Rosalia G. Schneider

The vast majority of missense variants observed in the human genome are of unknown clinical significance. We present AlphaMissense, an adaptation of AlphaFold fine-tuned on human and primate variant population frequency databases to predict missense variant pathogenicity. By combining structural context and evolutionary conservation, our model achieves state-of-the-art results across a wide range of genetic and experimental benchmarks, all without explicitly training on such data. The average pathogenicity score of genes is also predictive for their cell essentiality, capable of identifying short essential genes that existing statistical approaches are underpowered to detect. As a resource to the community, we provide a database of predictions for all possible human single amino acid substitutions and classify 89% of missense variants as either likely benign or likely pathogenic.

Color is one of the most common ways to convey information in visualization applications. Color vision deficiency (CVD) affects approximately 200 million individuals worldwide and considerably degrades their performance in understanding such contents by creating red-green or blue-yellow ambiguities. While several content-specific methods have been proposed to resolve these ambiguities, they cannot achieve this effectively in many situations for contents with a large variety of colors. More importantly, they cannot facilitate color identification. We propose a technique for using patterns to encode color information for individuals with CVD, in particular for dichromats. We present the first content-independent method to overlay patterns on colored visualization contents that not only minimizes ambiguities but also allows color identification. Further, since overlaying patterns does not compromise the underlying original colors, it does not hamper the perception of normal trichromats. We validated our method with two user studies: one including 11 subjects with CVD and 19 normal trichromats, and focused on images that use colors to represent multiple categories; and another one including 16 subjects with CVD and 22 normal trichromats, which considered a broader set of images. Our results show that overlaying patterns significantly improves the performance of dichromats in several color-based visualization tasks, making their performance almost similar to normal trichromats'. More interestingly, the patterns augment color information in a positive manner, allowing normal trichromats to perform with greater accuracy.

Computational design of protein-binding proteins is a fundamental capability with broad utility in biomedical research and biotechnology. Recent methods have made strides against some target proteins, but on-demand creation of high-affinity binders without multiple rounds of experimental testing remains an unsolved challenge. This technical report introduces AlphaProteo, a family of machine learning models for protein design, and details its performance on the de novo binder design problem. With AlphaProteo, we achieve 3- to 300-fold better binding affinities and higher experimental success rates than the best existing methods on seven target proteins. Our results suggest that AlphaProteo can generate binders "ready-to-use" for many research applications using only one round of medium-throughput screening and no further optimization.

This repository provide AlphaMissense predictions. Please see the README for more details. For questions about AlphaMissense or the prediction Database please email alphamissense@google.com.

This repository provide AlphaMissense predictions. Please see the README for more details. For questions about AlphaMissense or the prediction Database please email alphamissense@google.com.