MULTIPLE STRATEGIES IN COMPUTATIONAL PROTEIN DESIGN: STRUCTURAL INTUITION, PROBABILISTIC INVERSE FOLDING, AND GENERATIVE APPROACHES

Designing proteins with specific structural and functional features has become increasingly feasible through the integration of physics-based modeling, statistical inference, and generative algorithms. This work presents conceptual, methodological, and applied examples spanning multiple stages of protein design, from early structure-guided approaches to inverse backbone design and modern generative models. As a structure-guided example, simulations were used to investigate how sequence and geometry contribute to the mechanical behavior of supramolecular assemblies of one-dimensional coiled coils, revealing the molecular basis of their exceptional rigidity. Building on these insights, rational modifications—such as electrostatic patterning and hydrophobic tuning—were introduced to create assemblies that form liquid crystals at low concentrations and remain stable across a wide pH range. In inverse backbone design, a new probabilistic modeling framework enabled full-sequence design with minimal sampling, yielding sequences that reliably fold into targeted structures. This framework was further applied to generate rare geometric motifs, including hexameric coiled coils, with experimentally confirmed stability and compact higher-order assembly. For generative algorithms, a hallucination-based design strategy was developed to create proteins with precise structural constraints and functional attributes, such as internal cavities and ligand-binding capability. This approach integrated predictive modeling, geometric regularization, and binding-aware evaluation into an iterative optimization scheme capable of producing structurally novel and foldable sequences. Across all stages, the designs illustrate how complementary computational strategies can be systematically applied to distinct levels of the protein design problem—from backbone geometry and sidechain packing to supramolecular assembly and functional site engineering.

Author dc:creator

• Yang, Daibei

Advisor dc:contributor.advisor

• Saven, Jeffery, G.

• Chemistry
• Biochemistry, Biophysics, and Structural Biology

Language dc:language.iso

en

Repository record dc:identifier.uri

https://repository.upenn.edu/handle/20.500.14332/61728

OAI identifier oai:identifier

oai:repository.upenn.edu:20.500.14332/61728