1. Intersubunit contacts are often facilitated by specificity-determining positions Computational identification of protein positions that possibly account for precise recognition of the interaction partner

2. Abundance of sequence data Abundance of sequence data Little experimental information on protein function Little experimental information on protein function => annotation by homology Even less information on protein specificity Even less information on protein specificity => prediction of specificity-determining positions (SDPs)

3. SDP (Specificity-Determining Position) Alignment position that is conserved within groups of proteins having the same specificity (specificity groups) but differs between them Alignment position that is conserved within groups of proteins having the same specificity (specificity groups) but differs between them SDP is not equivalent to a functionally important position!

4. What can we infer from SDPs? Targets for protein functional redesign Targets for protein functional redesign Specificity signature Specificity signature Sites of protein-protein interaction Sites of protein-protein interaction

5. Talk overview SDPpred, an algorithm for identification of SDPs SDPpred, an algorithm for identification of SDPs A studied example: isocitrate/isopropylmalate dehydrogenases A studied example: isocitrate/isopropylmalate dehydrogenases Link to PPI Link to PPI

6. SDPpred Multiple protein alignment divided into specificity groups Multiple protein alignment divided into specificity groups SDPpred SDPs: positions best discriminating between specificity groups SDPs: positions best discriminating between specificity groups === AQP === %sp|Q9L772|AQPZ_BRUME -------------------------------------mlnklsaeffgtfwlvfggcgsa ilaa--afp-------elgigflgvalafgltvltmayavggisg--ghfnpavslgltv iiilgsts------------------------------slap------------------ qlwlfwvaplvgavigaiiwkgllgrd--------------------------------- ------ … === GLP === %sp|P11244|GLPF_ECOLI ----------------------------msqt---stlkgqciaeflgtglliffgvgcv aalkvag---------a-sfgqweisviwglgvamaiyltagvsg--ahlnpavtialwl glilaltd------------------------------dgn--------------g-vpr -flvplfgpivgaivgafayrkligrhlpcdicvveek--etttpseqkasl-------- ------ …

7. What is in the black box: the algorithm Mutual information I p reflect the extent to which an alignment position tends to be a SDP. Mutual information I p reflect the extent to which an alignment position tends to be a SDP. Statistical significance of I p. Statistical significance of I p. Expected mutual information I p exp of an alignment column. Expected mutual information I p exp of an alignment column. Z-score. Z-score. (Mirny&Gelfand, 2002, J Mol Biol, 321(1)) Are 5 SDP with Z-score >10.5 better than 10 SDP with Z-score >9.0? Bernoulli estimator for selection of proper number of SDPs Are 5 SDP with Z-score >10.5 better than 10 SDP with Z-score >9.0? Bernoulli estimator for selection of proper number of SDPs Smoothed amino acid frequencies: a leucine is more a methionine than a valine, and any arginine has a dash of lysine… Smoothed amino acid frequencies: a leucine is more a methionine than a valine, and any arginine has a dash of lysine… - ratio of occurences of amino acid α in group i in position p to the height of the alignment column - frequency of amino acid α in position p - fraction of proteins in group i

8. Other similar techniques Evolutionary trace (Lichtarge et al. 1996, 1997) Evolutionary trace (Lichtarge et al. 1996, 1997) Evolutionary rate shifts (Gaucher et al. 2002)  Evolutionary rate shifts (Gaucher et al. 2002)  Surface patches of slowly evolving residues (Rate4Site, Pupko et al. 2002)  Surface patches of slowly evolving residues (Rate4Site, Pupko et al. 2002)  PCA in sequence space (Casari et al. 1999, del Sol Mesa et al. 2003) PCA in sequence space (Casari et al. 1999, del Sol Mesa et al. 2003) Correlated mutations (Pazos and Valencia, 2002) Correlated mutations (Pazos and Valencia, 2002) Prediction of functional sub-types (Hannenhalli and Russell, 2000) and identification of PSDR (Mirny and Gelfand, 2002) Prediction of functional sub-types (Hannenhalli and Russell, 2000) and identification of PSDR (Mirny and Gelfand, 2002)

9. Special features of SDPpred Smoothed amino acid frequencies allow to account for functional (structural, chemical, evolutionary, …) similarities among amino acids Smoothed amino acid frequencies allow to account for functional (structural, chemical, evolutionary, …) similarities among amino acids Automatic cutoff setting -> no prior knowledge about protein family Automatic cutoff setting -> no prior knowledge about protein family Does not require 3D structure -> use of structural data solely for interpretation and verification of results Does not require 3D structure -> use of structural data solely for interpretation and verification of results – Kalinina OV, Mironov AA, Gelfand MS, Rakhmaninova AB. (2004) Protein Sci 13(2): 443-56 – Kalinina OV, Novichkov PS, Mironov AA, Gelfand MS, Rakhmaninova AB. (2004) Nucl Acids Res 32(Web Server issue): W424-8. – http://math.belozersky.msu.ru/~psn/ http://math.belozersky.msu.ru/~psn/

10. Example: isocitrate/isopropylmalate dehydrogenases (IDH/IMDH) IDH: catalyzes the oxidation of isocitrate to α- ketoglutorate and CO 2 (TCA) using either NAD or NADP as a cofactor in different organisms from bacteria to higher eukaryotes IDH: catalyzes the oxidation of isocitrate to α- ketoglutorate and CO 2 (TCA) using either NAD or NADP as a cofactor in different organisms from bacteria to higher eukaryotes IMDH: catalyzes oxidative decarboxylation of 3- isopropylmalate into 2-oxo-4-methylvalerate (leucine biosynthesis) in bacteria and fungi IMDH: catalyzes oxidative decarboxylation of 3- isopropylmalate into 2-oxo-4-methylvalerate (leucine biosynthesis) in bacteria and fungi

11. IDH/IMDH: combinations of specificities towards substrate and cofactor NAD-dependent IDHs NAD-dependent IDHs NADP-dependent IDHs from bacteria and archaea (type I) NADP-dependent IDHs from bacteria and archaea (type I) NADP-dependent IDHs from eukaryota (type II) NADP-dependent IDHs from eukaryota (type II) NAD-dependent IMDH NAD-dependent IMDH Mitochondria ArchaeaBacteria Eukaryota ArchaeaBacteriaEukaryota

12. IDH/IMDH: selecting specificity groups 1. All NAD-dependent vs. all NADP- dependent 2. All IDHs vs. all IMDHs 3. Four groups IDH (NAD) IDH (NADP) type II IDH (NADP) type II IDH (NADP) type II IMDH (NAD) IDH (NADP) type I IDH (NADP) type I IDH (NADP) type I

13. IDH/IMDH: predicted SDPs (cofactor-specific) NADP-dependent IDH from E. coli (1ai2) Substrate Cofactor Subunit I Subunit II SDPs

14. IDH/IMDH: predicted SDPs (substrate-specific) NADP-dependent IDH from E. coli (1ai2) Substrate Cofactor Subunit I Subunit II SDPs

15. IDH/IMDH: predicted SDPs (four groups) NADP-dependent IDH from E. coli (1ai2) Substrate Cofactor Subunit I Subunit II SDPs

16. IDH/IMDH: predicted SDPs (overview)

17. IDH/IMDH: SDPs predicted for different groupings All NAD- dependent vs. all NADP-dependent -> cofactor- specific SDPs All IDHs vs. all IMDHs -> substrate- specific SDPs Four groups 154Glu 158Asp 208Arg 229His 231Gly 233Ile 287Gln 300Ala 305Asn 308Tyr 327Asn 344Lys 345Tyr351Val 38Gly40Asp 100Lys 103Leu 105Thr 115Asn 155Asn 164Glu 241Phe 337Ala 341Thr 97Val 98Ala 104Thr 107Val152Phe 161Ala 162Gly 232Asn245Gly 31Tyr 323Ala 36Gly 45Met Color code: Contacts substrate Contacts cofactor Contacts the other subunit Contacts substrate AND cofactor Contacts substrate AND the other subunit

18. IDH/IMDH: SDPs in contact with cofactor Substrate (isocitrate) Cofactor (NADP) Nicotinamide nucleotide Adenine nucleotide 344Lys, 345Tyr, 351Val: cofactor-specific SDPs, known determinants of specificity to cofactor 100Lys, 104Thr, 105Thr, 107Val, 337Ala, 341Thr: substrate-specific and four group SDPs, functionally not characterized NADP-dependent IDH from E. coli (1ai2)

19. Clusters of SDPs on the intersubunit contact surface in the IDH/IMDH family… in the IDH/IMDH family… Cluster I Cluster II

20. …and in other protein families The LacI family of bacterial transcription factors The LacI family of bacterial transcription factors Bind specific operator sequences upon interaction with effector molecules, mainly various sugars Bind specific operator sequences upon interaction with effector molecules, mainly various sugars LacI (lactose repressor) from E.coli (1jwl) Effector DNA operator Cluster I Cluster II

21. Bacterial membrane transporters from the MIP family Bacterial membrane transporters from the MIP family Water and glycerol/water channels Glpf (glycerol facilitator) from E. coli (1fx8) Cluster I Cluster II Subunit I Substrate(glycerol)

22. Conclusions SDPpred, a method for identification of amino acids that account for differences in protein specificity SDPpred, a method for identification of amino acids that account for differences in protein specificity Results obtained for several protein families of different functional type agree with structural and experimental data Results obtained for several protein families of different functional type agree with structural and experimental data A substantial fraction of SDPs are located on the intersubunit contacts interface, where they form distinct spatial clasps A substantial fraction of SDPs are located on the intersubunit contacts interface, where they form distinct spatial clasps

23. Olga V. Kalinina Olga V. Kalinina Pavel S. Novichkov Pavel S. Novichkov Andrey A. Mironov Andrey A. Mironov Mikhail S. Gelfand Mikhail S. Gelfand Aleksandra B. Rakhmaninova Aleksandra B. Rakhmaninova Department of Bioengineering and Bioinformatics, Moscow State University, Moscow, Russia Department of Bioengineering and Bioinformatics, Moscow State University, Moscow, Russia Institute for Information Transmission Problems RAS, Moscow, Russia Institute for Information Transmission Problems RAS, Moscow, Russia State Scientific Center GosNIIGenetika, Moscow, Russia State Scientific Center GosNIIGenetika, Moscow, Russia Acknowledgements Acknowledgements Leonid A. Mirny Olga Laikova Vsevolod Makeev Roman Sutormin Shamil Sunyaev Aleksey Finkelstein Thank you!