Answer: use the j s and l s that give the best score Vectors from atom k to HS ERR V F 1223 A 0210 C E 9-8O8 R H ∽ R A 021141 01125 F NOTE: this gives an aLignment of how the residues of sequence a align with those of sequence b, when viewed from the perspective of i and k BUT, Which i' s and k's should you compare?
Answer: use the j’s and l’s that give the best score Vectors from atom k to: i k H S E H R R V F C A M G G V Q H S E R R H V F 12 2 3 1 1 10 1 0 2 1 0 1 23 1 0 1 7 4 1 0 2 14 1 0 1 25 G Q Vectors from atom i to: V G M A C NOTE: this gives an ALIGNMENT of how the residues of sequence A align with those of sequence B, when viewed from the perspective of i and k. BUT, which i’s and k’s should you compare?
ALL OF THEM Then combine the results and take a consensus via another round of dynamic programming = double dynamic programming Vectors from k= F 101 0 Protein a Vectors from k= v 25
ALL OF THEM! Then combine the results and take a consensus via another round of dynamic programming = “double dynamic programming” Vectors from k = F Vectors from i = C Vectors from i = C 12 2 3 1 1 10 1 0 2 1 0 1 23 1 0 1 7 4 1 0 2 14 1 0 1 25 Protein A Protein B 28 21 10 4 27 12 15 14 25 2 5 Vectors from k = V 16 1 2 1 21 1 1 1 4 0 0 5 4 1 1 4 5 1 1 2 15 1 0 1 25 1
Instead of using distances, use vectors to include some directionality Sj=a(ldj-dkl b) (V-VA|+b); Can also include other information about residues i and k if desired (e.g. sequence or environment information) Si=(a+F(k)Vi- vx1l b)
Instead of using distances, use vectors to include some directionality sij = a/(|dij - dkl| + b); sij = a/(|V ij - V kl| + b); Can also include other information about residues i and k if desired (e.g. sequence or environment information) sij = (a + F(i,k)/(|V ij - V kl| + b);
It is important to assess whether detected similarities are SIGNIFICANT Various statistical criteria have been used General idea: How"surprising"is the discovery of a shared structure?
It is important to assess whether detected similarities are SIGNIFICANT. Various statistical criteria have been used. General idea: How “surprising” is the discovery of a shared structure?
Structural classification of proteins Structure VS structure comparisons(e.g. using DALI reveal related groups of proteins Structurally-similar proteins with detectable sequence homology are assumed to be evolutionarily related Similarities between non-homologous proteins suggest convergent evolution to a favorable or useful fold A number of different groups have proposed classification schemes SCOP (by hand CATH (uSes SSAP FSSP (uses Dali
Structural Classification of Proteins • Structure vs. structure comparisons (e.g. using DALI) reveal related groups of proteins • Structurally-similar proteins with detectable sequence homology are assumed to be evolutionarily related • Similarities between non-homologous proteins suggest convergent evolution to a favorable or useful fold • A number of different groups have proposed classification schemes – SCOP (by hand) – CATH (uses SSAP) – FSSP (uses Dali)