Reciprocally coupled residues crucial for protein kinase Pak2 activity calculated by statistical coupling analysis

Small design from big alignment: engineering proteins with multiple sequence alignment as the starting point

Multiple sequence alignment (MSA) is a fundamental way to gain information that cannot be obtained from the analysis of any individual sequence included in the alignment. It provides ways to investigate the relationship between sequence and function from a perspective of evolution. Thus, the MSA of proteins can be employed as a reference for protein engineering. In this paper, we reviewed the recent advances to highlight how protein engineering was benefited from the MSA of proteins. These methods include (1) engineering the thermostability or solubility of proteins by making it closer to the consensus sequence of the alignment through introducing site mutations; (2) structure-based engineering proteins with comparative modeling; (3) creating paleoenzymes featured with high thermostability and promiscuity by constructing the ancestral sequences derived from multiple sequence alignment; and (4) incorporating site-mutations targeting the evolutionarily coupled sites identified from multiple sequence alignment.

Integrated Computational Approaches and Tools forAllosteric Drug Discovery

Understanding molecular mechanisms underlying the complexity of allosteric regulationin proteins has attracted considerable attention in drug discovery due to the benefits and versatilityof allosteric modulators in providing desirable selectivity against protein targets while minimizingtoxicity and other side effects. The proliferation of novel computational approaches for predictingligand-protein interactions and binding using dynamic and network-centric perspectives has ledto new insights into allosteric mechanisms and facilitated computer-based discovery of allostericdrugs. Although no absolute method of experimental and in silico allosteric drug/site discoveryexists, current methods are still being improved. As such, the critical analysis and integration ofestablished approaches into robust, reproducible, and customizable computational pipelines withexperimental feedback could make allosteric drug discovery more efficient and reliable. In this article,we review computational approaches for allosteric drug discovery and discuss how these tools can beutilized to develop consensus workflows for in silico identification of allosteric sites and modulatorswith some applications to pathogen resistance and precision medicine. The emerging realization thatallosteric modulators can exploit distinct regulatory mechanisms and can provide access to targetedmodulation of protein activities could open opportunities for probing biological processes and insilico design of drug combinations with improved therapeutic indices and a broad range of activities.

Disease-relevant mutations alter amino acid co-evolution networks in the second nucleotide binding domain of CFTR

Cystic Fibrosis (CF) is an inherited disease caused by mutations in the cystic fibrosis transmembrane conductance regulator (CFTR) ion channel. Mutations in CFTR cause impaired chloride ion transport in the epithelial tissues of patients leading to cardiopulmonary decline and pancreatic insufficiency in the most severely affected patients. CFTR is composed of twelve membrane-spanning domains, two nucleotide-binding domains (NBDs), and a regulatory domain. The most common mutation in CFTR is a deletion of phenylalanine at position 508 (ΔF508) in NBD1. Previous research has primarily concentrated on the structure and dynamics of the NBD1 domain; However numerous pathological mutations have also been found in the lesser-studied NBD2 domain. We have investigated the amino acid co-evolved network of interactions in NBD2, and the changes that occur in that network upon the introduction of CF and CF-related mutations (S1251N(T), S1235R, D1270N, N1303K(T)). Extensive coupling between the α- and β-subdomains were identified with residues in, or near Walker A, Walker B, H-loop and C-loop motifs. Alterations in the predicted residue network varied from moderate for the S1251T perturbation to more severe for N1303T. The S1235R and D1270N networks varied greatly compared to the wildtype, but these CF mutations only affect ion transport preference and do not severely disrupt CFTR function, suggesting dynamic flexibility in the network of interactions in NBD2. Our results also suggest that inappropriate interactions between the β-subdomain and Q-loop could be detrimental. We also identified mutations predicted to stabilize the NBD2 residue network upon introduction of the CF and CF-related mutations, and these predicted mutations are scored as benign by the MUTPRED2 algorithm. Our results suggest the level of disruption of the co-evolution predictions of the amino acid networks in NBD2 does not have a straightforward correlation with the severity of the CF phenotypes observed.

Unity and diversity among viral kinases

Viral kinases are known to undergo autophosphorylation and also phosphorylate viral and host substrates. Viral kinases have been implicated in various diseases and are also known to acquire host kinases for mimicking cellular functions and exhibit virulence. Although substantial analyses have been reported in the literature on diversity of viral kinases, there is a gap in the understanding of sequence and structural similarity among kinases from different classes of viruses. In this study, we performed a comprehensive analysis of protein kinases encoded in viral genomes. Homology search methods have been used to identify kinases from 104,282 viral genomic datasets. Serine/threonine and tyrosine kinases are identified only in 390 viral genomes. Out of seven viral classes that are based on nature of genetic material, only viruses having double-stranded DNA and single-stranded RNA retroviruses are found to encode kinases. The 716 identified protein kinases are classified into 63 subfamilies based on their sequence similarity within each cluster, and sequence signatures have been identified for each subfamily. 11 clusters are well represented with at least 10 members in each of these clusters. Kinases from dsDNA viruses, Phycodnaviridae which infect green algae and Herpesvirales that infect vertebrates including human, form a major group. From our analysis, it has been observed that the protein kinases in viruses belonging to same taxonomic lineages form discrete clusters and the kinases encoded in alphaherpesvirus form host-specific clusters. A comprehensive sequence and structure-based analysis enabled us to identify the conserved residues or motifs in kinase catalytic domain regions across all viral kinases. Conserved sequence regions that are specific to a particular viral kinase cluster and the kinases that show close similarity to eukaryotic kinases were identified by using sequence and three-dimensional structural regions of eukaryotic kinases as reference. The regions specific to each viral kinase cluster can be used as signatures in the future in classifying uncharacterized viral kinases. We note that kinases from giant viruses Marseilleviridae have close similarity to viral oncogenes in the functional regions and in putative substrate binding regions indicating their possible role in cancer.

P21-activated kinase 2 (PAK2) regulates glucose uptake and insulin sensitivity in neuronal cells

P21-activated kinases (PAKs) are recently reported as important players of insulin signaling and glucose homeostasis in tissues like muscle, pancreas and liver. However, their role in neuronal insulin signaling is still unknown. Present study reports the involvement of PAK2 in neuronal insulin signaling, glucose uptake and insulin resistance. Irrespective of insulin sensitivity, insulin stimulation decreased PAK2 activity. PAK2 downregulation displayed marked enhancement of GLUT4 translocation with increase in glucose uptake whereas PAK2 over-expression showed its reduction. Treatment with Akti-1/2 and wortmannin suggested that Akt and PI3K are mediators of insulin effect on PAK2 and glucose uptake. Rac1 inhibition demonstrated decreased PAK2 activity while inhibition of PP2A resulted in increased PAK2 activity, with corresponding changes in glucose uptake. Taken together, present study demonstrates an inhibitory role of insulin signaling (via PI3K-Akt) and PP2A on PAK2 activity and establishes PAK2 as a Rac1-dependent negative regulator of neuronal glucose uptake and insulin sensitivity.

Molecular Determinants Underlying Binding Specificities of the ABL Kinase Inhibitors: Combining Alanine Scanning of Binding Hot Spots with Network Analysis of Residue Interactions and Coevolution

Quantifying binding specificity and drug resistance of protein kinase inhibitors is of fundamental importance and remains highly challenging due to complex interplay of structural and thermodynamic factors. In this work, molecular simulations and computational alanine scanning are combined with the network-based approaches to characterize molecular determinants underlying binding specificities of the ABL kinase inhibitors. The proposed theoretical framework unveiled a relationship between ligand binding and inhibitor-mediated changes in the residue interaction networks. By using topological parameters, we have described the organization of the residue interaction networks and networks of coevolving residues in the ABL kinase structures. This analysis has shown that functionally critical regulatory residues can simultaneously embody strong coevolutionary signal and high network centrality with a propensity to be energetic hot spots for drug binding. We have found that selective (Nilotinib) and promiscuous (Bosutinib, Dasatinib) kinase inhibitors can use their energetic hot spots to differentially modulate stability of the residue interaction networks, thus inhibiting or promoting conformational equilibrium between inactive and active states. According to our results, Nilotinib binding may induce a significant network-bridging effect and enhance centrality of the hot spot residues that stabilize structural environment favored by the specific kinase form. In contrast, Bosutinib and Dasatinib can incur modest changes in the residue interaction network in which ligand binding is primarily coupled only with the identity of the gate-keeper residue. These factors may promote structural adaptability of the active kinase states in binding with these promiscuous inhibitors. Our results have related ligand-induced changes in the residue interaction networks with drug resistance effects, showing that network robustness may be compromised by targeted mutations of key mediating residues. This study has outlined mechanisms by which inhibitor binding could modulate resilience and efficiency of allosteric interactions in the kinase structures, while preserving structural topology required for catalytic activity and regulation.