[SocBiN] Phd position in Using Deep Learning to Understand Protein Structure Evolution”.

[Arne Elofsson]

https://su.varbi.com/what:job/jobID:830925/where:4/


Project description

Your studies in Bioinformatics will be in the project: "Using Deep Learning
to Understand Protein Structure Evolution”.

We have studied domain evolution and observed that structural change is
roughly linear with sequence change. However, larger structural changes
might also occur within a domain during evolution, particularly within
transporters3. Further, domains can be combined to create multi-domain
proteins. Most domain architectures arise from adding or removing a single
domain at the N- or C-terminal regions, while certain repeat-domain
families undergo more internal duplications. We also demonstrated that
eukaryotic proteins harbour substantially more disordered and linker
regions, which expand more rapidly than globular domains. Most phylogenetic
studies of proteins, including ours, have primarily focused on changes in
the amino-acid sequences. Today, accurate structural models from AlphaFold
make it possible to integrate structure into large-scale phylogenetic and
sequence analyses. Therefore, in this application, we propose revisiting
earlier studies, scaling them up, and adding structural information to the
analysis. This study will provide a detailed structural understanding of
changes within domains, how novel protein architectures emerge, and why
certain domains and architectures continue expanding and diversifying in
specific lineages. We believe this study will provide fundamental insights
into protein structure evolution and how evolutionary mechanisms and
selective pressures have shaped proteins. Together, the three aims of this
project will reveal how domain changes their structure and how
rearrangements shape structural protein diversity across the tree of life.

The Elofsson group is located at the Science for Life Laboratory. Elofsson
has worked on protein structure predictions and evolution for over two
decades. He has worked on various techniques using machine learning and
other computational techniques. Our most important contributions to this
work are the methods he has developed to identify the quality of protein
models, Pcons, and various versions of ProQ. The group comprises 5 PhD
students, one postdoc, and one senior researcher.
Qualification requirements

In order to be admitted to postgraduate education, the applicant must have
the general and specific entry requirements. The qualification requirements
must be met by the deadline for applications.

You meet general entry requirements if you have completed a second-cycle
degree, or completed courses equivalent to at least 240 higher education
credits, of which 60 credits must be in the second cycle, or have otherwise
acquired equivalent knowledge in Sweden or elsewhere.

In order to meet the specific entry requirements, for acceptance in the
Biochemistry, especially Bioinformatics, program the applicant must have
passed courses within the first and second cycles of at least 90 credits in
either, a) Chemistry/Molecular Biology/Biotechnology, or b) Computer
Science/Mathematics/Physics and at the second cycle level, 60 credits in
Life Science, Computer Science Mathematics, Physics or Bioinformatics
including a 30 credit Degree Project (thesis).

Yours

Arne

-----------------------------------------
  Arne Elofsson                  Science for Life Laboratory
  Tel:+46-(0)70 695 1045   Stockholm University
  http://bioinfo.se/               Box 1031,
  Email: arne at bioinfo.se   17121 Solna, Sweden
Bluesky: @arneelof.bsky.social
Twitter/X: @arneelof
  Mastodon: @arneelof at fediscience.org
  Zoom: https://stockholmuniversity.zoom.us/my/arneelof/
  Scholar: http://scholar.google.se/citations?user=s3OCM3AAAAAJ
  ORCID: 0000-0002-7115-9751 <https://orcid.org/0000-0002-7115-9751>
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