Browsing by Author "Liao, Li"
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Item Improving Inter-Helix Contact Prediction With Local 2D Topological Information(IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2023-05-08) Li, Jiefu; Sawhney, Aman; Lee, Jung-Youn; Liao, LiInter-helix contact prediction is to identify residue contact across different helices in α-helical integral membrane proteins. Despite the progress made by various computational methods, contact prediction remains as a challenging task, and there is no method to our knowledge that directly tap into the contact map in an alignment free manner. We build 2D contact models from an independent dataset to capture the topological patterns in the neighborhood of a residue pair depending it is a contact or not, and apply the models to the state-of-art method's predictions to extract the features reflecting 2D inter-helix contact patterns. A secondary classifier is trained on such features. Realizing that the achievable improvement is intrinsically hinged on the quality of original predictions, we devise a mechanism to deal with the issue by introducing, 1) partial discretization of original prediction scores to more effectively leverage useful information 2) fuzzy score to assess the quality of the original prediction to help with selecting the residue pairs where improvement is more achievable. The cross-validation results show that the prediction from our method outperforms other methods including the state-of-the-art method (DeepHelicon) by a notable degree even without using the refinement selection scheme. By applying the refinement selection scheme, our method outperforms the state-of-the-art method significantly in these selected sequences.Item Protein-protein interaction prediction based on multiple kernels and partial network with linear programming(BioMed Central, 2016-08-01) Huang, Lei; Liao, Li; Wu, Cathy H.; Lei Huang, Li Liao and Cathy H. Wu; Huang, Lei; Liao, Li; Wu, Cathy H.BACKGROUND: Prediction of de novo protein-protein interaction is a critical step toward reconstructing PPI networks, which is a central task in systems biology. Recent computational approaches have shifted from making PPI prediction based on individual pairs and single data source to leveraging complementary information from multiple heterogeneous data sources and partial network structure. However, how to quickly learn weights for heterogeneous data sources remains a challenge. In this work, we developed a method to infer de novo PPIs by combining multiple data sources represented in kernel format and obtaining optimal weights based on random walk over the existing partial networks. RESULTS: Our proposed method utilizes Barker algorithm and the training data to construct a transition matrix which constrains how a random walk would traverse the partial network. Multiple heterogeneous features for the proteins in the network are then combined into the form of weighted kernel fusion, which provides a new "adjacency matrix" for the whole network that may consist of disconnected components but is required to comply with the transition matrix on the training subnetwork. This requirement is met by adjusting the weights to minimize the element-wise difference between the transition matrix and the weighted kernels. The minimization problem is solved by linear programming. The weighted kernel fusion is then transformed to regularized Laplacian (RL) kernel to infer missing or new edges in the PPI network, which can potentially connect the previously disconnected components. CONCLUSIONS: The results on synthetic data demonstrated the soundness and robustness of the proposed algorithms under various conditions. And the results on real data show that the accuracies of PPI prediction for yeast data and human data measured as AUC are increased by up to 19 % and 11 % respectively, as compared to a control method without using optimal weights. Moreover, the weights learned by our method Weight Optimization by Linear Programming (WOLP) are very consistent with that learned by sampling, and can provide insights into the relations between PPIs and various feature kernel, thereby improving PPI prediction even for disconnected PPI networks.Item Targeting of plasmodesmal proteins requires unconventional signals(The Plant Cell, 2023-08-02) Luna, Gabriel Robles; Li, Jiefu; Wang, Xu; Liao, Li; Lee, Jung-YounEffective cellular signaling relies on precise spatial localization and dynamic interactions among proteins in specific subcellular compartments or niches, such as cell-to-cell contact sites and junctions. In plants, endogenous and pathogenic proteins gained the ability to target plasmodesmata, membrane-lined cytoplasmic connections, through evolution to regulate or exploit cellular signaling across cell wall boundaries. For example, the receptor-like membrane protein PLASMODESMATA-LOCATED PROTEIN 5 (PDLP5), a potent regulator of plasmodesmal permeability, generates feed-forward or feed-back signals important for plant immunity and root development. However, the molecular features that determine the plasmodesmal association of PDLP5 or other proteins remain largely unknown, and no protein motifs have been identified as plasmodesmal targeting signals. Here, we developed an approach combining custom-built machine-learning algorithms and targeted mutagenesis to examine PDLP5 in Arabidopsis thaliana and Nicotiana benthamiana. We report that PDLP5 and its closely related proteins carry unconventional targeting signals consisting of short stretches of amino acids. PDLP5 contains 2 divergent, tandemly arranged signals, either of which is sufficient for localization and biological function in regulating viral movement through plasmodesmata. Notably, plasmodesmal targeting signals exhibit little sequence conservation but are located similarly proximal to the membrane. These features appear to be a common theme in plasmodesmal targeting.