dc.rights.license | CC-BY-NC-ND | |
dc.contributor.advisor | Externe beoordelaar - External assesor, | |
dc.contributor.author | Kromdijk, Nicky | |
dc.date.accessioned | 2022-03-29T00:00:33Z | |
dc.date.available | 2022-03-29T00:00:33Z | |
dc.date.issued | 2022 | |
dc.identifier.uri | https://studenttheses.uu.nl/handle/20.500.12932/41442 | |
dc.description.abstract | In this review, I will explore how recent protein structure prediction methods that make use of deep
learning will impact the field of structural biology. I will particularly search for purposes where these
methods can be applied and what are their current pitfalls. After an intensive literature study, it became
clear that accurate prediction algorithms are useful to solve experimental structures faster and help
divide proteins into functional domains. Structural predictions improve interpreting the molecular
processes of (unknown) proteins and therefore provide insights into diseases and how to design potential
treatments. That way, researchers can rapidly work out the structure of every protein in new and
dangerous pathogens, speeding up the process of screening for drug targets. In addition to that, the open-source nature of these prediction methods enables scientists to continue the advances to create even
more powerful software. Besides great advances, limitations of these deep learning structure prediction
methods include that the predicted models only provide one conformational state of the protein and that
no ligands are included in the model which is crucial data for inferring exact biological function and
designing new drugs. Additionally, no folding information is present for the predicted structure. We should
not state that these algorithms solved the folding problem, as they solved the prediction problem.
Although scientists should remain critical of computational predictions, these technologies are still groundbreaking and will lead to more advances in the field of structural biology. | |
dc.description.sponsorship | Utrecht University | |
dc.language.iso | EN | |
dc.subject | How recent AI algorithms (AlphaFold, RoseTTaFold) will kick-start a new generation in biological research. | |
dc.title | The Rise of AI in Structural Biology | |
dc.type.content | Master Thesis | |
dc.rights.accessrights | Open Access | |
dc.subject.keywords | CASP;Structure prediction;AI;AlphaFold2;RoseTTaFold | |
dc.subject.courseuu | Bioinformatics and Biocomplexity | |
dc.thesis.id | 3090 | |