Determination of protein structural ensembles using cryo-electron microscopy.

Achieving a comprehensive understanding of the behaviour of proteins is greatly facilitated by the knowledge of their structures, thermodynamics and dynamics. All this information can be provided in an effective manner in terms of structural ensembles. A structural ensemble can be obtained by determining the structures, populations and interconversion rates for all the main states that a protein can occupy. To reach this goal, integrative methods that combine experimental and computational approaches provide powerful tools. Here we focus on cryo-electron microscopy, which has become over recent years an invaluable resource to bridge the gap from order to disorder in structural biology. In this review, we provide a perspective of the current challenges and opportunities in determining protein structural ensembles using integrative approaches that can combine cryo-electron microscopy data with other available sources of information, along with an overview of the tools available to the community

Funnel metadynamics as accurate binding free-energy method

A detailed description of the events ruling ligand/protein interac- tion and an accurate estimation of the drug affinity to its target is of great help in speeding drug discovery strategies. We have de- veloped a metadynamics-based approach, named funnel metady- namics, that allows the ligand to enhance the sampling of the target binding sites and its solvated states. This method leads to an effi- cient characterization of the binding free-energy surface and an accurate calculation of the absolute protein–ligand binding free energy. We illustrate our protocol in two systems, benzamidine/ trypsin and SC-558/cyclooxygenase 2. In both cases, the X-ray con- formation has been found as the lowest free-energy pose, and the computed protein–ligand binding free energy in good agreement with experiments. Furthermore, funnel metadynamics unveils im- portant information about the binding process, such as the presence of alternative binding modes and the role of waters. The results achieved at an affordable computational cost make funnel meta- dynamics a valuable method for drug discovery and for dealing with a variety of problems in chemistry, physics, and material science

Metainference: A Bayesian Inference Method for Heterogeneous Systems

Modelling a complex system is almost invariably a challenging task. The incorporation of experimental observations can be used to improve the quality of a model, and thus to obtain better predictions about the behavior of the corresponding system. This approach, however, is affected by a variety of different errors, especially when a system populates simultaneously an ensemble of different states and experimental data are measured as averages over such states. To address this problem we present a Bayesian inference method, called metainference, that is able to deal with errors in experimental measurements as well as with experimental measurements averaged over multiple states. To achieve this goal, metainference models a finite sample of the distribution of models using a replica approach, in the spirit of the replica-averaging modelling based on the maximum entropy principle. To illustrate the method we present its application to a heterogeneous model system and to the determination of an ensemble of structures corresponding to the thermal fluctuations of a protein molecule. Metainference thus provides an approach to model complex systems with heterogeneous components and interconverting between different states by taking into account all possible sources of errors.Comment: 29 pages, 10 figure

Metainference: a Bayesian inference method for heterogeneous systems

Modeling a complex system is almost invariably a challenging task. The incorporation of experimental observations can be used to improve the quality of a model and thus to obtain better predictions about the behavior of the corresponding system. This approach, however, is affected by a variety of different errors, especially when a system simultaneously populates an ensemble of different states and experimental data are measured as averages over such states. To address this problem, we present a Bayesian inference method, called “metainference,” that is able to deal with errors in experimental measurements and with experimental measurements averaged over multiple states. To achieve this goal, metainference models a finite sample of the distribution of models using a replica approach, in the spirit of the replica-averaging modeling based on the maximum entropy principle. To illustrate the method, we present its application to a heterogeneous model system and to the determination of an ensemble of structures corresponding to the thermal fluctuations of a protein molecule. Metainference thus provides an approach to modeling complex systems with heterogeneous components and interconverting between different states by taking into account all possible sources of errors

Ligand Discovery for the Alanine-Serine-Cysteine Transporter (ASCT2, SLC1A5) from Homology Modeling and Virtual Screening

The Alanine-Serine-Cysteine transporter ASCT2 (SLC1A5) is a membrane protein that transports neutral amino acids into cells in exchange for outward movement of intracellular amino acids. ASCT2 is highly expressed in peripheral tissues such as the lung and intestines where it contributes to the homeostasis of intracellular concentrations of neutral amino acids. ASCT2 also plays an important role in the development of a variety of cancers such as melanoma by transporting amino acid nutrients such as glutamine into the proliferating tumors. Therefore, ASCT2 is a key drug target with potentially great pharmacological importance. Here, we identify seven ASCT2 ligands by computational modeling and experimental testing. In particular, we construct homology models based on crystallographic structures of the aspartate transporter Glt(Ph) in two different conformations. Optimization of the models\u27 binding sites for protein-ligand complementarity reveals new putative pockets that can be targeted via structure-based drug design. Virtual screening of drugs, metabolites, fragments-like, and lead-like molecules from the ZINC database, followed by experimental testing of 14 top hits with functional measurements using electrophysiological methods reveals seven ligands, including five activators and two inhibitors. For example, aminooxetane-3-carboxylate is a more efficient activator than any other known ASCT2 natural or unnatural substrate. Furthermore, two of the hits inhibited ASCT2 mediated glutamine uptake and proliferation of a melanoma cancer cell line. Our results improve our understanding of how substrate specificity is determined in amino acid transporters, as well as provide novel scaffolds for developing chemical tools targeting ASCT2, an emerging therapeutic target for cancer and neurological disorders

Small-molecule sequestration of amyloid-β as a drug discovery strategy for Alzheimer's disease.

Disordered proteins are challenging therapeutic targets, and no drug is currently in clinical use that modifies the properties of their monomeric states. Here, we identify a small molecule (10074-G5) capable of binding and sequestering the intrinsically disordered amyloid-β (Aβ) peptide in its monomeric, soluble state. Our analysis reveals that this compound interacts with Aβ and inhibits both the primary and secondary nucleation pathways in its aggregation process. We characterize this interaction using biophysical experiments and integrative structural ensemble determination methods. We observe that this molecule increases the conformational entropy of monomeric Aβ while decreasing its hydrophobic surface area. We also show that it rescues a Caenorhabditis elegans model of Aβ-associated toxicity, consistent with the mechanism of action identified from the in silico and in vitro studies. These results illustrate the strategy of stabilizing the monomeric states of disordered proteins with small molecules to alter their behavior for therapeutic purposes

PLUMED: a portable plugin for free-energy calculations with molecular dynamics

Here we present a program aimed at free-energy calculations in molecular systems. It consists of a series of routines that can be interfaced with the most popular classical molecular dynamics (MD) codes through a simple patching procedure. This leaves the possibility for the user to exploit many different MD engines depending on the system simulated and on the computational resources available. Free-energy calculations can be performed as a function of many collective variables, with a particular focus on biological problems, and using state-of-the-art methods such as metadynamics, umbrella sampling and Jarzynski-equation based steered MD. The present software, written in ANSI-C language, can be easily interfaced with both fortran and C/C++ codes.Comment: to be submitted to Computer Physics Communication

Multiple Routes and Milestones in the Folding of HIV–1 Protease Monomer

Proteins fold on a time scale incompatible with a mechanism of random search in conformational space thus indicating that somehow they are guided to the native state through a funneled energetic landscape. At the same time the heterogeneous kinetics suggests the existence of several different folding routes. Here we propose a scenario for the folding mechanism of the monomer of HIV–1 protease in which multiple pathways and milestone events coexist. A variety of computational approaches supports this picture. These include very long all-atom molecular dynamics simulations in explicit solvent, an analysis of the network of clusters found in multiple high-temperature unfolding simulations and a complete characterization of free-energy surfaces carried out using a structure-based potential at atomistic resolution and a combination of metadynamics and parallel tempering. Our results confirm that the monomer in solution is stable toward unfolding and show that at least two unfolding pathways exist. In our scenario, the formation of a hydrophobic core is a milestone in the folding process which must occur along all the routes that lead this protein towards its native state. Furthermore, the ensemble of folding pathways proposed here substantiates a rational drug design strategy based on inhibiting the folding of HIV–1 protease

Sex-specific predictors of PCSK9 levels in a European population:The IMPROVE study

Background and aims: Proprotein convertase subtilisin/kexin type 9 (PCSK9) is one of the key regulators of low-density lipoprotein cholesterol plasma levels and circulating PCSK9, which differs between genders. PCSK9 represents a valid pharmacological target for preventing cardiovascular (CV) events. We aimed to investigate sex-related associations between PCSK9 plasma levels and biochemical and anthropomorphic factors, and familial and personal morbidities, in a large European cohort (n=3,673) of men (47.9%) and women (52.1%). Methods: Individuals (aged 54 to 79 years) free of CV diseases were enrolled in 7 centers of five European countries: Finland, France, Italy, the Netherlands, and Sweden. PCSK9 plasma levels were measured by ELISA. Results: PCSK9 was higher in women than in men. Multiple linear regression analysis showed that latitude, sex, and treatments with statins and fibrates were the strongest predictors of PCSK9 in the whole group. These variables, together with triglycerides and high-density lipoprotein cholesterol, were also associated with PCSK9 in men or women. Mean corpuscular hemoglobin concentration and pack-years were PCSK9 independent predictors in women, whereas hypercholesterolemia and physical activity were independent predictors in men. The associations between PCSK9 and latitude, uric acid, diabetes, hypercholesterolemia and physical activity were significantly different in men and women (pinteraction <0.05 for all). Conclusions: Besides confirming the association with lipids in the whole group, our study revealed previously unknown differences in PCSK9 predictors in men and women. These might be taken into account when defining individual risk for CV events and/or for refining PCSK9 lowering treatments

Sex-specific predictors of PCSK9 levels in a European population: the IMPROVE study

Background and aims: Proprotein convertase subtilisin/kexin type 9 (PCSK9) is one of the key regulators of low-density lipoprotein cholesterol plasma levels and circulating PCSK9, which differs between genders. PCSK9 represents a valid pharmacological target for preventing cardiovascular (CV) events. We aimed to investigate sex-related associations between PCSK9 plasma levels and biochemical and anthropomorphic factors, and familial and personal morbidities, in a large European cohort (n=3,673) of men (47.9%) and women (52.1%). Methods: Individuals (aged 54 to 79 years) free of CV diseases were enrolled in 7 centers of five European countries: Finland, France, Italy, the Netherlands, and Sweden. PCSK9 plasma levels were measured by ELISA. Results: PCSK9 was higher in women than in men. Multiple linear regression analysis showed that latitude, sex, and treatments with statins and fibrates were the strongest predictors of PCSK9 in the whole group. These variables, together with triglycerides and high-density lipoprotein cholesterol, were also associated with PCSK9 in men or women. Mean corpuscular hemoglobin concentration and pack-years were PCSK9 independent predictors in women, whereas hypercholesterolemia and physical activity were independent predictors in men. The associations between PCSK9 and latitude, uric acid, diabetes, hypercholesterolemia and physical activity were significantly different in men and women (pinteraction <0.05 for all). Conclusions: Besides confirming the association with lipids in the whole group, our study revealed previously unknown differences in PCSK9 predictors in men and women. These might be taken into account when defining individual risk for CV events and/or for refining PCSK9 lowering treatments