28 Jul 07 Introduction to Computational Proteomics

Jacques Colinge, Keiryn L. Bennett

Abbreviations: ESI, electrospray ionization; HMM, hidden Markov model; LC, liquid chromatography; MALDI, matrix assisted laser desorption ionization; MS, mass spectrometry; MS/MS, tandem mass spectrometry; PMF, peptide mass fingerprinting; PTM, posttranslational modifications; TOF, time-of-flight; SPC, shared peak count

Introduction

Proteomics is defined as the protein complement of the genome and involves the complete analysis of all the proteins in a given sample [1,2]. Several technologies are involved, and numerous questions concerning the proteins are addressed. What proteins are contained in a biological sample? At what concentration do the proteins exist? How do protein expression levels alter in different samples? What are the posttranslational modifications (PTMs)? Where in the cell [3] or an organism [4] are the proteins localised? How do the proteins interact with other proteins or molecules [5,6]?

The following discussion concentrates on computational aspects of protein identification. Characterization (identification of protein modifications), quantitation, and sample comparisons are also discussed briefly.

A typical proteomic experiment involves the analysis of complex samples, i.e., containing many proteins at varying concentrations [7]. Most of the currently available technology for identifying proteins from biological samples simply cannot contend with the complexity, and the majority of the low-abundance proteins are not observed. There are, however, a number of methods to separate the proteins contained in the original sample to obtain a simpler sample set that is amenable to in-depth analyses. Typical technologies are electrophoretic gels [8] and liquid chromatography [9] (LC) (see Figure 1A).

Figure 1. Steps in Sample Analysis by Proteomics

(A) Sample complexity reduction via an LC column. This is applicable to both proteins and peptides. It is possible to collect fractions at fixed or variable time intervals to obtain a series of less complex samples; however, direct MS analysis is also an option. The figure illustrates how peptides/proteins 1–11 are fractionated.

(B) Major steps in “bottom-up” proteomics and combinations thereof. Optional steps and essential steps are in rounded and bold rectangles, respectively. Green represents shotgun peptide sequencing entire sample digestion followed by multidimensional LC separation of peptides. Blue represents the classical gel approach, with or without (dashed arrows) peptide LC. Red combines protein and peptide LC.

(C) Data-dependent MS/MS analysis. Here, ESI of a liquid sample and alternation of the instrument between MS and MS/MS modes is illustrated. The data generated is a sequence of peptide experimental m/z associated with the corresponding fragments m/z. The complete analysis is named an LC-MS run.

A dominant and well-practiced technique in proteomics is referred to as the “bottom-up” approach. Proteins are digested into peptides (smaller components of the protein) by a proteolytic enzyme, e.g., trypsin. Analysis of the peptides is achieved by mass spectrometry (MS), and, from the data generated, the peptides (and subsequently the proteins) can be identified. The resultant mixture of peptides obtained from the digestion of several proteins is often highly complex, and a degree of separation can be achieved by peptide LC. Possible combinations of separation techniques are illustrated in Figure 1B.

Mass spectrometers comprise three main components: an ion-source, a fragmentation cell, and a mass analyzer. Each component is essentially independent from the others, and as such it is possible to combine the different technological aspects to produce different types of mass spectrometers. To measure its molecular mass, a molecule must be ionised. This occurs in the ion source of the mass spectrometer. The source can be based either on electrospray ionization [10] (ESI), which is therefore appropriate for liquid samples; or on matrix assisted laser desorption ionization [11] (MALDI), which is appropriate for samples that have been mixed with a matrix and crystallized on a metallic plate. The most common types of mass analyzers used in proteomic laboratories are (i) ion trap (IT), where the radio frequency of the trap is varied and the ejected ions are detected; and (ii) time-of-flight (TOF) analyzers, where the time required for an ion to “fly” through an electric field–free region of the instrument is recorded and correlated to the mass of the ion. Most current instruments include a fragmentation cell that uses an inert gas to break the peptides by collision-induced dissociation (CID). A fragmentation cell, however, is not always present (see next section), or fragmentation can occur “spontaneously” (in-source and post-source decay). All mass spectrometers do not measure mass directly, but rather the mass-to-charge ratio. Hence the measurements obtained are dependent on the charge state(s) of the molecule.

here the paper from PLOS

09 Jun 07 Sains 2020: Computational Biology

Musim panas 2005, berkumpul pakar (international expert group) dalam suatu workshop (disponsori oleh Microsoft) untuk mendefinisikan dan menghasilkan suatu visi baru dan ‘roadmap’ dari evolusi, tantangan dan potensi penelitian ‘computer science’ dan ‘computing in science’ untuk 15 tahun ke depan.

Dokumen hasil workshop, Towards 2020 Science, memuat tantangan dan kesempatan yang muncul akibat peningkatan sintesis dari komputasi dan sains. Dokumen juga menam- pilkan hasil identifikasi akan hal-hal penting yang diperlukan untuk mempercepat kemajuan saintifik, terutama yang di-drive ‘computational sciences’ dan the ‘new kinds’ of science’ : sintesis komputasi dan sains. Sintesis ini memuncul bidang baru dan advances mulai dari genomics dan proteomics, ilmu bumi dan klimatology, material nano, kimia dan fisika.

Para pakar berharap, Towards 2020 Science akan menjadi suatu ‘pathfinder’ bagi arah penelitian baru dalam sain dan komputasi.

Hal penting yang perlu di catat adalah bahwa menuju tahun 2020: a) riset lebih di arahkan ke Computational Biology (dan tahun 2020 riset computational biology sudah benar-benar ‘establish), b) Ilmu komputer akan menjadi tool utama (core tools) wajib bagi ilmu non-eksakta, sebagaimana matematika menjadi tool bagi semua ilmu.

Sudah saat bagi kita untuk ikut serta melakukan penelitian pada computional biology. Beberapa link sebagai pengantar computational biology:

http://www.biodirectory.com/biowiki/Main_Page

http://www.biodirectory.com/biowiki/Computational_biology

http://en.wikipedia.org/wiki/Computational_biology

http://www.soe.ucsc.edu/~karplus/compbio_pages.html

http://www.cmu.edu/bio/education/courses/03510/LectureNotes/

http://compbiol.plosjournals.org/