Browsing by Author "Liu, Yen-Yi"

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Computational analysis of a novel mutation in ETFDH gene highlights its long-range effects on the FAD-binding motif
(2011-10-21)
Er, Tze-Kiong
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Chen, Chih-Chieh
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Liu, Yen-Yi
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Chang, Hui-Chiu
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Chien, Yin-Hsiu
Background: Multiple acyl-coenzyme A dehydrogenase deficiency (MADD) is an autosomal recessive disease caused by the defects in the mitochondrial electron transfer system and the metabolism of fatty acids. Recently, mutations in electron transfer flavoprotein dehydrogenase (ETFDH) gene, encoding electron transfer flavoprotein: ubiquinone oxidoreductase (ETF:QO) have been reported to be the major causes of riboflavin-responsive MADD. To date, no studies have been performed to explore the functional impact of these mutations or their mechanism of disrupting enzyme activity. Results: High resolution melting (HRM) analysis and sequencing of the entire ETFDH gene revealed a novel mutation (p.Phe128Ser) and the hotspot mutation (p.Ala84Thr) from a patient with MADD. According to the predicted 3D structure of ETF: QO, the two mutations are located within the flavin adenine dinucleotide (FAD) binding domain; however, the two residues do not have direct interactions with the FAD ligand. Using molecular dynamics (MD) simulations and normal mode analysis (NMA), we found that the p. Ala84Thr and p.Phe128Ser mutations are most likely to alter the protein structure near the FAD binding site as well as disrupt the stability of the FAD binding required for the activation of ETF: QO. Intriguingly, NMA revealed that several reported disease-causing mutations in the ETF: QO protein show highly correlated motions with the FAD-binding site. Conclusions: Based on the present findings, we conclude that the changes made to the amino acids in ETF: QO are likely to influence the FAD-binding stability.
Computational Analysis of KRAS Mutations: Implications for Different Effects on the KRAS p.G12D and p.G13D Mutations
(2013-02-20)
Chen, Chih-Chieh
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Er, Tze-Kiong
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Liu, Yen-Yi
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Hwang, Jenn-Kang
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Jesus Barrio, Maria
Background: The issue of whether patients diagnosed with metastatic colorectal cancer who harbor KRAS codon 13 mutations could benefit from the addition of anti-epidermal growth factor receptor therapy remains under debate. The aim of the current study was to perform computational analysis to investigate the structural implications of the underlying mutations caused by c.38G>A (p.G13D) on protein conformation. Methods: Molecular dynamics (MD) simulations were performed to understand the plausible structural and dynamical implications caused by c.35G>A (p.G12D) and c.38G>A (p.G13D). The potential of mean force (PMF) simulations were carried out to determine the free energy profiles of the binding processes of GTP interacting with wild-type (WT) KRAS and its mutants (MT). Results: Using MD simulations, we observed that the root mean square deviation (RMSD) increased as a function of time for the MT c.35G>A (p.G12D) and MT c.38G>A (p.G13D) when compared with the WT. We also observed that the GTP-binding pocket in the c.35G>A (p.G12D) mutant is more open than that of the WT and the c.38G>A (p.G13D) proteins. Intriguingly, the analysis of atomic fluctuations and free energy profiles revealed that the mutation of c.35G>A (p.G12D) may induce additional fluctuations in the sensitive sites (P-loop, switch I and II regions). Such fluctuations may promote instability in these protein regions and hamper GTP binding. Conclusions: Taken together with the results obtained from MD and PMF simulations, the present findings implicate fluctuations at the sensitive sites (P-loop, switch I and II regions). Our findings revealed that KRAS mutations in codon 13 have similar behavior as KRAS WT. To gain a better insight into why patients with metastatic colorectal cancer (mCRC) and the KRAS c.38G>A (p.G13D) mutation appear to benefit from anti-EGFR therapy, the role of the KRAS c.38G>A (p.G13D) mutation in mCRC needs to be further investigated.
CPred: a web server for predicting viable circular permutations in proteins
(2012-07-01)
Lo, Wei-Cheng
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Wang, Li-Fen
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Liu, Yen-Yi
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Dai, Tian
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Hwang, Jenn-Kang
"Circular permutation (CP) is a protein structural rearrangement phenomenon, through which nature allows structural homologs to have different locations of termini and thus varied activities, stabilities and functional properties. It can be applied in many fields of protein research and bioengineering. The limitation of applying CP lies in its technical complexity, high cost and uncertainty of the viability of the resulting protein variants. Not every position in a protein can be used to create a viable circular permutant, but there is still a lack of practical computational tools for evaluating the positional feasibility of CP before costly experiments are carried out. We have previously designed a comprehensive method for predicting viable CP cleavage sites in proteins. In this work, we implement that method into an efficient and user-friendly web server named CPred (CP site predictor), which is supposed to be helpful to promote fundamental researches and biotechnological applications of CP. The CPred is accessible at http://sarst.life.nthu.edu.tw/CPred."
Deciphering the Preference and Predicting the Viability of Circular Permutations in Proteins
(2012-02-16)
Lo, Wei-Cheng
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Dai, Tian
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Liu, Yen-Yi
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Wang, Li-Fen
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Hwang, Jenn-Kang
Circular permutation (CP) refers to situations in which the termini of a protein are relocated to other positions in the structure. CP occurs naturally and has been artificially created to study protein function, stability and folding. Recently CP is increasingly applied to engineer enzyme structure and function, and to create bifunctional fusion proteins unachievable by tandem fusion. CP is a complicated and expensive technique. An intrinsic difficulty in its application lies in the fact that not every position in a protein is amenable for creating a viable permutant. To examine the preferences of CP and develop CP viability prediction methods, we carried out comprehensive analyses of the sequence, structural, and dynamical properties of known CP sites using a variety of statistics and simulation methods, such as the bootstrap aggregating, permutation test and molecular dynamics simulations. CP particularly favors Gly, Pro, Asp and Asn. Positions preferred by CP lie within coils, loops, turns, and at residues that are exposed to solvent, weakly hydrogen-bonded, environmentally unpacked, or flexible. Disfavored positions include Cys, bulky hydrophobic residues, and residues located within helices or near the protein's core. These results fostered the development of an effective viable CP site prediction system, which combined four machine learning methods, e. g., artificial neural networks, the support vector machine, a random forest, and a hierarchical feature integration procedure developed in this work. As assessed by using the hydrofolate reductase dataset as the independent evaluation dataset, this prediction system achieved an AUC of 0.9. Large-scale predictions have been performed for nine thousand representative protein structures; several new potential applications of CP were thus identified. Many unreported preferences of CP are revealed in this study. The developed system is the best CP viability prediction method currently available. This work will facilitate the application of CP in research and biotechnology.
Deriving correlated motions in proteins from X-ray structure refinement by using TLS parameters
(2013-04-10)
Liu, Yen-Yi
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Shih, Chien-Hua
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Hwang, Jenn-Kang
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Chen, Chih-Chieh
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生物資訊及系統生物研究所
Dynamic information in proteins may provide valuable information for understanding allosteric regulation of protein complexes or long-range effects of the mutations on enzyme activity. Experimental data such as X-ray B-factors or NMR order parameters provide a convenient estimate of atomic fluctuations (or atomic auto-correlated motions) in proteins. However, it is not as straightforward to obtain atomic cross-correlated motions in proteins - one usually resorts to more sophisticated computational methods such as Molecular Dynamics, normal mode analysis or atomic network models. In this report, we show that atomic cross-correlations can be reliably obtained directly from protein structure using X-ray refinement data. We have derived an analytic form of atomic correlated motions in terms of the original MS parameters used to refine the B-factors of X-ray structures. The correlated maps computed using this equation are well correlated with those of the method based on a mechanical model (the correlation coefficient is 0.75) for a non-homologous dataset comprising 100 structures. We have developed an approach to compute atomic cross-correlations directly from X-ray protein structure. Being in analytic form, it is fast and provides a feasible way to compute correlated motions in proteins in a high throughput way. In addition, avoiding sophisticated computational operations; it provides a quick, reliable way, especially for non-computational biologists, to obtain dynamics information directly from protein structure relevant to its function. (c) 2012 Elsevier B.V. All rights reserved.