Browsing by Author "Li, Zhixiu"

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    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.
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    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.
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    Intrinsically Semi-disordered State and Its Role in Induced Folding and Protein Aggregation
    (Springer, 2013) Zhang, Tuo; Faraggi, Eshel; Li, Zhixiu; Zhou, Yaoqi; Biochemistry and Molecular Biology, School of Medicine
    Intrinsically disordered proteins (IDPs) refer to those proteins without fixed three-dimensional structures under physiological conditions. Although experiments suggest that the conformations of IDPs can vary from random coils, semi-compact globules, to compact globules with different contents of secondary structures, computational efforts to separate IDPs into different states are not yet successful. Recently, we developed a neural-network-based disorder prediction technique SPINE-D that was ranked as one of the top performing techniques for disorder prediction in the biannual meeting of critical assessment of structure prediction techniques (CASP 9, 2010). Here, we further analyze the results from SPINE-D prediction by defining a semi-disordered state that has about 50% predicted probability to be disordered or ordered. This semi-disordered state is partially collapsed with intermediate levels of predicted solvent accessibility and secondary structure content. The relative difference in compositions between semi-disordered and fully disordered regions is highly correlated with amyloid aggregation propensity (a correlation coefficient of 0.86 if excluding four charged residues and proline, 0.73 if not). In addition, we observed that some semi-disordered regions participate in induced folding, and others play key roles in protein aggregation. More specifically, a semi-disordered region is amyloidogenic in fully unstructured proteins (such as alpha-synuclein and Sup35) but prone to local unfolding that exposes the hydrophobic core to aggregation in structured globular proteins (such as SOD1 and lysozyme). A transition from full disorder to semi-disorder at about 30-40 Qs is observed in the poly-Q (poly-glutamine) tract of huntingtin. The accuracy of using semi-disorder to predict binding-induced folding and aggregation is compared with several methods trained for the purpose. These results indicate the usefulness of three-state classification (order, semi-disorder, and full-disorder) in distinguishing nonfolding from induced-folding and aggregation-resistant from aggregation-prone IDPs and in locating weakly stable, locally unfolding, and potentially aggregation regions in structured proteins. A comparison with five representative disorder-prediction methods showed that SPINE-D is the only method with a clear separation of semi-disorder from ordered and fully disordered states.
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    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.
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    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.