Protein Analytics SIG



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The Protein Analytics Special Interest Group


  1. The idea was instigated in March 2014 following discussions initiated at the Protein Quantitation session in Eastbourne, at which the need for increased rigour and standardisation in protein quantification by mass spectrometry was highlighted.

First Meeting

  1. Alderley Park, March 30th.
  2. Twelve attendees from Universities of York, Liverpool, Cambridge, Manchester, and KCL, plus Covance, Quotient, Intertek, King's College Hospital, LGC, and Fera.
  3. Subsequently, Jerry Thomas, University of York, was designated to lead the SIG and David Knight, University of Manchester, agreed to lead a subgroup dealing with standards and benchmarking.

The SIG’s Purpose

  1. The group's main goals are:
    1. Sharing of protocols and methodology
    2. Publication of best practise guides
    3. Linking to other national and international organisations
    4. Harmonisation and unification of approaches

Next Steps

  1. The first planned output will be a training course, to be held in conjunction with the next BMSS Annual Meeting in Birmingham (September 2015).  Course material will form the basis of a best practice guide.
  2. An outline of the course is in circulation.  Volunteers to contribute to the course are being sought, and an initial meeting to discuss course content will be held.

For further information and to become a part of this SIG please contact the SIG co-leader's:

Dr Jerry Thomas
Dr David Knight


Proposed Topics for Course

  1. Mass spectrometry
    1. MS
    2. IDMS
    3. SRM & MRM (targeted)
    4. DIA (non-targeted)
    5. Peptide MS 
  2. Analytics
    1. Calibration
    2. Limits & uncertainty
    3. Acceptance criteria
    4. Accreditation
  3. Peptide standards
    1. Proteases &  proteolysis
    2. Criteria & choice
    3. Purity
    4. Quantity
    5. Sources
  4. Practice
    1. Equimolar release of peptides
    2. Selection and optimisation of transitions
    3. Running order & randomisation
    4. Quality control
    5. Data analysis





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