Evaluating, Comparing, and Interpreting Protein Domain Hierarchies

Neuwald, Andrew F.

doi:10.1089/cmb.2013.0098

Cited by 7 publications

(9 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The 262,126 sequences were aligned using MAPGAPS[ 68 ], a rapid and accurate alignment procedure. This was used as an input for mcBPPS [ 69 ], a Bayesian pattern based partitioning algorithm, which identifies residue positions in the alignment that most distinguish PTK sequences (‘foreground’) from STK (‘background’ sequences). In our analysis, we excluded tyrosine kinase-like sequences (TKLs), which share some of the PTK features in the kinase core.…”

Section: Methodsmentioning

confidence: 99%

Hydrophobic Core Variations Provide a Structural Framework for Tyrosine Kinase Evolution and Functional Specialization

et al. 2016

View full text Add to dashboard Cite

Protein tyrosine kinases (PTKs) are a group of closely related enzymes that have evolutionarily diverged from serine/threonine kinases (STKs) to regulate pathways associated with multi-cellularity. Evolutionary divergence of PTKs from STKs has occurred through accumulation of mutations in the active site as well as in the commonly conserved hydrophobic core. While the functional significance of active site variations is well understood, relatively little is known about how hydrophobic core variations contribute to PTK evolutionary divergence. Here, using a combination of statistical sequence comparisons, molecular dynamics simulations, mutational analysis and in vitro thermostability and kinase assays, we investigate the structural and functional significance of key PTK-specific variations in the kinase core. We find that the nature of residues and interactions in the hydrophobic core of PTKs is strikingly different from other protein kinases, and PTK-specific variations in the core contribute to functional divergence by altering the stability and dynamics of the kinase domain. In particular, a functionally critical STK-conserved histidine that stabilizes the regulatory spine in STKs is selectively mutated to an alanine, serine or glutamate in PTKs, and this loss-of-function mutation is accommodated, in part, through compensatory PTK-specific interactions in the core. In particular, a PTK-conserved phenylalanine in the I-helix appears to structurally and functionally compensate for the loss of STK-histidine by interacting with the regulatory spine, which has far-reaching effects on enzyme activity, inhibitor sensing, and stability. We propose that hydrophobic core variations provide a selective advantage during PTK evolution by increasing the conformational flexibility, and therefore the allosteric potential of the kinase domain. Our studies also suggest that Tyrosine Kinase Like kinases such as RAF are intermediates in PTK evolutionary divergence inasmuch as they share features of both PTKs and STKs in the core. Finally, our studies provide an evolutionary framework for identifying and characterizing disease and drug resistance mutations in the kinase core.

show abstract

Section: Methodsmentioning

confidence: 99%

Hydrophobic Core Variations Provide a Structural Framework for Tyrosine Kinase Evolution and Functional Specialization

et al. 2016

View full text Add to dashboard Cite

show abstract

“…Note that the probability distribution (as defined in Eq 4 of Methods) treats each column position as statistically independent; hence BPPS models correlations indirectly, based on the hierarchy. S5 Fig illustrates how BPPS sampling produces hierarchies that, based on measures of statistical significance, are generally superior to hierarchies created at the NCBI using a combination of phylogenetic analysis and manual-curation; for in-depth evaluations of protein domain hierarchies in this way, see [23, 24, 76]. …”

Section: Resultsmentioning

confidence: 99%

“…In principle, finding the global optimum for all but the simplest hierarchies is nearly impossible. In practice, however, we find that the various hierarchies obtained from run to run generally share the biologically most important features of the superfamily [76] and that the features most difficult to model optimally are least important. Therefore, failing to find the global optimum often forfeits little of biological significance.…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Inference of Functionally-Relevant N-acetyltransferase Residues Based on Statistical Correlations

Neuwald

Altschul

2016

PLoS Comput Biol

Self Cite

View full text Add to dashboard Cite

Over evolutionary time, members of a superfamily of homologous proteins sharing a common structural core diverge into subgroups filling various functional niches. At the sequence level, such divergence appears as correlations that arise from residue patterns distinct to each subgroup. Such a superfamily may be viewed as a population of sequences corresponding to a complex, high-dimensional probability distribution. Here we model this distribution as hierarchical interrelated hidden Markov models (hiHMMs), which describe these sequence correlations implicitly. By characterizing such correlations one may hope to obtain information regarding functionally-relevant properties that have thus far evaded detection. To do so, we infer a hiHMM distribution from sequence data using Bayes’ theorem and Markov chain Monte Carlo (MCMC) sampling, which is widely recognized as the most effective approach for characterizing a complex, high dimensional distribution. Other routines then map correlated residue patterns to available structures with a view to hypothesis generation. When applied to N-acetyltransferases, this reveals sequence and structural features indicative of functionally important, yet generally unknown biochemical properties. Even for sets of proteins for which nothing is known beyond unannotated sequences and structures, this can lead to helpful insights. We describe, for example, a putative coenzyme-A-induced-fit substrate binding mechanism mediated by arginine residue switching between salt bridge and π-π stacking interactions. A suite of programs implementing this approach is available (psed.igs.umaryland.edu).

show abstract

“…In some cases, these may be because of evolutionary events that defy preconceived notions and thus are inconsistent with a single phylogenetic tree. In a companion article (Neuwald, 2013), I explore this phenomenon and other aspects of domain hierarchies in greater detail.…”

Section: Neuwaldmentioning

confidence: 99%

A Bayesian Sampler for Optimization of Protein Domain Hierarchies

Neuwald

2014

Journal of Computational Biology

Self Cite

View full text Add to dashboard Cite

The process of identifying and modeling functionally divergent subgroups for a specific protein domain class and arranging these subgroups hierarchically has, thus far, largely been done via manual curation. How to accomplish this automatically and optimally is an unsolved statistical and algorithmic problem that is addressed here via Markov chain Monte Carlo sampling. Taking as input a (typically very large) multiple-sequence alignment, the sampler creates and optimizes a hierarchy by adding and deleting leaf nodes, by moving nodes and subtrees up and down the hierarchy, by inserting or deleting internal nodes, and by redefining the sequences and conserved patterns associated with each node. All such operations are based on a probability distribution that models the conserved and divergent patterns defining each subgroup. When we view these patterns as sequence determinants of protein function, each node or subtree in such a hierarchy corresponds to a subgroup of sequences with similar biological properties. The sampler can be applied either de novo or to an existing hierarchy. When applied to 60 protein domains from multiple starting points in this way, it converged on similar solutions with nearly identical log-likelihood ratio scores, suggesting that it typically finds the optimal peak in the posterior probability distribution. Similarities and differences between independently generated, nearly optimal hierarchies for a given domain help distinguish robust from statistically uncertain features. Thus, a future application of the sampler is to provide confidence measures for various features of a domain hierarchy.

show abstract

Evaluating, Comparing, and Interpreting Protein Domain Hierarchies

Cited by 7 publications

References 11 publications

Hydrophobic Core Variations Provide a Structural Framework for Tyrosine Kinase Evolution and Functional Specialization

Hydrophobic Core Variations Provide a Structural Framework for Tyrosine Kinase Evolution and Functional Specialization

Inference of Functionally-Relevant N-acetyltransferase Residues Based on Statistical Correlations

A Bayesian Sampler for Optimization of Protein Domain Hierarchies

Contact Info

Product

Resources

About