3. PRIMARY STRUCTURE:
● Linear sequence of amino acids
● Sequence encoded by DNA
● Determine the overall structure of the protein
SECONDARY STRUCTURE:
● local folding patterns within a polypeptide chain
● Include alpha helices and beta sheets
● Stabilised by hydrogen bonding between amino acid residues
TERTIARY STRUCTURE:
● 3D arrangement of a protein’s secondary structure elements
● Governed by hydrogen bonding, disulphide bonds, hydrophobic
interactions and electrostatic forces
BASICS OF PROTEIN STRUCTURE
4. QUATERNARY STRUCTURE:
● Proteins with multiple polypeptide chains
● Describes the arrangement and interactions between different subunits
5. PRINCIPLES OF PROTEIN FOLDING
1. HYDROPHOBIC EFFECT:
● The major driving force in protein folding
● Amino acids tend to cluster together to minimize contact with water
2. HYDROGEN BONDING:
● Formed between electronegative atoms(e.g. oxygen and nitrogen) in the peptide
backbone
● Contribute to the stability of secondary structures
3. VAN DER WAALS INTERACTIONS:
● Weak interactions between close-proximity atoms
● Stabilise the packing of non-polar side chains
6. 4. ELECTROSTATIC INTERACTIONS:
● Attractions and repulsions between charged amino acid chains
● Salt bridges contribute to protein stability
5. DISULPHIDE BONDS:
● Covalent bonds formed between sulphur atoms of two cysteine
residues
● Stabilise extracellular proteins
6. CHAPERONE PROTEINS:
● Assist in the correct folding of other proteins
● Prevent misfolding and aggregation in the crowded cellular
environment
7. 7. DENATURATION AND FOLDING PATHWAYS:
● Disruption of a protein’s structure, often reversible
● Describe the sequential steps a protein takes to reach the native conformation
8. KINETICS OF FOLDING:
● Protein folding is a dynamic process with specific rates for each step
● Folding kinetics influenced by temperature, pH and the presence of cofactors
8. Experimental Techniques:
● Cryo-Electron Microscopy (Cryo-EM)
• allowing researchers to capture high-resolution images of protein
structures
• Instrumental in studying large and complex protein structures that were
challenging to analyze using traditional methods.
● Single-Molecule Fluorescence Spectroscopy:
• Techniques such as single-molecule Förster resonance energy transfer
(smFRET) enable the observation of protein folding dynamics at the single-
molecule level.
• This provides valuable insights into the energy landscape and intermediate
states during the folding process.
RECENT ADVANCEMENTS IN PROTEIN FOLDING
9. ● Nuclear Magnetic Resonance (NMR):
• NMR spectroscopy has evolved to provide more detailed information on
protein dynamics and folding kinetics.
• Advances in isotope labelling and data analysis methods have improved
the accuracy of structural determination for smaller proteins.
10. X-ray Free Electron Lasers (XFELs):
• XFELs have allowed researchers to capture snapshots of proteins in action,
providing information on ultrafast processes involved in folding.
• These X-ray lasers overcome limitations posed by traditional X-ray
crystallography.
COMPUTATIONAL METHODS
1. Deep Learning and Neural Networks:
• Deep learning techniques, particularly neural networks, have been applied to
predict protein structures and folding pathways.
• AlphaFold, developed by DeepMind, is a notable example that demonstrated
breakthroughs in predicting protein structures accurately.
11. ● Fragment-Based Approaches:
• Fragment-based methods involve breaking down proteins into smaller units to predict the
overall structure.
• These approaches leverage experimental and computational data to assemble fragments
into a coherent 3D structure.
12. ● Molecular Dynamics Simulations:
• Continued improvements in computational power and algorithms have
enhanced the capability of molecular dynamics simulations.
• Advanced sampling techniques, like metadynamics and replica exchange,
enable the exploration of protein folding landscapes more efficiently.
● Machine Learning for Structure Prediction:
• Machine learning models are increasingly used for predicting protein folding
patterns and dynamics.
• These models integrate diverse data sources, such as sequence information,
evolutionary data, and physicochemical properties.
● Crowdsourcing and Gamification:
• Platforms like Foldit gamify protein folding problems, engaging citizen
scientists to contribute to solving complex folding challenges.
• The collective problem-solving approach has proven effective in certain cases.
13.
14. ALZHEIMER’S DISEASE
Alzheimer's disease is a progressive neurodegenerative
disorder that primarily affects the brain, leading to
cognitive decline and memory loss. It is the most common
cause of dementia among older adults.
15. CAUSES
● Genetics: Certain genetic mutations are associated
with an increased risk of developing Alzheimer's
disease. The most well-known risk gene is the Apo
lipoprotein E (APOE) gene.
● Age: The likelihood of developing Alzheimer's
increases with age, and most cases occur in
individuals over 65.
● Family History: Individuals with a family history of
Alzheimer's are at a higher risk.
● Down Syndrome: People with Down syndrome are at
an increased risk of developing Alzheimer
● Head Trauma: Severe head injuries, especially those
involving loss of consciousness
16. SYMPTOMS
● Memory Loss: Initially, individuals may experience
difficulty remembering recent events and
information.
● Cognitive Decline: Impaired thinking, reasoning, and
problem-solving become more apparent over time.
● Disorientation: People with Alzheimer's may
become confused about time, place, and the
identities of people around them.
● Language Problems: Difficulty finding the right
words thus struggling with verbal communication.
● Mood and Personality Changes: Mood swings, and
personality changes are common.
18. Misfolding of proteins leads to Alzheimer’s Disease
Beta-Amyloid Plaques:
Normal Function of Amyloid Precursor Protein (APP): Amyloid precursor
protein (APP) is a transmembrane protein involved in normal cellular
functions.
Abnormal Processing of APP: In Alzheimer's disease, APP undergoes abnormal
processing, leading to the generation of beta-amyloid fragments.
Aggregation of Beta-Amyloid: Beta-amyloid fragments tend to misfold and
aggregate into insoluble plaques disrupting the synaptic function
Neurofibrillary Tangles:
Normal Function of Tau Protein: Tau is a microtubule-stabilizing protein that
helps maintain the structural integrity of neurons.
19. Abnormal Phosphorylation of Tau: In Alzheimer's disease, tau proteins undergo
abnormal phosphorylation, leading to conformational changes.
Aggregation into Neurofibrillary Tangles: Misfolded tau proteins aggregate into
neurofibrillary tangles leading to neuronal dysfunction
20. ROLE OF AMYLOID BETA AND TAU PROTEINS IN
ALZHEIMER’S DISEASE BY PROTEIN FOLDING
1. Amyloid Beta (Aβ) Protein
Production and Accumulation:
(Aβ) is produced through cleavage of amyloid precursor protein (APP). In Alzheimer’s disease,
abnormal processing leads to the formation of plaques.
Plaque Formation:
21. (Aβ) peptides tend to aggregate and form plaques in the brain. These plaques
disrupt neuronal function, leading to synaptic dysfunction and contributing to
cognitive impairment.
Neurotoxicity:
(Aβ) aggregates can induce neurotoxicity, causing damage to neurons and
promoting inflammation. This neurotoxic effect is a key factor in Alzheimer’s
disease.
2. Tau Protein:
22. Hyperphosphorylation:
Tau becomes phosphorylated, leading to the loss of its normal
function and causing it to detach from microtubules.
Formation Of Neurofibrillary Tangles:
Hyperphosphorylated tau forms twisted tangles within the
neurons known as neurofibrillary tangles. These tangles
contribute to cell death and disrupt the cell transport system.
3. Protein Folding And Misfolding:
Proteins including both (Aβ) and Tau, have specific 3D
structures that is crucial for normal cellular processes.
In Alzheimer’s (Aβ) Misfolds form aggregates, while Tau
misfolds due to hyperphosphorylation leading to tangles and
plaques observed in the disease
23. 4. Interaction Between Aβ and Tau:
Aβ aggregates can interact with tau, promoting tau pathology and
causing neurodegeneration. This interaction amplifies the overall impact
on neuronal health.
5. Spread of Pathology:
Both Aβ and tau pathology can spread throughout the brain, exhibiting
prion-like behaviour. The spread contributes to the progressive nature
of Alzheimer’s disease.
6. Consequences on Cellular Homeostasis:
The misfolded Aβ and tau proteins disrupt cellular homeostasis, leading
to impaired axonal transport, mitochondrial dysfunction, and eventual
neuronal death.
24. THERAPEUTIC STRATEGIES AND FUTURE
OUTLOOK
ALZHEIMER’S DIAGNOSIS
• Detecting misfolded Aβ and tau proteins, whether through cerebrospinal fluid
analysis or neuroimaging, is crucial for early AD diagnosis and intervention.
• Misfolding and aggregation of Aβ and tau proteins is a focus of therapeutic
strategies, including immunotherapies.
THERAPEUTIC STRATEGIES FOR PROTEIN FOLDING
● Chaperone Based Folding:
• Utilize molecular chaperones to assist proper protein folding.
• Develop small molecules that enhance chaperone activity.
● Proteostasis Regulation:
• Target cellular pathways involved in proteostasis.
• Modulate ubiquitin-proteasome and autophagy systems.
25.
26. ● Drug Design for Misfolded Proteins:
• Design small molecules to stabilize correct protein conformations.
• Develop inhibitors targeting misfolded protein aggregates.
● Genetic Interventions:
• Explore gene therapy for correcting protein folding mutations.
• Investigate CRISPR-based approaches to modify misfolding-prone genes.
27. ● Advancement in Computational Methods:
• Enhance predictive models for protein folding using machine learning.
• Utilize quantum computing for more accurate simulations.
● Precision Medicine for Proteinopathies:
• Tailor therapeutic interventions based on individual genetic profiles.
• Implement personalized treatment strategies for protein misfolding disorders.
● Nanotechnology on Protein Folding:
• Develop nanomaterials for targeted delivery of folding modulators.
• Explore nanoscale devices for monitoring and influencing protein folding.
FUTURE OUTLOOK ON PROTEIN FOLDING
28. ● Integration of AI in Drug Discovery:
• Leverage artificial intelligence for accelerated drug discovery.
• Utilize deep learning to identify novel compounds targeting protein folding.
● Therapeutic Peptides and Proteins:
• Investigate engineered peptides as folding correctors.
• Develop therapeutic proteins to aid in proper folding processes
