Youxing Qu

Quantitative evaluation of protein–DNA interactions using an optimized knowledge-based potential

Computational evaluation of protein–DNA interaction is important for the identification of DNA-binding sites and genome annotation. It could validate the predicted binding motifs by sequence-based approaches through the calculation of the binding affinity between a protein and DNA. Such an evaluation should take into account structural information to deal with the complicated effects from DNA structural deformation, distance-dependent multi-body interactions and solvation contributions. In this paper, we present a knowledge-based potential built on interactions between protein residues and DNA tri-nucleotides. The potential, which explicitly considers the distance-dependent two-body, three-body and four-body interactions between protein residues and DNA nucleotides, has been optimized in terms of a Z-score. We have applied this knowledge-based potential to evaluate the binding affinities of zinc-finger protein–DNA complexes. The predicted binding affinities are in good agreement with the experimental data (with a correlation coefficient of 0.950). On a larger test set containing 48 protein–DNA complexes with known experimental binding free energies, our potential has achieved a high correlation coefficient of 0.800, when compared with the experimental data. We have also used this potential to identify binding motifs in DNA sequences of transcription factors (TF). The TFs in 79.4% of the known TF–DNA complexes have accurately found their native binding sequences from a large pool of DNA sequences. When tested in a genome-scale search for TF-binding motifs of the cyclic AMP regulatory protein (CRP) of Escherichia coli, this potential ranks all known binding motifs of CRP in the top 15% of all candidate sequences.

Structure determination of a new protein from backbone-centered NMR data and NMR-assisted structure prediction

Targeting of proteins for structure determination in structural genomic programs often includes the use of threading and fold recognition methods to exclude proteins belonging to well‐populated fold families, but such methods can still fail to recognize preexisting folds. The authors illustrate here a method in which limited amounts of structural data are used to improve an initial homology search and the data are subsequently used to produce a structure by data‐constrained refinement of an identified structural template. The data used are primarily NMR‐based residual dipolar couplings, but they also include additional chemical shift and backbone‐nuclear Overhauser effect data. Using this methodology, a backbone structure was efficiently produced for a 10 kDa protein (PF1455) from Pyrococcus furiosus. Its relationship to existing structures and its probable function are discussed. Proteins 2006.

PRIME: A Mass Spectrum Data Mining Tool for De Nova Sequencing and PTMs Identification

Abstractsequencing is one of the most promising proteomics techniques for identification of protein post-translation modifications (PTMs) in studying protein regulations and functions. We have developed a computer tool PRIME for identification of b and y ions in tandem mass spectra, a key challenging problem in de novo sequencing. PRIME utilizes a feature that ions of the same and different types follow different mass-difference distributions to separate b from y ions correctly. We have formulated the problem as a graph partition problem. A linear integer-programming algorithm has been implemented to solve the graph partition problem rigorously and efficiently. The performance of PRIME has been demonstrated on a large amount of simulated tandem mass spectra derived from Yeast genome and its power of detecting PTMs has been tested on 216 simulated phosphopeptides.

Protein fold recognition through application of residual dipolar coupling data.

Residual dipolar coupling (RDC) represents one of the most exciting emerging NMR techniques for studying protein structures. However, solving a protein structure using RDC data alone is a highly challenging problem as it often requires that the starting structure model be close to the actual structure of a protein, for the structure calculation procedure to be effective. We report in this paper a computer program, RDC-PROSPECT, for identification of a structural homolog or analog of a target protein in PDB, which best matches the 15N-1H RDC data of the protein recorded in a single ordering medium. The identified structural homolog/analog can then be used as a starting model for RDC-based structure calculation. Since RDC-PROSPECT uses only RDC data and predicted secondary structure information, its performance is virtually independent of sequence similarity between a target protein and its structural homolog/analog, making it applicable to protein targets out of the scope of current protein threading techniques. We have tested RDC-PROSPECT on all 15N-1H RDC data (representing 33 proteins) available in the BMRB database and the literature. The program correctly identified the structural folds for approximately 80% of the target proteins, significantly better than previously reported results, and achieved an average alignment accuracy of 97.9% residues within 4-residue shift. Through a careful algorithmic design, RDC-PROSPECT is at least one order of magnitude faster than previously reported algorithms for principal alignment frame search, making our algorithm fast enough for large-scale applications.