Biology subjects

Faizi, M.

Publications and source records attributed to Faizi, M..

4 recordsLinked to original sources

bioRxiv2025

A generative language model decodes contextual constraints on codon choice for mRNA design

The genetic code allows multiple synonymous codons to encode the same amino acid, creating a vast sequence space for protein-coding regions. Codon choice can impact mRNA function and protein output, a consideration newly relevant with advances in mRNA technology. Genomes preferentially use some codons, but simple optimization methods that select preferred codons miss complex contextual patterns. We present Trias, an encoder-decoder language model trained on millions of eukaryotic coding sequences. Trias learns codon usage rules directly from sequence data, integrating local and global dependencies to generate species-specific codon sequences that align with biological constraints. Without explicit training on protein expression, Trias generates sequences and scores that correlate strongly with experimental measurements of mRNA stability, ribosome load, and protein output. The model outperforms commercial codon optimization tools in generating sequences resembling high-expression codon sequence variants. By modeling codon usage in context, Trias offers a data-driven framework for synthetic mRNA design and for understanding the molecular and evolutionary principles behind codon choice.

genomics↗

bioRxiv2023

MSModDetector: A Tool for Detecting Mass Shifts and Post-Translational Modifications in Individual Ion Mass Spectrometry Data

MotivationPost-translational modifications (PTMs) on proteins regulate protein structures and functions. A single protein molecule can possess multiple modification sites that can accommodate various PTM types, leading to a variety of different patterns, or combinations of PTMs, on that protein. Different PTM patterns can give rise to distinct biological functions. To facilitate the study of multiple PTMs, top-down mass spectrometry (MS) has proven to be a useful tool to measure the mass of intact proteins, thereby enabling even widely separated PTMs to be assigned to the same protein molecule and allowing determination of how many PTMs are attached to a single protein. ResultsWe developed a Python module called MSModDetector that studies PTM patterns from individual ion mass spectrometry (I MS) data. I MS is an intact protein mass spectrometry approach that generates true mass spectra without the need to infer charge states. The algorithm first detects and quantifies mass shifts for a protein of interest and subsequently infers potential PTM patterns using linear programming. The algorithm is evaluated on simulated I MS data and experimental I MS data for the tumor suppressor protein p53. We show that MSModDetector is a useful tool for comparing a proteins PTM pattern landscape across different conditions. An improved analysis of PTM patterns will enable a deeper understanding of PTM-regulated cellular processes. AvailabilityThe source code is available at https://github.com/marjanfaizi/MSModDetector together with the scripts used for analyses and to generate the figures presented in this study.

bioinformatics↗

bioRxiv2018

Quantitative insights into the cyanobacterial cell economy

Phototrophic microorganisms are promising resources for green biotechnology. Compared to heterotrophic microorganisms, however, the cellular economy of phototrophic growth is still insufficiently understood. We provide a quantitative analysis of light-limited, light-saturated, and light-inhibited growth of the cyanobacterium Synechocystis sp. PCC 6803 using a reproducible cultivation setup. We report key physiological parameters, including growth rate, cell size, and photosynthetic activity over a wide range of light intensities. Intracellular proteins were quantified to monitor proteome allocation as a function of growth rate. Among other physiological adaptations, we identify an upregulation of the translational machinery and downregulation of light harvesting components with increasing light intensity and growth rate. The resulting growth laws are discussed in the context of a coarse-grained model of phototrophic growth and available data obtained by a comprehensive literature search. Our insights into quantitative aspects of cyanobacterial adaptations to different growth rates have implications to understand and optimize photosynthetic productivity.

microbiology↗

bioRxiv2017

A minimal model of protein allocation during phototrophic growth

Photoautotrophic growth depends upon an optimal allocation of finite cellular resources to diverse intracellular processes. Commitment of a certain mass fraction of the proteome to a specific cellular function, typically reduces the proteome available for other cellular functions. Here, we develop a minimal semi-quantitative kinetic model of cyanobacterial phototrophic growth to describe such trade-offs of cellular protein allocation. The model is based on coarse-grained descriptions of key cellular processes, in particular carbon uptake, metabolism, photosynthesis, and protein translation. The model is parametrized using literature data and experimentally obtained growth curves. Of particular interest are the resulting cyanobacterial growth laws as fundamental characteristics of cellular growth. We show that the model gives rise to similar growth laws as observed for heterotrophic organisms, with several important differences due to the distinction between light energy and carbon uptake. We discuss recent experimental data supporting the model results and show that minimal growth models have implications for our understanding of the limits of phototrophic growth and bridge a gap between molecular physiology and ecology.

systems biology↗