Yves Dehouck

Fast and accurate predictions of protein stability changes upon mutations using statistical potentials and neural networks

MOTIVATIONnThe rational design of proteins with modified properties, through amino acid substitutions, is of crucial importance in a large variety of applications. Given the huge number of possible substitutions, every protein engineering project would benefit strongly from the guidance of in silico methods able to predict rapidly, and with reasonable accuracy, the stability changes resulting from all possible mutations in a protein.nnnRESULTSnWe exploit newly developed statistical potentials, based on a formalism that highlights the coupling between four protein sequence and structure descriptors, and take into account the amino acid volume variation upon mutation. The stability change is expressed as a linear combination of these energy functions, whose proportionality coefficients vary with the solvent accessibility of the mutated residue and are identified with the help of a neural network. A correlation coefficient of R = 0.63 and a root mean square error of sigma(c) = 1.15 kcal/mol between measured and predicted stability changes are obtained upon cross-validation. These scores reach R = 0.79, and sigma(c) = 0.86 kcal/mol after exclusion of 10% outliers. The predictive power of our method is shown to be significantly higher than that of other programs described in the literature.nnnAVAILABILITYnhttp://babylone.ulb.ac.be/popmusic

BeAtMuSiC: Prediction of changes in protein-protein binding affinity on mutations.

The ability of proteins to establish highly selective interactions with a variety of (macro)molecular partners is a crucial prerequisite to the realization of their biological functions. The availability of computational tools to evaluate the impact of mutations on protein–protein binding can therefore be valuable in a wide range of industrial and biomedical applications, and help rationalize the consequences of non-synonymous single-nucleotide polymorphisms. BeAtMuSiC (http://babylone.ulb.ac.be/beatmusic) is a coarse-grained predictor of the changes in binding free energy induced by point mutations. It relies on a set of statistical potentials derived from known protein structures, and combines the effect of the mutation on the strength of the interactions at the interface, and on the overall stability of the complex. The BeAtMuSiC server requires as input the structure of the protein–protein complex, and gives the possibility to assess rapidly all possible mutations in a protein chain or at the interface, with predictive performances that are in line with the best current methodologies.

Sequence-structure signals of 3D domain swapping in proteins

Three-dimensional domain swapping occurs when two or more identical proteins exchange identical parts of their structure to generate an oligomeric unit. It affects proteins with diverse sequences and structures, and is expected to play important roles in evolution, functional regulation and even conformational diseases. Here, we search for traces of domain swapping in the protein sequence, by means of algorithms that predict the structure and stability of proteins using database-derived potentials. Regions whose sequences are not optimal with regard to the stability of the native structure, or showing marked intrinsic preferences for non-native conformations in absence of tertiary interactions are detected in most domain-swapping proteins. These regions are often located in areas crucial in the swapping process and are likely to influence it on a kinetic or thermodynamic level. In addition, cation-pi interactions are frequently observed to zip up the edges of the interface between intertwined chains or to involve hinge loop residues, thereby modulating stability. We end by proposing a set of mutations altering the swapping propensities, whose experimental characterization would contribute to refine our in silico derived hypotheses.

Flanking domain stability modulates the aggregation kinetics of a polyglutamine disease protein.

Spinocerebellar Ataxia Type 3 (SCA3) is one of nine polyglutamine (polyQ) diseases that are all characterized by progressive neuronal dysfunction and the presence of neuronal inclusions containing aggregated polyQ protein, suggesting that protein misfolding is a key part of this disease. Ataxin‐3, the causative protein of SCA3, contains a globular, structured N‐terminal domain (the Josephin domain) and a flexible polyQ‐containing C‐terminal tail, the repeat‐length of which modulates pathogenicity. It has been suggested that the fibrillogenesis pathway of ataxin‐3 begins with a non‐polyQ‐dependent step mediated by Josephin domain interactions, followed by a polyQ‐dependent step. To test the involvement of the Josephin domain in ataxin‐3 fibrillogenesis, we have created both pathogenic and nonpathogenic length ataxin‐3 variants with a stabilized Josephin domain, and have both stabilized and destabilized the isolated Josephin domain. We show that changing the thermodynamic stability of the Josephin domain modulates ataxin‐3 fibrillogenesis. These data support the hypothesis that the first stage of ataxin‐3 fibrillogenesis is caused by interactions involving the non‐polyQ containing Josephin domain and that the thermodynamic stability of this domain is linked to the aggregation propensity of ataxin‐3.

Detection of perturbation phases and developmental stages in organisms from DNA microarray time series data.

Available DNA microarray time series that record gene expression along the developmental stages of multicellular eukaryotes, or in unicellular organisms subject to external perturbations such as stress and diauxie, are analyzed. By pairwise comparison of the gene expression profiles on the basis of a translation-invariant and scale-invariant distance measure corresponding to least-rectangle regression, it is shown that peaks in the average distance values are noticeable and are localized around specific time points. These points systematically coincide with the transition points between developmental phases or just follow the external perturbations. This approach can thus be used to identify automatically, from microarray time series alone, the presence of external perturbations or the succession of developmental stages in arbitrary cell systems. Moreover, our results show that there is a striking similarity between the gene expression responses to these a priori very different phenomena. In contrast, the cell cycle does not involve a perturbation-like phase, but rather continuous gene expression remodeling. Similar analyses were conducted using three other standard distance measures, showing that the one we introduced was superior. Based on these findings, we set up an adapted clustering method that uses this distance measure and classifies the genes on the basis of their expression profiles within each developmental stage or between perturbation phases.

Modeling the temporal evolution of the Drosophila gene expression from DNA microarray time series

The time evolution of gene expression across the developmental stages of the host organism can be inferred from appropriate DNA microarray time series. Modeling this evolution aims eventually at improving the understanding and prediction of the complex phenomena that are the basis of life. We focus on the embryonic-to-adult development phases of Drosophila melanogaster, and chose to model the expression network with the help of a system of differential equations with constant coefficients, which are nonlinear in the transcript concentrations but linear in their logarithms. To reduce the dimensionality of the problem, genes having similar expression profiles are grouped into 17 clusters. We show that a simple linear model is able to reproduce the experimental data with very good precision, owing to the large number of parameters that represent the connections between the clusters. Remarkably, the parameter reduction allowed elimination of up to 80-85% of these connections while keeping fairly good precision. This result supports the low-connectivity hypothesis of gene expression networks, with about three connections per cluster, without introducing a priori hypotheses. The core of the network shows a few gene clusters with negative self-regulation, and some highly connected clusters involving proteins with crucial functions.

What is paradoxical about Levinthal paradox

Abstract We would be tempted to state that there has never been a Levinthal paradox. Indeed, Levinthal raised an interesting problem about protein folding, as he realized that proteins have no time to explore exhaustively their conformational space on the way to their native structure. He did not seem to find this paradoxical and immediately proposed a straightforward solution, which has essentially never been refuted. In other words, Levinthal solved his own paradox.

Neural Networks to predict protein stability changes upon mutation

Black box modelling is used here to improve the performances of the PoPMuSiC program that predicts protein stability changes caused by single-site mutations. For that purpose previously developed statistical energy functions are exploited, which are based on a formalism that highlights the coupling between 4 different protein descriptors (sequence, distance, torsion angles and solvent-accessibility), as well as the volume variation of the mutated amino acid. As the importance of the different types of interactions may depend on the protein region, the stability change is expressed as a linear combination of these energetic functions, whose proportionality coefficients vary with the solvent-accessibility of the mutated residue. Two alternative structures are considered for these coefficients: a Radial Basis Function network, and a MultiLayer Perceptron with sigmoid nodes. These two structures are identified, leading to an improvement of the predictive capabilities of PoPMuSiC, and are discussed in terms of their biophysical interpretation.

Design of modified proteins using knowledge-based approaches

PoPMuSiC (http://babylone.ulb.ac.be/popmusic) is a program that has been developed to predict rapidly changes in protein thermodynamic stability upon single-site mutations. We describe in this paper the theoretical model that underlies the PoPMuSiC software, and present a few applications to various issues of biological interest. In particular, we investigate the possible use of PoPMuSiC for the prediction of changes in protein-protein binding affinity upon mutation. We also summarize previous studies where PoPMuSiC has been used to modulate the relative stability of different protein structural states, to help the identification of mutations that stabilize and solubilize the Tobacco Etch Virus protease (TEV) and to detect structural weaknesses in proteins that are subject to domain swapping.

Modeling the temporal evolution of the Drosophila gene expression

Abstract The evolution of the gene expression pattern of Drosophila , from the embryonic to adult development phases, was studied on the basis of a microarray time series involving the expression levels of 4028 genes over 67 time-points. The genes presenting a similar temporal evolution of their expression levels were clustered together, so as to define a small number of representative classes. To model the network interactions responsible for the dynamic behavior of gene expression, a system of linear differential equations with constant coefficients was used. The parametric estimation of this model was performed in two stages: a first stage of linear parameter identification allowing an analytical approach to the solution, and a second stage of nonlinear parametric estimation which refines this solution. This model is shown to reproduce the experimental gene expression profiles with a fairly good precision.