Biomedical subjects

Jinbo Xu

Publications and source records attributed to Jinbo Xu.

9 recordsLinked to original sources

PubMed2006

Optimizing multiple spaced seeds for homology search.

Optimized spaced seeds improve sensitivity and specificity in local homology search. Several authors have shown that multiple seeds can have better sensitivity and specificity than single seeds. We describe a linear programming (LP)-based algorithm to optimize a set of seeds. Theoretically, our algorithm offers a performance guarantee: the sensitivity of a chosen seed set is at least 70% of what can be achieved, in most reasonable models of homologous sequences. In practice, our algorithm generates a solution which is at least 90% of the optimal. Our method not only achieves performance better than or comparable to that of a greedy algorithm, but also gives this area a mathematical foundation.

Algorithms↗

PubMed2006

Struct2net: integrating structure into protein-protein interaction prediction.

UNLABELLED: This paper presents a framework for predicting protein-protein interactions (PPI) that integrates structure-based information with other functional annotations, e.g. GO, co-expression and co-localization, etc., Given two protein sequences, the structure-based interaction prediction technique threads these two sequences to all the protein complexes in the PDB and then chooses the best potential match. Based on this match, structural information is incorporated into logistic regression to evaluate the probability of these two proteins interacting. This paper also describes a random forest classifier which can effectively combine the structure-based prediction results and other functional annotations together to predict protein interactions. Experimental results indicate that the predictive power of the structure-based method is better than many other information sources. Also, combining the structure-based method with other information sources allows us to achieve a better performance than when structure information is not used. We also tested our method on a set of approximately 1000 yeast genes and, interestingly, the predicted interaction network is a scale-free network. Our method predicted some potential interactions involving yeast homologs of human disease-related proteins. SUPPLEMENTARY INFORMATION: http://theory.csail.mit.edu/struct2net

Algorithms↗

PubMed2005

A tree-decomposition approach to protein structure prediction.

This paper proposes a tree decomposition of protein structures, which can be used to efficiently solve two key subproblems of protein structure prediction: protein threading for backbone prediction and protein side-chain prediction. To develop a unified tree-decomposition based approach to these two subproblems, we model them as a geometric neighborhood graph labeling problem. Theoretically, we can have a low-degree polynomial time algorithm to decompose a geometric neighborhood graph G = (V, E) into components with size O(|V|((2/3))log|V|). The computational complexity of the tree-decomposition based graph labeling algorithms is O(|V|Delta(tw+1)) where Delta is the average number of possible labels for each vertex and tw( = O(|V|((2/3))log|V|)) the tree width of G. Empirically, tw is very small and the tree-decomposition method can solve these two problems very efficiently. This paper also compares the computational efficiency of the tree-decomposition approach with the linear programming approach to these two problems and identifies the condition under which the tree-decomposition approach is more efficient than the linear programming approach. Experimental result indicates that the tree-decomposition approach is more efficient most of the time.

Algorithms↗

PubMed2005

Discovering sequence-structure motifs from protein segments and two applications.

We present a novel method for clustering short protein segments having strong sequence-structure correlations, and demonstrate that these clusters contain useful structural information via two applications. When applied to local tertiary structure prediction, we achieve approximately 60% accuracy with a novel dynamic programming algorithm. When applied to secondary structure prediction based on Support Vector Machines, we obtain a approximately 2% gain in Q3 performance by incorporating cluster-derived data into training and classification. These encouraging results illustrate the great potential of using conserved local motifs to tackle protein structure predictions and possibly other important problems in biology.

Algorithms↗

PubMed2003

Assessment of RAPTOR's linear programming approach in CAFASP3.

We have developed a new algorithm based on the mathematical theory of linear programming (LP) and implemented it in our program RAPTOR. Our new approach provides an elegant formulation of the protein-threading problem, overcomes the intractability problem of protein threading, in practice, and allows us to use existing powerful linear programming software to obtain optimal protein threading solutions. CASP5 and CAFASP3 gave us the first chance to test RAPTOR in an unbiased way. RAPTOR was ranked as the top individual (automatic) server for fold recognition by the CAFASP3 organizers. In this short article, we describe RAPTOR's LP formulation, assess RAPTOR's performance in CAFASP3/CASP5, explain why it has superceded other existing automatic individual methods, and point out its strengths, limitations, extensions, and prospects for improvement.

Algorithms↗

PubMed2003

RAPTOR: optimal protein threading by linear programming.

This paper presents a novel linear programming approach to do protein 3-dimensional (3D) structure prediction via threading. Based on the contact map graph of the protein 3D structure template, the protein threading problem is formulated as a large scale integer programming (IP) problem. The IP formulation is then relaxed to a linear programming (LP) problem, and then solved by the canonical branch-and-bound method. The final solution is globally optimal with respect to energy functions. In particular, our energy function includes pairwise interaction preferences and allowing variable gaps which are two key factors in making the protein threading problem NP-hard. A surprising result is that, most of the time, the relaxed linear programs generate integral solutions directly. Our algorithm has been implemented as a software package RAPTOR-RApid Protein Threading by Operation Research technique. Large scale benchmark test for fold recognition shows that RAPTOR significantly outperforms other programs at the fold similarity level. The CAFASP3 evaluation, a blind and public test by the protein structure prediction community, ranks RAPTOR as top 1, among individual prediction servers, in terms of the recognition capability and alignment accuracy for Fold Recognition (FR) family targets. RAPTOR also performs very well in recognizing the hard Homology Modeling (HM) targets. RAPTOR was implemented at the University of Waterloo and it can be accessed at http://www.cs.uwaterloo.ca/~j3xu/RAPTOR_form.htm.

Algorithms↗

PubMed2003

Protein threading by linear programming.

Protein three-dimensional structure prediction through threading approach has been extensively studied and various models and algorithms have been proposed. In order to further explore ways to improve accuracy and efficiency of the threading process, this paper investigates the effectiveness of a new method: protein threading via linear programming. Based on the contact map model of protein 3D structure, we formulate the protein threading problem as a large scale integer programming problem, then relax to a linear programming problem, and finally solve the integer program by a branch-and-bound method. The final solution is optimal with respect to energy functions incorporating pairwise interaction and allowing variable gaps. The algorithm has been implemented as software package RAPTOR--RApid Protein Threading predictOR. Experimental results for fold recognition show that RAPTOR significantly outperforms other programs at the fold similarity level. The RAPTOR webserver is at http://www.cs.uwaterloo.ca/-j3xu/RAPTOR_form.htm.

Algorithms↗

PubMed2002

An anti-CD45RO immunotoxin kills latently infected human immunodeficiency virus (HIV) CD4 T cells in the blood of HIV-positive persons.

Highly active antiretroviral therapy has decreased the morbidity and mortality of human immunodeficiency virus (HIV) infection, but latently infected cells remain for prolonged periods. CD4(+) CD45RO(+) T cells are a major latent virus reservoir in HIV-infected persons. Replication-competent, latently HIV-infected T cells can be generated in vitro by infecting peripheral blood mononuclear cells with HIV and then eliminating the HIV-producing cells with an anti-CD25 immunotoxin (IT). The CD25(-) latently infected cells then can be eliminated with an anti-CD45RO IT. This study determined whether this IT also could kill latently infected CD4 T cells from HIV-infected persons with or without detectable plasma viremia. The results show that ex vivo treatment of cells from HIV-positive persons by anti-CD45RO IT reduces the frequency of both productively and latently infected cells. In contrast, CD4(+) CD45RA(+) naive T cells and a proportion of CD4(+) CD45RO(lo) memory T cells are spared.

Antiretroviral Therapy, Highly Active↗

PubMedDate not supplied

Fold recognition by predicted alignment accuracy.

One of the key components in protein structure prediction by protein threading technique is to choose the best overall template for a given target sequence after all the optimal sequence-template alignments are generated. The chosen template should have the best alignment with the target sequence since the three-dimensional structure of the target sequence is built on the sequence-template alignment. The traditional method for template selection is called Z-score, which uses a statistical test to rank all the sequence-template alignments and then chooses the first-ranked template for the sequence. However, the calculation of Z-score is time-consuming and not suitable for genome-scale structure prediction. Z-scores are also hard to interpret when the threading scoring function is the weighted sum of several energy items of different physical meanings. This paper presents a Support Vector Machine (SVM) regression approach to directly predict the alignment accuracy of a sequence-template alignment, which is used to rank all the templates for a specific target sequence. Experimental results on a large-scale benchmark demonstrate that SVM regression performs much better than the composition-corrected Z-score method. SVM regression also runs much faster than the Z-score method.

Algorithms↗