arXiv research

A new method predicts protein functions using variable-length sequences.

New method maps protein sequences to embeddings encoding structural information.

As high-throughput biological sequencing becomes faster and cheaper, the need to extract useful information from sequencing becomes ever more paramount, often limited by low-throughput experimental characterizations. For proteins, accurate prediction of their functions directly from their primary amino-acid sequences h…

New Performer model tackles long-sequence protein modeling.

Recently exciting progress has been made on protein contact prediction, but the predicted contacts for proteins without many sequence homologs is still of low quality and not very useful for de novo structure prediction. This paper presents a new deep learning method that predicts contacts by integrating both evolution…

Automated protein structure prediction from cryo-EM data.

The knowledge regarding the function of proteins is necessary as it gives a clear picture of biological processes. Nevertheless, there are many protein sequences found and added to the databases but lacks functional annotation. The laboratory experiments take a considerable amount of time for annotation of the sequence…

Deep learning models optimize protein sequences.

Bayesian context trees capture complex dependencies in categorical sequences.

ProGen models protein sequences for synthetic biology.

This thesis improves protein contact prediction using unsupervised and supervised methods.

A new machine-learned CG model predicts protein structures efficiently.

PANDA predicts protein binding affinity changes from sequences, outperforming existing methods.

ProteinNet provides a standardized data set for protein structure prediction.

WideDTA predicts drug-target binding affinity using text-based information.

The effective representation of proteins is a crucial task that directly affects the performance of many bioinformatics problems. Related proteins usually bind to similar ligands. Chemical characteristics of ligands are known to capture the functional and mechanistic properties of proteins suggesting that a ligand base…

PLUS pre-trains protein sequences with structural info, improving performance.

ChemBoost predicts protein-ligand binding affinity using SMILES syntax.

A faster method for optimizing DNA and protein sequences using machine learning.

Study shows protein folding rate linked to topological changes.

Optimal transport embedding learns feature sets efficiently.

A new framework uses text descriptions to improve protein design.

Study infers evolutionary interactions from protein sequences using regularization methods.

The worldwide surge of multiresistant microbial strains has propelled the search for alternative treatment options. The study of Protein-Protein Interactions (PPIs) has been a cornerstone in the clarification of complex physiological and pathogenic processes, thus being a priority for the identification of vital compon…

MutaGAN predicts mutations of evolving protein populations using GANs.

BoGA combines evolutionary search with Bayesian optimization for efficient protein design.

Deep learning predicts protein structures accurately.

PS8-Net improves eight-state protein secondary structure prediction accuracy.

A new method estimates protein evolutionary fields and couplings from alignments.

Motivation. Protein contact map describes the pairwise spatial and functional relationship of residues in a protein and contains key information for protein 3D structure prediction. Although studied extensively, it remains very challenging to predict contact map using only sequence information. Most existing methods pr…

New method optimizes protein design by sampling from realistic inputs.

Tail-GNNs improve protein function prediction using relational reinforcement.

Artificial neural networks (ANNs) have gained a well-deserved popularity among machine learning tools upon their recent successful applications in image- and sound processing and classification problems. ANNs have also been applied for predicting the family or function of a protein, knowing its residue sequence. Here w…

This paper uses bandit theory and Thompson Sampling to optimize protein sequences.

Motivation: Drug discovery demands rapid quantification of compound-protein interaction (CPI). However, there is a lack of methods that can predict compound-protein affinity from sequences alone with high applicability, accuracy, and interpretability. Results: We present a seamless integration of domain knowledges and …

Seq-SetNet processes sequence sets directly, improving protein structure prediction.

ProtTrans models predict protein features without evolutionary info.

AbDiffuser generates full-atom antibodies with sequence and structure fidelity.

When analyzing the genome, researchers have discovered that proteins bind to DNA based on certain patterns of the DNA sequence known as "motifs". However, it is difficult to manually construct motifs due to their complexity. Recently, externally learned memory models have proven to be effective methods for reasoning ov…

TAPE benchmarks protein learning tasks, finds self-supervised pretraining boosts performance.

AFP-CKSAAP predicts antifreeze proteins using k-spaced amino acid pairs with deep neural networks.

New tools evaluate and optimize conditional sequence models in bioinformatics.

Flexible Kernels for Protein Property Prediction

RNA-binding proteins (RBPs) play crucial roles in many biological processes, e.g. gene regulation. Computational identification of RBP binding sites on RNAs are urgently needed. In particular, RBPs bind to RNAs by recognizing sequence motifs. Thus, fast locating those motifs on RNA sequences is crucial and time-efficie…

New method detects and compares folding pathways of knotted proteins.

Machine learning predicts protein structures and simulates dynamics.

Experimental determination of protein function is resource-consuming. As an alternative, computational prediction of protein function has received attention. In this context, protein structural classification (PSC) can help, by allowing for determining structural classes of currently unclassified proteins based on thei…

Long, flexible physical filaments are naturally tangled and knotted, from macroscopic string down to long-chain molecules. The existence of knotting in a filament naturally affects its configuration and properties, and may be very stable or disappear rapidly under manipulation and interaction. Knotting has been previou…