Distance geometry problems arise in the interpretation of NMR data and in the determination of protein structure. We formulate the distance geometry problem as a global minimization problem with special structure, and show that global smoothing techniques and a continuation approach for global optimization can be used to determine solutions of distance geometry problems with a nearly 100% probability of success.

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We investigate several approaches to coarse grained normal mode analysis on protein residual-level structural fluctuations by choosing different ways of representing the residues and the forces among them. Single-atom representations using the backbone atoms Cα, C, N, and Cβ are considered. Combinations of some of these atoms are also tested. The force constants between the representative atoms are extracted from the Hessian matrix of the energy function and served as the force constants between the corresponding residues. The residue mean-square-fluctuations and their correlations with the experimental B-factors are calculated for a large set of proteins. The results are compared with all-atom normal mode analysis and the residue-level Gaussian Network Model. The coarse-grained methods perform more efficiently than all-atom normal mode analysis, while their B-factor correlations are also higher. Their B-factor correlations are comparable with those estimated by the Gaussian Network Model and in many cases better. The extracted force constants are surveyed for different pairs of residues with different numbers of separation residues in sequence. The statistical averages are used to build a refined Gaussian Network Model, which is able to predict residue-level structural fluctuations significantly better than the conventional Gaussian Network Model in many test cases.

Replacing rigid metal oxides with flexible alternatives as a next-generation transparent conductor is important for flexible optoelectronic devices. Recently, nanowire networks have emerged as a new type of transparent conductor and have attracted wide attention because of their all-solution-based process manufacturing and excellent flexibility. However, the intrinsic percolation characteristics of the network determine that its fine pattern behavior is very different from that of continuous films, which is a critical issue for their practical application in high-resolution devices. Herein, a simple optimization approach is proposed to address this issue through the architectural engineering of the nanowire network. The aligned and random silver nanowire networks are fabricated and compared in theory and experimentally. Remarkably, network performance can be notably improved with an aligned structure, which is helpful for external quantum efficiency and the luminance of quantum dot light-emitting diodes (QLEDs) when the network is applied as the bottom-transparent electrode. More importantly, the advantage introduced by network alignment is also of benefit to fine pattern performance, even when the pattern width is narrowed to 30 μm, which leads to improved luminescent properties and lower failure rates in fine QLED strip applications. This paradigm illuminates a strategy to optimize nanowire network based transparent conductors and can promote their practical application in high-definition flexible optoelectronic devices.