Author
Jianyi Yang
Other affiliations: Nanyang Technological University, University of North Dakota, Xiangtan University ...read more
Bio: Jianyi Yang is an academic researcher from Nankai University. The author has contributed to research in topics: Protein structure prediction & Threading (protein sequence). The author has an hindex of 29, co-authored 61 publications receiving 8751 citations. Previous affiliations of Jianyi Yang include Nanyang Technological University & University of North Dakota.
Papers
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TL;DR: A stand-alone I-TASSER Suite that can be used for off-line protein structure and function prediction and three complementary algorithms to enhance function inferences are developed, the consensus of which is derived by COACH4 using support vector machines.
Abstract: The lowest free-energy conformations are identified by structure clustering. A second round of assembly simulation is conducted, starting from the centroid models, to remove steric clashes and refine global topology. Final atomic structure models are constructed from the low-energy conformations by a two-step atomic-level energy minimization approach. The correctness of the global model is assessed by the confidence score, which is based on the significance of threading alignments and the density of structure clustering; the residue-level local quality of the structural models and B factor of the target protein are evaluated by a newly developed method, ResQ, built on the variation of modeling simulations and the uncertainty of homologous alignments through support vector regression training. For function annotation, the structure models with the highest confidence scores are matched against the BioLiP5 database of ligand-protein interactions to detect homologous function templates. Functional insights on ligand-binding site (LBS), Enzyme Commission (EC) and Gene Ontology (GO) are deduced from the functional templates. We developed three complementary algorithms (COFACTOR, TM-SITE and S-SITE) to enhance function inferences, the consensus of which is derived by COACH4 using support vector machines. Detailed instructions for installation, implementation and result interpretation of the Suite can be found in the Supplementary Methods and Supplementary Tables 1 and 2. The I-TASSER Suite pipeline was tested in recent communitywide structure and function prediction experiments, including CASP10 (ref. 1) and CAMEO2. Overall, I-TASSER generated the correct fold with a template modeling score (TM-score) >0.5 for 10 out of 36 “New Fold” (NF) targets in the CASP10, which have no homologous templates in the Protein Data Bank (PDB). Of the 110 template-based modeling targets, 92 had a TM-score >0.5, and 89 had the templates drawn closer to the native with an average r.m.s. deviation improvement of 1.05 Å in the same threadingaligned regions6. In CAMEO, COACH generated LBS predictions for 4,271 targets with an average accuracy 0.86, which was 20% higher than that of the second-best method in the experiment. Here we illustrate I-TASSER Suite–based structure and function modeling using six examples (Fig. 1b–g) from the communitywide blind tests1,2. R0006 and R0007 are two NF targets from CASP10, and I-TASSER constructed models of correct fold with a TM-score of 0.62 for both targets (Fig. 1b,c). An illustration of local quality estimation by ResQ is shown for T0652, which has an average error 0.75 Å compared to the actual deviation of the model from the native (Fig. 1h). The four LBS prediction examples (Fig. 1d–g) are from CASP10 (ref. 1) and CAMEO2; COACH generated ligand models all with a ligand r.m.s. deviation below 2 Å. COACH also correctly assigned the threeand fourdigit EC numbers to the enzyme targets C0050 and C0046 (Supplementary Table 3). In summary, we developed a stand-alone I-TASSER Suite that can be used for off-line protein structure and function prediction. The I-TASSER Suite: protein structure and function prediction
4,693 citations
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TL;DR: Focuses have been made on the introduction of new methods for atomic-level structure refinement, local structure quality estimation and biological function annotations, which are designed to address the requirements from the user community and to increase the accuracy of modeling predictions.
Abstract: The I-TASSER server (http://zhanglab.ccmb.med.umich.edu/I-TASSER) is an online resource for automated protein structure prediction and structure-based function annotation. In I-TASSER, structural templates are first recognized from the PDB using multiple threading alignment approaches. Full-length structure models are then constructed by iterative fragment assembly simulations. The functional insights are finally derived by matching the predicted structure models with known proteins in the function databases. Although the server has been widely used for various biological and biomedical investigations, numerous comments and suggestions have been reported from the user community. In this article, we summarize recent developments on the I-TASSER server, which were designed to address the requirements from the user community and to increase the accuracy of modeling predictions. Focuses have been made on the introduction of new methods for atomic-level structure refinement, local structure quality estimation and biological function annotations. We expect that these new developments will improve the quality of the I-TASSER server and further facilitate its use by the community for high-resolution structure and function prediction.
1,698 citations
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TL;DR: A deep residual network for predicting interresidue orientations, in addition to distances, and a Rosetta-constrained energy-minimization protocol for rapidly and accurately generating structure models guided by these restraints are developed.
Abstract: The prediction of interresidue contacts and distances from coevolutionary data using deep learning has considerably advanced protein structure prediction. Here, we build on these advances by developing a deep residual network for predicting interresidue orientations, in addition to distances, and a Rosetta-constrained energy-minimization protocol for rapidly and accurately generating structure models guided by these restraints. In benchmark tests on 13th Community-Wide Experiment on the Critical Assessment of Techniques for Protein Structure Prediction (CASP13)- and Continuous Automated Model Evaluation (CAMEO)-derived sets, the method outperforms all previously described structure-prediction methods. Although trained entirely on native proteins, the network consistently assigns higher probability to de novo-designed proteins, identifying the key fold-determining residues and providing an independent quantitative measure of the "ideality" of a protein structure. The method promises to be useful for a broad range of protein structure prediction and design problems.
1,026 citations
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TL;DR: Two new methods, one based on binding-specific substructure comparison (TM-Site) and another on sequence profile alignment (S-SITE), for complementary binding site predictions are developed, which demonstrate a new robust approach to protein-ligand binding site recognition, ready for genome-wide structure-based function annotations.
Abstract: Motivation: Identification of protein–ligand binding sites is critical to protein function annotation and drug discovery. However, there is no method that could generate optimal binding site prediction for different protein types. Combination of complementary predictions is probably the most reliable solution to the problem. Results: We develop two new methods, one based on binding-specific substructure comparison (TM-SITE) and another on sequence profile alignment (S-SITE), for complementary binding site predictions. The methods are tested on a set of 500 non-redundant proteins harboring 814 natural, drug-like and metal ion molecules. Starting from low-resolution protein structure predictions, the methods successfully recognize 451% of binding residues with average Matthews correlation coefficient (MCC) significantly higher (with P-value 510 –9 in student t-test) than other state-of-the-art methods, including COFACTOR, FINDSITE and ConCavity. When combining TM-SITE and S-SITE with other structure-based programs, a consensus approach (COACH) can increase MCC by 15% over the best individual predictions. COACH was examined in the recent community-wide COMEO experiment and consistently ranked as the best method in last 22 individual datasets with the Area Under the Curve score 22.5% higher than the second best method. These data demonstrate a new robust approach to protein–ligand binding site recognition, which is ready for genome-wide structure-based function annotations.
715 citations
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TL;DR: A new COFACTOR webserver for automated structure-based protein function annotation and was ranked as the best method for protein–ligand binding site predictions in the recent community-wide CASP9 experiment.
Abstract: We have developed a new COFACTOR webserver for automated structure-based protein function annotation. Starting from a structural model, given by either experimental determination or computational modeling, COFACTOR first identifies template proteins of similar folds and functional sites by threading the target structure through three representative template libraries that have known protein-ligand binding interactions, Enzyme Commission number or Gene Ontology terms. The biological function insights in these three aspects are then deduced from the functional templates, the confidence of which is evaluated by a scoring function that combines both global and local structural similarities. The algorithm has been extensively benchmarked by large-scale benchmarking tests and demonstrated significant advantages compared to traditional sequence-based methods. In the recent community-wide CASP9 experiment, COFACTOR was ranked as the best method for protein-ligand binding site predictions. The COFACTOR sever and the template libraries are freely available at http://zhanglab.ccmb.med.umich.edu/COFACTOR.
608 citations
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TL;DR: For example, AlphaFold as mentioned in this paper predicts protein structures with an accuracy competitive with experimental structures in the majority of cases using a novel deep learning architecture. But the accuracy is limited by the fact that no homologous structure is available.
Abstract: Proteins are essential to life, and understanding their structure can facilitate a mechanistic understanding of their function. Through an enormous experimental effort1–4, the structures of around 100,000 unique proteins have been determined5, but this represents a small fraction of the billions of known protein sequences6,7. Structural coverage is bottlenecked by the months to years of painstaking effort required to determine a single protein structure. Accurate computational approaches are needed to address this gap and to enable large-scale structural bioinformatics. Predicting the three-dimensional structure that a protein will adopt based solely on its amino acid sequence—the structure prediction component of the ‘protein folding problem’8—has been an important open research problem for more than 50 years9. Despite recent progress10–14, existing methods fall far short of atomic accuracy, especially when no homologous structure is available. Here we provide the first computational method that can regularly predict protein structures with atomic accuracy even in cases in which no similar structure is known. We validated an entirely redesigned version of our neural network-based model, AlphaFold, in the challenging 14th Critical Assessment of protein Structure Prediction (CASP14)15, demonstrating accuracy competitive with experimental structures in a majority of cases and greatly outperforming other methods. Underpinning the latest version of AlphaFold is a novel machine learning approach that incorporates physical and biological knowledge about protein structure, leveraging multi-sequence alignments, into the design of the deep learning algorithm. AlphaFold predicts protein structures with an accuracy competitive with experimental structures in the majority of cases using a novel deep learning architecture.
10,601 citations
01 Aug 2000
TL;DR: Assessment of medical technology in the context of commercialization with Bioentrepreneur course, which addresses many issues unique to biomedical products.
Abstract: BIOE 402. Medical Technology Assessment. 2 or 3 hours. Bioentrepreneur course. Assessment of medical technology in the context of commercialization. Objectives, competition, market share, funding, pricing, manufacturing, growth, and intellectual property; many issues unique to biomedical products. Course Information: 2 undergraduate hours. 3 graduate hours. Prerequisite(s): Junior standing or above and consent of the instructor.
4,833 citations
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TL;DR: A stand-alone I-TASSER Suite that can be used for off-line protein structure and function prediction and three complementary algorithms to enhance function inferences are developed, the consensus of which is derived by COACH4 using support vector machines.
Abstract: The lowest free-energy conformations are identified by structure clustering. A second round of assembly simulation is conducted, starting from the centroid models, to remove steric clashes and refine global topology. Final atomic structure models are constructed from the low-energy conformations by a two-step atomic-level energy minimization approach. The correctness of the global model is assessed by the confidence score, which is based on the significance of threading alignments and the density of structure clustering; the residue-level local quality of the structural models and B factor of the target protein are evaluated by a newly developed method, ResQ, built on the variation of modeling simulations and the uncertainty of homologous alignments through support vector regression training. For function annotation, the structure models with the highest confidence scores are matched against the BioLiP5 database of ligand-protein interactions to detect homologous function templates. Functional insights on ligand-binding site (LBS), Enzyme Commission (EC) and Gene Ontology (GO) are deduced from the functional templates. We developed three complementary algorithms (COFACTOR, TM-SITE and S-SITE) to enhance function inferences, the consensus of which is derived by COACH4 using support vector machines. Detailed instructions for installation, implementation and result interpretation of the Suite can be found in the Supplementary Methods and Supplementary Tables 1 and 2. The I-TASSER Suite pipeline was tested in recent communitywide structure and function prediction experiments, including CASP10 (ref. 1) and CAMEO2. Overall, I-TASSER generated the correct fold with a template modeling score (TM-score) >0.5 for 10 out of 36 “New Fold” (NF) targets in the CASP10, which have no homologous templates in the Protein Data Bank (PDB). Of the 110 template-based modeling targets, 92 had a TM-score >0.5, and 89 had the templates drawn closer to the native with an average r.m.s. deviation improvement of 1.05 Å in the same threadingaligned regions6. In CAMEO, COACH generated LBS predictions for 4,271 targets with an average accuracy 0.86, which was 20% higher than that of the second-best method in the experiment. Here we illustrate I-TASSER Suite–based structure and function modeling using six examples (Fig. 1b–g) from the communitywide blind tests1,2. R0006 and R0007 are two NF targets from CASP10, and I-TASSER constructed models of correct fold with a TM-score of 0.62 for both targets (Fig. 1b,c). An illustration of local quality estimation by ResQ is shown for T0652, which has an average error 0.75 Å compared to the actual deviation of the model from the native (Fig. 1h). The four LBS prediction examples (Fig. 1d–g) are from CASP10 (ref. 1) and CAMEO2; COACH generated ligand models all with a ligand r.m.s. deviation below 2 Å. COACH also correctly assigned the threeand fourdigit EC numbers to the enzyme targets C0050 and C0046 (Supplementary Table 3). In summary, we developed a stand-alone I-TASSER Suite that can be used for off-line protein structure and function prediction. The I-TASSER Suite: protein structure and function prediction
4,693 citations
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TL;DR: The emergence of MCR-1 heralds the breach of the last group of antibiotics, polymyxins, by plasmid-mediated resistance, in Enterobacteriaceae and emphasise the urgent need for coordinated global action in the fight against pan-drug-resistant Gram-negative bacteria.
Abstract: Summary Background Until now, polymyxin resistance has involved chromosomal mutations but has never been reported via horizontal gene transfer. During a routine surveillance project on antimicrobial resistance in commensal Escherichia coli from food animals in China, a major increase of colistin resistance was observed. When an E coli strain, SHP45, possessing colistin resistance that could be transferred to another strain, was isolated from a pig, we conducted further analysis of possible plasmid-mediated polymyxin resistance. Herein, we report the emergence of the first plasmid-mediated polymyxin resistance mechanism, MCR-1, in Enterobacteriaceae. Methods The mcr-1 gene in E coli strain SHP45 was identified by whole plasmid sequencing and subcloning. MCR-1 mechanistic studies were done with sequence comparisons, homology modelling, and electrospray ionisation mass spectrometry. The prevalence of mcr-1 was investigated in E coli and Klebsiella pneumoniae strains collected from five provinces between April, 2011, and November, 2014. The ability of MCR-1 to confer polymyxin resistance in vivo was examined in a murine thigh model. Findings Polymyxin resistance was shown to be singularly due to the plasmid-mediated mcr-1 gene. The plasmid carrying mcr-1 was mobilised to an E coli recipient at a frequency of 10 −1 to 10 −3 cells per recipient cell by conjugation, and maintained in K pneumoniae and Pseudomonas aeruginosa . In an in-vivo model, production of MCR-1 negated the efficacy of colistin. MCR-1 is a member of the phosphoethanolamine transferase enzyme family, with expression in E coli resulting in the addition of phosphoethanolamine to lipid A. We observed mcr-1 carriage in E coli isolates collected from 78 (15%) of 523 samples of raw meat and 166 (21%) of 804 animals during 2011–14, and 16 (1%) of 1322 samples from inpatients with infection. Interpretation The emergence of MCR-1 heralds the breach of the last group of antibiotics, polymyxins, by plasmid-mediated resistance. Although currently confined to China, MCR-1 is likely to emulate other global resistance mechanisms such as NDM-1. Our findings emphasise the urgent need for coordinated global action in the fight against pan-drug-resistant Gram-negative bacteria. Funding Ministry of Science and Technology of China, National Natural Science Foundation of China.
3,647 citations
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University of Washington1, Harvard University2, University of Texas Southwestern Medical Center3, University of Cambridge4, Stanford University5, Lawrence Berkeley National Laboratory6, North-West University7, University of the Free State8, University of Graz9, Medical University of Graz10, University of Victoria11, University of British Columbia12, University of California, Berkeley13
TL;DR: In this article, a three-track network is proposed to combine information at the one-dimensional (1D) sequence level, the 2D distance map level, and the 3D coordinate level.
Abstract: DeepMind presented notably accurate predictions at the recent 14th Critical Assessment of Structure Prediction (CASP14) conference. We explored network architectures that incorporate related ideas and obtained the best performance with a three-track network in which information at the one-dimensional (1D) sequence level, the 2D distance map level, and the 3D coordinate level is successively transformed and integrated. The three-track network produces structure predictions with accuracies approaching those of DeepMind in CASP14, enables the rapid solution of challenging x-ray crystallography and cryo-electron microscopy structure modeling problems, and provides insights into the functions of proteins of currently unknown structure. The network also enables rapid generation of accurate protein-protein complex models from sequence information alone, short-circuiting traditional approaches that require modeling of individual subunits followed by docking. We make the method available to the scientific community to speed biological research.
1,907 citations