Protein Function and Gene Ontology

False (B)

The reliability and confidence level of the annotations.

<p>Cellular Component</p> Signup and view all the answers

<p>Molecular Function = Tasks performed by individual gene products Biological Process = Broad biological goals or objectives Cellular Component = Subcellular structures or locations Evidence Codes = Indicate the reliability of the annotation</p> Signup and view all the answers

<p>Papers and experiments (A)</p> Signup and view all the answers

<p>True (A)</p> Signup and view all the answers

<p>Competitors may develop similar drugs (B)</p> Signup and view all the answers

<p>True (A)</p> Signup and view all the answers

<ol> <li>Ligands known, receptor unknown; 2. Receptor known, ligands unknown; 3. Receptor-ligand complex known.</li> </ol> Signup and view all the answers

<p>QSAR</p> Signup and view all the answers

<p>AutoDock Vina = Traditional docking method using simple energy scores GLIDE = Traditional docking method that adjusts ligand conformations DeepDock = Advanced ML method that improves scoring functions DiffDock = Advanced ML method that utilizes diffusive models</p> Signup and view all the answers

<p>STRING (D)</p> Signup and view all the answers

<p>True (A)</p> Signup and view all the answers

<p>Machine learning and sequence analysis using k-nearest neighbour.</p> Signup and view all the answers

<p>CAFA</p> Signup and view all the answers

<p>STRING = Identifies protein interactions NetGo2.0 = Predicts GO terms using machine learning DALI = Structures alignment and scores Prosite = Identifies enzyme active site patterns</p> Signup and view all the answers

<p>Advanced Sequence-Based Approaches (A)</p> Signup and view all the answers

<p>True (A)</p> Signup and view all the answers

<p>Increased confidence due to identification of similar functional residues.</p> Signup and view all the answers

<p>HMMs</p> Signup and view all the answers

<p>Addition of a protein language model (A)</p> Signup and view all the answers

<p>Mendelian scale (C)</p> Signup and view all the answers

<p>True (A)</p> Signup and view all the answers

<p>deep learning</p> Signup and view all the answers

<p>lower case letters</p> Signup and view all the answers

<p>Signal Peptides = Direct proteins to cellular locations Low Complexity Regions = Regions biased toward a few amino acids Coiled-Coils = Intertwined alpha helices Disordered Proteins = Proteins with flexible structural conformations</p> Signup and view all the answers

<p>Machine learning (D)</p> Signup and view all the answers

<p>False (B)</p> Signup and view all the answers

<p>Low complexity regions</p> Signup and view all the answers

<p>C-</p> Signup and view all the answers

<p>False (B)</p> Signup and view all the answers

<p>It has an AUC of 0.814 for predicting disorder. (A)</p> Signup and view all the answers

<p>True (A)</p> Signup and view all the answers

<p>Determine a safe dosage and assess side effects.</p> Signup and view all the answers

<p>lead</p> Signup and view all the answers

<p>Phase I = Determine safe dosage and side effects Phase II = Refine side effects and initial effectiveness Phase III = Prove effectiveness and evaluate safety Phase IV = Post-marketing surveillance</p> Signup and view all the answers

<p>A small molecule identified with the desired therapeutic effect (D)</p> Signup and view all the answers

<p>False (B)</p> Signup and view all the answers

<p>Obtain approval for the drug’s use and marketing.</p> Signup and view all the answers

<p>DisProt</p> Signup and view all the answers

<p>DNA-binding, RNA-binding, Protein-binding, Linkers (D)</p> Signup and view all the answers

A controlled vocabulary system used to describe the functions of genes and gene products (proteins and RNA) across all organisms.

The specific tasks performed by individual gene products, like binding to a molecule or catalyzing a reaction.

The overall biological objective or goal that a gene product contributes to, such as cell division or metabolism.

The location within a cell where a gene product is active, like a specific organelle or complex.

An annotation that indicates the source of information used to assign a GO term, helping to assess its reliability.

Using existing knowledge about protein homology and families to predict the function of new or uncharacterized proteins.

The process of grouping related GO terms to gain a broader understanding of the integrated functions of a set of proteins.

Specialized databases containing curated information on protein domains, motifs, and profiles, aiding in functional annotation.

A collection of short patterns that can be used to identify specific functional sites in proteins, such as active enzyme sites.

More extensive profiles than motifs, encompassing larger sequence patterns that pinpoint specific functions, such as carrier proteins.

Databases that analyze protein-protein interactions, helping to infer function based on the principle that interacting proteins often have similar functions.

A database that uses diverse sources of information, including curated interactions and predictions from various bioinformatics methods, to identify proteins interacting with a query protein.

A machine learning approach that integrates multiple data sources, such as BLAST results, InterPro features, amino acid frequencies, and STRING network information, to predict protein function.

A method for functional prediction based on the similarity of 3D structures, assuming that proteins with similar structures often have similar functions.

A database search tool that compares the 3D structure of a query protein to a database of known structures, providing a score for similarity.

A type of structural searching that focuses on identifying proteins with similar folds, suggesting potential functional relationships.

A mathematical relationship that connects the activity of a molecule to its structure.

A method that tests the ability of a docking algorithm to predict the correct pose of a ligand within a protein, by using the known structure of a protein-ligand complex.

An approach where the ligand's conformation is adjusted while keeping the protein structure mostly rigid, allowing only side-chain adjustments.

An evaluation method where a docking algorithm is trained on one protein and then tested on a homologous protein.

It uses diffusion models to search and score ligands. It works by taking known complexes, adding noise, and then removing the noise.

A sequence of amino acids at the beginning of a protein that directs it to the correct location within the cell and is often removed after transport.

A method for predicting transmembrane regions within a protein sequence by analyzing the hydrophobicity of amino acids.

A region in a protein sequence with a high concentration of a few specific amino acids, making it difficult to align with other sequences.

Two or three alpha helices intertwined, forming a structural motif in proteins.

A protein that lacks a defined three-dimensional structure and is highly flexible, often involved in protein recognition.

Regions within a protein that lack a defined structure and are often found at the ends of the protein or as loops.

A deep learning algorithm designed to accurately predict both transmembrane structures and signal peptides in protein sequences.

A program used to identify low complexity regions in protein sequences and replace them with lowercase letters in BLAST searches.

A technique used to identify coiled-coil structures in proteins.

A program that predicts disordered regions in protein sequences based on machine learning.

A protein or molecule whose activity is modified to achieve therapeutic effects.

A small molecule identified through biological or computational screening with the desired effect (typically IC50 ≤ 1 µM).

A chemically optimized version of a hit, designed to enhance therapeutic efficacy and minimize adverse effects.

A measure of how much of a drug is needed to achieve a desired effect.

The first phase of clinical trials, involving a small group of individuals to determine a safe dosage and assess side effects.

The second phase of clinical trials, focusing on refining results from Phase I and determining initial effectiveness.

The third phase of clinical trials, involving a large group of patients to definitively prove effectiveness and further evaluate safety.

The process of submitting data to regulatory agencies for approval of drug use and marketing.

The post-marketing surveillance phase, monitoring long-term safety and effectiveness of a drug.

A database of curated information about intrinsically disordered proteins and regions, providing valuable insights into their functions.