Papers Protein Language Model
“Protein Language Model” 태그가 달린 논문 119편 · 필터 해제
Generative modeling of intrinsically disordered protein regions by reinforcing sparse autoencoder features
Intrinsically disordered protein regions (IDRs) play central roles in cellular processes such as transcriptional regulation, signal transduction, and subcellular localization, yet their functional design remains challeng…
Protein Language ModelReinforcement LearningProtein DesignInterpreting Protein Language Model Embeddings via Orthogonal Projection for Protein Fitness Prediction
Recently, there has been a growing adoption of protein language models (PLMs) in biomedical science. Their embeddings provide a rich numerical representation of protein sequences which achieve state-of-the-art performanc…
Protein Language ModelLC-SEPLM: long-range contact-supervised adaptation for sequence-only protein representation learning
Protein language models learn transferable sequence representations. However, because they primarily model contextual dependencies along amino-acid sequences, their training objectives do not explicitly constrain the mod…
Representation LearningProtein Language ModelStructure-Regularized Interpretable TCR-Epitope Prediction
T cell receptor (TCR)-epitope binding prediction is essential for understanding adaptive immunity and developing immunotherapies. Existing sequence- and structure-based models often generalize poorly to unseen epitopes a…
Protein Language Model3D Masked Autoencoders are Robust Learners of Volumetric and Multimodal Cellular Representations for Microscopy
Self-supervised learning in fluorescence microscopy often relies on 2D projections, despite the inherently three-dimensional nature of cells. We present a systematic comparison of 2D and 3D masked autoencoders (MAE-2D vs…
Self-Supervised LearningRepresentation LearningProtein Language ModelProtein-Based Fish Species Identification: Dataset, Models, and Insights from Native Bangladeshi Fish
Correct identification of fish species is highly significant for food security, economic development, and climate resilience in Bangladesh. Protein sequences directly reflect functional and evolutionary constraints which…
Protein Language ModelViral Proteins Reveal Geometry of Protein Language Models
Protein language models are trained on highly imbalanced datasets, raising the question of how they represent underrepresented biological sequences. Using viral proteins as a case study across ESM model families, we iden…
Protein Language ModelComputational Modeling of Antibody-Antigen Complexes: PLM-Based and MSA-Based Approaches
Antibodies play a central role in the immune response by specifically recognizing and neutralizing antigens, and therapeutic antibodies have become major drugs for cancer and autoimmune diseases. However, their discovery…
Protein Language ModelSutra: Tensor-Op RNNs as a Compilation Target for Vector Symbolic Architectures
Sutra is a typed, purely functional programming language whose compiled forward pass is a PyTorch neural network. The compiler beta-reduces the whole program -- primitives, control flow, string I/O -- to one fused tensor…
Protein Language ModelEvoStruct: Bridging Evolutionary and Structural Priors for Antibody CDR Design via Protein Language Model Adaptation
Equivariant graph neural network (GNN) methods for antibody complementarity-determining region (CDR) design achieve the highest sequence recovery but suffer from severe vocabulary collapse. The current best GNN methods o…
Protein Language ModelGraph Neural NetworkProtein DesignSCOPE: Siamese Contrastive Operon Pair Embeddings for Functional Sequence Representation and Classification
Identifying operons is a fundamental step in understanding prokaryotic gene regulation, as classifying genes into operons supports the reconstruction of regulatory networks, functional annotation of unannotated genes, an…
Protein Language ModelBinary ClassificationStructural Interpretations of Protein Language Model Representations via Differentiable Graph Partitioning
Protein language models such as ESM-2 learn rich residue representations that achieve strong performance on protein function prediction, but their features remain difficult to interpret as structural $\&$ evolutionary si…
Protein Function PredictionProtein Language Modelgraph partitioningCo-Generative De Novo Functional Protein Design
De novo functional protein design aims to generate protein sequences that realize specified biochemical functions without relying on evolutionary templates, enabling broad applications in biotechnology and medicine. Exis…
Protein Language ModelProtein DesignMulti-Scale Reversible Chaos Game Representation: A Unified Framework for Sequence Classification
Biological classification with interpretability remains a challenging task. For this, we introduce a novel encoding framework, Multi-Scale Reversible Chaos Game Representation (MS-RCGR), that transforms biological sequen…
Protein Language ModelBiologically-Grounded Multi-Encoder Architectures as Developability Oracles for Antibody Design
Generative models can now propose thousands of \emph{de novo} antibody sequences, yet translating these designs into viable therapeutics remains constrained by the cost of biophysical characterization. Here we present Cr…
Protein Language ModelQ-BIOLAT: Binary Latent Protein Fitness Landscapes for QUBO-Based Optimization
Protein fitness optimization is inherently a discrete combinatorial problem, yet most learning-based approaches rely on continuous representations and are primarily evaluated through predictive accuracy. We introduce Q-B…
Protein Language ModelImmSET: Sequence-Based Predictor of TCR-pMHC Specificity at Scale
T cells are a critical component of the adaptive immune system, playing a role in infectious disease, autoimmunity, and cancer. T cell function is mediated by the T cell receptor (TCR) protein, a highly diverse receptor …
Protein Language ModelCross-Granularity Representations for Biological Sequences: Insights from ESM and BiGCARP
Recent advances in general-purpose foundation models have stimulated the development of large biological sequence models. While natural language shows symbolic granularity (characters, words, sentences), biological seque…
Protein Language ModelBeyond Muon: MUD (MomentUm Decorrelation) for Faster Transformer Training
Orthogonalized-momentum optimizers such as Muon improve transformer training by approximately whitening/orthogonalizing matrix-valued momentum updates via a short polar-decomposition iteration. However, polar-factor appr…
Protein Language ModelReverse Distillation: Consistently Scaling Protein Language Model Representations
Unlike the predictable scaling laws in natural language processing and computer vision, protein language models (PLMs) scale poorly: for many tasks, models within the same family plateau or even decrease in performance, …
Protein Language Model