Sourav Chowdhury

Antifolate resistance in Plasmodium falciparum dihydrofolate reductase (pfDHFR) remains a major challenge for malaria control. To understand how this enzyme maintains function under antifolate selection, we developed PfPATH, a computational framework that integrates mutational fitness measurements, evolutionary interactions, and structural information to map adaptive trajectories. PfPATH adaptive walks reveal that evolution is constrained to a narrow ridge in sequence space defined by a small subset of residues. Most resistance mutations are individually deleterious and become viable only when supported by stabilizing changes, restricting adaptation to a few high-resistance pathways. Structure network analyses show that these mutations do not disrupt the global enzyme architecture but instead reorganize internal communication to preserve catalytic function. Consistent with this, molecular dynamics and free-energy analyses indicate that resistant variants remain stable while sampling a broader ensemble of low-energy conformational substates. Together, these results reveal a narrow and predictable route to antifolate escape.

Electrolytes are essential parts of the environment for all life forms, where proteins, water, and solutes interplay to support vital activities. However, a fundamental understanding of the effect of ionic solutes on proteins remains elusive for more than a century^1–3. Here we show how some ionic solutes can serve as potent denaturants despite the absence of direct protein-ion interactions. We demonstrate dramatic differences between denaturation potency of different ionic solutes with lithium bromide (LiBr) being the strongest denaturant and sodium bromide (NaBr) being the least potent. Experiments and simulations indicate the presence of certain ions disrupts the structure of water network, thereby induce protein denaturation indirectly via an entropy-driven mechanism. We further introduce a scalable strategy for protein waste revalorization, distinguished by the closed-loop recycling of denaturants, straightforward protein separation, and facile manufacturing, all enabled by the entropy-driven denaturation by LiBr. Through successful isolation and systematic study of indirect solute effects, our findings suggest a unified and generally applicable framework for decoding of the protein-water-solute nexus, where all current studies can be easily incorporated. Besides, our regeneration approach underscores the feasibility of repurposing protein waste into valuable biomaterials in a sustainable way with wide-reaching application potential.

Enzymes in a pathway often form metabolons through weak protein-protein interactions (PPI) that localize and protect labile metabolites. Due to their transient nature, the structural architecture of these enzyme assemblies has largely remained elusive, limiting our abilities to re-engineer novel metabolic pathways. Here we delineate a complete PPI map of 1225 interactions in the E. coli 1-carbon metabolism pathway using bimolecular fluorescence complementation that can capture transient interactions in vivo and show strong intra- and inter-pathway clusters within the folate and purine biosynthesis pathways. Scanning mutagenesis experiments along with AlphaFold predictions and meta-dynamics simulations reveal that most proteins use conserved “dedicated” interfaces distant from their active sites to interact with multiple partners. Diffusion-reaction simulations with shared interaction surfaces and realistic PPI networks reveal a dramatic speedup in metabolic pathway fluxes. Overall, this study sheds light on the fundamental features of metabolon biophysics and structural aspects of transient binary complexes. Significance statement: Enzymes from the same metabolic pathway often form dynamic assemblies called metabolons, which channel metabolites as well as protect labile intermediates. Yet very little is known about their structural features or what makes these interactions transient. Paucity of such information has particularly affected our ability to engineer novel metabolic pathways, construct multi-scale mathematical models of cells, etc. We address this by obtaining a comprehensive map for 1225 interaction pairs in the essential 1-carbon metabolism pathway of E. coli . Using both high-throughput experiments and computation, we uncover that metabolon proteins tend to use a conserved dedicated interface to interact with their partners. These results shed light on structural and energetic aspects of PPI in metabolons at near atomic level of resolution.

Natural killer cells (NK) are a first line of immune defense to eliminate infected, transformed and stressed cells by releasing cytotoxic granules ¹ . NK activation is controlled by the balance of signals transmitted by activating and inhibitory receptors but activating receptor engagement is required to trigger cytotoxicity. The activating receptor NKp46, encoded by the NCR1 gene, is expressed by virtually all NK cells and is the most evolutionarily ancient NK receptor. NKp46 plays a major role in NK recognition of cancer cells, since NKp46 blocking antibodies potently inhibit NK killing of many cancer targets ^2,3 . Although a few viral, fungal and soluble host ligands ⁴ have been identified, the endogenous cell-surface ligand of this important activating NK receptor is unknown. Here we show that NKp46 recognizes and is activated by the P-domain of externalized calreticulin (ecto-CRT). CRT, normally localized to the ER, translocates to the cell surface during ER stress and is a hallmark of chemotherapy-treated dying cancer cells that induce an immune response (immunogenic cell death, ICD) ⁵ . NKp46 caps with ecto-CRT in NK immune synapses formed with ecto-CRT-bearing target cells. ER stress, induced by ZIKV infection, ICD-causing chemotherapy drugs and some senescence activators, externalizes CRT and triggers NKp46 signaling. NKp46-mediated killing is inhibited by CRT knockout or knockdown or anti-CRT antibodies and is enhanced by ectopic expression of GPI-anchored CRT. NCR1/Ncr1 -deficient human and mouse NK are impaired in killing ZIKV-infected, ER-stressed, and senescent cells and cancer cells that endogenously or ectopically express ecto-CRT. Importantly, NKp46 recognition of ecto-CRT controls the growth of B16 melanoma and RAS-driven lung cancer in mouse models and enhances tumor-infiltrating NK degranulation and cytokine secretion. Thus, ecto-CRT is a danger-associated molecular pattern (DAMP) that is an endogenous NKp46 ligand that promotes innate immune elimination of ER-stressed cells.

Antibiotic resistance is a worldwide challenge. A potential approach to block resistance is to simultaneously inhibit WT and known escape variants of the target bacterial protein. Here, we applied an integrated computational and experimental approach to discover compounds that inhibit both WT and trimethoprim (TMP) resistant mutants of E. coli dihydrofolate reductase (DHFR). We identified a novel compound (CD15-3) that inhibits WT DHFR and its TMP resistant variants L28R, P21L and A26T with IC₅₀ 50–75 µM against WT and TMP-resistant strains. Resistance to CD15-3 was dramatically delayed compared to TMP in in vitro evolution. Whole genome sequencing of CD15-3-resistant strains showed no mutations in the target folA locus. Rather, gene duplication of several efflux pumps gave rise to weak (about twofold increase in IC₅₀) resistance against CD15-3. Altogether, our results demonstrate the promise of strategy to develop evolution drugs - compounds which constrain evolutionary escape routes in pathogens.

Elucidating intracellular drug targets has been a difficult problem. While machine learning analysis of omics data has been a promising approach, going from large-scale trends to specific targets remains a challenge. Here, we developed a systems-guided hierarchic workflow that utilizes metabolic and structural analysis to narrow in on specific targets suggested by statistical and machine learning analysis of metabolomics data. Utilizing a novel multi-valent DHFR-targeting antibiotic compound, CD15-3, as a case study, we first measured global metabolomics and applied statistics and machine learning to locate broad areas of metabolic perturbation under antibiotic stress. We then tested the ability of suggested compounds to rescue growth and performed metabolic modelling to identify pathways whose inhibition was consistent with growth rescue patterns. Next, we utilized protein structural similarity to further prioritize candidate drug targets within these pathways. Overexpression and in vitro activity assays of a top candidate target, HPPK (folK), showed complete recovery from drug induced growth inhibition and with microscopy. As interest in ‘white-box’ machine learning methods continues to grow, this study demonstrates how established machine learning methods can be combined with mechanistic analyses to improve the resolution of drug target finding workflows.

Cataract is one of the most prevalent protein aggregation disorders and still the most common cause of vision loss worldwide. The metabolically quiescent core region of the human lens lacks cellular or protein turnover; it has therefore evolved remarkable mechanisms to resist light-scattering protein aggregation for a lifetime. We now report that one such mechanism involves an unusually abundant lens metabolite, myo- inositol, suppressing aggregation of lens crystallins. We quantified aggregation suppression using our previously well-characterized in vitro aggregation assays of oxidation-mimicking human γD-crystallin variants and investigated myo- inositol’s molecular mechanism of action using solution NMR, negative-stain TEM, differential scanning fluorometry, thermal scanning Raman spectroscopy, turbidimetry in redox buffers, and free thiol quantitation. Unlike many known chemical chaperones, myo- inositol’s primary target was neither the native nor the unfolded state of the protein, nor the final aggregated state, but rather the rate-limiting bimolecular step on the aggregation pathway. Given recent metabolomic evidence that it is severely depleted in human cataractous lenses compared to age-matched controls, we suggest that maintaining or restoring healthy levels of myo- inositol in the lens may be a simple, safe, and globally accessible strategy to prevent or delay lens opacification due to age-onset cataract.

Antibiotic resistance is a worldwide challenge. A potential approach to block resistance is to simultaneously inhibit WT and known escape variants of the target bacterial protein. Here we applied an integrated computational and experimental approach to discover compounds that inhibit both WT and trimethoprim (TMP) resistant mutants of E. coli dihydrofolate reductase (DHFR). We identified a novel compound (CD15-3) that inhibits WT DHFR and its TMP resistant variants L28R, P21L and A26T with IC ₅₀ 50-75 µM against WT and TMP-resistant strains. Resistance to CD15-3 was dramatically delayed compared to TMP in in vitro evolution. Whole genome sequencing of CD15-3 resistant strains showed no mutations in the target folA locus. Rather, gene duplication of several efflux pumps gave rise to weak (about twofold increase in IC ₅₀ ) resistance against CD15-3. Altogether, our results demonstrate the promise of strategy to develop evolution drugs - compounds which block evolutionary escape routes in pathogens.

Aggregation of Cu-Zn superoxide dismutase (SOD1) is implicated in the motor neuron disease, ALS. Although more than 140 disease mutations of SOD1 are available, their stability or aggregation behaviors in membrane environment are not correlated with disease pathophysiology. Here, we use multiple mutational variants of SOD1 to show that the absence of Zn, and not Cu, significantly impacts membrane attachment of SOD1 through two loop regions facilitating aggregation driven by lipid induced conformational changes. These loop regions influence both the primary (through Cu intake) and the gain of function (through aggregation) of SOD1 presumably through a shared conformational landscape. Combining experimental and theoretical frameworks using representative ALS disease mutants, we develop a ‘ co-factor derived membrane association model ’ wherein mutational stress closer to the Zn (but not to the Cu) pocket is responsible for membrane association mediated toxic aggregation and survival time scale after ALS diagnosis.