The foldability landscape of model proteins

Molecular evolution may be considered as a walk in a multidimensional fitness landscape, where the fitness at each point is associated with features such as the function, stability, and survivability of these molecules. We present a simple model for the evolution of protein sequences on a landscape with a precisely defined fitness function. We use simple lattice models to represent protein structures, with the ability of a protein sequence to fold into the structure with lowest energy, quantified as the foldability, representing the fitness of the sequence. The foldability of the sequence is characterized based on the spin glass model of protein folding. We consider evolution as a walk in this foldability landscape and study the nature of the landscape and the resulting dynamics. Selective pressure is explicitly included in this model in the form of a minimum foldability requirement. We find that different native structures are not evenly distributed in interaction space, with similar structures and structures with similar optimal foldabilities clustered together. Evolving proteins marginally fulfill the selective criteria of foldability. As the selective pressure is increased, evolutionary trajectories become increasingly confined to “neutral networks,” where the sequence and the interactions can be significantly changed while a constant structure is maintained. © 1997 John Wiley & Sons, Inc. Biopoly 42: 427–438, 1997     

Bacterial surface proteins. Some structural, functional and evolutionary aspects

The structure of several eubacterial and archaebacterial surface (glyco)proteins as determined by three-dimensional electron microscopy is described. Particular emphasis is placed on surface proteins which interact with membranes. Some structure-function relationships deduced from the structural information, such as shape maintenance and molecular recognition phenomena, are discussed.     

Inferring functional linkages between proteins from evolutionary scenarios

Identifying potential protein interactions is of great importance in understanding the topologies of cellular networks, which is much needed and valued in current systematic biological studies. The development of our computational methods to predict protein-protein interactions have been spurred on by the massive sequencing efforts of the genomic revolution. Among these methods is phylogenetic profiling, which assumes that proteins under similar evolutionary pressures with similar phylogenetic profiles might be functionally related. Here, we introduce a method for inferring functional linkages between proteins from their evolutionary scenarios. The term evolutionary scenario refers to a series of events that occurred in speciation over time, which can be reconstructed given a phylogenetic profile and a species tree. Common evolutionary pressures on two proteins can then be inferred by comparing their evolutionary scenarios, which is a direct indication of their functional linkage. This scenario method has proven to have better performance compared with the classical phylogenetic profile method, when applied to the same test set. In addition, predicted results of the two methods are found to be fairly different, suggesting the possibility of merging them in order to achieve a better performance. We analyzed the influence of the topology of the phylogenetic tree on the performance of this method, and found it to be robust to perturbations in the topology of the tree. However, if a completely random tree is incorporated, performance will decline significantly. The evolutionary scenario method was used for inferring functional linkages in 67 species, and 40,006 linkages were predicted. We examine our prediction for budding yeast and find that almost all predicted linkages are supported by further evidence.     

Ligand binding to proteins: the binding landscape model.

Models of ligand binding are often based on four assumptions: (1) steric fit: that binding is determined mainly by shape complementarity; (2) native binding: that ligands mainly bind to native states; (3) locality: that ligands perturb protein structures mainly at the binding site; and (4) continuity: that small changes in ligand or protein structure lead to small changes in binding affinity. Using a generalization of the 2D HP lattice model, we study ligand binding and explore these assumptions. We first validate the model by showing that it reproduces typical binding behaviors. We observe ligand-induced denaturation, ANS and heme-like binding, and "lock-and-key" and "induced-fit" specific binding behaviors characterized by Michaelis-Menten or more cooperative types of binding isotherms. We then explore cases where the model predicts violations of the standard assumptions. For example, very different binding modes can result from two ligands of identical shape. Ligands can sometimes bind highly denatured states more tightly than native states and yet have Michaelis-Menten isotherms. Even low-population binding to denatured states can cause changes in global stability, hydrogen-exchange rates, and thermal B-factors, contrary to expectations, but in agreement with experiments. We conclude that ligand binding, similar to protein folding, may be better described in terms of energy landscapes than in terms of simpler mass-action models.     

Proteomics enhances evolutionary and functional analysis of reproductive proteins

Reproductive proteins maintain species‐specific barriers to fertilization, affect the outcome of sperm competition, mediate reproductive conflicts between the sexes, and potentially contribute to the formation of new species. However, the specific proteins and molecular mechanisms that underlie these processes are understood in only a handful of cases. Advances in genomic and proteomic technologies enable the identification of large suites of reproductive proteins, making it possible to dissect reproductive phenotypes at the molecular level. We first review these technological advances and describe how reproductive proteins are identified in diverse animal taxa. We then discuss the dynamic evolution of reproductive proteins and the potential selective forces that act on them. Finally, we describe molecular and genomic tools for functional analysis and detail how evolutionary data may be used to make predictions about interactions among reproductive proteins.     

The functional conservation of proteins in evolutionary alleles and the dominant role of enhancers in evolution.

Drosophila paired- embryos can be rescued to viable adults by the evolutionary alleles prd-Gsb and prd-Pax3, which express the Drosophila Gooseberry and mouse Pax3 proteins under the control of the paired cis-regulatory region. As prd-Gsb uncovers a prd function involved in the proper abdominal segmentation of adults, evolutionary alleles, defined and constructed in this manner, may often be weak and thus serve to discover hitherto unknown functions of a gene. Our findings show that the Gooseberry and Pax3 proteins have conserved most or all functions of the related Drosophila Paired protein although their C-terminal halves appear unrelated in sequence but not in 3-D structure essential for function. It follows that the acquisition of new cis-regulatory regions rather than the divergence of the C-terminal coding regions has been the primary device for the functional diversification of the Drosophila genes paired and gooseberry and the mouse Pax3 gene. The operation of this mechanism in insects as well as vertebrates suggests a major role in evolution.     

Evolution of model proteins on a foldability landscape

We model the evolution of simple lattice proteins as a random walk in a fitness landscape, where the fitness represents the ability of the protein to fold. At higher selective pressure, the evolutionary trajectories are confined to neutral networks where the native structure is conserved and the dynamics are non self-averaging and nonexponential. The optimizability of the corresponding native structure has a strong effect on the size of these neutral networks and thus on the nature of the evolutionary process. Proteins 29:461–466, 1997. © 1997 Wiley-Liss, Inc.     

An integrated probabilistic model for functional prediction of proteins.

We develop an integrated probabilistic model to combine protein physical interactions, genetic interactions, highly correlated gene expression networks, protein complex data, and domain structures of individual proteins to predict protein functions. The model is an extension of our previous model for protein function prediction based on Markovian random field theory. The model is flexible in that other protein pairwise relationship information and features of individual proteins can be easily incorporated. Two features distinguish the integrated approach from other available methods for protein function prediction. One is that the integrated approach uses all available sources of information with different weights for different sources of data. It is a global approach that takes the whole network into consideration. The second feature is that the posterior probability that a protein has the function of interest is assigned. The posterior probability indicates how confident we are about assigning the function to the protein. We apply our integrated approach to predict functions of yeast proteins based upon MIPS protein function classifications and upon the interaction networks based on MIPS physical and genetic interactions, gene expression profiles, tandem affinity purification (TAP) protein complex data, and protein domain information. We study the recall and precision of the integrated approach using different sources of information by the leave-one-out approach. In contrast to using MIPS physical interactions only, the integrated approach combining all of the information increases the recall from 57% to 87% when the precision is set at 57%-an increase of 30%.     

The evolutionary landscape of antifolate resistance in Plasmodium falciparum

Resistance to antifolates in Plasmodium falciparum is well described and has been observed in clinical settings for decades. At the molecular level, point mutations in the dhfr gene that lead to resistance have been identified, and the crystal structure of the wildtype and mutant dihydrofolate reductase enzymes have been solved in complex with native substrate and drugs. However, we are only beginning to understand the complexities of the evolutionary pressures that lead to the evolution of drug resistance in this system. Microbial systems that allow heterologous expression of malarial proteins provide a tractable way to investigate patterns of evolution that can inform our eventual understanding of the more complex factors that influence the evolution of drug resistance in clinical settings. In this paper we will review work in Escherichia coli and Saccharomyces cerevisiae expression systems that explore the fitness landscape of mutations implicated in drug resistance and show that (i) a limited number of evolutionary pathways to resistance are followed with high probability; (ii) fitness costs associated with the maintenance of high levels of resistance are modest; and (iii) different antifolates may exert opposing selective forces.     

The evolutionary landscape of cytosolic microbial sensors in humans

Host-pathogen interactions are generally initiated by host recognition of microbial components or danger signals triggered by microbial invasion. This recognition involves germline-encoded microbial sensors or pattern-recognition receptors (PRRs). By studying the way in which natural selection has driven the evolution of these microbial sensors in humans, we can identify genes playing an essential role and distinguish them from other, more redundant genes. We characterized the sequence diversity of the NOD-like receptor family, including the NALP and NOD/IPAF subfamilies, in various populations worldwide and compared this diversity with that of other PRR families, such as Toll-like receptors (TLRs) and RIG-I-like receptors (RLRs). We found that most NALPs had evolved under strong selective constraints, suggesting that their functions are essential and possibly much broader than previously thought. Conversely, most NOD/IPAF subfamily members were subject to more relaxed selective constraints, suggesting greater redundancy. Furthermore, some NALP genes, including NLRP1, NLRP14, and CIITA, were found to have evolved adaptively. We identified those variants conferring a selective advantage on some human populations as the most likely targets of positive selection. More generally, the strength of selection differed considerably between the major families of microbial sensors. Endosomal TLRs and most NALPs were found to evolve under stronger purifying selection than most NOD/IPAF subfamily members and cell-surface TLRs and RLRs, suggesting some degree of redundancy in the signaling pathways triggered by these molecules. This study provides novel perspectives and experimentally testable hypotheses concerning the relative biological relevance of the various families of microbial sensors in humans.     

The Zn-peptidase superfamily: functional convergence after evolutionary divergence.

Zn-dependent carboxypeptidases (ZnCP) cleave off the C-terminal amino acid residues from proteins and peptides. Here we describe a superfamily that unites classical ZnCP with other enzymes, most of which are known (or likely) to participate in metal-dependent peptide bond cleavage, but not necessarily in polypeptide substrates. It is demonstrated that aspartoacylase (ASP gene) and succinylglutamate desuccinylase (ASTE gene) are members of the ZnCP family. The Zn-binding site along with the structural core of the protein is shown to be conserved between ZnCP and another large family of hydrolases that includes mostly aminopeptidases (ZnAP). Both families (ZnCP and ZnAP) include not only proteases but also enzymes that perform N-deacylation, and enzymes that catalyze N-desuccinylation of amino acids. This is a result of functional convergence that apparently occurred after the divergence of the two families.     

Predicted functional RNAs within coding regions constrain evolutionary rates of yeast proteins

Functional RNAs (fRNAs) are being recognized as an important regulatory component in biological processes. Interestingly, recent computational studies suggest that the number and biological significance of functional RNAs within coding regions (coding fRNAs) may have been underestimated. We hypothesized that such coding fRNAs will impose additional constraint on sequence evolution because the DNA primary sequence has to simultaneously code for functional RNA secondary structures on the messenger RNA in addition to the amino acid codons for the protein sequence. To test this prediction, we first utilized computational methods to predict conserved fRNA secondary structures within multiple species alignments of Saccharomyces sensu strico genomes. We predict that as much as 5% of the genes in the yeast genome contain at least one functional RNA secondary structure within their protein-coding region. We then analyzed the impact of coding fRNAs on the evolutionary rate of protein-coding genes because a decrease in evolutionary rate implies constraint due to biological functionality. We found that our predicted coding fRNAs have a significant influence on evolutionary rates (especially at synonymous sites), independent of other functional measures. Thus, coding fRNA may play a role on sequence evolution. Given that coding regions of humans and flies contain many more predicted coding fRNAs than yeast, the impact of coding fRNAs on sequence evolution may be substantial in genomes of higher eukaryotes.     

The evolutionary landscape of the Mycobacterium tuberculosis genome

Mycobacterium tuberculosis is one of the most deadly human pathogens. The major mechanism for the adaptations of M. tuberculosis is nucleotide substitution. Previous studies have relied on the nonsynonymous-to-synonymous substitution rate (d_N/d_S) ratio as a measurement of selective constraint based on the assumed selective neutrality of synonymous substitutions. However, this assumption has been shown to be untrue in many cases. In this study, we used the substitution rate in intergenic regions (d_i) of the M. tuberculosis genome as the neutral reference, and conducted a genome-wide profiling for d_i, d_S, and the rate of insertions/deletions (indel rate) as compared with the genome of M. canettii using a 50 kb sliding window. We demonstrate significant variations in all of the three evolutionary measurements across the M. tuberculosis genome, even for regions in close vicinity. Furthermore, we identified a total of 233 genes with their d_S deviating significantly from d_i within the same window. Interestingly, d_S also varies significantly in some of the windows, indicating drastic changes in mutation rate and/or selection pressure within relatively short distances in the M. tuberculosis genome. Importantly, our results indicate that selection on synonymous substitutions is common in the M. tuberculosis genome. Therefore, the d_N/d_S ratio test must be applied carefully for measuring selection pressure on M. tuberculosis genes.     

Simple evolutionary pathways to complex proteins.

A recent paper in this journal has challenged the idea that complex adaptive features of proteins can be explained by known molecular, genetic, and evolutionary mechanisms. It is shown here that the conclusions of this prior work are an artifact of unwarranted biological assumptions, inappropriate mathematical modeling, and faulty logic. Numerous simple pathways exist by which adaptive multi-residue functions can evolve on time scales of a million years (or much less) in populations of only moderate size. Thus, the classical evolutionary trajectory of descent with modification is adequate to explain the diversification of protein functions.     

The application of functional morphology to evolutionary studies

Studies of functional morphology contribute in several ways to the understanding of evolutionary patterns and processes. The former include the unravelling of dependencies of characters and the construction of biomechanically feasible transformation schemes. Among the latter are the identification of structural novelties that facilitate a cascade of diverse structural changes, and the Identification of mechanisms that enable the incorporation of evolutionary novelties into the integrated organism. The study of mechanisms that maintain the match between form and function during evolutionary (and developmental) changes is a new and important area for evolutionary biologists.     

The functional landscape of a presynaptic nerve terminal

The neuromuscular junction (NMJ) is the original model synapse, and while others have emerged, especially as models of plasticity involving coincidence detectors, the NMJ continues to provide useful new information. It remains, for example, one of the best understood synapses in terms of the relationship between structure and function. In particular, the advent of new tools for fluorescence imaging has allowed the processes of vesicle exocytosis, endocytosis, and receptor activation to be spatially mapped in considerable detail. Here, we will focus on the spatial properties of transmitter release at the presynaptic motor terminal at the mouse NMJ. The preparation offers several experimental advantages, such as easy accessibility, a nearly planar, unobstructed view of several hundred square microns of synaptic membrane, as well as a highly stereotyped, consistent structure of a fully differentiated adult mammalian synapse.     

The evolutionary landscape of PRC1 core components in green lineage

Planta - The origin and evolution of plant PRC1 core components. Polycomb repressive complex1 (PRC1) plays critical roles in epigenetic silencing of homeotic genes and determination of cell fate....     

Uncoupling proteins outside the animal and plant kingdoms: functional and evolutionary aspects

The appearance of intracellular oxidative phosphorylation at the time of acquisition of mitochondria in Eukarya was very soon accompanied by the emergence of uncoupling protein, a carrier specialized in free fatty acid-mediated H⁺ recycling that can modulate the tightness of coupling between mitochondrial respiration and ATP synthesis, thereby maintaining a balance between energy supply and demand in the cell and defending cells against damaging reactive oxygen species production when electron carriers of the respiratory chain become overreduced. The simultaneous occurrence of redox free energy-dissipating oxidase, which has the same final effect, could be related to the functional interactions between both dissipative systems.