Biology subjects

Murtada, M. H.

Publications and source records attributed to Murtada, M. H..

3 recordsLinked to original sources

bioRxiv2024

Language Models for Molecular Dynamics

Molecular Dynamics (MD) simulations provide accurate descriptions of the motions of molecular systems, yet their computational demands pose significant challenges in applications in molecular biology and materials science. Given the success of deep learning methods in a wide range of fields, a timely question concerns whether these methods could be leveraged to improve the efficiency of MD simulations. To investigate this possibility, we introduce Molecular Dynamics Language Models (MDLMs), to enable the generation of MD trajectories. In the present implementation, an MDLM is trained on a short classical MD trajectory of a protein, where structural accuracy is maintained through kernel density estimations derived from extensive MD datasets. We illustrate the application of this MDLM in the case of the determination of the free energy landscape a small protein, showing that this approach makes it possible to discover conformational states undersampled in the training data. These results provide initial evidence for the use of language models for the efficient implementation of molecular dynamics.

bioinformatics↗

bioRxiv2024

AlphaFold-Metainference: Prediction of Structural Ensembles of Disordered Proteins

Deep learning methods of predicting protein structures have reached an accuracy comparable to that of high-resolution experimental methods. It is thus possible to generate accurate models of the native states of hundreds of millions of proteins. An open question, however, concerns whether these advances can be translated to disordered proteins, which should be represented as structural ensembles because of their heterogeneous and dynamical nature. To address this problem, we introduce the AlphaFold-Metainference method to use AlphaFold-derived distances as structural restraints in molecular dynamics simulations to construct structural ensembles of ordered and disordered proteins. The results obtained using AlphaFold-Metainference illustrate the possibility of making predictions of the conformational properties of disordered proteins using deep learning methods trained on the large structural databases available for folded proteins.

biophysics↗

bioRxiv2024

The diversity of SNCA transcripts in neurons, and its impact on antisense oligonucleotide therapeutics

The role of the SNCA gene locus in driving Parkinsons disease (PD) through rare and common genetic variation is well-recognized, but the transcriptional diversity of SNCA in vulnerable cell types remains unclear. We performed SNCA long-read RNA sequencing in human dopaminergic neurons and show that annotated SNCA transcripts account for only 5% of expression. Rather, the majority of expression (75%) at the SNCA locus originates from transcripts with alternative 5 and 3 untranslated regions. Importantly, 10% originates from transcripts encoding open reading frames not previously annotated, which are translated and detectable in human postmortem brain. Defining the 3 untranslated regions enabled the rational design of antisense oligonucleotides targeting the majority of SNCA transcripts, leading to the effective reversal of PD pathology, including protein aggregation, mitochondrial dysfunction, and toxicity. Resolving the complexity of the SNCA transcriptional landscape impacts RNA therapies and highlights differences in protein isoforms and their contribution to disease.

neuroscience↗