns and different sequence arrangements, and their molecular weights also vary within a large range, increasing the structural complexity and diversity of CS/DS. The structure-function relationship of CS/DS PGs directly and indirectly involves them in a variety of physiological and pathological processes. Accumulating evidence suggests that CS/DS serves as an important cofactor for many cell behaviors. Understanding the molecular basis of these interactions helps to elucidate the occurrence and development of various diseases and the development of new therapeutic approaches. The present article reviews the physiological and pathological processes in which CS and DS participate through their interactions with different proteins. Moreover, classic and emerging glycosaminoglycan (GAG)-protein interaction analysis tools and their applications in CS/DS-protein characterization are also discussed.

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/

Influences of the glial environment on the elongation of axons after injury: transplantation studies in adult rodents

1981 ◽  
Vol 95 (1) ◽  
pp. 231-240 ◽  
Author(s):  
A. J. Aguayo ◽  
S. David ◽  
G. M. Bray
Keyword(s):  
Spinal Cord ◽  
Peripheral Nerve ◽  
Brain Stem ◽  
Neural Development ◽  
Cns Injury ◽  
Sprague Dawley ◽  
Inherited Disorders ◽  
Thoracic Spinal Cord ◽  
Thoracic Spinal ◽  
Cord Injury

Tissue transplantation methods, previously used to study neural development, myelination and inherited disorders of myelin can be applied also to the investigation of repair and regeneration in the mammalian CNS. The elongation of axons from injured peripheral nerve of CNS has been studied in adult mice and rats by observing the growth of axons into PNS or CNS tissue grafts. Following spinal cord injury and also after transplantation of optic nerves into the PNS there is axonal sprouting but these neuronal processes fail to elongate more than a few mm into the surrounding glia. On the other hand if segments of a peripheral nerve are grafted into the transected spinal cord, axons arising from spinal neurons and dorsal root ganglia become associated with the transplanted Schwann cells and elongate along the graft, approximately 1 cm. Recently the elongation of axons from spinal and medullary neurones was studied using a new experimental model which employed PNS grafts as ‘bridges’ to connect the spinal cord and the brain stem. In a series of adult C57BL/6J mice and Sprague Dawley rats, autologous segments of sciatic nerve were used to create ‘bridges’ between the lower cervical or upper thoracic spinal cord and the medulla oblongata. The spinal cord between these two levels was left intact. Grafted segments examined by light and electron microscope 1-7 months after surgery were well innervated by Schwann cell ensheathed axons that had grown the entire length of the graft (2 cm in mice and 3.5 cm in rats). The origin and termination of these axons were determined by transecting the regenerated grafts and applying horseradish peroxidase to the cut ends. Retrogradely labelled neurones were found to be distributed widely in the gray matter of the spinal cord and medulla near the sites of insertion of the graft. Anterogradely labelled fibres coursing within the graft penetrated the CNS for short distances, approximately 2 mm. These new results indicate that following CNS injury a conducive glial environment does allow spinal and brain stem neurones to elongate axons for distances that can be greater than those they usually extend for in the intact animal. This evidence that the regenerative response of similar axons differs in CNS and PNS neuroglia supports the hypothesis that influences arising from the environment play an important role in the success or failure of regeneration. The regenerative potentiality of central neurones may be expressed only when the CNS neuroglial environment is changed to resemble that in the PNS.

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.

Prediction of Disease Comorbidity Using HeteSim Scores based on Multiple Heterogeneous Networks

2019 ◽  
Vol 19 (4) ◽  
pp. 232-241 ◽  
Author(s):  
Xuegong Chen ◽  
Wanwan Shi ◽  
Lei Deng
Keyword(s):  
Protein Interactions ◽  
Experimental Studies ◽  
Treatment Strategies ◽  
Computational Method ◽  
Biological Information ◽  
Support Vector ◽  
Protein Protein Interactions ◽  
Efficient Treatment ◽  
Disease Associations ◽  
Previous State

Background: Accumulating experimental studies have indicated that disease comorbidity causes additional pain to patients and leads to the failure of standard treatments compared to patients who have a single disease. Therefore, accurate prediction of potential comorbidity is essential to design more efficient treatment strategies. However, only a few disease comorbidities have been discovered in the clinic. Objective: In this work, we propose PCHS, an effective computational method for predicting disease comorbidity. Materials and Methods: We utilized the HeteSim measure to calculate the relatedness score for different disease pairs in the global heterogeneous network, which integrates six networks based on biological information, including disease-disease associations, drug-drug interactions, protein-protein interactions and associations among them. We built the prediction model using the Support Vector Machine (SVM) based on the HeteSim scores. Results and Conclusion: The results showed that PCHS performed significantly better than previous state-of-the-art approaches and achieved an AUC score of 0.90 in 10-fold cross-validation. Furthermore, some of our predictions have been verified in literatures, indicating the effectiveness of our method.

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 De novo design of a reversible phosphorylation-dependent switch for membrane targeting

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Leon Harrington ◽  
Jordan M. Fletcher ◽  
Tamara Heermann ◽  
Derek N. Woolfson ◽  
Petra Schwille
Keyword(s):  
Protein Interactions ◽  
Lipid Membrane ◽  
De Novo ◽  
Protein Localization ◽  
Protein Structures ◽  
Spatiotemporal Pattern ◽  
Membrane Targeting ◽  
Protein Protein Interactions ◽  
Reversible Phosphorylation ◽  
Potential Applications

AbstractModules that switch protein-protein interactions on and off are essential to develop synthetic biology; for example, to construct orthogonal signaling pathways, to control artificial protein structures dynamically, and for protein localization in cells or protocells. In nature, the E. coli MinCDE system couples nucleotide-dependent switching of MinD dimerization to membrane targeting to trigger spatiotemporal pattern formation. Here we present a de novo peptide-based molecular switch that toggles reversibly between monomer and dimer in response to phosphorylation and dephosphorylation. In combination with other modules, we construct fusion proteins that couple switching to lipid-membrane targeting by: (i) tethering a ‘cargo’ molecule reversibly to a permanent membrane ‘anchor’; and (ii) creating a ‘membrane-avidity switch’ that mimics the MinD system but operates by reversible phosphorylation. These minimal, de novo molecular switches have potential applications for introducing dynamic processes into designed and engineered proteins to augment functions in living cells and add functionality to protocells.

Review and comparative assessment of similarity-based methods for prediction of drug–protein interactions in the druggable human proteome

2018 ◽  
Vol 20 (6) ◽  
pp. 2066-2087 ◽  
Author(s):  
Chen Wang ◽  
Lukasz Kurgan
Keyword(s):  
Protein Interactions ◽  
Drug Targets ◽  
Characteristic Curve ◽  
Protein Structures ◽  
Predictive Performance ◽  
Computational Prediction ◽  
Comprehensive Analysis ◽  
Model Combining ◽  
Benchmark Database ◽  
Key Aspects

AbstractDrug–protein interactions (DPIs) underlie the desired therapeutic actions and the adverse side effects of a significant majority of drugs. Computational prediction of DPIs facilitates research in drug discovery, characterization and repurposing. Similarity-based methods that do not require knowledge of protein structures are particularly suitable for druggable genome-wide predictions of DPIs. We review 35 high-impact similarity-based predictors that were published in the past decade. We group them based on three types of similarities and their combinations that they use. We discuss and compare key aspects of these methods including source databases, internal databases and their predictive models. Using our novel benchmark database, we perform comparative empirical analysis of predictive performance of seven types of representative predictors that utilize each type of similarity individually and all possible combinations of similarities. We assess predictive quality at the database-wide DPI level and we are the first to also include evaluation over individual drugs. Our comprehensive analysis shows that predictors that use more similarity types outperform methods that employ fewer similarities, and that the model combining all three types of similarities secures area under the receiver operating characteristic curve of 0.93. We offer a comprehensive analysis of sensitivity of predictive performance to intrinsic and extrinsic characteristics of the considered predictors. We find that predictive performance is sensitive to low levels of similarities between sequences of the drug targets and several extrinsic properties of the input drug structures, drug profiles and drug targets. The benchmark database and a webserver for the seven predictors are freely available at http://biomine.cs.vcu.edu/servers/CONNECTOR/.

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