Bioinformatics and Molecular Modeling

Module: Structural Bioinformatics
1. Introduction to bioinformatics
2. Genome organisation and evolution
3. Databases, archives, and information retrieval
4. Substitution matrices, pairwise and multiple alignments, database searching, and phylogenetic trees
5. Protein structure and architecture
6. Protein structure prediction and validation: comparative modelling, threading, and ab initio approaches
Module: Molecular Mechanics and Dynamics
1. Elements of statistical thermodynamics
2. Introduction to molecular mechanics
o Force fields
o Solvent models and periodic boundary conditions
o Geometry optimisation
3. Conformational search
o Systematic methods
o Stochastic methods
4. Molecular dynamics (MD)
o Equations of motion and trajectory calculation
o Microcanonical (NVE), canonical (NVT), and isothermal-isobaric (NPT) ensembles
o Trajectory analysis: energy profiles, RMSD, RMSF, geometric parameters, hydrogen bonds, cluster analysis, principal component analysis
o Applications and limitations of MD
5. Enhanced sampling techniques
o Simulated annealing
o Umbrella sampling
o Replica exchange MD
o Metadynamics
o Accelerated MD
6. Free energy calculations in complex systems
o Potential of mean force (PMF)
o Alchemical perturbations: Free Energy Perturbation and Thermodynamic Integration
o End-point methods: MM-PBSA
Module: Data Modelling in Biotechnology
1. Introduction to cheminformatics and computer-aided drug design (CADD):
· basics of computer science
· ligand-based and structure-based approaches
· introduction to QSAR modelling and Virtual Screening
2. Computer representation of small molecules:
· molecular graphs, matrices, and connection tables
· CT files, line notations, molecular fingerprints
3. Molecular descriptors:
· definition, characteristics, and classification by dimensionality
· ECFP, LogP, geometric descriptors, electronic descriptors, molecular fields, MLP, Virtual LogP
· advantages and limitations of the main descriptor families
4. History and development of QSAR modelling.
5. Traditional regression QSAR:
· meaning and requirements
· steps in the development of predictive regression models
· least squares algorithm and multiple linear regression
· principal component analysis (PCA) and genetic algorithms
· coefficient of determination (R²), Pearson correlation coefficient (R), PRESS, Q²
· model application and applicability domain analysis
6. Modern classification QSAR:
· meaning and requirements
· introduction to Machine Learning concepts and the Random Forest algorithm
· steps in the development of predictive classification models
· Precision, Recall, Matthews Correlation Coefficient (MCC)
· model application and applicability domain analysis
7. Theoretical foundations of molecular docking simulations preparatory to practical exercises:
· meaning, classification, and model validation
· searching algorithms
· scoring functions: meaning and classifications
8. Theoretical foundations of virtual screening preparatory to practical exercises:
· ligand-based and structure-based strategies
· steps in predictive model development and the concept of enrichment
· sensitivity, specificity, accuracy, topN% enrichment factor, ROC curve.