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1. Cholesterol Chip for the Study of Cholesterol–Protein Interactions Using SPR

   https://doi.org/10.3390/bios12100788  

   He, Peng; Faris, Shannon; Sagabala, Reddy Sudheer; Datta, Payel; Xu, Zihan; Callahan, Brian; Wang, Chunyu; Boivin, Benoit; Zhang, Fuming; Linhardt, Robert J (October 2022, Biosensors)

   Cholesterol, an important lipid in animal membranes, binds to hydrophobic pockets within many soluble proteins, transport proteins and membrane bound proteins. The study of cholesterol–protein interactions in aqueous solutions is complicated by cholesterol’s low solubility and often requires organic co-solvents or surfactant additives. We report the synthesis of a biotinylated cholesterol and immobilization of this derivative on a streptavidin chip. Surface plasmon resonance (SPR) was then used to measure the kinetics of cholesterol interaction with cholesterol-binding proteins, hedgehog protein and tyrosine phosphatase 1B. 

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2. Teaching virtual protein‐centric CUREs and UREs using computational tools

   Bell, Anthony. (September 2020, Biochemistry and molecular biology education)

   Responding to the need to teach remotely due to COVID-19, we used readily available computational approaches (and developed associated tutorials (https://mdh-cures-community.squarespace.com/virtual-cures-and-ures)) to teach virtual Course-Based Undergraduate Research Experience (CURE) laboratories that fulfil generally accepted main components of CUREs or Undergraduate Research Experiences (UREs): Scientific Background, Hypothesis Development, Proposal, Experiments, Teamwork, Data Analysis, Conclusions, and Presentation1. We then developed and taught remotely, in three phases, protein-centric CURE activities that are adaptable to virtually any protein, emphasizing contributions of noncovalent interactions to structure, binding and catalysis (an ASBMB learning framework2 foundational concept). The courses had five learning goals (unchanged in the virtual format),focused on i) use of primary literature and bioinformatics, ii) the roles of non-covalent interactions, iii) keeping accurate laboratory notebooks, iv) hypothesis development and research proposal writing, and, v) presenting the project and drawing evidence based conclusions The first phase, Developing a Research Proposal, contains three modules, and develops hallmarks of a good student-developed hypothesis using available literature (PubMed3) and preliminary observations obtained using bioinformatics, Module 1: Using Primary Literature and Data Bases (Protein Data Base4, Blast5 and Clustal Omega6), Module 2: Molecular Visualization (PyMol7 and Chimera8), culminating in a research proposal (Module 3). Provided rubrics guide student expectations. In the second phase, Preparing the Proteins, students prepared necessary proteins and mutants using Module 4: Creating and Validating Models, which leads users through creating mutants with PyMol, homology modeling with Phyre29 or Missense10, energy minimization using RefineD11 or ModRefiner12, and structure validation using MolProbity13. In the third phase, Computational Experimental Approaches to Explore the Questions developed from the Hypothesis, students selected appropriate tools to perform their experiments, chosen from computational techniques suitable for a CURE laboratory class taught remotely. Questions, paired with computational approaches were selected from Modules 5: Exploring Titratable Groups in a Protein using H++14, 6: Exploring Small Molecule Ligand Binding (with SwissDock15), 7: Exploring Protein-Protein Interaction (with HawkDock16), 8: Detecting and Exploring Potential Binding Sites on a Protein (with POCASA17 and SwissDock), and 9: Structure-Activity Relationships of Ligand Binding & Drug Design (with SwissDock, Open Eye18 or the Molecular Operating Environment (MOE)19). All involve freely available computational approaches on publicly accessible web-based servers around the world (with the exception of MOE). Original literature/Journal club activities on approaches helped students suggest tie-ins to wet lab experiments they could conduct in the future to complement their computational approaches. This approach allowed us to continue using high impact CURE teaching, without changing our course learning goals. Quantitative data (including replicates) was collected and analyzed during regular class periods. Students developed evidence-based conclusions and related them to their research questions and hypotheses. Projects culminated in a presentation where faculty feedback was facilitated with the Virtual Presentation platform from QUBES20 These computational approaches are readily adaptable for topics accessible for first to senior year classes and individual research projects (UREs). We used them in both partial and full semester CUREs in various institutional settings. We believe this format can benefit faculty and students from a wide variety of teaching institutions under conditions where remote teaching is necessary. 

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3. Predicting protein conformational motions using energetic frustration analysis and AlphaFold2

   https://doi.org/10.1073/pnas.2410662121  

   Guan, Xingyue; Tang, Qian-Yuan; Ren, Weitong; Chen, Mingchen; Wang, Wei; Wolynes, Peter G; Li, Wenfei (August 2024, Proceedings of the National Academy of Sciences)

   Proteins perform their biological functions through motion. Although high throughput prediction of the three-dimensional static structures of proteins has proved feasible using deep-learning-based methods, predicting the conformational motions remains a challenge. Purely data-driven machine learning methods encounter difficulty for addressing such motions because available laboratory data on conformational motions are still limited. In this work, we develop a method for generating protein allosteric motions by integrating physical energy landscape information into deep-learning-based methods. We show that local energetic frustration, which represents a quantification of the local features of the energy landscape governing protein allosteric dynamics, can be utilized to empower AlphaFold2 (AF2) to predict protein conformational motions. Starting from ground state static structures, this integrative method generates alternative structures as well as pathways of protein conformational motions, using a progressive enhancement of the energetic frustration features in the input multiple sequence alignment sequences. For a model protein adenylate kinase, we show that the generated conformational motions are consistent with available experimental and molecular dynamics simulation data. Applying the method to another two proteins KaiB and ribose-binding protein, which involve large-amplitude conformational changes, can also successfully generate the alternative conformations. We also show how to extract overall features of the AF2 energy landscape topography, which has been considered by many to be black box. Incorporating physical knowledge into deep-learning-based structure prediction algorithms provides a useful strategy to address the challenges of dynamic structure prediction of allosteric proteins. 

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4. Ca^XML : Chemistry‐informed machine learning explains mutual changes between protein conformations and calcium ions in calcium‐binding proteins using structural and topological features

   https://doi.org/10.1002/pro.70023  

   Zhang, Pengzhi; Nde, Jules; Eliaz, Yossi; Jennings, Nathaniel; Cieplak, Piotr; Cheung, Margaret_S (January 2025, Protein Science)

   Abstract Proteins' flexibility is a feature in communicating changes in cell signaling instigated by binding with secondary messengers, such as calcium ions, associated with the coordination of muscle contraction, neurotransmitter release, and gene expression. When binding with the disordered parts of a protein, calcium ions must balance their charge states with the shape of calcium‐binding proteins and their versatile pool of partners depending on the circumstances they transmit. Accurately determining the ionic charges of those ions is essential for understanding their role in such processes. However, it is unclear whether the limited experimental data available can be effectively used to train models to accurately predict the charges of calcium‐binding protein variants. Here, we developed a chemistry‐informed, machine‐learning algorithm that implements a game theoretic approach to explain the output of a machine‐learning model without the prerequisite of an excessively large database for high‐performance prediction of atomic charges. We used the ab initio electronic structure data representing calcium ions and the structures of the disordered segments of calcium‐binding peptides with surrounding water molecules to train several explainable models. Network theory was used to extract the topological features of atomic interactions in the structurally complex data dictated by the coordination chemistry of a calcium ion, a potent indicator of its charge state in protein. Our design created a computational tool of Ca^XML, which provided a framework of explainable machine learning model to annotate ionic charges of calcium ions in calcium‐binding proteins in response to the chemical changes in an environment. Our framework will provide new insights into protein design for engineering functionality based on the limited size of scientific data in a genome space. 

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5. mebipred : identifying metal-binding potential in protein sequence

   https://doi.org/10.1093/bioinformatics/btac358  

   Aptekmann, A. A.; Buongiorno, J.; Giovannelli, D.; Glamoclija, M.; Ferreiro, D. U.; Bromberg, Y.; Cowen, ed., Lenore (May 2022, Bioinformatics)

   Abstract Motivationmetal-binding proteins have a central role in maintaining life processes. Nearly one-third of known protein structures contain metal ions that are used for a variety of needs, such as catalysis, DNA/RNA binding, protein structure stability, etc. Identifying metal-binding proteins is thus crucial for understanding the mechanisms of cellular activity. However, experimental annotation of protein metal-binding potential is severely lacking, while computational techniques are often imprecise and of limited applicability. Resultswe developed a novel machine learning-based method, mebipred, for identifying metal-binding proteins from sequence-derived features. This method is over 80% accurate in recognizing proteins that bind metal ion-containing ligands; the specific identity of 11 ubiquitously present metal ions can also be annotated. mebipred is reference-free, i.e. no sequence alignments are involved, and is thus faster than alignment-based methods; it is also more accurate than other sequence-based prediction methods. Additionally, mebipred can identify protein metal-binding capabilities from short sequence stretches, e.g. translated sequencing reads, and, thus, may be useful for the annotation of metal requirements of metagenomic samples. We performed an analysis of available microbiome data and found that ocean, hot spring sediments and soil microbiomes use a more diverse set of metals than human host-related ones. For human microbiomes, physiological conditions explain the observed metal preferences. Similarly, subtle changes in ocean sample ion concentration affect the abundance of relevant metal-binding proteins. These results highlight mebipred’s utility in analyzing microbiome metal requirements. Availability and implementationmebipred is available as a web server at services.bromberglab.org/mebipred and as a standalone package at https://pypi.org/project/mymetal/. Supplementary informationSupplementary data are available at Bioinformatics online. 

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