Preface
e AAAI-13 workshop Artificial Intelligence and Robotics Methods in Computational
Biology provided a forum for AI and Robotics researchers with diverse backgrounds in
search, planning, machine learning, data mining, evolutionary computation, and so on, to
exchange views, treatments, and findings on important open problems relating to biomolecular structure prediction and design, motion simulation, and assembly/docking prediction.
A total of eight papers were accepted.
Evolutionary search algorithms were proposed to model protein loops and backbones. In
“Pareto-based Optimal Sampling Method and Its Applications in Protein Structural
Conformation Sampling,” the authors propose the POS algorithm to compute conformations of loops. Pareto-based multiobjective analysis over different potential energy functions
is used to guide the algorithm towards energetically-relevant conformations while reducing
artifacts due to a specific energy function. A proof-of-concept application showcases the
potential of POS for decoy sampling in ab-initio structure predicition. In “An Evolutionaryinspired Algorithm to Guide Stochastic Search for Near-native Protein Conformations with
Multiobjective Analysis,” the authors propose their MOEA and MOEA-PC algorithms to
investigate the promise of evolutionary search algorithms for effective decoy sampling in abinitio structure prediction. Various Pareto-based metrics are investigated to showcase the
effectiveness of guiding the search by multiple objectives defined over groups of terms of a
potential energy function, resulting in higher exploration capability and higher-quality
decoys.
Once decoys are sampled, then in “A Contact-assisted Approach to Protein Structure
Prediction and Its Assessment in CASP10,” the authors investigate different metrics for
selecting promising decoys for prediction and/or further energetic refinement in the context
of hard structure prediction. Contact-based metrics are found promising, and coupled with
other structure evaluators and modeling tools, shown by the authors to help improve structure selection and prediction in some hard cases.
Robotics-inspired algorithms were proposed to compute motions connecting structural
states in peptides, model folding paths in proteins, and even predict large-scale molecular
ordering events. In “A Multi-Tree Approach to Compute Transition Paths on Energy
Landscapes,” the authors propose to map the connectivity between given stable structural
states, such as known free-energy minima from experiment or computation. e proposed
method builds on the Transition-RRT framework from robot motion planning, enhancing
the exploration capability through multiple trees, revealing a roadmap of conformational
transitions between stable states. Interesting detailed analysis on the transitions and transition states of a small peptide showed the power of the method and its promise for computing transition trajectories in proteins. In “Rigidity Analysis for Protein Motion and Folding
Core Identification,” the authors build on the PRM framework to compute paths from a
given folded structure of a protein to unfolded conformations. Rigidity-based analysis of
paths is employed to reveal a folding core of residues. In “Geometrical Insights into the
Process of Antibody Aggregation,” the authors propose a graph-based algorithm to simulate, at a large-scale, molecular events triggered by ligand-receptor binding in anaphylactic
shock. Dynamic events are encoded according to experimental measurements, revealing
interesting geometrical formations of receptors corroborated by experimental findings.
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In “Packing Models for Multi-Domain Biomolecular Structures in Crystals with
P212121 Space-Group Symmetry,” the authors propose a method to reduce the computational
cost of expensive calculations to determine protein structures from data obtained by X-ray
crystallography experiments. e underlying idea is to introduce more advanced mathematical concepts in molecular replacement methods. An algorithm originating rom robotics is
also applied to improve the accuracy of results. e approach is applied to multi-domain proteins with a frequent type of structural symmetry.
In “Using Protein Fragments for Searching and Data-Mining Protein Databases,” the
authors elucidate important relationships between sequence, structure, and function in proteins through visualization of the protein structure space. Novel analysis is introduced,
building on the fragbag representation that allows linear embedding of the known protein
structure space, to reveal function and sequence preservation or diversity on different
regions of the structure map.
Amarda Shehu, Juan Cortés, Jianlin Cheng
Workshop Cochairs
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