Structural Biology Commons^™

Decoding The Allosteric Grammar Of Protein Kinases: A Dual-Stream Framework Integrating Protein Language Models And Energy Landscape Frustration Analysis, Will Gatlin, Max Ludwick, Lucas Turano, Brandon Foley, Kamila Riedlova, Vít Škrhák, Marian Novotný, David Hoksza, Gennady M. Verkhivker

Mathematics, Physics, and Computer Science Faculty Articles and Research

The spatial and energetic encoding of allosteric regulatory sites remains a major challenge in structural biology, frequently representing a “blind spot” for sequence-based artificial intelligence (AI) models. We present a protein language model (PLM)-guided approach complemented by the energy landscape frustration analysis as a dual-stream framework to investigate the relationship between AI prediction of binding sites and biophysical organization of regulatory pockets across the human kinome. By probing a fine-tuned residue-level PLM classifier across 453 kinase structures, a clear performance gap is discovered between highly predictable orthosteric pockets (Types I, I.5, and II) and poorly resolved distal allosteric sites (Type …

Tree Story, Jia Hu

Masters Theses

What is Nature?

Nature is a system of intelligence. It means designing for efficiency—often by learning from strategies that have evolved over time. In my research, I use patterns to interpret and decode nature.

To explore nature, I began with the red cedar tree, aiming to simulate and predict its growth patterns—forms shaped by both internal biology and external forces. By analyzing its geometry, I sought to understand how trees embody the dynamic relationship between organism and environment. These patterns reveal the adaptive logic of life.

Patterns are central to understanding nature. While tree geometry may appear chaotic, it follows …

Characterization Of Sars-Cov-2 Replication And Transcription Complexes Via Structural And Evolutionary Approaches, Amelie Ghirardo, Ben Shabatian, Avishai Aghelian, Kyle Tau, Eleonora Gianti

Undergraduate Research

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) caused around 700M cases and over 7M COVID-19-related deaths recorded worldwide (World Health Organization, March 2025). Aiming to effectively combat this and other disease-causing Coronaviruses (CoV), unprecedented research efforts led to the development of new vaccines and antiviral therapies. Due to emergence of variants of concern (VOCs) with increased transmissibility, immune evasion from vaccination, and potential to resist the available treatments, SARS-CoV-2 continues to represent a major threat to global health. Hence, there is a pressing need to discover new antivirals with broad-spectrum efficacy against multiple SARS-CoV-2 variants and related CoVs. This project …

Bloom: Behavioral Learning And Outcome Observation In Microbes, Sean Sarwar Haque, Luke Compton Wharton, Ming Lin, Razvan Voicu

Symposium of Student Scholars

Understanding how pathogens respond to physical changes in their environment is crucial for developing effective treatments and preventative measures. Current research often relies on static models or experimental data that either fail to capture the dynamic interactions within cellular environments or are not generalizable to other types of pathogens. This project aims to address this gap by creating a comprehensive cell simulation that models pathogens and their response to chemical, physical, and physiological changes. The proposed solution is a simulation that integrates biological data and computational modeling to replicate the behavior of pathogens in real time as they are affected …

"Deep Learning For Microscope Image Denoising", Nasreen Buhn, Sriya Adunur, Guy Hagen, Jonathan Ventura

College of Engineering Summer Undergraduate Research Program

In order to avoid damaging live cells, optical microscope imaging must be conducted under low-excitation light intensity and/or short exposure times, resulting in low signal-to-noise ratios (SNR). Deep learning methods offer an effective solution for removing microscope noise, utilizing algorithms that are able to reconstruct finer features in low SNR images. This research explores the denoising capability of several deep learning methods based on PSNR and SSIM. Tested methods include traditional approaches (BMED), supervised learning (CARE and Restormer), and unsupervised methods (Noise2Fast, N2V, SSD-Unsupervised, and SASSID). The Restormer model, which employs an encoder-decoder transformer architecture and progressive learning, stood out …

Optimization And Application Of Graph Neural Networks, Shuo Zhang

Dissertations, Theses, and Capstone Projects

Graph Neural Networks (GNNs) are widely recognized for their potential in learning from graph-structured data and solving complex problems. However, optimal performance and applicability of GNNs have been an open-ended challenge. This dissertation presents a series of substantial advances addressing this problem. First, we investigate attention-based GNNs, revealing a critical shortcoming: their ignorance of cardinality information that impacts their discriminative power. To rectify this, we propose Cardinality Preserved Attention (CPA) models that can be applied to any attention-based GNNs, which exhibit a marked improvement in performance. Next, we introduce the Directional Node Pair (DNP) descriptor and the Robust Molecular Graph …

The Metabolomics Workbench File Status Website: A Metadata Repository Promoting Fair Principles Of Metabolomics Data, Christian D. Powell, Hunter N. B. Moseley

Markey Cancer Center Faculty Publications

Background: An updated version of the mwtab Python package for programmatic access to the Metabolomics Workbench (MetabolomicsWB) data repository was released at the beginning of 2021. Along with updating the package to match the changes to MetabolomicsWB’s ‘mwTab’ file format specification and enhancing the package’s functionality, the included validation facilities were used to detect and catalog file inconsistencies and errors across all publicly available datasets in MetabolomicsWB.

Results: The MetabolomicsWB File Status website was developed to provide continuous validation of MetabolomicsWB data files and a useful interface to all found inconsistencies and errors. This list of detectable issues/errors include format …

Kegg_Pull: A Software Package For The Restful Access And Pulling From The Kyoto Encyclopedia Of Gene And Genomes, Erik D. Huckvale, Hunter N. B. Moseley

Markey Cancer Center Faculty Publications

Background: The Kyoto Encyclopedia of Genes and Genomes (KEGG) provides organized genomic, biomolecular, and metabolic information and knowledge that is reasonably current and highly useful for a wide range of analyses and modeling. KEGG follows the principles of data stewardship to be findable, accessible, interoperable, and reusable (FAIR) by providing RESTful access to their database entries via their web-accessible KEGG API. However, the overall FAIRness of KEGG is often limited by the library and software package support available in a given programming language. While R library support for KEGG is fairly strong, Python library support has been lacking. Moreover, there …

A Pipeline To Generate Deep Learning Surrogates Of Genome-Scale Metabolic Models, Achilles Rasquinha

School of Computing: Dissertations, Theses, and Student Research

Genome-Scale Metabolic Models (GEMMs) are powerful reconstructions of biological systems that help metabolic engineers understand and predict growth conditions subjected to various environmental factors around the cellular metabolism of an organism in observation, purely in silico. Applications of metabolic engineering range from perturbation analysis and drug-target discovery to predicting growth rates of biotechnologically important metabolites and reaction objectives within dierent single-cell and multi-cellular organism types. GEMMs use mathematical frameworks for quantitative estimations of flux distributions within metabolic networks. The reasons behind why an organism activates, stuns, or fluctuates between alternative pathways for growth and survival, however, remain relatively unknown. GEMMs …

Challenges Of Constructing Entrainment Map For Arbitrary Circadian Models, Yuxuan (Nelson) Wu

Honors Theses

The entrainment map, developed by Dr.Diekman and Dr.Bose, is claimed to be a 1-dimensional map that produces a better prediction for phase-locking than methods than the phase response curve for circadian models. In his paper, he constructs the entrainment map for the two-dimensional circadian model, the Novak-Tyson model, and the other two higher-dimensional circadian models. For this thesis, we concentrate on exploring if it is viable to construct the entrainment map for other circadian models that are not included in his paper: the Becker-Weimann model and the Relogio model. In addition, we discuss the challenges of constructing the entrainment map …

Automated Parsing Of Flexible Molecular Systems Using Principal Component Analysis And K-Means Clustering Techniques, Matthew J. Nwerem

Computational and Data Sciences (MS) Theses

Computational investigation of molecular structures and reactions of biological and pharmaceutical interests remains a grand scientific challenge due to the size and conformational flexibility of these systems. The work requires parsing and analyzing thousands of conformations in each molecular state for meaningful chemical information and subjecting the ensemble to costly quantum chemical calculations. The current status quo typically involves a manual process where the investigator must look at each conformation, separating each into structural families. This process is time-intensive and tedious, making this process infeasible in some cases, and limiting the ability of theoreticians to study these systems. However, the …

Awegnn: Auto-Parametrized Weighted Element-Specific Graph Neural Networks For Molecules., Timothy Szocinski, Duc Duy Nguyen, Guo-Wei Wei

Mathematics Faculty Publications

While automated feature extraction has had tremendous success in many deep learning algorithms for image analysis and natural language processing, it does not work well for data involving complex internal structures, such as molecules. Data representations via advanced mathematics, including algebraic topology, differential geometry, and graph theory, have demonstrated superiority in a variety of biomolecular applications, however, their performance is often dependent on manual parametrization. This work introduces the auto-parametrized weighted element-specific graph neural network, dubbed AweGNN, to overcome the obstacle of this tedious parametrization process while also being a suitable technique for automated feature extraction on these internally complex …

Functional Role Of The N-Terminal Domain In Connexin 46/50 By In Silico Mutagenesis And Molecular Dynamics Simulation, Umair Khan

University Honors Theses

Connexins form intercellular channels known as gap junctions that facilitate diverse physiological roles, from long-range electrical and chemical coupling to nutrient exchange. Recent structural studies on Cx46 and Cx50 have defined a novel and stable open state and implicated the amino-terminal (NT) domain as a major contributor to functional differences between connexin isoforms. This thesis presents two studies which use molecular dynamics simulations with these new structures to provide mechanistic insight into the function and behavior of the NTH in Cx46 and Cx50. In the first, residues in the NTH that differ between Cx46 and Cx50 are swapped between the …

Development Of Computational To Ols To Target Microrna, Luo Song

Dissertations and Theses (Open Access)

MicroRNAs (a.k.a, miRNAs) play an important role in disease development. However, few of their structures have been determined and structure-based computational methods remain challenging in accurately predicting their interactions with small molecules. To address this issue, my thesis is to develop integrated approaches to screening for novel inhibitors by targeting specific structure motifs in miRNAs. The project starts with implementing a tool to find potential miRNA targets with desired motifs. I combined both sequence information of miRNAs and known RNA structure data from Protein Data Bank (PDB) to predict the miRNA structure and identify the motif to target, then I …

New Methods For Deep Learning Based Real-Valued Inter-Residue Distance Prediction, Jacob Barger

Theses

Background: Much of the recent success in protein structure prediction has been a result of accurate protein contact prediction--a binary classification problem. Dozens of methods, built from various types of machine learning and deep learning algorithms, have been published over the last two decades for predicting contacts. Recently, many groups, including Google DeepMind, have demonstrated that reformulating the problem as a multi-class classification problem is a more promising direction to pursue. As an alternative approach, we recently proposed real-valued distance predictions, formulating the problem as a regression problem. The nuances of protein 3D structures make this formulation appropriate, allowing predictions …

Cylindrical Similarity Measurement For Helices In Medium-Resolution Cryo-Electron Microscopy Density Maps, Salim Sazzed, Peter Scheible, Maytha Alshammari, Willy Wriggers, Jing He

College of Sciences Posters

Cryo-electron microscopy (cryo-EM) density maps at medium resolution (5-10 Å) reveal secondary structural features such as α-helices and β-sheets, but they lack the side chains details that would enable a direct structure determination. Among the more than 800 entries in the Electron Microscopy Data Bank (EMDB) of medium-resolution density maps that are associated with atomic models, a wide variety of similarities can be observed between maps and models. To validate such atomic models and to classify structural features, a local similarity criterion, the F1 score, is proposed and evaluated in this study. The F1 score is theoretically normalized to a …

Effective Statistical Energy Function Based Protein Un/Structure Prediction, Avdesh Mishra

LSU New Orleans Theses and Dissertations

Proteins are an important component of living organisms, composed of one or more polypeptide chains, each containing hundreds or even thousands of amino acids of 20 standard types. The structure of a protein from the sequence determines crucial functions of proteins such as initiating metabolic reactions, DNA replication, cell signaling, and transporting molecules. In the past, proteins were considered to always have a well-defined stable shape (structured proteins), however, it has recently been shown that there exist intrinsically disordered proteins (IDPs), which lack a fixed or ordered 3D structure, have dynamic characteristics and therefore, exist in multiple states. Based on …

"Flagella Base Model" And "Flagellin Monomer", Brandon Lasalle, Rebecca Roston

3-D Printed Model Structural Files

"Flagella Base Model" and "Flagellin monomer"

Description: This is a teaching model of the proteins that make a bacterial flagella. All models are depicted in space-fill. The Flagellin monomer and the Flagella base can slot together to show protein quaternary structure and filamentous protein assembly.

Printable models are already uploaded to Shapeways.com in the MacroMolecules shop under the names "Flagella Base Model" and "Flagellin monomer".

This model has been printed successfully using these parameters on Shapeways’ laser sintering printer in the following material: Processed Versatile Plastic (Strong & Flexible Plastic).

Model designer: Brandon Lasalle Authors: Brandon Lasalle and Rebecca Roston …

Data Analytics Pipeline For Rna Structure Analysis Via Shape, Quinn Nelson

UNO Student Research and Creative Activity Fair

Coxsackievirus B3 (CVB3) is a cardiovirulent enterovirus from the family Picornaviridae. The RNA genome houses an internal ribosome entry site (IRES) in the 5’ untranslated region (5’UTR) that enables cap-independent translation. Ample evidence suggests that the structure of the 5’UTR is a critical element for virulence. We probe RNA structure in solution using base-specific modifying agents such as dimethyl sulfate as well as backbone targeting agents such as N-methylisatoic anhydride used in Selective 2’-Hydroxyl Acylation Analyzed by Primer Extension (SHAPE). We have developed a pipeline that merges and evaluates base-specific and SHAPE data together with statistical analyses that provides confidence …

Multi-Resolution Analysis Of Large Molecular Structures And Interactions, Kasra Manavi

Computer Science ETDs

Simulation of large molecular structures and their interactions has become a major component of modern biomolecular research. Methods to simulate these type of molecules span a wide array of resolutions, from all atom molecular dynamics to model interaction energetics to systems of linear equations to evaluate population kinetics. In recent years, there has been an acceleration of molecular structural information production, primarily from x-ray crystallography and electron microscopy. This data has provided modelers the ability to produce better representations of these molecular structures. The purpose of this research is to take advantage of this information to develop multi-resolution models for …

Amino Acid Pop-Set: Model File Name: Amino-Acid-Wgrp-Pop_Sc3.Stl, Michelle Howell, Rebecca Roston

3-D Printed Model Structural Files

This is a teaching model for protein primary structure. It consists of four amino acids (tryptophan, proline, arginine, and glycine) depicted in stick and space-fill representations, five peptide bonds depicted in space-fill, and an N-terminus and a C-terminus depicted in space-fill. It is designed so that students can make various peptides to explore the amount of space of the electron clouds of the amino acids and bonds, and explore the psi and phi angles for the peptides. The printable model is already uploaded to Shapeways.com in the MacroMolecules shop under the name “Amino acid pop-set”. This model has …

Lipoprotein Signal Peptidase Ii: Model File Name: 5dir-Lipoii-Reps_Sc1-5.Stl, Michelle Howell, Rebecca Roston

3-D Printed Model Structural Files

This is a teaching model of lipoprotein signal peptidase II (PDB: 5DIR). It is designed with different regions of the protein depicted in space-filling, ribbon, stick, and backbone-only representations to explore protein secondary structure and illustrate how much space the protein takes up. The printable model is already uploaded to Shapeways.com in the MacroMolecules shop under the name “Lipoprotein signal peptidase II” and is intended to accompany the “Crambin”, “Cytochrome c” and “3 water molecules” models. This model has been printed successfully using these parameters on Shapeways’ laser sintering printer in …

3 Water Molecules: Model File Name: 3hoh-Final.Stl, Michelle Howell, Rebecca Roston

3-D Printed Model Structural Files

This is a teaching model of 3 water molecules depicted in space-fill. It is designed to the same scale as the “Lipoprotein signal peptidase II”, “Crambin”, and “Cytochrome c” models to illustrate the amount of space taken up by proteins. The printable model is already uploaded to Shapeways.com in the MacroMolecules shop under the name “3 water molecules” and is intended to accompany the “Lipoprotein signal peptidase II”, “Crambin”, and “Cytochrome c” models. This model has been printed successfully using these parameters on Shapeways’ laser sintering …

Crambin: Model File Name: 2fd7-Crambin-Stick_Sc1-5.Stl, Michelle Howell, Rebecca Roston

3-D Printed Model Structural Files

This is a teaching model of cytochrome c (PDB: 2FD7). It is designed in a stick representation to explore protein secondary structure and how much space the protein takes up. The printable model is already uploaded to Shapeways.com in the MacroMolecules shop under the name “Crambin” and is intended to accompany the “Lipoprotein signal peptidase II”, “Cytochrome c”, and “3 water molecules” models. This model has been printed successfully using these parameters on Shapeways’ laser sintering printer in the following material: Processed Versatile Plastic (Strong & Flexible Plastic).

Cytochrome C: Model File Name: 1b7v-Cytc-Stick_Sc1-5.Stl, Michelle Howell, Rebecca Roston

3-D Printed Model Structural Files

This is a teaching model of cytochrome c (PDB: 1B7V). It is designed in a stick representation to explore protein secondary structure and how much space the protein takes up. The printable model is already uploaded to Shapeways.com in the MacroMolecules shop under the name “Cytochrome c” and is intended to accompany the “Lipoprotein signal peptidase II”, “Crambin”, and “3 water molecules” models. This model has been printed successfully using these parameters on Shapeways’ laser sintering printer in the following material: Processed Versatile Plastic (Strong & Flexible Plastic).

Human Hexokinase I - Allosteric Regulation: Model File Name: 1dgk-Editb22-Allostery_Sc06.Stl, Michelle Howell, Rebecca Roston

3-D Printed Model Structural Files

This is a teaching model of human Hexokinase I in a surface representation with small molecules ADP and G6P included (PDB: 1DGK). It is designed to be hollow with a lever to mimic allosteric regulation. The printable model is already uploaded to Shapeways.com in the MacroMolecules shop under the name “Human Hexokinase I - Allosteric regulation model”. This model has been printed successfully using these parameters on Shapeways’ laser sintering printer in the following material: Processed Versatile Plastic (Strong & Flexible Plastic).

Model File Name: 1ehz-Trna(Thick).Stl, Michelle Howell, Karin Van Dijk, Rebecca Roston

3-D Printed Model Structural Files

Model file name: 1ehz-tRNA(thick).stl

Authors: Michelle E Howell, Karin van Dijk, Rebecca L Roston

This is a teaching model of Phe-tRNA in a thick stick-representation (PDB: 1ehz), designed to go with a teaching module comparing DNA and RNA basic structures and functions. The printable model is already uploaded to Shapeways.com under the name “tRNA-Phe small”. This model has been printed successfully using these parameters on Shapeways’ laser sintering printer in the following materials: Strong & Flexible Plastic and Elasto Plastic.

Model File Name: 1lmb-Dbd-Tfmono.Stl, Michelle Howell, Karin V. Van Dijk, Rebecca Roston

3-D Printed Model Structural Files

Model file name: 1lmb-DBD-TFmono.stl

Authors: Michelle E Howell, Karin van Dijk, Rebecca L Roston

This is a teaching model of the DNA binding domain of a Lambda repressor transcription factor monomer that accompanies the full color DNA helix (PDB: 1lmb). It is in a cartoon ribbon representation with key amino acid residues in stick representation to highlight interactions with the DNA. This model is designed to accompany a teaching module illustrating transcription factor-DNA binding. The printable model is already uploaded to Shapeways.com in the MacroMolecules shop under the name “Lambda TF DBD”. This model has been printed …

Model File Name: 1ehz-Trna(Thin-Big).X3d, Michelle Howell, Karin V. Van Dijk, Rebecca Roston

3-D Printed Model Structural Files

Model file name: 1ehz-tRNA(thin-big).x3d

Authors: Michelle E Howell, Karin van Dijk, Rebecca L Roston

This is a teaching model of Phe-tRNA in a large, but thin stick-representation (PDB: 1ehz), designed to go with a teaching module comparing DNA and RNA basic structures and functions. The printable model is already uploaded to Shapeways.com in the MacroMolecules shop under the name “Full color Phe-tRNA (1ehz)”. This model has been printed successfully using these parameters on Shapeways’ laser sintering printer in the following material: Strong & Flexible Plastic.

Model File Name: 1lmb-Dnalong_Fordimer-Test6b.Stl, Michelle Howell, Karin V. Van Dijk, Rebecca Roston

3-D Printed Model Structural Files

Model file name: 1lmb-DNAlong_fordimer-test6b.stl

Authors: Michelle E Howell, Karin van Dijk, Rebecca L Roston

This is a teaching model of the 19-bp segment of DNA to which Lambda repressor transcription factor interacts (PDB: 1lmb). It is in a stick representation and has been designed with sites to add magnets to illustrate binding interactions with the transcription factor. Sphere magnets with a 1/8” diameter can be purchased separately from K&J Magnets. The model can interact with the dimer form of the transcription factor. This model is designed to accompany a teaching module illustrating transcription factor-DNA binding. The printable model …