Samuel Sledzieski

Learning the language of protein-protein interactions

The paper introduces MINT, a protein language model designed to model protein-protein interactions, outperforming existing models in tasks like binding affinity prediction and mutational effect estimation.

PLANET-MD: Ultra-fast Proteome-scale Prediction of Allosteric Networks in Proteins

PLANET-MD predicts dynamic protein properties from sequence or structure with high accuracy and speed, enabling comprehensive proteome-scale analysis of protein dynamics.

Decoding the Functional Interactome of Non-Model Organisms with PHILHARMONIC

PHILHARMONIC infers protein interaction networks and functional organization in non-model organisms using deep learning and spectral clustering, validated in Drosophila and coral symbionts.

Learning the Language of Antibody Hypervariability

This paper introduces AbMAP, a transfer learning framework for antibody sequence analysis, showing it can predict structure, binding, and paratope with high accuracy and enable antibody optimization.

Democratizing Protein Language Models with Parameter-Efficient Fine-Tuning

This paper applies parameter-efficient fine-tuning methods to proteomics, achieving competitive or better performance than traditional fine-tuning with significantly reduced computational resources.

Transfer of Knowledge from Model Organisms to Evolutionarily Distant Non-Model Organisms: The Coral Pocillopora damicornis Membrane Signaling Receptome

The paper develops a method to identify homologous membrane receptors in non-model corals by combining remote homology detection and functional analysis, mapping over 1000 human receptors to coral receptors.

Learning the Drug-Target Interaction Lexicon

ConPLex predicts drug-target interactions using a deep learning model that combines pre-trained protein language models with contrastive co-embedding, achieving high accuracy and scalability.

Adapting protein language models for rapid DTI prediction

This paper presents a deep learning approach using pre-trained protein language models for drug-target interaction prediction, achieving higher accuracy and faster training than existing methods.

Contrasting drugs from decoys

This paper introduces CON-Plex, a drug-target interaction model that improves specificity by distinguishing drugs from decoys using contrastive learning.

Single-cell mosaicism analysis reveals cell-type-specific somatic mutational burden in Alzheimer's Dementia

This study analyzes somatic mosaicism in Alzheimer's Dementia using single-cell data, finding increased mutational burden in specific cell types and genes associated with disease processes.

Sequence-based prediction of protein-protein interactions: a structure-aware interpretable deep learning model

D-SCRIPT predicts protein-protein interactions from sequence using a structure-aware deep learning model, improving cross-species generalization and functional characterization of proteins.

TreeFix-TP: Phylogenetic Error-Correction for Infectious Disease Transmission Network Inference

TreeFix-TP improves transmission phylogeny inference by correcting phylogenetic errors using intra-host diversity and host information, enhancing accuracy in outbreak analysis.