Graph convolutional network for protein sequence sampling and prediction using structural data
Orellana, G. A.; Caceres-Delpiano, J.; Ibanez, R.; Alvarez, L.
Show abstract
The increasing integration between protein engineering and machine learning has led to many interesting results. A problem still to solve is to evaluate the likelihood that a sequence will fold into a target structure. This problem can be also viewed as sequence prediction from a known structure. In the current work, we propose improvements in the recent architecture of Geometric Vector Perceptrons [1] in order to optimize the sampling of sequences from a known backbone structure. The proposed model differs from the original in that there is: (i) no updating in the vectorial embedding, only in the scalar one, (ii) only one layer of decoding. The first aspect improves the accuracy of the model and reduces the use of memory, the second allows for training of the model with several tasks without incurring data leakage. We treat the trained classifier as an Energy-Based Model and sample sequences by sampling amino acids in a non-autoreggresive manner in the empty positions of the sequence using energy-guided criteria and followed by random mutation optimization. We improve the median identity of samples from 40.2% to 44.7%. An additional question worth investigating is whether sampled and original sequences fold into similar structures independent of their identity. We chose proteins in our test set whose sampled sequences show low identity (under 30%) but for which our model predicted favorable energies. We used AlphaFold [2, 3] and observed that the predicted structures for sampled sequences highly resemble the predicted structures for original sequences, with an average TM-score of 0.84.
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Pair-EGRET: enhancing the prediction of protein-proteininteraction sites through graph attention networks and protein language models 96%
- Embedding-based alignment: combining protein language models and alignment approaches to detect structural similarities in the twilight-zone 96%
- Patch-DCA: Improved Protein Interface Prediction by utilizing Structural Information and Clustering DCA scores 96%
Similar papers in this journal
Similar papers in this journal
- SAINT-Angle: self-attention augmented inception-inside-inception network and transfer learning improve protein backbone torsion angle prediction 96%
- MULAN: Multimodal Protein Language Model for Sequence and Structure Encoding 96%
- Estimating Protein Complex Model Accuracy Using Graph Transformers and Pairwise Similarity Graphs 94%
Similar papers in this journal
- Combined topological data analysis and geometric deep learning reveal niches by the quantification of protein binding pockets 97%
- Building explainable graph neural network by sparse learning for the drug-protein binding prediction 95%
- ProALIGN: Directly learning alignments for protein structure prediction via exploiting context-specific alignment motifs 95%
Similar papers in this journal
- Struct2Graph: A graph attention network for structure based predictions of protein-protein interactions 96%
- Multi-Head Attention-based U-Nets for Predicting Protein Domain Boundaries Using 1D Sequence Features and 2D Distance Maps 96%
- Predicting RNA Sequence-Structure Likelihood via Structure-Aware Deep Learning 95%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.