Saad Naseem is a postdoctoral scholar whose research applies biology to the recovery of critical materials, with a focus on the biorecovery of rare earth elements (REEs) using the methylotrophic bacterium Methylobacterium extorquens AM1. He designs and automates high-throughput bioprocess development workflows—building 96-well colorimetric assays and deploying them on a Hamilton Vantage liquid-handling platform—to rapidly screen and optimize conditions for selective metal capture. His work bridges wet-lab bioprocessing with computational methods: he is increasingly focused on machine learning and graph neural networks for predicting substrate utilization and guiding enzyme and strain engineering. Grounded in a background in enzyme mechanism, biocatalysis, and structural biology, Saad’s research aims to turn microbial and enzymatic systems into scalable, data-driven tools for sustainable materials recovery and bioproduct development.
Before and during his postdoctoral appointment at Lawrence Berkeley National Laboratory, Saad developed deep expertise in protein biochemistry, X-ray crystallography, and analytical chemistry (HPLC, LC-MS, NMR), alongside molecular biology and gene-editing techniques. As a postdoctoral scholar he has led a rare earth element biorecovery program, established automated high-throughput assay pipelines, and integrated machine-learning approaches into bioprocess design. His doctoral research at Texas A&M characterized eight enzymes in the lumichrome catabolic pathway and included solving the apo-structure of chorismate dehydratase.
Education
- Ph.D. in Chemistry, Texas A&M University
- M.S. in Computer Science (Machine Learning Specialization), Georgia Institute of Technology (in progress)
- B.S. in Chemistry, Lahore University of Management Sciences, Pakistan
Notable Papers and Publications
- Naseem, S.; Miller, M. A.; Martinez-Gomez, N. C.; Sun, N.; Joachimiak, M. P. MicroGrowAgents: An Agentic AI System for Microbial Cultivation Engineering. bioRxiv 2026,
https://doi.org/10.64898/2026.06.04.729985 - Naseem, S, et al. Machine Learning-Driven Prediction of Substrate Utilization in Pseudomonas putida KT2440 Using Graph Neural Networks and Phenotype Microarray Data. Manuscript in preparation.
- Sinha S, Jian X, Adak S, Naseem S, Steiner JL, Fedoseyenko D, Thiagarayaselvam A, Begley TP*. Vitamin B2 Catabolism: Nature’s Route from Riboflavin to Acetoacetate and Pyruvate. ACS Cent. Sci. 2025, 11, 2353–2365.
- Mahanta N, Hicks KA, Naseem S, Fedoseyenko D, Dairi T, Ealick SE, Begley TP*. Menaquinone Biosynthesis: Biochemical and Structural Studies on Chorismate Dehydratase. Biochemistry 2019, 58(14), 1837–1840.