Your browser doesn't support javascript.
Evaluation of protein descriptors in computer-aided rational protein engineering tasks and its application in property prediction in SARS-CoV-2 spike glycoprotein.
Lim, Hocheol; Jeon, Hyeon-Nae; Lim, Seungcheol; Jang, Yuil; Kim, Taehee; Cho, Hyein; Pan, Jae-Gu; No, Kyoung Tai.
  • Lim H; The Interdisciplinary Graduate Program in Integrative Biotechnology and Translational Medicine, Yonsei University, Incheon, Republic of Korea.
  • Jeon HN; Department of Biotechnology, Yonsei University, Seoul, Republic of Korea.
  • Lim S; Department of Biotechnology, Yonsei University, Seoul, Republic of Korea.
  • Jang Y; Department of Safety Engineering, Chungbuk National University, Cheongju, Republic of Korea.
  • Kim T; The Interdisciplinary Graduate Program in Integrative Biotechnology and Translational Medicine, Yonsei University, Incheon, Republic of Korea.
  • Cho H; The Interdisciplinary Graduate Program in Integrative Biotechnology and Translational Medicine, Yonsei University, Incheon, Republic of Korea.
  • Pan JG; The Interdisciplinary Graduate Program in Integrative Biotechnology and Translational Medicine, Yonsei University, Incheon, Republic of Korea.
  • No KT; Infectious Disease Research Center (Superbacteria Group), Korea Research Institute of Bioscience and Biotechnology (KRIBB), Daejeon, Republic of Korea.
Comput Struct Biotechnol J ; 20: 788-798, 2022.
Article in English | MEDLINE | ID: covidwho-1676691
ABSTRACT
The importance of protein engineering in the research and development of biopharmaceuticals and biomaterials has increased. Machine learning in computer-aided protein engineering can markedly reduce the experimental effort in identifying optimal sequences that satisfy the desired properties from a large number of possible protein sequences. To develop general protein descriptors for computer-aided protein engineering tasks, we devised new protein descriptors, one sequence-based descriptor (PCgrades), and three structure-based descriptors (PCspairs, 3D-SPIEs_5.4 Å, and 3D-SPIEs_8Å). While the PCgrades and PCspairs include general and statistical information in physicochemical properties in single and pairwise amino acids respectively, the 3D-SPIEs include specific and quantum-mechanical information with parameterized quantum mechanical calculations (FMO2-DFTB3/D/PCM). To evaluate the protein descriptors, we made prediction models with the new descriptors and previously developed descriptors for diverse protein datasets including protein expression and binding affinity change in SARS-CoV-2 spike glycoprotein. As a result, the newly devised descriptors showed a good performance in diverse datasets, in which the PCspairs showed the best performance ( R 2 = 0.783 for protein expression and R 2 = 0.711 for binding affinity). As a result, the newly devised descriptors showed a good performance in diverse datasets, in which the PCspairs showed the best performance. Similar approaches with those descriptors would be promising and useful if the prediction models are trained with sufficient quantitative experimental data from high-throughput assays for industrial enzymes or protein drugs.
Keywords

Full text: Available Collection: International databases Database: MEDLINE Type of study: Experimental Studies / Prognostic study Language: English Journal: Comput Struct Biotechnol J Year: 2022 Document Type: Article

Similar

MEDLINE

...
LILACS

LIS


Full text: Available Collection: International databases Database: MEDLINE Type of study: Experimental Studies / Prognostic study Language: English Journal: Comput Struct Biotechnol J Year: 2022 Document Type: Article