Interaction Signatures Stabilizing the NAD(P)-Binding Rossmann Fold: A Structure Network Approach

Abstract

The fidelity of the folding pathways being encoded in the amino acid sequence is met with challenge in instances where proteins with no sequence homology, performing different functions and no apparent evolutionary linkage, adopt a similar fold. The problem stated otherwise is that a limited fold space is available to a repertoire of diverse sequences. The key question is what factors lead to the formation of a fold from diverse sequences. Here, with the NAD(P)-binding Rossmann fold domains as a case study and using the concepts of network theory, we have unveiled the consensus structural features that drive the formation of this fold. We have proposed a graph theoretic formalism to capture the structural details in terms of the conserved atomic interactions in global milieu, and hence extract the essential topological features from diverse sequences. A unified mathematical representation of the different structures together with a judicious concoction of several network parameters enabled us to probe into the structural features driving the adoption of the NAD(P)-binding Rossmann fold. The atomic interactions at key positions seem to be better conserved in proteins, as compared to the residues participating in these interactions. We propose a “spatial motif” and several “fold specific hot spots” that form the signature structural blueprints of the NAD(P)-binding Rossmann fold domain. Excellent agreement of our data with previous experimental and theoretical studies validates the robustness and validity of the approach. Additionally, comparison of our results with statistical coupling analysis (SCA) provides further support. The methodology proposed here is general and can be applied to similar problems of interest.

Comparative analysis of plant lycopene cyclases

“…9). It was reported that the glycine or alanine residues of the GXXXG/A motif form van der Waals interactions with either a valine or isoleucine residue located either seven or eight residues further back along the polypeptide chain and such motif holds together the secondary structures in core protein (Kleiger and Eisenberg, 2002;Bhattacharyya et al, 2012). Therefore, V/IXGX (1-2) GXXGXXXG/A is more strongly indicative than previously described motif (GX (1-2) GXXG), (Kleiger and Eisenberg, 2002).…”

“…Meanwhile, lycopene cyclase domain is related to FAD/ NADP(H) binding Rossmann fold domain superfamily. Rossmann folds can often be identified by GX (1-2) GXXG Motif (Kleiger and Eisenberg, 2002;Bhattacharyya et al, 2012). It was proposed that the first glycine provides a tight turn of the main-chain from the b-strand into the loop, and the second glycine allows close contact of the main-chain to the pyrophosphate of the nucleotide (Wierenga et al, 1986;Kleiger and Eisenberg, 2002).…”

An automated approach to network features of protein structure ensembles

“…Protein sidechain networks (PScN) are constructed by considering amino acid residues as nodes and edges are constructed between the nodes on the basis of noncovalent interactions between them (as evaluated from the normalized number of contacts between them) for each system. The details of the construction of such a graph at a particular interaction cut‐off ( I min ) and the implications of such graphs have been previously described . The noncovalent interaction between side chain atoms of amino acid residues (with the exception of Gly where Cα atom) are considered, ignoring the interaction between sequence neighbors (±2 immediate neighbors).…”

“…The interactions in PScN representation can be quantified by considering the residue connections ranging from atom–atom contact to highly packed interactions like aromatic stacking . Herein, this representation enables the construction of networks based on weak and strong noncovalent interactions at the sidechain level and this unique approach has been proven to provide valuable biological insights …”

“…Such an approach has found vast applications in the study and analysis of macromolecular structures to obtain valuable insights into several phenomena of biological relevance . A combination of MD simulation techniques and PScN/PBN to analyze protein structures has led to interesting results in several previous studies, such as ligand induced modulation of rigidity/flexibility/function, transition between conformational substates, homology modeling and structural refinement, understanding of topological features of proteins, their folds and functions, protein structure comparison, identification of active site/aromatic clusters, identifying “hotspots” in the structure, and many more. They have also been conveniently used in understanding allosteric communication pathways or “junction points” responsible for long‐range signal transmission in proteins .…”