The SIRAH force field: A suite for simulations of complex biological systems at the coarse-grained and multiscale levels.

The different combinations of molecular dynamics simulations with coarse-grained representations have acquired considerable popularity among the scientific community. Especially in biocomputing, the significant speedup granted by simplified molecular models opened the possibility of increasing the diversity and complexity of macromolecular systems, providing realistic insights on large assemblies for more extended time windows. However, a holistic view of biological ensembles' structural and dynamic features requires a self-consistent force field, namely, a set of equations and parameters that describe the intra and intermolecular interactions among moieties of diverse chemical nature (i.e., nucleic and amino acids, lipids, solvent, ions, etc.). Nevertheless, examples of such force fields are scarce in the literature at the fully atomistic and coarse-grained levels. Moreover, the number of force fields capable of handling simultaneously different scales is restricted to a handful. Among those, the SIRAH force field, developed in our group, furnishes a set of topologies and tools that facilitate the setting up and running of molecular dynamics simulations at the coarse-grained and multiscale levels. SIRAH uses the same classical pairwise Hamiltonian function implemented in the most popular molecular dynamics software. In particular, it runs natively in AMBER and Gromacs engines, and porting it to other simulation packages is straightforward. This review describes the underlying philosophy behind the development of SIRAH over the years and across families of biological molecules, discussing current limitations and future implementations.

How to Make the CUTiest Sensor in Three Simple Steps for Computational Pedestrians.

Genetically encoded FRET sensors for revealing local concentrations of second messengers in living cells have enormously contributed to our understanding of physiological and pathological processes. However, the development of sensors remains an intricate process. Using simulation techniques, we recently introduced a new architecture to measure intracellular concentrations of cAMP named CUTie, which works as a FRET tag for arbitrary targeting domains. Although our method showed quasi-quantitative predictive power in the design of cAMP and cGMP sensors, it remains intricate and requires specific computational skills. Here, we provide a simplified computer-aided protocol to design tailor-made CUTie sensors based on arbitrary cyclic nucleotide-binding domains. As a proof of concept, we applied this method to construct a new CUTie sensor with a significantly higher cAMP sensitivity (EC50 = 460 nM).This simple protocol, which integrates our previous experience, only requires free web servers and can be straightforwardly used to create cAMP sensors adapted to the physicochemical characteristics of known cyclic nucleotide-binding domains.

CUTie2: The Attack of the Cyclic Nucleotide Sensor Clones.

The detection of small molecules in living cells using genetically encoded FRET sensors has revolutionized our understanding of signaling pathways at the sub-cellular level. However, engineering fluorescent proteins and specific binding domains to create new sensors remains challenging because of the difficulties associated with the large size of the polypeptides involved, and their intrinsically huge conformational variability. Indeed, FRET sensors' design still relies on vague structural notions, and trial and error combinations of linkers and protein modules. We recently designed a FRET sensor for the second messenger cAMP named CUTie (Cyclic nucleotide Universal Tag for imaging experiments), which granted sub-micrometer resolution in living cells. Here we apply a combination of sequence/structure analysis to produce a new-generation FRET sensor for the second messenger cGMP based on Protein kinase G I (PKGI), which we named CUTie2. Coarse-grained molecular dynamics simulations achieved an exhaustive sampling of the relevant spatio-temporal coordinates providing a quasi-quantitative prediction of the FRET efficiency, as confirmed by in vitro experiments. Moreover, biochemical characterization showed that the cGMP binding module maintains virtually the same affinity and selectivity for its ligand thant the full-length protein. The computational approach proposed here is easily generalizable to other allosteric protein modules, providing a cost effective-strategy for the custom design of FRET sensors.

Assessing SIRAH's Capability to Simulate Intrinsically Disordered Proteins and Peptides.

The challenges posed by intrinsically disordered proteins (IDPs) to atomistic and coarse-grained (CG) simulations are boosting efforts to develop and reparametrize current force fields. An assessment of the dynamical behavior of IDPs' and unstructured peptides with the CG SIRAH force field suggests that the current version achieves a fair description of IDPs' conformational flexibility. Moreover, we found a remarkable capability to capture the effect of point mutations in loosely structured peptides.

Coarse-Grained Parameters for Divalent Cations within the SIRAH Force Field.

Although molecular dynamics simulations allow for the study of interactions among virtually all biomolecular entities, metal ions still pose significant challenges in achieving an accurate structural and dynamical description of many biological assemblies, particularly to coarse-grained (CG) models. Although the reduced computational cost of CG methods often makes them the technique of choice for the study of large biomolecular systems, the parameterization of metal ions is still very crude or not available for the vast majority of CG force fields. Here, we show that incorporating statistical data retrieved from the Protein Data Bank (PDB) to set specific Lennard-Jones interactions can produce structurally accurate CG molecular dynamics simulations using the SIRAH force field. We provide a set of interaction parameters for calcium, magnesium, and zinc ions, which cover more than 80% of the metal-bound structures reported in the PDB. Simulations performed on several proteins and DNA systems show that it is possible to preclude the use of topological constraints by modifying specific Lennard-Jones interactions.

The SIRAH 2.0 Force Field: Altius, Fortius, Citius.

A new version of the coarse-grained (CG) SIRAH force field for proteins has been developed. Modifications to bonded and non-bonded interactions on the existing molecular topologies significantly ameliorate the structural description and flexibility of a non-redundant set of proteins. The SIRAH 2.0 force field has also been ported to the popular simulation package AMBER, which along with the former implementation in GROMACS expands significantly the potential range of users and performance of this CG force field on CPU/GPU codes. As a non-trivial example of its application, we undertook the structural and dynamical analysis of the most abundant and conserved calcium-binding protein, calmodulin (CaM). CaM is composed of two calcium-binding motifs called EF-hands, which in the presence of calcium specifically recognize a cognate peptide by embracing it. CG simulations of CaM bound to four calcium ions in the presence or absence of a binding peptide (holo and apo forms, respectively) resulted in good and stable ion coordination. The simulation of the holo form starting from an experimental structure sampled near-native conformations, retrieving quasi-atomistic precision. Removing the binding peptide enabled the EF-hands to perform large reciprocal movements, comparable to those observed in NMR structures. On the other hand, the isolated peptide starting from the helical conformation experienced spontaneous unfolding, in agreement with previous experimental data. However, repositioning the peptide in the neighborhood of one EF-hand not only prevented the peptide from unfolding but also drove CaM to a fully bound conformation, with both EF-hands embracing the cognate peptide, resembling the experimental holo structure. Therefore, SIRAH 2.0 shows the capacity to handle a number of structurally and dynamically challenging situations, including metal ion coordination, unbiased conformational sampling, and specific protein-peptide recognition.

New red-shifted fluorescent biosensor for monitoring intracellular redox changes.

Maintenance of intracellular redox homeostasis is critical for cell survival, proliferation, differentiation, and signaling. In this regard, major changes in the intracellular redox milieu may lead to cell death whereas subtle increases in the level of certain oxidizing species may act as signals that regulate a plethora of cellular processes. Redox-sensitive variants of green fluorescent proteins (roGFP2 and rxYFP) were developed and proved useful to monitor intracellular redox changes in a non-invasive and online manner. With the aim to extend the spectral range of the fluorescent redox biosensors, we here describe the generation, biochemical characterization and biological validation of a new redox reporter based on the red-shifted mRuby2 protein (rxmRuby2). Spectrofluorimetric analysis performed with the recombinant biosensor shows a reversible redox response produced by two redox-active cysteine residues predicted by molecular modeling. rxmRuby2 is highly selective for the couple glutathione/glutathione disulfide in the presence of the oxidoreductase glutaredoxin. The estimated redox potential of rxmRuby2 (E° -265 ±â€¯22 mV) makes it suitable for its use in reducing subcellular compartments. Titration assays demonstrated the capacity of rxmRuby2 to monitor redox changes within a physiological pH range. rxmRuby2 responded sensitively and reversibly to different redox stimuli applied to HeLa and HEK293 cells expressing transiently and/or stable the biosensor. Fusing rxmRuby2 to the Clover fluorescent protein allowed normalization of the redox signal to the expression level of the reporter protein and/or to other factors that may affect fluorescence. The new red-shifted redox biosensor show promises for deep-tissue and in vivo imaging applications.