you can try using ENTPRISE! Below is the link and the papers

http://cssb.biology.gatech.edu/entprise

Gao, M, Zhou H, Skolnick J. 2015. Insights into Disease-Associated Mutations in the Human Proteome through Protein Structural Analysis. Structure. 23(7):1362-1369.

Zhou, H, Gao M, Skolnick J. Submitted. ENTPRISE: An algorithm for predicting human disease-associated amino acid mutations from sequence entropy and predicted protein structures. PLOS One.

you can try using ENTPRISE! Below is the link and the papers

http://cssb.biology.gatech.edu/entprise

Gao, M, Zhou H, Skolnick J. 2015. Insights into Disease-Associated Mutations in the Human Proteome through Protein Structural Analysis. Structure. 23(7):1362-1369.

Zhou, H, Gao M, Skolnick J. Submitted. ENTPRISE: An algorithm for predicting human disease-associated amino acid mutations from sequence entropy and predicted protein structures. PLOS One.

Thank  you very much  Bharath Srinivasan  for valuable link 

Thank you very much Bharath Sirnivasan, your response was helpful. 

Thank you Tobias for your help 

SNPeffect, MutPred, SNPs3D, Polyphen-2 are good as they contain structural and functional features. SAAPdap/SAAPpred is also very good for analysing structural effects of SNPs as it report effects specifically and then makes a prediction based on these using machine learning. 

What kind of mutations are you looking at specifically as certain methods can have different sensitivities to different mutation types, such as those in cancer.

Thank you Camilla for your answer it is really helping, i am looking for missense mutations that associated with hypertension. 

Dear  Hamdan It is highly needed  and following with you too