Protein Thermostability Prediction within Homologous Families Using Temperature-Dependent Statistical Potentials

Abstract

The ability to rationally modify targeted physical and biological features of a protein of interest holds promise in numerous academic and industrial applications and paves the way towards de novo protein design. In particular, bioprocesses that utilize the remarkable properties of enzymes would often benefit from mutants that remain active at temperatures that are either higher or lower than the physiological temperature, while maintaining the biological activity. Many in silico methods have been developed in recent years for predicting the thermodynamic stability of mutant proteins, but very few have focused on thermostability. To bridge this gap, we developed an algorithm for predicting the best descriptor of thermostability, namely the melting temperature , from the protein's sequence and structure. Our method is applicable when the of proteins homologous to the target protein are known. It is based on the design of several temperature-dependent statistical potentials, derived from datasets consisting of either mesostable or thermostable proteins. Linear combinations of these potentials have been shown to yield an estimation of the protein folding free energies at low and high temperatures, and the difference of these energies, a prediction of the melting temperature. This particular construction, that distinguishes between the interactions that contribute more than others to the stability at high temperatures and those that are more stabilizing at low , gives better performances compared to the standard approach based on -independent potentials which predict the thermal resistance from the thermodynamic stability. Our method has been tested on 45 proteins of known that belong to 11 homologous families. The standard deviation between experimental and predicted 's is equal to 13.6°C in cross validation, and decreases to 8.3°C if the 6 worst predicted proteins are excluded. Possible extensions of our approach are discussed.

Improved insights into protein thermal stability: from the molecular to the structurome scale

“…Usually, moreover, the studies of protein structure and stability are performed using force fields that do not take into account this T-dependence, which adds further uncertainty to the problem and the risk of misinterpreting the obtained results. Indeed, only few types of temperature-dependent potentials have been defined in the literature [7,[19][20][21]. They are all based on a simplified representation of the protein structure.…”

“…Here, we review the first technique [7,19], which we developed and used to get more insight into the T-dependence of amino acid interactions. The idea behind it consists of taking advantage of the bias of the potentials towards the dataset from which they are extracted.…”

Towards an accurate prediction of the thermal stability of homologous proteins

“…Indeed, only few tools [12][13][14][15][16][17][18] have been built to predict on a large scale the melting temperature T m which is the best descriptor of the thermal stability. In contrast, the thermodynamic stability, or more precisely the thermodynamic stability changes upon mutations, has been much more studied and a lot of prediction tools are available in the literature.…”

“…Some of them have already been presented elsewhere [12][13][14][15][16][17][18], and others are new or have not yet been tested. The guiding thread consists in analyzing systematically and on a common test set the performance of each individual method and to combine the best of them with the aim of building a tool that yields the most accurate T m -predictions.…”

Unfolding stabilities of two structurally similar proteins as probed by temperature-induced and force-induced molecular dynamics simulations

“…It should also be mentioned that, apart the TFTs, studies on understanding disparity in structural stabilities of β-lactamases homologous proteins (Vanhove, Houba, Lamotte-Brasseur, & Frere, 1995) p. 53 homologous proteins (Brandt, Kaar, Fersht, & Veprintsev, 2012), homologous POZ/PTB domains of potassium channel KCTD family (Pirone et al, 2013), adenylate kinases (Bae & Phillips 2004) and proteins derived from thermophilic and mesophilic organisms (Merkley, Parson, & Daggett, 2010;Razvi & Scholtz, 2006;Szilágyi & Závodszky, 2000) have been well documented in the literature. Moreover, computational methods to predict T m (midpoint of temperature-induced protein unfolding curves) of proteins belonging to various homologous proteins have also been proposed (Pucci, Dhanani, Dehouck, & Rooman, 2014).…”