Practical aspects of protein co-evolution

PyCoM: a python library for large-scale analysis of residue-residue coevolution data

MOTIVATION

Computational methods to detect correlated amino acid positions in proteins have become a valuable tool to predict intra- and inter-residue protein contacts, protein structures, and effects of mutation on protein stability and function. While there are many tools and webservers to compute coevolution scoring matrices, there is no central repository of alignments and coevolution matrices for large-scale studies and pattern detection leveraging on biological and structural annotations already available in UniProt.

RESULTS

We present a Python library, PyCoM, which enables users to query and analyze coevolution matrices and sequence alignments of 457 622 proteins, selected from UniProtKB/Swiss-Prot database (length ≤ 500 residues), from a precompiled coevolution matrix database (PyCoMdb). PyCoM facilitates the development of statistical analyses of residue coevolution patterns using filters on biological and structural annotations from UniProtKB/Swiss-Prot, with simple access to PyCoMdb for both novice and advanced users, supporting Jupyter Notebooks, Python scripts, and a web API access. The resource is open source and will help in generating data-driven computational models and methods to study and understand protein structures, stability, function, and design.

AVAILABILITY AND IMPLEMENTATION

PyCoM code is freely available from https://github.com/scdantu/pycom and PyCoMdb and the Jupyter Notebook tutorials are freely available from https://pycom.brunel.ac.uk.

Coevolution based immunoinformatics approach considering variability of epitopes to combat different strains: A case study using spike protein of SARS-CoV-2

In the recent past several vaccines were developed to combat the COVID-19 disease. Unfortunately, the protective efficacy of the current vaccines has been reduced due to the high mutation rate in SARS-CoV-2. Here, we successfully implemented a coevolution based immunoinformatics approach to design an epitope-based peptide vaccine considering variability in spike protein of SARS-CoV-2. The spike glycoprotein was investigated for B- and T-cell epitope prediction. Identified T-cell epitopes were mapped on previously reported coevolving amino acids in the spike protein to introduce mutation. The non-mutated and mutated vaccine components were constructed by selecting epitopes showing overlapping with the predicted B-cell epitopes and highest antigenicity. Selected epitopes were linked with the help of a linker to construct a single vaccine component. Non-mutated and mutated vaccine component sequences were modelled and validated. The in-silico expression level of the vaccine constructs (non-mutated and mutated) in E. coli K12 shows promising results. The molecular docking analysis of vaccine components with toll-like receptor 5 (TLR5) demonstrated strong binding affinity. The time series calculations including root mean square deviation (RMSD), radius of gyration (RGYR), and energy of the system over 100 ns trajectory obtained from all atom molecular dynamics simulation showed stability of the system. The combined coevolutionary and immunoinformatics approach used in this study will certainly help to design an effective peptide vaccine that may work against different strains of SARS-CoV-2. Moreover, the strategy used in this study can be implemented on other pathogens.

Toward novel treatment against filariasis: Insight into genome-wide co-evolutionary analysis of filarial nematodes and Wolbachia

Infectious diseases caused by filarial nematodes are major health problems for humans and animals globally. Current treatment using anti-helminthic drugs requires a long treatment period and is only effective against the microfilarial stage. Most species of filarial nematodes harbor a specific strain of Wolbachia bacteria, which are essential for the survival, development, and reproduction of the nematodes. This parasite-bacteria obligate symbiosis offers a new angle for the cure of filariasis. In this study, we utilized publicly available genome data and putative protein sequences from seven filarial nematode species and their symbiotic Wolbachia to screen for protein-protein interactions that could be a novel target against multiple filarial nematode species. Genome-wide in silico screening was performed to predict molecular interactions based on co-evolutionary signals. We identified over 8,000 pairs of gene families that show evidence of co-evolution based on high correlation score and low false discovery rate (FDR) between gene families and obtained a candidate list that may be keys in filarial nematode-Wolbachia interactions. Functional analysis was conducted on these top-scoring pairs, revealing biological processes related to various signaling processes, adult lifespan, developmental control, lipid and nucleotide metabolism, and RNA modification. Furthermore, network analysis of the top-scoring genes with multiple co-evolving pairs suggests candidate genes in both Wolbachia and the nematode that may play crucial roles at the center of multi-gene networks. A number of the top-scoring genes matched well to known drug targets, suggesting a promising drug-repurposing strategy that could be applicable against multiple filarial nematode species.

Bioinformatic Analysis of Na+, K+-ATPase Regulation through Phosphorylation of the Alpha-Subunit N-Terminus

The Na⁺, K⁺-ATPase is an integral membrane protein which uses the energy of ATP hydrolysis to pump Na⁺ and K⁺ ions across the plasma membrane of all animal cells. It plays crucial roles in numerous physiological processes, such as cell volume regulation, nutrient reabsorption in the kidneys, nerve impulse transmission, and muscle contraction. Recent data suggest that it is regulated via an electrostatic switch mechanism involving the interaction of its lysine-rich N-terminus with the cytoplasmic surface of its surrounding lipid membrane, which can be modulated through the regulatory phosphorylation of the conserved serine and tyrosine residues on the protein's N-terminal tail. Prior data indicate that the kinases responsible for phosphorylation belong to the protein kinase C (PKC) and Src kinase families. To provide indications of which particular enzyme of these families might be responsible, we analysed them for evidence of coevolution via the mirror tree method, utilising coevolution as a marker for a functional interaction. The results obtained showed that the most likely kinase isoforms to interact with the Na⁺, K⁺-ATPase were the θ and η isoforms of PKC and the Src kinase itself. These theoretical results will guide the direction of future experimental studies.

FunOrder 2.0 - a method for the fully automated curation of co-evolved genes in fungal biosynthetic gene clusters

Coevolution is an important biological process that shapes interacting proteins - may it be physically interacting proteins or consecutive enzymes in a metabolic pathway, such as the biosynthetic pathways for secondary metabolites. Previously, we developed FunOrder, a semi-automated method for the detection of co-evolved genes, and demonstrated that FunOrder can be used to identify essential genes in biosynthetic gene clusters from different ascomycetes. A major drawback of this original method was the need for a manual assessment, which may create a user bias and prevents a high-throughput application. Here we present a fully automated version of this method termed FunOrder 2.0. In the improved version, we use several mathematical indices to determine the optimal number of clusters in the FunOrder output, and a subsequent k-means clustering based on the first three principal components of a principal component analysis of the FunOrder output to automatically detect co-evolved genes. Further, we replaced the BLAST tool with the DIAMOND tool as a prerequisite for using larger proteome databases. Potentially, FunOrder 2.0 may be used for the assessment of complete genomes, which has not been attempted yet. However, the introduced changes slightly decreased the sensitivity of this method, which is outweighed by enhanced overall speed and specificity.

Computational Docking Reveals Co-Evolution of C4 Carbon Delivery Enzymes in Diverse Plants

Proteins are modular functionalities regulating multiple cellular activities in prokaryotes and eukaryotes. As a consequence of higher plants adapting to arid and thermal conditions, C4 photosynthesis is the carbon fixation process involving multi-enzymes working in a coordinated fashion. However, how these enzymes interact with each other and whether they co-evolve in parallel to maintain interactions in different plants remain elusive to date. Here, we report our findings on the global protein co-evolution relationship and local dynamics of co-varying site shifts in key C4 photosynthetic enzymes. We found that in most of the selected key C4 photosynthetic enzymes, global pairwise co-evolution events exist to form functional couplings. Besides, protein-protein interactions between these enzymes may suggest their unknown functionalities in the carbon delivery process. For PEPC and PPCK regulation pairs, pocket formation at the interactive interface are not necessary for their function. This feature is distinct from another well-known regulation pair in C4 photosynthesis, namely, PPDK and PPDK-RP, where the pockets are necessary. Our findings facilitate the discovery of novel protein regulation types and contribute to expanding our knowledge about C4 photosynthesis.

CView: A network based tool for enhanced alignment visualization

To date basic visualization of sequence alignments have largely focused on displaying per-site columns of nucleotide, or amino acid, residues along with associated frequency summarizations. The persistence of this tendency to the recent tools designed for viewing mapped read data indicates that such a perspective not only provides a reliable visualization of per-site alterations, but also offers implicit reassurance to the end-user in relation to data accessibility. However, the initial insight gained is limited, something that is especially true when viewing alignments consisting of many sequences representing differing factors such as location, date and subtype. A basic alignment viewer can have potential to increase initial insight through visual enhancement, whilst not delving into the realms of complex sequence analysis. We present CView, a visualizer that expands on the per-site representation of residues through the incorporation of a dynamic network that is based on the summarization of diversity present across different regions of the alignment. Within the network, nodes are based on the clustering of sequence fragments that span windows placed consecutively along the alignment. Edges are placed between nodes of neighbouring windows where they share sequence identification(s), i.e. different regions of the same sequence(s). Thus, if a node is selected on the network, then the relationship that sequences passing through that node have to other regions of diversity within the alignment can be observed through path tracing. In addition to augmenting visual insight, CView provides export features including variant summarization, per-site residue and kmer frequencies, consensus sequence, alignment dissection as well as clustering; each useful across a range of research areas. The software has been designed to be user friendly, intuitive and interactive. It is open source and an executable jar, source code, quick start, usage tutorial and test data are available (under the GNU General Public License) from https://sourceforge.net/projects/cview/.

The coevolution of large and small terminases of bacteriophages is a result of purifying selection leading to phenotypic stabilization

Genome packaging in many dsDNA phages requires a series of precisely coordinated actions of two phage-coded proteins, namely, large terminase (TerL) and small terminase (TerS) with DNA and ATP, and with each other. Despite the strict functional conservation, TerL and TerS homologs exhibit large sequence variations. We investigated the sequence variability across eight phage types and observed a coevolutionary framework wherein the genealogy of TerL homologs mirrored that of the corresponding TerS homologs. Furthermore, a high purifying selection observed (dN/dS«1) indicated strong structural constraints on both TerL and TerS, and identify coevolving residues in TerL and TerS of phage T4 and lambda. Using the highly coevolving (correlation coefficient of 0.99) TerL and TerS of phage N4, we show that their biochemical features are similar to the phylogenetically divergent phage λ terminases. We also demonstrate using the Surface Plasma Resonance (SPR) technique that phage N4 TerL transiently interacts with TerS.

FunOrder: A robust and semi-automated method for the identification of essential biosynthetic genes through computational molecular co-evolution

Secondary metabolites (SMs) are a vast group of compounds with different structures and properties that have been utilized as drugs, food additives, dyes, and as monomers for novel plastics. In many cases, the biosynthesis of SMs is catalysed by enzymes whose corresponding genes are co-localized in the genome in biosynthetic gene clusters (BGCs). Notably, BGCs may contain so-called gap genes, that are not involved in the biosynthesis of the SM. Current genome mining tools can identify BGCs, but they have problems with distinguishing essential genes from gap genes. This can and must be done by expensive, laborious, and time-consuming comparative genomic approaches or transcriptome analyses. In this study, we developed a method that allows semi-automated identification of essential genes in a BGC based on co-evolution analysis. To this end, the protein sequences of a BGC are blasted against a suitable proteome database. For each protein, a phylogenetic tree is created. The trees are compared by treeKO to detect co-evolution. The results of this comparison are visualized in different output formats, which are compared visually. Our results suggest that co-evolution is commonly occurring within BGCs, albeit not all, and that especially those genes that encode for enzymes of the biosynthetic pathway are co-evolutionary linked and can be identified with FunOrder. In light of the growing number of genomic data available, this will contribute to the studies of BGCs in native hosts and facilitate heterologous expression in other organisms with the aim of the discovery of novel SMs.

The discovery and description of novel fungal secondary metabolites promises novel antibiotics, pharmaceuticals, and other useful compounds. A way to identify novel secondary metabolites is to express the corresponding genes in a suitable expression host. Consequently, a detailed knowledge or an accurate prediction of these genes is necessary. In fungi, the genes are co-localized in so-called biosynthetic gene clusters. Notably, the clusters may also contain genes that are not necessary for the biosynthesis of the secondary metabolites, so-called gap genes. We developed a method to detect co-evolved genes within the clusters and demonstrated that essential genes are co-evolving and can thus be differentiated from the gap genes. This adds an additional layer of information, which can support researchers with their decisions on which genes to study and express for the discovery of novel secondary metabolites.

The Role of Methionine Residues in the Regulation of Liquid-Liquid Phase Separation

Membraneless organelles are non-stoichiometric supramolecular structures in the micron scale. These structures can be quickly assembled/disassembled in a regulated fashion in response to specific stimuli. Membraneless organelles contribute to the spatiotemporal compartmentalization of the cell, and they are involved in diverse cellular processes often, but not exclusively, related to RNA metabolism. Liquid-liquid phase separation, a reversible event involving demixing into two distinct liquid phases, provides a physical framework to gain insights concerning the molecular forces underlying the process and how they can be tuned according to the cellular needs. Proteins able to undergo phase separation usually present a modular architecture, which favors a multivalency-driven demixing. We discuss the role of low complexity regions in establishing networks of intra- and intermolecular interactions that collectively control the phase regime. Post-translational modifications of the residues present in these domains provide a convenient strategy to reshape the residue-residue interaction networks that determine the dynamics of phase separation. Focus will be placed on those proteins with low complexity domains exhibiting a biased composition towards the amino acid methionine and the prominent role that reversible methionine sulfoxidation plays in the assembly/disassembly of biomolecular condensates.

Co-evolutionary landscape at the interface and non-interface regions of protein-protein interaction complexes

Proteins involved in interactions throughout the course of evolution tend to co-evolve and compensatory changes may occur in interacting proteins to maintain or refine such interactions. However, certain residue pair alterations may prove to be detrimental for functional interactions. Hence, determining co-evolutionary pairings that could be structurally or functionally relevant for maintaining the conservation of an inter-protein interaction is important. Inter-protein co-evolution analysis in several complexes utilizing multiple existing methodologies suggested that co-evolutionary pairings can occur in spatially proximal and distant regions in inter-protein interactions. Subsequently, the Co-Var (Correlated Variation) method based on mutual information and Bhattacharyya coefficient was developed, validated, and found to perform relatively better than CAPS and EV-complex. Interestingly, while applying the Co-Var measure and EV-complex program on a set of protein-protein interaction complexes, co-evolutionary pairings were obtained in interface and non-interface regions in protein complexes. The Co-Var approach involves determining high degree co-evolutionary pairings that include multiple co-evolutionary connections between particular co-evolved residue positions in one protein with multiple residue positions in the binding partner. Detailed analyses of high degree co-evolutionary pairings in protein-protein complexes involved in cancer metastasis suggested that most of the residue positions forming such co-evolutionary connections mainly occurred within functional domains of constituent proteins and substitution mutations were also common among these positions. The physiological relevance of these predictions suggested that Co-Var can predict residues that could be crucial for preserving functional protein-protein interactions. Finally, Co-Var web server (http://www.hpppi.iicb.res.in/ishi/covar/index.html) that implements this methodology identifies co-evolutionary pairings in intra and inter-protein interactions.

Phylogenetic Permulations: A Statistically Rigorous Approach to Measure Confidence in Associations in a Phylogenetic Context

Many evolutionary comparative methods seek to identify associations between phenotypic traits or between traits and genotypes, often with the goal of inferring potential functional relationships between them. Comparative genomics methods aimed at this goal measure the association between evolutionary changes at the genetic level with traits evolving convergently across phylogenetic lineages. However, these methods have complex statistical behaviors that are influenced by nontrivial and oftentimes unknown confounding factors. Consequently, using standard statistical analyses in interpreting the outputs of these methods leads to potentially inaccurate conclusions. Here, we introduce phylogenetic permulations, a novel statistical strategy that combines phylogenetic simulations and permutations to calculate accurate, unbiased P values from phylogenetic methods. Permulations construct the null expectation for P values from a given phylogenetic method by empirically generating null phenotypes. Subsequently, empirical P values that capture the true statistical confidence given the correlation structure in the data are directly calculated based on the empirical null expectation. We examine the performance of permulation methods by analyzing both binary and continuous phenotypes, including marine, subterranean, and long-lived large-bodied mammal phenotypes. Our results reveal that permulations improve the statistical power of phylogenetic analyses and correctly calibrate statements of confidence in rejecting complex null distributions while maintaining or improving the enrichment of known functions related to the phenotype. We also find that permulations refine pathway enrichment analyses by correcting for nonindependence in gene ranks. Our results demonstrate that permulations are a powerful tool for improving statistical confidence in the conclusions of phylogenetic analysis when the parametric null is unknown.

Protein Complexes Form a Basis for Complex Hybrid Incompatibility

Proteins are the workhorses of the cell and execute many of their functions by interacting with other proteins forming protein complexes. Multi-protein complexes are an admixture of subunits, change their interaction partners, and modulate their functions and cellular physiology in response to environmental changes. When two species mate, the hybrid offspring are usually inviable or sterile because of large-scale differences in the genetic makeup between the two parents causing incompatible genetic interactions. Such reciprocal-sign epistasis between inter-specific alleles is not limited to incompatible interactions between just one gene pair; and, usually involves multiple genes. Many of these multi-locus incompatibilities show visible defects, only in the presence of all the interactions, making it hard to characterize. Understanding the dynamics of protein-protein interactions (PPIs) leading to multi-protein complexes is better suited to characterize multi-locus incompatibilities, compared to studying them with traditional approaches of genetics and molecular biology. The advances in omics technologies, which includes genomics, transcriptomics, and proteomics can help achieve this end. This is especially relevant when studying non-model organisms. Here, we discuss the recent progress in the understanding of hybrid genetic incompatibility; omics technologies, and how together they have helped in characterizing protein complexes and in turn multi-locus incompatibilities. We also review advances in bioinformatic techniques suitable for this purpose and propose directions for leveraging the knowledge gained from model-organisms to identify genetic incompatibilities in non-model organisms.

Systematic prediction of genes functionally associated with bacterial retrons and classification of the encoded tripartite systems

Bacterial retrons consist of a reverse transcriptase (RT) and a contiguous non-coding RNA (ncRNA) gene. One third of annotated retrons carry additional open reading frames (ORFs), the contribution and significance of which in retron biology remains to be determined. In this study we developed a computational pipeline for the systematic prediction of genes specifically associated with retron RTs based on a previously reported large dataset representative of the diversity of prokaryotic RTs. We found that retrons generally comprise a tripartite system composed of the ncRNA, the RT and an additional protein or RT-fused domain with diverse enzymatic functions. These retron systems are highly modular, and their components have coevolved to different extents. Based on the additional module, we classified retrons into 13 types, some of which include additional variants. Our findings provide a basis for future studies on the biological function of retrons and for expanding their biotechnological applications.

Co-complex protein membership evaluation using Maximum Entropy on GO ontology and InterPro annotation

Motivation

Protein–protein interactions (PPI) play a crucial role in our understanding of protein function and biological processes. The standardization and recording of experimental findings is increasingly stored in ontologies, with the Gene Ontology (GO) being one of the most successful projects. Several PPI evaluation algorithms have been based on the application of probabilistic frameworks or machine learning algorithms to GO properties. Here, we introduce a new training set design and machine learning based approach that combines dependent heterogeneous protein annotations from the entire ontology to evaluate putative co-complex protein interactions determined by empirical studies.

Results

PPI annotations are built combinatorically using corresponding GO terms and InterPro annotation. We use a S.cerevisiae high-confidence complex dataset as a positive training set. A series of classifiers based on Maximum Entropy and support vector machines (SVMs), each with a composite counterpart algorithm, are trained on a series of training sets. These achieve a high performance area under the ROC curve of ≤0.97, outperforming go2ppi—a previously established prediction tool for protein-protein interactions (PPI) based on Gene Ontology (GO) annotations.

Availability and implementation

https://github.com/ima23/maxent-ppi

Supplementary information

Supplementary data are available at Bioinformatics online.

Characterizing human genomic coevolution in locus-gene regulatory interactions

Background

Coevolution has been used to identify and predict interactions and functional relationships between proteins of many different organisms including humans. Current efforts in annotating the human genome increasingly show that non-coding DNA sequence has important functional and regulatory interactions. Furthermore, regulatory elements do not necessarily reside in close proximity of the coding region for their target genes.

Results

We characterize coevolution as it appears in locus-gene interactions in the human genome, focusing on expression Quantitative Trait - Locus (eQTL) interactions. Our results show that in these interactions the conservation status of the loci is predictive of the conservation status of their target genes. Furthermore, comparing the phylogenetic histories of intra-chromosomal pairs of loci and transcription start sites, we find that pairs that appear coevolved are enriched for cis-eQTL interactions. Exploring this property we found that coevolution might be useful in prioritizing association tests in cis-eQTL detection.

Conclusions

The relationship between the conservation status of pairs of loci and protein coding transcription start sites reveal correlations with regulatory interactions. Pairs that appear coevolved are enriched for intra-chromosomal regulatory interactions, thus our results suggest that measures of coevolution can be useful for prediction and detection of new interactions. Measures of coevolution are genome-wide and could potentially be used to prioritize the detection of distant or inter-chromosomal interactions such as trans-eQTL interactions in the human genome.

TIC236 links the outer and inner membrane translocons of the chloroplast

The two-membrane envelope is a defining feature of chloroplasts. Chloroplasts evolved from a Gram-negative cyanobacterial endosymbiont. During evolution, genes of the endosymbiont have been transferred to the host nuclear genome. Most chloroplast proteins are synthesized in the cytosol as higher-molecular-mass preproteins with an N-terminal transit peptide. Preproteins are transported into chloroplasts by the TOC and TIC (translocons at the outer- and inner-envelope membranes of chloroplasts, respectively) machineries1,2, but how TOC and TIC are assembled together is unknown. Here we report the identification of the TIC component TIC236; TIC236 is an integral inner-membrane protein that projects a 230-kDa domain into the intermembrane space, which binds directly to the outer-membrane channel TOC75. The knockout mutation of TIC236 is embryonically lethal. In TIC236-knockdown mutants, a smaller amount of the inner-membrane channel TIC20 was associated with TOC75; the amount of TOC-TIC supercomplexes was also reduced. This resulted in a reduced import rate into the stroma, though outer-membrane protein insertion was unaffected. The size and the essential nature of TIC236 indicate that-unlike in mitochondria, in which the outer- and inner-membrane translocons exist as separate complexes and a supercomplex is only transiently assembled during preprotein translocation3,4-a long and stable protein bridge in the intermembrane space is required for protein translocation into chloroplasts. Furthermore, TIC236 and TOC75 are homologues of bacterial inner-membrane TamB5 and outer-membrane BamA, respectively. Our evolutionary analyses show that, similar to TOC75, TIC236 is preserved only in plants and has co-evolved with TOC75 throughout the plant lineage. This suggests that the backbone of the chloroplast protein-import machinery evolved from the bacterial TamB-BamA protein-secretion system.

Inferring Methionine Sulfoxidation and serine Phosphorylation crosstalk from Phylogenetic analyses

Background

The sulfoxidation of methionine residues within the phosphorylation motif of protein kinase substrates, may provide a mechanism to couple oxidative signals to changes in protein phosphorylation. Herein, we hypothesize that if the residues within a pair of phosphorylatable-sulfoxidable sites are functionally linked, then they might have been coevolving. To test this hypothesis a number of site pairs previously detected on human stress-related proteins has been subjected to analysis using eukaryote ortholog sequences and a phylogenetic approach.

Results

Overall, the results support the conclusion that in the eIF2α protein, serine phosphorylation at position 218 and methionine oxidation at position 222, belong to the same functional network. First, the observed data were much better fitted by Markovian models that assumed coevolution of both sites, with respect to their counterparts assuming independent evolution (p-value = 0.003). Second, this conclusion was robust with respect to the methods used to reconstruct the phylogenetic relationship between the 233 eukaryotic species analyzed. Third, the co-distribution of phosphorylatable and sulfoxidable residues at these positions showed multiple origins throughout the evolution of eukaryotes, which further supports the view of an adaptive value for this co-occurrence. Fourth, the possibility that the coevolution of these two sites might be due to structure-driven compensatory mutations was evaluated. The results suggested that factors other than those merely structural were behind the observed coevolution. Finally, the relationship detected between other modifiable site pairs from ataxin-2 (S814-M815), ataxin-2-like (S211-M215) and Pumilio homolog 1 (S124-M125), reinforce the view of a role for phosphorylation-sulfoxidation crosstalk.

Conclusions

For the four stress-related proteins analyzed herein, their respective pairs of PTM sites (phosphorylatable serine and sulfoxidable methionine) were found to be evolving in a correlated fashion, which suggests a relevant role for methionine sulfoxidation and serine phosphorylation crosstalk in the control of protein translation under stress conditions.

Electronic supplementary material

The online version of this article (doi:10.1186/s12862-017-1017-9) contains supplementary material, which is available to authorized users.

Exploiting sequence and stability information for directing nanobody stability engineering

BACKGROUND

Variable domains of camelid heavy-chain antibodies, commonly named nanobodies, have high biotechnological potential. In view of their broad range of applications in research, diagnostics and therapy, engineering their stability is of particular interest. One important aspect is the improvement of thermostability, because it can have immediate effects on conformational stability, protease resistance and aggregation propensity of the protein.

METHODS

We analyzed the sequences and thermostabilities of 78 purified nanobody binders. From this data, potentially stabilizing amino acid variations were identified and studied experimentally.

RESULTS

Some mutations improved the stability of nanobodies by up to 6.1°C, with an average of 2.3°C across eight modified nanobodies. The stabilizing mechanism involves an improvement of both conformational stability and aggregation behavior, explaining the variable degree of stabilization in individual molecules. In some instances, variations predicted to be stabilizing actually led to thermal destabilization of the proteins. The reasons for this contradiction between prediction and experiment were investigated.

CONCLUSIONS

The results reveal a mutational strategy to improve the biophysical behavior of nanobody binders and indicate a species-specificity of nanobody architecture.

GENERAL SIGNIFICANCE

This study illustrates the potential and limitations of engineering nanobody thermostability by merging sequence information with stability data, an aspect that is becoming increasingly important with the recent development of high-throughput biophysical methods.

Coevolution of RAC Small GTPases and their Regulators GEF Proteins

RAC proteins are small GTPases involved in important cellular processes in eukaryotes, and their deregulation may contribute to cancer. Activation of RAC proteins is regulated by DOCK and DBL protein families of guanine nucleotide exchange factors (GEFs). Although DOCK and DBL proteins act as GEFs on RAC proteins, DOCK and DBL family members are evolutionarily unrelated. To understand how DBL and DOCK families perform the same function on RAC proteins despite their unrelated primary structure, phylogenetic analyses of the RAC, DBL, and DOCK families were implemented, and interaction patterns that may suggest a coevolutionary process were searched. Interestingly, while RAC and DOCK proteins are very well conserved in humans and among eukaryotes, DBL proteins are highly divergent. Moreover, correlation analyses of the phylogenetic distances of RAC and GEF proteins and covariation analyses between residues in the interacting domains showed significant coevolution rates for both RAC–DOCK and RAC–DBL interactions.

Detection of significant protein coevolution

MOTIVATION

The evolution of proteins cannot be fully understood without taking into account the coevolutionary linkages entangling them. From a practical point of view, coevolution between protein families has been used as a way of detecting protein interactions and functional relationships from genomic information. The most common approach to inferring protein coevolution involves the quantification of phylogenetic tree similarity using a family of methodologies termed mirrortree. In spite of their success, a fundamental problem of these approaches is the lack of an adequate statistical framework to assess the significance of a given coevolutionary score (tree similarity). As a consequence, a number of ad hoc filters and arbitrary thresholds are required in an attempt to obtain a final set of confident coevolutionary signals.

RESULTS

In this work, we developed a method for associating confidence estimators (P values) to the tree-similarity scores, using a null model specifically designed for the tree comparison problem. We show how this approach largely improves the quality and coverage (number of pairs that can be evaluated) of the detected coevolution in all the stages of the mirrortree workflow, independently of the starting genomic information. This not only leads to a better understanding of protein coevolution and its biological implications, but also to obtain a highly reliable and comprehensive network of predicted interactions, as well as information on the substructure of macromolecular complexes using only genomic information.

AVAILABILITY AND IMPLEMENTATION

The software and datasets used in this work are freely available at: http://csbg.cnb.csic.es/pMT/.

CONTACT

pazos@cnb.csic.es

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.