Biology subjects

Perez-Riba, A.

Publications and source records attributed to Perez-Riba, A..

3 recordsLinked to original sources

bioRxiv2021

Consensus tandem tetratricopeptide repeat proteins are complex superhelical nanosprings

Tandem-repeat proteins comprise small secondary structure motifs that stack to form one-dimensional arrays with distinctive mechanical properties that are proposed to direct their cellular functions. Here, we use single-molecule optical tweezers to study the folding of consensus-designed tetratricopeptide repeats (CTPRs) -- superhelical arrays of short helix-turn-helix motifs. We find that CTPRs display a spring-like mechanical response in which individual repeats undergo rapid equilibrium fluctuations between folded and unfolded conformations. We rationalise the force response using Ising models and dissect the folding pathway of CTPRs under mechanical load, revealing how the repeat arrays form from the centre towards both termini simultaneously. Strikingly, we also directly observe the proteins superhelical tertiary structure in the force signal. Using protein engineering, crystallography and single-molecule experiments, we show how the superhelical geometry can be altered by carefully placed amino-acid substitutions and examine how these sequence changes affect intrinsic repeat stability and inter-repeat coupling. Our findings provide the means to dissect and modulate repeat-protein stability and dynamics, which will be essential for researchers to understand the function of natural repeat proteins and to exploit artificial repeats proteins in nanotechnology and biomedical applications. Significance statementRepetition of biological building blocks is crucial to modulating and diversifying structure and function of biomolecules across all organisms. In tandem-repeat proteins, the linear arrangement of small structural motifs leads to the formation of striking supramolecular shapes. Using a combination of single-molecule biophysical techniques and modelling approaches, we dissect the spring-like nature of a designed repeat protein and demonstrate how its shape and mechanics can be manipulated by design. These novel insights into the biomechanical and biochemical characteristics of this protein class give us a methodological basis from which to understand the biological functions of repeat proteins and to exploit them in nanotechnology and biomedicine.

biophysics↗

bioRxiv2019

Designing real novel proteins using deep graph neural networks

Protein structure and function is determined by the arrangement of the linear sequence of amino acids in 3D space. Despite substantial advances, precisely designing sequences that fold into a predetermined shape (the "protein design" problem) remains difficult. We show that a deep graph neural network, ProteinSolver, can solve protein design by phrasing it as a constraint satisfaction problem (CSP). To sidestep the considerable issue of optimizing the network architecture, we first develop a network that is accurately able to solve the related and straightforward problem of Sudoku puzzles. Recognizing that each protein design CSP has many solutions, we train this network on millions of real protein sequences corresponding to thousands of protein structures. We show that our method rapidly designs novel protein sequences and perform a variety of in silico and in vitro validations suggesting that our designed proteins adopt the predetermined structures. One Sentence SummaryA neural network optimized using Sudoku puzzles designs protein sequences that adopt predetermined structures.

bioinformatics↗

bioRxiv2017

PyFolding: An open-source software package for graphing, analysis and simulation of thermodynamic and kinetic models of protein folding

Abstract]For many years, curve fitting software has been heavily utilized to fit simple models to various types of biophysical data. Although such software packages are easy to use for simple functions, they are often expensive and present substantial impediments to applying more complex models or for the analysis of large datasets. One field that is relient on such data analysis is the thermodynamics and kinetics of protein folding. Over the past decade, increasingly sophisticated analytical models have been generated, but without simple tools to enable routine analysis. Consequently, users have needed to generate their own tools or otherwise find willing collaborators. Here we present PyFolding, a free, open source, and extensible Python framework for graphing, analysis and simulation of the biophysical properties of proteins. To demonstrate the utility of PyFolding, we have used it to analyze and model experimental protein folding and thermodynamic data. Examples include: (i) multi-phase kinetic folding fitted to linked equations, (ii) global fitting of multiple datasets and (iii) analysis of repeat protein thermodynamics with Ising model variants. Moreover, we demonstrate how Pyfolding is easily extensible to novel functionality beyond applications in protein folding via the addition of new models. Example scripts to perform these and other operations are supplied with the software, and we encourage users to contribute notebooks and models to create a community resource. Finally, we show that PyFolding can be used in conjunction with Jupyter notebooks as an easy way to share methods and analysis for publication and amongst research teams.

biochemistry↗