Efficient molecular surface generation using level-set methods


Can T., Chen C., Wang Y.

JOURNAL OF MOLECULAR GRAPHICS & MODELLING, cilt.25, sa.4, ss.442-454, 2006 (SCI-Expanded) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 25 Sayı: 4
  • Basım Tarihi: 2006
  • Doi Numarası: 10.1016/j.jmgm.2006.02.012
  • Dergi Adı: JOURNAL OF MOLECULAR GRAPHICS & MODELLING
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.442-454
  • Anahtar Kelimeler: molecular surface, surface generation, cavity, visualization, level sets, PROTEIN, CAVITIES, PACKING
  • Orta Doğu Teknik Üniversitesi Adresli: Evet

Özet

Molecules interact through their surface residues. Calculation of the molecular surface of a protein structure is thus an important step for a detailed functional analysis. One of the main considerations in comparing existing methods for molecular surface computations is their speed. Most of the methods that produce satisfying results for small molecules fail to do so for large complexes. In this article, we present a level-set-based approach to compute and visualize a molecular surface at a desired resolution. The emerging level-set methods have been used for computing evolving boundaries in several application areas from fluid mechanics to computer vision. Our method provides a uniform framework for computing solvent-accessible, solvent-excluded surfaces and interior cavities. The computation is carried out very efficiently even for very large molecular complexes with tens of thousands of atoms. We compared our method to some of the most widely used molecular visualization tools (Swiss-PDB Viewer, PyMol, and Chimera) and our results show that we can calculate and display a molecular surface 1.5-3.14 times faster on average than all three of the compared programs. Furthermore, we demonstrate that our method is able to detect all of the interior inaccessible cavities that can accommodate one or more water molecules. (c) 2006 Elsevier Inc. All rights reserved.