University of Essex

BS281-5-SP-CO:
Protein Bioinformatics

The details

Year: 2024/25

Department: Life Sciences (School of)

Campus: Colchester Campus

Term: Spring

Level: Undergraduate: Level 5

Status: Current

Start date: Monday 13 January 2025

End date: Friday 21 March 2025

Credits: 15

Last updated: 14 February 2024

Requisites for this module

Pre-requisites: BS131

Co-requisites: (none)

Pre and / or co-requisites: (none)

Prohibited modules: (none)

Key module (requisite for): (none)

Key module for

BSC C700 Biochemistry,
BSC C701 Biochemistry (Including Placement Year),
BSC C703 Biochemistry (Including Year Abroad),
BSC CR00 Biochemistry (Including Foundation Year),
MSCIC098 Biochemistry and Biotechnology (Including Year Abroad),
MSCIC099 Biochemistry and Biotechnology (Including Placement Year),
MSCICZ99 Biochemistry and Biotechnology

Module description

This module will focus on how to decode the role and the molecular evolution of a protein starting from its sequence.

The lectures will teach the theory and the computer practicals will introduce some of the many computer and web resources available to perform bioinformatics analyses.

Module aims

The aim of this module is:

To introduce the different resources and tools available on-line for the study of protein structure and interaction.

Module learning outcomes

By the end of this module, students will be expected to be able to:

Use and retrieve information from protein databases and protein portals.

Align protein sequences, and edit them in the light of amino acid similarities.

Predict protein secondary and tertiary structure and protein-protein interaction.

Observe and modify protein structure files using PyMol.

Use webtools to recognise motifs in a protein sequence.

Define the principles of molecular dynamics and perform energy minimisation.

Module information

The post-genome era has seen significant advances in our ability to obtain biological data, be it protein sequences or structures. Bioinformatics have become an indispensible skill for the next generation of biochemists and biologists in order to retrieve, analyse and interpret data.

Learning and teaching methods

This module will be delivered via:

Eight 1-hour lectures.
One revision class.
Seven 2-hour workshops.

Bibliography

Lesk, A. (2019) Introduction to Bioinformatics. 5th Revised edition. Oxford: Oxford University Press. Available at: https://app.kortext.com/Shibboleth.sso/Login?entityID=https://idp0.essex.ac.uk/shibboleth&target=https://app.kortext.com/borrow/371502.
Xu, D. and Xu, Y. (2004) ‘Protein Databases on the Internet’, Current Protocols in Molecular Biology, 68(1). Available at: https://doi.org/10.1002/0471142727.mb1904s68.
Stephens, Z.D. et al. (2015) ‘Big Data: Astronomical or Genomical?’, PLOS Biology, 13(7). Available at: https://doi.org/10.1371/journal.pbio.1002195.
Berman, Helen M. et al. (2013) ‘How Community Has Shaped the Protein Data Bank’, Structure, 21(9), pp. 1485–1491. Available at: https://doi.org/10.1016/j.str.2013.07.010.
Berman, Helen M. et al. (2013) ‘Trendspotting in the Protein Data Bank’, FEBS Letters, 587(8), pp. 1036–1045. Available at: https://doi.org/10.1016/j.febslet.2012.12.029.
Zeeberg, B.R. et al. (2004) ‘Mistaken Identifiers: Gene name errors can be introduced inadvertently when using Excel in bioinformatics’, BMC Bioinformatics, 5(1). Available at: https://doi.org/10.1186/1471-2105-5-80.
Callaway, E. (2020) ‘“It will change everything”: DeepMind’s AI makes gigantic leap in solving protein structures’, Nature, 588(7837), pp. 203–204. Available at: https://doi.org/10.1038/d41586-020-03348-4.
Kuhlman, B. and Bradley, P. (2019) ‘Advances in protein structure prediction and design’, Nature Reviews Molecular Cell Biology, 20(11), pp. 681–697. Available at: https://doi.org/10.1038/s41580-019-0163-x.
Brooke, G.N. and Prischi, F. (2020) ‘Structural and functional modelling of SARS-CoV-2 entry in animal models’, Scientific Reports, 10(1). Available at: https://doi.org/10.1038/s41598-020-72528-z.
Prischi, F. et al. (2010) ‘Structural bases for the interaction of frataxin with the central components of iron–sulphur cluster assembly’, Nature Communications, 1(1). Available at: https://doi.org/10.1038/ncomms1097.
Crosara, K.T.B. et al. (2018) ‘Merging in - silico and in vitro salivary protein complex partners using the STRING database: A tutorial’, Journal of Proteomics, 171, pp. 87–94. Available at: https://doi.org/10.1016/j.jprot.2017.08.002.
Lupoli, F. and Pastore, A. (2017) ‘Why Studying Rare Diseases Is So Important: Do You Know of This Disease?’, Frontiers for Young Minds, 5. Available at: https://doi.org/10.3389/frym.2017.00003.

The above list is indicative of the essential reading for the course.
The library makes provision for all reading list items, with digital provision where possible, and these resources are shared between students.
Further reading can be obtained from this module's reading list.

Assessment items, weightings and deadlines

Coursework / exam	Description	Coursework weighting
Coursework	Worksheet	40%
Coursework	Problem Solving Activity	55%
Coursework	Engagement	5%

Exam format definitions

Remote, open book: Your exam will take place remotely via an online learning platform. You may refer to any physical or electronic materials during the exam.
In-person, open book: Your exam will take place on campus under invigilation. You may refer to any physical materials such as paper study notes or a textbook during the exam. Electronic devices may not be used in the exam.
In-person, open book (restricted): The exam will take place on campus under invigilation. You may refer only to specific physical materials such as a named textbook during the exam. Permitted materials will be specified by your department. Electronic devices may not be used in the exam.
In-person, closed book: The exam will take place on campus under invigilation. You may not refer to any physical materials or electronic devices during the exam. There may be times when a paper dictionary, for example, may be permitted in an otherwise closed book exam. Any exceptions will be specified by your department.

Your department will provide further guidance before your exams.

Overall assessment

Coursework	Exam
100%	0%

Reassessment

Coursework	Exam
100%	0%

Module supervisor and teaching staff

Supervisor: Prof Vassiliy Bavro, email: v.bavro@essex.ac.uk.

Teaching staff: Dr Marcus Edwards, Dr James Birrell

Contact details: School Undergraduate Office, email: bsugoffice (Non essex users should add @essex.ac.uk to create the full email address)

Availability

Available to incoming Essex Abroad / Exchange students: No

Available to Outside Option: No

Available to Audit: No

External examiner

Name: Prof Richard Bowater

Institution: University of East Anglia

Academic role: Professor of Biochemistry and Molecular Biology Education

Resources

Teaching materials: Available via Moodle

Lecture recording: Of 30 hours, 30 (100%) hours available to students:
0 hours not recorded due to service coverage or fault;
0 hours not recorded due to opt-out by lecturer(s), module, or event type.

Further information

Department website: Life Sciences (School of)

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Module Directory

BS281-5-SP-CO:Protein Bioinformatics