Desarrollo In silico de sensores fluorescentes para disecar vías de señalización celular

El campo de fuerza para simulaciones de grano grueso y multiescala denominado SIRAH viene siendo desarrollado en la última década por el grupo de Simulaciones Biomoleculares del Institut Pasteur de Montevideo. Posee parámetros para proteínas, ácidos nucleicos, lípidos, glicanos y han incorporado estudios de modificaciones post-traduccionales. Debido a que los campos de fuerza necesitan estar en continua actualización para mantenerse vigentes e incorporar parámetros de nuevas especies moleculares, durante esta tesis se colaboró en la actualización del campo de fuerzas SIRAH2.0 y se desarrollaron parámetros de iones divalentes como zinc, magnesio y se extendió la aplicabilidad de los iones calcio que ya se encontraban parametrizado. A su vez, era de suma importancia verificar la capacidad de SIRAH para predecir proteínas intrínsecamente desestructuradas (IDPs), extendiendo la aplicabilidad del campo de fuerzas sin necesidad de introducir cambios relevantes en la parametrización. A su vez, abordé el estudio y desarrollo de sensores genéticamente codificados para nucleótidos cíclicos como 3 ́5 ́-adenosín/guanosín monofosfato cíclico (AMPc/GMPc respectivamente). La búsqueda de sensores genéticamente codificados ha permitido el estudio de manera no invasiva y en tiempo real de la compartimentalización de señales subcelulares. Para ello, nuestro grupo ya había desarrollado un biosensor primero en su clase, en la que un módulo fluorescente es insertado dentro del dominio de enlace a AMPc de la subunidad regulatoria de la proteína PKA (Proteína Quinasa dependiente de AMPc) mientras el segundo se encuentra en su extremo C-terminal. De esta manera, el extremo N-terminal de la cadena polipeptídica puede ser fusionado a cualquier proteína de interés. Esta arquitectura novedosa mantiene la funcionalidad de dicha proteína y garantiza la localización del sensor en el punto de interés sin deber confiar en la colocalización. Teniendo este precedente, en la presente tesis se desarrolla la construcción de un sensor para GMPc, cuyo dominio de unión a nucleótido cíclico es PKG (Proteína Quinasa dependiente de GMPc). Por otro lado, gracias al primer sensor de AMPc, se pudo establecer que la compartimentalización mínima en el sarcómero puede ser establecida en una escala de nanómetros. Por lo que, en este proyecto de investigación de doctorado, se busca construir la unidad estructural mínima de un signalosoma de la cadena beta adrenérgica en el sarcómero del músculo cardíaco para entender la disposición de las diferentes proteínas, el volumen que ocupan y la capacidad de moléculas como el AMPc para llegar a sus proteínas blanco. Gracias a esto y estudios del Dr. Baillie del Grupo de Molecular Pharmacology en Glasgow University, pudimos evidenciar los sitios de contacto entre proteínas como las troponinas, PKAs y fosfodiesterasas. Estos resultados llevan al primer ejemplo de un modelo 3D del denominado “islas de AMPc”, vagamente propuesto hace tres décadas aproximadamente, pero, hasta el momento, no bien definido en términos estructurales. Estos resultados llevan a una predicción de tamaños relativos y concentraciones que muestran al músculo cardíaco como una pieza de relojería donde todo debe trabajar de manera unísona y precisa. Finalmente, la experiencia obtenida durante mis estudios doctorales nos llevó a proponer un procedimiento simplificado pero general para generar sensores fluorescentes para nucleótidos cíclicos con una afinidad arbitraria por su ligando. Vale la pena destacar, que el procedimiento de diseño puede basarse en conceptos simples, sin la necesidad de ser un experto computacional, utilizando servidores disponibles en línea

Ligand-protein interactions in lysozyme investigated through a dual-resolution model

A fully atomistic modelling of biological macromolecules at relevant length- and time-scales is often cumbersome or not even desirable, both in terms of computational effort required and it a posteriori analysis. This difficulty can be overcome with the use of multi-resolution models, in which different regions of the same system are concurrently described at different levels of detail. In enzymes, computationally expensive atomistic detail is crucial in the modelling of the active site in order to capture e.g. the chemically subtle process of ligand binding. In contrast, important yet more collective properties of the remainder of the protein can be reproduced with a coarser description. In the present work, we demonstrate the effectiveness of this approach through the calculation of the binding free energy of hen egg white lysozyme (HEWL) with the inhibitor di-N-acetylchitotriose. Particular attention is posed to the impact of the mapping, i.e. the selection of atomistic and coarse-grained residues, on the binding free energy. It is shown that, in spite of small variations of the binding free energy with respect to the active site resolution, the separate contributions coming from different energetic terms (such as electrostatic and van der Waals interactions) manifest a stronger dependence on the mapping, thus pointing to the existence of an optimal level of intermediate resolution

The power of coarse graining in biomolecular simulations

Computational modeling of biological systems is challenging because of the multitude of spatial and temporal scales involved. Replacing atomistic detail with lower resolution, coarse grained (CG), beads has opened the way to simulate large-scale biomolecular processes on time scales inaccessible to all-atom models. We provide an overview of some of the more popular CG models used in biomolecular applications to date, focusing on models that retain chemical specificity. A few state-of-the-art examples of protein folding, membrane protein gating and self-assembly, DNA hybridization, and modeling of carbohydrate fibers are used to illustrate the power and diversity of current CG modeling

Effects and limitations of a nucleobase-driven backmapping procedure for nucleic acids using steered molecular dynamics

Coarse-grained models can be of great help to address the problem of structure prediction in nucleic acids. On one hand they can make the prediction more efficient, while on the other hand they can also help to identify the essential degrees of freedom and interactions for the description of a number of structures. With the aim to provide an all-atom representation in an explicit solvent to the predictions of our SPlit and conQueR (SPQR) coarse-grained model of RNA, we recently introduced a backmapping procedure which enforces the predicted structure into an atomistic one by means of steered molecular dynamics. These simulations minimize the ERMSD, a particular metric which deals exclusively with the relative arrangement of nucleobases, between the atomistic representation and the target structure. In this paper, we explore the effects of this approach on the resulting interaction networks and backbone conformations by applying it on a set of fragments using as a target their native structure. We find that the geometry of the target structures can be reliably recovered, with limitations in the regions with unpaired bases such as bulges. In addition, we observe that the folding pathway can also change depending on the parameters used in the definition of the ERMSD and the use of other metrics such as the RMSD

Computational Modeling of Realistic Cell Membranes

Cell membranes contain a large variety of lipid types and are crowded with proteins, endowing them with the plasticity needed to fulfill their key roles in cell functioning. The compositional complexity of cellular membranes gives rise to a heterogeneous lateral organization, which is still poorly understood. Computational models, in particular molecular dynamics simulations and related techniques, have provided important insight into the organizational principles of cell membranes over the past decades. Now, we are witnessing a transition from simulations of simpler membrane models to multicomponent systems, culminating in realistic models of an increasing variety of cell types and organelles. Here, we review the state of the art in the field of realistic membrane simulations and discuss the current limitations and challenges ahead

Computational studies on Membrane Proteins (bovine CNGA1 & mouse TSPO)

Around thirty percent of total proteins are present in the membrane and play an important role to communicate intracellular and extracellular region. Their presence in the membrane is one of the limiting steps to determine protein structure and to understand their mechanisms. Hence bioinformatics techniques and computational tools play an important role to overcome these issues in characterizing the structural/functional mechanism of membrane proteins. In this thesis, I have developed and used state of the art computational techniques applied to two different pharmaceutically relevant membrane proteins, Cyclic nucleotide-gated channels (CNG) and translocator membrane protein (TSPO). CNG ion channels are embedded into the neuronal membrane. Till date, the structure and their gating mechanism are subject to interest. Different approaches like electrophysiology, single molecule force spectroscopy, biophysics, etc. have been employed to study these channels. Here I studied the gating mechanism of the CNGA1 ion channel by use of homology modeling and coarse-grained molecular dynamics. TSPO is a key biomarker for the diagnostics of inflammation in the brain. Limited Structural and functional information available on mammalian TSPOs homodimers. Computational studies suggested that the NMR-solved structure is not prone to dimer formation and is not stable in a membrane environment and has been an object of vivid criticism. To address this issue we use homology modeling technique and molecular dynamics approach. Principle results are: 1. I have successfully created homology models for CNGA1 homotetramer and performed coarse-grained simulation in the presence and absence of cGMP molecule and developed the coarse-grained force-field parameters for cGMP. 2. I have proposed a new model of the functionally relevant dimeric form of mTSPO. The model is fully consistent with solid-state NMR spectral data. Our predictions provide for the first time structural insights on this pharmaceutically important target fully consistent with experimental data. 3. During these studies, and in order to optimize the preparation of the systems it was necessary to develop an automated tool for creating the input files for doing coarse-grained simulations. These tools are shared with the community through a publically available online web-server that simplifies the task of generating input files which help in performing simulation and retrieving the result data for small simulations. The web-server, MERMAID is available at MERMAID (http://molsim.sci.univr.it/mangesh/). The application of novel computational approaches in this thesis allowed me to characterize extensively both systems by offering a rational to a huge amount of experimental data on biological relevant systems

Two decades of Martini:Better beads, broader scope

The Martini model, a coarse-grained force field for molecular dynamics simulations, has been around for nearly two decades. Originally developed for lipid-based systems by the groups of Marrink and Tieleman, the Martini model has over the years been extended as a community effort to the current level of a general-purpose force field. Apart from the obvious benefit of a reduction in computational cost, the popularity of the model is largely due to the systematic yet intuitive building-block approach that underlies the model, as well as the open nature of the development and its continuous validation. The easy implementation in the widely used Gromacs software suite has also been instrumental. Since its conception in 2002, the Martini model underwent a gradual refinement of the bead interactions and a widening scope of applications. In this review, we look back at this development, culminating with the release of the Martini 3 version in 2021. The power of the model is illustrated with key examples of recent important findings in biological and material sciences enabled with Martini, as well as examples from areas where coarse-grained resolution is essential, namely high-throughput applications, systems with large complexity, and simulations approaching the scale of whole cells. This article is categorized under: Software > Molecular Modeling Molecular and Statistical Mechanics > Molecular Dynamics and Monte-Carlo Methods Structure and Mechanism > Computational Materials Science Structure and Mechanism > Computational Biochemistry and Biophysics

A Molecular Dynamics Investigation of the Interactions between DNA and Other Biological Molecules

We used molecular dynamics simulations to investigate interactions between DNA and three antineoplastic drugs from the anthracycline family, viz. daunomycin, doxorubicin and idarubicin. This encompassed three important aspects of DNA/drug interactions, viz. conformational perturbations, dynamics and energetics. First, we investigated the structural perturbations caused by intercalation of the drugs into DNA. We found, using the software PyralleX which simulates X-ray diffraction patterns, that the DNA tends to change into an intermediary conformation between canonical forms. Daunomycin, among the three drugs, caused the greatest conformational shift in the DNA. Structural perturbations were shared with the base pairs adjacent to the intercalation sites. Second, we studied the effects of groove-binding on the supercoiling behaviour of closed-circular DNA using the coarse-grained force field SIRAH. In the case without drugs, we saw an accelerating upward trend in the supercoiling rate with the salinity of the solution. However, with the drugs, supercoiling was found to retard in hypernatremic environments. Anthracyclines were found to form multilayer complex systems within themselves, which were capable of bridging across two segments of DNA and stabilising the DNA structure. Third, we calculated the free energy changes associated with the intercalation of anthracyclines into DNA, using hybrid coarse-grained / all-atom models for simulation and the novel "extended-system adaptive biasing force" method for analysis. The free energy changes of intercalation of daunomycin and doxorubicin were calculated theoretically to be (-7.27 +/- 0.23) kcal/mol and (-8.61 +/- 0.33) kcal/mol respectively, which are in close agreement with previous experimental data. It was found that the calculated free energy change of idarubicin’s intercalation is (-7.75 +/- 0.17) kcal/mol, i.e. between those of the previous two drugs. This work has demonstrated a new way of evaluating free energy changes of interactions, which could help in speeding up time-consuming drug discovery processes