Key information

Next planned course: Spring 2022
Location: UiT
Course responsibleAdele Kim Williamson

Grading: 80% attendance and evaluation of reports (pass/fail)
Credits: 5 ECTS  

Registration

Students must register to BioCat.

Content

The purpose of this course is to provide students with an overview of available methods to discover novel, biotechnologically-applicable enzymes from biological and environmental samples.

The course will focus on the two core strategies for enzyme bioprospecting/ bio-discovery: sequence-based prediction, and functional screening. Lectures will introduce the types of programs and databases used for in silico analysis of sequence data, and discuss available high-throughput methods for functional screening. Evaluation of extreme organisms/ environments as potential sources of novel enzymes will also be covered, as well as the ‘goals’ of employing novel enzymes in biocatalytic processes (e.g. novel chemistry or improved operating optima). Protein expression methods and enzyme engineering will be mentioned, but not covered in depth.

As the context of this course is biocatalysis in industrial or biotechnological settings, all concepts will be illustrated by real-world examples of enzyme discovery for applied purposes, with examples drawn from both the literature and active research from course contributors. Several in-depth case studies will be used to demonstrate key concepts and two guest lectures will feature speakers from industry.

Computer laboratory sessions will allow students to put this information into practice, by using bioinformatic tools to ‘discover’ novel enzymes from sequence sets, while a project-based assessment will encourage students to draw on knowledge acquired from the course to describe how both sequence-based and function-based methods can be applied for novel enzyme discovery.  

Teaching

The course will be given intensively over 1 week, followed by own project work (approximately 1.5 week).

The course will involve lectures, seminars/ group work based around specific case studies , and computer laboratories.

Own project work will involve writing a section of a research/project proposal based on knowledge/ skills acquired during the course, and evaluation of a ‘mock’ proposal by peer review

Work requirement

  • Minimum 80% attendance
  • Approved project work

Exam and evaluation

Pass/fail evaluation on reports, based on external evaluation

Syllabus

Course material will be provided during the course

Learning outcome

This course aims to enable the candidate to propose a reasonable strategy for bio-mining organisms(s)/ genomes/ other datasets for enzyme candidates taking into consideration: 1. Current knowledge of the enzyme in question, and 2. Particular goals of biocatalytic / industrial process being targeted. The candidate should also be able to discuss and compare the relative advantages and disadvantages of their proposal, and to evaluate alternative methods available in this rapidly-developing field.

Knowledge

By the end of this course the student have:

  • Detailed knowledge of strategies available for bio-mining novel organisms and an appreciation of the strengths/ weaknesses of these
  • Familiarity with the toolbox of (publically available) programs  for sequence-based in silico analysis
  • Familiarity with principles of  functional screening and has a basic knowledge of specific types of high-throughput assays
  • Awareness of different biological/ environmental sources of enzyme leads, and can evaluate  the suitability of these for the relevant application

Skills

By the end of this course the student can:

  • Evaluate applicability of bio-mining strategies to a particular biocatalytic problem/ sample type
  • Identify homologous proteins from sequence data
  • Construct sequence-based alignments of homologous proteins and use this information to extract meaningful information about an uncharacterized protein sequence
  • Design a functional screening workflow to identify lead
  • Propose subsequent experiments/ workflows to verify their in silico prediction /functional screening results

General competence

During this course the student will gain experience in:

  • Critical evaluation of literature and extraction of information from primary literature sources
  • Scientific writing skills
  • Experimental planning
  • Introduction to peer-review process (possible group work)