Machine Learning in Bioinformatics

Genolator: A Multimodal Large Language Model Fusing Natural Language, Genomic, and Structural Tokens for Protein Function Interpretation

Genolator is a multimodal large language model that combines genomic and structural data with natural language to interpret protein function, outperforming existing models in accuracy.

TooTranslator: Zero-Shot Classification of Specific Substrates for Transport Proteins by Language Embedding Alignment of Proteins and Chemicals

TooTranslator uses language embedding alignment to predict substrates of transport proteins, achieving 80% accuracy with top-100 predictions for unseen substrates.

Evaluating the Significance of Embedding-Based Protein Sequence Alignment with Clustering and Double Dynamic Programming for Remote Homology

This study evaluates an embedding-based protein sequence alignment method using clustering and double dynamic programming, showing improved performance in detecting remote homology compared to traditional and state-of-the-art approaches.

E1: Retrieval-Augmented Protein Encoder Models

The paper introduces Profluent-E1, retrieval-augmented protein language models that improve performance by conditioning on homologous sequences, achieving state-of-the-art results in fitness prediction and contact-map prediction.

SignalGen: A Protein Language Model Based AI Agent For Optimal Signal Peptide Prediction

SignalGen predicts optimal signal peptides for protein expression by integrating mature protein sequences, organism, and localization using a Latent Residual Transformer.

Integrating Millions of Years of Evolutionary Information into Protein Structure Models for Function Prediction

This paper introduces ESMSCOP, a contrast-aware pre-training framework that integrates evolutionary and structural information for improved protein function prediction.

Zero-shot segmentation using embeddings from a protein language model identifies functional regions in the human proteome

The paper introduces a zero-shot protein segmentation method using ProtT5 embeddings to identify and categorize functional protein segments without sequence conservation reliance.

Graph attention with structural features improves the generalizability of identifying functional sequences at a protein interface

The paper improves prediction of functional protein interface sequences by integrating structural and sequence features using a graph attention model, achieving better generalization across diverse variants.

MotifAE Reveals Functional Sequence Patterns from Protein Language Model: Unsupervised Discovery and Interpretability Analysis

MotifAE discovers functional sequence patterns from protein language models through an unsupervised framework, improving motif identification and enabling stability prediction.

Identification of short peptides that correlate with cytoplasmic retention of human proteins

The paper identifies 15 candidate cytoplasmic retention peptides in human proteins, with 10 having unrelated sequences, and shows that retention sequences are located at specific sites across protein isoforms.

MkAtt-SDN2GO: Multi-kernel Attentive-SDN2GO Network for Protein Function Prediction in Humans

MkAtt-SDN2GO improves protein function prediction by integrating multi-kernel convolution and attention mechanisms, achieving better performance than SDN2GO on Gene Ontology terms.

PLiCat: Decoding protein-lipid interactions by large language model

PLiCat uses large language models to classify protein-lipid interactions across eight lipid categories and predict lipid-binding sites and mutation impacts.

ProAffinity++

ProAffinity++ predicts protein-protein binding affinities using sequence and structure representations, outperforming existing methods in accuracy and handling complex cases like antigen-antibody interactions.

Which pLM to choose?

This study benchmarks fourteen protein-language models (pLMs) to determine which best captures biological properties, finding that mid-scale models often outperform larger ones for general tasks.

ProteinSight: A Volumetric Deep Learning Model for Carotenoid-Binding Site Prediction

ProteinSight predicts carotenoid-binding sites using a 3D U-Net model, showing high sensitivity and specificity on a curated test set and identifying potential sites on Human Serum Albumin.

DomDiff: protein family and domain annotation via diffusion model and ESM2 embedding

DomDiff improves domain boundary identification and classification using a diffusion model with ESM2 embeddings, outperforming existing methods and excelling in rare families.

Democratizing Protein Language Model Training, Sharing and Collaboration

The paper introduces SaprotHub, a platform that simplifies training and sharing of protein language models, enabling non-experts to build and collaborate on customized models.

StoPred: Accurate Stoichiometry Prediction for ProteinComplexes Using Protein Language Models and GraphAttention

StoPred predicts protein complex stoichiometry using protein language models and graph attention networks, achieving higher accuracy for both homomeric and heteromeric complexes than existing methods.

AstraPTM2: A Context-Aware Transformer for Broad-Spectrum PTM Prediction

AstraPTM2 predicts 39 PTM types on full-length proteins using a transformer model with structural and sequence features, achieving high AUROC and macro-F1 scores.

Towards functional annotation with latent protein languagemodel features

The paper develops a pipeline to annotate protein functions using latent features from protein language models, identifying missing annotations and structurally matching proteins.

ProTrek: Navigating the Protein Universe through Tri-Modal Contrastive Learning

ProTrek introduces a tri-modal contrastive learning model for protein sequence, structure, and function, achieving significant improvements in speed and accuracy over existing tools.

Qimai: a multi-agent framework for zero-shot DNA-protein interaction prediction

Qimai improves DNA-protein interaction prediction for unseen proteins by integrating deep learning with LLM-based reasoning, achieving higher AUC-PR, AUC-ROC, and MCC than standalone models.

Efficient Protein Engineering via Integrated Language Models and Bayesian Optimization

This study uses protein language models and Bayesian optimization to improve protein engineering efficiency by predicting function from sequences and optimizing variant search.

InterPLM: Discovering Interpretable Features in Protein Language Models via Sparse Autoencoders

This paper introduces InterPLM, a framework using sparse autoencoders to extract interpretable features from protein language models, revealing both known and novel biological concepts.

DeepGene: An Efficient Foundation Model for Genomics based on Pan-genome Graph Transformer

DeepGene is a foundation model for genomics that addresses genetic language diversity, model efficiency, and length extrapolation, achieving top performance on 9 out of 28 genome understanding tasks.

Fine-Tuning Protein Language Models Unlocks the Potential of Underrepresented Viral Proteomes

This study fine-tunes pre-trained protein language models on viral protein sequences to improve their performance on downstream tasks and representation quality.

PlasmoFP: leveraging deep learning to predict protein function of uncharacterized proteins across the malaria parasite genus

PlasmoFP uses deep learning to predict protein function in Plasmodium species, reducing unannotated proteins from 15-59% to 3-28% and increasing fully annotated proteins from 7-42% to 36-68%.

Paying Attention to Attention: High Attention Sites as Indicators of Protein Family and Function in Language Models

This paper identifies High Attention (HA) sites in protein sequences using the ESM model to improve protein family classification and function prediction.

A curriculum learning approach to training antibody language models

This paper introduces a curriculum learning approach for training antibody language models, showing that a 650M-parameter model outperforms existing mixed models in downstream tasks.

OmicsFUSION: A Pretrained Hyena-Based Framework for Encoding, Reconstruction, and Representation of the Omics and DNA Data

The paper introduces a framework that integrates DNA sequences with omics data using a pretrained model, enabling reconstruction and unified representation of genomic and functional information.