Method for transferring mutations between plasmids.

Despite occurring in an essential molecule, mutations in RNA polymerase readily emerge and elicit complex pleiotropic effects across different levels of biological organization, which are all modulated by environment. We investigated the impact of temperature on the effects of six mutations on sequence, structure, transcriptome, and organismal traits. We found temperature altered the transcriptomic response and key organismal traits such as growth rate and biofilm formation in a genotype-specific manner. Critically, mechanistic insights into the possible drivers of mutational effects emerged only when examining the relationships between different levels of organization: location of mutations in the tertiary structure and distance to key interacting molecules partly explained the observed transcriptomic differences, which in turn drove the impact of mutations on organismal traits. While falling short of capturing the full complexity of the system, our findings underscore the benefits of integrating insights across multiple biological levels to understand the relationship between environment and mutational effects in molecules with extensive pleiotropic effects.

Mutation↗

PubMed2001

Molecular dynamics and thermodynamics of protein-RNA interactions: mutation of a conserved aromatic residue modifies stacking interactions and structural adaptation in the U1A-stem loop 2 RNA complex.

Molecular dynamics (MD) simulations and free energy component analysis have been performed to evaluate the molecular origins of the 5.5 kcal/mol destabilization of the complex formed between the N-terminal RNP domain of U1A and stem loop 2 of U1 snRNA upon mutation of a conserved aromatic residue, Phe56, to Ala. MD simulations, including counterions and water, have been carried out on the wild type and Phe56Ala peptide-stem loop 2 RNA complexes, the free wild type and Phe56Ala peptides, and the free stem loop 2 RNA. The MD structure of the Phe56Ala-stem loop 2 complex is similar to that of the wild type complex except the stacking interaction between Phe56 and A6 of stem loop 2 is absent and loop 3 of the peptide is more dynamic. However, the MD simulations predict large changes in the structure and dynamics of helix C and increased dynamic range of loop 3 for the free Phe56Ala peptide compared to the wild type peptide. Since helix C and loop 3 are highly variable regions of RNP domains, this indicates that a significant contribution to the reduced affinity of the Phe56Ala peptide for RNA results from cooperation between highly conserved and highly variable regions of the RNP domain of U1A. Surprisingly, these structural effects, which are manifested as cooperative free energy changes, occur in the free peptide, rather than in the complex, and are revealed only by study of both the initial and final states of the complexation process. Free energy component analysis correctly accounts for the destabilization of the Phe56Ala-stem loop 2 complex, and indicates that approximately 80% of the destabilization is due to the loss of the stacking interaction and approximately 20% is due to differences in U1A adaptation.

Mutation↗

PubMed2001

Progress in predicting inter-residue contacts of proteins with neural networks and correlated mutations.

This article presents recent progress in predicting inter-residue contacts of proteins with a neural network-based method. Improvement over the results obtained at the previous CASP3 competition is attained by using as input to the network a complex code, which includes evolutionary information, sequence conservation, correlated mutations, and predicted secondary structures. The predictor was trained and cross-validated on a data set comprising the contact maps of 173 non-homologous proteins as computed from their well-resolved three-dimensional structures. The method could assign protein contacts with an average accuracy of 0.21 and with an improvement over a random predictor of a factor greater than 6, which is higher than that previously obtained with methods only based either on neural networks or on correlated mutations. Although far from being ideal, these scores are the highest reported so far for predicting protein contact maps. On 29 targets automatically predicted by the server (CORNET) the average accuracy is 0.14. The predictor is poorly performing on all alpha proteins, not represented in the training set. On all beta and mixed proteins (22 targets) the average accuracy is 0.16. This set comprises proteins of different complexity and different chain length, suggesting that the predictor is capable of generalization over a broad number of features.

Mutation↗