Jean Marc Kwasigroch

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.

Cation-π, amino-π, π-π, and H-bond interactions stabilize antigen-antibody interfaces.

The identification of immunogenic regions on the surface of antigens, which are able to stimulate an immune response, is a major challenge for the design of new vaccines. Computational immunology aims at predicting such regions—in particular B‐cell epitopes—but is far from being reliably applicable on a large scale. To gain understanding into the factors that contribute to the antigen–antibody affinity and specificity, we perform a detailed analysis of the amino acid composition and secondary structure of antigen and antibody surfaces, and of the interactions that stabilize the complexes, in comparison with the composition and interactions observed in other heterodimeric protein interfaces. We make a distinction between linear and conformational B‐cell epitopes, according to whether they consist of successive residues along the polypeptide chain or not. The antigen–antibody interfaces were shown to differ from other protein–protein interfaces by their smaller size, their secondary structure with less helices and more loops, and the interactions that stabilize them: more H‐bond, cation–π, amino–π, and π–π interactions, and less hydrophobic packing; linear and conformational epitopes can clearly be distinguished. Often, chains of successive interactions, called cation/amino–π and π–π chains, are formed. The amino acid composition differs significantly between the interfaces: antigen–antibody interfaces are less aliphatic and more charged, polar and aromatic than other heterodimeric protein interfaces. Moreover, paratopes and epitopes—albeit to a lesser extent—have amino acid compositions that are distinct from general protein surfaces. This specificity holds promise for improving B‐cell epitope prediction. Proteins 2014; 82:1734–1746.

Modelling and Bioinformatics Analysis of the Dimeric Structure of House Dust Mite Allergens from Families 5 and 21: Der f 5 Could Dimerize as Der p 5

Abstract Allergy represents an increasing thread to public health in both developed and emerging countries and the dust mites Dermatophagoides pteronyssinus (Der p), Blomia tropicalis (Blo t), Dermatophagoides farinae (Der f), Lepidoglyphus destructor (Lep d) and Suidasia medanensis (Sui m) strongly contribute to this problem. Their allergens are classified in several families among which families 5 and 21 which are the subject of this work. Indeed, their biological function as well as the mechanism or epitopes by which they are contributing to the allergic response remain unknown and their tridimensional structures have not been resolved experimentally except for Blo t 5 and Der p 5. Blo t 5 is a monomeric three helical bundle, whereas Der p 5 shows a three helical bundle with a kinked N-terminal helix that assembles in an entangled dimeric structure with a large hydrophobic cavity. This cavity could be involved in the binding of hydrophobic ligands, which in turn could be responsible for the shift of the immune response from tolerance to allergic inflammation. We used molecular modelling approaches to bring out if other house dust mite allergens of families 5 and 21 (Der f 5, Sui m 5, Lep d 5, Der p 21 and Der f 21) could dimerize and form a large cavity in the same way as Der p 5. Monomeric models were first performed with MODELLER using the experimental structures of Der p 5 and Blo t 5 as templates. The ClusPro server processed the selected monomers in order to assess their capacity to form dimeric structures with a positive result for Der p 5 and Der f 5 only. The other allergens (Blo t 5, Sui m 5, Lep d 5, Der p 21 and Der f 21) did not present such a propensity. Moreover, we identified mutations that should destabilize and/or prevent the formation of the Der p 5 dimeric structure. The production of these mutated proteins could help us to understand the role of the dimerization process in the allergic response induced by Der p 5, and if Der p 5 and Der f 5 behave similarly.

SODa: An Mn/Fe superoxide dismutase prediction and design server

BackgroundSuperoxide dismutases (SODs) are ubiquitous metalloenzymes that play an important role in the defense of aerobic organisms against oxidative stress, by converting reactive oxygen species into nontoxic molecules. We focus here on the SOD family that uses Fe or Mn as cofactor.ResultsThe SODa webtool http://babylone.ulb.ac.be/soda predicts if a target sequence corresponds to an Fe/Mn SOD. If so, it predicts the metal ion specificity (Fe, Mn or cambialistic) and the oligomerization mode (dimer or tetramer) of the target. In addition, SODa proposes a list of residue substitutions likely to improve the predicted preferences for the metal cofactor and oligomerization mode. The method is based on residue fingerprints, consisting of residues conserved in SOD sequences or typical of SOD subgroups, and of interaction fingerprints, containing residue pairs that are in contact in SOD structures.ConclusionSODa is shown to outperform and to be more discriminative than traditional techniques based on pairwise sequence alignments. Moreover, the fact that it proposes selected mutations makes it a valuable tool for rational protein design.

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.

Prelude&Fugue, predicting local protein structure, early folding regions and structural weaknesses

UNLABELLED Prelude&Fugue are bioinformatics tools aiming at predicting the local 3D structure of a protein from its amino acid sequence in terms of seven backbone torsion angle domains, using database-derived potentials. Prelude(&Fugue) computes all lowest free energy conformations of a protein or protein region, ranked by increasing energy, and possibly satisfying some interresidue distance constraints specified by the user. (Prelude&)Fugue detects sequence regions whose predicted structure is significantly preferred relative to other conformations in the absence of tertiary interactions. These programs can be used for predicting secondary structure, tertiary structure of short peptides, flickering early folding sequences and peptides that adopt a preferred conformation in solution. They can also be used for detecting structural weaknesses, i.e. sequence regions that are not optimal with respect to the tertiary fold. AVAILABILITY http://babylone.ulb.ac.be/Prelude_and_Fugue.

Quantification of biases in predictions of protein stability changes upon mutations

Motivation: Bioinformatics tools that predict protein stability changes upon point mutations have made a lot of progress in the last decades and have become accurate and fast enough to make computational mutagenesis experiments feasible, even on a proteome scale. Despite these achievements, they still suffer from important issues that must be solved to allow further improving their performances and utilizing them to deepen our insights into protein folding and stability mechanisms. One of these problems is their bias toward the learning datasets which, being dominated by destabilizing mutations, causes predictions to be better for destabilizing than for stabilizing mutations. Results: We thoroughly analyzed the biases in the prediction of folding free energy changes upon point mutations (&Dgr;&Dgr;G0) and proposed some unbiased solutions. We started by constructing a dataset Ssym of experimentally measured &Dgr;&Dgr;G0s with an equal number of stabilizing and destabilizing mutations, by collecting mutations for which the structure of both the wild‐type and mutant protein is available. On this balanced dataset, we assessed the performances of 15 widely used &Dgr;&Dgr;G0 predictors. After the astonishing observation that almost all these methods are strongly biased toward destabilizing mutations, especially those that use black‐box machine learning, we proposed an elegant way to solve the bias issue by imposing physical symmetries under inverse mutations on the model structure, which we implemented in PoPMuSiCsym. This new predictor constitutes an efficient trade‐off between accuracy and absence of biases. Some final considerations and suggestions for further improvement of the predictors are discussed. Supplementary information: Supplementary data are available at Bioinformatics online. Note: The article 10.1093/bioinformatics/bty340/, published alongside this paper, also addresses the problem of biases in protein stability change predictions.

SCooP: an accurate and fast predictor of protein stability curves as a function of temperature

Motivation The molecular bases of protein stability remain far from elucidated even though substantial progress has been made through both computational and experimental investigations. One of the most challenging goals is the development of accurate prediction tools of the temperature dependence of the standard folding free energy &Dgr;G(T). Such predictors have an enormous series of potential applications, which range from drug design in the biopharmaceutical sector to the optimization of enzyme activity for biofuel production. There is thus an important demand for novel, reliable and fast predictors. Results We present the SCooP algorithm, which is a significant step towards accurate temperature‐dependent stability prediction. This automated tool uses the protein structure and the host organism as sole entries and predicts the full T‐dependent stability curve of monomeric proteins assumed to follow a two‐state folding transition. Equivalently, it predicts all the thermodynamic quantities associated to the folding transition, namely the melting temperature Tm, the standard folding enthalpy &Dgr;Hm measured at Tm, and the standard folding heat capacity &Dgr;Cp. The cross‐validated performances are good, with correlation coefficients between predicted and experimental values equal to [0.80, 0.83, 0.72] for &Dgr;Hm, &Dgr;Cp and Tm, respectively, which increase up to [0.88, 0.90, 0.78] upon the removal of 10% outliers. Moreover, the stability curve prediction of a target protein is very fast: it takes less than a minute. SCooP can thus potentially be applied on a structurome scale. This opens new perspectives of large‐scale analyses of protein stability, which is of considerable interest for protein engineering. Availability and implementation The SCooP webserver is freely available at http://babylone.ulb.ac.be/SCooP. Contact fapucci@ulb.ac.be, jkwasigr@ulb.ac.be or mrooman@ulb.ac.be Supplementary information Supplementary data are available at Bioinformatics online.