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Binding landscapes

BINDING LANDSCAPES

Characterizing the binding selectivity landscape of interacting proteins is crucial both for elucidating the underlying mechanisms of their interaction and for developing selective inhibitors. However, current mapping methods are laborious and cannot provide a sufficiently comprehensive description of the landscape. We develop novel and efficient strategies for comprehensively mapping the binding landscape of proteins using a combination of experimental multi-target selective library screening and next-generation sequencing analysis. Our protocols combine protein randomization, yeast surface display technology, deep sequencing, and a few experimental ΔΔGbind data points on purified proteins to generate ΔΔGbind values for the remaining numerous mutants of the same protein complex.

Figure 1.tif

Yeast-surface display of APPI-3M. (A) Schematic drawing of the pairwise selective screen using the YSD system. A naïve library of mutated APPI-3M variants was displayed on the yeast cell surface and presented to pairs of proteases. Each protease in the pair (denoted A or B) was labeled with a different fluorescent dye – Alexa Fluor-650 or Alexa Fluor-488 (represented by green and blue stars, respectively). (B) Pairwise selective screen. Flow cytometry sorting was used to screen the library to isolate APPI-3M variants with enhanced selectivity toward each of the four serine proteases (Meso: mesotrypsin; KLK6; Anionic: anionic trypsin; Cationic: cationic trypsin). In each sort, two variant populations were collected inside the black gates, yielding sorted library populations of protease-selective APPI-3M variants. Green and blue colors represent a high and low cell densities, respectively.

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​Mapping the sclerostin–LRP4 binding interface identifies critical interaction hotspots in loops 1 and 3 of sclerostin​

Katchkovsky S, Meiri R, Lacham-Hartman S, Orenstein Y, Levaot N & Papo N. FEBS Letters 2024

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Deep neural networks for predicting the affinity landscape of protein-protein interactions 

 

Meiri R, Lotati S. L. A, Orenstein Y & Papo N. iScience 2024

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Climbing Up and Down Binding Landscapes through Deep Mutational Scanning of Three Homologous Protein–Protein Complexes.

Heyne M, Shirian J, Cohen I, Peleg Y, Radisky ES, Papo N, Shifman JM. J Am Chem Soc. 2021

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Predicting mutant outcome by combining deep mutational scanning and machine learning.

Sarfati H, Naftaly S, Papo N, Keasar C. Proteins. 2021.


Quantitative mapping of binding specificity landscapes for homologous targets by using a high-throughput method.

Aharon L, Aharoni SL, Radisky ES, Papo N. Biochem J. 2020.

 

Generating quantitative binding landscapes through fractional binding selections combined with deep sequencing and data normalization.

Heyne M, Papo N, Shifman JM. Nat Commun. 2020.

 

Residue-level determinants of angiopoietin-2 interactions with its receptor Tie2.

Bakhman A, Rabinovich E, Shlamkovich T, Papo N, Kosloff M. Proteins. 2019.

 

Mapping protein selectivity landscapes using multi-target selective screening and next-generation sequencing of combinatorial libraries.

Naftaly S, Cohen I, Shahar A, Hockla A, Radisky ES, Papo N. Nat Commun. 2018.

 

Editorial overview: Engineering and design: New trends in designer proteins.

Shifman JM, Papo N.  Curr Opin Struct Biol. 2017.


Identifying Residues that Determine SCF Molecular-Level Interactions through a Combination of Experimental and In silico Analyses.

Rabinovich E, Heyne M, Bakhman A, Kosloff M, Shifman JM, Papo N. J Mol Biol. 2017.

 

Protein Engineering by Combined Computational and In Vitro Evolution Approaches.

Rosenfeld L, Heyne M, Shifman JM, Papo N. Trends Biochem Sci. 2016.

 

Combinatorial and Computational Approaches to Identify Interactions of Macrophage Colony-stimulating Factor (M-CSF) and Its Receptor c-FMS.

Rosenfeld L, Shirian J, Zur Y, Levaot N, Shifman JM, Papo N. J Biol Chem. 2015.

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