Biomedical subjects

Yuedong Yang

Publications and source records attributed to Yuedong Yang.

5 recordsLinked to original sources

PubMed2026

AI proteomics: from protein identification to virtual cells.

Artificial intelligence (AI) is transforming scientific research, including proteomics. In this Perspective, we highlight key mass spectrometry (MS)-based proteomics areas where AI is driving innovation, ranging from protein identification to building AI virtual cells. These include improving peptide and protein identification and quantification; characterizing protein-protein interactions and protein complexes; advancing spatial and perturbation proteomics; integrating multi-omics data; and, ultimately, enabling AI virtual cells. Finally, we call for global collaboration among data producers, data consumers and other stakeholders to establish an AI-friendly ecosystem for MS-based proteomics, laying the foundation for transformative advancements in proteomics driven by AI.

Proteomics↗

PubMed2006

Genetic algorithms for protein conformation sampling and optimization in a discrete backbone dihedral angle space.

We have investigated protein conformation sampling and optimization based on the genetic algorithm and discrete main chain dihedral state model. An efficient approach combining the genetic algorithm with local minimization and with a niche technique based on the sharing function is proposed. Using two different types of potential energy functions, a Go-type potential function and a knowledge-based pairwise potential energy function, and a test set containing small proteins of varying sizes and secondary structure compositions, we demonstrated the importance of local minimization and population diversity in protein conformation optimization with genetic algorithms. Some general properties of the sampled conformations such as their native-likeness and the influences of including side-chains are discussed.

Algorithms↗

PubMed2005

Transition state ensemble for the folding of B domain of protein A: a comparison of distributed molecular dynamics simulations with experiments.

Folding pathways of the B domain of staphylococcal protein A have been sampled with a distributed computing approach. Starting from an extended structure, the method employs an index measuring topological similarity to the native structure to selectively sample trajectory branches leading to the native fold. Unperturbed and continuous folding trajectories are drawn on a physics-based atomic potential energy surface with an implicit solvent. The sampled folding trajectories demonstrate a similar sequence of events: the earlier stage involves a partial formation of helix 2 and to a less extent of helix 1 at their N terminals, followed by the hydrophobic collapse between residues F14, I17, and L18 on helix 1 and residues R28, F31, and I32 on helix 2, which results in the rigidification of the helix turn from R28 to I32. Helix 2 is then able to extend, allowing for the formation to turn 2. The above description explains one experimental result why a G30A mutant of the protein was observed to be the fastest folder among proteins of its size. And the ensemble of structures right before the final collapse is in good agreement with the transition state ensemble mapped by another recent experiment with Fersht Phi values. We emphasize that because the approach here does not provide quantifications of the free energy landscape, our model of the transition state ensemble emerges from comparisons of simulations and previous experimental results rather from the simulation results alone. On the other hand, as our approach does not rely on a low-dimensional free energy surface, it can complement methods based on the construction of free energy surfaces.

Amino Acid Sequence↗

PubMed2005

Structural basis for SUMO-E2 interaction revealed by a complex model using docking approach in combination with NMR data.

The interaction between small ubiquitin-related modifier SUMO and its conjugating-enzyme Ubc9 (E2) is an essential step in SUMO conjugation cascade. However, an experimental structure of such a transient complex is still unavailable. Here, a structural model of SUMO-3-Ubc9 complex was obtained with HADDOCK, combining NMR chemical shift mapping information. Docking calculations were performed using SUMO-3 and Ubc9 structures as input. The resulting complex reveals that the complementary surface electrostatic potentials contribute dominantly to the specific interaction. At the interface, similar numbers of oppositely-charged conserved residues are identified on the respective binding partners. Hydrogen bonds are formed in the vicinity of the interface to stabilize the complex. Comparison of the structure of SUMO-3-Ubc9 complex generated by HADDOCK and the experimental structures in free form indicates that SUMO-3 and Ubc9 maintain their respective fold as a whole after docking. However, the N-terminal helix alpha1 and its subsequent L1 loop of Ubc9 experience sizeable changes upon complex formation. They cooperatively move towards the hydrophilic side of the beta-sheet of SUMO-3. Our observations are consistent with the data from previous Ubc9 mutational analysis and conformational flexibility studies. Together, we have proposed that the SUMO-3-Ubc9 interaction is strongly electrostatically driven and the N terminus of Ubc9 shifts to SUMO-3 to facilitate the interaction. The NMR-based structural model, which provides considerable insights into the molecular basis of the specific SUMO-E2 recognition and interaction, implicates the general interaction mode between SUMO-3 and Ubc9 homologues from yeast to humans.

Binding Sites↗

PubMed2005

Solution structure of AF-6 PDZ domain and its interaction with the C-terminal peptides from Neurexin and Bcr.

AF-6 is a key molecule essential for structure organization of cell-cell junction of polarized epithelia. It belongs to a novel cell-cell adhesion system. The AF-6 PDZ domain mediates interactions by binding to a specific amino acid sequence in target proteins. Here we report the solution structure of the AF-6 PDZ domain determined by NMR. Previously, the AF-6 PDZ domain was considered to be a class II PDZ domain. However we found that a unique hydrophilic amino acid, Gln70, at position alphaB1 makes the alphaB/betaB groove of the AF-6 PDZ domain significantly different from that of the canonical class II PDZ domain. The AF-6 PDZ domain does not have the second hydrophobic binding pocket, and the N-terminal end of alphaB is closer to betaB. Using BIACORE and NMR chemical shift perturbation experiments, we have studied the binding characteristics of the PDZ domain to the C-terminal peptide of Neurexin, KKNKDKEYYV, and that of Bcr, KRQSILFSTEV. The C-terminal peptide of Neurexin is a class II ligand, whereas that of Bcr is a class I ligand. The dissociation constants of these ligands were 4.08 x 10(-7) and 2.23 x 10(-6) m, respectively. Each of the four C-terminal positions in Neurexin and Bcr may contribute to the interaction. The three-dimensional models of the AF-6 PDZ-Neurexin C-terminal peptide complex and the AF-6 PDZ-Bcr C-terminal peptide complex were built up by molecular dynamics simulations. Unlike the canonical class II PDZ domain, Ala74 at alphaB5 rather than the residue at alphaB1 makes direct hydrophobic contact with the side chain of Tyr at the -2 position of the ligand.

Amino Acid Sequence↗