Towards protein folding with evolutionary techniques

Abstract

We present design details and first tests of a new evolutionary algorithm approach to ab initio protein folding. It does not focus on dihedral angles exclusively, but mainly operates on introduction, extension, break-up, and destruction of secondary structure elements, given as correlated dihedral angle values. In first test applications to polyalanines (up to 60 residues) and random primary sequences (up to 40 residues), we demonstrate that this use of prior knowledge is well balanced: On the one hand, it ensures quick introduction of secondary structure elements if they are favorable for a given primary sequence, but still allows for efficient location of pure random coil solutions without enforcing any secondary structure elements, if folds of this type are preferred by the given primary sequence. Furthermore, the algorithm is clearly able to pack several secondary structure elements into favorable tertiary structure arrangements, although no part of the algorithm is explicitly designed to do this. In first test examples on real-life peptides between 21 and 44 residues from the Protein Data Bank, the quality of the results depends on the force field used (as expected); nevertheless, we can show that the algorithm is able to find structures in good agreement with the targets easily and consistently, if the force field allows for that.

Configurational Sampling of Noncovalent (Atmospheric) Molecular Clusters: Sulfuric Acid and Guanidine

“…Various promising techniques have therefore been developed for exploring the PES at lower cost but with a sufficient accuracy: basin hopping, 6 umbrella sampling, 7 neural networks, 8,9 or genetic algorithms (GA) methods, which have recently been shown to be quite successful. [10][11][12][13][14] Furthermore, exploration of a PES using just high-level quantum chemical methods is computationally very expensive. Therefore, configurational exploration at low level of theory is utilized.…”

Prediction of protein solubility in Escherichia coli using logistic regression

“…Indeed, ab initio solubility prediction requires folding prediction where interactions with the solvent and with other proteins need to be considered. Some attempts to obtain ab initio predictions of the folding of soluble proteins (i.e., considering protein-water interactions) have been made (Bradley et al, 2005;Klepeis and Floudas 1999;Klepeis et al, 2003;Koskowski and Hartke, 2005;Scheraga, 1996). Despite all these efforts, a tool for full and reliable ab initio solubility predictions is not yet available.…”

Efficient global optimization of reactive force‐field parameters

“…[40,41] It combines threadlevel and MPI-level parallelism to achieve high scalability on shared memory as well as distributed memory architectures. The OGOLEM framework embodies our accumulated knowledge on nondeterministic global optimization in general and on EA s in particular [42,43] for various applications: cluster structures, [44][45][46][47][48][49][50][51][52][53][54] protein folding, [55] potential fitting, [34,35,[56][57][58][59][60] molecular design, [61] and abstract benchmarks. [62] EAs [19] borrow nomenclature from natural selection and evolution processes.…”