Protein structural models selection using 4-mer sequence and combined single and consensus scores

Title from PDF of title page (University of Missouri--Columbia, viewed on September 10, 2012).The entire thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file; a non-technical public abstract appears in the public.pdf file.Thesis advisor: Dr. Dong XuIncludes bibliographical references.M. S. University of Missouri--Columbia 2012."May 2012"Quality assessment for protein structure models is an important issue in protein structure prediction. Consensus methods assess each model based on its structural similarity to all the other models in a model set, while single scoring methods, such as Opus-ca and RW, evaluate each model based on its structural properties. In this work, a novel method proposed and developed to effectively combine consensus methods and single scoring methods for better quality assessment. At first, a new method called Single Position Specific Probability (SPSP) Score is proposed based on consensus method using 4-mer sequence. Specifically, every letter in the 4-mer sequence represents a state for a local region consisting of four amino acids. A machine learning method (Neural Network) helped to combine several single scoring methods, RW, DDFire, and OPusCa with consensus methods, SPSP and Consensus Global Distance Test-Total Score (CGDT-TS) to achieve a good combination of all the terms. The method was tested on two benchmark datasets and achieved improvements over the state-of-the-art methods. The first benchmark was on Yang Zhang's data containing 56 targets. The second benchmark was from Rosetta data containing 35 targets. For Zhang's data, the CGDT score is 0.6058, while combined method achieved 0.6105. For Rosetta data, the CGDT score achieved 0.4255, while combined method achieved 0.4529

QAUST: Protein Function Prediction Using Structure Similarity, Protein Interaction, and Functional Motifs

The number of available protein sequences in public databases is increasing exponentially. However, a significant percentage of these sequences lack functional annotation, which is essential for the understanding of how biological systems operate. Here, we propose a novel method, Quantitative Annotation of Unknown STructure (QAUST), to infer protein functions, specifically Gene Ontology (GO) terms and Enzyme Commission (EC) numbers. QAUST uses three sources of information: structure information encoded by global and local structure similarity search, biological network information inferred by protein–protein interaction data, and sequence information extracted from functionally discriminative sequence motifs. These three pieces of information are combined by consensus averaging to make the final prediction. Our approach has been tested on 500 protein targets from the Critical Assessment of Functional Annotation (CAFA) benchmark set. The results show that our method provides accurate functional annotation and outperforms other prediction methods based on sequence similarity search or threading. We further demonstrate that a previously unknown function of human tripartite motif-containing 22 (TRIM22) protein predicted by QAUST can be experimentally validated

Chebyshev Polynomial-Based Fog Computing Scheme Supporting Pseudonym Revocation for 5G-Enabled Vehicular Networks

he privacy and security of the information exchanged between automobiles in 5G-enabled vehicular networks is at risk. Several academics have offered a solution to these problems in the form of an authentication technique that uses an elliptic curve or bilinear pair to sign messages and verify the signature. The problem is that these tasks are lengthy and difficult to execute effectively. Further, the needs for revoking a pseudonym in a vehicular network are not met by these approaches. Thus, this research offers a fog computing strategy for 5G-enabled automotive networks that is based on the Chebyshev polynomial and allows for the revocation of pseudonyms. Our solution eliminates the threat of an insider attack by making use of fog computing. In particular, the fog server does not renew the signature key when the validity period of a pseudonym-ID is about to end. In addition to meeting privacy and security requirements, our proposal is also resistant to a wide range of potential security breaches. Finally, the Chebyshev polynomial is used in our work to sign the message and verify the signature, resulting in a greater performance cost efficiency than would otherwise be possible if an elliptic curve or bilinear pair operation had been employed

Applying a Hydrophilic Modified Hollow Fiber Membrane to Reduce Fouling in Artificial Lungs

Membranes for use in high gas exchange lung applications are riddled with fouling. The goal of this research is to create a membrane that can function in an artificial lung until the actual lung becomes available for the patient. The design of the artificial lung is based on new hollow fiber membranes (HFMs), due to which the current devices have short and limited periods of low fouling. By successfully modifying membranes with attached peptoids, low fouling can be achieved for longer periods of time. Hydrophilic modification of porous polysulfone (PSF) membranes can be achieved gradually by polydopamine (PSU-PDA) and peptoid (PSU-PDA-NMEG5). Polysulfone (PSU-BSA-35Mg), polysulfone polydopamine (PSUPDA-BSA-35Mg) and polysulfone polydopamine peptoid (PSU-PDA-NMEG5-BSA35Mg) were tested by potting into the new design of gas exchange modules. Both surfaces of the modified membranes were found to be highly resistant to protein fouling permanently. The use of different peptoids can facilitate optimization of the low fouling on the membrane surface, thereby allowing membranes to be run for significantly longer time periods than has been currently achieved

Protein structural model selection by combining consensus and single scoring methods.

Quality assessment (QA) for predicted protein structural models is an important and challenging research problem in protein structure prediction. Consensus Global Distance Test (CGDT) methods assess each decoy (predicted structural model) based on its structural similarity to all others in a decoy set and has been proved to work well when good decoys are in a majority cluster. Scoring functions evaluate each single decoy based on its structural properties. Both methods have their merits and limitations. In this paper, we present a novel method called PWCom, which consists of two neural networks sequentially to combine CGDT and single model scoring methods such as RW, DDFire and OPUS-Ca. Specifically, for every pair of decoys, the difference of the corresponding feature vectors is input to the first neural network which enables one to predict whether the decoy-pair are significantly different in terms of their GDT scores to the native. If yes, the second neural network is used to decide which one of the two is closer to the native structure. The quality score for each decoy in the pool is based on the number of winning times during the pairwise comparisons. Test results on three benchmark datasets from different model generation methods showed that PWCom significantly improves over consensus GDT and single scoring methods. The QA server (MUFOLD-Server) applying this method in CASP 10 QA category was ranked the second place in terms of Pearson and Spearman correlation performance

Performance Analysis of Adopting FSO Technology for Wireless Data Center Network

Free Space Optical Communication (FSO) is a promising technology to address wired Data Center Network (DCN) challenges like power consumption, low scalability and flexibility, congestion and cabling. Scholars have developed indirect line-of-sight (LoS) FSO schemes by reflecting the FSO beams via switchable mirrors. These schemes have introduced extra overhead delay to establish indirect LoS links, defined herein as the rack-to-rack FSO link setup process. The purpose of this work is to study and model this setup process with the consideration of the DC workloads. We found that the process involves a sequence of i.i.d random variables that contribute differently to its delay. Also, the process shows a statistical characteristic close to M/M/K. However, the number of FSO links, K, is random with time, which necessitates careful modeling. Finally, the PDF of the process total response time is close to the hypoexponential distribution, and it maintains its main characteristics even with different distributions for the service time

A Comprehensive Review of Aminochalcones

Chalcones, members of the flavonoid family, display a plethora of interesting biological activities including but not limited to antioxidant, anticancer, antimicrobial, anti-inflammatory, and antiprotozoal activities. The literature cites the synthesis and activity of a range of natural, semisynthetic, and synthetic chalcones. The current review comprehensively covers the literature on amino-substituted chalcones and includes chalcones with amino-groups at various positions on the aromatic rings as well as those with amino-groups containing mono alkylation, dialkylation, alkenylation, acylation, and sulfonylation. The aminochalcones are categorized according to their structure, and the corresponding biological activities are discussed as well. Some compounds showed high potency against cancer cells, microbes, and malaria, whereas others did not. The purpose of this review is to serve as a one-stop location for information on the aminochalcones reported in the literature in recent years

Comparison of 1NE3 from benchmark 1.

<p>Comparison of 1NE3 from benchmark 1.</p

Performance on benchmark 3 with 4 features.

<p>Performance on benchmark 3 with 4 features.</p