The Modular Organization of Domain Structures: Insights into Protein–Protein Binding

Domains are the building blocks of proteins and play a crucial role in protein–protein interactions. Here, we propose a new approach for the analysis and prediction of domain–domain interfaces. Our method, which relies on the representation of domains as residue-interacting networks, finds an optimal decomposition of domain structures into modules. The resulting modules comprise highly cooperative residues, which exhibit few connections with other modules. We found that non-overlapping binding sites in a domain, involved in different domain–domain interactions, are generally contained in different modules. This observation indicates that our modular decomposition is able to separate protein domains into regions with specialized functions. Our results show that modules with high modularity values identify binding site regions, demonstrating the predictive character of modularity. Furthermore, the combination of modularity with other characteristics, such as sequence conservation or surface patches, was found to improve our predictions. In an attempt to give a physical interpretation to the modular architecture of domains, we analyzed in detail six examples of protein domains with available experimental binding data. The modular configuration of the TEM1-β-lactamase binding site illustrates the energetic independence of hotspots located in different modules and the cooperativity of those sited within the same modules. The energetic and structural cooperativity between intramodular residues is also clearly shown in the example of the chymotrypsin inhibitor, where non–binding site residues have a synergistic effect on binding. Interestingly, the binding site of the T cell receptor β chain variable domain 2.1 is contained in one module, which includes structurally distant hot regions displaying positive cooperativity. These findings support the idea that modules possess certain functional and energetic independence. A modular organization of binding sites confers robustness and flexibility to the performance of the functional activity, and facilitates the evolution of protein interactions.     

The relative efficiency of modular and non-modular networks of different size

Most biological networks are modular but previous work with small model networks has indicated that modularity does not necessarily lead to increased functional efficiency. Most biological networks are large, however, and here we examine the relative functional efficiency of modular and non-modular neural networks at a range of sizes. We conduct a detailed analysis of efficiency in networks of two size classes: ‘small’ and ‘large’, and a less detailed analysis across a range of network sizes. The former analysis reveals that while the modular network is less efficient than one of the two non-modular networks considered when networks are small, it is usually equally or more efficient than both non-modular networks when networks are large. The latter analysis shows that in networks of small to intermediate size, modular networks are much more efficient that non-modular networks of the same (low) connective density. If connective density must be kept low to reduce energy needs for example, this could promote modularity. We have shown how relative functionality/performance scales with network size, but the precise nature of evolutionary relationship between network size and prevalence of modularity will depend on the costs of connectivity.     

An Analytically Solvable Model for Rapid Evolution of Modular Structure

Biological systems often display modularity, in the sense that they can be decomposed into nearly independent subsystems. Recent studies have suggested that modular structure can spontaneously emerge if goals (environments) change over time, such that each new goal shares the same set of sub-problems with previous goals. Such modularly varying goals can also dramatically speed up evolution, relative to evolution under a constant goal. These studies were based on simulations of model systems, such as logic circuits and RNA structure, which are generally not easy to treat analytically. We present, here, a simple model for evolution under modularly varying goals that can be solved analytically. This model helps to understand some of the fundamental mechanisms that lead to rapid emergence of modular structure under modularly varying goals. In particular, the model suggests a mechanism for the dramatic speedup in evolution observed under such temporally varying goals.     

Relative impacts of environmental variation and evolutionary history on the nestedness and modularity of tree–herbivore networks

Nestedness and modularity are measures of ecological networks whose causative effects are little understood. We analyzed antagonistic plant–herbivore bipartite networks using common gardens in two contrasting environments comprised of aspen trees with differing evolutionary histories of defence against herbivores. These networks were tightly connected owing to a high level of specialization of arthropod herbivores that spend a large proportion of the life cycle on aspen. The gardens were separated by ten degrees of latitude with resultant differences in abiotic conditions. We evaluated network metrics and reported similar connectance between gardens but greater numbers of links per species in the northern common garden. Interaction matrices revealed clear nestedness, indicating subsetting of the bipartite interactions into specialist divisions, in both the environmental and evolutionary aspen groups, although nestedness values were only significant in the northern garden. Variation in plant vulnerability, measured as the frequency of herbivore specialization in the aspen population, was significantly partitioned by environment (common garden) but not by evolutionary origin of the aspens. Significant values of modularity were observed in all network matrices. Trait-matching indicated that growth traits, leaf morphology, and phenolic metabolites affected modular structure in both the garden and evolutionary groups, whereas extra-floral nectaries had little influence. Further examination of module configuration revealed that plant vulnerability explained considerable variance in web structure. The contrasting conditions between the two gardens resulted in bottom-up effects of the environment, which most strongly influenced the overall network architecture, however, the aspen groups with dissimilar evolutionary history also showed contrasting degrees of nestedness and modularity. Our research therefore shows that, while evolution does affect the structure of aspen–herbivore bipartite networks, the role of environmental variations is a dominant constraint.     

Linear Motif-Mediated Interactions Have Contributed to the Evolution of Modularity in Complex Protein Interaction Networks

The modular architecture of protein-protein interaction (PPI) networks is evident in diverse species with a wide range of complexity. However, the molecular components that lead to the evolution of modularity in PPI networks have not been clearly identified. Here, we show that weak domain-linear motif interactions (DLIs) are more likely to connect different biological modules than strong domain-domain interactions (DDIs). This molecular division of labor is essential for the evolution of modularity in the complex PPI networks of diverse eukaryotic species. In particular, DLIs may compensate for the reduction in module boundaries that originate from increased connections between different modules in complex PPI networks. In addition, we show that the identification of biological modules can be greatly improved by including molecular characteristics of protein interactions. Our findings suggest that transient interactions have played a unique role in shaping the architecture and modularity of biological networks over the course of evolution.     

Z-disc-associated,Alternatively Spliced,PDZ Motif-containing Protein (ZASP) Mutations in the Actin-binding Domain Cause Disruption of Skeletal Muscle Actin Filaments in Myofibrillar Myopathy

The core of skeletal muscle Z-discs consists of actin filaments from adjacent sarcomeres that are cross-linked by α-actinin homodimers. Z-disc-associated, alternatively spliced, PDZ motif-containing protein (ZASP)/Cypher interacts with α-actinin, myotilin, and other Z-disc proteins via the PDZ domain. However, these interactions are not sufficient to maintain the Z-disc structure. We show that ZASP directly interacts with skeletal actin filaments. The actin-binding domain is between the modular PDZ and LIM domains. This ZASP region is alternatively spliced so that each isoform has unique actin-binding domains. All ZASP isoforms contain the exon 6-encoded ZASP-like motif that is mutated in zaspopathy, a myofibrillar myopathy (MFM), whereas the exon 8–11 junction-encoded peptide is exclusive to the postnatal long ZASP isoform (ZASP-LΔex10). MFM is characterized by disruption of skeletal muscle Z-discs and accumulation of myofibrillar degradation products. Wild-type and mutant ZASP interact with α-actin, α-actinin, and myotilin. Expression of mutant, but not wild-type, ZASP leads to Z-disc disruption and F-actin accumulation in mouse skeletal muscle, as in MFM. Mutations in the actin-binding domain of ZASP-LΔex10, but not other isoforms, cause disruption of the actin cytoskeleton in muscle cells. These isoform-specific mutation effects highlight the essential role of the ZASP-LΔex10 isoform in F-actin organization. Our results show that MFM-associated ZASP mutations in the actin-binding domain have deleterious effects on the core structure of the Z-discs in skeletal muscle.     

Evolving modular genetic regulatory networks with a recursive,top-down approach

Being able to design genetic regulatory networks (GRNs) to achieve a desired cellular function is one of the main goals of synthetic biology. However, determining minimal GRNs that produce desired time-series behaviors is non-trivial. In this paper, we propose a ‘top-down’ approach to evolving small GRNs and then use these to recursively boot-strap the identification of larger, more complex, modular GRNs. We start with relatively dense GRNs and then use differential evolution (DE) to evolve interaction coefficients. When the target dynamical behavior is found embedded in a dense GRN, we narrow the focus of the search and begin aggressively pruning out excess interactions at the end of each generation. We first show that the method can quickly rediscover known small GRNs for a toggle switch and an oscillatory circuit. Next we include these GRNs as non-evolvable subnetworks in the subsequent evolution of more complex, modular GRNs. Successful solutions found in canonical DE where we truncated small interactions to zero, with or without an interaction penalty term, invariably contained many excess interactions. In contrast, by incorporating aggressive pruning and the penalty term, the DE was able to find minimal or nearly minimal GRNs in all test problems.     

Dual Role of an N-terminal Amyloidogenic Mutation in Apolipoprotein A-I: DESTABILIZATION OF HELIX BUNDLE AND ENHANCEMENT OF FIBRIL FORMATION*

A number of naturally occurring mutations of apolipoprotein (apo) A-I, the major protein of HDL, are known to be associated with hereditary amyloidosis and atherosclerosis. Here, we examined the effects of the G26R point mutation in apoA-I (apoA-I_Iowa) on the structure, stability, and aggregation propensity to form amyloid fibril of full-length apoA-I and the N-terminal fragment of apoA-I. Circular dichroism and fluorescence measurements demonstrated that the G26R mutation destabilizes the N-terminal helix bundle domain of full-length protein, leading to increased hydrophobic surface exposure, whereas it has no effect on the initial structure of the N-terminal 1–83 fragment, which is predominantly a random coil structure. Upon incubation for extended periods at neutral pH, the N-terminal 1–83 variants undergo a conformational change to β-sheet-rich structure with a great increase in thioflavin T fluorescence, whereas no structural change is observed in full-length proteins. Comparison of fibril-forming propensity among substituted mutants at Gly-26 position of 1–83 fragments demonstrated that the G26R mutation enhances the nucleation step of fibril formation, whereas G26K and G26E mutations have small or inhibiting effects on the formation of fibrils. These fibrils of the 1–83 variants have long and straight morphology as revealed by atomic force microscopy and exhibited significant toxicity with HEK293 cells. Our results indicate dual critical roles of the arginine residue at position 26 in apoA-I_Iowa: destabilization of the N-terminal helix bundle structure in full-length protein and enhancement of amyloid fibril formation by the N-terminal 1–83 fragment.     

Mutational fitness effects in RNA and single-stranded DNA viruses: common patterns revealed by site-directed mutagenesis studies

The fitness effects of mutations are central to evolution, yet have begun to be characterized in detail only recently. Site-directed mutagenesis is a powerful tool for achieving this goal, which is particularly suited for viruses because of their small genomes. Here, I discuss the evolutionary relevance of mutational fitness effects and critically review previous site-directed mutagenesis studies. The effects of single-nucleotide substitutions are standardized and compared for five RNA or single-stranded DNA viruses infecting bacteria, plants or animals. All viruses examined show very low tolerance to mutation when compared with cellular organisms. Moreover, for non-lethal mutations, the mean fitness reduction caused by single mutations is remarkably constant (0.10–0.13), whereas the fraction of lethals varies only modestly (0.20–0.41). Other summary statistics are provided. These generalizations about the distribution of mutational fitness effects can help us to better understand the evolution of RNA and single-stranded DNA viruses.     

Interactions between Aβ and Mutated Tau Lead to Polymorphism and Induce Aggregation of Aβ-Mutated Tau Oligomeric Complexes

One of the main hallmarks of the fronto-temporal dementia with Parkinsonism linked to chromosome 17 (FTDP-17) is the accumulation of neurofibrillary tangles in the brain as an outcome of the aggregation of mutated tau protein. This process occurs due to a number of genetic mutations in the MAPT gene. One of these mutations is the ∆K280 mutation in the tau R2 repeat domain, which promotes the aggregation vis-à-vis that for the wild-type tau. Experimental studies have shown that in Alzheimer’s disease Aβ peptide forms aggregates both with itself and with wild-type tau. By analogy, in FTDP-17, it is likely that there are interactions between Aβ and mutated tau, but the molecular mechanisms underlying such interactions remain to be elucidated. Thus, to investigate the interactions between Aβ and mutated tau, we constructed fourteen ∆K280 mutated tau-Aβ_17-42 oligomeric complexes. In seven of the mutated tau-Aβ_17-42 oligoemric complexes the mutated tau oligomers exhibited hydrophobic interactions in their core domain, and in the other seven mutated tau-Aβ_17-42 oligoemric complexes the mutated tau oligomers exhibited salt-bridge interactions in their core domain. We considered two types of interactions between mutated tau oligomers and Aβ oligomers: interactions of one monomer of the Aβ oligomer with one monomer of the mutated tau oligomer to form a single-layer conformation, and interactions of the entire Aβ oligomer with the entire mutated tau oligomer to form a double-layer conformation. We also considered parallel arrangements of Aβ trimers alternating with mutated tau trimers in a single-layer conformation. Our results demonstrate that in the interactions of Aβ and mutated tau oligomers, polymorphic mutated tau-Aβ_17-42 oligomeric complexes were observed, with a slight preference for the double-layer conformation. Aβ trimers alternating with mutated tau trimers constituted a structurally stable confined β-structure, albeit one that was energetically less stable than all the other constructed models.     

Long-term experimental evolution of HIV-1 reveals effects of environment and mutational history

An often-returning question for not only HIV-1, but also other organisms, is how predictable evolutionary paths are. The environment, mutational history, and random processes can all impact the exact evolutionary paths, but to which extent these factors contribute to the evolutionary dynamics of a particular system is an open question. Especially in a virus like HIV-1, with a large mutation rate and large population sizes, evolution is expected to be highly predictable if the impact of environment and history is low, and evolution is not neutral. We investigated the effect of environment and mutational history by analyzing sequences from a long-term evolution experiment, in which HIV-1 was passaged on 2 different cell types in 8 independent evolutionary lines and 8 derived lines, 4 of which involved a switch of the environment. The experiments lasted for 240–300 passages, corresponding to approximately 400–600 generations or almost 3 years. The sequences show signs of extensive parallel evolution—the majority of mutations that are shared between independent lines appear in both cell types, but we also find that both environment and mutational history significantly impact the evolutionary paths. We conclude that HIV-1 evolution is robust to small changes in the environment, similar to a transmission event in the absence of an immune response or drug pressure. We also find that the fitness landscape of HIV-1 is largely smooth, although we find some evidence for both positive and negative epistatic interactions between mutations.

Analysis of the longest evolutionary experiment with HIV-1 to-date reveals continuous viral adaptation over several years. The authors quantify the environment-specific mutations that arise and determine the fraction of mutations that co-occur with significantly different frequencies than expected by chance.     

Deleterious- and Disease-Allele Prevalence in Healthy Individuals: Insights from Current Predictions,Mutation Databases,and Population-Scale Resequencing

We have assessed the numbers of potentially deleterious variants in the genomes of apparently healthy humans by using (1) low-coverage whole-genome sequence data from 179 individuals in the 1000 Genomes Pilot Project and (2) current predictions and databases of deleterious variants. Each individual carried 281–515 missense substitutions, 40–85 of which were homozygous, predicted to be highly damaging. They also carried 40–110 variants classified by the Human Gene Mutation Database (HGMD) as disease-causing mutations (DMs), 3–24 variants in the homozygous state, and many polymorphisms putatively associated with disease. Whereas many of these DMs are likely to represent disease-allele-annotation errors, between 0 and 8 DMs (0–1 homozygous) per individual are predicted to be highly damaging, and some of them provide information of medical relevance. These analyses emphasize the need for improved annotation of disease alleles both in mutation databases and in the primary literature; some HGMD mutation data have been recategorized on the basis of the present findings, an iterative process that is both necessary and ongoing. Our estimates of deleterious-allele numbers are likely to be subject to both overcounting and undercounting. However, our current best mean estimates of ∼400 damaging variants and ∼2 bona fide disease mutations per individual are likely to increase rather than decrease as sequencing studies ascertain rare variants more effectively and as additional disease alleles are discovered.     

Detecting Clusters of Mutations

Positive selection for protein function can lead to multiple mutations within a small stretch of DNA, i.e., to a cluster of mutations. Recently, Wagner proposed a method to detect such mutation clusters. His method, however, did not take into account that residues with high solvent accessibility are inherently more variable than residues with low solvent accessibility. Here, we propose a new algorithm to detect clustered evolution. Our algorithm controls for different substitution probabilities at buried and exposed sites in the tertiary protein structure, and uses random permutations to calculate accurate P values for inferred clusters. We apply the algorithm to genomes of bacteria, fly, and mammals, and find several clusters of mutations in functionally important regions of proteins. Surprisingly, clustered evolution is a relatively rare phenomenon. Only between 2% and 10% of the genes we analyze contain a statistically significant mutation cluster. We also find that not controlling for solvent accessibility leads to an excess of clusters in terminal and solvent-exposed regions of proteins. Our algorithm provides a novel method to identify functionally relevant divergence between groups of species. Moreover, it could also be useful to detect artifacts in automatically assembled genomes.     

A Highlights from MBoC Selection: Enhanced dimerization drives ligand-independent activity of mutant epidermal growth factor receptor in lung cancer

Mutations within the epidermal growth factor receptor (EGFR/erbB1/Her1) are often associated with tumorigenesis. In particular, a number of EGFR mutants that demonstrate ligand-independent signaling are common in non–small cell lung cancer (NSCLC), including kinase domain mutations L858R (also called L834R) and exon 19 deletions (e.g., ΔL747-P753insS), which collectively make up nearly 90% of mutations in NSCLC. The molecular mechanisms by which these mutations confer constitutive activity remain unresolved. Using multiple subdiffraction-limit imaging modalities, we reveal the altered receptor structure and interaction kinetics of NSCLC-associated EGFR mutants. We applied two-color single quantum dot tracking to quantify receptor dimerization kinetics on living cells and show that, in contrast to wild-type EGFR, mutants are capable of forming stable, ligand-independent dimers. Two-color superresolution localization microscopy confirmed ligand-independent aggregation of EGFR mutants. Live-cell Förster resonance energy transfer measurements revealed that the L858R kinase mutation alters ectodomain structure such that unliganded mutant EGFR adopts an extended, dimerization-competent conformation. Finally, mutation of the putative dimerization arm confirmed a critical role for ectodomain engagement in ligand-independent signaling. These data support a model in which dysregulated activity of NSCLC-associated kinase mutants is driven by coordinated interactions involving both the kinase and extracellular domains that lead to enhanced dimerization.     

The quantitative-genetic and QTL architecture of trait integration and modularity in Brassica rapa across simulated seasonal settings

Within organisms, groups of traits with different functions are frequently modular, such that variation among modules is independent and variation within modules is tightly integrated, or correlated. Here, we investigated patterns of trait integration and modularity in Brassica rapa in response to three simulated seasonal temperature/photoperiod conditions. The goals of this research were to use trait correlations to understand patterns of trait integration and modularity within and among floral, vegetative and phenological traits of B. rapa in each of three treatments, to examine the QTL architecture underlying patterns of trait integration and modularity, and to quantify how variation in temperature and photoperiod affects the correlation structure and QTL architecture of traits. All floral organs of B. rapa were strongly correlated, and contrary to expectations, floral and vegetative traits were also correlated. Extensive QTL co-localization suggests that covariation of these traits is likely due to pleiotropy, although physically linked loci that independently affect individual traits cannot be ruled out. Across treatments, the structure of genotypic and QTL correlations was generally conserved. Any observed variation in genetic architecture arose from genotype × environment interactions (GEIs) and attendant QTL × E in response to temperature but not photoperiod.     

Coming to Grips with Evolvability

To explain the evolution of complex organisms by random mutation, drift, and selection is not a trivial task. This becomes obvious if we imagine an organism in which most genes affect most traits and all mutations are immediately expressed in the phenotype. Most of the mutations will be deleterious. Computer programmers experienced a similar problem when trying to evolve computer programs by introducing random changes to a conventional computer code, realizing that almost all random changes are “lethal.” Everyone who has done any programming knows that conventional computer languages are very brittle! Real organisms are not organized in this way but rather involve mediation between the genes and the phenotypic traits, namely development, also sometimes called the genotype–phenotype map. This map of genetic effects is structured in a way that enables evolvability, that is, enhances the probability that mutations will improve the performance of the organism. Here we outline two properties of organismal development, namely modularity and robustness. Modularity refers to the situation in which genes affect a restricted number of functionally related phenotypic characters. Robustness describes a situation in which cryptic mutations can accumulate without effect on fitness but can become visible to selection in a new environment or genetic background. We discuss recent empirical evidence in support of both phenomena and their effect on evolvability and also briefly address their evolution.     

Sensitive and specific detection of K-ras mutations in colon tumors by short oligonucleotide mass analysis

Short oligonucleotide mass analysis (SOMA) is a technique by which small sequences of mutated and wild-type DNA, produced by PCR amplification and restriction digestion, are characterized by HPLC-electrospray ionization tandem mass spectrometry. We have adapted the method to specifically detect two common point mutations at codon 12 of the c-K-ras gene. Mutations in DNA from 121 colon tumor samples were identified by SOMA and validated by comparison with sequencing. SOMA correctly identified 26 samples containing the 12GAT mutation and four samples containing the 12AGT mutation. Sequencing did not reveal mutant DNA in three samples out of the 26 samples shown by SOMA to contain the 12GAT mutation. In these three samples, the presence of mutant DNA was confirmed by SOMA analysis after selective PCR amplification in the presence of BstN1 restriction enzyme. Additional mutations in codons 12 and 13 were revealed by sequencing in 24 additional samples, and their presence did not interfere with the correct identification of G to A or G to T mutations in codon 12. These results provide the basis for a sensitive and specific method to detect c-K-ras codon 12-mutated DNA at levels below 10–12% of wild-type DNA.     

Integration of New Genes into Cellular Networks,and Their Structural Maturation

It has been recently discovered that new genes can originate de novo from noncoding DNA, and several biological traits including expression or sequence composition form a continuum from noncoding sequences to conserved genes. In this article, using yeast genes I test whether the integration of new genes into cellular networks and their structural maturation shows such a continuum by analyzing their changes with gene age. I show that 1) The number of regulatory, protein–protein, and genetic interactions increases continuously with gene age, although with very different rates. New regulatory interactions emerge rapidly within a few million years, while the number of protein–protein and genetic interactions increases slowly, with a rate of 2–2.25 × 10⁻⁸/year and 4.8 × 10⁻⁸/year, respectively. 2) Gene essentiality evolves relatively quickly: the youngest essential genes appear in proto-genes ∼14 MY old. 3) In contrast to interactions, the secondary structure of proteins and their robustness to mutations indicate that new genes face a bottleneck in their evolution: proto-genes are characterized by high β-strand content, high aggregation propensity, and low robustness against mutations, while conserved genes are characterized by lower strand content and higher stability, most likely due to the higher probability of gene loss among young genes and accumulation of neutral mutations.     

Pedigree-based and phylogenetic methods support surprising patterns of mutation rate and spectrum in the gray mouse lemur

Mutations are the raw material on which evolution acts, and knowledge of their frequency and genomic distribution is crucial for understanding how evolution operates at both long and short timescales. At present, the rate and spectrum of de novo mutations have been directly characterized in relatively few lineages. Our study provides the first direct mutation-rate estimate for a strepsirrhine (i.e., the lemurs and lorises), which comprises nearly half of the primate clade. Using high-coverage linked-read sequencing for a focal quartet of gray mouse lemurs (Microcebus murinus), we estimated the mutation rate to be among the highest calculated for a mammal at 1.52 × 10^–8 (95% credible interval: 1.28 × 10⁻⁸–1.78 × 10⁻⁸) mutations/site/generation. Further, we found an unexpectedly low count of paternal mutations, and only a modest overrepresentation of mutations at CpG sites. Despite the surprising nature of these results, we found both the rate and spectrum to be robust to the manipulation of a wide range of computational filtering criteria. We also sequenced a technical replicate to estimate a false-negative and false-positive rate for our data and show that any point estimate of a de novo mutation rate should be considered with a large degree of uncertainty. For validation, we conducted an independent analysis of context-dependent substitution types for gray mouse lemur and five additional primate species for which de novo mutation rates have also been estimated. These comparisons revealed general consistency of the mutation spectrum between the pedigree-based and the substitution-rate analyses for all species compared.Subject terms: Evolution, Molecular evolution