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We recently used computational protein design to create an extremely stable, globular protein, Top7, with a sequence and fold not observed previously in nature. Since Top7 was created in the absence of genetic selection, it provides a rare opportunity to investigate aspects of the cellular protein production and surveillance machinery that are subject to natural selection. Here we show that a portion of the Top7 protein corresponding to the final 49 C-terminal residues is efficiently mis-translated and accumulates at high levels in Escherichia coli. We used circular dichroism, size-exclusion chromatography, small-angle X-ray scattering, analytical ultra-centrifugation, and NMR spectroscopy to show that the resulting C-terminal fragment (CFr) protein adopts a compact, extremely stable, homo-dimeric structure. Based on the solution structure, we engineered an even more stable variant of CFr by disulfide-induced covalent circularisation that should be an excellent platform for design of novel functions. The accumulation of high levels of CFr exposes the high error rate of the protein translation machinery. The rarity of correspondingly stable fragments in natural proteins coupled with the observation that high quality ribosome binding sites are found to occur within E. coli protein-coding regions significantly less often than expected by random chance implies a stringent evolutionary pressure against protein sub-fragments that can independently fold into stable structures. The symmetric self-association between two identical mis-translated CFr sub-domains to generate an extremely stable structure parallels a mechanism for natural protein-fold evolution by modular recombination of protein sub-structures.

Native protein structures achieve stability in part by burying hydrophobic side-chains. About 75% of all amino acid residues buried in protein interiors are non-polar. Buried residues are not uniformly distributed in protein sequences, but sometimes cluster as contiguous polypeptide stretches that run through the interior of protein domain structures. Such regions have an intrinsically high local sequence density of non-polar residues, creating a potential problem: local non-polar sequences also promote protein misfolding and aggregation into non-native structures such as the amyloid fibrils in Alzheimer's disease. Here we show that long buried blocks of sequence in protein domains of known structure have, on average, a lower content of non-polar amino acids (about 70%) than do isolated buried residues (about 80%). This trend is observed both in small and in large protein domains and is independent of secondary structure. Long, completely non-polar buried stretches containing many large side-chains are particularly avoided. Aspartate residues that are incorporated in long buried stretches were found to make fewer polar interactions than those in short stretches, hinting that they may be destabilizing to the native state. We suggest that evolutionary pressure is acting on non-native properties, causing buried polar residues to be placed at positions where they would break up aggregation-prone non-polar sequences, perhaps even at some cost to native state stability.

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.

Understanding the molecular mechanisms of the specific interaction between transcription factor proteins and DNA is key to comprehend the regulation of gene expression and to develop technologies to engineer transcription factors. Thus far, although there have been several attempts to elucidate protein-DNA interaction through amino acid-base recognition codes, sequence based profiles, or physical models of interaction, the greatest successes in engineering DNA binding specificity remain experimental. Here we present the first systematic evidence of correlated evolutionary pressure at interacting amino acid residues and DNA base-pairs in transcription factors, and show that it can be used to rationally engineer DNA binding specificity. The correlation is between the relative evolutionary importance of protein residues and DNA bases, measured, respectively, in terms of the Evolutionary Trace (ET) rank and information entropy. The evolutionarily most important residues interact with the most conserved base-pairs within the response element while residues of least importance interact with the most variable base-pairs. The correlation averages 0.74 over 12 unrelated families of transcriptional regulators, including nuclear hormone receptors, basic helix-loop-helix, ETS- and homeo-domain family. To test the predictive power of this correlation, we targeted a mutational swap of top-ranked ET residues in a transcription factor, LRH-1. This redirects LRH-1 binding as predicted and showed that, in this case, evolutionary importance and binding specificity are coupled sufficiently strongly for the Evolutionary Trace to guide the computational design of DNA binding specificity. This establishes the existence of evolutionary importance correlation at protein-DNA interfaces, and demonstrates that it is a useful principle for the rational engineering of binding specificity.

Here, we present statistical analysis of conservation profiles in families of homologous sequences for nine proteins whose folding nucleus was determined by protein engineering methods. We show that in all but one protein (AcP) folding nucleus residues are significantly more conserved than the rest of the protein. Two aspects of our study are especially important: (i) grouping of amino acid residues into classes according to their physical-chemical properties and (ii) proper normalization of amino acid probabilities that reflects the fact that evolutionary pressure to conserve some amino acid types may itself affect concentration of various amino acid types in protein families. Neglect of any of those two factors may make physical and biological "signals" from conservation profiles disappear.

Protein families are a rich source of information; sequence conservation and sequence correlation are two of the main properties that can be derived from the analysis of multiple sequence alignments. Sequence conservation is related to the direct evolutionary pressure to retain the chemical characteristics of some positions in order to maintain a given function. Sequence correlation is attributed to the small sequence adjustments needed to maintain protein stability against constant mutational drift. Here, we showed that sequence conservation and correlation were each frequently informative enough to detect incorrectly folded proteins. Furthermore, combining conservation, correlation, and polarity, we achieved an almost perfect discrimination between native and incorrectly folded proteins. Thus, we made use of this information for threading by evaluating the models suggested by a threading method according to the degree of proximity of the corresponding correlated, conserved, and apolar residues. The results showed that the fold recognition capacity of a given threading approach could be improved almost fourfold by selecting the alignments that score best under the three different sequence-based approaches.

It has been observed that the evolutionary distances of interacting proteins often display a higher level of similarity than those of non-interacting proteins. This finding indicates that interacting proteins are subject to common evolutionary constraints and constitutes the basis of a method to predict protein interactions known as mirrortree. It has been difficult, however, to identify the direct cause of the observed similarities between evolutionary trees. One possible explanation is the existence of compensatory mutations between partners’ binding sites to maintain proper binding. This explanation, however, has been recently challenged; and it has been suggested that the signal of correlated evolution uncovered by the mirrortree method is unrelated to any correlated evolution between binding sites. We examined the contribution of binding sites to the correlation between evolutionary trees of interacting domains. We showed that binding neighborhoods of interacting proteins have, on average, higher coevolutionary signal compared to the regions outside binding sites; although when the binding neighborhood was removed, the remaining domain sequence still contained some coevolutionary signal. In conclusion, the correlation between evolutionary trees of interacting domains cannot exclusively be attributed to the correlated evolution of the binding sites or to common evolutionary pressure exerted on the whole protein domain sequence, both factors contribute to the signal measured by the mirror tree approach. In addition, we showed that binding neighborhoods of interacting proteins have, on average, higher coevolutionary signal compared to the regions outside binding sites.

We discuss the question of which residues are sufficiently important for protein-protein interaction to be under notable evolutionary pressure. Its interest stems from the applicability of this knowledge in the reverse direction, to detect a protein-protein interface on a single protomer, starting from the rate of mutation of participating residues. Using the analysis of trajectories produced by the molecular dynamics simulations, we suggest that, in the case of water soluble proteins, a large fraction of evolutionarily privileged residues can be found by considering the dynamic behavior of the protein interface and by looking for residues which exchange water molecules with the bulk of the solvent outstandingly slowly (tentatively termed "dry residues"). We show that the dry interface residues are better conserved across homologues than the generic "geometric footprint" and can be quite reliably detected through comparative analysis of protein homologues, without strong dependence on the choice of method. Furthermore, we show that dry residues distinguish themselves through a set of biophysical properties consistent with the known mechanisms of protein oligomerization: their compositional shift toward nonpolar, overlap, and co-location with residues exhibiting low mobility, their two- to threefold increased propensity over the rest of the geometric footprint to form hydrogen bonds, and four- to almost tenfold increased likelihood to participate in formation of salt bridges. These properties, consistently, help understand the observed increase in the evolutionary pressure that dry residues experience.

The identification of the whole set of protein interactions taking place in an organism is one of the main tasks in genomics, proteomics and systems biology. One of the computational techniques used by many investigators for studying and predicting protein interactions is the comparison of evolutionary histories (phylogenetic trees), under the hypothesis that interacting proteins would be subject to a similar evolutionary pressure resulting in a similar topology of the corresponding trees. Here, we present a new approach to predict protein interactions from phylogenetic trees, which incorporates information on the overall evolutionary histories of the species (i.e. the canonical "tree of life") in order to correct by the expected background similarity due to the underlying speciation events. We test the new approach in the largest set of annotated interacting proteins for Escherichia coli. This assessment of co-evolution in the context of the tree of life leads to a highly significant improvement (P(N) by sign test approximately 10E-6) in predicting interaction partners with respect to the previous technique, which does not incorporate information on the overall speciation tree. For half of the proteins we found a real interactor among the 6.4% top scores, compared with the 16.5% by the previous method. We applied the new method to the whole E.coli proteome and propose functions for some hypothetical proteins based on their predicted interactors. The new approach allows us also to detect non-canonical evolutionary events, in particular horizontal gene transfers. We also show that taking into account these non-canonical evolutionary events when assessing the similarity between evolutionary trees improves the performance of the method predicting interactions.

In an effort to better understand beta-sheet assembly, we have investigated the evolutionary behavior of neighboring residues on adjacent antiparallel beta-strands. Residue pairs were classified according to solvent exposure as well as by whether their backbone NH and C==O groups are hydrogen bonded. The conservation and covariation of 19,241 pairs in 219 sequence alignments was analyzed. Buried pairs were found to be the most conserved, while stronger covariation was detected in the solvent-exposed pairs. However, residues on neighboring strands showed a degree of conservation and covariation similar to that of well-separated residues on the same strand, suggesting that evolutionary pressure to maintain complementarity between pairs on neighboring strands is weak. Moreover, in spite of the preference of certain amino acid pairs to occupy neighboring positions on adjacent strands, such favored pairs are neither more strongly mutually conserved nor covary more strongly than pairs of the same type in non-interacting positions. Although the beta-sheet pairs did not show outstanding evolutionary coupling, in many protein families significant conservation and covariation patterns were detected for some of the residue pairs. Overall, the weak evolutionary conservation and covariation of the beta-sheet pairs indicates that sheet structure is unlikely to be dictated by specific side-chain interactions.

A procedure for simulating the RNA folding process using the principles of genetic algorithm is proposed. The method allows one to simulate a folding pathway of RNA, including such processes as disruption of temporarily formed structures, the folding of a molecule during its synthesis and pseudoknot formation. The simulations are able to predict functional metastable foldings and kinetically driven transitions to more stable structures. The analysis of free energies for intermediate foldings allows estimation of the ranges of kinetic refolding barriers and suggests that in some RNAs the selective evolutionary pressure suppresses the possibilities for alternative structures that could form in the course of transcription. It is shown that the folding pathway simulation can result in structure predictions that are more consistent with phylogenetically proven structures than minimum energy solutions. This suggest that RNA folding kinetics is very important for the formation of functional RNA structures. Therefore, apart form its value for predictions of RNA structures, the proposed computer simulations can be a powerful tool in the studies of RNA folding features.