Biology subjects

Rachman, M.

Publications and source records attributed to Rachman, M..

3 recordsLinked to original sources

bioRxiv2026

Toward a Random Background for Ligand Optimization

Ligand optimization is central to drug discovery as hundreds of analogs might be designed and synthesized between an initial hit and a therapeutic candidate. The efficiency of this process is unclear, at least partly because there is no random background for optimization against which to compare. Such a random background might emerge from synthetically accessible but otherwise systematic random small substitutions across starting ligands, measuring likelihood of achieving a substantial improvement in affinity/potency or other property by any single perturbation. Recent literature and ligand-affinity/potency databases suggest that perhaps 10% of analogs with minor modifications improve upon a parents potency substantially (by [≥]10-fold), but this number is clouded by reporting bias, intentional improvement, and inter-group reproducibility. To begin to establish a background expectation for ligand optimization, we comprehensively and systematically modified 18 lead molecules across six targets with single atom changes; 257 compounds were synthesized. Unexpectedly, 11.2% of these random small perturbation analogs improved potency by [≥]10-fold over their parents. Conversely, these more potent analogs typically had worse in vitro pharmacokinetics (e.g. reduced metabolic stability, lower plasma free fraction). While it was possible to find analogs where the potency increase compensated for inferior exposure and half-life, resulting in more potent compounds in vivo, overall a frustrated landscape for ligand optimization is revealed. This study begins to establish a background expectation for ligand potency optimization and offers a simple strategy to do so. It also begins to quantify the challenges confronting the field in moving beyond in vitro potency.

pharmacology and toxicology↗

bioRxiv2025

In silico analysis of the Chikungunya virus and SARS-CoV-2 Macrodomain

The macrodomain of the Chikungunya virus NSP3 protein (ChikV Mac1) hydrolyzes mono-ADP ribose post-translational modifications on the human host proteins. Mutations in Mac1 reduce ChikV virulence. Thus, ChikV Mac1 is a viral drug target. While no potent ChikV Mac1 inhibitors are available, high-affinity inhibitors were developed for Mac1 in Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). Here, we rationalize this difference in the ligand binding affinity in terms of three differences in the binding site structure and dynamics. First, the apo ChikV Mac1 binding site exhibits substantially more conformational heterogeneity than that of SARS-CoV-2, based on microsecond-scale molecular dynamics simulations; it also has a less druggable binding site, based on program SiteMap. Second, water binding sites overlapping with the ligand binding site in ChikV Mac1 are predicted by program WaterMap to have a stronger affinity for water molecules than the corresponding sites in SARS-CoV-2 Mac1, thus decreasing ligand binding affinity. Finally, a smaller number of Mac1 residues interacts persistently with ADP-ribose in holo ChikV Mac1 than in SARS-CoV-2. With these rationalizations in hand, we designed ligands of ChikV Mac1 using a fragment growth strategy; subsequent molecular dynamics simulations of a representative ligand in complex with ChikV Mac1 substantiated our design.

bioinformatics↗

bioRxiv2022

Structure-based inhibitor optimization for the Nsp3 Macrodomain of SARS-CoV-2

The nonstructural protein 3 (NSP3) of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) contains a conserved macrodomain enzyme (Mac1) that is critical for pathogenesis and lethality. While small molecule inhibitors of Mac1 have great therapeutic potential, at the outset of the COVID-19 pandemic there were no well-validated inhibitors for this protein nor, indeed, the macrodomain enzyme family, making this target a pharmacological orphan. Here, we report the structure-based discovery and development of several different chemical scaffolds exhibiting low- to sub-micromolar affinity for Mac1 through iterations of computer-aided design, structural characterization by ultra-high resolution protein crystallography, and binding evaluation. Potent scaffolds were designed with in silico fragment linkage and by ultra-large library docking of over 450 million molecules. Both techniques leverage the computational exploration of tangible chemical space and are applicable to other pharmacological orphans. Overall, 160 ligands in 119 different scaffolds were discovered, and 152 Mac1-ligand complex crystal structures were determined, typically to 1 [A] resolution or better. Our analyses discovered selective and cell-permeable molecules, unexpected ligand-mediated protein dynamics within the active site, and key inhibitor motifs that will template future drug development against Mac1. Significance StatementSARS-CoV-2 encodes a viral macrodomain protein (Mac1) that hydrolyzes ribo-adenylate marks on viral proteins, disrupting the innate immune response to the virus. Catalytic mutations in the enzyme make the related SARS-1 virus less pathogenic and non-lethal in animals, suggesting that Mac1 will be a good antiviral target. However, no potent inhibitors of this protein class have been described, and pharmacologically the enzyme remains an orphan. Here, we computationally designed potent inhibitors of Mac1, determining 150 inhibitor-enzyme structures to ultra-high resolution by crystallography. In silico fragment linking and molecular docking of > 450 million virtual compounds led to inhibitors with submicromolar activity. These molecules may template future drug discovery efforts against this crucial but understudied viral target.

biophysics↗