Ludovico Sutto

Effects of oncogenic mutations on the conformational free-energy landscape of EGFR kinase

Activating mutations in the epidermal growth factor receptor (EGFR) tyrosine kinase are frequently found in many cancers. It has been suggested that changes in the equilibrium between its active and inactive conformations are linked to its oncogenic potential. Here, we quantify the effects of some of the most common single (L858R and T790M) and double (T790M-L858R) oncogenic mutations on the conformational free-energy landscape of the EGFR kinase domain by using massive molecular dynamics simulations together with parallel tempering, metadynamics, and one of the best force-fields available. Whereas the wild-type EGFR catalytic domain monomer is mostly found in an inactive conformation, our results show a clear shift toward the active conformation for all of the mutants. The L858R mutation stabilizes the active conformation at the expense of the inactive conformation and rigidifies the αC-helix. The T790M gatekeeper mutant favors activation by stabilizing a hydrophobic cluster. Finally, T790M with L858R shows a significant positive epistasis effect. This combination not only stabilizes the active conformation, but in nontrivial ways changes the free-energy landscape lowering the transition barriers.

Consequences of localized frustration for the folding mechanism of the IM7 protein

In the laboratory, IM7 has been found to have an unusual folding mechanism in which an “on-pathway” intermediate with nonnative interactions is formed. We show that this intermediate is a consequence of an unusual cluster of highly frustrated interactions in the native structure. This cluster is involved in the binding of IM7 to its target, Colicin E7. Redesign of residues in this cluster to eliminate frustration is predicted by simulations to lead to faster folding without the population of an intermediate ensemble.

New advances in metadynamics

Metadynamics is an algorithm for accelerating rare events and reconstructing the associated free energy surface. It works by biasing the evolution of the system by a history‐dependent potential that is adaptively constructed in the space of a suitably chosen set of collective variables. Since its first appearance, the method has been successfully applied in several domains of science. Its widespread adoption is not only due to its efficiency, flexibility, and availability but also to its continuous evolution and its combination with complementary enhanced sampling algorithms. Here, we focus on the progress made in the development of more general and powerful collective variables and on the very recent and exciting evolutions of the method.

The different flexibility of c-Src and c-Abl kinases regulates the accessibility of a druggable inactive conformation.

c-Src and c-Abl are two closely related protein kinases that constitute important anticancer targets. Despite their high sequence identity, they show different sensitivities to the anticancer drug imatinib, which binds specifically to a particular inactive conformation in which the Asp of the conserved DFG motif points outward (DFG-out). We have analyzed the DFG conformational transition of the two kinases using massive molecular dynamics simulations, free energy calculations, and isothermal titration calorimetry. On the basis of the reconstruction of the free energy surfaces for the DFG-in to DFG-out conformational changes of c-Src and c-Abl, we propose that the different flexibility of the two kinases results in a different stability of the DFG-out conformation and might be the main determinant of imatinib selectivity.

From residue coevolution to protein conformational ensembles and functional dynamics.

Significance Evolutionary-related protein sequences have been selected to preserve a common function and fold. Residues in contact in this conserved structure are coupled by evolution and show correlated mutational patterns. The exponential growth of sequenced genomes makes it possible to detect these coevolutionary coupled pairs and to infer three-dimensional folds from predicted contacts. But how far can we push the prediction of native folds? Can we predict the conformational heterogeneity of a protein directly from sequences? We address these questions developing an accurate contact prediction algorithm and a protein coarse-grained model, and exploring conformational landscapes congruent with coevolution. We find that both structural and dynamical properties can be already recovered using evolutionary information only. The analysis of evolutionary amino acid correlations has recently attracted a surge of renewed interest, also due to their successful use in de novo protein native structure prediction. However, many aspects of protein function, such as substrate binding and product release in enzymatic activity, can be fully understood only in terms of an equilibrium ensemble of alternative structures, rather than a single static structure. In this paper we combine coevolutionary data and molecular dynamics simulations to study protein conformational heterogeneity. To that end, we adapt the Boltzmann-learning algorithm to the analysis of homologous protein sequences and develop a coarse-grained protein model specifically tailored to convert the resulting contact predictions to a protein structural ensemble. By means of exhaustive sampling simulations, we analyze the set of conformations that are consistent with the observed residue correlations for a set of representative protein domains, showing that (i) the most representative structure is consistent with the experimental fold and (ii) the various regions of the sequence display different stability, related to multiple biologically relevant conformations and to the cooperativity of the coevolving pairs. Moreover, we show that the proposed protocol is able to reproduce the essential features of a protein folding mechanism as well as to account for regions involved in conformational transitions through the correct sampling of the involved conformers.

Use of the Metropolis algorithm to simulate the dynamics of protein chains

The Metropolis implementation of the Monte Carlo algorithm has been developed to study the equilibrium thermodynamics of many-body systems. Choosing small trial moves, the trajectories obtained applying this algorithm agree with those obtained by Langevins dynamics. Applying this procedure to a simplified protein model, it is possible to show that setting a threshold of 1∘ on the movement of the dihedrals of the protein backbone in a single Monte Carlo step, the mean quantities associated with the off-equilibrium dynamics (e.g., energy, RMSD, etc.) are well reproduced, while the good description of higher moments requires smaller moves. An important result is that the time duration of a Monte Carlo step depends linearly on the temperature, something which should be accounted for when doing simulations at different temperatures.

Design of HIV-1-PR inhibitors that do not create resistance: Blocking the folding of single monomers

The main problems found in designing drugs are those of optimizing the drug–target interaction and of avoiding the insurgence of resistance. We suggest a scheme for the design of inhibitors that can be used as leads for the development of a drug and that do not face either of these problems, and then apply it to the case of HIV‐1‐PR. It is based on the knowledge that the folding of single‐domain proteins, such as each of the monomers forming the HIV‐1‐PR homodimer, is controlled by local elementary structures (LES), stabilized by local contacts among hydrophobic, strongly interacting, and highly conserved amino acids that play a central role in the folding process. Because LES have evolved over many generations to recognize and strongly interact with each other so as to make the protein fold fast and avoid aggregation with other proteins, highly specific (and thus little toxic) as well as effective folding‐inhibitor molecules suggest themselves: short peptides (or eventually their mimetic molecules) displaying the same amino acid sequence of that of LES (p‐LES). Aside from being specific and efficient, these inhibitors are expected not to induce resistance; in fact, mutations in HIV‐1‐PR that successfully avoid the action of p‐LES imply the destabilization of one or more LES and thus should lead to protein denaturation. Making use of Monte Carlo simulations, we first identify the LES of the HIV‐1‐PR and then show that the corresponding p‐LES peptides act as effective inhibitors of the folding of the protease.

The Effect of Mutations on Drug Sensitivity and Kinase Activity of Fibroblast Growth Factor Receptors: A Combined Experimental and Theoretical Study

Fibroblast growth factor receptors (FGFRs) are recognized therapeutic targets in cancer. We here describe insights underpinning the impact of mutations on FGFR1 and FGFR3 kinase activity and drug efficacy, using a combination of computational calculations and experimental approaches including cellular studies, X-ray crystallography and biophysical and biochemical measurements. Our findings reveal that some of the tested compounds, in particular TKI258, could provide therapeutic opportunity not only for patients with primary alterations in FGFR but also for acquired resistance due to the gatekeeper mutation. The accuracy of the computational methodologies applied here shows a potential for their wider application in studies of drug binding and in assessments of functional and mechanistic impacts of mutations, thus assisting efforts in precision medicine.

Sequence of events in folding mechanism: Beyond the Gō model

Simplified Gō models, where only native contacts interact favorably, have proven useful to characterize some aspects of the folding of small proteins. The success of these models is limited by the fact that all residues interact in the same way so that the folding features of a protein are determined only by the geometry of its native conformation. We present an extended version of a Cα‐based Gō model where different residues interact with different energies. The model is used to calculate the thermodynamics of three small proteins (Protein G, Src‐SH3, and CI2) and the effect of mutations (ΔΔGU‐N, ΔΔG‡‐N, ΔΔG‡‐U, and φ‐values) on the wild‐type sequence. The model allows us to investigate some of the most controversial areas in protein folding, such as its earliest stages and the nature of the unfolded state, subjects that have lately received particular attention.

Conformational Changes and Free Energies in a Proline Isomerase

Proteins are dynamic molecules and their ability to adopt alternative conformations is central to their biological function. The structural and biophysical properties of transiently and sparsely populated states are, however, difficult to study and an atomic-level description of those states is challenging. We have used enhanced-sampling all-atom, explicit-solvent molecular simulations, guided by structural information from X-ray crystallography and NMR, to describe quantitatively the transition between the major and a minor state of Cyclophilin A, thus providing new insight into how dynamics can affect enzyme function. We calculate the conformational free energy between the two states, and comparison with experiments demonstrates a surprisingly high accuracy for both the wild type protein and a mutant that traps the protein in its alternative conformation. Our results demonstrate how the combination of state-of-the-art force fields and enhanced sampling methods can provide a detailed and quantitative description of the conformational changes in proteins such as those observed in Cyclophilin A.