September
13-17, 2014 - Ljubljana, Slovenia
13th International Conference on Parallel Problem Solving
From Nature
Workshop “Natural
computing for protein structure prediction”
Overview
Independent
of its starting conformation, a protein in its natural environment folds into a
unique three dimensional structure, the native structure. Understanding the
native structure of a protein is crucial, as the structure can provide insight
into the functional roles of a protein and the specific mechanisms of its
biological function. The experimental determination of the native conformation
(using X-ray crystallography or NMR spectroscopy) is difficult and
time-consuming. As a result, the output of experimentally determined protein
structures lags behind the output of protein sequences, and the computational
prediction of protein structure remains a “holy grail” of computational
biology.
Work
related to the prediction of protein structure comprises the prediction of
secondary and tertiary structure. In the case of secondary structure
prediction, machine learning methods such as neural nets and support vector
machines can achieve up to 80% of overall accuracy in globular proteins. In the
case of the prediction of the final tertiary structure, the methods range from
comparative modeling with resolved structures to ab initio modeling approaches. In comparative modeling, the search
space is restricted by the assumption that the target protein adopts a
structure close to the experimentally determined structure of another
homologous protein.
In
contrast to this, ab initio
prediction is more challenging as it uses information from the amino acid
sequence only. Typical approaches to ab
initio prediction simplify the complexity of the interactions and the
nature of the amino acid elements, e.g. through the use of lattice-based or
low-resolution models such as the representation employed by the first stage of
the Rosetta technique. Even with such simplified models, the energy landscapes
in these problems present a multitude of local energy minima that are typically
separated by high barriers. Given these challenges, recent research in
this field has therefore focused on the development of improved sampling
protocols for protein structure prediction, often using meta-heuristic
techniques routed in the field of natural computation.
The
aim of this workshop is to provide a forum for the exchange and communication
of ideas, proposals and results related to the use of nature-inspired
techniques in problems related to computational protein structure prediction.
In tackling this important problem, nature-inspired techniques are currently
being used in a variety of ways, but presentations related to this work are
often distributed across a range of sessions / conferences / journals dependent
on the particular sub-problem considered / algorithm used. It is hoped that
this workshop will act as a meeting point for those authors and attendants of
the PPSN conference who have a current or developing interest in this area.
Topic areas include (but
are not restricted to):
• Use of
natural and evolutionary computing algorithms in protein structure prediction
(secondary and tertiary).
• Use
of natural computing for determining protein classification and protein
function.
• Hybrid
combinations of algorithms applied to protein structure prediction or protein
classification.
• Use
of artificial life models like cellular automata or Lindenmayer systems.
• Use
of simple and detailed lattice models and CASP (Critical Assessment of
Techniques for Protein Structure Prediction) evaluation measures (RMSD, GDT,
…).
• Multi-objective
approaches.
• Modeling
of temporal folding.
• Integration
of visualization methods of protein structure within the process with natural
computing alternatives.
• Use
of surrogate models in order to reduce the computational time of current
approaches.
• Parallel
implementations (Threads, MPI, OpenMP, GPUs or FPGAs).
Format &
Submission:
Authors interested in presenting their works should submit an extended
abstract describing their work (max one A4 page). The workshop will run over a
half day where the authors will have the opportunity for presenting and
discussing their developments in the related topics.
A printed version of the abstracts will be provided to the workshop
attendants.
Dates:
- Submission
deadline extended: it is open for new presentations until August
2014.
- PPSN
Conference: 13-17 September 2014
- Workshop
date: 13 September 2014
Presentations
and schedule:
Chair: José
Santos
16:00-16:30
José Santos, Pablo Villot, Martin Diéguez, “Cellular
automata for modeling protein folding in lattice models”.
16:30-17:00 Sune
S. Nielsen, Wiktor Jurkowski, Grégoire Danoy, Juan Luis Jiménez Laredo,
Reinhard Schneider, El-Ghazali Talbi and Pascal Bouvry, “Evolutionary multi objective optimisation
with diversity as objective for the protein structure similarity problem”.
17:00-17:30 Shaun M. Kandathil, Simon C. Lovell and
Julia Handl, “Low-resolution
conformational exploration for Rosetta ab initio by bi-level optimisation of
structural features”.
17:30-18:00 Amarda Shehu and
Kenneth A. De Jong, “Memetic, multi-objective, off-lattice, and multiscale evolutionary algorithms for de novo and guided
protein structure modelling”.
Organizers:
|
santos@udc.es |
University of A Coruña, Spain |
|
|
|
gtoscano@tamps.cinvestav.mx |
Cinvestav, Information Technology Lab
-Tamaulipas, México |
|
|
|
j.handl@manchester.ac.uk |
University of Manchester, UK |
|
