Structural biology of plant sulfur metabolism: from sulfate to glutathione

2019 ◽  
Vol 70 (16) ◽  
pp. 4089-4103 ◽  
Author(s):  
Joseph M Jez
Keyword(s):  
Structural Biology ◽  
Protein Interactions ◽  
Sulfur Metabolism ◽  
Essential Element ◽  
Glutathione Synthetase ◽  
Protein Protein Interactions ◽  
Glutathione Biosynthesis ◽  
Wide Range ◽  
Aps Reductase ◽  
Level Information

Abstract Sulfur is an essential element for all organisms. Plants must assimilate this nutrient from the environment and convert it into metabolically useful forms for the biosynthesis of a wide range of compounds, including cysteine and glutathione. This review summarizes structural biology studies on the enzymes involved in plant sulfur assimilation [ATP sulfurylase, adenosine-5'-phosphate (APS) reductase, and sulfite reductase], cysteine biosynthesis (serine acetyltransferase and O-acetylserine sulfhydrylase), and glutathione biosynthesis (glutamate-cysteine ligase and glutathione synthetase) pathways. Overall, X-ray crystal structures of enzymes in these core pathways provide molecular-level information on the chemical events that allow plants to incorporate sulfur into essential metabolites and revealed new biochemical regulatory mechanisms, such as structural rearrangements, protein–protein interactions, and thiol-based redox switches, for controlling different steps in these pathways.

scholarly journals Ways into Understanding HIF Inhibition

Cancers ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 159
Author(s):  
Tina Schönberger ◽  
Joachim Fandrey ◽  
Katrin Prost-Fingerle
Keyword(s):  
Protein Interactions ◽  
Target Genes ◽  
Protein Protein Interactions ◽  
Low Oxygen ◽  
Drug Candidates ◽  
Open Questions ◽  
Wide Range ◽  
Hif Pathway

Hypoxia is a key characteristic of tumor tissue. Cancer cells adapt to low oxygen by activating hypoxia-inducible factors (HIFs), ensuring their survival and continued growth despite this hostile environment. Therefore, the inhibition of HIFs and their target genes is a promising and emerging field of cancer research. Several drug candidates target protein–protein interactions or transcription mechanisms of the HIF pathway in order to interfere with activation of this pathway, which is deregulated in a wide range of solid and liquid cancers. Although some inhibitors are already in clinical trials, open questions remain with respect to their modes of action. New imaging technologies using luminescent and fluorescent methods or nanobodies to complement widely used approaches such as chromatin immunoprecipitation may help to answer some of these questions. In this review, we aim to summarize current inhibitor classes targeting the HIF pathway and to provide an overview of in vitro and in vivo techniques that could improve the understanding of inhibitor mechanisms. Unravelling the distinct principles regarding how inhibitors work is an indispensable step for efficient clinical applications and safety of anticancer compounds.

Elucidating Protein-protein Interactions Through Computational Approaches and Designing Small Molecule Inhibitors Against them for Various Diseases

2018 ◽  
Vol 18 (20) ◽  
pp. 1719-1736 ◽  
Author(s):  
Sharanya Sarkar ◽  
Khushboo Gulati ◽  
Manikyaprabhu Kairamkonda ◽  
Amit Mishra ◽  
Krishna Mohan Poluri
Keyword(s):  
Small Molecule ◽  
Protein Interactions ◽  
Small Molecule Inhibitors ◽  
Computational Techniques ◽  
Protein Protein Interactions ◽  
Computational Tools ◽  
Computational Approaches ◽  
Protein Protein Interaction ◽  
Wide Range ◽  
Therapeutic Measures

Background: To carry out wide range of cellular functionalities, proteins often associate with one or more proteins in a phenomenon known as Protein-Protein Interaction (PPI). Experimental and computational approaches were applied on PPIs in order to determine the interacting partners, and also to understand how an abnormality in such interactions can become the principle cause of a disease. Objective: This review aims to elucidate the case studies where PPIs involved in various human diseases have been proven or validated with computational techniques, and also to elucidate how small molecule inhibitors of PPIs have been designed computationally to act as effective therapeutic measures against certain diseases. Results: Computational techniques to predict PPIs are emerging rapidly in the modern day. They not only help in predicting new PPIs, but also generate outputs that substantiate the experimentally determined results. Moreover, computation has aided in the designing of novel inhibitor molecules disrupting the PPIs. Some of them are already being tested in the clinical trials. Conclusion: This review delineated the classification of computational tools that are essential to investigate PPIs. Furthermore, the review shed light on how indispensable computational tools have become in the field of medicine to analyze the interaction networks and to design novel inhibitors efficiently against dreadful diseases in a shorter time span.

Metallocluster transactions: dynamic protein interactions guide the biosynthesis of Fe–S clusters in bacteria

2018 ◽  
Vol 46 (6) ◽  
pp. 1593-1603 ◽  
Author(s):  
Chenkang Zheng ◽  
Patricia C. Dos Santos
Keyword(s):  
Protein Interactions ◽  
Protein Complexes ◽  
Specific Protein ◽  
Biological Synthesis ◽  
Cluster Assembly ◽  
Sequence Motifs ◽  
Protein Protein Interactions ◽  
Cysteine Desulfurase ◽  
Wide Range ◽  
Domains Of Life

Iron–sulfur (Fe–S) clusters are ubiquitous cofactors present in all domains of life. The chemistries catalyzed by these inorganic cofactors are diverse and their associated enzymes are involved in many cellular processes. Despite the wide range of structures reported for Fe–S clusters inserted into proteins, the biological synthesis of all Fe–S clusters starts with the assembly of simple units of 2Fe–2S and 4Fe–4S clusters. Several systems have been associated with the formation of Fe–S clusters in bacteria with varying phylogenetic origins and number of biosynthetic and regulatory components. All systems, however, construct Fe–S clusters through a similar biosynthetic scheme involving three main steps: (1) sulfur activation by a cysteine desulfurase, (2) cluster assembly by a scaffold protein, and (3) guided delivery of Fe–S units to either final acceptors or biosynthetic enzymes involved in the formation of complex metalloclusters. Another unifying feature on the biological formation of Fe–S clusters in bacteria is that these systems are tightly regulated by a network of protein interactions. Thus, the formation of transient protein complexes among biosynthetic components allows for the direct transfer of reactive sulfur and Fe–S intermediates preventing oxygen damage and reactions with non-physiological targets. Recent studies revealed the importance of reciprocal signature sequence motifs that enable specific protein–protein interactions and consequently guide the transactions between physiological donors and acceptors. Such findings provide insights into strategies used by bacteria to regulate the flow of reactive intermediates and provide protein barcodes to uncover yet-unidentified cellular components involved in Fe–S metabolism.

scholarly journals Practical Model Selection for Prospective Virtual Screening

2018 ◽  
Author(s):  
Shengchao Liu ◽  
Moayad Alnammi ◽  
Spencer S. Ericksen ◽  
Andrew F. Voter ◽  
Gene E. Ananiev ◽  
...  
Keyword(s):  
Random Forest ◽  
Virtual Screening ◽  
Protein Interactions ◽  
High Throughput Screening ◽  
Screening Methods ◽  
Protein Protein Interactions ◽  
Screening Algorithm ◽  
Screening Performance ◽  
Wide Range ◽  
Public Datasets

AbstractVirtual (computational) high-throughput screening provides a strategy for prioritizing compounds for experimental screens, but the choice of virtual screening algorithm depends on the dataset and evaluation strategy. We consider a wide range of ligand-based machine learning and docking-based approaches for virtual screening on two protein-protein interactions, PriA-SSB and RMI-FANCM, and present a strategy for choosing which algorithm is best for prospective compound prioritization. Our workflow identifies a random forest as the best algorithm for these targets over more sophisticated neural network-based models. The top 250 predictions from our selected random forest recover 37 of the 54 active compounds from a library of 22,434 new molecules assayed on PriA-SSB. We show that virtual screening methods that perform well in public datasets and synthetic benchmarks, like multi-task neural networks, may not always translate to prospective screening performance on a specific assay of interest.

scholarly journals Discovery, development and application of drugs targeting BCL-2 pro-survival proteins in cancer

2021 ◽  
Author(s):  
Erinna F. Lee ◽  
W. Douglas Fairlie
Keyword(s):  
Structural Biology ◽  
Protein Interactions ◽  
Basic Science ◽  
Great Success ◽  
Protein Protein Interactions ◽  
New Class ◽  
Success Stories ◽  
History Of ◽  
Approved Drugs ◽  
Close Relatives

The discovery of a new class of small molecule compounds that target the BCL-2 family of anti-apoptotic proteins is one of the great success stories of basic science leading to translational outcomes in the last 30 years. The eponymous BCL-2 protein was identified over 30 years ago due to its association with cancer. However, it was the unveiling of the biochemistry and structural biology behind it and its close relatives’ mechanism(s)-of-action that provided the inspiration for what are now known as ‘BH3-mimetics’, the first clinically approved drugs designed to specifically inhibit protein–protein interactions. Herein, we chart the history of how these drugs were discovered, their evolution and application in cancer treatment.

scholarly journals Exploring the Human-Nipah Virus Protein-Protein Interactome

2017 ◽  
Vol 91 (23) ◽  
Author(s):  
Luis Martinez-Gil ◽  
Natalia M. Vera-Velasco ◽  
Ismael Mingarro
Keyword(s):  
Protein Interactions ◽  
Affinity Purification ◽  
Nipah Virus ◽  
Productive Infection ◽  
Virus Protein ◽  
Protein Protein Interactions ◽  
Data Set ◽  
Wide Range ◽  
Host Virus Interactions ◽  
Virus Interactions

ABSTRACT Nipah virus is an emerging, highly pathogenic, zoonotic virus of the Paramyxoviridae family. Human transmission occurs by close contact with infected animals, the consumption of contaminated food, or, occasionally, via other infected individuals. Currently, we lack therapeutic or prophylactic treatments for Nipah virus. To develop these agents we must now improve our understanding of the host-virus interactions that underpin a productive infection. This aim led us to perform the present work, in which we identified 101 human-Nipah virus protein-protein interactions (PPIs), most of which (88) are novel. This data set provides a comprehensive view of the host complexes that are manipulated by viral proteins. Host targets include the PRP19 complex and the microRNA (miRNA) processing machinery. Furthermore, we explored the biologic consequences of the interaction with the PRP19 complex and found that the Nipah virus W protein is capable of altering p53 control and gene expression. We anticipate that these data will help in guiding the development of novel interventional strategies to counter this emerging viral threat. IMPORTANCE Nipah virus is a recently discovered virus that infects a wide range of mammals, including humans. Since its discovery there have been yearly outbreaks, and in some of them the mortality rate has reached 100% of the confirmed cases. However, the study of Nipah virus has been largely neglected, and currently we lack treatments for this infection. To develop these agents we must now improve our understanding of the host-virus interactions that underpin a productive infection. In the present work, we identified 101 human-Nipah virus protein-protein interactions using an affinity purification approach coupled with mass spectrometry. Additionally, we explored the cellular consequences of some of these interactions. Globally, this data set offers a comprehensive and detailed view of the host machinery's contribution to the Nipah virus's life cycle. Furthermore, our data present a large number of putative drug targets that could be exploited for the treatment of this infection.

scholarly journals Chasing coevolutionary signals in intrinsically disordered proteins complexes

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Javier A. Iserte ◽  
Tamas Lazar ◽  
Silvio C. E. Tosatto ◽  
Peter Tompa ◽  
Cristina Marino-Buslje
Keyword(s):  
Protein Interactions ◽  
Intrinsically Disordered Proteins ◽  
Disordered Proteins ◽  
Protein Protein Interactions ◽  
Contact Prediction ◽  
Intrinsically Disordered ◽  
Regulatory Processes ◽  
Wide Range ◽  
Predict Protein Structure ◽  
Biological Interpretation

Abstract Intrinsically disordered proteins/regions (IDPs/IDRs) are crucial components of the cell, they are highly abundant and participate ubiquitously in a wide range of biological functions, such as regulatory processes and cell signaling. Many of their important functions rely on protein interactions, by which they trigger or modulate different pathways. Sequence covariation, a powerful tool for protein contact prediction, has been applied successfully to predict protein structure and to identify protein–protein interactions mostly of globular proteins. IDPs/IDRs also mediate a plethora of protein–protein interactions, highlighting the importance of addressing sequence covariation-based inter-protein contact prediction of this class of proteins. Despite their importance, a systematic approach to analyze the covariation phenomena of intrinsically disordered proteins and their complexes is still missing. Here we carry out a comprehensive critical assessment of coevolution-based contact prediction in IDP/IDR complexes and detail the challenges and possible limitations that emerge from their analysis. We found that the coevolutionary signal is faint in most of the complexes of disordered proteins but positively correlates with the interface size and binding affinity between partners. In addition, we discuss the state-of-art methodology by biological interpretation of the results, formulate evaluation guidelines and suggest future directions of development to the field.

scholarly journals A massively parallel barcoded sequencing pipeline enables generation of the first ORFeome and interactome map for rice

2020 ◽  
Vol 117 (21) ◽  
pp. 11836-11842 ◽  
Author(s):  
Shayne D. Wierbowski ◽  
Tommy V. Vo ◽  
Pascal Falter-Braun ◽  
Timothy O. Jobe ◽  
Lars H. Kruse ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Massively Parallel ◽  
Model Organisms ◽  
Protein Protein Interactions ◽  
Numerous Model ◽  
High Quality Protein ◽  
Protein Interactome ◽  
Wide Range ◽  
Dna Elements ◽  
General Tool

Systematic mappings of protein interactome networks have provided invaluable functional information for numerous model organisms. Here we developPCR-mediatedLinkage of barcodedAdaptersTo nucleic acidElements forsequencing (PLATE-seq) that serves as a general tool to rapidly sequence thousands of DNA elements. We validate its utility by generating the ORFeome forOryza sativacovering 2,300 genes and constructing a high-quality protein–protein interactome map consisting of 322 interactions between 289 proteins, expanding the known interactions in rice by roughly 50%. Our work paves the way for high-throughput profiling of protein–protein interactions in a wide range of organisms.

Structural biology and drug discovery for protein–protein interactions

2012 ◽  
Vol 33 (5) ◽  
pp. 241-248 ◽  
Author(s):  
Harry Jubb ◽  
Alicia P. Higueruelo ◽  
Anja Winter ◽  
Tom L. Blundell
Keyword(s):  
Drug Discovery ◽  
Structural Biology ◽  
Protein Interactions ◽  
Protein Protein Interactions

The interaction of IQGAPs with calmodulin-like proteins

2011 ◽  
Vol 39 (2) ◽  
pp. 694-699 ◽  
Author(s):  
Sevvel Pathmanathan ◽  
Elaine Hamilton ◽  
Erwan Atcheson ◽  
David J. Timson
Keyword(s):  
Protein Interactions ◽  
Light Chain ◽  
Protein Protein Interactions ◽  
Iq Motif ◽  
Calcium Dependent ◽  
Essential Light Chain ◽  
Cellular Processes ◽  
Wide Range ◽  
Related Proteins ◽  
Do So

Since their identification over 15 years ago, the IQGAP (IQ-motif-containing GTPase-activating protein) family of proteins have been implicated in a wide range of cellular processes, including cytoskeletal reorganization, cell–cell adhesion, cytokinesis and apoptosis. These processes rely on protein–protein interactions, and understanding these (and how they influence one another) is critical in determining how the IQGAPs function. A key group of interactions is with calmodulin and the structurally related proteins myosin essential light chain and S100B. These interactions occur primarily through a series of IQ motifs, which are α-helical segments of the protein located towards the middle of the primary sequence. The three human IQGAP isoforms (IQGAP1, IQGAP2 and IQGAP3) all have four IQ motifs. However, these have different affinities for calmodulin, myosin light chain and S100B. Whereas all four IQ motifs of IQGAP1 interact with calmodulin in the presence of calcium, only the last two do so in the absence of calcium. IQ1 (the first IQ motif) interacts with the myosin essential light chain Mlc1sa and the first two undergo a calcium-dependent interaction with S100B. The significance of the interaction between Mlc1sa and IQGAP1 in mammals is unknown. However, a similar interaction involving the Saccharomyces cerevisiae IQGAP-like protein Iqg1p is involved in cytokinesis, leading to speculation that there may be a similar role in mammals.

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