Lecture 26: Proteins, Peptides, Combinatorial Chemistry, CADD, AI PDF

Principles of Drug Action 1. Optimizing Pharmacokinetics Properties Protein and Peptide Drugs 2. Combinatorial & Parallel Chemistry in Drug Discovery 3. Drug Design and Molecular Modeling 4. Artificial Intelligence in Drug Discovery Lectu...

Principles of Drug Action 1. Optimizing Pharmacokinetics Properties Protein and Peptide Drugs 2. Combinatorial & Parallel Chemistry in Drug Discovery 3. Drug Design and Molecular Modeling 4. Artificial Intelligence in Drug Discovery Lecture#26  Medicinal Chemistry Drug Distribution; Absorption: Drug Physicochemical Physiochemical properties Discovery Properties of Drugs-1 of Drug Absorption and membrane transport Optimization Functional Group (FG) & Acidity and Basicity of FG Development Optimizing drug- (Drug Target) target interactions Physicochemical Properties Enzymes (Improving Pharmacodynamic of Drugs-2 and 3 Receptors Properties) - Forces Involved in Drug-Receptor Interactions Optimizing - Chirality Metabolism: Chemical Basis access to the - Salts, Solubility of Drug Metabolism target Small (Optimizing molecules pharmacokinetic Excretion Oligonucleoti properties)  Learning Objectives What are peptides and proteins? How to improve their pharmacokinetics properties? Understand the concept of combinatorial chemistry in drug discovery Explain the concept of parallel synthesis Molecular modeling and artificial intelligence in drug design  1. Peptide, Polypeptides, and Proteins 1. Peptides, Polypeptides, and Proteins Polypeptides composed of covalently linked amino acids. Polypeptides with 2-40 amino acids are called peptides (e.g., gonadotropin-releasing hormone contains 10 residues and corticotrophin has 39 residues) Polypeptides with >40 amino acids are called proteins (e.g., insulin has 51 residues, and somatotropin has 191 residues). Compounds Number of Amino Acids Gonadotropin-releasing hormone 10 Somatotropin 191 Corticotrophin 39 Insulin 51  1.1. Why proteins are important? Nucleotide Sequence Protein Sequence Protein Structure Function of a protein determined by its non- covalent 3D structure >95% of all drug targets are proteins  1.2. Some Classes of Protein Pharmaceuticals 1. Monoclonal antibodies – Treatment of cancer, rheumatoid arthritis; used for diagnostic purposes 2. Cytokines (secreted protein factors that are involved in both short- and long-range cell-cell communication) – Interleukins 1, 2, 3, 4 Treatment of cancer, AIDS; radiation- or drug-induced bone marrow suppression – Interferons (, , ) Treatment of cancer, allergies, arthritis, and infectious diseases – Colony stimulating factors Treatment of cancer, low blood count; AIDS therapy  Some Classes of Protein Pharmaceuticals 3. Vaccines (peptides, parts of proteins, killed bacteria) – Vaccines against hepatitis B, malaria, diphtheria, tetanus 4. Recombinant therapeutic proteins (herceptin, humulin, etc.) 5. Blood products – Blood clotting factors Treatment of hemophilia and related clotting disorders BeneFix (F IX) (Factor IX, Antihemorrhagic) Novoseven (F VIIa) (Factor VIIa, Antihemorrhagic) 6. Peptide and Protein Hormones (oxytocin, pitocin) – Human growth factor Treatment of growth deficiency in children – Epidermal growth factor Treatment of wounds, skin ulcers, cancer – Insulin Treatment of diabetes melitus Humulin (Antidiabetic agent, Buffered Insulin Human) – Gonadotropin-releasing hormone (GnRH) (Gonadorelin, Leuprolide, Goserlin, Nafarelin, Histrelin, Triptorelin, Somatostatin)  1.3. Amino acids, peptides, and protein structures amino alpha The general formula for an group carbon amino acid The building block of O proteins Amino group (NH2) H3N+ Carboxyl group Hydrogen O R group is commonly one of 20 different side chains H R specific for each amino acid At pH 7 both the amino and carboxyl carboxyl groups are ionized group side chain group  1.3. Amino Acids Alanine Ala A The Building Blocks of Proteins Cysteine Cys C Aspartic Acid Asp D Glutamic Acid Glu E Twenty types of amino acids. Phenylalanine Phe F Glycine Gly G All residues have main polar chain atoms Histidine His H (N and carbonyl). Isoleucine Ile I Lysine Lys K The central carbon atom is called the C- Leucine Leu L atom and is a chiral center. Methionine Met M Asparagine Asn N All amino acids found in proteins encoded Proline Pro P Glutamine Gln Q by the genome have the L-configuration Arginine Arg R at this chiral center. Serine Ser S Threonine Thr T Valine Val V Tryptophan Trp W Groups of Amino Acids based on Tyrosine Tyr Y their physicochemical properties: Tiny, Small, Aliphatic, Aromatic, Hydrophobic, Acidic, Basic, polar, charged.  Aromatic Amino Acids N N N W H C C H2N COOH H2N COOH H H Charged Amino Acids H N NH3+ R COO - D NH C C H2N COOH H2N COOH H H  1.4. Peptide bond The newly created C-N bond between the two separate amino acids is called a peptide bond. The term 'peptide bond' implies the existence of the peptide group which is commonly written in text as -CONH-. 1.5. Peptides Pharmacokinetics Disadvantages of peptide-like drugs Pharmacokinetic properties are often unsatisfactory Susceptible to peptidase, digestive, and metabolic enzymes Poor absorption from the digestive tract Difficulty in crossing cell membranes Poor aqueous solubility Lack of oral activity Poor bioavailability Peptidomimetics: Modify peptide-like drugs.  Strategies - altering stereochemistry 1. Inverting an asymmetric centre R1 O R1 O H H N L N L N L N D H H O R2 O R2 2. Replacing amino acids 3. Pegylation and/or fatty acylation  Hormones of hypothalamic origin Gonadotropin-releasing hormone (GnRH) https://www.researchgate.net/figure/HPG-axis-in-males-and-females-Gonadotrophin-releasing-hormone-GnRH-is-released-in-a_fig1_339602868 Pharmacy Alert GnRH Agonists D-amino acid substitutions that hinder enzymatic degradation https://www.semanticscholar.org/paper/GnRH-in-the-Human-Female-Reproductive-Axis.- Limonta-Marelli/aa71fe1ae8695d7a9f7f84d99bb7f39a06021a28 Management of endometriosis and advanced prostate cancer Pharmacy Alert Ozempic (Semiglutide) Glucagon-like peptide-1 (GLP-1) receptor agonist Stimulates insulin secretion Lowers glucagon secretion More delay in gastric emptying  Protein Structure and function, Combinatorial & Parallel Chemistry 2. Combinatorial Synthesis 2.1. Methodology 2. Combinatorial Chemistry Synthesis or biosynthesis of chemical libraries of molecules for the purpose of biological screening 2.1. Methodology The automated synthesis of a large number of compounds in a short time period using a defined reaction route and a large variety of reactants Mixtures of compounds formed in each reaction vessel PARALLEL SYNTHESIS Useful for finding lead compounds Useful for SAR, drug optimization, and finding lead compounds Synthesis- either solution or solid-phase.  Protein Structure and function, Combinatorial & Parallel Chemistry 2. Combinatorial Synthesis 2.2. Solid-phase synthesis 2.2. Solid-phase synthesis Reactants are bound to a polymeric surface and modified whilst still attached. The final product is released at the end of the synthesis Beads must be able to swell in the solvent used Beads must remain stable Most reactions occur in the bead interior 2.2.1. Solid-Phase Peptide Synthesis Protecting groups (PG) will be present on the amine nitrogen for iterative deprotection as well as any potentially reactive side chain g  Protein Structure and function, Combinatorial & Parallel Chemistry 2. Combinatorial Synthesis 2.3. Advantage of Solid-phase synthesis 2.3. Advantages of Solid-Phase Synthesis Reactants are bound to a solid support (e.g. beads) The reactant and subsequent intermediates remain attached to the bead during the synthetic sequence Excess reagents can be used to drive reactions to completion Excess reagents and by-products are easily removed Automation is possible Beads can be mixed and reacted in the same reaction vessel Products formed are distinctive for each bead and physically distinct Individual beads can be separated to isolate individual products  2. Combinatorial Synthesis 2.4. Combinatorial Library 2.4. Combinatorial Library: Number of possible different compounds in a library (N) Number of building blocks used in each step (b) Number of synthetic steps (x). If an equal number of building blocks is used in each synthetic step, then the following Equation holds N = bx If the number of building blocks in each step is varied (e.g., b, c, and d for a three-step synthesis) then the following equation is relevant N = bcd  2. Combinatorial Synthesis 2.4. Combinatorial Library 2.5. Approaches in Combinatorial Synthesis 2.5.1. Parallel Synthesis 2.5. Approaches in Combinatorial Synthesis 2.5.1. Parallel Synthesis  2. Combinatorial Synthesis 2.4. Combinatorial Library 2.5. Approaches in Combinatorial Synthesis 2.5.1. Parallel Synthesis Size of compound library:  2. Combinatorial Synthesis 2.5. Approaches in Combinatorial Synthesis 2.5.2. Combinatorial synthesis (Mix and Split) 2.5.2. Combinatorial Synthesis Mix and Split Solid-phase Synthesis  Key Takeaways How to classify peptide versus protein (number of amino acids)? Concept of Parallel and Combinatorial Chemistry Solid phase chemistry and application Count library size by using building blocks and steps Testing the library of compounds: – using the Deconvolution technique for big libraries – testing individual compounds for parallel (single/small) compound libraries.  3. COMPUTER-Aided Drug Designing (CADD) 3. Computer-Aided Drug Design (CADD) Rational Drug Design Structure-Based Drug Design Intelligent Drug Design Why CADD? Traditional drug development: random screening, chance discovery A typical process for a drug: ~12 years and over $800 million! Limitation: lengthy, expensive, and intellectually inelegant CADD exploits state-of-the-art technologies to speed up the drug development process Rational Drug Design: (i) 3D superimposition of available compound libraries; (ii) Performing virtual docking  3. CADD 3.1. How CADD Work? 3.1. Is the structure of the target known? 3.2. Development of Computers 3. CADD 3.1. How CADD Work? - hardware 3.2. Development of Computers - algorithms – software's - computation chemists - 3D representation of molecules based on graphic or mathematical representations of chemical structures  3.3. Molecular Modeling 3.3. Molecular Modeling in Drug Design Known bioactive conformation of a specific drug. One of the main goals in the design of new molecules is to decrease the energy of this conformation in order to increase its population, therefore increasing its biological activity. 3.3.1. Internal Energy of a 3. CADD 3.3. Molecular Modeling 3.3.1. Internal Energy Molecule All chemical systems contain a certain amount of internal energy consisting of potential and kinetic energy. The potential energy is directly related to chemical bonding and non-bonding interactions, whereas kinetic energy is related to random molecular motions. Each geometry (Conformation) of a molecule has its specific internal energy; this is due to different non-bonding interactions.  3. CADD 3.3. Molecular Modeling 3.3.1. Internal Energy 3.3.1. Internal Energy Associated to Conformation  3. CADD Force Field Components 3.3. Molecular Modeling 3.3.1. Internal Energy A typical force field consists of bond stretching, bending, torsional rotation, van der Waals interaction, electrostatic interaction, and hydrogen bonding energy function. The energy of a conformer is the sum of all those contributions.  3. CADD 3.3. Molecular Modeling 3.3.1. Internal Energy 3.3.2. Transition State 3.3.2. Transition State The conversion of a conformer of a molecule into another one involves a high-energy geometry, also known as the transition state. The high energy operates as a barrier that the molecule has to cross in order to change its three-dimensional structure.  3. CADD 3.4. Minimization Models 3.4.1. Molecular mechanics minimization 3.4. Minimization Models 3.4.1. Molecular mechanics minimization Energy minimization Identifying stable conformations Energy calculations for specific conformations Generating different conformations Studying molecular motion  3. CADD 3.4. Minimization Models 3.4.1. Molecular mechanics minimization How Does Minimization of Energy Work? Small modifications of the molecular geometry are considered if the resulting geometry has a lower energy than the original, then another step is made in the same direction. Otherwise, a smaller step in a different direction is carried out. This process is continued until the energy cannot decrease. Minimization is a Time-Consuming Treatment  3. CADD Typical Minimization Example 3.4. Minimization Models 3.4.1. Molecular mechanics minimization The minimization of the one conformer may require thousands of iterations, each one requiring the calculation of the new energy. The minimization treatment has to be applied thousands of times (one minimization for each conformer generated)  3. CADD Energy of conformer 3.4. Minimization Models 3.4.1. Molecular mechanics minimization The conformational potential surface contains local minima (Low energy conformers), barriers (high energy conformers), and a global minimum (the conformation of lowest energy). Saddle points correspond to points where not all the derivatives of the energy with respect to the coordinates are equal to zero. Local Minima and Global Minimum  3. CADD 3.4.2. Molecular Dynamics (MD) 3.4. Minimization Models 3.4.1. Molecular mechanics minimization 3.4.2. Molecular dynamics minimization MD provides an alternative approach to determine the preferred conformers and the global minimum of a molecule. This is achieved by the simulation of the dynamic motions of the molecule as it vibrates and undergoes internal rotation.  3. CADD 3.4. Minimization Models 3.4.1. Molecular mechanics minimization 3.4.2. Molecular dynamics minimization Simulated Annealing: A Special Type of Dynamics Simulated annealing is a technique where the molecule is heated and then cooled very slowly so that conformational changes occurring will be at a local minimum. This process is repeated many times until several very closely related and low-energy conformations are obtained. The conformer of the lowest energy is assumed to be the global minimum.  3. CADD 3.4. Minimization Models 3.5. Conformational Analysis 3.5. Conformational Analysis in Drug Design Conformational analyses are used to predict the biological properties of candidate prototype structures. The goal is to determine which conformer of the drug is active and stable (lowest energy). Global minimum It cannot be assumed that the lowest energy structure of the molecule binds to the receptor; the bioactive conformation can be a higher energy conformation of the molecule.  3. CADD 3.4. Minimization Models 3.6. Docking 3.5. Conformational Analysis 3.6. Docking Dock known chemicals from an in silico database into the receptor target site (database searching or screening). A molecular graphics term for the computer-assisted movement of a molecule into its receptor. Docking is an energy-based operation for exploring the binding modes of two interacting molecules. https://www.sciencedirect.com/science/article/abs/pii/B9780323897754000146  3. CADD 3.6. Docking 3.4. Minimization Models 3.5. Conformational Analysis 3.6. Docking Stretching, bending, torsion, electrostatic, Van der Waals, solvation An energy-driven procedure that takes into account van der Waals, hydrogen bonding, and electrostatic forces allows one to dock a ligand to a binding site by optimizing the interaction of the ligand and its receptor. Calculating the binding energies Energies gives scoring functions that give a measure of the ligand-protein binding constant. https://www.mdpi.com/1422-0067/20/18/4574 4. Artificial Intelligence in Drug Discovery https://spj.science.org/doi/10.34133/2022/9816939  4.1. Neural Network https://nexocode.com/blog/posts/artificial-intelligence-in-drug-discovery-and-development/ 4.2. Applications of AI https://phys.org/news/2023-05-ai-advantages-drug.html  Key Takeaways Understand Minimization of energy for molecules. Able to identify Global minima and Local minima. Understand process of molecular modeling. Importance of conformation analysis in CADD. (To find the bioactive and most stable conformer) Understand molecular docking. Artificial intelligence process and applications