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Register a new userCosmic Rays and the Chiral Puzzle of Life
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Living organisms exhibit consistent homochirality. It is argued that the specific, binary choice made is not an accident but is a consequence of parity violation in the weak interaction expressed by cosmic irradiation. The secondary muons and pairs are spin- and magnetic moment- polarized and may introduce a small, net chiral preference when they interact electromagnetically or quantum mechanically with molecules that have made the transition to self-replication. Although this preference is likely to be very small, it may suffice to give a chirally-dominant outcome after billions of replications, especially if combined with chirally-unbiased conflict between the two choices. Examples of mechanisms that can manifest the three essential steps of polarization, preference and domination are presented and some variations and possible implications are discussed.
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Biological molecules chose one of two structurally, chiral systems which are related by reflection in a mirror. It is proposed that this choice was made, causally, by magnetically polarized and physically chiral cosmic-rays, which are known to have a large role in mutagenesis. It is shown that the cosmic rays can impose a small, but persistent, chiral bias in the rate at which they induce structural changes in simple, chiral monomers that are the building blocks of biopolymers. A much larger effect should be present with helical biopolymers, in particular, those that may have been the progenitors of RNA and DNA. It is shown that the interaction can be both electrostatic, just involving the molecular electric field, and electromagnetic, also involving a magnetic field. It is argued that this bias can lead to the emergence of a single, chiral life form over an evolutionary timescale. If this mechanism dominates, then the handedness of living systems should be universal. Experiments are proposed to assess the efficacy of this process.
The investigation into the possible effects of cosmic rays on living organisms will also offer great interest. - Victor F. Hess, Nobel Lecture, December 12, 1936 High-energy radiation bursts are commonplace in our Universe. From nearby solar flares to distant gamma ray bursts, a variety of physical processes accelerate charged particles to a wide range of energies, which subsequently reach the Earth. Such particles contribute to a number of physical processes occurring in the Earth system. A large fraction of the energy of charged particles gets deposited in the atmosphere, ionizing the atmosphere, causing changes in its chemistry and affecting the global electric circuit. Remaining secondary particles contribute to the background dose of cosmic rays on the surface and parts of the subsurface region. Life has evolved over the past ~ 3 billion years in presence of this background radiation, which itself has varied considerably during the period. As demonstrated by the Miller-Urey experiment, lightning plays a very important role in the formation of complex organic molecules, which are the building blocks of more complex structures forming life. There is growing evidence of increase in the lightning rate with increasing flux of charged particles. Is there a connection between enhanced rate of cosmic rays and the origin of life? Cosmic ray secondaries are also known to damage DNA and cause mutations, leading to cancer and other diseases. It is now possible to compute radiation doses from secondary particles, in particular muons and neutrons. Have the variations in cosmic ray flux affected the evolution of life on earth? We describe the mechanisms of cosmic rays affecting terrestrial life and review the potential implications of the variation of high-energy astrophysical radiation on the history of life on earth.
In multi-resolution simulations, different system components are simultaneously modelled at different levels of resolution, these being smoothly coupled together. In the case of enzyme systems, computationally expensive atomistic detail is needed in the active site to capture the chemistry of substrate binding. Global properties of the rest of the protein also play an essential role, determining the structure and fluctuations of the binding site; however, these can be modelled on a coarser level. Similarly, in the most computationally efficient scheme only the solvent hydrating the active site requires atomistic detail. We present a methodology to couple atomistic and coarse-grained protein models, while solvating the atomistic part of the protein in atomistic water. This allows a free choice of which protein and solvent degrees of freedom to include atomistically, without loss of accuracy in the atomistic description. This multi-resolution methodology can successfully model stable ligand binding, and we further confirm its validity via an exploration of system properties relevant to enzymatic function. In addition to a computational speedup, such an approach can allow the identification of the essential degrees of freedom playing a role in a given process, potentially yielding new insights into biomolecular function.
The elastic network (EN) is a prime model that describes the long-time dynamics of biomolecules. However, the use of harmonic potentials renders this model insufficient for studying large conformational changes of proteins (e.g. stretching of proteins, folding and thermal unfolding). Here, we extend the capabilities of the EN model by using a harmonic approximation described by Lennard-Jones (LJ) interactions for far contacts and native contacts obtained from the standard overlap criterion as in the case of Go-like models. While our model is validated against the EN model by reproducing the equilibrium properties for a number of proteins, we also show that the model is suitable for the study of large conformation changes by providing various examples. In particular, this is illustrated on the basis of pulling simulations that predict with high accuracy the experimental data on the rupture force of the studied proteins. Furthermore, in the case of DDFLN4 protein, our pulling simulations highlight the advantages of our model with respect to Go-like approaches, where the latter fail to reproduce previous results obtained by all-atom simulations that predict an additional characteristic peak for this protein. In addition, folding simulations of small peptides yield different folding times for alpha-helix and beta-hairpin, in agreement with experiment, in this way providing further opportunities for the application of our model in studying large conformational changes of proteins. In contrast to the EN model, our model is suitable for both normal mode analysis and molecular dynamics simulation. We anticipate that the proposed model will find applications in a broad range of problems in biology, including, among others, protein folding and thermal unfolding.
We describe a new generation of algorithms capable of mapping the structure and conformations of macromolecules and their complexes from large ensembles of heterogeneous snapshots, and demonstrate the feasibility of determining both discrete and continuous macromolecular conformational spectra. These algorithms naturally incorporate conformational heterogeneity without resort to sorting and classification, or prior knowledge of the type of heterogeneity present. They are applicable to single-particle diffraction and image datasets produced by X-ray lasers and cryo-electron microscopy, respectively, and particularly suitable for systems not easily amenable to purification or crystallization.
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