Biomedical subjects

Wenqi Huang

Publications and source records attributed to Wenqi Huang.

3 recordsLinked to original sources

PubMed2006

Energy optimization for off-lattice protein folding.

Two three-dimensional AB off-lattice protein models consisting of hydrophobic and hydrophilic monomers are studied in this paper. By incorporating an extra energy contribution into the original energy function, the protein folding is converted from a constraint optimization problem into an unconstrained one which can be solved by the well-known gradient method. From the initial configurations randomly generated by the heuristic strategy proposed in this paper, our algorithm can find better results than those by nPERM for the four Fibonacci sequences. Based on the initial configurations obtained by energy landscape paving (ELP) routine, some of our results for the lowest energies are better than the best values reported in the literature.

Computer Simulation↗

PubMed2006

Structure optimization in a three-dimensional off-lattice protein model.

We studied a three-dimensional off-lattice AB model with two species of monomers, hydrophobic (A) and hydrophilic (B), and present two optimization algorithms: face-centered-cubic (FCC)-lattice pruned-enriched-Rosenbluth method (PERM) and subsequent conjugate gradient (PERM++) minimization and heuristic conjugate gradient (HCG) simulation based on "off-trap" strategy. In PERM++, we apply the PERM to the FCC-lattice to produce the initial conformation, and conjugate gradient minimization is then used to reach the minimum energy state. Both algorithms have been tested in the three-dimensional AB model for all sequences with lengths 13 < or = n < or = 55. The numerical results show that the proposed methods are very promising for finding the ground states of proteins. In several cases, we renew the putative ground states energy values.

Algorithms↗

PubMed2005

Growth algorithm for finding low energy configurations of simple lattice proteins.

PERM and its new variant nPERMis have been developed to optimize the energy function of protein folding based on HP simple lattice model and were found to outperform all other previous fully blind general purpose algorithms. Using the concept of core-guiding and life-forecasting, we propose a new version of nPERMis, called nPERMh. A major difference with respect to nPERMis is that criteria for further growth of new residue are based on the species of current growing monomer and its position in the HP sequence. Seventeen sequences of length ranging from 46 to 124 residues were tested by nPERMh on the cubic lattice and our algorithm proved very efficient. It should be pointed out that our new version of nPERMis is exclusively designed for conformational search. We hope that similar methods will ultimately be useful for finding native states of more realistic protein models.

Journal Article↗