arXiv research

New method maps protein sequences to embeddings encoding structural information.

Deep learning predicts protein structures accurately.

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…

PGEL learns embeddings to diversify protein motifs while maintaining biological function.

ProtTrans models predict protein features without evolutionary info.

Identification of high affinity drug-target interactions is a major research question in drug discovery. Proteins are generally represented by their structures or sequences. However, structures are available only for a small subset of biomolecules and sequence similarity is not always correlated with functional similar…

Automated protein structure prediction from cryo-EM data.

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

Protein Thoughts interprets protein interactions with clear reasoning, improving prediction accuracy.

Deep learning models optimize protein sequences.

Flexible Kernels for Protein Property Prediction

ProGen models protein sequences for synthetic biology.

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.

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

Amino acid sequence portrays most intrinsic form of a protein and expresses primary structure of protein. The order of amino acids in a sequence enables a protein to acquire a particular stable conformation that is responsible for the functions of the protein. This relationship between a sequence and its function motiv…

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…

GEFA predicts drug-target affinity using graph neural networks.

Rapid progress in deep learning has spurred its application to bioinformatics problems including protein structure prediction and design. In classic machine learning problems like computer vision, progress has been driven by standardized data sets that facilitate fair assessment of new methods and lower the barrier to …

Motivation: Prediction of the interaction affinity between proteins and compounds is a major challenge in the drug discovery process. WideDTA is a deep-learning based prediction model that employs chemical and biological textual sequence information to predict binding affinity. Results: WideDTA uses four text-based inf…

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

Optimal transport embedding learns feature sets efficiently.

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

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.

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

CogMol designs novel drug-like molecules for SARS-CoV-2 targets.

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…

Graph embedding method captures both local and global network structure.

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…

Tail-GNNs improve protein function prediction using relational reinforcement.

New Performer model tackles long-sequence protein modeling.

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…

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…

Focusing on a small set of proteins that i) fold in a concerted, all-or-none fashion and ii) do not contain knots or slipknots, we show that the Gauss linking integral, the torsion and the number of sequence-distant contacts provide information regarding the folding rate. Our results suggest that the global topology/ge…

Generative Distribution Embeddings learn multiscale representations of distributions.

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

We present a new method for design problems wherein the goal is to maximize or specify the value of one or more properties of interest. For example, in protein design, one may wish to find the protein sequence that maximizes fluorescence. We assume access to one or more, potentially black box, stochastic "oracle" predi…

Bayesian context trees capture complex dependencies in categorical sequences.

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…

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.

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.