Prediction of protein aggregation tendency based on the support vector machine algorithm

  • سال انتشار: 1400
  • محل انتشار: اولین همایش بین المللی و دهمین همایش ملی بیوانفورماتیک ایران
  • کد COI اختصاصی: IBIS10_035
  • زبان مقاله: انگلیسی
  • تعداد مشاهده: 120
دانلود فایل این مقاله

نویسندگان

Fatemeh Eshari

Protein Biotechnology Research Lab (PBRL), School of Biology, College of Science, University of Tehran, Tehran, Iran

Amirreza Farajnezhadi

Protein Biotechnology Research Lab (PBRL), School of Biology, College of Science, University of Tehran, Tehran, Iran

Fahime Momeni

Protein Biotechnology Research Lab (PBRL), School of Biology, College of Science, University of Tehran, Tehran, Iran

Mehran Habibi-Rezaei

Protein Biotechnology Research Lab (PBRL), School of Biology, College of Science, University of Tehran, Tehran, Iran

چکیده

Protein aggregation plays an important role in various diseases, for instance, type ۲ diabetes (T۲D),Alzheimer’s disease (AD), Parkinson’s disease (PD), prion encephalopathies, and Huntington’s disease.Moreover, it has been recognized as a field with increasing importance in the biopharmaceutical industriesbecause of its occurrence during bioprocessing steps to ensure the drug's effectiveness and decreaseassociated risks, such as increased immunogenicity. Therefore, aggregation prediction of proteins underdifferent conditions has great importance for successful biopharmaceutics’ development and theranosticapproaches. In order to predict the aggregation propensity of proteins, a machine learning method wasproposed to evaluate the aggregation propensity of hexapeptides of WALTZ-DB ۲.۰ databank, using theSupport Vector Machine (SVM) algorithm based on the sequences of segments and beta-sheet formationpropensity of residues as an intrinsic feature. To analyze the capability of the proposed method, twoparameters were considered, which are F-measure and Matthews Correlation Coefficient (MCC), owing totheir evaluative power. Finally, the applied approach was compared with the Pasta ۲.۰ server that uses similarinputs to make predictions. The mentioned parameters of the proposed method were resulted to be ۰.۸۳۰ and۰.۶۳۳ for the proposed method, and ۰.۶۸۸ and ۰.۳۸۲ for the Pasta۲ server, respectively. As a result, the newsuggested strategy superiorly evaluates the aggregation propensity, which is essential for the basic andapplied approaches.

کلیدواژه ها

Protein aggregation; Bioproducts engineering; Machine learning; SVM; Pasta ۲.۰

مقالات مرتبط جدید

اطلاعات بیشتر در مورد COI

COI مخفف عبارت CIVILICA Object Identifier به معنی شناسه سیویلیکا برای اسناد است. COI کدی است که مطابق محل انتشار، به مقالات کنفرانسها و ژورنالهای داخل کشور به هنگام نمایه سازی بر روی پایگاه استنادی سیویلیکا اختصاص می یابد.

کد COI به مفهوم کد ملی اسناد نمایه شده در سیویلیکا است و کدی یکتا و ثابت است و به همین دلیل همواره قابلیت استناد و پیگیری دارد.