AbMelt: Learning antibody thermostability from molecular dynamics.

Antibody thermostability is challenging to predict from sequence and/or structure. This difficulty is likely due to the absence of direct entropic information. Herein, we present AbMelt where we model the inherent flexibility of homologous antibody structures using molecular dynamics simulations at three temperatures and learn the relevant descriptors to predict the temperatures of aggregation (T), melt onset (T), and melt (T).

A Computational Strategy for the Rapid Identification and Ranking of Patient-Specific T Cell Receptors Bound to Neoantigens.

T cell receptor (TCR) recognition of a peptide-major histocompatibility complex (pMHC) is crucial for adaptive immune response. The identification of therapeutically relevant TCR-pMHC protein pairs is a bottleneck in the implementation of TCR-based immunotherapies. The ability to computationally design TCRs to target a specific pMHC requires automated integration of next-generation sequencing, protein-protein structure prediction, molecular dynamics, and TCR ranking.

MolPROP: Molecular Property prediction with multimodal language and graph fusion.

Pretrained deep learning models self-supervised on large datasets of language, image, and graph representations are often fine-tuned on downstream tasks and have demonstrated remarkable adaptability in a variety of applications including chatbots, autonomous driving, and protein folding. Additional research aims to improve performance on downstream tasks by fusing high dimensional data representations across multiple modalities. In this work, we explore a novel fusion of a pretrained language model, ChemBERTa-2, with graph neural networks for the task of molecular property prediction.

AbLEF: antibody language ensemble fusion for thermodynamically empowered property predictions.

Motivation: Pre-trained protein language and/or structural models are often fine-tuned on drug development properties (i.e. developability properties) to accelerate drug discovery initiatives.

A microfluidic strategy to capture antigen-specific high affinity B cells.

Assessing B cell affinity to pathogen-specific antigens prior to or following exposure could facilitate the assessment of immune status. Current standard tools to assess antigen-specific B cell responses focus on equilibrium binding of the secreted antibody in serum. These methods are costly, time-consuming, and assess antibody affinity under zero-force.

A dynamic biomimetic model of the membrane-bound CD4-CD3-TCR complex during pMHC disengagement.

The coordinated (dis)engagement of the membrane-bound T cell receptor (TCR)-CD3-CD4 complex from the peptide-major histocompatibility complex (pMHC) is fundamental to TCR signal transduction and T cell effector function. As such, an atomic-scale understanding would not only enhance our basic understanding of the adaptive immune response but would also accelerate the rational design of TCRs for immunotherapy. In this study, we explore the impact of the CD4 coreceptor on the TCR-pMHC (dis)engagement by constructing a molecular-level biomimetic model of the CD3-TCR-pMHC and CD4-CD3-TCR-pMHC complexes within a lipid bilayer.

Receptor-ligand non-equilibrium kinetics (RLNEK) 1.0: An integrated Trackmate laminar flow chamber analysis.

Although parallel plate flow chamber assays are widely performed, extraction of kinetic parameters is limited to specialized labs with mathematical expertise and customized video-microscopy tracking tools. The recent development of Trackmate has increased researcher accessibility to tracking particles in video-microscopy experiments; however, there is a lack of tools that analyze this tracking information. We report a software tool, compatible with Trackmate, that extracts Receptor Ligand Non-Equilibrium Kinetic (RLNEK) parameters from video-microscopy data.

A computational algorithm to assess the physiochemical determinants of T cell receptor dissociation kinetics.

The rational design of T Cell Receptors (TCRs) for immunotherapy has stagnated due to a limited understanding of the dynamic physiochemical features of the TCR that elicit an immunogenic response. The physiochemical features of the TCR-peptide major histocompatibility complex (pMHC) bond dictate bond lifetime which, in turn, correlates with immunogenicity. Here, we: i) characterize the force-dependent dissociation kinetics of the bond between a TCR and a set of pMHC ligands using Steered Molecular Dynamics (SMD); and ii) implement a machine learning algorithm to identify which physiochemical features of the TCR govern dissociation kinetics.

Using molecular dynamics simulations to interrogate T cell receptor non-equilibrium kinetics.

An atomic-scale mechanism of T Cell Receptor (TCR) mechanosensing of peptides in the binding groove of the peptide-major histocompatibility complex (pMHC) may inform the design of novel TCRs for immunotherapies. Using steered molecular dynamics simulations, our study demonstrates that mutations to peptides in the binding groove of the pMHC - which are known to discretely alter the T cell response to an antigen - alter the MHC conformation at equilibrium. This subsequently impacts the overall strength (duration and length) of the TCR-pMHC bond under constant load.