Heat conductivity of DNA double helix

Thermal conductivity of isolated single molecule DNA fragments is of importance for nanotechnology, but has not yet been measured experimentally. Theoretical estimates based on simplified (1D) models predict anomalously high thermal conductivity. To investigate thermal properties of single molecule DNA we have developed a 3D coarse-grained (CG) model that retains the realism of the full all-atom description, but is significantly more efficient. Within the proposed model each nucleotide is represented by 6 particles or grains; the grains interact via effective potentials inferred from classical molecular dynamics (MD) trajectories based on a well-established all-atom potential function. Comparisons of 10 ns long MD trajectories between the CG and the corresponding all-atom model show similar root-mean-square deviations from the canonical B-form DNA, and similar structural fluctuations. At the same time, the CG model is 10 to 100 times faster depending on the length of the DNA fragment in the simulation. Analysis of dispersion curves derived from the CG model yields longitudinal sound velocity and torsional stiffness in close agreement with existing experiments. The computational efficiency of the CG model makes it possible to calculate thermal conductivity of a single DNA molecule not yet available experimentally. For a uniform (polyG-polyC) DNA, the estimated conductivity coefficient is 0.3 W/mK which is half the value of thermal conductivity for water. This result is in stark contrast with estimates of thermal conductivity for simplified, effectively 1D chains ("beads on a spring") that predict anomalous (infinite) thermal conductivity. Thus, full 3D character of DNA double-helix retained in the proposed model appears to be essential for describing its thermal properties at a single molecule level.Comment: 16 pages, 12 figure

New computational methods for structural modeling protein-protein and protein-nucleic acid interactions

Programa de Doctorat en Biomedicina[eng] The study of the 3D structural details of protein-protein and protein-DNA interactions is essential to understand biomolecular functions at the molecular level. Given the difficulty of the structural determination of these complexes by experimental techniques, computational tools are becoming a powerful to increase the actual structural coverage of protein-protein and protein-DNA interactions. pyDock is one of these tools, which uses its scoring function to determine the quality of models generated by other tools. pyDock is usually combined with the model sampling methods FTDOCK or ZDOCK. This combination has shown a consistently good prediction performance in community-wide assessment experiments like CAPRI or CASP and has provided biological insights and insightful interpretation of experiments by modeling many biomolecular interactions of biomedical and biotechnological interest. This software combination has demonstrated good predictive performance in the blinded evaluation experiments CAPRI and CASP. It has provided biological insights by modeling many biomolecular interactions of biomedical and biotechnological interest. Here, we describe a pyDock software update, which includes its adaptation to the newest python code, the capability of including cofactor and other small molecules, and an internal parallelization to use the computational resources more efficiently. A strategy was designed to integrate the template-based docking and ab initio docking approaches by creating a new scoring function based on the pyDock scoring energy basis function and the TM-score measure of structural similarity of protein structures. This strategy was partially used for our participation in the 7th CAPRI, the 3rd CASP-CAPRI and the 4th CASP-CAPRI joint experiments. These experiments were challenging, as we needed to model protein-protein complexes, multimeric oligomerization proteins, protein-peptide, and protein-oligosaccharide interactions. Many proposed targets required the efficient integration of rigid-body docking, template-based modeling, flexible optimization, multi- parametric scoring, and experimental restraints. This was especially relevant for the multi- molecular assemblies proposed in the 3er and 4th CASP-CAPRI joint experiments. In addition, a case study, in which electron transfer protein complexes were modelled to test the software new capabilities. Good results were achieved as the structural models obtained help explaining the differences in photosynthetic efficiency between red and green algae

In silico investigation of the mechanism of ricin-catalysed depurination reaction and design of novel ricin inhibitors

Includes abstract.Includes bibliographical references.Ricin is a dimeric enzyme found in the castor bean plant. It is extremely toxic with a fatal dose for humans ranging from 0.1-1.0 ug/kg. This has lead to its use as a biological weapon. Cell death is caused when ricin ceases the protein synthesis by removing a specific adenine (A-4324) of the GAGA tetra loop of 28S ribosomal RNA. Despite this destructive feature, ricin has been touted as a potential therapeutic agent where applications such as immunotoxins to treat cancer, AIDS and other diseases are actively being pursued. However, the prime challenge in such applications is the non specific cytotoxicity of ricin, which cannot currently be treated due to the absence of an effective antidote. The primary objective of this thesis is to describe the catalytic mechanism of ricin using computational reaction dynamics. For an accurate simulation of the ricin-catalysed reaction, a reasonable model of the target natural substrate is required

Dissecting Mg2+-RNA interactions using atomistic molecular dynamics

The central dogma of molecular biology summarizes one of the most important mechanisms for the functioning of living organisms, stating that deoxyribonucleic acid (DNA) is transcribed into ribonucleic acid (RNA), which is then translated into proteins. However, it is still not sufficient to capture how important RNA are for cellular life. Nucleic acids are at the core of any living cell on this planet and thus deserve indisputably deserve scientific attention. In particular, RNA molecules are proposed as the key chemical species that ignited the beginning of life on prebiotic earth. Independently of this hypothesis, studying RNA molecules today is essential for numerous applications in life sciences, spanning from drug development to cancer treatment. That being said, in the last half century there have been unprecedented efforts into understanding RNAs and their role in the cell to the utmost detail. RNA is transcribed from DNA and translated into proteins, which then perform an abundance of functions in the cell. On top of that, it can catalyze chemical reactions, regulate gene expression and even carry genetic information which is retrotranscribed into DNA. The outstanding versatility of RNA molecules is due to their unique chemical features, resulting in a very flexible backbone combined with strong interactions between the nucleobases. The balance between canonical base pairs and a multitude of backbone conformations is the main factor for RNA being well structured yet dynamical. On the other hand, RNA folding can only occur in the presence of positively charged particles that compensate the electrostatic repulsion arising from the negatively charged sugar-phosphate backbone, inevitably tying nucleic acids and ions together. Metal ions are instrumental for proper RNA folding and dynamics, while also being crucial cofactors for ribozyme catalysis. Monovalent cations (Na+, K+) are the workhorses compensating the overall negatively charged nucleic acids, while divalent cations are frequently the protagonists of relevant folding events and catalysis. Mg2+ ions, which are the most freely available divalent cations in cells, commonly perform as structural pillars in RNA tertiary structures. Despite the ubiquitous presence of Mg2+ around RNA, the experimental characterization of their interaction is challenging, because Mg2+ do not offer a direct spectroscopic handle for detection and requires high-resolution X-ray crystallography. On top of that, their assignment through X-ray diffraction is difficult, since the Mg2+ is isoelectronic with water and Na+ ions. Therefore, the use of theoretical and computational tools can clearly help reinforce the experimental characterization of Mg2+-RNA interaction and contribute to the most needed dynamical view of these molecules. The results presented in this thesis aim to provide a meaningful description of the interaction between Mg2+ ions and RNA through atomistic molecular dynamics coupled with enhanced sampling techniques. The simulations done in this work were designed to tackle the two most fundamental issues in describing divalent ions interaction with RNA using molecular dynamics. First, the quality and fidelity of the models used, and second the proper sampling of rare events. Through the employment of modified state-of-the-art simulations techniques, I was able to predict Mg2+ binding sites and their correspondent affinities on an RNA duplex. The affinities qualitatively agree with the interaction frequency trends observed in the structural databases (PDB 1 or NDB 2). Furthermore, I evaluated relevant aspects of RNA simulation concerning force field choices for Mg2+ ions, RNA backbone non-bridging oxygens, and water. Lastly, I developed a robust methodological framework that allows for future molecular dynamics simulations aimed to study multiple concurrent binding events associated with high free-energy barriers. Since RNA folding is intrinsically dependent on ionic conditions, I hope that this work will facilitate future research on this important subject

Improving the resolution of interaction maps: A middleground between high-resolution complexes and genome-wide interactomes

Protein-protein interactions are ubiquitous in Biology and therefore central to understand living organisms. In recent years, large-scale studies have been undertaken to describe, at least partially, protein-protein interaction maps or interactomes for a number of relevant organisms including human. Although the analysis of interaction networks is proving useful, current interactomes provide a blurry and granular picture of the molecular machinery, i.e. unless the structure of the protein complex is known the molecular details of the interaction are missing and sometime is even not possible to know if the interaction between the proteins is direct, i.e. physical interaction or part of functional, not necessary, direct association. Unfortunately, the determination of the structure of protein complexes cannot keep pace with the discovery of new protein-protein interactions resulting in a large, and increasing, gap between the number of complexes that are thought to exist and the number for which 3D structures are available. The aim of the thesis was to tackle this problem by implementing computational approaches to derive structural models of protein complexes and thus reduce this existing gap. Over the course of the thesis, a novel modelling algorithm to predict the structure of protein complexes, V-D2OCK, was implemented. This new algorithm combines structure-based prediction of protein binding sites by means of a novel algorithm developed over the course of the thesis: VORFFIP and M-VORFFIP, data-driven docking and energy minimization. This algorithm was used to improve the coverage and structural content of the human interactome compiled from different sources of interactomic data to ensure the most comprehensive interactome. Finally, the human interactome and structural models were compiled in a database, V-D2OCK DB, that offers an easy and user-friendly access to the human interactome including a bespoken graphical molecular viewer to facilitate the analysis of the structural models of protein complexes. Furthermore, new organisms, in addition to human, were included providing a useful resource for the study of all known interactomes