ProteinMAE: masked autoencoder for protein surface self-supervised learning.

SUMMARY: The biological functions of proteins are determined by the chemical and geometric properties of their surfaces. Recently, with the booming progress of deep learning, a series of learning-based surface descriptors have been proposed and achieved inspirational performance in many tasks such as protein design, protein-protein interaction prediction, etc. However, they are still limited by the problem of label scarcity, since the labels are typically obtained through wet experiments. Inspired by the great success of self-supervised learning in natural language processing and computer vision, we introduce ProteinMAE, a self-supervised framework specifically designed for protein surface representation to mitigate label scarcity. Specifically, we propose an efficient network and utilize a large number of accessible unlabeled protein data to pretrain it by self-supervised learning. Then we use the pretrained weights as initialization and fine-tune the network on downstream tasks. To demonstrate the effectiveness of our method, we conduct experiments on three different downstream tasks including binding site identification in protein surface, ligand-binding protein pocket classification, and protein-protein interaction prediction. The extensive experiments show that our method not only successfully improves the network's performance on all downstream tasks, but also achieves competitive performance with state-of-the-art methods. Moreover, our proposed network also exhibits significant advantages in terms of computational cost, which only requires less than a tenth of memory cost of previous methods. AVAILABILITY AND IMPLEMENTATION: https://github.com/phdymz/ProteinMAE.

Distinct binding interactions trigger opposite conformational modulations on pathogenic and wildtype Huntingtin exon 1 proteins.

Abnormal elongation of the polyglutamine tract transforms exon 1 of the Huntingtin protein (Htt-exon-1) from wildtype to pathogenic form, and causes Huntington's disease. As an intrinsically disordered protein, the structural ensemble of Htt-exon-1 is highly heterogeneous and the detailed conformation of toxic species is still under debate. Ispinesib, a potential small-molecule drug, has been identified to selectively link the pathogenic Htt-exon-1 into the autophagosome to degrade, thus opening an innovative therapeutic direction. However, the molecular mechanisms behind this selectivity remain largely elusive. Herein, we carry out extensive molecular dynamics simulations with an enhanced sampling method to investigate the ispinesib inducing conformational changes of pathogenic and wildtype Htt-exon-1 and the corresponding binding mechanisms. Our simulations reveal that the ispinesib binding induces opposite conformational changes in pathogenic and wildtype Htt-exon-1, i.e., the 'entropy collapse' with significant reduction of ß-sheets versus the 'entropy expansion' with a slight increase of α-helices. Network analyses further reveal that there are two stable binding sites in the pathogenic Htt-exon-1, while the binding on the wildtype Htt-exon-1 is highly dynamic and non-specific. These dramatic differences originate from the underlying distinct binding interactions. More specifically, stronger hydrogen bonds serve as the specific binding anchors in pathogenic Htt-exon-1, while stronger hydrophobic interactions dominate in the dynamic binding with wildtype Htt-exon-1. Our simulations provide an atomistic mechanism for the ispinesib selective binding on the pathogenic Htt-exon-1, and further shed light on the general mechanisms of small molecule modulation on intrinsically disordered proteins.