Publications by authors named "Fanhao Wang"

Despite the exciting progress in target-specific protein binder design, peptide binder design remains challenging due to the flexibility of peptide structures and the scarcity of protein-peptide complex structure data. In this study, we curated a large synthetic data set, referred to as PepPC-F, from the abundant protein-protein interface data and developed DiffPepBuilder, a target-specific peptide binder generation method that utilizes an SE(3)-equivariant diffusion model trained on PepPC-F to codesign peptide sequences and structures. DiffPepBuilder also introduces disulfide bonds to stabilize the generated peptide structures.

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In this work, we introduced a siderophore information database (SIDERTE), a digitized siderophore information database containing 649 unique structures. Leveraging this digitalized data set, we gained a systematic overview of siderophores by their clustering patterns in the chemical space. Building upon this, we developed a functional group-based method for predicting new iron-binding molecules with experimental validation.

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Strigolactones (SLs) represent a recently identified class of plant hormones that are crucial for plant tillering and mycorrhizal symbiosis. The gene, an essential receptor within the SLs signaling pathway, has been well-examined in crops, like rice ( L.) and ( L.

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Article Synopsis
  • Maize is a crucial global food crop but its yields are threatened by insect pests, prompting research into genetic modifications to enhance resistance.
  • The cry1Ah gene was mutated and introduced into a specific maize variety using advanced techniques, resulting in nine successful transgenic plants that express a protective protein effectively.
  • The transgenic maize showed a complete mortality rate of corn borers in lab tests and significant insecticidal activity in field trials, indicating strong commercialization potential for this genetically modified crop.
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A persistent goal for drug design is to generate novel chemical compounds with desirable properties in a labor-, time-, and cost-efficient manner. Deep generative models provide alternative routes to this goal. Numerous model architectures and optimization strategies have been explored in recent years, most of which have been developed to generate two-dimensional molecular structures.

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