Browsing by Author "Li, Zhixiu"

Now showing 1 - 4 of 4
  • Thumbnail Image
    Item
    Computational protein design: assessment and applications
    (2015) Li, Zhixiu; Zhou, Yaoqi
    Computational protein design aims at designing amino acid sequences that can fold into a target structure and perform a desired function. Many computational design methods have been developed and their applications have been successful during past two decades. However, the success rate of protein design remains too low to be of a useful tool by biochemists whom are not an expert of computational biology. In this dissertation, we first developed novel computational assessment techniques to assess several state-of-the-art computational techniques. We found that significant progresses were made in several important measures by two new scoring functions from RosettaDesign and from OSCAR-design, respectively. We also developed the first machine-learning technique called SPIN that predicts a sequence profile compatible to a given structure with a novel nonlocal energy-based feature. The accuracy of predicted sequences is comparable to RosettaDesign in term of sequence identity to wild type sequences. In the last two application chapters, we have designed self-inhibitory peptides of Escherichia coli methionine aminopeptidase (EcMetAP) and de novo designed barstar. Several peptides were confirmed inhibition of EcMetAP at the micromole-range 50% inhibitory concentration. Meanwhile, the assessment of designed barstar sequences indicates the improvement of OSCAR-design over RosettaDesign.
  • Thumbnail Image
    Item
    Direct prediction of profiles of sequences compatible to a protein structure by neural networks with fragment-based local and energy-based nonlocal profiles
    (Wiley Online Library, 2014-10) Li, Zhixiu; Yang, Yuedong; Faraggi, Eshel; Zhou, Jian; Zhou, Yaoqi; Department of BioHealth Informatics, IU School of Informatics and Computing
    Locating sequences compatible with a protein structural fold is the well-known inverse protein-folding problem. While significant progress has been made, the success rate of protein design remains low. As a result, a library of designed sequences or profile of sequences is currently employed for guiding experimental screening or directed evolution. Sequence profiles can be computationally predicted by iterative mutations of a random sequence to produce energy-optimized sequences, or by combining sequences of structurally similar fragments in a template library. The latter approach is computationally more efficient but yields less accurate profiles than the former because of lacking tertiary structural information. Here we present a method called SPIN that predicts Sequence Profiles by Integrated Neural network based on fragment-derived sequence profiles and structure-derived energy profiles. SPIN improves over the fragment-derived profile by 6.7% (from 23.6 to 30.3%) in sequence identity between predicted and wild-type sequences. The method also reduces the number of residues in low complex regions by 15.7% and has a significantly better balance of hydrophilic and hydrophobic residues at protein surface. The accuracy of sequence profiles obtained is comparable to those generated from the protein design program RosettaDesign 3.5. This highly efficient method for predicting sequence profiles from structures will be useful as a single-body scoring term for improving scoring functions used in protein design and fold recognition. It also complements protein design programs in guiding experimental design of the sequence library for screening and directed evolution of designed sequences. The SPIN server is available at http://sparks-lab.org.
  • Thumbnail Image
    Item
    The Role of Semidisorder in Temperature Adaptation of Bacterial FlgM Proteins
    (Elsevier B.V., 2013-12-03) Wang, Jihua; Yang, Yuedong; Cao, Zanxia; Li, Zhixiu; Zhao, Huiying; Zhou, Yaoqi; Department of Biochemistry & Molecular Biology, IU School of Medicine
    Probabilities of disorder for FlgM proteins of 39 species whose optimal growth temperature ranges from 273 K (0°C) to 368 K (95°C) were predicted by a newly developed method called Sequence-based Prediction with Integrated NEural networks for Disorder (SPINE-D). We showed that the temperature-dependent behavior of FlgM proteins could be separated into two subgroups according to their sequence lengths. Only shorter sequences evolved to adapt to high temperatures (>318 K or 45°C). Their ability to adapt to high temperatures was achieved through a transition from a fully disordered state with little secondary structure to a semidisordered state with high predicted helical probability at the N-terminal region. The predicted results are consistent with available experimental data. An analysis of all orthologous protein families in 39 species suggests that such a transition from a fully disordered state to semidisordered and/or ordered states is one of the strategies employed by nature for adaptation to high temperatures.
  • Thumbnail Image
    Item
    Self-derived structure-disrupting peptides targeting methionine aminopeptidase in pathogenic bacteria: a new strategy to generate antimicrobial peptides
    (Federation of American Society of Experimental Biology (FASEB), 2019-02) Zhan, Jian; Jia, Husen; Semchenko, Evgeny A.; Bian, Yunqiang; Zhou, Amy M.; Li, Zhixiu; Yang, Yuedong; Wang, Jihua; Sarkar, Sohinee; Totsika, Makrina; Blanchard, Helen; Jen, Freda E.-C.; Ye, Qizhuang; Haselhorst, Thomas; Jennings, Michael P.; Seib, Kate L.; Zhou, Yaoqi; Biochemistry and Molecular Biology, School of Medicine
    Bacterial infection is one of the leading causes of death in young, elderly, and immune-compromised patients. The rapid spread of multi-drug-resistant (MDR) bacteria is a global health emergency and there is a lack of new drugs to control MDR pathogens. We describe a heretofore-unexplored discovery pathway for novel antibiotics that is based on self-targeting, structure-disrupting peptides. We show that a helical peptide, KFF- EcH3, derived from the Escherichia coli methionine aminopeptidase can disrupt secondary and tertiary structure of this essential enzyme, thereby killing the bacterium (including MDR strains). Significantly, no detectable resistance developed against this peptide. Based on a computational analysis, our study predicted that peptide KFF- EcH3 has the strongest interaction with the structural core of the methionine aminopeptidase. We further used our approach to identify peptide KFF- NgH1 to target the same enzyme from Neisseria gonorrhoeae. This peptide inhibited bacterial growth and was able to treat a gonococcal infection in a human cervical epithelial cell model. These findings present an exciting new paradigm in antibiotic discovery using self-derived peptides that can be developed to target the structures of any essential bacterial proteins.