LEAP: highly accurate prediction of protein loop conformations by integrating coarse-grained sampling and optimized energy scores with all-atom refinement of backbone and side chains

Prediction of protein loop conformations without any prior knowledge (ab initio prediction) is an unsolved problem. Its solution will significantly impact protein homology and template-based modeling as well as ab initio protein-structure prediction. Here, we developed a coarse-grained, optimized scoring function for initial sampling and ranking of loop decoys. The resulting decoys are then further optimized in backbone and side-chain conformations and ranked by all-atom energy scoring functions. The final integrated technique called loop prediction by energy-assisted protocol achieved a median value of 2.1 Å root mean square deviation (RMSD) for 325 12-residue test loops and 2.0 Å RMSD for 45 12-residue loops from critical assessment of structure-prediction techniques (CASP) 10 target proteins with native core structures (backbone and side chains). If all side-chain conformations in protein cores were predicted in the absence of the target loop, loop-prediction accuracy only reduces slightly (0.2 Å difference in RMSD for 12-residue loops in the CASP target proteins). The accuracy obtained is about 1 Å RMSD or more improvement over other methods we tested. The executable file for a Linux system is freely available for academic users at http://sparks-lab.org

Studies on the degraduation of wood sawdust by Lentinus squarrosulus (Mont.) Singer

Lentinus squarrosulus (Mont.) Singer, a basidiomycete also known as a white rot fungi, was immobilized on sodium alginate and tested for the effectiveness to degrade wood sawdust (WSD). Untreated and 0.1 M HCl-pretreated WSD samples were separately reacted in a micro-carrier bioreactor (mCBR) and the extent of degradation to form protein, glucose and ethanol was investigated. Pretreatment enhanced the production of both proteins and ethanol by average value of 72.0% over untreated WSD samples, after hydraulic retention time of 72 h. The maximum production of protein observed was 0.94 mg/ml-reaction volume and that of ethanol was 6.6 mg/ml-reaction volume, whereas glucose concentration fluctuated due to interconversion to ethanol. This report shows that L. squarrosulus (Mont.) Singer have the potentials of degrading WSD samples to important chemical compounds that are not hazardous to the environment. Key words: Lentinus squarrosulus, wood sawdust, sodium alginate, bioreactor, ethanol. African Journal of Biotechnology Vol.3(8) 2004: 395-39

Protein binding site prediction using an empirical scoring function

Most biological processes are mediated by interactions between proteins and their interacting partners including proteins, nucleic acids and small molecules. This work establishes a method called PINUP for binding site prediction of monomeric proteins. With only two weight parameters to optimize, PINUP produces not only 42.2% coverage of actual interfaces (percentage of correctly predicted interface residues in actual interface residues) but also 44.5% accuracy in predicted interfaces (percentage of correctly predicted interface residues in the predicted interface residues) in a cross validation using a 57-protein dataset. By comparison, the expected accuracy via random prediction (percentage of actual interface residues in surface residues) is only 15%. The binding sites of the 57-protein set are found to be easier to predict than that of an independent test set of 68 proteins. The average coverage and accuracy for this independent test set are 30.5 and 29.4%, respectively. The significant gain of PINUP over expected random prediction is attributed to (i) effective residue-energy score and accessible-surface-area-dependent interface-propensity, (ii) isolation of functional constraints contained in the conservation score from the structural constraints through the combination of residue-energy score (for structural constraints) and conservation score and (iii) a consensus region built on top-ranked initial patches

<ORIGINAL>Appearance and distribution of osteoclast precursors and the morphological change during mouse mandibular osteogenesis

The appearance and distribution of osteoclast precursors and the morphological change of the precursors were examined during mouse mandibular osteogenesis, using enzyme histochemistry of tartrate-resistant acid phosphatase (TRAPase). Osteogenic tissue was not observed in the prospective region of the mandibles at embryonic day 12 (E12), but TRAPase-positive cells often existed in the region. Immature osteoblasts were seen as a population in E13 mandibles, and a thin layer of bone matrix had been formed at the central part of the osteogenic region in E14 mandibles. A number of TRAPase-positive osteoclast precursors were tandemly localized along the region. At these stages, the TRAPase-positive cells were oval and round in the vicinity of blood vessels, but at an earlier stage of the osteogenesis, the positive cells extended long processes towards the interspace between the osteoblasts. The present results demonstrated the morphology characteristic of the TRAPase-positive osteoclast precursors during osteoclast differentiation, suggesting the possibility that there is a cell-cell interaction between the osteoclast precursors and the osteoblasts in vivo

Performance Analysis of Simulation-based Multi-objective Optimization of Bridge Construction Processes Using High Performance Computing

Bridges constitute a crucial component of urban highways due to the complexity and uncertain nature of their construction process. Simulation is an alternative method of analyzing and planning the construction processes, especially the ones with repetitive and cyclic nature, and it helps managers to make appropriate decisions. Furthermore, there is an inverse relationship between the cost and time of a project and finding a proper trade-off between these two key elements using optimization methods is important. Thus, the integration of simulation models with optimization techniques leads to an advancement in the decision making process. In addition, the large number of resources required in complex and large scale bridge construction projects results in a very large search space. Therefore, there is a need for using parallel computing in order to reduce the computational time of the simulation-based optimization. Most of the construction simulation tools need an integration platform to be combined with optimization techniques. Also, these simulation tools are not usually compatible with Linux environment which is used in most of the massive parallel computing systems or clusters. In this research, an integrated simulation-based optimization framework is proposed within one platform to alleviate those limitations. A master-slave (or global) parallel Genetic Algorithm (GA) is used as a parallel computing technique to decrease the computation time and to efficiently use the full capacity of the computer. In addition, sensitivity analysis is applied to identify the promising configuration for GA and analyzing the impact of GA parameters on the overall performance of the specific simulation-based optimization problem used in this research. Finally, a case study is implemented and tested on a server machine as well as a cluster to explore the feasibility of the proposed approach. The results of this research showed better performance of the proposed framework in comparison with other GA optimization techniques from the points of view of the quality of the optimum solutions and the computation time. Also, acceptable improvements in the computation time were achieved for both deterministic and probabilistic simulation models using master-salve parallel paradigm (8.32 and 20.3 times speedups were achieved using 12 cores, respectively). Moreover, performing the proposed framework on multiple nodes using a cluster system led to 31% saving on the computation time on average. Furthermore, the GA was tuned using sensitivity analyses which resulted in the best parameters (500 generations, population size of 200 and 0.7 as the crossover probability)

SVMTriP: A Method to Predict Antigenic Epitopes Using Support Vector Machine to Integrate Tri-Peptide Similarity and Propensity

Identifying protein surface regions preferentially recognizable by antibodies (antigenic epitopes) is at the heart of new immuno-diagnostic reagent discovery and vaccine design, and computational methods for antigenic epitope prediction provide crucial means to serve this purpose. Many linear B-cell epitope prediction methods were developed, such as BepiPred, ABCPred, AAP, BCPred, BayesB, BEOracle/BROracle, and BEST, towards this goal. However, effective immunological research demands more robust performance of the prediction method than what the current algorithms could provide. In this work, a new method to predict linear antigenic epitopes is developed; Support Vector Machine has been utilized by combining the Tri-peptide similarity and Propensity scores (SVMTriP). Applied to non-redundant B-cell linear epitopes extracted from IEDB, SVMTriP achieves a sensitivity of 80.1% and a precision of 55.2% with a five-fold cross-validation. The AUC value is 0.702. The combination of similarity and propensity of tri-peptide subsequences can improve the prediction performance for linear B-cell epitopes. Moreover, SVMTriP is capable of recognizing viral peptides from a human protein sequence background. A web server based on our method is constructed for public use. The server and all datasets used in the current study are available at http://sysbio.unl.edu/SVMTriP

Simulation-Based Optimization of Energy Consumption and Occupants Comfort in Open-Plan Office Buildings Using Probabilistic Occupancy Prediction Model

Considering the ever-growing increase in the world energy consumption and the fact that buildings contribute a large portion of the global energy consumption arises a need for detailed investigation towards more effective energy performance of buildings. Thus, monitoring, estimating, and reducing buildings’ energy consumption have always been important concerns for researchers and practitioners in the field of energy management. Since more than 80% of energy consumption happens during the operation phase of a building’s life cycle, efficient management of building operation is a promising way to reduce energy usage in buildings. Among the parameters influencing the total building energy consumption, building occupants’ presence and preferences could have high impacts on the energy usage of a building. To consider the effect of occupancy on building energy performance, different occupancy models, which aim to estimate the space utilization patterns, have been developed by researches. However, providing a comprehensive occupancy model, which could capture all important occupancy features, is still under development. Moreover, researchers investigated the effect of the application of occupancy-centered control strategies on the efficiency of the energy-consuming systems. However, there are still many challenges in this area of research mainly related to collecting, processing, and analyzing the occupancy data and the application of intelligent control strategies. In addition, generally, there is an inverse relationship between the energy consumption of operational systems and the comfort level of occupants using these systems. As a result, finding a balance between these two important concepts is crucial to improve the building operation. The optimal operation of building energy-consuming systems is a complex procedure for decision-makers, especially in terms of minimizing the energy cost and the occupants’ discomfort. On this premise, this research aims to develop a new simulation-based multi-objective optimization model of the energy consumption in open-plan offices based on occupancy dynamic profiles and occupants’ preferences and has the following objectives: (1) developing a method for extracting detailed occupancy information with varying time-steps from collected Real-Time Locating System (RTLS) occupancy data. This method captures different resolution levels required for the application of intelligent, occupancy-centered local control strategies of different building systems; (2) developing a new time-dependent inhomogeneous Markov chain occupancy prediction model based on the derived occupancy information, which distinguishes the temporal behavior of different occupants within an open-plan office; (3) improving the performance of the developed occupancy prediction model by determining the near-optimum length of the data collection period, selecting the near-optimum training dataset, and finding the most satisfying temporal resolution level for analyzing the occupancy data; (4) developing local control algorithms for building energy-consuming systems; and (5) integrating the energy simulation model of an open-plan office with an optimization algorithm to optimally control the building energy-consuming systems and to analyze the trade-off between building energy consumption and occupants’ comfort. It is found that the occupancy perdition model is able to estimate occupancy patterns of the open-plan office with 92% and 86% accuracy at occupant and zone levels, respectively. Also, the proposed integrated model improves the thermal condition by 50% along with 2% savings in energy consumption by developing intelligent, optimal, and occupancy-centered local control strategies

Conformational B-Cell Epitope Prediction on Antigen Protein Structures: A Review of Current Algorithms and Comparison with Common Binding Site Prediction Methods

Accurate prediction of B-cell antigenic epitopes is important for immunologic research and medical applications, but compared with other bioinformatic problems, antigenic epitope prediction is more challenging because of the extreme variability of antigenic epitopes, where the paratope on the antibody binds specifically to a given epitope with high precision. In spite of the continuing efforts in the past decade, the problem remains unsolved and therefore still attracts a lot of attention from bioinformaticists. Recently, several discontinuous epitope prediction servers became available, and it is intriguing to review all existing methods and evaluate their performances on the same benchmark. In addition, these methods are also compared against common binding site prediction algorithms, since they have been frequently used as substitutes in the absence of good epitope prediction methods