We are interested in:

• Protein Structure Prediction
• Struture-based Function Annotation
• Protein Design
• Modeling of G Protein-Coupled Receptor and Ligand-Receptor Interactions
• Amyloid Diseases and Fiber Aggregation
• Modeling of Protein-Protein Interactions
• RNA Alternative Splicing
• Ligand Screening and Structure-Based Drug Design

Protein structure prediction refers to the effort to construct 3-dimensional shape of protein molecules from the amino acid sequence by computational calculations. Our lab has developed a number of algorithms for protein 3D structure prediction, including I-TASSER for iterative protein structure assembly, QUARK for ab initio protein folding, and MUSTER and LOMETS for protein template structure identification, some of which have been considered as the world's best and widely used by the community.

The Critical Assessment of Structure Prediction (CASP) is a community-wide experiment, which designs to benchmark the state-of-the-art of protein structure prediction in every two years since 1994. Our lab has participated as "Zhang-Server" in the automated structure prediction section in the experiments held in 2006, 2008 and 2010. The method was ranked at the top position in all three experiments (CASP 7-9) (Table 1).

Table 1. Top ten groups in automated structure prediction in CASP 7-9, ranked based on cumulative GDT-TS score of first model.
(Data were taken from http://predictioncenter.org. When multiple servers are from same lab, the best server was listed)


The most difficult problem in protein structure prediction is the modeling of proteins which have no solved structures that can be used as template, commonly referred "ab initio" or "free modeling (FM)" modeling. Figure 1 shows a successful example of ab initio modeling on a FM target (T0604_1) in CASP9, where the first model by the I-TASSER server has a RMSD 2.66 Angstroms to the X-ray crystal structure.


Figure 1. The first model by the I-TASSER server versus the crystal structure of T0604_1, a FM target in CASP9.
This is the VP0956 protein from Vibrio parahaemolyticus, solved by the Northeast Structural Genomics Consortium.


Despite the successes, there are still significant unsolved problems in protein structure prediction, which will be the target of our lab in the next few years. These include:

1. How to build structures of experimental resolution (below 1-2 Angstroms, useful for drug screening) when homologous templates are available?
2. How to identify distantly homologous templates with accurate query-template alignments?
3. How to fold proteins (especially the beta-proteins) with correct topology by ab initio modeling, when no templates exist?
4. How to fold membrane proteins?

G protein-coupled receptors, or GPCRs, are integral membrane proteins embedded in the cell surface that transmit signals to cells in response to stimuli and mediate physiological functions through interaction with heterotrimeric G proteins (Figure 5). Many diseases involve the malfunction of these receptors, making them important drug targets. More than 50% of all modern drugs target GPCRs, which represent 25% of the 100 top-selling drugs worldwide.

Figure 5. GPCRs comprise the largest family of membrane proteins and act as cell receptors for cellular signal transduction.

We are working on the development of the new GPCR modeling tool, GPCR-ITASSER, which extends I-TASSER by incooporating the protein-membrane interactions and the mutagenesis restraints into the knowledge-based force field. The ligand-GPCR interactions are then modeled by BSP-SLIM, a blind molecular docking tool designed for low-resolution protein-ligand docking. The method was tested (as "UMich-Zhang") in the recent community-wide GPCR Dock experiment in 2010. Figure 6 shows the result of our lab on all three ligand-GPCR complexes, where the first receptor models are 2.4 and 1.6 Angstroms to the crystal structure in the transmembrane region for the CXCR4 chemokine and dopamine D3 receptors, respectively. The three ligands, antagonists IT1t, CVX15, and eticlopride, are all in the same pocket as that in the crystal structure (Figure 6).


Figure 6. The first ligand-receptor docking model generated by GPCR-ITASSER and BSP-SLIM in GPCR-Dock 2010.
Left: CXCR4 chemokine receptor with IT1t; middle: CXCR receptor with CVX15; right: dopamine D3 with eticlopride.

Table 2 shows a summary of the top 10 groups (out of 35) in GPCRDock 2010, together with the cumulative Z-score on all three targets for both receptor and ligand models. The most significant success of our models is on the distant homology target CXCR4/CVX15, as Kufareva et al. (the assessors) commented, "Modeling the CXCR4/CVX15 peptide complex represented the biggest challenge of GPCR Dock 2010. The top model of this complex (by UMich-Zhang) has the Z-score of 2.45 thus far exceeding other models in accuracy."

Table 2. The best 10 groups in GPCRDock 2010 based on total Z-score of receptor and ligand models.
(Data were take from Kufareva et al. Structure. 2011, 19:1108)



We are now working on the application of the GPCR-ITASSER and BSP-SLIM pipeline to the modeling of all GPCRs in the human genome, to generate high-resolution structures of the receptors as well as the ligand-receptor associations with the aid of experimental restraints collected in GPCRRD. One goal is to systematically annotate the physiological roles of all GPCRs in associated pathways and to identify new therapies to regulate these interactions.

Every protein interacts (at least transiently) with about 9 other proteins, which forms complicated interaction networks within a cell (Figure 9). Since most proteins carry out their biological function through the interaction with other proteins, many diseases can be treated by designing new drugs to inhibit or activate the protein-protein interactions, where the knowledge of the protein-protein complex structures is essential.


Figure 9. Rhodopseudomonas palustris protein-protein interaction network.

To predict 3D structure of protein-protein complexes from sequence, we developed a new dimeric threading algorithm, COTH, to recognize template structure of protein complexes from solved complex structural databases. COTH aligns multiple-chain sequences simultaneously through the PDB library using scoring functions including multiple sequence profiles and structural information, with the assistance of interface predictions from BSpred. The COTH algorithm demonstrated significant advantage compared to other homology-based template identification methods (Figure 10).



Figure 10. TM-score of templates identified by COTH versus that from other homology-based methods.

Following COTH, we are working on the development of Dimer-ITASSER by extending the I-TASSER algorithm for multiple-chain full-length complex structure prediction. Since the folding principles of protein domains and complexes are essentially the same, we are hopeful to exploit I-TASSER iterative threading assembly methodology to significantly refine the template structures as identified from COTH, with the focus on the modeling of binding-induced side-chain and backbone conformational changes. One of the long-term goals is to utilize the developed Dimer-ITASSER to reconstruct the structure-based protein-protein network across genomes. A systematic, atom-level description of protein-protein complexes will be essential for the understanding of cellular processes and for the development of novel reagents to regulate the protein-protein interaction networks.

After many years of wild guesses, it is now known that the number of human genes is about 20,000, which is surprisingly less than that of a roundworm or a fruit fly. How can such a small number of genes give rise to an organism as complex as Mozart or Einstein? One of the important mechanisms that the human body uses to increase the complexity of proteome is through RNA alternative splicing, i.e. different proteins can be generated by combining different entries of exons from the same gene (Figure 11), while another mechanism is through post-translational modification (PTM). It is believed that more than 90% of human genes have alternative splicing isoforms.

Figure 11. An illustration of RNA alternative splicing, where the combination of Exons 1, 2, 3 gives rise to Protein A and 1, 2, 4 to Protein B.

Many RNA alternative splicing products result in diseases, including cancer. To examine the role of alternative spliced isoforms in human breast cancer at the level of protein structure, we modeled the structures of three pairs of protein isoforms (calumenin, cell devision cycle 42, and polypyrimindine tract binding proteins) which were found only in breast cancer cells by our collaborators (Menon and Omenn). We observed that, despite the high sequence identity (>90%), structural variations between isoforms occur at the critical active motifs. This observation opens a new avenue to the study of cancer on the basis of protein structure variations in alternatively spliced isoforms (Figure 12).



Figure 12. Sequence and structure variations in the breast cancer associated isoforms, ENSP00000249364 and
ENSP00000408838, from the calumenin proteins. The arrows point to the important variation motifs occurring in the
isoforms. Left panel is the TM-align structural alignment of the models generated by I-TASSER for the isoforms.

One issue in the determination of alternative splicing genes is the high error rate in the high-throughput data of protein identification and quantification. To improve data quality from protein structure prediction, we are working on the application of the I-TASSER modeling and the confidence scoring system to distinguish between true and false RNA alternative splicing isoforms from the human genome sequences.