Accurately Predicting Mutation-Caused Stability Changes from Protein Sequences Using Extreme Gradient Boosting

Abstract

Accurately predicting the impact of point mutation on protein stability has crucial roles in protein design and engineering. In this study, we proposed a novel method (BoostDDG) to predict stability changes upon point mutations from protein sequences based on the extreme gradient boosting. We extracted features comprehensively from evolutional information and predicted structures and performed feature selection by a strategy of sequential forward selection. The features and parameters were optimized by homologue-based cross-validation to avoid overfitting. Finally, we found that 14 features from six groups led to the highest Pearson correlation coefficient (PCC) of 0.535, which is consistent with the 0.540 on an independent test. Our method was indicated to consistently outperform other sequence-based methods on three precompiled test sets, and 7363 variants on two proteins (PTEN and TPMT). These results highlighted that BoostDDG is a powerful tool for predicting stability changes upon point mutations from protein sequences.

Using AlphaFold to predict the impact of single mutations on protein stability and function

“…However, AlphaFold may be used to help predict the impact of a mutation on protein stability or function using AlphaFold 3D models for 3D-structure based ∆∆G predictors. Indeed, it was reported many times that 3D-based predictors perform better than 1D-based [20,21,22], so the availability of a pool of high-quality 3D predicted structures could be a plus. However, the performance of the resulting predictions is going to be far from perfect: the 3D-structure based ∆∆G predictors are far from being perfect even using 3D structures from PDB [23], they show correlation of 0.59 or less in independent tests [24].…”

An End-to-end Oxford Nanopore Basecaller Using Convolution-augmented Transformer

“…We deployed a Gated Linear Units (GLU) [30] and a fully-connected layer before and after the convolution module respectively and the kernel sizes are [3,5,7,31×3] for the overall six encoder blocks.…”

“…Basecalling has a pivotal role in ONT sequencing, largely determining the usability of sequencing results for downstream applications [3][4][5]. However, generating high-quality sequencing reads remains a challenging task.…”

SE(3) Equivalent Graph Attention Network as an Energy-Based Model for Protein Side Chain Conformation

“…Deep learning has been shown to have the ability to capture the hidden high-order dependencies between source and target [15]. A number of deep-learning based methods including our previous works have successfully leveraged the deep learning methods in the field of protein design [16]- [18], protein engineering [19], [20] and protein structure prediction [21], [22]. Therefore, it is promising to learn protein energy function fully from crystal structure data by deep learning methods.…”