MTech Theses

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

Fri, 01 Aug 2025 00:00:00 GMT

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.

Challenges in the prediction of half-life of lncRNAs

Thu, 01 Jan 2026 00:00:00 GMT

Challenges in the prediction of half-life of lncRNAs Shuhail, Shaikh; Raghava, Gajendra Pal Singh (Advisor) Long non-coding RNAs (lncRNAs) are key regulators of gene expression, and their stability, commonly quantified as half-life, plays a critical role in cellular function. Recent computational efforts have attempted to predict RNA half-life from sequence, with limited success. For instance, Shi et al. applied deep learning models and initially reported spearman correlations of 0.7–0.8, but performance dropped to 0.06–0.09 after five-fold validation. In this study, we developed machine learning and deep learning models using sequence-derived features to predict lncRNA half-life. Among the approaches tested, Random Forest based on nucleotide composition features performed best, achieving a spearman correlation of 0.9862 on the training dataset but only 0.0592 on the validation dataset. Furthermore, clustering analysis revealed that different transcript groups exhibited nearly identical mean half-life distributions, indicating that sequence-derived features alone do not meaningfully stratify lncRNAs by stability. These results, consistent with prior studies, demonstrate the persistent difficulty of predicting RNA half-life in silico. Further, inclusion of features such as RNA–binding protein motifs, structure–based minimum free energy and sub–cellular localization did not improve the model performances. This suggests that RNA stability is regulated by features beyond those included. Therefore, in this paper, we outline the approaches studied and the challenges to predict the RNA stability, further highlighting the need to integrate multi-omic strategy or design an algorithm to predict RNA half-life.

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

Fri, 01 Aug 2025 00:00:00 GMT

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

Fri, 01 Aug 2025 00:00:00 GMT

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.