The Role of Evolutionary Selection in the Dynamics of Protein Structure Evolution

An immunoinformatics approach for a potential NY-ESO-1 and WT1 based multi-epitope vaccine designing against triple-negative breast cancer

Breast cancer emerges as one of the most prevalent malignancies in women, its incidence showing a concerning upward trend. Among the diverse array of breast cancer subtypes, triple-negative breast cancer (TNBC) assumes notable significance, due to lack of estrogen, progesterone, and HER-2 receptors. More focus has to be placed on creating effective therapy due to the high prevalence and rising incidence of TNBC. Currently, conventional passive treatments have several drawbacks that have not yet been resolved. On the other hand, as innovative immunotherapy approaches, cancer vaccines have offered promising prospects in combatting advanced stages of TNBC. Therefore, the main objective of this study was to utilize WT1 and NY-ESO-1 antigenic proteins in designing a multiepitope vaccine against TNBC. Initially, to generate robust immune responses, we identified antigenic epitopes of both proteins and assessed their immunogenicity. In order to reduce junctional immunogenicity, promiscuous epitopes were joined using the suitable adjuvant (50S ribosomal L7/L12 protein) and incorporated appropriate linkers (GPGPG, AAY, and EAAAK). The best predicted 3D model was refined and validated to achieve an excellent 3D model. Molecular docking analysis and dynamic simulation were conducted to demonstrate the structural stability and integrity of the vaccine/TLR-4 complex. Finally, the vaccine was cloned into the vector pET28 (+). Thus, analysis of the constructed vaccine through immunoinformatics indicates its capability to elicit robust humoral and cellular immune responses in the targeted organism. As such, it holds promise as a therapeutic weapon against TNBC and may open doors for further research in the field.

Persistent homology reveals strong phylogenetic signal in 3D protein structures

Changes that occur in proteins over time provide a phylogenetic signal that can be used to decipher their evolutionary history and the relationships between organisms. Sequence comparison is the most common way to access this phylogenetic signal, while those based on 3D structure comparisons are still in their infancy. In this study, we propose an effective approach based on Persistent Homology Theory (PH) to extract the phylogenetic information contained in protein structures. PH provides efficient and robust algorithms for extracting and comparing geometric features from noisy datasets at different spatial resolutions. PH has a growing number of applications in the life sciences, including the study of proteins (e.g. classification, folding). However, it has never been used to study the phylogenetic signal they may contain. Here, using 518 protein families, representing 22,940 protein sequences and structures, from 10 major taxonomic groups, we show that distances calculated with PH from protein structures correlate strongly with phylogenetic distances calculated from protein sequences, at both small and large evolutionary scales. We test several methods for calculating PH distances and propose some refinements to improve their relevance for addressing evolutionary questions. This work opens up new perspectives in evolutionary biology by proposing an efficient way to access the phylogenetic signal contained in protein structures, as well as future developments of topological analysis in the life sciences.

Integrative approaches for characterizing protein dynamics: NMR, CryoEM, and computer simulations

Proteins are inherently dynamic and their internal motions are essential for biological function. Protein motions cover a broad range of timescales: 10-14-10 s, spanning from sub-picosecond vibrational motions of atoms via microsecond loop conformational rearrangements to millisecond large amplitude domain reorientations. Observing protein dynamics over all timescales and connecting motions and structure to biological mechanisms requires integration of multiple experimental and computational techniques. This review reports on state-of-the-art approaches for assessing dynamics in biological systems using recent examples of virus assemblies, enzymes, and molecular machines. By integrating NMR spectroscopy in solution and the solid state, cryo electron microscopy, and molecular dynamics simulations, atomistic pictures of protein motions are obtained, not accessible from any single method in isolation. This information provides fundamental insights into protein behavior that can guide the development of future therapeutics.

Identification of Two Flip-Over Genes in Grass Family as Potential Signature of C4 Photosynthesis Evolution

In flowering plants, C4 photosynthesis is superior to C3 type in carbon fixation efficiency and adaptation to extreme environmental conditions, but the mechanisms behind the assembly of C4 machinery remain elusive. This study attempts to dissect the evolutionary divergence from C3 to C4 photosynthesis in five photosynthetic model plants from the grass family, using a combined comparative transcriptomics and deep learning technology. By examining and comparing gene expression levels in bundle sheath and mesophyll cells of five model plants, we identified 16 differentially expressed signature genes showing cell-specific expression patterns in C3 and C4 plants. Among them, two showed distinctively opposite cell-specific expression patterns in C3 vs. C4 plants (named as FOGs). The in silico physicochemical analysis of the two FOGs illustrated that C3 homologous proteins of LHCA6 had low and stable pI values of ~6, while the pI values of LHCA6 homologs increased drastically in C4 plants Setaria viridis (7), Zea mays (8), and Sorghum bicolor (over 9), suggesting this protein may have different functions in C3 and C4 plants. Interestingly, based on pairwise protein sequence/structure similarities between each homologous FOG protein, one FOG PGRL1A showed local inconsistency between sequence similarity and structure similarity. To find more examples of the evolutionary characteristics of FOG proteins, we investigated the protein sequence/structure similarities of other FOGs (transcription factors) and found that FOG proteins have diversified incompatibility between sequence and structure similarities during grass family evolution. This raised an interesting question as to whether the sequence similarity is related to structure similarity during C4 photosynthesis evolution.

Evolution of drug resistance drives destabilization of flap region dynamics in HIV-1 protease

The HIV-1 protease is one of several common key targets of combination drug therapies for human immunodeficiency virus infection and acquired immunodeficiency syndrome. During the progression of the disease, some individual patients acquire drug resistance due to mutational hotspots on the viral proteins targeted by combination drug therapies. It has recently been discovered that drug-resistant mutations accumulate on the "flap region" of the HIV-1 protease, which is a critical dynamic region involved in nonspecific polypeptide binding during invasion and infection of the host cell. In this study, we utilize machine learning-assisted comparative molecular dynamics, conducted at single amino acid site resolution, to investigate the dynamic changes that occur during functional dimerization and drug binding of wild-type and common drug-resistant versions of the main protease. We also use a multiagent machine learning model to identify conserved dynamics of the HIV-1 main protease that are preserved across simian and feline protease orthologs. We find that a key conserved functional site in the flap region, a solvent-exposed isoleucine (Ile50) that controls flap dynamics is functionally targeted by drug resistance mutations, leading to amplified molecular dynamics affecting the functional ability of the flap region to hold the drugs. We conclude that better long-term patient outcomes may be achieved by designing drugs that target protease regions that are less dependent upon single sites with large functional binding effects.

Exploring the structural acrobatics of fold-switching proteins using simplified structure-based models

Metamorphic proteins are a paradigm of the protein folding process, by encoding two or more native states, highly dissimilar in terms of their secondary, tertiary, and even quaternary structure, on a single amino acid sequence. Moreover, these proteins structurally interconvert between these native states in a reversible manner at biologically relevant timescales as a result of different environmental cues. The large-scale rearrangements experienced by these proteins, and their sometimes high mass interacting partners that trigger their metamorphosis, makes the computational and experimental study of their structural interconversion challenging. Here, we present our efforts in studying the refolding landscapes of two quintessential metamorphic proteins, RfaH and KaiB, using simplified dual-basin structure-based models (SBMs), rigorously footed on the energy landscape theory of protein folding and the principle of minimal frustration. By using coarse-grained models in which the native contacts and bonded interactions extracted from the available experimental structures of the two native states of RfaH and KaiB are merged into a single Hamiltonian, dual-basin SBM models can be generated and savvily calibrated to explore their fold-switch in a reversible manner in molecular dynamics simulations. We also describe how some of the insights offered by these simulations have driven the design of experiments and the validation of the conformational ensembles and refolding routes observed using this simple and computationally efficient models.

Combining Protein Conformational Diversity and Phylogenetic Information Using CoDNaS and CoDNaS-Q

CoDNaS (http://ufq.unq.edu.ar/codnas/) and CoDNaS-Q (http://ufq.unq.edu.ar/codnasq) are repositories of proteins with different degrees of conformational diversity. Following the ensemble nature of the native state, conformational diversity represents the structural differences between the conformers in the ensemble. Each entry in CoDNaS and CoDNaS-Q contains a redundant collection of experimentally determined conformers obtained under different conditions. These conformers represent snapshots of the protein dynamism. While CoDNaS contains examples of conformational diversity at the tertiary level, a recent development, CoDNaS-Q, contains examples at the quaternary level. In the emerging age of accurate protein structure prediction by machine learning approaches, many questions remain open regarding the characterization of protein dynamism. In this context, most bioinformatics resources take advantage of distinct features derived from protein alignments, however, the complexity and heterogeneity of information makes it difficult to recover reliable biological signatures. Here we present five protocols to explore tertiary and quaternary conformational diversity at the individual protein level as well as for the characterization of the distribution of conformational diversity at the protein family level in a phylogenetic context. These protocols can provide curated protein families with experimentally known conformational diversity, facilitating the exploration of sequence determinants of protein dynamism. © 2023 Wiley Periodicals LLC. Basic Protocol 1: Assessing conformational diversity with CoDNaS Alternate Protocol 1: Assessing conformational diversity at the quaternary level with CoDNaS-Q Basic Protocol 2: Exploring conformational diversity in a protein family Alternate Protocol 2: Exploring quaternary conformational diversity in a protein family Basic Protocol 3: Representing conformational diversity in a phylogenetic context.

Heterophilic and homophilic cadherin interactions in intestinal intermicrovillar links are species dependent

Enterocytes are specialized epithelial cells lining the luminal surface of the small intestine that build densely packed arrays of microvilli known as brush borders. These microvilli drive nutrient absorption and are arranged in a hexagonal pattern maintained by intermicrovillar links formed by 2 nonclassical members of the cadherin superfamily of calcium-dependent cell adhesion proteins: protocadherin-24 (PCDH24, also known as CDHR2) and the mucin-like protocadherin (CDHR5). The extracellular domains of these proteins are involved in heterophilic and homophilic interactions important for intermicrovillar function, yet the structural determinants of these interactions remain unresolved. Here, we present X-ray crystal structures of the PCDH24 and CDHR5 extracellular tips and analyze their species-specific features relevant for adhesive interactions. In parallel, we use binding assays to identify the PCDH24 and CDHR5 domains involved in both heterophilic and homophilic adhesion for human and mouse proteins. Our results suggest that homophilic and heterophilic interactions involving PCDH24 and CDHR5 are species dependent with unique and distinct minimal adhesive units.

Heterogeneity in conservation of multifunctional partner enzymes with meiotic importance, CDK2 kinase and BRCA1 ubiquitin ligase

The evolution of proteins can be accompanied by changes not only to their amino acid sequences, but also their structural and spatial molecular organization. Comparison of the protein conservation within different taxonomic groups (multifunctional, or highly specific) allows to clarify their specificity and the direction of evolution. Two multifunctional enzymes, cyclin-dependent kinase 2 (CDK2) and BRCA1 ubiquitin ligase, that are partners in some mitotic and meiotic processes were investigated in the present work. Two research methods, bioinformatics and immunocytochemical, were combined to examine the conservation levels of the two enzymes. It has been established that CDK2 is a highly conserved protein in different taxonomic lineages of the eukaryotic tree. Immunocytochemically, a conserved CDK2 pattern was revealed in the meiotic autosomes of five rodent species and partially in domestic turkey and clawed frog. Nevertheless, variable CDK2 distribution was detected at the unsynapsed segments of the rodent X chromosomes. BRCA1 was shown to be highly conserved only within certain mammalian taxa. It was also noted that in those rodent nuclei, where BRCA1 specifically binds to antigens, asynaptic regions of sex chromosomes were positive. BRCA1 staining was not always accompanied by specific binding, and a high nonspecificity in the nucleoplasm was observed. Thus, the studies revealed different conservation of the two enzymes at the level of protein structure as well as at the level of chromosome behavior. This suggests variable rates of evolution due to both size and configuration of the protein molecules and their multifunctionality.

In silico and gene expression analysis of the acute inflammatory response of gilthead seabream (Sparus aurata) after subcutaneous administration of carrageenin

Inflammation is one of the main causes of loss of homeostasis at both the systemic and molecular levels. The aim of this study was to investigate in silico the conservation of inflammation-related proteins in the gilthead seabream (Sparus aurata L.). Open reading frames of the selected genes were used as input in the STRING database for protein-protein interaction network analysis, comparing them with other teleost protein sequences. Proteins of the large yellow croaker (Larimichthys crocea L.) presented the highest percentages of identity with the gilthead seabream protein sequence. The gene expression profile of these proteins was then studied in gilthead seabream specimens subcutaneously injected with carrageenin (1%) or phosphate-buffered saline (control) by analyzing skin samples from the injected zone 12 and 24 h after injection. Gene expression analysis indicated that the mechanisms necessary to terminate the inflammatory response to carrageenin and recover skin homeostasis were activated between 12 and 24 h after injection (at the tested dose). The gene analysis performed in this study could contribute to the identification of the main mechanisms of acute inflammatory response and validate the use of carrageenin as an inflammation model to elucidate these mechanisms in fish.

In Silico Study of Mutational Stability of SARS-CoV-2 Proteins

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), an enveloped RNA virus transmits by droplet infection thus affects the respiratory system. Different genomes have been reported globally for SARS-CoV-2 with moderate level of mutation which makes it harder to combat the virus. Mutational profiling and the relevant evolutionary aspect of coronavirus proteins namely spike glycoprotein, membrane protein, envelope protein, nucleoprotein, ORF1ab, ORF3a, ORF6, ORF7a, ORF7b and ORF8 were studied by in silico experiments. Clustering of the protein sequences and calculation of residue relative abundance were done to get an idea about the protein conservancy as well as finding out some representative sequences for phylogenetic and ancestral reconstruction. By mutational profiling and mutation analysis, the effect of mutations on the protein stability and their functional implication were studied. This study indicates the mutational effect on the proteins and their relevance in evolution, which directs us towards a better understanding of these variations and diversification of SARS-CoV-2 for useful future therapeutic study and thus aid in designing therapeutic agents keeping the highly variable regions in mind.

Exposing the distinctive modular behavior of β-strands and α-helices in folded proteins

Although folded proteins are commonly depicted as simplistic combinations of β-strands and α-helices, the actual properties and functions of these secondary-structure elements in their native contexts are just partly understood. The principal reason is that the behavior of individual β- and α-elements is obscured by the global folding cooperativity. In this study, we have circumvented this problem by designing frustrated variants of the mixed α/β-protein S6, which allow the structural behavior of individual β-strands and α-helices to be targeted selectively by stopped-flow kinetics, X-ray crystallography, and solution-state NMR. Essentially, our approach is based on provoking intramolecular "domain swap." The results show that the α- and β-elements have quite different characteristics: The swaps of β-strands proceed via global unfolding, whereas the α-helices are free to swap locally in the native basin. Moreover, the α-helices tend to hybridize and to promote protein association by gliding over to neighboring molecules. This difference in structural behavior follows directly from hydrogen-bonding restrictions and suggests that the protein secondary structure defines not only tertiary geometry, but also maintains control in function and structural evolution. Finally, our alternative approach to protein folding and native-state dynamics presents a generally applicable strategy for in silico design of protein models that are computationally testable in the microsecond-millisecond regime.

Exploring the sequence fitness landscape of a bridge between protein folds

Most foldable protein sequences adopt only a single native fold. Recent protein design studies have, however, created protein sequences which fold into different structures apon changes of environment, or single point mutation, the best characterized example being the switch between the folds of the GA and GB binding domains of streptococcal protein G. To obtain further insight into the design of sequences which can switch folds, we have used a computational model for the fitness landscape of a single fold, built from the observed sequence variation of protein homologues. We have recently shown that such coevolutionary models can be used to design novel foldable sequences. By appropriately combining two of these models to describe the joint fitness landscape of GA and GB, we are able to describe the propensity of a given sequence for each of the two folds. We have successfully tested the combined model against the known series of designed GA/GB hybrids. Using Monte Carlo simulations on this landscape, we are able to identify pathways of mutations connecting the two folds. In the absence of a requirement for domain stability, the most frequent paths go via sequences in which neither domain is stably folded, reminiscent of the propensity for certain intrinsically disordered proteins to fold into different structures according to context. Even if the folded state is required to be stable, we find that there is nonetheless still a wide range of sequences which are close to the transition region and therefore likely fold switches, consistent with recent estimates that fold switching may be more widespread than had been thought.

Effect of Protein Structure on Evolution of Cotranslational Folding

Cotranslational folding depends on the folding speed and stability of the nascent protein. It remains difficult, however, to predict which proteins cotranslationally fold. Here, we simulate evolution of model proteins to investigate how native structure influences evolution of cotranslational folding. We developed a model that connects protein folding during and after translation to cellular fitness. Model proteins evolved improved folding speed and stability, with proteins adopting one of two strategies for folding quickly. Low contact order proteins evolve to fold cotranslationally. Such proteins adopt native conformations early on during the translation process, with each subsequently translated residue establishing additional native contacts. On the other hand, high contact order proteins tend not to be stable in their native conformations until the full chain is nearly extruded. We also simulated evolution of slowly translating codons, finding that slower translation speeds at certain positions enhances cotranslational folding. Finally, we investigated real protein structures using a previously published data set that identified evolutionarily conserved rare codons in Escherichia coli genes and associated such codons with cotranslational folding intermediates. We found that protein substructures preceding conserved rare codons tend to have lower contact orders, in line with our finding that lower contact order proteins are more likely to fold cotranslationally. Our work shows how evolutionary selection pressure can cause proteins with local contact topologies to evolve cotranslational folding.

Alien Domains Shaped the Modular Structure of Plant NLR Proteins

Plant innate immunity mostly relies on nucleotide-binding (NB) and leucine-rich repeat (LRR) intracellular receptors to detect pathogen-derived molecules and to induce defense responses. A multitaxa reconstruction of NB-domain associations allowed us to identify the first NB–LRR arrangement in the Chlorophyta division of the Viridiplantae. Our analysis points out that the basic NOD-like receptor (NLR) unit emerged in Chlorophytes by horizontal transfer and its diversification started from Toll/interleukin receptor–NB–LRR members. The operon-based genomic structure of Chromochloris zofingiensis NLR copies suggests a functional origin of NLR clusters. Moreover, the transmembrane signatures of NLR proteins in the unicellular alga C. zofingiensis support the hypothesis that the NLR-based immunity system of plants derives from a cell-surface surveillance system. Taken together, our findings suggest that NLRs originated in unicellular algae and may have a common origin with cell-surface LRR receptors.

Shared Signature Dynamics Tempered by Local Fluctuations Enables Fold Adaptability and Specificity

Recent studies have drawn attention to the evolution of protein dynamics, in addition to sequence and structure, based on the premise structure-encodes-dynamics-encodes-function. Of interest is to understand how functional differentiation is accomplished while maintaining the fold, or how intrinsic dynamics plays out in the evolution of structural variations and functional specificity. We performed a systematic computational analysis of 26,899 proteins belonging to 116 CATH superfamilies. Characterizing cooperative mechanisms and convergent/divergent features that underlie the shared/differentiated dynamics of family members required a methodology that lends itself to efficient analyses of large ensembles of proteins. We therefore introduced, SignDy, an integrated pipeline for evaluating the signature dynamics of families based on elastic network models. Our analysis confirmed that family members share conserved, highly cooperative (global) modes of motion. Importantly, our analysis discloses a subset of motions that sharply distinguishes subfamilies, which lie in a low-to-intermediate frequency regime of the mode spectrum. This regime has maximal impact on functional differentiation of families into subfamilies, while being evolutionarily conserved among subfamily members. Notably, the high-frequency end of the spectrum also reveals evolutionary conserved features across and within subfamilies; but in sharp contrast to global motions, high-frequency modes are minimally collective. Modulation of robust/conserved global dynamics by low-to-intermediate frequency fluctuations thus emerges as a versatile mechanism ensuring the adaptability of selected folds and the specificity of their subfamilies. SignDy further allows for dynamics-based categorization as a new layer of information relevant to distinctive mechanisms of action of subfamilies, beyond sequence or structural classifications.

Molecular function limits divergent protein evolution on planetary timescales

Functional conservation is known to constrain protein evolution. Nevertheless, the long-term divergence patterns of proteins maintaining the same molecular function and the possible limits of this divergence have not been explored in detail. We investigate these fundamental questions by characterizing the divergence between ancient protein orthologs with conserved molecular function. Our results demonstrate that the decline of sequence and structural similarities between such orthologs significantly slows down after ~1-2 billion years of independent evolution. As a result, the sequence and structural similarities between ancient orthologs have not substantially decreased for the past billion years. The effective divergence limit (>25% sequence identity) is not primarily due to protein sites universally conserved in all linages. Instead, less than four amino acid types are accepted, on average, per site across orthologous protein sequences. Our analysis also reveals different divergence patterns for protein sites with experimentally determined small and large fitness effects of mutations.

Editorial note

This article has been through an editorial process in which the authors decide how to respond to the issues raised during peer review. The Reviewing Editor's assessment is that all the issues have been addressed (see decision letter).

ProSNEx: a web-based application for exploration and analysis of protein structures using network formalism

ProSNEx (Protein Structure Network Explorer) is a web service for construction and analysis of Protein Structure Networks (PSNs) alongside amino acid flexibility, sequence conservation and annotation features. ProSNEx constructs a PSN by adding nodes to represent residues and edges between these nodes using user-specified interaction distance cutoffs for either carbon-alpha, carbon-beta or atom-pair contact networks. Different types of weighted networks can also be constructed by using either (i) the residue-residue interaction energies in the format returned by gRINN, resulting in a Protein Energy Network (PEN); (ii) the dynamical cross correlations from a coarse-grained Normal Mode Analysis (NMA) of the protein structure; (iii) interaction strength. Upon construction of the network, common network metrics (such as node centralities) as well as shortest paths between nodes and k-cliques are calculated. Moreover, additional features of each residue in the form of conservation scores and mutation/natural variant information are included in the analysis. By this way, tool offers an enhanced and direct comparison of network-based residue metrics with other types of biological information. ProSNEx is free and open to all users without login requirement at http://prosnex-tool.com.

Protein ensembles link genotype to phenotype

Classically, phenotype is what is observed, and genotype is the genetic makeup. Statistical studies aim to project phenotypic likelihoods of genotypic patterns. The traditional genotype-to-phenotype theory embraces the view that the encoded protein shape together with gene expression level largely determines the resulting phenotypic trait. Here, we point out that the molecular biology revolution at the turn of the century explained that the gene encodes not one but ensembles of conformations, which in turn spell all possible gene-associated phenotypes. The significance of a dynamic ensemble view is in understanding the linkage between genetic change and the gained observable physical or biochemical characteristics. Thus, despite the transformative shift in our understanding of the basis of protein structure and function, the literature still commonly relates to the classical genotype-phenotype paradigm. This is important because an ensemble view clarifies how even seemingly small genetic alterations can lead to pleiotropic traits in adaptive evolution and in disease, why cellular pathways can be modified in monogenic and polygenic traits, and how the environment may tweak protein function.

Characterization of the GPR1/FUN34/YaaH protein family in the green microalga Chlamydomonas suggests their role as intracellular membrane acetate channels

The unicellular green microalga Chlamydomonas reinhardtii is a powerful photosynthetic model organism which is capable of heterotrophic growth on acetate as a sole carbon source. This capacity has enabled its use for investigations of perturbations in photosynthetic machinery as mutants can be recovered heterotrophically. Fixation of acetate into cellular carbon metabolism occurs first by its conversion into acetyl-CoA by a respective synthase and the generation of succinate by the glyoxylate cycle. These metabolic steps have been recently determined to largely occur in the peroxisomes of this alga; however, little is known about the trafficking and import of acetate or its subcellular compartmentalization. Recently, the genes of five proteins belonging to the GPR1/FUN34/YaaH (GFY) superfamily were observed to exhibit increased expression in C. reinhardtii upon acetate addition, however, no further characterization has been reported. Here, we provide several lines of evidence to implicate Cr GFY1-5 as channels which share structural homology with bacterial succinate-acetate channels and specifically localize to microbodies, which are surprisingly distinct from the glyoxylate cycle-containing peroxisomes. We demonstrate structural models, gene expression profiling, and in vivo fluorescence localization of all five isoforms in the algal cell to further support this role.

Practical applications of DNA genotyping in diagnostic pathology

INTRODUCTION

Among various human tissue identity testing platforms, short tandem repeat (STR) genotyping has emerged as the most powerful and cost-effective method. Beyond forensic applications, tissue identity testing has become increasingly important in modern medical practice, in areas such as diagnostic pathology. Areas covered: A brief overview of various molecular/genetic techniques for identity testing is provided. This includes restriction fragment length polymorphism, single nucleotide polymorphism array and STR genotyping by multiplex PCR. Diagnostic applications of STR genotyping are covered in greater details: genotyping diagnosis of gestational trophoblastic disease, resolving tissue specimen mislabeling or histologic contaminant or 'floaters', bone marrow engraftment/chimerism analysis and interrogation of the primary source of malignancy in patients receiving organ donation. Four clinical cases are then presented to further illustrate these important clinical applications along with discussion of the interpretation, limitations, and pitfalls of STR genotyping. Expert commentary: STR genotyping is currently the most applicable method of identity testing and has extended its role well into the practice of diagnostic pathology with novel and powerful applications beyond forensics.

Enhancing Statistical Multiple Sequence Alignment and Tree Inference Using Structural Information

For highly divergent sequences, there is often insufficient information to reliably construct alignments and phylogenetic trees. Since protein structure may be strongly conserved despite large divergences in sequence, structural information can be used to help identify homology in such cases.While there exist well-studied models of sequence evolution, structurally informed alignment methods have typically made use of geometric measures of deviation that do not take into account the underlying mutational processes. In order to integrate structural information into sequence-based evolutionary models, we recently developed a stochastic model of structural evolution on a phylogenetic tree and implemented this as the StructAlign plugin for the StatAlign statistical alignment package.In this chapter, we will outline the types of analyses that can be carried out using StructAlign, illustrating how the inclusion of structural information can be used to inform joint estimation of alignments and trees. StructAlign can also be used to infer branch-specific rates of structural evolution, and analysis of an example globin dataset highlights strong variation in the inferred rate across the tree. While structure is more highly conserved within clades, the rate of structural divergence as a function of sequence variation is larger between functionally divergent proteins. Allowing for the rate of structural divergence to vary over the tree results in an improved fit to the empirically observed pairwise RMSD values.

ProteomeVis: a web app for exploration of protein properties from structure to sequence evolution across organisms' proteomes

Motivation

Protein evolution spans time scales and its effects span the length of an organism. A web app named ProteomeVis is developed to provide a comprehensive view of protein evolution in the Saccharomyces cerevisiae and Escherichia coli proteomes. ProteomeVis interactively creates protein chain graphs, where edges between nodes represent structure and sequence similarities within user-defined ranges, to study the long time scale effects of protein structure evolution. The short time scale effects of protein sequence evolution are studied by sequence evolutionary rate (ER) correlation analyses with protein properties that span from the molecular to the organismal level.

Results

We demonstrate the utility and versatility of ProteomeVis by investigating the distribution of edges per node in organismal protein chain universe graphs (oPCUGs) and putative ER determinants. S.cerevisiae and E.coli oPCUGs are scale-free with scaling constants of 1.79 and 1.56, respectively. Both scaling constants can be explained by a previously reported theoretical model describing protein structure evolution. Protein abundance most strongly correlates with ER among properties in ProteomeVis, with Spearman correlations of -0.49 (P-value < 10-10) and -0.46 (P-value < 10-10) for S.cerevisiae and E.coli, respectively. This result is consistent with previous reports that found protein expression to be the most important ER determinant.

Availability and implementation

ProteomeVis is freely accessible at http://proteomevis.chem.harvard.edu.

Supplementary information

Supplementary data are available at Bioinformatics online.

Enzyme function and its evolution

With rapid increases over recent years in the determination of protein sequence and structure, alongside knowledge of thousands of enzyme functions and hundreds of chemical mechanisms, it is now possible to combine breadth and depth in our understanding of enzyme evolution. Phylogenetics continues to move forward, though determining correct evolutionary family trees is not trivial. Protein function prediction has spawned a variety of promising methods that offer the prospect of identifying enzymes across the whole range of chemical functions and over numerous species. This knowledge is essential to understand antibiotic resistance, as well as in protein re-engineering and de novo enzyme design.