Papers by Silvia Crivelli
2005 IEEE Computational Systems Bioinformatics Conference - Workshops (CSBW'05), 2000
We describe a protein structure prediction method that predicts the three-dimensional structure o... more We describe a protein structure prediction method that predicts the three-dimensional structure of new folds via minimizations of a physics-based energy function. The method is one of the few attempts to use an all-atom physics-based energy function throughout all stages of the optimization. It differs from the Stochastic Perturbation with Soft Constraints method[1] developed by some of the authors and applied in previous CASP competitions in that (1) it incorporates knowledge from known proteins to design the initial configurations and it combines filtering techniques with a physics-based energy function to enhance the structure discrimination ability and performance of the method. Our method is based on the hypothesis that although the fold recognition servers can only provide limited and incomplete folding information for the targets in the new folds category, that information may be valuable for guiding the global optimization process to find the native conformation. The method is composed of two phases: the setup phase creates a variety of partially folded or fully folded initial configurations guided by knowledge from known proteins; the global optimization phase improves the initial configurations by applying a sophisticated optimization algorithm to optimize the coil and loop regions followed by local minimizations to optimize the entire structure.
European Conference on Parallel Processing, 1999
We discuss the parallelization of our protein structure prediction algorithm on distributed-memor... more We discuss the parallelization of our protein structure prediction algorithm on distributed-memory computers. Because the computation can be represented as a search through a vast tree of possible solutions, a hierarchical approach that assigns subtrees to different groups of processors allows us to partition the work efficiently and maintain information updated without incurring significant communication overhead. Our results show that
The protein structure prediction problem continues to elude scientists. Despite the introduction ... more The protein structure prediction problem continues to elude scientists. Despite the introduction of many methods, only modest gains were made over the last decade for certain classes of prediction targets. To address this challenge, a socialmedia based worldwide collaborative effort, named WeFold, was undertaken by 13 labs. During the collaboration, the laboratories were simultaneously competing with each other. Here, we present the first attempt at "coopetition" in scientific research applied to the protein structure prediction and refinement problems. The coopetition was possible by allowing the participating labs to contribute different components of their protein structure prediction pipelines and create new hybrid pipelines that they tested during CASP10. This manuscript describes both successes and areas needing improvement as identified throughout the first WeFold experiment and discusses the efforts that are underway to advance this initiative. A footprint of all contributions and structures are publicly accessible at http://www.wefold.org.
Wuhan University Journal of Natural Sciences, 1996
Ijpeds, 2006
In this paper, we present a new, easy to implement algorithm for detecting the termination of a p... more In this paper, we present a new, easy to implement algorithm for detecting the termination of a parallel asynchronous computation on distributedmemory MIMD computers. We demonstrate that it operates concurrently with the main computation, adding minimal overhead, and we prove that it correctly detects termination when it occurs. Experimental results confirm that the termination detection routine imposes an overhead smaller than the experimental uncertainty.
Parallel Processing for Scientific Computing, 1993
In this paper, we compare the costs of computing a single eigenvalue of a symmetric tridiagonal m... more In this paper, we compare the costs of computing a single eigenvalue of a symmetric tridiagonal matrix by serial bisection and by parallel multisection on a mesh multiprocessor. We show how the optimal method for computing one eigenvalue depends on such variables as the matrix order and parameters of the multiprocessor used. We present the results of experiments on the
Parallel Processing for Scientific Computing, 1993
Parallel Computing
ABSTRACT
2005 IEEE Computational Systems Bioinformatics Conference - Workshops (CSBW'05), 2005
ProteinShop and POSE are graphical infrastructures for the interactive modeling, manipulation, op... more ProteinShop and POSE are graphical infrastructures for the interactive modeling, manipulation, optimization and analysis of molecules. They were designed to bring interactive computer graphics in the field of molecular modeling to a level not attempted by other visualization programs. To achieve that goal, we adapted inverse kinematics algorithms commonly used in robotics to permit interactive manipulation of protein structures in a natural and intuitive way.
Parallel Computing, 1995
In [20], Simon proves that bisection is not the optimal method for computing an eigenvalue on a s... more In [20], Simon proves that bisection is not the optimal method for computing an eigenvalue on a single vector processor. In this paper, we show that his analysis does not extend in a straightforward way to the computation of an eigenvalue on a distributed-memory MIMD multiprocessor. In particular, we show how the optimal number of sections (and processors) to use
The protein structure prediction problem continues to elude scientists. Even though many new meth... more The protein structure prediction problem continues to elude scientists. Even though many new methods have been introduced, certain classes of prediction targets such as free modeling targets remain a challenge based on blind predictions in the several previous Critical Assessment of Techniques for Protein Structure Prediction (CASP) experiments [1]. To meet this challenge, a large-scale collaborative effort called WeFold was undertaken by thirteen labs, each with their own specialties and approaches in addressing the problem. In this talk, we will present the different methods or branches collaboratively designed and tested during the WeFold experiment, as well as their predictive ability, outcomes, and lessons learned. Independent branches involved in the collaborative effort yielded several high-ranking predictions among all group and method submissions in CASP10 for human, free modeling (template free), and refinement targets. Remarkably, two WeFold methods were able to produce t...
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Papers by Silvia Crivelli