Biomedical subjects

Jonathan P K Doye

Publications and source records attributed to Jonathan P K Doye.

7 recordsLinked to original sources

PubMed2006

Structural transitions in the 309-atom magic number Lennard-Jones cluster.

The thermal behavior of the 309-atom Lennard-Jones cluster, whose structure is a complete Mackay icosahedron, has been studied by parallel tempering Monte Carlo simulations. Surprisingly for a magic number cluster, the heat capacity shows a very pronounced peak before melting, which is attributed to several coincident structural transformation processes. The main transformation is somewhat akin to surface roughening and involves a cooperative condensation of vacancies and adatoms that leads to the formation of pits and islands one or two layers thick on the Mackay icosahedron. The second transition in order of importance involves a whole scale transformation of the cluster structure and leads to a diverse set of twinned structures that are assemblies of face-centered-cubic tetrahedra with six atoms along their edges, i.e., one atom more than the edges of the 20 tetrahedra that make up the 309-atom Mackay icosahedron. A surface reconstruction of the icosahedron from a Mackay to an anti-Mackay overlayer is also observed, but with a lower probability.

Journal Article↗

PubMed2005

Mapping the magic numbers in binary Lennard-Jones clusters.

Using a global optimization approach that directly searches for the composition of greatest stability, we have been able to find the particularly stable structures for binary Lennard-Jones clusters with up to 100 atoms for a range of Lennard-Jones parameters. In particular, we have shown that just having atoms of different sizes leads to a remarkable stabilization of polytetrahedral structures, including both polyicosahedral clusters and at larger sizes structures with disclination lines.

Journal Article↗

PubMed2005

Identifying communities within energy landscapes.

Potential energy landscapes can be represented as a network of minima linked by transition states. The community structure of such networks has been obtained for a series of small Lennard-Jones (LJ) clusters. This community structure is compared to the concept of funnels in the potential energy landscape. Two existing algorithms have been used to find community structure, one involving removing edges with high betweenness, the other involving optimization of the modularity. The definition of the modularity has been refined, making it more appropriate for networks such as these where multiple edges and self-connections are not included. The optimization algorithm has also been improved, using Monte Carlo methods with simulated annealing and basin hopping, both often used successfully in other optimization problems. In addition to the small clusters, two examples with known heterogeneous landscapes, the 13-atom cluster (LJ13) with one labeled atom and the 38-atom cluster (LJ38) , were studied with this approach. The network methods found communities that are comparable to those expected from landscape analyses. This is particularly interesting since the network model does not take any barrier heights or energies of minima into account. For comparison, the network associated with a two-dimensional hexagonal lattice is also studied and is found to have high modularity, thus raising some questions about the interpretation of the community structure associated with such partitions.

Journal Article↗

PubMed2005

Characterizing the network topology of the energy landscapes of atomic clusters.

By dividing potential energy landscapes into basins of attractions surrounding minima and linking those basins that are connected by transition state valleys, a network description of energy landscapes naturally arises. These networks are characterized in detail for a series of small Lennard-Jones clusters and show behavior characteristic of small-world and scale-free networks. However, unlike many such networks, this topology cannot reflect the rules governing the dynamics of network growth, because they are static spatial networks. Instead, the heterogeneity in the networks stems from differences in the potential energy of the minima, and hence the hyperareas of their associated basins of attraction. The low-energy minima with large basins of attraction act as hubs in the network. Comparisons to randomized networks with the same degree distribution reveals structuring in the networks that reflects their spatial embedding.

Journal Article↗

PubMed2005

Self-similar disk packings as model spatial scale-free networks.

The network of contacts in space-filling disk packings, such as the Apollonian packing, is examined. These networks provide an interesting example of spatial scale-free networks, where the topology reflects the broad distribution of disk areas. A wide variety of topological and spatial properties of these systems is characterized. Their potential as models for networks of connected minima on energy landscapes is discussed.

Journal Article↗

PubMed2004

Inhibition of protein crystallization by evolutionary negative design.

Why are proteins so hard to crystallize? We propose an 'evolutionary negative design' principle to explain this difficulty. Proteins have evolved to avoid crystallization because crystallization compromises the viability of the cell. Evolutionary negative design is supported by much evidence in the literature, including the effect of mutations on the crystallizability of a protein, the correlations found in the properties of crystal contacts in bioinformatics databases, and the positive use of protein crystallization by bacteria and viruses.

Biophysical Phenomena↗

PubMed2002

Network topology of a potential energy landscape: a static scale-free network.

Here we analyze the topology of the network formed by the minima and transition states on the potential energy landscape of small clusters. We find that this network has both a small-world and scale-free character. In contrast to other scale-free networks, where the topology results from the dynamics of the network growth, the potential energy landscape is a static entity. Therefore, a fundamentally different organizing principle underlies this behavior: The potential energy landscape is highly heterogeneous with the low-energy minima having large basins of attraction and acting as the highly connected hubs in the network.

Models, Theoretical↗