Fiasco: GUI and GPGPU integration

From WCB 2014, Lyon, France.

The focus of this work is the use of constraint-based technology in the investigation of protein structures. We developed a system composed of two main parts: (1) a Graphical User Interface, to help the user, in particular users coming from Biology, in modeling protein structure studies with CP, and (2) a constraint solver targeted at protein structure analysis. Parts of the system have been presented in four previous works. Our current work provides a comprehensive constraint system for modeling generic structural protein properties and investigating different types of problems (e.g., structure prediction, studies of flexibility). Moreover the solver makes use, in some parts, of GPU computation following the ideas presented in, as explained below.

  1. The Graphical User Interface. The user interface is developed in Java and allows the user to impose constraints on protein structures as well as to visualize the results of the solver’s execution. The interface allows the user to select the primary structure of a target protein as input and to launch the solver. In turn, the solver computes a preliminary protein’s structure using default constraints, according to the sequence of amino acids given in input. The structure is displayed to the user, who can choose to add new constraints (e.g., distance constraints between secondary structure elements) and re-run the solver. This process can be iterated until a desired solution is found.
  2.  The Solver. The constraint solver is composed by two sub-systems: (a) a parallel Multi-Agents System, and (b) the FIASCO constraint solver.
    (a) applies a multi-agents system (MAS) perspective, where concurrent agents explore the folding of different parts of a protein. The strength of the approach lies in the agents’ ability to apply different types of knowledge, expressed in the form of declarative constraints, to prune the search space of folding alternatives. Distinct agents are in charge of retrieving, filtering, and coordinating local information about parts of a protein, aiming to reach a global consensus. This system is further enhanced by the use of a General-Purpose Graphical Processing Unit (GPGPU) to implement the MAS infrastructure, with significant performance improvements over the sequential implementation—available if the tool is not run on a suitable GPU. The multi-agents system is used to predict the preliminary protein’s structure on which the user will impose additional constraints to be satisfied by FIASCO. GPGPUs have been recently used in the implementation of a generic constraint solver.
    (b) is an efficient C++-based resolution engine that can be used to model and solve a class of problems derived from loop modeling instances. FIASCO provides a uniform and efficient modeling platform for studying different structural properties—that have been, so far, addressed only using significantly distinct methods and tools. The declarative nature of constraint-based methods supports a level of elaboration tolerance that is not offered by other frameworks for protein structure prediction, facilitating the integration of additional knowledge in guiding the studies (e.g., availability of information on secondary structure).

 

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The FIASCO solver is published

It’s a pleasure to announce that the FIASCO solver has been published to a major journal:

Have a nice reading.

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News from CMSB 2013

This is a short post from the 11-th International Conference on Computational Methods in System Biology.

We presented our paper: “Constraint Programming in Community-based Gene Regulatory Network Inference”. The talk focused on the use of  constraints programming to in the context of gene regulatory network inference. Gene Regulatory Network (GRN) inference is a major objective of

Systems Biology. The complexity of biological systems and the lack of
adequate data have posed many challenges to the inference
problem. Community networks integrate predictions from
individual methods in a “meta predictor”, in order to compose the
advantages of different methods and soften individual
limitations. This paper proposes a novel methodology to integrate
prediction ensembles using Constraint Programming, a declarative
modeling paradigm, which allows the formulation of dependencies among
components of the problem, enabling the integration of diverse forms
of knowledge. We show the potential of this
method: the addition of biological constraints can offer improvements
in the prediction accuracy, and the method shows promising results in
assessing biological hypothesis using constraints.

Here you can find the slides presented at CMSB 13.

Ferdinando.

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News from CP 2012

Dear readers,

This is a short post from the 18-th International Conference on Principles and Practice of Constraint Programming.

Alessandro Dal Palu’ presenting the impact on the use of constraints in protein flexibility analysis.

We presented our paper: “A Filtering Technique for Fragment Assembly-based Proteins Loop Modeling with Constraints”. The talk focused on the use of novel constraints and propagation to speed up the analysis of flexible regions of proteins.
Protein flexibility is of fundamental importance to understand the ways in which drugs exert biological effects. In particular the study of their binding-site location, biding orientation and binding kinetics is of interest to study how the affinity between a drug and its target could be increased.
In the work presented at CP’12 we focused on testing the capabilities of FIASCO in producing biologically meaningful and efficient results for the problem of protein loop sampling. A link to the slides presented at CP’12 can be found here.

Ferdinando Fioretto (left) and Alessandro Dal Palu’ (right) at the poster session of CP’12.

The positive results produced by FIASCO and the good feedback obtained from the Constraints community is a great signal that Constraint Programming Technologies are a successful solution to tackle problems related to protein flexibility structure analysis.

We are currently working on a new version of FIASCO, which will be able to generate protein structures ensemble uniformly distributed over a specific region of the euclidean space. This is useful for example in drug design, where a number of energetically similar protein conformations is assumed to pre-exist and the interaction between the ligand (drug) and the protein is simulated employing such structure ensemble.

It’s now time to go back to work!

Ferdinando.

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FIASCO: a novel solution for the structural analysis of proteins

FIASCO (Fragment-based Interactive Assembly for protein Structure prediction with COnstraints) is a C++ constraint solver optimized to address studies related to the analysis and prediction of the three-dimensional structure of proteins.
Its conceptual declarative nature allows an easy manipulation of constraints targeted at describing protein sequence and structural properties, interaction forces fields, spatial local and global constraints, etc.

Theoretical and Practical info about FIASCO can be retrieved following the tags above (Main Publications, Software, Extra Material, etc.)

What can FIASCO do? Some application examples.

  • Protein 3D structure prediction
  • Protein loops modeling
  • Reconstruction of protein parts (sub-structures)
  • Analysis of proteins flexibility

Thanks for reading these pages. For any question feel free to contact us (see Team TAG above for email addresses).

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