Laboratory of Protein and Nucleic Acid Chemistry

Our overarching goal is to understand DNA replication and protein translation at the molecular level. In our research lab in the Chemistry Department at Ben-Gurion University of the Negev we are utilizing innovative biophysical tools and approaches to assess the structural nature and the biomolecular interactions in protein-protein and protein-nucleic acid complexes, and, in turn, we learn how these interactions determine and impact biological catalysis of these domains of life. Our interdisciplinary approach, spanning and integrating chemistry and biology, employs a wide range of techniques.

BASIC RESEARCH

DNA replication has been studied extensively in many model organisms, the organization of the protein components at the replication fork of M. tuberculosis, and their activities are poorly understood. Understanding the fundamental mechanisms that regulate DNA replication in M. tuberculosisis is critical for developing new therapeutic approaches to control bacterial proliferation. To date, the minimal M. tuberculosis replisome arrangement that is required for coordinated DNA synthesis is not known, and the role that many protein components play at the replication fork, as well as the interactions between them, are still largely unexplored. Mounting evidence collected over the past few years supports the idea that DNA replication in mycobacteria is unique and maybe coordinated differently from that found in other bacteria and humans.

In filling these knowledge gaps, my lab aims to shed light on the activities at the replication fork of M. tuberculosis. In particular, we will unravel the sequence on the DNA recognized by the DnaG primase, an essential enzyme that binds to a specific sequence on the genome and performs catalytic activity necessary for normal DNA replication. Revealing the primase binding signature on genomic DNA was made possible through protein-DNA binding microarray containing a massive amount of DNA sequences with their assigned binding scores to the primase (A. Afek and S. Ilic et al., 2019 iScience). We then sought to leverage advances in data science and machine learning to analyze DNA-primase interactions (A. Soffer et al., 2020, 2020, Nucleic Acid Research). Machine-learning algorithms were used to characterize specific DNA sequences that lead to DNA sequence recognition which is an essential step in the recruitment of DNA polymerase on the DNA replication fork.

APPLIED RESEARCH

The imminent need for the development of new antibacterial drugs will lead us to develop inhibitors targeted against components in central molecular biology domains in a bacterial cell, such as DNA replication and protein translation.

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Mycobacterium Tuberculosis is a pathogenic bacterium and the causative agent of tuberculosis, which infects a third of the world population and kills more than 1.5 million people worldwide every year.

Small-molecule inhibitors for bacterial DNA replication

In addition to the basic research, my lab develops inhibitors that target the DNA replication machinery in M. tuberculosis. Our recently published hybrid approach will be used to identify such inhibitors for M. tuberculosis. In this approach, an NMR fragment-based screening is combined with virtual screening to select inhibitors against a different target, the primase domain of bacteriophage T7. Proof of concept for the workflow has already yielded five inhibitors (Ilic S. et al., Scientific Reports 2016, PCT patent filed with BGN, 2016 WO-2018/073828). Three compounds were found to inhibit the related M. tuberculosis DnaG primase. Intrigued by these recent results a postdoctoral researcher in my lab is now synthesizing derivatives with improved binding/inhibitory properties and patentable chemical structures (Singh M. et al., 2020 Chemistry European Journal).

Ilic S. et al., Scientific Reports 2016

Small-molecule inhibitors for bacterial protein translation

For this purpose, we used a fragment-based screening workflow in which the first step was the novel exploitation of NMR transverse relaxation times to identify fragment molecules that bind specifically to RNA hairpin 91 in the ribosomal PTC of M. tuberculosis. This initial screening was followed by computational optimization of the fragment molecules into larger molecules with drug-like properties. Specifically, a virtual filtration followed by a high-throughput docking procedure yielded drug-sized molecules. We trained various machine-learning models for predicting the docking binding free energy as a function of geometric features extracted from each of the above molecules. As superior inhibitors, the machine-learning model predicted two molecules that exhibited IC₅₀ values superior to that of chloramphenicol, an antibiotic drug that acts on the ribosomal PTC (Tam B. et al, Chemical Science 2019, a US provisional patent application is filed, Serial Number 62/789,570, BGU-P-087-US). Intrigued by these recent results we synthesize derivatives with improved binding/inhibitory properties (Singh M. et al, Chemistry- A European Journal). Our studies will yield new antituberculous agents and will provide new tools for fragment-based lead discovery.

Tam B. et al., Chemical Science 2019

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