Bioinformatics Practical Course

Mentor:

Dr. Qais Yousef

Ph.D. in Computer Engineering/ Industrial Automation Systems and Swarm Intelligence

Have worked on numerus researches in this field.

Mobile: +962-795037290

Email:   info@atitgroup.com  


 Course Details:

This course presents major ideas and techniques for auxiliary bioinformatics and the advanced applications. Points included incorporate arrangement, structure and capacity databases of DNA and protein particles, propelled succession and structure arrangement strategies, techniques for protein collapsing and protein structure expectation (homologous demonstrating, threading and ab initio folding) basics of molecular dynamics and Monte Carlo simulation, principle and application of machine learning, and techniques of protein structure determination (X-ray crystallography, NMR and cryo-EM). Accentuation is on the comprehension of the ideas instructed and the pragmatic usage, with the target to help understudies to utilize the front line bioinformatics devices/strategies to tackle issues in their own exploration. Prerequisite: Good ability of computer coding (with at least one language, such as MATLAB) are highly recommended.   


Schedule and location: 

Three months. The practical course will be held in ATIT Academy or Online.


Projects and Assignments:

Each session will contain practical project. Moreover, there will be homework assignments, including code writing and literature reading.


Textbook: 

No one reference will be considered for this course, however, related materials will be shared with students.


Table of content:

1. Bioinformatics databases

1.1. Introduction

1.1.1. Motivation

1.1.2. Central dogma of life

1.1.3. Type of bioinformatics databases

1.2. Nucleotide sequence databases

1.2.1. EMBL

1.2.2. GeneBank

1.2.3. DDBJ

1.3. Protein amino acid sequence databases

1.3.1. How protein sequences are determined

1.3.1.1. DNA/mRNA coding

1.3.1.2. Edman degradation reaction

1.3.1.3. Mass spectrometry

1.3.2. SwissProt/TrEMBL

1.3.3. PIR

1.3.4. UniProt

1.3.4.1. UniProtKB/Swiss-Prot and UniProtKB/TrEMBL

1.3.4.2. UniParc

1.3.4.3. UniRef

1.4. Protein structure databases

1.4.1. History of structural biology

1.4.2. Protein Data Bank

1.4.3. SCOP

1.4.4. CATH

1.5. Protein function databases

1.5.1. Pfam-protein family database

1.5.2. GO-gene ontology

1.5.3. PROSITE-protein function pattern and profile

1.5.4. ENZYME-Enzyme commission

1.5.5. BioLiP-ligand protein binding interaction 

2. Pair-wise sequence alignments and database search

2.1. Biological motivation-why sequence alignment?

2.2. What is a sequence alignment?

2.2.1. Scoring matrix

2.2.1.1. PAM

2.2.1.2. BLOSUM

2.2.2. Gap penalty

2.3. Dynamics programming

2.3.1. Needleman-Wunsch: global alignment algorithm

2.3.2. Smith-Waterman: local alignment algorithm

2.3.3. Gotoh algorithm 

2.4. Heuristic methods

2.4.1. FASTA

2.4.2. BLAST

2.5. Statistics of sequence alignment score

2.5.1. E-Value

2.5.2. P-Value

3. Phylogenic tree & multiple sequence alignments

3.1. Neighbor-joining method and phylogenetic tree

3.2. How to construct multiple sequence alignments?

3.2.1. ClustalW

3.2.2. PSI-BLAST

3.2.2.1. PSI-Blast pipeline

3.2.2.2. Profile pseudocount

3.2.2.3. PSSM-position specific scoring matrix

3.2.2.4. Installing and running PSI-Blast programs

3.2.2.5. Interpret PSI-Blast out

3.2.3. Hidden Markov Models

3.2.3.1. Viterbi algorithm

3.2.3.2. HMM based multiple-sequence alignment

3.2.3.2.1. Creating HMM by iteration

3.2.3.2.2. HMMER

3.2.3.2.3. SAM

3.3. Sequence profile & profile based alignments

3.3.1. What is sequence profile?

3.3.2. Henikoff weighting scheme

3.3.3. Profile-to-sequence alignment

3.3.4. Profile-to-profile alignment

4. Protein structure alignments

4.1. Structure superposition versus structural alignment

4.2. Structure superposition methods

4.2.1. RMSD

4.2.2. TM-score

4.3. Structure alignment methods

4.3.1. DALI

4.3.2. CE

4.3.3. TM-align

4.4. How to define the fold of proteins?

4.5. Number of protein folds in the PDB

5. Protein secondary structure predictions

5.1. What is protein secondary structure?

5.2. Hydrogen bond

5.3. How to define a secondary structure element?

5.4. Basics of machine learning and neural network methods

5.5. Methods for predicting secondary structure

5.5.1. Chou and Fasman method

5.5.2. PHD

5.5.3. PSIPRED

5.5.4. PSSpred

6. Introduction to Monte Carlo Simulation

6.1. Introduction: why Monte Carlo simulation?

6.2. Monte Carlo Sampling of Probabilities

6.2.1. Random number generator

6.2.2. How to test a random number generator?

6.2.3. Sampling of rectangular distributions

6.2.4. Sampling of probability distribution

6.2.4.1. Reverse transform method

6.2.4.2. Rejection sampling method

6.3. Boltzmann distribution

6.4. Metropolis method

6.5. Advanced Metropolis methods

6.5.1. Replica exchange simulation

6.5.2. Simulated annealing

7. Protein folding and protein structure modeling

7.1. Basic concepts

7.2. Ab initio modeling

7.2.1. Anfinsen thermodynamic hypothesis

7.2.2. Molecular dynamics simulation

7.2.2.1. CHARMM

7.2.2.2. AMBER

7.2.3. Knowledge-based free modeling

7.2.3.1. Bowie-Eisenberg approach

7.2.3.2.ROSETTA 7.2.3.3.QUARK

7.2.3.3. Why is beta-protein so difficult to fold?

7.3. Comparative modeling (homology modeling)

7.3.1. Principle of homology modeling

7.3.2. PSI-BLAST

7.3.3. Modeler

7.4. Threading and fold-recognition

7.4.1. What is threading?

7.4.2. Threading programs

7.4.2.1. Bowie-Luthy-Eisenberg

7.4.2.2. HHpred

7.4.2.3. MUSTER

7.4.3. Meta-server threading

7.4.3.1.3D-jury

7.4.3.2. LOMETS

7.5. Combined modeling approaches

7.5.1. TASSER/I-TASSER

7.5.1.1. Force field design

7.5.1.2. Search engine: replica-exchange Monte Carlo simulation

7.5.1.3. Major issues and recent development 

7.6. CASP: A blind test on protein structure predictions 

8. Protein function and structure-based function annotation

8.1. Gene ontology

8.2. Enzyme classification

8.3. Ligand-protein interaction

8.4. Structure-based function prediction

8.4.1. Concavity

8.4.2. FindSite

8.4.3. COFACTOR

8.4.4. COACH

9. Principle of X-ray Crystallography & Molecular Replacement

9.1. What is X-ray Crystallography?

9.2. Why can a wave be represented by exp (iα)?

9.3. How to calculate scattering on two electrons?

9.4. What is Laue condition?

9.5. What is Bragg’s law?

9.6. How to calculate electron density of crystal?

9.7. What is Patterson function?

9.8. How to calculate electron density of crystal?

9.9. What is the idea of Molecular Replacement?

9.10. How to judge quality of MR?

9.11. What are often-used software for MR?

10. Introduction to nuclear magnetic resonance (NMR)

10.1. Basic magnetic property of nuclei

10.1.1. Magnetic moment

10.1.2. Nuclei in external magnetic field

10.1.3. Nuclear shielding of magnetic field

10.2. Chemical shift

10.3. NMR spectrum

10.3.1. Correlation spectroscopy (COSY)

10.3.2. Heteronuclear single-quantum correlation spectroscopy (HSQC)

10.3.3. Nuclear Overhauser effect spectroscopy (NOESY)

10.4. From NOE to 3D structure model

  • Each student will be assigned a short Review paper (aside from the class-shared paper) to work on at home, and is required to submit a part of it at the beginning of every session starting the 4th session. The submitted assignments will be discussed in the next session with each student individually.   
  • Questions and discussions are highly encouraged during class.  

Remarks:

  • Each student MUST bring in a laptop with Windows OS installed on as well as Microsoft Office or Apache OpenOffice to every class session.  

 

Prepared by: ATIT Academy