Fundamentals to function: Quantitative and scalable approaches for measuring protein stability.

Cell Syst

Department of Bioengineering, Stanford University, Stanford, CA 94305, USA; ChEM-H, Stanford University, Stanford, CA 94305, USA; Department of Genetics, Stanford University, Stanford, CA 94305, USA; Chan Zuckerberg Biohub, San Francisco, CA 94110, USA. Electronic address:

Published: June 2021

AI Article Synopsis

  • The process of folding amino acids into proteins is intricate and plays a crucial role in their diverse functions, with proteins existing as dynamic conformational ensembles rather than static structures.
  • Recent advancements have improved our ability to predict protein structures from sequences, but understanding the underlying energy landscapes and physical parameters is essential for assessing how mutations affect protein function, especially in diseases.
  • This review highlights different methods for quantifying protein stability at various scales, which will help create models that can better capture the complexity of protein behavior and their functional implications.

Article Abstract

Folding a linear chain of amino acids into a three-dimensional protein is a complex physical process that ultimately confers an impressive range of diverse functions. Although recent advances have driven significant progress in predicting three-dimensional protein structures from sequence, proteins are not static molecules. Rather, they exist as complex conformational ensembles defined by energy landscapes spanning the space of sequence and conditions. Quantitatively mapping the physical parameters that dictate these landscapes and protein stability is therefore critical to develop models that are capable of predicting how mutations alter function of proteins in disease and informing the design of proteins with desired functions. Here, we review the approaches that are used to quantify protein stability at a variety of scales, from returning multiple thermodynamic and kinetic measurements for a single protein sequence to yielding indirect insights into folding across a vast sequence space. The physical parameters derived from these approaches will provide a foundation for models that extend beyond the structural prediction to capture the complexity of conformational ensembles and, ultimately, their function.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9337709PMC
http://dx.doi.org/10.1016/j.cels.2021.05.009DOI Listing

Publication Analysis

Top Keywords

protein stability
12
three-dimensional protein
8
conformational ensembles
8
physical parameters
8
protein
6
fundamentals function
4
function quantitative
4
quantitative scalable
4
scalable approaches
4
approaches measuring
4

Similar Publications

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!