---
url: /mosaic/examples/protein-design.md
---
# Protein Design Explorer

Explore synthesized proteins generated via
[RFDiffusion](https://www.bakerlab.org/2023/07/11/diffusion-model-for-protein-design/).
"Minibinders" are small proteins that bind to a specific protein target.
When designing a minibinder, a researcher inputs the structure of the
target protein and other parameters into the AI diffusion model. Often, a
single, promising (parent) *version* can be run through the model again to
produce additional, similar designs to better sample the design space.

The pipeline generates tens of thousands of protein designs. The metric
*pAE* (predicted alignment error) measures how accurate a model was at
predicting the minibinder shape, whereas *pLDDT* (predicted local distance
difference test) measures a model's confidence in minibinder structure
prediction. For *pAE* lower is better, for *pLDDT* higher is better.

Additional parameters include *partial t* to set the time steps used by
the model, *noise* to create more diversity of designs, *gradient decay
function* and *gradient scale* to guide prioritizing different positions
at different time points, and *movement* to denote whether the minibinder
was left in its original position ("og") or moved to a desirable position
("moved").

The dashboard below enables exploration of the results to identify
promising protein designs and assess the effects of process parameters.

**Credit**: Adapted from a [UW CSE 512](https://courses.cs.washington.edu/courses/cse512/24sp/) project by Christina Savvides, Alexander Shida, Riti Biswas, and Nora McNamara-Bordewick. Data from the [UW Institute for Protein Design](https://www.ipd.uw.edu/).

## Specification

```py \[Python]
import vgplot as vg

proteins = vg.parquet("data/protein-design.parquet")

query = vg.selection.crossfilter()
point = vg.selection.intersect(empty=True)
plddt_domain = vg.param([67, 94.5])
pae_domain = vg.param([5, 29])
scheme = vg.param("observable10")

view = vg.vconcat(
    vg.hconcat(
        vg.menu(source=proteins, column="partial_t", label="Partial t", bind=query),
        vg.menu(source=proteins, column="noise", label="Noise", bind=query),
        vg.menu(
            source=proteins,
            column="gradient_decay_function",
            label="Gradient Decay",
            bind=query,
        ),
        vg.menu(
            source=proteins, column="gradient_scale", label="Gradient Scale", bind=query
        ),
    ),
    vg.vspace("1.5em"),
    vg.hconcat(
        vg.plot(
            vg.rect_y(
                data=proteins,
                filter_by=query,
                x=vg.bin("plddt_total", steps=60),
                y=vg.count(),
                z="version",
                fill="version",
                order="z",
                reverse=True,
                inset_left=0.5,
                inset_right=0.5,
            ),
            vg.width(600),
            vg.height(55),
            vg.x_axis(None),
            vg.y_axis(None),
            vg.x_domain(plddt_domain),
            vg.color_domain("Fixed"),
            vg.color_scheme(scheme),
            vg.margin_left(40),
            vg.margin_right(0),
            vg.margin_top(0),
            vg.margin_bottom(0),
        ),
        vg.hspace(5),
        vg.color_legend(plot="scatter", columns=1, bind=query),
    ),
    vg.hconcat(
        vg.plot(
            vg.frame(stroke="#ccc"),
            vg.raster(
                data=proteins,
                filter_by=query,
                x="plddt_total",
                y="pae_interaction",
                fill="version",
                pad=0,
            ),
            vg.interval_xy(
                bind=query, brush=vg.brush(stroke="currentColor", fill="transparent")
            ),
            vg.dot(
                data=proteins,
                filter_by=point,
                x="plddt_total",
                y="pae_interaction",
                fill="version",
                stroke="currentColor",
                stroke_width=0.5,
            ),
            vg.name("scatter"),
            vg.opacity_domain([0, 2]),
            vg.opacity_clamp(True),
            vg.color_domain("Fixed"),
            vg.color_scheme(scheme),
            vg.x_domain(plddt_domain),
            vg.y_domain(pae_domain),
            vg.x_label_anchor("center"),
            vg.y_label_anchor("center"),
            vg.margin_top(0),
            vg.margin_left(40),
            vg.margin_right(0),
            vg.width(600),
            vg.height(450),
        ),
        vg.plot(
            vg.rect_x(
                data=proteins,
                filter_by=query,
                x=vg.count(),
                y=vg.bin("pae_interaction", steps=60),
                z="version",
                fill="version",
                order="z",
                reverse=True,
                inset_top=0.5,
                inset_bottom=0.5,
            ),
            vg.width(55),
            vg.height(450),
            vg.x_axis(None),
            vg.y_axis(None),
            vg.margin_top(0),
            vg.margin_left(0),
            vg.margin_right(0),
            vg.y_domain(pae_domain),
            vg.color_domain("Fixed"),
            vg.color_scheme(scheme),
        ),
    ),
    vg.vspace("1em"),
    vg.table_input(
        bind=point,
        filter_by=query,
        source=proteins,
        columns=[
            "version",
            "pae_interaction",
            "plddt_total",
            "noise",
            "gradient_decay_function",
            "gradient_scale",
            "movement",
        ],
        width=680,
        height=215,
    ),
)

```