Biology subjects

Pitman, M.

Publications and source records attributed to Pitman, M..

5 recordsLinked to original sources

bioRxiv2026

MAHLER: Integrating Metadynamics and Inverse Folding to Predict Antibody-Antigen Kinetics

Binding kinetics are crucial for antibody function, shaping pharmacokinetics and in vivo efficacy beyond what equilibrium affinity captures. We present "Metadynamics-Anchored Hybrid Learning for Engineering off-Rates (MAHLER)", a fully open-source machine learning/physics hybrid method that predicts relative antibody-antigen residence times at scale. Incorporating inverse-folding models into molecular dynamics simulations, MAHLER shows first-in-class screening-grade accuracy in calculating relative antibody-antigen dissociation kinetics across a family of point mutants. After initial antigen-specific setup, each prediction takes only 4 minutes on a single NVIDIA A100 GPU, compared to days even with already enhanced molecular dynamics simulations. This provides practical kinetics-aware complement to current computational design approaches that focus primarily on binding affinity for antibody-antigen complexes.

biophysics↗

bioRxiv2026

On improving experimental binding affinity predictions with synthetic data

The success of deep learning binding affinity prediction models depends critically on expanding experimental data with reliable synthetic data. We extend the Structurally Augmented IC50 Repository (SAIR) with {approx}80K absolute free energy perturbation (AFEP) calculations and present two distinct data splits, SAIR-FEP and SAIR-OOD (out-of-distribution), to simulate realistic drug discovery scenarios. We compare sequence-based proteochemometric (PCM) models and state-of-the-art, structure-based deep learning models and demonstrate that PCM models can be enhanced by physics-based descriptors. While structure-based deep learning methods capture finer geometric detail, their performance is highly sensitive to the input structure. By filtering for high-confidence, co-folded complexes, we show that the performance improves predictably, whereas training on all complexes blindly does not yield performance gains. Finally, using the SAIR-OOD split, we demonstrate that simultaneous training on synthetic and experimental data improves performance on publicly available, experimental benchmarks. These results provide a clear strategy for using synthetic data to advance experimental binding affinity predictions.

molecular biology↗

bioRxiv2022

An optimized method for high-quality RNA extraction from distinctive intrinsic laryngeal muscles in the rat model.

Challenges related to high-quality RNA extraction from post-mortem tissue have limited RNA-sequencing (RNA-seq) application in certain skeletal muscle groups, including the intrinsic laryngeal muscles (ILMs). The present study identified critical factors contributing to substandard RNA extraction from the ILMs and established a suitable method that permitted high-throughput analysis. Here, standard techniques for tissue processing were adapted, and an effective means to control confounding effects during specimen preparation was determined. The experimental procedure consistently provided sufficient intact total RNA (N = 68) and RIN ranging between 7.0 and 8.6, which was unprecedented using standard RNA purification protocols. This study confirmed the reproducibility of the workflow through repeated trials at different postnatal time points and across the distinctive ILMs. High-throughput diagnostics from 90 RNA samples indicated no sequencing alignment scores below 70%, validating the extraction strategy. Significant differences between the standard and experimental conditions suggest circumvented challenges and broad applicability to other skeletal muscles. This investigation remains ongoing given the prospect of therapeutic insights to voice, swallowing, and airway disorders. The present methodology supports pioneering global transcriptome investigations in the larynx previously unfounded in literature.

molecular biology↗

bioRxiv2018

Elastic and Rigidified CENP-A Nucleosomes govern Centromeric Chromatin Plasticity

Histone variants fine-tune transcription, replication, DNA damage repair, and faithful chromosome segregation. Whether and how nucleosome variants encode unique mechanical properties to their cognate chromatin structures remains elusive. Here, using novel in silico and in vitro nanoindentation methods, extending to in vivo dissections, we report that histone variant nucleosomes are intrinsically more elastic than their canonical counterparts. Furthermore, binding proteins which discriminate between histone variant nucleosomes suppress this innate elasticity and also compact chromatin. Interestingly, when we overexpress the binding proteins in vivo, we also observe increased compaction of chromatin enriched for histone variant nucleosomes, correlating with diminished access. Together, these data suggest a plausible link between innate mechanical properties possessed by histone variant nucleosomes, the adaptability of chromatin states in vivo, and the epigenetic plasticity of the underlying locus.\n\nSignificanceNucleosomes are the base unit which organize eukaryotic genomes. Besides the canonical histone, histone variants create unique local chromatin domains that fine-tune transcription, replication, DNA damage repair, and faithful chromosome segregation. We developed computational and single-molecule nanoindentation tools to determine mechanical properties of histone variant nucleosomes. We found that the CENP-A nucleosome variant is more elastic than the canonical H3 nucleosome but becomes stiffer when bound to its partner CENP-C. In addition, CENP-C induces cross-array clustering, creating a chromatin state that less accessible. These data suggest that innate material properties of nucleosomes can influence the ultimate chromatin state, thereby influence biological outcomes.

biochemistry↗

bioRxiv2017

Internal modifications in the CENP-A nucleosome modulate centromeric dynamics

Post-translational modifications (PTMs) of core histones have studied for over 2 decades, and are correlated with changes in transcriptional status, chromatin fiber folding, and nucleosome dynamics. However, within the centromere-specific histone H3 variant CENP-A, few modifications have been reported, and their functions remain largely unexplored. In this multidisciplinary report, we utilize in silico computational and in vivo approaches to dissect lysine 124 of human CENP-A, which was previously reported to be acetylated in advance of replication. Computational modeling demonstrates that acetylation of K124 causes tightening of the histone core, and hinders accessibility to its C-terminus, which in turn diminishes CENP-C binding. Additionally, CENP-A K124ac/H4 K79ac containing nucleosomes are prone to DNA sliding. In vivo experiments using an acetyl or unacetylatable mimic (CENP-A K124Q and K124A respectively) reveal alterations in CENP-C levels, and a modest increase in mitotic errors. Furthermore, mutation of K124 results in alterations in centromeric replication timing, with the permanently acetylated form replicating centromeres early, and the unacetylable form replicating centromeres late. Purification of native CENP-A proteins followed by mass spectrometry analysis reveal that while CENP-A K124 is acetylated at G1/S, it switches to monomethylation during early and mid-S phase. Finally, we provide evidence that the HAT p300 is involved in this cycle. Taken together, our data suggest that cyclical modifications within the CENP-A nucleosome can influence the binding of key kinetochore proteins, the integrity of mitosis and centromeric replication. These data support the emerging paradigm that core modifications in histone variant nucleosomes transduce defined changes to key biological processes.

biochemistry↗