The long-term objective of the proposed project is to provide a comprehensive platform, MUFOLD, for efficient and consistently accurate protein tertiary structure prediction. MUFOLD will help experimental biologists understand structures and functions of the proteins of their interest thereby facilitating hypotheses for experimental design. We will focus on the Funding Opportunity Announcement's second objective -- High- Accuracy Models for Remote Homologs of Known Structures which states the quality of these models should be close to X-ray structures or high-resolution NMR structures with less than 2 Angstrom RMSD for backbone and side-chain atoms consistently for all protein targets. Specifically, we will integrate bioinformatics techniques, graph and network theories, computational algorithms, global optimization methods, statistics evaluations, etc. to develop a template-based structure prediction system, in which model generation, model quality assessment (QA), and model refinement will be seamlessly integrated together. At first, we will apply relevant information from the known template database (PDB) in depth as well as multi-layer QA methods to guide an efficient model generation in a small and targeted conformation space, which will facilitate computational efficiency and a limited number of models for QA methods to select. Secondly, we will improve the overall discerning power of QA by integrating various QA scores of a model and its structural relationships to other models generated for the same target protein. Thirdly, we will develop a population-based model refinement protocol, which integrates different levels of QA and efficient model generation techniques to improve the overall quality of models. Our goals are 1) to improve the prediction speed such that the prediction for a target protein with 200~300 residues can be finished in minutes on a multi-core desktop machine; 2) to enhance the QA ability of selecting the best models from the generated candidates, and decrease the current average ~10-point GDT-TS loss from the best available model to <5 points; 3) to achieve the prediction accuracy for remote homolog proteins within 2 Angstrom RMSD for backbone and side-chain atoms on average; and 4) to collaborate with PSI (Protein Structure Initiative) and others for various applications, such as performing homolog modeling for proteins with sequence similarity to newly determined structures, building complete models for incomplete structures, and predicting potential mutation sites to make protein soluble.
Protein structure prediction can provide valuable information for understanding disease mechanisms and designing drugs. Current computational methods are still far from consistently providing accurate structures. With rapid accumulating protein sequences derived from next-generation sequencing, software tools that can significantly improve the accuracy and efficiency of protein structure prediction are urgently needed, and our proposed development will address this need by developing a set of integrated novel methodologies.
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|Keasar, Chen; McGuffin, Liam J; Wallner, Björn et al. (2018) An analysis and evaluation of the WeFold collaborative for protein structure prediction and its pipelines in CASP11 and CASP12. Sci Rep 8:9939|
|Liu, Ye; Yu, Zhengfei; Zhu, Jingxuan et al. (2018) Why Is a High Temperature Needed by Thermus thermophilus Argonaute During mRNA Silencing: A Theoretical Study. Front Chem 6:223|
|Wang, Juexin; Sheridan, Robert; Sumer, S Onur et al. (2018) G2S: a web-service for annotating genomic variants on 3D protein structures. Bioinformatics 34:1949-1950|
|Fang, Chao; Shang, Yi; Xu, Dong (2018) MUFOLD-SS: New deep inception-inside-inception networks for protein secondary structure prediction. Proteins 86:592-598|
|Rao, R Shyama Prasad; Zhang, Ning; Xu, Dong et al. (2018) CarbonylDB: a curated data-resource of protein carbonylation sites. Bioinformatics 34:2518-2520|
|Wang, Duolin; Zeng, Shuai; Xu, Chunhui et al. (2017) MusiteDeep: a deep-learning framework for general and kinase-specific phosphorylation site prediction. Bioinformatics 33:3909-3916|
|Zhu, Jingxuan; Lv, Yishuo; Han, Xiaosong et al. (2017) Understanding the differences of the ligand binding/unbinding pathways between phosphorylated and non-phosphorylated ARH1 using molecular dynamics simulations. Sci Rep 7:12439|
|Zhang, Li; Wang, Han; Yan, Lun et al. (2017) OMPcontact: An Outer Membrane Protein Inter-Barrel Residue Contact Prediction Method. J Comput Biol 24:217-228|
|Han, Weiwei; Zhu, Jingxuan; Wang, Song et al. (2017) Understanding the Phosphorylation Mechanism by Using Quantum Chemical Calculations and Molecular Dynamics Simulations. J Phys Chem B 121:3565-3573|
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