Coevolutionary Analysis of Protein Subfamilies by Sequence Reweighting.
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ABSTRACT:
SUBMITTER: Malinverni D
PROVIDER: S-EPMC6992422 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Recent developments in global statistical methodologies have advanced the analysis of large collections of protein sequences for coevolutionary information. Coevolution between amino acids in a protein arises from compensatory mutations that are needed to maintain the stability or function of a protein over the course of evolution. This gives rise to quantifiable correlations between amino acid sites within the multiple sequence alignment of a protein family. Here, we use the maximum entropy-based approach called mean field Direct Coupling Analysis (mfDCA) to infer a Potts model Hamiltonian governing the correlated mutations in a protein family. We use the inferred pairwise statistical couplings to generate the sequence-dependent heterogeneous interaction energies of a structure-based model (SBM) where only native contacts are considered. Considering the ribosomal S6 protein and its circular permutants as well as the SH3 protein, we demonstrate that these models quantitatively agree with experimental data on folding mechanisms. This work serves as a new framework for generating coevolutionary data-enriched models that can potentially be used to engineer key functional motions and novel interactions in protein systems.
| S-EPMC4815312 | biostudies-literature
Project description:BACKGROUND:The polyamine oxidases (PAOs) catabolize the oxidative deamination of the polyamines (PAs) spermine (Spm) and spermidine (Spd). Most of the phylogenetic studies performed to analyze the plant PAO family took into account only a limited number and/or taxonomic representation of plant PAOs sequences. RESULTS:Here, we constructed a plant PAO protein sequence database and identified four subfamilies. Subfamily PAO back conversion 1 (PAObc1) was present on every lineage included in these analyses, suggesting that BC-type PAOs might play an important role in plants, despite its precise function is unknown. Subfamily PAObc2 was exclusively present in vascular plants, suggesting that t-Spm oxidase activity might play an important role in the development of the vascular system. The only terminal catabolism (TC) PAO subfamily (subfamily PAOtc) was lost in Superasterids but it was present in all other land plants. This indicated that the TC-type reactions are fundamental for land plants and that their function could being taken over by other enzymes in Superasterids. Subfamily PAObc3 was the result of a gene duplication event preceding Angiosperm diversification, followed by a gene extinction in Monocots. Differential conserved protein motifs were found for each subfamily of plant PAOs. The automatic assignment using these motifs was found to be comparable to the assignment by rough clustering performed on this work. CONCLUSIONS:The results presented in this work revealed that plant PAO family is bigger than previously conceived. Also, they delineate important background information for future specific structure-function and evolutionary investigations and lay a foundation for the deeper characterization of each plant PAO subfamily.
| S-EPMC6341606 | biostudies-literature
Project description:The interaction between the Heat Shock Proteins 70 and 40 is at the core of the ATPase regulation of the chaperone machinery that maintains protein homeostasis. However, the structural details of the interaction remain elusive and contrasting models have been proposed for the transient Hsp70/Hsp40 complexes. Here we combine molecular simulations based on both coarse-grained and atomistic models with coevolutionary sequence analysis to shed light on this problem by focusing on the bacterial DnaK/DnaJ system. The integration of these complementary approaches resulted in a novel structural model that rationalizes previous experimental observations. We identify an evolutionarily conserved interaction surface formed by helix II of the DnaJ J-domain and a structurally contiguous region of DnaK, involving lobe IIA of the nucleotide binding domain, the inter-domain linker, and the β-basket of the substrate binding domain.
| S-EPMC5519331 | biostudies-literature
Project description:MotivationAssigning new sequences to known protein families and subfamilies is a prerequisite for many functional, comparative and evolutionary genomics analyses. Such assignment is commonly achieved by looking for the closest sequence in a reference database, using a method such as BLAST. However, ignoring the gene phylogeny can be misleading because a query sequence does not necessarily belong to the same subfamily as its closest sequence. For example, a hemoglobin which branched out prior to the hemoglobin alpha/beta duplication could be closest to a hemoglobin alpha or beta sequence, whereas it is neither. To overcome this problem, phylogeny-driven tools have emerged but rely on gene trees, whose inference is computationally expensive.ResultsHere, we first show that in multiple animal and plant datasets, 18 to 62% of assignments by closest sequence are misassigned, typically to an over-specific subfamily. Then, we introduce OMAmer, a novel alignment-free protein subfamily assignment method, which limits over-specific subfamily assignments and is suited to phylogenomic databases with thousands of genomes. OMAmer is based on an innovative method using evolutionarily-informed k-mers for alignment-free mapping to ancestral protein subfamilies. Whilst able to reject non-homologous family-level assignments, we show that OMAmer provides better and quicker subfamily-level assignments than approaches relying on the closest sequence, whether inferred exactly by Smith-Waterman or by the fast heuristic DIAMOND.AvailabilityOMAmer is available from the Python Package Index (as omamer), with the source code and a precomputed database available at https://github.com/DessimozLab/omamer.Supplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC8479680 | biostudies-literature
Project description:Protein-protein interactions play a central role in cellular function. Improving the understanding of complex formation has many practical applications, including the rational design of new therapeutic agents and the mechanisms governing signal transduction networks. The generally large, flat, and relatively featureless binding sites of protein complexes pose many challenges for drug design. Fragment docking and direct coupling analysis are used in an integrated computational method to estimate druggable protein-protein interfaces. (i) This method explores the binding of fragment-sized molecular probes on the protein surface using a molecular docking-based screen. (ii) The energetically favorable binding sites of the probes, called hot spots, are spatially clustered to map out candidate binding sites on the protein surface. (iii) A coevolution-based interface interaction score is used to discriminate between different candidate binding sites, yielding potential interfacial targets for therapeutic drug design. This approach is validated for important, well-studied disease-related proteins with known pharmaceutical targets, and also identifies targets that have yet to be studied. Moreover, therapeutic agents are proposed by chemically connecting the fragments that are strongly bound to the hot spots.
| S-EPMC5167203 | biostudies-literature
Project description:The replication of human immunodeficiency virus-1 (HIV-1) requires reverse transcription of the viral RNA genome and integration of newly synthesized pro-viral DNA into the host genome. This is mediated by the viral proteins reverse transcriptase (RT) and integrase (IN). The formation and stabilization of the pre-integration complex (PIC), which is an essential step for reverse transcription, nuclear import, chromatin targeting, and subsequent integration, involves direct and indirect modes of interaction between RT and IN proteins. While epitope-based treatments targeting IN-viral DNA and IN-RT complexes appear to be a promising combination for an anti-HIV treatment, the mechanisms of IN-RT interactions within the PIC are not well understood due to the transient nature of the protein complex and the intrinsic flexibility of its components. Here, we identify potentially interacting regions between the IN and RT proteins within the PIC through the coevolutionary analysis of amino acid sequences of the two proteins. Our results show that specific regions in the two proteins have strong coevolutionary signatures, suggesting that these regions either experience direct and prolonged interactions between them that require high affinity and/or specificity or that the regions are involved in interactions mediated by dynamic conformational changes and, hence, may involve both direct and indirect interactions. Other regions were found to exhibit weak, but positive correlations, implying interactions that are likely transient and/or have low affinity. We identified a series of specific regions of potential interactions between the IN and RT proteins (e.g., specific peptide regions within the C-terminal domain of IN were identified as potentially interacting with the Connection domain of RT). Coevolutionary analysis can serve as an important step in predicting potential interactions, thus informing experimental studies. These studies can be integrated with structural data to gain a better understanding of the mechanisms of HIV protein interactions.
| S-EPMC4854294 | biostudies-literature
Project description:Quantifying the relationship between protein sequence and structure is key to understanding the protein universe. A fundamental measure of this relationship is the total number of amino acid sequences that can fold to a target protein structure, known as the "sequence capacity," which has been suggested as a proxy for how designable a given protein fold is. Although sequence capacity has been extensively studied using lattice models and theory, numerical estimates for real protein structures are currently lacking. In this work, we have quantitatively estimated the sequence capacity of 10 proteins with a variety of different structures using a statistical model based on residue-residue co-evolution to capture the variation of sequences from the same protein family. Remarkably, we find that even for the smallest protein folds, such as the WW domain, the number of foldable sequences is extremely large, exceeding the Avogadro constant. In agreement with earlier theoretical work, the calculated sequence capacity is positively correlated with the size of the protein, or better, the density of contacts. This allows the absolute sequence capacity of a given protein to be approximately predicted from its structure. On the other hand, the relative sequence capacity, i.e., normalized by the total number of possible sequences, is an extremely tiny number and is strongly anti-correlated with the protein length. Thus, although there may be more foldable sequences for larger proteins, it will be much harder to find them. Lastly, we have correlated the evolutionary age of proteins in the CATH database with their sequence capacity as predicted by our model. The results suggest a trade-off between the opposing requirements of high designability and the likelihood of a novel fold emerging by chance.
| S-EPMC5647607 | biostudies-literature
Project description:BackgroundThe pyridine nucleotide disulfide reductase (PNDR) is a large and heterogeneous protein family divided into two classes (I and II), which reflect the divergent evolution of its characteristic disulfide redox active site. However, not all the PNDR members fit into these categories and this suggests the need of further studies to achieve a more comprehensive classification of this complex family.ResultsA workflow to improve the clusterization of protein families based on the array of linear conserved motifs is designed. The method is applied to the PNDR large family finding two main groups, which correspond to PNDR classes I and II. However, two other separate protein clusters, previously classified as class I in most databases, are outgrouped: the peroxide reductases (NAOX, NAPE) and the type II NADH dehydrogenases (NDH-2). In this way, two novel PNDR classes III and IV for NAOX/NAPE and NDH-2 respectively are proposed. By knowledge-driven biochemical and functional data analyses done on the new class IV, a linear array of motifs putatively related to Cu(II)-reductase activity is detected in a specific subset of NDH-2.ConclusionThe results presented are a novel contribution to the classification of the complex and large PNDR protein family, supporting its reclusterization into four classes. The linear array of motifs detected within the class IV PNDR subfamily could be useful as a signature for a particular subgroup of NDH-2.
| S-EPMC1847454 | biostudies-literature
Project description:Background: Amino acid exchanges within proteins sometimes compensate for one another and could therefore be co-evolved. It is essential to investigate the intricate relationship between the extent of coevolution and the evolutionary variability exerted at individual protein sites, as well as the whole protein. Methods: In this study, we have used a reliable set of coevolutionary connections (sites within 10Å spatial distance) and investigated their correlation with the evolutionary diversity within the respective protein sites. Results: Based on our observations, we propose an interesting hypothesis that higher numbers of coevolutionary connections are associated with lesser evolutionary variable protein sites, while higher numbers of the coevolutionary connections can be observed for a protein family that has higher evolutionary variability. Our findings also indicate that highly coevolved sites located in a solvent accessible state tend to be less evolutionary variable. This relationship reverts at the whole protein level where cytoplasmic and extracellular proteins show moderately higher anti-correlation between the number of coevolutionary connections and the average evolutionary conservation of the whole protein. Conclusions: Observations and hypothesis presented in this study provide intriguing insights towards understanding the critical relationship between coevolutionary and evolutionary changes observed within proteins. Our observations encourage further investigation to find out the reasons behind subtle variations in the relationship between coevolutionary connectivity and evolutionary diversity for proteins located at various cellular localizations and/or involved in different molecular-biological functions.
| S-EPMC5506539 | biostudies-literature
Project description:The goal of the 1000 Genomes Consortium is to characterize human genome structural variation (SV), including forms of copy number variations such as deletions, duplications, and insertions. Mobile element insertions, particularly Alu elements, are major contributors to genomic SV among humans. During the pilot phase of the project we experimentally validated 645 (611 intergenic and 34 exon targeted) polymorphic "young" Alu insertion events, absent from the human reference genome. Here, we report high resolution sequencing of 343 (322 unique) recent Alu insertion events, along with their respective target site duplications, precise genomic breakpoint coordinates, subfamily assignment, percent divergence, and estimated A-rich tail lengths. All the sequenced Alu loci were derived from the AluY lineage with no evidence of retrotransposition activity involving older Alu families (e.g., AluJ and AluS). AluYa5 is currently the most active Alu subfamily in the human lineage, followed by AluYb8, and many others including three newly identified subfamilies we have termed AluYb7a3, AluYb8b1, and AluYa4a1. This report provides the structural details of 322 unique Alu variants from individual human genomes collectively adding about 100 kb of genomic variation. Many Alu subfamilies are currently active in human populations, including a surprising level of AluY retrotransposition. Human Alu subfamilies exhibit continuous evolution with potential drivers sprouting new Alu lineages.
| S-EPMC4607524 | biostudies-literature