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Biology subjects

Han, H. L.

Publications and source records attributed to Han, H. L..

5 recordsLinked to original sources

Potent substoichiometric inhibition of alpha-synuclein aggregation by de novo oligomer-binding proteins

The intrinsically disordered, non-amyloid-{beta} component domain of -synuclein (Syn) drives aggregation in Parkinson's disease but lacks a stable epitope for structure-based drug design. We used deep learning-based protein design to generate de novo binders capturing this region in a {beta}-strand conformation. Three designs bound Syn with nanomolar affinities, with solution NMR spectroscopy supporting the intended fold. All three suppressed fibril formation at substoichiometric ratios, and fitting of aggregation kinetics resolved the microscopic steps inhibited by each. Notably, one binder preferentially engaged oligomers and inhibited secondary nucleation, both closely linked to toxicity. In cells, all three suppressed seeded Syn assembly, with the oligomer binder being the most effective. Together, these findings establish a set of molecules that target distinct aggregation intermediates and mechanistically reshape Syn assembly.

biophysics↗

Neurodegeneration emerges at a cellular tipping point between protein accumulation and removal.

Protein aggregates are a pathological hallmark across neurodegenerative diseases. Yet, the disconnect between molecular-level aggregation and the emergence of disease severely limits mechanistic understanding of neurodegeneration. Here, we bridge this disconnect by showing that a cellular tipping point emerges as a universal feature across diseases from the competition between aggregate accumulation and removal. We map the resulting cellular phase transition with our high-throughput live-cell assay, measuring the tipping point that separates healthy cells from those with large aggregate loads. Using super-resolution imaging of brain tissue from Alzheimers and Parkinsons disease, we quantify how the balance of accumulation and removal is shifted in disease. We validate our framework by predicting how designed aggregation inhibitors shift the tipping point to restore cellular homeostasis. Our results provide a mechanistic framework connecting molecular-level aggregation to disease, paving the way for a quantitative, unified understanding of neurodegeneration and enabling predictions of therapeutic efficacy.

neuroscience↗

Small-molecule binding and sensing with a designed protein family

Despite transformative advances in protein design with deep learning, the design of small-molecule-binding proteins and sensors for arbitrary ligands remains a grand challenge. Here we combine deep learning and physics-based methods to generate a family of proteins with diverse and designable pocket geometries, which we employ to computationally design binders for six chemically and structurally distinct small-molecule targets. Biophysical characterization of the designed binders revealed nanomolar to low micromolar binding affinities and atomic-level design accuracy. The bound ligands are exposed at one edge of the binding pocket, enabling the de novo design of chemically induced dimerization (CID) systems; we take advantage of this to create a biosensor with nanomolar sensitivity for cortisol. Our approach provides a general method to design proteins that bind and sense small molecules for a wide range of analytical, environmental, and biomedical applications.

biochemistry↗

Blueprinting expandable nanomaterials with standardized protein building blocks

A wooden house frame consists of many different lumber pieces, but because of the regularity of these building blocks, the structure can be designed using straightforward geometrical principles. The design of multicomponent protein assemblies in comparison has been much more complex, largely due to the irregular shapes of protein structures1. Here we describe extendable linear, curved, and angled protein building blocks, as well as inter-block interactions that conform to specified geometric standards; assemblies designed using these blocks inherit their extendability and regular interaction surfaces, enabling them to be expanded or contracted by varying the number of modules, and reinforced with secondary struts. Using X-ray crystallography and electron microscopy, we validate nanomaterial designs ranging from simple polygonal and circular oligomers that can be concentrically nested, up to large polyhedral nanocages and unbounded straight "train track" assemblies with reconfigurable sizes and geometries that can be readily blueprinted. Because of the complexity of protein structures and sequence-structure relationships, it has not been previously possible to build up large protein assemblies by deliberate placement of protein backbones onto a blank 3D canvas; the simplicity and geometric regularity of our design platform now enables construction of protein nanomaterials according to "back of an envelope" architectural blueprints.

biochemistry↗

Design of amyloidogenic peptide traps

Segments of proteins with {beta}-strand propensity can self associate to form amyloid fibrils associated with many diseases. These regions often adopt alternative structures in their folded states, or are intrinsically disordered in solution, making it difficult to generate binders or inhibitors with existing strategies. Here we describe a general approach to bind such segments in {beta}-strand and {beta}-hairpin conformations using de novo designed scaffolds that contain deep peptide binding clefts flanked by {beta}-strands that form hydrogen bonds to the peptide upon binding. The designs bind their cognate peptides in vitro with nanomolar affinities and in mammalian cells. The crystal structure of a designed protein-peptide complex is close to the design model, and NMR characterization reveals how the peptide binding cleft is protected in the apo state. We use the approach to design binders to segments of the amyloid forming proteins Transthyretin, Tau, Serum amyloid A1 and A{beta}42. The A{beta} binders block assembly of A{beta} fibrils as effectively as the most potent of the clinically tested antibodies to date.

synthetic biology↗