Material Transport Automation in Semiconductor Factories and Protein Structure Prediction of the Beta-Helix Motif By Side-Chain Packing

Abstract

Automation is an integral part of semiconductor factories. Factory automation involves many aspects of the manufacturing operations: material processes, transport, storage, and material tracking. This talk specifically addresses the optimization of the resource allocation mechanism of the material transport system implemented by Brooks Automation, a semiconductor capital equipment vendor located in Chelmsford, MA. Optimization is measured in terms of the throughput of the transport system, resource utilization, and responsiveness. Two resource allocation algorithms are discussed: a greedy approach and a min-weight max-cardinality matching (assignment) algorithm. Production simulation were executed on several real factory layouts using both techniques. Results are shown and discussed. Further improvements are also briefly considered. The protein structure prediction problem, deriving the 3D form of a protein from only the amino acid sequence, has often been referred to as the holy grail of computational biology. Knowing the structure of a protein helps researchers to determine its function, and to design drugs to block the protein, if it's malicious, or create synthetic copies, if it's helpful. Physical methods for structure prediction are costly and time-consuming, so fast computational methods are in high demand. Unfortunately, predicting protein structure is extremely difficult, and for the most part unsolved. One popular method is threading, which attempts to place a sequence onto a known structure from another protein. This can give an approximation of the backbone, a sort of general outline of the protein, but for drug design, a much more precise view is needed, specifically one including the side- chains, which are unique for each amino acid. Various programs have been written that predict if a sequence will form a specific, known motif. BETAWRAP detects proteins that may form a single- stranded, right-handed beta-helix, a motif found predominantly in human- pathenogenic organisms, and gives some topological information about which residues in the sequence are likely to form which parts of the predicted beta-helix. By taking the backbone from a known beta-helix and BETAWRAP's parse of the predicted beta-helix, we can use a side-chain packing program to create a 3D model of the protein and use simulated physical methods to evaluate the quality if the model. This can supply molecular biologists with several possible structures, and can also weed out false positives detected by BETAWRAP.