Interesting thoughts and tutorials from the White Lab at the University of Rochester.. Topics include mathematical modeling, programing, computational chemistry and statistics. This is a continuation of random posts from my time in grad school and post-doc. Back then, this was called Crows and Cats Blog. Some of those older are best viewed at my old url:

EDS Lammps Coordination Number

This tutorial will show you how to reproduce the method in the recent White et al. water AIMD paper (White, Knight, Hocky, & Voth, 2017). The goal of the method is to minimally bias a water simulation to match a reference RDF. We will first convert a reference RDF into a set of reference scalar coordination number moments. Then, an NVT simulation is run to find a minimal bias which causes the coordination numbers to match their reference values. It won’t be covered here, but the bias can then be used to run NVE simulations or in conjunctino with other free-energy methods. This tutorial will use classical simulations in Lammps instead of AIMD in CP2K so that results can be obtained in a reasonable amount of time.

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Peptide self-assembly of FF

I’ve decided to start summarizing literature topics on areas I’m interested in on my blog. I’m starting with peptide self-assembly of diphenylalanine. This is a very incomplete survey of work on it.

One of the most common self-assembling motifs is diphenylalanine, or FF in the amino acid code alphabet. FF is the shortest self-assembling peptide sequence. It is stable up to 100°C and 150°C with dry heating (Sedman, Adler-Abramovich, Allen, Gazit, & Tendler, 2006), it has a Young’s modulous of 19 GPa (somewhere between human bone and concrete) (Kol et al., 2005) and is stable in harsh solvents such as acetone and alcohol (Adler-Abramovich et al., 2006). FF, or some variation of it, is thought to become an important building block in future biological applications (Yan, Zhu, & Li, 2010).

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McGovern-de Pablo Boundary Corrected Hills

Metadynamics is a way of overcoming energetic barriers in molecular simulation (Laio & Parrinello, 2002). It’s part of a class of algorithms that focus on making rare-events more frequent in simulations. It’s been getting quite popular: it even has it’s own wikipedia page. One problem with the method is that it has systematic errors at boundaries and this was recently fixed via a method introduced by Michael McGovern and Juan de Pablo (McGovern & de Pablo, 2013). I’m recording the equations here in, since I use an implementation of it and the derivatives weren’t shown in the manuscript, which are necessary for the implementation.

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