Year-2025

Benchmarking fold recovery: implicit-solvent atomistic & coarse grained simulations of two vs non two state proteins

2026-04-20T22:00:32Z

Benchmarking fold recovery: implicit-solvent atomistic & coarse grained simulations of two vs non two state proteins Das, Mimansha; Murugan, N. Arul (Advisor) Protein folding is a fundamental biological process through which a polypeptide adopts its functional three-dimensional structure. In this study, we systematically investigated the structural response of proteins to thermal denaturation using both atomistic and coarse-grained (CG) molecular dynamics simulations. A curated set of 138 proteins from the PFDB (89 two-state and 49 non-two-state folders) was subjected to a heat–quench protocol (300 K → 1000 K → 300 K) in implicit solvent. Structural recovery was assessed through RMSD and radius of gyration (Rg) calculations after Kabsch superimposition, alongside MM/PBSA energy evaluations. Post-quench alignment revealed distinct behaviors: two-state proteins consistently showed lower RMSD, greater compaction (ΔRg < 0), and higher native contact retention than non-two-state proteins. Furthermore, a significant inverse correlation was observed between log10(kf) and final RMSD in the two-state subset, linking folding rate to structural resilience. Results from CG simulations mirrored these trends, validating their utility for rapid, cost-effective screening. These findings underscore the importance of structural alignment in post-simulation analysis and highlight heat–quench recovery as a powerful proxy for foldability. The combined pipeline offers a scalable framework for evaluating folding kinetics and native-state robustness across protein families.

Role of cofactors/co-enzymes in stabilizing protein-ligand complexes

2026-04-20T22:00:31Z

Role of cofactors/co-enzymes in stabilizing protein-ligand complexes Anand, Harnoor Kaur; Murugan, N. Arul (Advisor) Flavin adenine dinucleotide (FAD) is a ubiquitous redox cofactor that plays a fundamental role in various metabolic and enzymatic reactions. It is involved in electron transfer processes essential for cellular respiration, energy production, and redox homeostasis. Beyond its classical biochemical functions, recent research has suggested that FAD may serve as a promising drug target, particularly in the context of neurological diseases, where alterations in flavoproteins and redox imbalances contribute to disease progression. Despite its well-established role in enzymatic catalysis, the contribution of FAD to the overall stability of protein-ligand complexes remains an open question. Investigating how FAD influences ligand binding and protein conformational dynamics is crucial for understanding its potential as a therapeutic target. In this study, we employed molecular dynamics (MD) simulations in an explicit solvent environment to analyze the interaction energy of protein-ligand complexes containing FAD. A set of diverse protein systems with FAD-dependent interactions was selected, and each complex was subjected to long-timescale MD simulations to capture its structural and energetic properties. By applying energy decomposition analysis, we calculated the interaction energies of all residues within the system, with a particular focus on FAD and the ligand. This allowed us to quantify the contribution of FAD to the stability of the complex and assess its role in modulating ligand interactions. To further understand the structural consequences of FAD removal, volumetric analysis was performed to compare the binding pocket volumes in the presence and absence of FAD. This analysis provided insights into whether the cofactor-induced conformational changes in the binding site, potentially affecting ligand binding and protein function. The comparison of pocket volumes aimed to reveal possible allosteric effects exerted by FAD, shedding light on its stabilizing role within the protein-ligand system. Our findings highlight the importance of FAD in modulating protein-ligand interactions, both from an energetic and structural perspective. The study provides a detailed understanding of how FAD contributes to the stability of protein-ligand complexes and underscores its potential as a target in drug discovery efforts for neurological diseases. By elucidating the molecular mechanisms through which FAD influences binding interactions, this work lays the foundation for future studies exploring FAD-targeted therapeutic strategies. These insights could be particularly valuable in the design of small-molecule inhibitors or modulators that exploit FAD’s structural and energetic role in protein function.

Targeting neutrophil elastase in alpha-1 antitrypsin deficiency: a GenAI-based approach for peptide inhibitor discovery

2026-04-18T07:36:57Z

Targeting neutrophil elastase in alpha-1 antitrypsin deficiency: a GenAI-based approach for peptide inhibitor discovery Paul, Prateek; Dhanjal, Jaspreet Kaur (Advisor) Alpha-1 Antitrypsin Deficiency (AATD) is a hereditary disorder primarily affecting the lungs and liver, often resulting in early-onset chronic obstructive pulmonary disease (COPD) due to the unchecked activity of Neutrophil Elastase (NE). Current protein replacement therapies have limited efficacy and accessibility, underscoring the need for alternative therapeutic approaches. In this study, we developed a computational pipeline to design peptide-based inhibitors targeting NE, with the aim of mitigating AATD-associated tissue damage. The workflow began by modeling the AAT–NE complex using available crystal structures (PDB IDs: 1HP7 and 5ABW). Protein-protein docking via ZDOCK confirmed critical interaction sites, notably between Met358 of AAT and the catalytic Ser195 of NE. Structural stability and interaction dynamics were evaluated through molecular dynamics simulations (MDS) in GROMACS, followed by binding free energy calculations using MM-PBSA and MM-GBSA methods. Leveraging these insights, a library of peptide candidates was generated using ProteinMPNN and structurally modeled with ESMFold. Peptides were strategically trimmed near the in-teraction interface and screened for cell-penetrating potential and toxicity. Subsequent docking and MD simulations with NE enabled the identification of stable, high-affinity binders based on binding energies and residue-level interaction profiles. This GenAI-guided pipeline offers a promising route for developing novel peptide therapeutics for AATD by selectively inhibiting NE activity.

Advancing gene signature discovery with generative models: a case study ovarian cancer

2026-04-18T07:28:19Z

Advancing gene signature discovery with generative models: a case study ovarian cancer Anand, Alok; Sethi, Tavpritesh (Advisor) Ovarian cancer is a highly aggressive malignancy with poor survival rates, largely due to late detection and extensive tumor heterogeneity. This study introduces a computational framework, NIGAM (Normalize, Identify, Generate, Authenticate, Meta-analyze) , to overcome limitations posed by small sample sizes in transcriptomic datasets. Gene expression data from ovarian cancer microarray studies were preprocessed (normalized), key underlying dimensions were identified, synthetic data were generated which were then authenticated via statistical and biological enrichment. Finally, the key findings were meta-analyzed to derive signatures. In the case study on Ovarian Cancer, a comparative analysis between original and augmented data revealed significant improvements in detecting biologically relevant signals. Pathways emerging only after augmentation included those associated with key cancer hallmarks such as uncontrolled proliferation, genomic instability, and angiogenesis. From these datasets, eight genes AURKA, DAPK1, MCM2, WNT2B, CNRIP1, CXXC5, PEX5L, and SEL1L2 were identified as novel candidates, with 50% of these supported by existing literature and pathway databases. AURKA and MCM2, in particular, showed strong alignment with known ovarian cancer biology. The remaining genes, lacking prior association, may represent unexplored therapeutic or diagnostic targets. Observed trends in p-values and fold changes confirmed that increasing augmented sample size enhanced statistical robustness. Our innovation, NIGAM and its application to Ovarian cancer demonstrates how an end-to-end framework incorporating AI, biology and data-science can enable biomarker discovery, speed up discovery of diagnostic panels and lead to precision treatment.