20361854
Protein Metal Binding Residue Prediction Based on Neural Networks
Over one-third of protein structures contain metal ions, which are the necessary elements in life systems. Traditionally, structural biologists were used to investigate properties of metalloproteins (proteins which bind with metal ions) by physical means and interpreting the function formation and reaction mechanism of enzyme by their structures and observations from experiments in vitro. Most of proteins have primary structures (amino acid sequence information) only; however, the 3-dimension structures are not always available. In this paper, a direct analysis method is proposed to predict the protein metal-binding amino acid residues from its sequence information only by neural networks with sliding window-based feature extraction and biological feature encoding techniques. In four major bulk elements (Calcium, Potassium, Magnesium, and Sodium), the metal-binding residues are identified by the proposed method with higher than 90% sensitivity and very good accuracy under 5-fold cross validation. With such promising results, it can be extended and used as a powerful methodology for metal-binding characterization from rapidly increasing protein sequences in the future.
5848136
Protein metal binding residue prediction based on neural networks
Over one-third of protein structures contain metal ions, which are the necessary elements in life systems. Traditionally, structural biologists were used to investigate properties of metalloproteins (proteins which bind with metal ions) by physical means and interpreting the function formation and reaction mechanism of enzyme by their structures and observations from experiments in vitro. Most of proteins have primary structures (amino acid sequence information) only; however, the 3-dimension structures are not always available. In this paper, a direct analysis method is proposed to predict the protein metal-binding amino acid residues from its sequence information only by neural networks with sliding window-based feature extraction and biological feature encoding techniques. In four major bulk elements (Calcium, Potassium, Magnesium, and Sodium), the metal-binding residues are identified by the proposed method with higher than 90% sensitivity and very good accuracy under 5-fold cross validation. With such promising results, it can be extended and used as a powerful methodology for metal-binding characterization from rapidly increasing protein sequences in the future.
14104203
SM: real-time multicast protocols for simultaneous message delivery
Traditionally, a multicast message is sent once by a sender. All receivers will receive the same message but may receive it at different times. This may result in unfair advantage for some receivers. Simultaneous Multicast (SM) applications require that all receivers receive the same message at the same time. SM protocols therefore need to meet a strict deadline to ensure that each receiver will receive a message at the same time, regardless of the network conditions, heterogeneity of the environment and differences between the local clocks in the receivers. Such a real time SM protocol is proposed so that the coordinated deadline can be met by controlling the reserved bandwidth and without the overhead of clock synchronization. A sensing protocol is also proposed to continuously search for better levels of quality and to avoid the overhead of an external feedback protocol. The protocols are bandwidth conscious, enforce fairness and provide scalability.
1161117
Fast Similarity Search in the Presence of Noise, Scaling, and Translation in Time-Series Databases
We introduce a new model of similarity of time sequences that captures the intuitive notion that two sequences should be considered similar if they have enough non-overlapping time-ordered pairs of subsequences thar are similar. The model allows the amplitude of one of the two sequences to be scaled by any suitable amount and its offset adjusted appropriately. Two subsequences are considered similar if one can be enclosed within an envelope of a specified width drawn around the other. The model also allows non-matching gaps in the matching subsequences. The matching subsequences need not be aligned along the time axis. Given this model of similarity, we present fast search techniques for discovering all similar sequences in a set of sequences. These techniques can also be used to find all (sub)sequences similar to a given sequence. We applied this matching system to the U.S. mutual funds data and discovered interesting matches.
18826501
Supporting Fault-Tolerant Real-Time Applications Using the RED-Linux General Scheduling Framework
In this paper, w e study the fault-tolerant support for real-time applications. In particular, we study the scheduling issues and kernel support for fault monitors and the primary-backup task model. Using the pow erful sc heduling framework in RED-Linux, we can support a jitter-less fault monitoring. We can also provide the task execution isolation so that an erroneous runaw ay task will not take aw ay additional CPU budget from other concurrently running tasks. Finally, we provide a group mechanism to allow the primary and backup jobs of a fault-tolerant task to share both the CPU budget as well as other resources. All these mechanisms make the implementation of fault-tolerant real-time systems easier.
4707602
An initial strategy for comparing proteins at the domain architecture level
MOTIVATION
Ideally, only proteins that exhibit highly similar domain architectures should be compared with one another as homologues or be classified into a single family. By combining three different indices, the Jaccard index, the Goodman-Kruskal gamma function and the domain duplicate index, into a single similarity measure, we propose a method for comparing proteins based on their domain architectures.


RESULTS
Evaluation of the method using the eukaryotic orthologous groups of proteins (KOGs) database indicated that it allows the automatic and efficient comparison of multiple-domain proteins, which are usually refractory to classic approaches based on sequence similarity measures. As a case study, the PDZ and LRR_1 domains are used to demonstrate how proteins containing promiscuous domains can be clearly compared using our method. For the convenience of users, a web server was set up where three different query interfaces were implemented to compare different domain architectures or proteins with domain(s), and to identify the relationships among domain architectures within a given KOG from the Clusters of Orthologous Groups of Proteins database.


CONCLUSION
The approach we propose is suitable for estimating the similarity of domain architectures of proteins, especially those of multidomain proteins.


AVAILABILITY
http://cmb.bnu.edu.cn/pdart/.
13196530
Amino Acid Substitution Matrices from an Artificial Neural Network Model
An amino acid substitution matrix specifies probabilities of substitutions for each pair of the 20 amino acids. Log-odds scores transformed from the values in substitution matrices are widely used to construct protein sequence alignments. Any given substitution matrix is suited to matching sequences diverged by a specific evolutionary distance. However, for a given set of sequences, it is not always clear what matrix should be used. We used an artificial neural network model to predict probabilities of amino acid substitutions with alignment samples of different evolutionary distances. From this internal description, substitution matrices suitable for detecting relationships at any chosen evolutionary distance can be instantly generated. By using the additional information of evolutionary distances, the average cross entropy error of our neural network model is lower than that of a series of BLOSUM and PET matrices over all testing sets. Our model is more accurate on the prediction of amino acid substitution probabilities.