Protein molecules carry out a vast array of biological functions. Indeed, almost every cellular process, from genome regulation to energy metabolism, requires a unique set of proteins with their precise concentration in a cell. Understanding the details of the process of protein synthesis and its regulation remains one of the fundamental questions of genetics and evolutionary cell biology. In our lab, we study this by integrating the theoretical and computational models of protein synthesis with high-throughput sequencing data.
Recent Publications:
Translation initiation is the primary determinant of the rate of protein production. The variation in the rate with which this step occurs can cause up to three orders of magnitude differences in cellular protein levels. Several mRNA features, including mRNA stability in proximity to the start codon, coding sequence length, and presence of specific motifs in the mRNA molecule, have been shown to influence the translation initiation rate. These molecular factors acting at different strengths allow precise control of in vivo translation initiation rate and thus the rate of protein synthesis. However, despite the paramount importance of translation initiation rate in protein synthesis, accurate prediction of the absolute values of initiation rate remains a challenge. In fact, as of now, there is no available model for predicting the initiation rate in Saccharomyces cerevisiae. To address this, we train a machine learning model for predicting the in vivo initiation rate in S. cerevisiae transcripts. The model is trained using a diverse set of mRNA transcripts, enabling the comparison of initiation rates across different transcripts. Our model exhibited excellent accuracy in predicting the translation initiation rate and demonstrated its effectiveness with both endogenous and exogenous transcripts. Then, by combining the machine learning model with the Monte-Carlo search algorithm, we have also devised a method to optimize the nucleotide sequence of any gene to achieve a specific target initiation rate. The machine learning model we’ve developed for predicting translation initiation rates, along with the gene optimization method, are deployed as a web server. Both web servers are accessible for free at the following link: ajeetsharmalab.com/TIRPredictor. Thus, this research advances our fundamental understanding of translation initiation processes, with direct applications in biotechnology.
Ref. Biotechnology Journal, 19, e2400081 (2024)
Decoding stoichiometric protein synthesis in E. coli through translation rate parameters
E. coli is one of the most widely used organisms for understanding the principles of cellular and molecular genetics. However, we are yet to understand the origin of several experimental observations related to the regulation of gene expression in E. coli. One of the prominent examples in this context is the proportional synthesis in multiprotein complexes where all of their obligate subunits are produced in proportion to their stoichiometry. In this work, by combining the next-generation sequencing data with the stochastic simulations of protein synthesis, we explain the origin of proportional protein synthesis in multicomponent complexes. We find that the estimated initiation rates for the translation of all subunits in those complexes are proportional to their stoichiometry. This constraint on protein synthesis kinetics enforces proportional protein synthesis without requiring any feedback mechanism. We also find that the translation initiation rates in E. coli are influenced by the coding sequence length and the enrichment of A and C nucleotides near the start codon. Thus, this study rationalizes the role of conserved and nonrandom features of genes in regulating the translation kinetics and unravels a key principle of the regulation of protein synthesis.
Ref. Biophysical Reports 3.4 (2023)
Optimization of ribosome utilization in Saccharomyces cerevisiae
Resource optimization in protein synthesis is often looked at from the perspective of translation efficiency—the rate at which proteins are synthesized from a single transcript. The higher the rate of protein synthesis, the more efficiently a transcript is translated. However, the production of a ribosome consumes significantly more cellular resources than an mRNA molecule. Therefore, there should be a stronger selection pressure for optimizing ribosome usage than translation efficiency. This paper reports strong evidence of such optimization which becomes more prominent in highly expressed transcripts that consume a significant amount of cellular resources. The ribosome usage is optimized by the biases in codon usage and translation initiation rates. This optimization significantly reduces the ribosome requirement in Saccharomyces cerevisiae. We also find that a low ribosome density on mRNA transcripts helps optimize ribosome utilization. Therefore, protein synthesis occurs in a low ribosome density regime where translation–initiation is the rate-limiting step. Our results suggest that optimizing ribosome usage is one of the major forces shaping evolutionary selection pressure, and thus provide a new perspective to resource optimization in protein synthesis.
Ref: PNAS Nexus 2, pgad074 (2023)
We appreciate receiving financial support from the following funding agencies listed below.
Computational Biology and Biophysics Lab 