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

SP5: Improving Protein Fold Recognition by Using Torsion Angle Profiles and Profile-Based Gap Penalty Model

How to recognize the structural fold of a protein is one of the challenges in protein structure prediction. We have developed a series of single (non-consensus) methods (SPARKS, SP2, SP3, SP4) that are based on weighted matching of two to four sequence and structure-based profiles. There is a robust improvement of the accuracy and sensitivity of fold recognition as the number of matching profiles increases. Here, we introduce a new profile-profile comparison term based on real-value dihedral torsion angles. Together with updated real-value solvent accessibility profile and a new variable gap-penalty model based on fractional power of insertion/deletion profiles, the new method (SP5) leads to a robust improvement over previous SP method. There is a 2% absolute increase (5% relative improvement) in alignment accuracy over SP4 based on two independent benchmarks. Moreover, SP5 makes 7% absolute increase (22% relative improvement) in success rate of recognizing correct structural folds, and 32% relative improvement in model accuracy of models within the same fold in Lindahl benchmark. In addition, modeling accuracy of top-1 ranked models is improved by 12% over SP4 for the difficult targets in CASP 7 test set. These results highlight the importance of harnessing predicted structural properties in challenging remote-homolog recognition. The SP5 server is available at http://sparks.informatics.iupui.edu

Web-based toolkits for topology prediction of transmembrane helical proteins, fold recognition, structure and binding scoring, folding-kinetics analysis and comparative analysis of domain combinations

We have developed the following web servers for protein structural modeling and analysis at http:// theory.med.buffalo.edu: THUMBUP, UMDHMMTMHP and TUPS, predictors of trans-membrane helical protein topology based on a mean-burial-propensity scale of amino acid residues (THUMBUP), hidden Markov model (UMDHMMTMHP) and their combinations (TUPS); SPARKS 2.0 and SP3, two profile– profile alignment methods, that match input query sequence(s) to structural templates by integrating sequence profile with knowledge-based structural score (SPARKS 2.0) and structure-derived profile (SP3); DFIRE, a knowledge-based potential for scoring free energy of monomers (DMONOMER), loop conformations (DLOOP), mutant stability (DMUTANT) and binding affinity of protein–protein/ peptide/DNA complexes (DCOMPLEX & DDNA); TCD, a program for protein-folding rate and transition-state analysis of small globular proteins; and DOGMA, a web-server that allows comparative analysis of domain combinations between plant and other 55 organisms. These servers provide tools for prediction and/or analysis of proteins on the secondary structure, tertiary structure and interaction levels, respectively

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

Logging Crew Attributes by Region in the Southeast USA

Urbanization, shrinking markets, and reduced forestry investment may affect harvesting efficiency in regions of the US South. To monitor these conditions, logging businesses have been tracked by surveys conducted by universities and trade associations. This project used a sampling approach coordinated with FIA utilization studies to sample logging crews based on a harvesting location. The approach was used to develop relationships among firm attributes and site attributes in six southeastern states (AL, GA, FL, NC, SC, and VA) from 2011 to 2018. The data included harvest attributes (location, harvest size and stand type) and logging firm attributes (production, crew labor, crew number, the number of machines by type, and machine age). For crew capital value, an equation was developed for this study using machine number and average machine age. The data from logging crews on 419 harvests were analyzed by region, harvest size, and stand type. Mean values for crew labor ranged from 3.1 to 7.1 workers. The average capital value per crew ranged from $220,000 to$524,000 per crew in the Coastal Plain with a narrower range in the Piedmont. In the Coastal Plain, higher productivity was detected for larger harvests and pine versus hardwood and mixed stands; however, in the Piedmont those trends were less obvious. Ratio of feller-bunchers, skidders and loaders were mostly 1:1:1 or 1:2:1 with 41% and 24% of samples, respectively. There were notable trends among Coastal Plain loggers regarding capital value and productivity with evidence supported by a production function. The differences in Piedmont (e.g., ownership size, market access, terrain, population density, etc.) may combine to limit daily production and labor productivity

Web-based toolkits for topology prediction of transmembrane helical proteins, fold recognition, structure and binding scoring, folding-kinetics analysis and comparative analysis of domain combinations

We have developed the following web servers for protein structural modeling and analysis at : THUMBUP, UMDHMM(TMHP) and TUPS, predictors of transmembrane helical protein topology based on a mean-burial-propensity scale of amino acid residues (THUMBUP), hidden Markov model (UMDHMM(TMHP)) and their combinations (TUPS); SPARKS 2.0 and SP(3), two profile–profile alignment methods, that match input query sequence(s) to structural templates by integrating sequence profile with knowledge-based structural score (SPARKS 2.0) and structure-derived profile (SP(3)); DFIRE, a knowledge-based potential for scoring free energy of monomers (DMONOMER), loop conformations (DLOOP), mutant stability (DMUTANT) and binding affinity of protein–protein/peptide/DNA complexes (DCOMPLEX & DDNA); TCD, a program for protein-folding rate and transition-state analysis of small globular proteins; and DOGMA, a web-server that allows comparative analysis of domain combinations between plant and other 55 organisms. These servers provide tools for prediction and/or analysis of proteins on the secondary structure, tertiary structure and interaction levels, respectively

Study on multi-axis sine vibration test control techniques

This paper describes several key aspects about multi-axis sine vibration test control techniques including the identification of the system frequency response function, synchronization of the input and output signals, the generation of the sinewave, the control algorithm, etc. A multi-axis sine vibration controller is developed based on these key techniques and the major framework of the controller is introduced. Finally, a dual axial experiment is carried out by the controller. The test results show the feasibility of the control algorithm and the good control strategy of the multi-axis sine vibration controller in which the key techniques are realized

Template-Based Structure Prediction and Classification of Transcription Factors in \u3ci\u3eArabidopsis thaliana\u3c/i\u3e

Transcription factors (TFs) play important roles in plants. However, there is no systematic study of their structures and functions of most TFs in plants. Here, we performed template-based structure prediction for all TFs in Arabidopsis thaliana, with their full-length sequences as well as C-terminal and N-terminal regions. A total of 2,918 model structures were obtained with a high confidence score. We find that TF families employ only a smaller number of templates for DNA-binding domains (DBD) but a diverse number of templates for transcription regulatory domains (TRD). Although TF families are classified according to DBD, their sizes have a significant correlation with the number of unique non-DNA-binding templates employed in the family (Pearson correlation coefficient of 0.74). That is, the size of TF family is related to its functional diversity. Network analysis reveals new connections between TF families based on shared TRD or DBD templates; 81% TF families share DBD and 67% share TRD templates. Two large fully connected family clusters in this network are observed along with 69 island families. In addition, 25 genes with unknown functions are found to be DNA-binding and/or TF factors according to predicted structures. This work provides a global view of the classification of TFs based on their DBD or TRD templates, and hence, a deeper understanding of DNA-binding and regulatory functions from structural perspective. All structural models of TFs are deposited in the online database for public usage at http://sysbio.unl.edu/AthTF