A Constraint Solver for Flexible Protein Models

This paper proposes the formalization and implementation of a novel class of constraints aimed at modeling problems related to placement of multi-body systems in the 3-dimensional space. Each multi-body is a system composed of body elements, connected by joint relationships and constrained by geometric properties. The emphasis of this investigation is the use of multi-body systems to model native conformations of protein structures---where each body represents an entity of the protein (e.g., an amino acid, a small peptide) and the geometric constraints are related to the spatial properties of the composing atoms. The paper explores the use of the proposed class of constraints to support a variety of different structural analysis of proteins, such as loop modeling and structure prediction. The declarative nature of a constraint-based encoding provides elaboration tolerance and the ability to make use of any additional knowledge in the analysis studies. The filtering capabilities of the proposed constraints also allow to control the number of representative solutions that are withdrawn from the conformational space of the protein, by means of criteria driven by uniform distribution sampling principles. In this scenario it is possible to select the desired degree of precision and/or number of solutions. The filtering component automatically excludes configurations that violate the spatial and geometric properties of the composing multi-body system. The paper illustrates the implementation of a constraint solver based on the multi-body perspective and its empirical evaluation on protein structure analysis problems

Exploring the use of GPGPUs in Constraint Solving

This dissertation presents an experimental study aimed at assessing the feasibility of parallelizing the constraint solving process using Graphical Processing Units (GPU s). GPUs support a form of data parallelism that appears to be suitable to the type of processing required to cycle through constraints and domain values during consistency checking and propagation. The dissertation also illustrates an implementation of a constraint solver capable of hybrid propagations (i.e., alternating CPU and GPU) and parallel search, and demonstrates the potential for competitiveness against sequential implementations. We consider the Protein Structure Prediction problem as a hard combinatorial real-world problem as case study to show the advantages of combining parallel search and parallel constraint propagation on a GPU architecture. We present the formalization and implementation of a novel class of constraints to support a variety of different structural analysis of proteins, such as loop modeling and structure prediction.. We demonstrate the suitability of a GPU approach to implement such MAS infrastructure, with significant performance improvements over the sequential implementation and other methods.openDottorato di ricerca in InformaticaopenCampeotto, Federic

A declarative concurrent system for protein structure prediction on GPU

This paper provides a novel perspective in the protein structure prediction (PSP) problem. The PSP problem focuses on determining putative 3D structures of a protein starting from its primary sequence. The proposed approach relies on a multi-agent 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. The paper makes also an important contribution in demonstrating the suitability of a general-purpose graphical processing unit approach to implement such MAS infrastructure, with significant performance improvements over the sequential implementation and other method

A GPU Implementation of Large Neighborhood Search for Solving Constraint Optimization Problems.

Constraint programming has gained prominence as an effective and declarative paradigm for modeling and solving complex combinatorial problems. Techniques based on local search have proved practical tosolve real-world problems, providing a good compromise between optimality and efficiency. In spite of the natural presence of concurrency, there has been relatively limited effort to use novel massively parallel architectures, such as those found in modern Graphical Processing Units (GPUs), to speedup local search techniques in constraint programming. This paper describes a novel framework which exploits parallelism from a popular local search method (the Large Neighborhood Search method) using GPUs

Protein Loop Modeling via Constraints and Fragment Assembly

Methods to predict the structure of a protein often rely on the knowledge of macro-sub-structures and their exact or approximate relative positions in space. The parts connecting these sub-structures are called loops and, in general, they are characterized by a high degree of freedom. The modeling of loops is, thus, a critical problem in predicting protein conformations that are biologically realistic. This paper introduces a constraint that models a general multi-body system, and shows its application to the protein loop modeling, based on fragments assembly, along with filtering techniques, inspired by inverse kinematics, that can drastically reduce the search space of potential conformations

GD-Gibbs: A GPU-based sampling algorithm for solving distributed constraint optimization problems

Researchers have recently introduced a promising new class of Distributed Constraint Optimization Problem (DCOP) algorithms that is based on sampling. This paradigm is very amenable to parallelization since sampling algorithms require a lot of samples to ensure convergence, and the sampling process can be designed to be executed in parallel. This paper presents GPU-based D-Gibbs (GD-Gibbs), which extends the Distributed Gibbs (D-Gibbs) sampling algorithm and harnesses the power of parallel computation of GPUs to solve DCOPs. Experimental results show that GD-Gibbs is faster than several other benchmark algorithms on a distributed meeting scheduling problem