Experimental　and　theoretical　studies　have　showed　that　the　native-state　topology　conceals　a　wealth　of　information　about　protein　folding/unfolding.　In　this　study,　a　method　based　on　the　Gaussian　network　model　(GNM)　is　developed　to　study　some　properties　of　protein　unfolding　and　explore　the　role　of　topology　in　protein　unfolding　process.　The　GNM　has　been　successful　in　predicting　atomic　fluctuations　around　an　energy　minimum.　However,　in　the　GNM,　the　normal　mode　description　is　linear　and　cannot　be　accurate　in　studying　protein　folding/unfolding,　which　has　many　local　minima　in　the　energy　landscape.　To　describe　the　nonlinearity　of　the　conformational　changes　during　protein　unfolding,　a　method　based　on　the　iterative　use　of　normal　mode　calculation　is　proposed.　The　protein　unfolding　process　is　mimicked　through　breaking　the　native　contacts　between　the　residues　one　by　one　according　to　the　fluctuations　of　the　distance　between　them.　With　this　approach,　the　unfolding　processes　of　two　proteins,　C12　and　barnase,　are　simulated.　It　is　found　that　the　sequence　of　protein　unfolding　events　revealed　by　this　method　is　consistent　with　that　obtained　from　thermal　unfolding　by　molecular　dynamics　and　Monte　Carlo　simulations.　The　results　indicate　that　this　method　is　effective　in　studying　protein　unfolding.　In　this　method,　only　the　native　contacts　are　considered,　which　implies　that　the　native　topology　may　play　an　important　role　in　the　protein　unfolding　process.　The　simulation　results　also　show　that　the　unfolding　pathway　is　robust　against　the　introduction　of　some　noise,　or　stochastic　characters.　Furthermore,　several　conformations　selected　from　the　unfolding　process　are　studied　to　show　that　the　denatured　state　does　not　behave　as　a　random　coil,　but　seems　to　have　highly　cooperative　motions,　which　may　help　and　promote　the　polypeptide　chain　to　fold　into　the　native　state　correctly　and　speedily.