4,021 research outputs found
Protein-Protein Docking with F2Dock 2.0 and GB-Rerank
Rezaul Chowdhury is with UT Austin; Muhibur Rasheed is with UT Austin; Maysam Moussalem is with UT Austin; Donald Keidel is with The Scripps Research Institute; Arthur Olson is with The Scripps Research Institute; Michel Sanner is with The Scripps Research Institute; Chandrajit Bajaj is with The Scripps Research Institute.Motivation -- Computational simulation of protein-protein docking can expedite the process of molecular modeling and drug discovery. This paper reports on our new F2 Dock protocol which improves the state of the art in initial stage rigid body exhaustive docking search, scoring and ranking by introducing improvements in the shape-complementarity and electrostatics affinity functions, a new knowledge-based interface propensity term with FFT formulation, a set of novel knowledge-based filters and finally a solvation energy (GBSA) based reranking technique. Our algorithms are based on highly efficient data structures including the dynamic packing grids and octrees which significantly speed up the computations and also provide guaranteed bounds on approximation error. Results -- The improved affinity functions show superior performance compared to their traditional counterparts in finding correct docking poses at higher ranks. We found that the new filters and the GBSA based reranking individually and in combination significantly improve the accuracy of docking predictions with only minor increase in computation time. We compared F2 Dock 2.0 with ZDock 3.0.2 and found improvements over it, specifically among 176 complexes in ZLab Benchmark 4.0, F2 Dock 2.0 finds a near-native solution as the top prediction for 22 complexes; where ZDock 3.0.2 does so for 13 complexes. F2 Dock 2.0 finds a near-native solution within the top 1000 predictions for 106 complexes as opposed to 104 complexes for ZDock 3.0.2. However, there are 17 and 15 complexes where F2 Dock 2.0 finds a solution but ZDock 3.0.2 does not and vice versa; which indicates that the two docking protocols can also complement each other. Availability -- The docking protocol has been implemented as a server with a graphical client (TexMol) which allows the user to manage multiple docking jobs, and visualize the docked poses and interfaces. Both the server and client are available for download. Server: http://www.cs.utexas.edu/~bajaj/cvc/softâware/f2dock.shtml. Client: http://www.cs.utexas.edu/~bajaj/cvc/softâware/f2dockclient.shtml.The research of C.B., R.C., M.M., and M.R. of University of Texas, was supported in part by National Science Foundation (NSF) grant CNS-0540033, and grants from the National Institutes of Health (NIH) R01-GM074258, R01-GM073087, R01-EB004873. The research of M.M. was additionally supported by an NSF Graduate Research Fellowship. The research of M.S. and A.O. of TSRI was supported in part by a subcontract on NIH grant R01-GM073087. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Computer Science
PRED-CLASS: cascading neural networks for generalized protein classification and genome-wide applications
A cascading system of hierarchical, artificial neural networks (named
PRED-CLASS) is presented for the generalized classification of proteins into
four distinct classes-transmembrane, fibrous, globular, and mixed-from
information solely encoded in their amino acid sequences. The architecture of
the individual component networks is kept very simple, reducing the number of
free parameters (network synaptic weights) for faster training, improved
generalization, and the avoidance of data overfitting. Capturing information
from as few as 50 protein sequences spread among the four target classes (6
transmembrane, 10 fibrous, 13 globular, and 17 mixed), PRED-CLASS was able to
obtain 371 correct predictions out of a set of 387 proteins (success rate
approximately 96%) unambiguously assigned into one of the target classes. The
application of PRED-CLASS to several test sets and complete proteomes of
several organisms demonstrates that such a method could serve as a valuable
tool in the annotation of genomic open reading frames with no functional
assignment or as a preliminary step in fold recognition and ab initio structure
prediction methods. Detailed results obtained for various data sets and
completed genomes, along with a web sever running the PRED-CLASS algorithm, can
be accessed over the World Wide Web at http://o2.biol.uoa.gr/PRED-CLAS
Hierarchical Orthogonal Matrix Generation and Matrix-Vector Multiplications in Rigid Body Simulations
In this paper, we apply the hierarchical modeling technique and study some
numerical linear algebra problems arising from the Brownian dynamics
simulations of biomolecular systems where molecules are modeled as ensembles of
rigid bodies. Given a rigid body consisting of beads, the
transformation matrix that maps the force on each bead to 's
translational and rotational forces (a vector), and the row
space of , we show how to explicitly construct the matrix
consisting of orthonormal basis vectors of
(orthogonal complement of ) using only operations
and storage. For applications where only the matrix-vector multiplications
and are needed, we introduce
asymptotically optimal hierarchical algorithms without
explicitly forming . Preliminary numerical results are presented to
demonstrate the performance and accuracy of the numerical algorithms
Pipelined Two-Operand Modular Adders
Pipelined two-operand modular adder (TOMA) is one of basic components used in digital signal processing (DSP) systems that use the residue number system (RNS). Such modular adders are used in binary/residue and residue/binary converters, residue multipliers and scalers as well as within residue processing channels. The design of pipelined TOMAs is usually obtained by inserting an appriopriate number of latch layers inside a nonpipelined TOMA structure. Hence their area is also determined by the number of latches and the delay by the number of latch layers. In this paper we propose a new pipelined TOMA that is based on a new TOMA, that has the smaller area and smaller delay than other known structures. Comparisons are made using data from the very large scale of integration (VLSI) standard cell library
Time-domain analysis of RF and microwave autonomous circuits by vector fitting-based approach
This work presents a new method for the analysis of RF and microwave autonomous circuits directly in the time-domain, which is the most effective approach at simulation level to evaluate nonlinear phenomena. For RF and microwave autonomous circuits, time-domain simulations usually experiment convergence problems or numerical inaccuracies due to the presence of distributed elements, preventing de-facto their use. The proposed solution is based on the Vector Fitting algorithm applied directly at circuit level. A case study relative to a RF hybrid oscillator is presented for practical demonstration and evaluation of performance reliability of the proposed method
Implementation and Evaluation of Algorithmic Skeletons: Parallelisation of Computer Algebra Algorithms
This thesis presents design and implementation approaches for the parallel algorithms of computer algebra. We use algorithmic skeletons and also further approaches, like data parallel arithmetic and actors. We have implemented skeletons for divide and conquer algorithms and some special parallel loops, that we call ârepeated computation with a possibility of premature terminationâ. We introduce in this thesis a rational data parallel arithmetic. We focus on parallel symbolic computation algorithms, for these algorithms our arithmetic provides a generic parallelisation approach.
The implementation is carried out in Eden, a parallel functional programming language based on Haskell. This choice enables us to encode both the skeletons and the programs in the same language. Moreover, it allows us to refrain from using two different languagesâone for the implementation and one for the interfaceâfor our implementation of computer algebra algorithms.
Further, this thesis presents methods for evaluation and estimation of parallel execution times. We partition the parallel execution time into two components. One of them accounts for the quality of the parallelisation, we call it the âparallel penaltyâ. The other is the sequential execution time. For the estimation, we predict both components separately, using statistical methods. This enables very confident estimations, although using drastically less measurement points than other methods. We have applied both our evaluation and estimation approaches to the parallel programs presented in this thesis. We haven also used existing estimation methods.
We developed divide and conquer skeletons for the implementation of fast parallel multiplication. We have implemented the Karatsuba algorithm, Strassenâs matrix multiplication algorithm and the fast Fourier transform. The latter was used to implement polynomial convolution that leads to a further fast multiplication algorithm. Specially for our implementation of Strassen algorithm we have designed and implemented a divide and conquer skeleton basing on actors. We have implemented the parallel fast Fourier transform, and not only did we use new divide and conquer skeletons, but also developed a map-and-transpose skeleton. It enables good parallelisation of the Fourier transform. The parallelisation of Karatsuba multiplication shows a very good performance. We have analysed the parallel penalty of our programs and compared it to the serial fractionâan approach, known from literature. We also performed execution time estimations of our divide and conquer programs.
This thesis presents a parallel map+reduce skeleton scheme. It allows us to combine the usual parallel map skeletons, like parMap, farm, workpool, with a premature termination property. We use this to implement the so-called âparallel repeated computationâ, a special form of a speculative parallel loop. We have implemented two probabilistic primality tests: the RabinâMiller test and the Jacobi sum test. We parallelised both with our approach. We analysed the task distribution and stated the fitting configurations of the Jacobi sum test. We have shown formally that the Jacobi sum test can be implemented in parallel. Subsequently, we parallelised it, analysed the load balancing issues, and produced an optimisation. The latter enabled a good implementation, as verified using the parallel penalty. We have also estimated the performance of the tests for further input sizes and numbers of processing elements. Parallelisation of the Jacobi sum test and our generic parallelisation scheme for the repeated computation is our original contribution.
The data parallel arithmetic was defined not only for integers, which is already known, but also for rationals. We handled the common factors of the numerator or denominator of the fraction with the modulus in a novel manner. This is required to obtain a true multiple-residue arithmetic, a novel result of our research. Using these mathematical advances, we have parallelised the determinant computation using the GauĂ elimination. As always, we have performed task distribution analysis and estimation of the parallel execution time of our implementation. A similar computation in Maple emphasised the potential of our approach. Data parallel arithmetic enables parallelisation of entire classes of computer algebra algorithms.
Summarising, this thesis presents and thoroughly evaluates new and existing design decisions for high-level parallelisations of computer algebra algorithms
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