Protein Classification A comparison of function inference techniques
Why do we need automatedclassification? Sequencing a genome is only the first step. Between 35-50% of the proteins in sequenced genomes have no assigned functionality. Direct observation of function is costly, time consuming, and difficult.
Protein DomainsThe tertiary structure of many proteins is built fromseveral domains.Often each domain has a separate function toperform for the protein, such as:•binding a small ligand (e.g., a peptide in themolecule shown here)•spanning the plasma membrane (transmembrane proteins)•containing the catalytic site (enzymes)•DNA-binding (in transcription factors)•providing a surface to bind specifically to anotherproteinIn some (but not all) cases, each domain in aprotein is encoded by a separate exon in the geneencoding that protein.
Inference through sequencesimilarity ProtoMap: Automatic Classification of Protein Sequences, a Hierarchy of Protein Families, and Local Maps of the Protein Space (1999)
Observations Sometimes you don’t know where the domains are. It is generally accepted that two sequences with over 30% identity are likely to have the same fold. Homologous proteins have similar functions. Homology is a transitive relationship.
Departures Authors do not attempt to define protein domains or motifs. Not dependant on predefined groups or classifications. Chart the space of all proteins in SWISSPROT, as opposed to individual families Produce global organization of sequences.
Algorithm Overview We construct a weighted graph where the nodes are protein sequences and the edges are similarity scores. Cluster the network considering only those edges above some threshold. Decrease similarity threshold and repeat.
Measuring Sequence Similarity Expectation value used. This the normalized probability of the similarity occurring at random. Lower value implies logarithmically stronger similarity. λS − ln KS= ln 2 E = N /2 S
Finding Homologies Very difficult to distinguish a clear threshold between homology and chance similarity. Authors chose e = .1, .1, and .001 for SW, FASTA, and BLAST, respectively. Spent a lot of time empirically determining these thresholds.
Clustering Clustering is done iteratively. Start with a threshold of E < 10-100 Cluster and increase threshold by a factor of 105 Sublinear threshold prevents the collapse of sequence space
ProtoMap: Results Produces well-defined groups which correlate strongly to protein families in PROSITE and Pfam.
ProtoMap: Limitations Analysis performs poorly by families dominated by short/local domains (PH, EGF, ER_TARGET, C2, SH2, SH3, ect…) High scoring, low complexity segments can lead to nonhomogeneous clusters. “Hard” clustering vs. “Soft” clustering Has difficulty classifying multidomain proteins.
Inference through proteininteraction networks Functional Classification of Proteins for the Prediction of Cellular Function from a Protein- Protein Interaction Network (2003)
PRODISTIN• Very similar to ProtoMap,only the data used toproduce the graph is a listof binary protein-proteininteractions instead ofsequence similarity scores• Sequence similarity not adominating factor inPRODISTIN clusters
Problems with PRODISTIN • Paucity of protein-protein interaction data (average # of connections = 2.6) • Either very robust or very indiscriminant
Problems: Multidomain and Nonlocal Proteins• protein kinases• hydrolases• ubiquitin…PRODISTIN: Present problems in clustering bybiochemical functionProtoMap: Can create undesired connection amongunrelated groups
Scale-Free Networks • Node connection probability follows a power law distribution • Maximum degree of separation grows as O(lg n) • Highly robust under noise, except at hubs and superhubs. kiP(linking to node i) ~ ∑kj j
Metabolic Networks• The E. coli metabolic network is scale-free.• Actually, the metabolic networks of all organisms inall three domains of life appear to be scale-free (43examined)• The network diameter of all 43 metabolic networks isthe same, irrespective of the number of proteinsinvolved.• Is this counter-intuitive? Yes. http://biocomplexity.indiana.edu/research/bionet/
Protein Domain Networks • Protein Domains – Nature’s take on writing modular code • Reconciles apparent paradox of a fixed network diameter across species – despite vast differences in complexity (some human proteins have 130 domains) • Occurrence of specific protein domains in multidomain proteins is scale-free.http://mbe.oupjournals.org/cgi/content/full/18/9/1694
Protein Domain Graphs• Prosite domains have a distribution following thepower-law function f(x) = a(b + x)-c, with c = .89.There are few highly connected domains and manyrarely connected ones.• ProDom and Pfam domains follow the powerfunction P ( k ) ≈ k − γ y = 2.5 for ProDom y = 1.7 for Pfam
Conclusions• The accuracy of both ProtoMap and PRODISTIN islimited because they make the tacit assumption of arandom network topology.• Protein-Protein interaction networks have scale-free topology, foiling PRODISTIN• Protein Domain networks have scale-free topology,foiling ProtoMap• Any protein classification algorithm that performsbetter than ProtoMap is probably going to have toaddress this issue.