50 resultados para REDUNDANT


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The significant contribution of naturally occurring disulfide bonds to protein stability has encouraged development of methods to engineer non-native disulfides in proteins. These have yielded mixed results. We summarize applications of the program MODIP for disulfide engineering. The program predicts sites in proteins where disulfides can be stably introduced. The program has also been used as an aid in conformational analysis of naturally occurring disulfides in a-helices, antiparallel and parallel beta-strands. Disulfides in a-helices occur only at N-termini, where the first cysteine residue is the N-cap residue of the helix. The disulfide occurs as a CXXC motif and can possess redox activity. In antiparallel beta-strands, disulfides occur exclusively at non-hydrogen bonded (NHB) registered pairs of antiparallel beta-sheets with only 1 known natural example occurring at a hydrogen bonded (HB) registered pair. Conformational analysis suggests that disulfides between HB residue pairs are under torsional strain. A similar analysis to characterize disulfides in parallel beta-strands was carried out. We observed that only 9 instances of cross-strand disulfides exist in a non-redundant dataset. Stereochemical analysis shows that while tbe chi(square) angles are similar to those of other disulfides, the chi(1) and chi(2) angles show more variation and that one of tbe strands is generally an edge strand.

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Programming for parallel architectures that do not have a shared address space is extremely difficult due to the need for explicit communication between memories of different compute devices. A heterogeneous system with CPUs and multiple GPUs, or a distributed-memory cluster are examples of such systems. Past works that try to automate data movement for distributed-memory architectures can lead to excessive redundant communication. In this paper, we propose an automatic data movement scheme that minimizes the volume of communication between compute devices in heterogeneous and distributed-memory systems. We show that by partitioning data dependences in a particular non-trivial way, one can generate data movement code that results in the minimum volume for a vast majority of cases. The techniques are applicable to any sequence of affine loop nests and works on top of any choice of loop transformations, parallelization, and computation placement. The data movement code generated minimizes the volume of communication for a particular configuration of these. We use a combination of powerful static analyses relying on the polyhedral compiler framework and lightweight runtime routines they generate, to build a source-to-source transformation tool that automatically generates communication code. We demonstrate that the tool is scalable and leads to substantial gains in efficiency. On a heterogeneous system, the communication volume is reduced by a factor of 11X to 83X over state-of-the-art, translating into a mean execution time speedup of 1.53X. On a distributed-memory cluster, our scheme reduces the communication volume by a factor of 1.4X to 63.5X over state-of-the-art, resulting in a mean speedup of 1.55X. In addition, our scheme yields a mean speedup of 2.19X over hand-optimized UPC codes.

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Dynamic power dissipation due to redundant switching is an important metric in data-path design. This paper focuses on the use of ingenious operand isolation circuits for low power design. Operand isolation attempts to reduce switching by clamping or latching the output of a first level of combinational circuit. This paper presents a novel method using power supply switching wherein both PMOS and NMOS stacks of a circuit are connected to the same power supply. Thus, the output gets clamped or latched to the power supply value with minimal leakage. The proposed circuits make use of only two transistors to clamp the entire Multiple Input Multiple Output (MIMO) block. Also, the latch-based designs have higher drive strength in comparison to the existing methods. Simulation results have shown considerable area reduction in comparison to the existing techniques without increasing timing overhead.

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Heterodimeric proteins with homologous subunits of same fold are involved in various biological processes. The objective of this study is to understand the evolution of structural and functional features of such heterodimers. Using a non-redundant dataset of 70 such heterodimers of known 3D structure and an independent dataset of 173 heterodimers from yeast, we note that the mean sequence identity between interacting homologous subunits is only 23-24% suggesting that, generally, highly diverged paralogues assemble to form such a heterodimer. We also note that the functional roles of interacting subunits/domains are generally quite different. This suggests that, though the interacting subunits/domains are homologous, the high evolutionary divergence characterize their high functional divergence which contributes to a gross function for the heterodimer considered as a whole. The inverse relationship between sequence identity and RMSD of interacting homologues in heterodimers is not followed. We also addressed the question of formation of homodimers of the subunits of heterodimers by generating models of fictitious homodimers on the basis of the 3D structures of the heterodimers. Interaction energies associated with these homodimers suggests that, in overwhelming majority of the cases, such homodimers are unlikely to be stable. Majority of the homologues of heterodimers of known structures form heterodimers (51.8%) and a small proportion (14.6%) form homodimers. Comparison of 3D structures of heterodimers with homologous homodimers suggests that interfacial nature of residues is not well conserved. In over 90% of the cases we note that the interacting subunits of heterodimers are co-localized in the cell. Proteins 2015; 83:1766-1786. (c) 2015 Wiley Periodicals, Inc.

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Most pattern mining methods yield a large number of frequent patterns, and isolating a small relevant subset of patterns is a challenging problem of current interest. In this paper, we address this problem in the context of discovering frequent episodes from symbolic time-series data. Motivated by the Minimum Description Length principle, we formulate the problem of selecting relevant subset of patterns as one of searching for a subset of patterns that achieves best data compression. We present algorithms for discovering small sets of relevant non-redundant episodes that achieve good data compression. The algorithms employ a novel encoding scheme and use serial episodes with inter-event constraints as the patterns. We present extensive simulation studies with both synthetic and real data, comparing our method with the existing schemes such as GoKrimp and SQS. We also demonstrate the effectiveness of these algorithms on event sequences from a composable conveyor system; this system represents a new application area where use of frequent patterns for compressing the event sequence is likely to be important for decision support and control.