Annotating Putative D. discoideum Proteins Using I-TASSER.
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ABSTRACT:
SUBMITTER: McCullough J
PROVIDER: S-EPMC8280847 | biostudies-literature |
REPOSITORIES: biostudies-literature
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| S-EPMC1869010 | biostudies-literature
Project description:As genome sequencing outstrips the rate of high-quality, low-throughput biochemical and genetic experimentation, accurate annotation of protein function becomes a bottleneck in the progress of the biomolecular sciences. Most gene products are now annotated by homology, in which an experimentally determined function is applied to a similar sequence. This procedure becomes error-prone between more divergent sequences and can contaminate biomolecular databases. Here, we propose a computational method of assignment of function, termed Generalized Functional Linkages (GFL), that combines nonhomology-based methods with other types of data. Functional linkages describe pairwise relationships between proteins that work together to perform a biological task. GFL provides a Bayesian framework that improves annotation by arbitrating a competition among biological process annotations to best describe the target protein. GFL addresses the unequal strengths of functional linkages among proteins, the quality of existing annotations, and the similarity among them while incorporating available knowledge about the cellular location or individual molecular function of the target protein. We demonstrate GFL with functional linkages defined by an algorithm known as zorch that quantifies connectivity in protein-protein interaction networks. Even when using proteins linked only by indirect or high-throughput interactions, GFL predicts the biological processes of many proteins in Saccharomyces cerevisiae, improving the accuracy of annotation by 20% over majority voting.
| S-EPMC2584710 | biostudies-literature
Project description:BACKGROUND:Predicting 3-dimensional protein structures from amino-acid sequences is an important unsolved problem in computational structural biology. The problem becomes relatively easier if close homologous proteins have been solved, as high-resolution models can be built by aligning target sequences to the solved homologous structures. However, for sequences without similar folds in the Protein Data Bank (PDB) library, the models have to be predicted from scratch. Progress in the ab initio structure modeling is slow. The aim of this study was to extend the TASSER (threading/assembly/refinement) method for the ab initio modeling and examine systemically its ability to fold small single-domain proteins. RESULTS:We developed I-TASSER by iteratively implementing the TASSER method, which is used in the folding test of three benchmarks of small proteins. First, data on 16 small proteins (< 90 residues) were used to generate I-TASSER models, which had an average Calpha-root mean square deviation (RMSD) of 3.8A, with 6 of them having a Calpha-RMSD < 2.5A. The overall result was comparable with the all-atomic ROSETTA simulation, but the central processing unit (CPU) time by I-TASSER was much shorter (150 CPU days vs. 5 CPU hours). Second, data on 20 small proteins (< 120 residues) were used. I-TASSER folded four of them with a Calpha-RMSD < 2.5A. The average Calpha-RMSD of the I-TASSER models was 3.9A, whereas it was 5.9A using TOUCHSTONE-II software. Finally, 20 non-homologous small proteins (< 120 residues) were taken from the PDB library. An average Calpha-RMSD of 3.9A was obtained for the third benchmark, with seven cases having a Calpha-RMSD < 2.5A. CONCLUSION:Our simulation results show that I-TASSER can consistently predict the correct folds and sometimes high-resolution models for small single-domain proteins. Compared with other ab initio modeling methods such as ROSETTA and TOUCHSTONE II, the average performance of I-TASSER is either much better or is similar within a lower computational time. These data, together with the significant performance of automated I-TASSER server (the Zhang-Server) in the 'free modeling' section of the recent Critical Assessment of Structure Prediction (CASP)7 experiment, demonstrate new progresses in automated ab initio model generation. The I-TASSER server is freely available for academic users http://zhang.bioinformatics.ku.edu/I-TASSER.
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Project description:I-TASSER is a hierarchical protocol for automated protein structure prediction and structure-based function annotation. Starting from the amino acid sequence of target proteins, I-TASSER first generates full-length atomic structural models from multiple threading alignments and iterative structural assembly simulations followed by atomic-level structure refinement. The biological functions of the protein, including ligand-binding sites, enzyme commission number, and gene ontology terms, are then inferred from known protein function databases based on sequence and structure profile comparisons. I-TASSER is freely available as both an on-line server and a stand-alone package. This unit describes how to use the I-TASSER protocol to generate structure and function prediction and how to interpret the prediction results, as well as alternative approaches for further improving the I-TASSER modeling quality for distant-homologous and multi-domain protein targets.
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Project description:Molecular replacement (MR) is one of the most common techniques used for solving the phase problem in X-ray crystal diffraction. The success rate of MR however drops quickly when the sequence identity between query and templates is reduced, while the I-TASSER-MR server is designed to solve the phase problem for proteins that lack close homologous templates. Starting from a sequence, it first generates full-length models using I-TASSER by iterative structural fragment reassembly. A progressive sequence truncation procedure is then used for editing the models based on local variations of the structural assembly simulations. Next, the edited models are submitted to MR-REX to search for optimal placements in the crystal unit-cells through replica-exchange Monte Carlo simulations, with the phasing results used by CNS for final atomic model refinement and selection. The I-TASSER-MR algorithm was tested in large-scale benchmark datasets and solved 36% more targets compared to using the best threading templates. The server takes primary sequence and raw crystal diffraction data as input, with output containing annotated phase information and refined structure models. It also allows users to choose between different methods for setting B-factors and the number of models used for phasing. The online server is freely available at http://zhanglab.ccmb.med.umich.edu/I-TASSER-MR.
| S-EPMC5793832 | biostudies-literature
Project description:MotivationAccurate and efficient predictions of protein structures play an important role in understanding their functions. Iterative Threading Assembly Refinement (I-TASSER) is one of the most successful and widely used protein structure prediction methods in the recent community-wide CASP experiments. Yet, the computational efficiency of I-TASSER is one of the limiting factors that prevent its application for large-scale structure modeling.ResultsWe present I-TASSER for Graphics Processing Units (GPU-I-TASSER), a GPU accelerated I-TASSER protein structure prediction tool for fast and accurate protein structure prediction. Our implementation is based on OpenACC parallelization of the replica-exchange Monte Carlo simulations to enhance the speed of I-TASSER by extending its capabilities to the GPU architecture. On a benchmark dataset of 71 protein structures, GPU-I-TASSER achieves on average a 10× speedup with comparable structure prediction accuracy compared to the CPU version of the I-TASSER.Availability and implementationThe complete source code for GPU-I-TASSER can be downloaded and used without restriction from https://zhanggroup.org/GPU-I-TASSER/.Supplementary informationSupplementary data are available at Bioinformatics online.
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