Protein folding as an evolutionary process
ABSTRACT Protein folding is often depicted as a motion along descending paths on a free energy landscape that results in a concurrent decrease in the conformational entropy of the polypeptide chain. However, to provide a description that is consistent with other natural processes, protein folding is formulated from the principle of increasing entropy. It then becomes evident that protein folding is an evolutionary process among many others. During the course of folding protein structural hierarchy builds up in succession by diminishing energy density gradients in the quest for a stationary state determined by surrounding density-in-energy. Evolution toward more probable states, eventually attaining the stationary state, naturally selects steeply ascending paths on the entropy landscape that correspond to steeply descending paths on the free energy landscape. The dissipative motion of the non-Euclidian manifold is non-deterministic by its nature which clarifies why it is so difficult to predict protein folding.
Full-textDOI: · Available from: Arto Annila, Jul 01, 2015
- SourceAvailable from: Yudong Zhang[Show abstract] [Hide abstract]
ABSTRACT: In order to solve the HP model of the protein folding problem, we investigated traditional energy function and pointed out that its discrete property cannot give direction of the next step to the searching point, causing a challenge to optimization algorithms. Therefore, we introduced the simplified energy function into a turn traditional discrete energy function to continuous one. The simplified energy function totals the distance between all pairs of hydrophobic amino acids. To optimize the simplified energy function, we introduced the latest swarm intelligence algorithm, the firefly algorithm (FA). FA is a hot nature-inspired technique and has been used for solving nonlinear multimodal optimization problems in dynamic environment. We also proposed the code scheme strategy to apply FA to the simplified HP model with the clash test strategy. The experiment took 14 sequences of different chain lengths from 18 to 100 as the dataset and compared the FA with standard genetic algorithm and immune genetic algorithm. Each algorithm ran 20 times. The averaged energy convergence results show that FA achieves the lowest values. It concludes that it is effective to solve 2D HP model by the firefly algorithm and the simplified energy function.Mathematical Problems in Engineering 02/2013; 2013. DOI:10.1155/2013/398141 · 1.08 Impact Factor
Article: Threads of Time[Show abstract] [Hide abstract]
ABSTRACT: The concept of time’s arrow is examined using the principle of least action as given in its original non-Abelian form. When every entity of nature is considered to be composed of quantized actions, such an entity will change, either by absorbing quanta from surrounding actions or by emitting quanta to the surrounding actions. In natural processes, quanta disperse from high-energy density actions to low-energy density actions in quest of consuming free energy in least time. We propose that the flux of quanta embodies the flow of time, and therefore the irreversible consumption of free energy creates time’s arrow in a fundamental physical sense. The cosmological arrow of time results from universal processes that take place, most notably, in stars and other celestial systems, where matter, that is, bound actions, combusts to photons, that is, freely propagating actions. The biological arrow of time manifests itself in maturation processes where quanta absorb to emerging functional structures, leading eventually to aging processes where quanta, on balance, emit from disintegrating organs. Mathematical analysis of an evolutionary equation of motion, given in general terms of a spontaneous symmetry breaking process of actions, reveals the reason why future paths—and the future itself—remain inherently intractable.06/2012; 2012. DOI:10.5402/2012/850957
- Protein Engineering, 02/2012; , ISBN: 978-953-51-0037-9