scholarly journals Generating quantitative binding landscapes through fractional binding selections, deep sequencing and data normalization

2019 ◽  
Author(s):  
Michael Heyne ◽  
Niv Papo ◽  
Julia Shifman
Keyword(s):  
Free Energy ◽  
Protein Interactions ◽  
Binding Free Energy ◽  
Surface Display ◽  
Yeast Surface Display ◽  
Protein Protein Interactions ◽  
Protein Mutants ◽  
Display Technology ◽  
Data Points ◽  
Affinity Interaction

AbstractQuantifying the effects of various mutations on binding free energy is crucial for understanding the evolution of protein-protein interactions and would greatly facilitate protein engineering studies. Yet, measuring changes in binding free energy (ΔΔGbind) remains a tedious task that requires expression of each mutant, its purification, and affinity measurements. We developed a new approach that allows us to quantify ΔΔGbindfor thousands of protein mutants in one experiment. Our protocol combines protein randomization, Yeast Surface Display technology, Next Generation Sequencing, and a few experimental ΔΔGbinddata points on purified proteins to generate ΔΔGbindvalues for the remaining numerous mutants of the same protein complex. Using this methodology, we comprehensively map the single-mutant binding landscape of one of the highest-affinity interaction between BPTI and Bovine Trypsin. We show that ΔΔGbindfor this interaction could be quantified with high accuracy over the range of 12 kcal/mol displayed by various BPTI single mutants.

scholarly journals Quantitative yeast-yeast two hybrid for discovery and binding affinity estimation of protein-protein interactions

2020 ◽  
Author(s):  
Kaitlyn Bacon ◽  
Abigail Blain ◽  
John Bowen ◽  
Matthew Burroughs ◽  
Nikki McArthur ◽  
...  
Keyword(s):  
Binding Affinity ◽  
Protein Interactions ◽  
Quantitative Estimation ◽  
Surface Display ◽  
Combinatorial Libraries ◽  
Yeast Surface Display ◽  
Binding Affinities ◽  
Protein Protein Interactions ◽  
Yeast Two Hybrid ◽  
Two Hybrid

AbstractQuantifying the binding affinity of protein-protein interactions is important for elucidating connections within biochemical signaling pathways, as well as characterization of binding proteins isolated from combinatorial libraries. We describe a quantitative yeast-yeast two hybrid (qYY2H) system that not only enables discovery of specific protein-protein interactions, but also efficient, quantitative estimation of their binding affinities (KD). In qYY2H, the bait and prey proteins are expressed as yeast cell surface fusions using yeast surface display. We developed a semi-empirical framework for estimating the KD of monovalent bait-prey interactions, using measurements of the apparent KD of yeast-yeast binding, which is mediated by multivalent interactions between yeast-displayed bait and prey. Using qYY2H, we identified interaction partners of SMAD3 and the tandem WW domains of YAP from a cDNA library and characterized their binding affinities. Finally, we showed that qYY2H could also quantitatively evaluate binding interactions mediated by post-translational modifications on the bait protein.

scholarly journals Yeast Surface Display System: Strategies for Improvement and Biotechnological Applications

2022 ◽  
Vol 9 ◽  
Author(s):  
Karla V. Teymennet-Ramírez ◽  
Fernando Martínez-Morales ◽  
María R. Trejo-Hernández
Keyword(s):  
Protein Interactions ◽  
Genetic Manipulation ◽  
Surface Display ◽  
Yeast Surface Display ◽  
Yeast Cells ◽  
Biotechnological Applications ◽  
Protein Protein Interactions ◽  
Post Translational Modifications ◽  
Yeast Surface ◽  
Antibody Design

Yeast surface display (YSD) is a “whole-cell” platform used for the heterologous expression of proteins immobilized on the yeast’s cell surface. YSD combines the advantages eukaryotic systems offer such as post-translational modifications, correct folding and glycosylation of proteins, with ease of cell culturing and genetic manipulation, and allows of protein immobilization and recovery. Additionally, proteins displayed on the surface of yeast cells may show enhanced stability against changes in temperature, pH, organic solvents, and proteases. This platform has been used to study protein-protein interactions, antibody design and protein engineering. Other applications for YSD include library screening, whole-proteome studies, bioremediation, vaccine and antibiotics development, production of biosensors, ethanol production and biocatalysis. YSD is a promising technology that is not yet optimized for biotechnological applications. This mini review is focused on recent strategies to improve the efficiency and selection of displayed proteins. YSD is presented as a cutting-edge technology for the vectorial expression of proteins and peptides. Finally, recent biotechnological applications are summarized. The different approaches described herein could allow for a better strategy cascade for increasing protein/peptide interaction and production.

scholarly journals SAAMBE-SEQ: a sequence-based method for predicting mutation effect on protein–protein binding affinity

2020 ◽  
Author(s):  
Gen Li ◽  
Swagata Pahari ◽  
Adithya Krishna Murthy ◽  
Siqi Liang ◽  
Robert Fragoza ◽  
...  
Keyword(s):  
Free Energy ◽  
Protein Binding ◽  
Binding Affinity ◽  
Protein Interactions ◽  
Structural Information ◽  
Binding Free Energy ◽  
Supplementary Information ◽  
Sequence Information ◽  
Protein Protein Interactions ◽  
Genome Scale

Abstract Motivation Vast majority of human genetic disorders are associated with mutations that affect protein–protein interactions by altering wild-type binding affinity. Therefore, it is extremely important to assess the effect of mutations on protein–protein binding free energy to assist the development of therapeutic solutions. Currently, the most popular approaches use structural information to deliver the predictions, which precludes them to be applicable on genome-scale investigations. Indeed, with the progress of genomic sequencing, researchers are frequently dealing with assessing effect of mutations for which there is no structure available. Results Here, we report a Gradient Boosting Decision Tree machine learning algorithm, the SAAMBE-SEQ, which is completely sequence-based and does not require structural information at all. SAAMBE-SEQ utilizes 80 features representing evolutionary information, sequence-based features and change of physical properties upon mutation at the mutation site. The approach is shown to achieve Pearson correlation coefficient (PCC) of 0.83 in 5-fold cross validation in a benchmarking test against experimentally determined binding free energy change (ΔΔG). Further, a blind test (no-STRUC) is compiled collecting experimental ΔΔG upon mutation for protein complexes for which structure is not available and used to benchmark SAAMBE-SEQ resulting in PCC in the range of 0.37–0.46. The accuracy of SAAMBE-SEQ method is found to be either better or comparable to most advanced structure-based methods. SAAMBE-SEQ is very fast, available as webserver and stand-alone code, and indeed utilizes only sequence information, and thus it is applicable for genome-scale investigations to study the effect of mutations on protein–protein interactions. Availability and implementation SAAMBE-SEQ is available at http://compbio.clemson.edu/saambe_webserver/indexSEQ.php#started. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Mapping, Structure and Modulation of PPI

2021 ◽  
Vol 9 ◽  
Author(s):  
Elisa Martino ◽  
Sara Chiarugi ◽  
Francesco Margheriti ◽  
Gianpiero Garau
Keyword(s):  
Free Energy ◽  
Protein Interactions ◽  
Hot Spots ◽  
Molecular Level ◽  
Protein Complexes ◽  
Binding Free Energy ◽  
Detailed Knowledge ◽  
Protein Protein Interactions ◽  
Advanced Technologies ◽  
Successful Design

Because of the key relevance of protein–protein interactions (PPI) in diseases, the modulation of protein-protein complexes is of relevant clinical significance. The successful design of binding compounds modulating PPI requires a detailed knowledge of the involved protein-protein system at molecular level, and investigation of the structural motifs that drive the association of the proteins at the recognition interface. These elements represent hot spots of the protein binding free energy, define the complex lifetime and possible modulation strategies. Here, we review the advanced technologies used to map the PPI involved in human diseases, to investigate the structure-function features of protein complexes, and to discover effective ligands that modulate the PPI for therapeutic intervention.

scholarly journals Thermodynamic measures of cancer: Gibbs free energy and entropy of protein–protein interactions

2016 ◽  
Vol 42 (3) ◽  
pp. 339-350 ◽  
Author(s):  
Edward A. Rietman ◽  
John Platig ◽  
Jack A. Tuszynski ◽  
Giannoula Lakka Klement
Keyword(s):  
Free Energy ◽  
Gibbs Free Energy ◽  
Protein Interactions ◽  
Protein Protein Interactions

scholarly journals Yeast surface display of full-length human microtubule-associated protein tau

2019 ◽  
Author(s):  
Shiyao Wang ◽  
Yong Ku Cho
Keyword(s):  
Conformational Change ◽  
Protein Interactions ◽  
Surface Display ◽  
Full Length ◽  
Adult Brain ◽  
Yeast Surface Display ◽  
Post Translational Modification ◽  
Protein Tau ◽  
Microtubule Associated Protein Tau ◽  
Intrinsically Disordered

AbstractMicrotubule-associated protein tau is an intrinsically-disordered, highly soluble protein found primarily in neurons. Under normal conditions, tau regulates the stability of axonal microtubules and intracellular vesicle transport. However, in patients of neurodegeneration such as Alzheimer’s disease (AD), tau forms neurofibrillary deposits, which correlates well with the disease progression. Identifying molecular signatures in tau, such as post-translational modification, truncation, and conformational change has great potential to detect earliest signs of neurodegeneration, and develop therapeutic strategies. Here we show that full-length human tau, including the longest isoform found in the adult brain can be robustly displayed on the surface of yeastSaccharomyces cerevisiae. Yeast-displayed tau binds to anti-tau antibodies that cover epitopes ranging from the N-terminus to the 4R repeat region. Unlike tau expressed in the yeast cytosol, surface-displayed tau was not phosphorylated at sites found in AD patients (probed by antibodies AT8, AT270, AT180, PHF-1). However, yeast-displayed tau showed clear binding to paired helical filament (PHF) tau conformation-specific antibodies Alz-50, MC-1, and Tau-2. Although the tau possessed a conformation found in PHFs, oligomerization or aggregation into larger filaments were undetected. Taken together, yeast-displayed tau enables robust measurement of protein interactions, and is of particular interest for characterizing conformational change.

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