Please Enter Keywords
资源 63
[Lecture] Interpreting genetic variants and designing proteins using deep learning
Apr. 18, 2024

from clipboard

Speaker: Pro. Zhang Haicang (Institute of computing technology, Chinese Academy Sciences)

Time: 13:30-14:30 p.m., April 18, 2024, GMT+8

Venue: Room 411, Jinguang Life Science Building, PKU


Accurate prediction of damaging missense variants is crucial in genetic studies and clinical diagnosis. While many methods have been developed, their performance has been limited. We propose two deep learning-based methods, MVP and gMVP, for predicting the functional effects of missense variants. MVP utilizes a deep residual network to leverage large training datasets and numerous correlated predictors, while gMVP incorporates graph attention networks to incorporate co-evolutionary and structural information. Both MVP and gMVP significantly improve performance in deep mutational scanning data and cross-gene de novo variants enrichment. Additionally, we introduce DiffAffinity for predicting mutational effects on protein-protein binding. DiffAffinity employs a diffusion-based model to learn the generative process of side-chain conformations at protein-protein interfaces, achieving state-of-the-art performance.
De novo protein structure and sequence design play a critical role in protein engineering and drug discovery. We present CarbonDesign and CarbonNovo for protein sequence design and structure and sequence Co-design, respectively. CarbonDesign utilizes a novel architecture, Inverseformer, to learn representations from backbone structures and an amortized Markov Random Fields model for sequence decoding. CarbonNovo is the first end-to-end generative method for structure and sequence co-design, based on a unified energy-based model. Specifically tailored for antibody design, AbX integrates evolutionary, physical, and geometric constraints into a diffusion-based generative model, addressing the scarcity of labeled antibody-antigen complex data. All these methods achieve state-of-the-art performance in protein sequence and structure design.

Source: School of Life Sciences, PKU