Geometrical Concept-reduction in conformational space.and his Φ-ψ Map. G. N. Ramachandran

Transcription

1 Geometrical Concept-reduction in conformational space.and his Φ-ψ Map G. N. Ramachandran

2 Communication paths in trna-synthetase: Insights from protein structure networks and MD simulations Saraswathi Vishveshwara Molecular Biophysics Unit Indian Institute of Science Bangalore, India Indo-US workshop, IISc, December 11, 2007

3 Outline Allosteric effects in proteins Tracking conformational changes Protein Structure Networks Network in dynamical equilibrium trna synthetases Simulations on Methionyl trna synthetase comlexes Communication pathways

4 Allosteric effect Definition: Allostery or a different shape is the coupling of conformational changes between two widely separated sites in a protein. Allosteric proteins bind to two ligands. The binding of one of them alters the affinity of the protein to the other ligand. Allosteric proteins can be multimers, monomer with multidomains

5 Allosteric Models Concerted hypothesis of allostery by Monad et al. (MWC model) Induced fit models: Monad, Koshland Population Shift Model From the Review by Gunasekaran, Ma, and Ruth Nussinov PROTEINS: Structure, Function, and Bioinformatics 57: (2004)

6 Importance of alternate conformations Models differ in their emphasis on whether the allosteric effect is Kinetic or Thermodynamic? Irrespective of the model, it is clear that alternate conformations are taken up in the ligand-bound forms

7 Tracking the changes in conformational states Structures of different liganded states Experimental: X-ray, NMR Structures from simulations: a large ensemble Monitoring of structural changes Gross changes: RMSD Local changes: Residue-wise RMSD, cross correlations Collective movements such as hinge bending, domain movements: Normal mode analysis Gaussian Network model Essential dynamics

8 RMSD Trajectories RMSD as a function of simulation time continuous line: w.r.t <MD> broken line: w.r.t crystal structure RMSD as a function of residue number

9 Pair-wise Cross Correlation C ij = ( r ) ( ) i ri rj rj ( )( 2 2 r 2 2 ) i ri rj rj Cij the cross correlation between the residues i and j ri, rj the coordinates at a given time point, <ri>, <rj> are the averages over the trajectory

10 Collective modes from Essential Dynamics Molecule of N atoms has 3N degrees of freedom. Essential dynamics is a way of capturing important collective modes Covariance matrix (M=[m ij ]) m ij =(1/S) Σ t (x i (t)-<x i >)(x j (t)-<x j >) where S = total number of configurations t = time in picoseconds i = i th coordinate (i=1,2,...,3n) ('N' being number of atoms) <x i >= average value of x i over all configurations Find Eigenvalues and normalized Eigenvectors of Covariance matrix A. Amadei, A.B. Linssen, H.J. Berendsen, Proteins: Struct. Func. Gen., 17, 412 (1993).

11 Conformational changes at Network level Network represents global connectivity. Connections are defined at residue level. Connections are made at desired level of interaction strength between residues.

12 Protein structures as Graphs

13 Protein Graphs Main Chain Interaction (back bone level ) Nodes : Amino acid Residues Edges : Spatial neighbours within fixed distance S.M.Patra, Kannan, Vishveshwara, Biophys. Chem (2000); JMB (1999)

14 Side Chain Interaction High (Iij=11%) Low (Iij=4%) Iij=0% a) High Contact b) Low Contact c)no interaction High and low contact criteria. A pair of phenylalanine rings interacting with each other are shown. The lines between the phenylalanines indicate the atoms that are within a distance of 4.5Å. I ij = (n ij (N i *N j )) 100 I min is user defined interaction cutoff. An (ij) residue pair with I ij > I min is connected by an edge.

15 Backbone-based versus the Side-chain-based Protein Structure Graphs Backbone-based (coarse grained) Based on C-alpha-C-alpha distance The extent of side-chain interaction is not considered Side-chain-based The interactions between sidechains are quantified, hence a weighted graph can be constructed or graphs can be constructed on the basis of the strength of interaction

16 Graph spectral parameters Provide information on the clusters of interacting residues Detect cluster centres, which play a crucial role in the integrity of the cluster Vishveshwara, Brinda and Kannan JTCC, 2002

17 Advantages of Graph Spectral Analysis Solution to weighted graph Identification of Cluster Centre Centre of a Graph E(v) = max d(v,v i ) v i G Clusters (at interaction strength Imin =6%) in Ornithine Decarboxylase

18 Construction of Graphs and Networks Interacting biomolecular residues Adjacency Matrix PSN Size of largest cluster, Degree distribution, Hubs PSG Side chain Clusters (DFS) Brinda, Vishveshwara, Biophysical J, 2005

19 Hubs in Protein Structure Networks Asp Arg Arg Ser Pro His Hubs - highly connected amino acids in the protein structure. Identified as amino acids having a contact number of >= 4 at a given I min.

20 Aminoacyl trna Synthetase (AARS) trna Catalytic domain 70A Anticodon binding domain Communication between the anticodon region and the active-site region is crucial for the faithful translation of the genetic code

21 Aminoacyl trna Synthetases (AARS) Charge an amino acid to the cognate trna in protein synthesis. 20 AARS enzymes two classes based on structural and sequence motifs. Possess proof-reading capacities to ensure correct aminoacylation and determines the fidelity of translation. Other functions: Cytokine like activity, Translational and Transcriptional control, Mitochondrial RNA Splicing, DNA binding etc.

22 Classification of AARS AARS are classified into two classes: CLASS I RS Leu Ile Val Cys Met Arg Glu Gln Lys Tyr Trp Oligomeric α α α α α 2 α α α α α 2 α 2 State CLASS II RS Ia Ib Ic Ser Thr Gly Ala Pro His Asp Asn Lys Phe Oligomeric α 2 α 2 (αβ) 2 α 4 α 2 α 2 α 2 α 2 α 2 (αβ) 2 State IIa IIb II c

23 Features distinguishing class I and class II aars. Feature Class I Class II Aminoacylation Fold of the ATP binding domain Leu, Ile, Val, Cys, Met, Arg, Glu, Gln, Lys, Tyr, Trp Rossmann Fold Ser, Thr, Gly, Ala, Pro, His, Asp, Asn, Phe, Lys Antiparallel Beta fold Sequence motifs Aminoacylation of ribose Amino acid binding at the active site trna acceptor end HIGH, KMSKS, GXGXGXER 2 OH Deep Pocket Bent FRXE/D, R/HXXF 3 OH Surface Straight

24 System selected for investigation: Methionine trna Synthetase

25 MD simulations on MetRS complexes A:MetRS B: MetRS+MetAMP C:MetRS+tRNA D:MetRS+tRNA+MetAMP

26 MetRS complexes Simulations RMSD Profiles

27 The CP domain opens up when MetRS is bound to both the trna and the activated methionine

28 Conformation of trna MetRS+tRNA MetRS+tRNA+MetAMP

29 Interactions at the active-site

30 How is the message communicated between the anticodon region and the activation site, which are separated by about 70 A?

31 Dynamics of structure networks

32 Analysis of Network parameters from MD trajectories Insights into biological processes such as The mechanism of protein folding (Ghosh, Brinda, Vishveshwara, Biophysical J, 2006) Long distance communications (allosteric effect)

33 Identification of communication pathways in MetRS from network analysis of MD trajectories Amit Ghosh and S Vishveshwara PNAS September 2007

34 MetRS+tRNA+MetAMP MetRS Leu13-His28-Lys388-Trp461 Dynamical cross correlation maps representing the collective atomic fluctuations

35 Limitation of cross correlation map Cross Correlated residues identified from MD simulations need not be connected in space. Network analysis overcomes this limitation The residues connecting (non-covalent) the correlated ones are identified from the protein structure network, by the analysis of the shortest path between the selected residues

36 Choice of interaction strength for path analysis Imin (%) Size of the largest cluster in MetRS (averaged over MD snapshots) as a function of interaction strength

37 Communication pathways from the anti-codon region to the aminoacylation site Deduced from dynamic cross correlations and identification of shortest paths connected through non-covalent interactions at Imin=3%

38 Communication paths between the anti-codon region and the active site in MetRS

39 Summary of the method of path identification

40 Summary of identified communication paths

41 Tryptophenyl trna Synthetase Functional dimer Complicated communication system (Work in progress)

42 Summary Allosteric effect involves conformational change, which can be identified at different levels. Residue-wise RMSD, Cross correlations, Essential dynamics The global non-covalent connectivity in proteins can be represented as graphs and networks The communication pathways between the anti-codon region and the amino acylation site have been deduced from the dynamic cross correlations and the network analysis of the MD trajectories of Methionyl trna Synthetase

43 Acknowledgements Amit Ghosh: MetRS communication paths Priti Hansia: TrpRS Drs. Brinda, Kannan, swarna M. Patra- structure network Department of Biotechnology, Government of India Super-computer Education and Research Centre (SERC), IISc

44