Modeling Sequence-Space Exploration and Emergence of Epistatic Signals in Protein Evolution.
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ABSTRACT:
SUBMITTER: Bisardi M
PROVIDER: S-EPMC8789065 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:MotivationProtein homology detection, a fundamental problem in computational biology, is an indispensable step toward predicting protein structures and understanding protein functions. Despite the advances in recent decades on sequence alignment, threading and alignment-free methods, protein homology detection remains a challenging open problem. Recently, network methods that try to find transitive paths in the protein structure space demonstrate the importance of incorporating network information of the structure space. Yet, current methods merge the sequence space and the structure space into a single space, and thus introduce inconsistency in combining different sources of information.MethodWe present a novel network-based protein homology detection method, CMsearch, based on cross-modal learning. Instead of exploring a single network built from the mixture of sequence and structure space information, CMsearch builds two separate networks to represent the sequence space and the structure space. It then learns sequence-structure correlation by simultaneously taking sequence information, structure information, sequence space information and structure space information into consideration.ResultsWe tested CMsearch on two challenging tasks, protein homology detection and protein structure prediction, by querying all 8332 PDB40 proteins. Our results demonstrate that CMsearch is insensitive to the similarity metrics used to define the sequence and the structure spaces. By using HMM-HMM alignment as the sequence similarity metric, CMsearch clearly outperforms state-of-the-art homology detection methods and the CASP-winning template-based protein structure prediction methods.Availability and implementationOur program is freely available for download from http://sfb.kaust.edu.sa/Pages/Software.aspxContact: xin.gao@kaust.edu.saSupplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC4908355 | biostudies-literature
Project description:The amino acid sequence of a protein affects both its structure and its function. Thus, the ability to modify the sequence, and hence the structure and activity, of individual proteins in a systematic way, opens up many opportunities, both scientifically and (as we focus on here) for exploitation in biocatalysis. Modern methods of synthetic biology, whereby increasingly large sequences of DNA can be synthesised de novo, allow an unprecedented ability to engineer proteins with novel functions. However, the number of possible proteins is far too large to test individually, so we need means for navigating the 'search space' of possible protein sequences efficiently and reliably in order to find desirable activities and other properties. Enzymologists distinguish binding (Kd) and catalytic (kcat) steps. In a similar way, judicious strategies have blended design (for binding, specificity and active site modelling) with the more empirical methods of classical directed evolution (DE) for improving kcat (where natural evolution rarely seeks the highest values), especially with regard to residues distant from the active site and where the functional linkages underpinning enzyme dynamics are both unknown and hard to predict. Epistasis (where the 'best' amino acid at one site depends on that or those at others) is a notable feature of directed evolution. The aim of this review is to highlight some of the approaches that are being developed to allow us to use directed evolution to improve enzyme properties, often dramatically. We note that directed evolution differs in a number of ways from natural evolution, including in particular the available mechanisms and the likely selection pressures. Thus, we stress the opportunities afforded by techniques that enable one to map sequence to (structure and) activity in silico, as an effective means of modelling and exploring protein landscapes. Because known landscapes may be assessed and reasoned about as a whole, simultaneously, this offers opportunities for protein improvement not readily available to natural evolution on rapid timescales. Intelligent landscape navigation, informed by sequence-activity relationships and coupled to the emerging methods of synthetic biology, offers scope for the development of novel biocatalysts that are both highly active and robust.
| S-EPMC4349129 | biostudies-literature
Project description:Translational arrest peptides (APs) are short stretches of polypeptides that induce translational stalling when synthesized on a ribosome. Mechanical pulling forces acting on the nascent chain can weaken or even abolish stalling. APs can therefore be used as in vivo force sensors, making it possible to measure the forces that act on a nascent chain during translation with single-residue resolution. It is also possible to score the relative strengths of APs by subjecting them to a given pulling force and ranking them according to stalling efficiency. Using the latter approach, we now report an extensive mutagenesis scan of a strong mutant variant of the Mannheimia succiniciproducens SecM AP and identify mutations that further increase the stalling efficiency. Combining three such mutations, we designed an AP that withstands the strongest pulling force we are able to generate at present. We further show that diproline stretches in a nascent protein act as very strong APs when translation is carried out in the absence of elongation factor P. Our findings highlight critical residues in APs, show that certain amino acid sequences induce very strong translational arrest and provide a toolbox of APs of varying strengths that can be used for in vivo force measurements.
| S-EPMC4400336 | biostudies-literature
Project description:The currently known protein sequences are not distributed equally in sequence space, but cluster into families. Analyzing the cluster size distribution gives a glimpse of the large and unknown extant protein sequence space, which has been explored during evolution. For six protein superfamilies with different fold and function, the cluster size distributions followed a power law with slopes between 2.4 and 3.3, which represent upper limits to the cluster distribution of extant sequences. The power law distribution of cluster sizes is in accordance with percolation theory and strongly supports connectedness of extant sequence space. Percolation of extant sequence space has three major consequences: (1) It transforms our view of sequence space as a highly connected network where each sequence has multiple neighbors, and each pair of sequences is connected by many different paths. A high degree of connectedness is a necessary condition of efficient evolution, because it overcomes the possible blockage by sign epistasis and reciprocal sign epistasis. (2) The Fisher exponent is an indicator of connectedness and saturation of sequence space of each protein superfamily. (3) All clusters are expected to be connected by extant sequences that become apparent as a higher portion of extant sequence space becomes known. Being linked to biochemically distinct homologous families, bridging sequences are promising enzyme candidates for applications in biotechnology because they are expected to have substrate ambiguity or catalytic promiscuity.
| S-EPMC5738032 | biostudies-literature
Project description:BackgroundGene finding is a complicated procedure that encapsulates algorithms for coding sequence modeling, identification of promoter regions, issues concerning overlapping genes and more. In the present study we focus on coding sequence modeling algorithms; that is, algorithms for identification and prediction of the actual coding sequences from genomic DNA. In this respect, we promote a novel multivariate method known as Canonical Powered Partial Least Squares (CPPLS) as an alternative to the commonly used Interpolated Markov model (IMM). Comparisons between the methods were performed on DNA, codon and protein sequences with highly conserved genes taken from several species with different genomic properties.ResultsThe multivariate CPPLS approach classified coding sequence substantially better than the commonly used IMM on the same set of sequences. We also found that the use of CPPLS with codon representation gave significantly better classification results than both IMM with protein (p < 0.001) and with DNA (p < 0.001). Further, although the mean performance was similar, the variation of CPPLS performance on codon representation was significantly smaller than for IMM (p < 0.001).ConclusionsThe performance of coding sequence modeling can be substantially improved by using an algorithm based on the multivariate CPPLS method applied to codon or DNA frequencies.
| S-EPMC3473301 | biostudies-literature
Project description:The Lactamase Engineering Database (www.LacED.uni-stuttgart.de) was developed to facilitate the classification and analysis of TEM β-lactamases. The current version contains 474 TEM variants. Two hundred fifty-nine variants form a large scale-free network of highly connected point mutants. The network was divided into three subnetworks which were enriched by single phenotypes: one network with predominantly 2be and two networks with 2br phenotypes. Fifteen positions were found to be highly variable, contributing to the majority of the observed variants. Since it is expected that a considerable fraction of the theoretical sequence space is functional, the currently sequenced 474 variants represent only the tip of the iceberg of functional TEM β-lactamase variants which form a huge natural reservoir of highly interconnected variants. Almost 50% of the variants are part of a quartet. Thus, two single mutations that result in functional enzymes can be combined into a functional protein. Most of these quartets consist of the same phenotype, or the mutations are additive with respect to the phenotype. By predicting quartets from triplets, 3,916 unknown variants were constructed. Eighty-seven variants complement multiple quartets and therefore have a high probability of being functional. The construction of a TEM β-lactamase network and subsequent analyses by clustering and quartet prediction are valuable tools to gain new insights into the viable sequence space of TEM β-lactamases and to predict their phenotype. The highly connected sequence space of TEM β-lactamases is ideally suited to network analysis and demonstrates the strengths of network analysis over tree reconstruction methods.
| S-EPMC4862526 | biostudies-literature
Project description:Rapidly evolving microbes are a challenge to model because of the volatile, complex, and dynamic nature of their populations. We developed the DISSEQT pipeline (DIStribution-based SEQuence space Time dynamics) for analyzing, visualizing, and predicting the evolution of heterogeneous biological populations in multidimensional genetic space, suited for population-based modeling of deep sequencing and high-throughput data. The pipeline is openly available on GitHub (https://github.com/rasmushenningsson/DISSEQT.jl, accessed 23 June 2019) and Synapse (https://www.synapse.org/#!Synapse: syn11425758, accessed 23 June 2019), covering the entire workflow from read alignment to visualization of results. Our pipeline is centered around robust dimension and model reduction algorithms for analysis of genotypic data with additional capabilities for including phenotypic features to explore dynamic genotype-phenotype maps. We illustrate its utility and capacity with examples from evolving RNA virus populations, which present one of the highest degrees of genetic heterogeneity within a given population found in nature. Using our pipeline, we empirically reconstruct the evolutionary trajectories of evolving populations in sequence space and genotype-phenotype fitness landscapes. We show that while sequence space is vastly multidimensional, the relevant genetic space of evolving microbial populations is of intrinsically low dimension. In addition, evolutionary trajectories of these populations can be faithfully monitored to identify the key minority genotypes contributing most to evolution. Finally, we show that empirical fitness landscapes, when reconstructed to include minority variants, can predict phenotype from genotype with high accuracy.
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Project description:Models of protein evolution tend to ignore functional constraints, although structural constraints are sometimes incorporated. Here we propose a probabilistic framework for codon substitution that evaluates joint effects of relative solvent accessibility (RSA), a structural constraint; and gene expression, a functional constraint. First, we explore the relationship between RSA and codon usage at the genomic scale as well as at the individual gene scale. Motivated by these results, we construct our framework by determining how probable is an amino acid, given RSA and gene expression, and then evaluating the relative probability of observing a codon compared to other synonymous codons. We come to the biologically plausible conclusion that both RSA and gene expression are related to amino acid frequencies, but, among synonymous codons, the relative probability of a particular codon is more closely related to gene expression than RSA. To illustrate the potential applications of our framework, we propose a new codon substitution model. Using this model, we obtain estimates of 2N s, the product of effective population size N, and relative fitness difference of allele s. For a training data set consisting of human proteins with known structures and expression data, 2N s is estimated separately for synonymous and nonsynonymous substitutions in each protein. We then contrast the patterns of synonymous and nonsynonymous 2N s estimates across proteins while also taking gene expression levels of the proteins into account. We conclude that our 2N s estimates are too concentrated around 0, and we discuss potential explanations for this lack of variability.
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Project description:The mechanisms of viral RNA genome segmentation are unknown. On extensive passage of foot-and-mouth disease virus in baby hamster kidney-21 cells, the virus accumulated multiple point mutations and underwent a transition akin to genome segmentation. The standard single RNA genome molecule was replaced by genomes harboring internal in-frame deletions affecting the L- or capsid-coding region. These genomes were infectious and killed cells by complementation. Here we show that the point mutations in the nonstructural protein-coding region (P2, P3) that accumulated in the standard genome before segmentation increased the relative fitness of the segmented version relative to the standard genome. Fitness increase was documented by intracellular expression of virus-coded proteins and infectious progeny production by RNAs with the internal deletions placed in the sequence context of the parental and evolved genome. The complementation activity involved several viral proteins, one of them being the leader proteinase L. Thus, a history of genetic drift with accumulation of point mutations was needed to allow a major variation in the structure of a viral genome. Thus, exploration of sequence space by a viral genome (in this case an unsegmented RNA) can reach a point of the space in which a totally different genome structure (in this case, a segmented RNA) is favored over the form that performed the exploration.
| S-EPMC4020086 | biostudies-literature
Project description:Sequence similarity/database searching is a cornerstone of molecular biology. PairsDB is a database intended to make exploring protein sequences and their similarity relationships quick and easy. Behind PairsDB is a comprehensive collection of protein sequences and BLAST and PSI-BLAST alignments between them. Instead of running BLAST or PSI-BLAST individually on each request, results are retrieved instantaneously from a database of pre-computed alignments. Filtering options allow you to find a set of sequences satisfying a set of criteria-for example, all human proteins with solved structure and without transmembrane segments. PairsDB is continually updated and covers all sequences in Uniprot. The data is stored in a MySQL relational database. Data files will be made available for download at ftp://nic.funet.fi/pub/sci/molbio. PairsDB can also be accessed interactively at http://pairsdb.csc.fi. PairsDB data is a valuable platform to build various downstream automated analysis pipelines. For example, the graph of all-against-all similarity relationships is the starting point for clustering protein families, delineating domains, improving alignment accuracy by consistency measures, and defining orthologous genes. Moreover, query-anchored stacked sequence alignments, profiles and consensus sequences are useful in studies of sequence conservation patterns for clues about possible functional sites.
| S-EPMC2238971 | biostudies-literature