High-throughput profiling of point mutations across the HIV-1 genome

Simulation-guided tunable DNA probe design for mismatch tolerant hybridization

The ability to both sensitively and specifically assess the sequence composition of a nucleic acid strand is an ever-growing field. Designing a detection scheme that can perform this function when the sequence of the target being detected deviates significantly from the canonical sequence however is difficult in part because probe/primer design is based on established Watson-Crick base-pairing rules. We present here a robust and tunable toehold-based exchange probe that can detect a sequence with a variable number of SNPs of unknown identity by inserting a series of controlled, sequential mismatches into the protector seal of the toehold probe, in an effort to make the protector seal "sloppy". We show that the mismatch-tolerant system follows predicted behavior closely even with targets containing up to four mismatches that thermodynamically deviate from the canonical sequence by up to 15 kcal/mole. The system also performs faithfully regardless of the global mismatch position on either the protector seal or target. Lastly, we demonstrate the generalizability of the approach by testing the increasingly mismatch-tolerant protectors on HIV clinical samples to show that the system is capable of resolving multiple, iteratively mutated sequences derived from numerous HIV sub-populations with remarkable precision.

Computational analysis of cas proteins unlocks new potential in HIV-1 targeted gene therapy

Introduction: The human immunodeficiency virus type 1 (HIV-1) pandemic has been slowed with the advent of anti-retroviral therapy (ART). However, ART is not a cure and as such has pushed the disease into a chronic infection. One potential cure strategy that has shown promise is the Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)/Cas gene editing system. It has recently been shown to successfully edit and/or excise the integrated provirus from infected cells and inhibit HIV-1 in vitro, ex vivo, and in vivo. These studies have primarily been conducted with SpCas9 or SaCas9. However, additional Cas proteins are discovered regularly and modifications to these known proteins are being engineered. The alternative Cas molecules have different requirements for protospacer adjacent motifs (PAMs) which impact the possible targetable regions of HIV-1. Other modifications to the Cas protein or gRNA handle impact the tolerance for mismatches between gRNA and the target. While reducing off-target risk, this impacts the ability to fully account for HIV-1 genetic variability. Methods: This manuscript strives to examine these parameter choices using a computational approach for surveying the suitability of a Cas editor for HIV-1 gene editing. The Nominate, Diversify, Narrow, Filter (NDNF) pipeline measures the safety, broadness, and effectiveness of a pool of potential gRNAs for any PAM. This technique was used to evaluate 46 different potential Cas editors for their HIV therapeutic potential. Results: Our examination revealed that broader PAMs that improve the targeting potential of editors like SaCas9 and LbCas12a have larger pools of useful gRNAs, while broader PAMs reduced the pool of useful SpCas9 gRNAs yet increased the breadth of targetable locations. Investigation of the mismatch tolerance of Cas editors indicates a 2-missmatch tolerance is an ideal balance between on-target sensitivity and off-target specificity. Of all of the Cas editors examined, SpCas-NG and SPRY-Cas9 had the highest number of overall safe, broad, and effective gRNAs against HIV. Discussion: Currently, larger proteins and wider PAMs lead to better targeting capacity. This implies that research should either be targeted towards delivering longer payloads or towards increasing the breadth of currently available small Cas editors. With the discovery and adoption of additional Cas editors, it is important for researchers in the HIV-1 gene editing field to explore the wider world of Cas editors.

A molecular switch modulates assembly and host factor binding of the HIV-1 capsid

The HIV-1 capsid is a fullerene cone made of quasi-equivalent hexamers and pentamers of the viral CA protein. Typically, quasi-equivalent assembly of viral capsid subunits is controlled by a molecular switch. Here, we identify a Thr-Val-Gly-Gly motif that modulates CA hexamer/pentamer switching by folding into a 3₁₀ helix in the pentamer and random coil in the hexamer. Manipulating the coil/helix configuration of the motif allowed us to control pentamer and hexamer formation in a predictable manner, thus proving its function as a molecular switch. Importantly, the switch also remodels the common binding site for host factors that are critical for viral replication and the new ultra-potent HIV-1 inhibitor lenacapavir. This study reveals that a critical assembly element also modulates the post-assembly and viral replication functions of the HIV-1 capsid and provides new insights on capsid function and inhibition.

Limits to detecting epistasis in the fitness landscape of HIV

The rapid evolution of HIV is constrained by interactions between mutations which affect viral fitness. In this work, we explore the role of epistasis in determining the mutational fitness landscape of HIV for multiple drug target proteins, including Protease, Reverse Transcriptase, and Integrase. Epistatic interactions between residues modulate the mutation patterns involved in drug resistance, with unambiguous signatures of epistasis best seen in the comparison of the Potts model predicted and experimental HIV sequence “prevalences” expressed as higher-order marginals (beyond triplets) of the sequence probability distribution. In contrast, experimental measures of fitness such as viral replicative capacities generally probe fitness effects of point mutations in a single background, providing weak evidence for epistasis in viral systems. The detectable effects of epistasis are obscured by higher evolutionary conservation at sites. While double mutant cycles in principle, provide one of the best ways to probe epistatic interactions experimentally without reference to a particular background, we show that the analysis is complicated by the small dynamic range of measurements. Overall, we show that global pairwise interaction Potts models are necessary for predicting the mutational landscape of viral proteins.

HIV-1 capsid variability: viral exploitation and evasion of capsid-binding molecules

The HIV-1 capsid, a conical shell encasing viral nucleoprotein complexes, is involved in multiple post-entry processes during viral replication. Many host factors can directly bind to the HIV-1 capsid protein (CA) and either promote or prevent HIV-1 infection. The viral capsid is currently being explored as a novel target for therapeutic interventions. In the past few decades, significant progress has been made in our understanding of the capsid–host interactions and mechanisms of action of capsid-targeting antivirals. At the same time, a large number of different viral capsids, which derive from many HIV-1 mutants, naturally occurring variants, or diverse lentiviruses, have been characterized for their interactions with capsid-binding molecules in great detail utilizing various experimental techniques. This review provides an overview of how sequence variation in CA influences phenotypic properties of HIV-1. We will focus on sequence differences that alter capsid–host interactions and give a brief account of drug resistant mutations in CA and their mutational effects on viral phenotypes. Increased knowledge of the sequence-function relationship of CA helps us deepen our understanding of the adaptive potential of the viral capsid.

Anti-SARS-CoV-2 Vaccines and Monoclonal Antibodies Facing Viral Variants

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is genetically variable, allowing it to adapt to various hosts including humans. Indeed, SARS-CoV-2 has accumulated around two mutations per genome each month. The first relevant event in this context was the occurrence of the mutant D614G in the Spike gene. Moreover, several variants have emerged, including the well-characterized 20I/501Y.V1, 20H/501Y.V2, and 20J/501Y.V3 strains, in addition to those that have been detected within clusters, such as 19B/501Y or 20C/655Y in France. Mutants have also emerged in animals, including a variant transmitted to humans, namely, the Mink variant detected in Denmark. The emergence of these variants has affected the transmissibility of the virus (for example, 20I/501Y.V1, which was up to 82% more transmissible than other preexisting variants), its severity, and its ability to escape natural, adaptive, vaccine, and therapeutic immunity. In this respect, we review the literature on variants that have currently emerged, and their effect on vaccines and therapies, and, in particular, monoclonal antibodies (mAbs). The emergence of SARS-CoV-2 variants must be examined to allow effective preventive and curative control strategies to be developed.

Predominance of positive epistasis among drug resistance-associated mutations in HIV-1 protease

Drug-resistant mutations often have deleterious impacts on replication fitness, posing a fitness cost that can only be overcome by compensatory mutations. However, the role of fitness cost in the evolution of drug resistance has often been overlooked in clinical studies or in vitro selection experiments, as these observations only capture the outcome of drug selection. In this study, we systematically profile the fitness landscape of resistance-associated sites in HIV-1 protease using deep mutational scanning. We construct a mutant library covering combinations of mutations at 11 sites in HIV-1 protease, all of which are associated with resistance to protease inhibitors in clinic. Using deep sequencing, we quantify the fitness of thousands of HIV-1 protease mutants after multiple cycles of replication in human T cells. Although the majority of resistance-associated mutations have deleterious effects on viral replication, we find that epistasis among resistance-associated mutations is predominantly positive. Furthermore, our fitness data are consistent with genetic interactions inferred directly from HIV sequence data of patients. Fitness valleys formed by strong positive epistasis reduce the likelihood of reversal of drug resistance mutations. Overall, our results support the view that strong compensatory effects are involved in the emergence of clinically observed resistance mutations and provide insights to understanding fitness barriers in the evolution and reversion of drug resistance.

Deep sequence analysis of HIV adaptation following vertical transmission reveals the impact of immune pressure on the evolution of HIV

Human immunodeficiency virus (HIV) can adapt to an individual’s T cell immune response via genomic mutations that affect antigen recognition and impact disease outcome. These viral adaptations are specific to the host’s human leucocyte antigen (HLA) alleles, as these molecules determine which peptides are presented to T cells. As HLA molecules are highly polymorphic at the population level, horizontal transmission events are most commonly between HLA-mismatched donor/recipient pairs, representing new immune selection environments for the transmitted virus. In this study, we utilised a deep sequencing approach to determine the HIV quasispecies in 26 mother-to-child transmission pairs where the potential for founder viruses to be pre-adapted is high due to the pairs being haplo-identical at HLA loci. This scenario allowed the assessment of specific HIV adaptations following transmission in either a non-selective immune environment, due to recipient HLA mismatched to original selecting HLA, or a selective immune environment, mediated by matched donor/recipient HLA. We show that the pattern of reversion or fixation of HIV adaptations following transmission provides insight into the replicative cost, and likely compensatory networks, associated with specific adaptations in vivo. Furthermore, although transmitted viruses were commonly heavily pre-adapted to the child’s HLA genotype, we found evidence of de novo post-transmission adaptation, representing new epitopes targeted by the child’s T cell response. High-resolution analysis of HIV adaptation is relevant when considering vaccine and cure strategies for individuals exposed to adapted viruses via transmission or reactivated from reservoirs.

Highly mutable pathogens utilise genetic variations within T cell epitopes as a mechanism of immune escape (viral adaptation). The diversity of the human leucocyte antigen (HLA) molecules that present viral targets to T cells in human populations partially protects against rapid population-level accumulation of human immunodeficiency virus (HIV) adaptations through horizontal transmissions. In contrast, vertical transmissions occur between haplo-identical mother/child pairs, and potentially include adaptive changes through father-mother-child transmission, representing a pathway to complete pre-adaptation to HLA alleles in child hosts over only two transmission events. We utilised next-generation sequencing to examine HIV evolution in the unique setting of vertical HIV transmission. We predict the in vivo replicative cost and immune benefit of specific HIV adaptations that could be used to inform vaccine design and cure strategies to combat viral immune adaptation.

MaveDB: an open-source platform to distribute and interpret data from multiplexed assays of variant effect

Multiplex assays of variant effect (MAVEs), such as deep mutational scans and massively parallel reporter assays, test thousands of sequence variants in a single experiment. Despite the importance of MAVE data for basic and clinical research, there is no standard resource for their discovery and distribution. Here, we present MaveDB ( https://www.mavedb.org ), a public repository for large-scale measurements of sequence variant impact, designed for interoperability with applications to interpret these datasets. We also describe the first such application, MaveVis, which retrieves, visualizes, and contextualizes variant effect maps. Together, the database and applications will empower the community to mine these powerful datasets.

Application of Deep Mutational Scanning in Hepatitis C Virus

Mutagenesis is one of the key techniques in virus research. The recent development of deep mutational scanning allows the assessment of replication fitness effects of a large number of viral mutants in a high-throughput manner. Here, we describe a protocol for studying hepatitis C virus (HCV) using deep mutational scanning, which includes the methodologies for mutant library construction, passaging, sequencing, and data analysis.

Dynamic interactions between RNA viruses and human hosts unravelled by a decade of next generation sequencing

BACKGROUND

Next generation sequencing (NGS) methods have significantly contributed to a paradigm shift in genomic research for nearly a decade now. These methods have been useful in studying the dynamic interactions between RNA viruses and human hosts.

SCOPE OF THE REVIEW

In this review, we summarise and discuss key applications of NGS in studying the host - pathogen interactions in RNA viral infections of humans with examples.

MAJOR CONCLUSIONS

Use of NGS to study globally relevant RNA viral infections have revolutionized our understanding of the within host and between host evolution of these viruses. These methods have also been useful in clinical decision-making and in guiding biomedical research on vaccine design.

GENERAL SIGNIFICANCE

NGS has been instrumental in viral genomic studies in resolving within-host viral genomic variants and the distribution of nucleotide polymorphisms along the full-length of viral genomes in a high throughput, cost effective manner. In the future, novel advances such as long read, single molecule sequencing of viral genomes and simultaneous sequencing of host and pathogens may become the standard of practice in research and clinical settings. This will also bring on new challenges in big data analysis.

Quantitative characterization of all single amino acid variants of a viral capsid-based drug delivery vehicle

Self-assembling proteins are critical to biological systems and industrial technologies, but predicting how mutations affect self-assembly remains a significant challenge. Here, we report a technique, termed SyMAPS (Systematic Mutation and Assembled Particle Selection), that can be used to characterize the assembly competency of all single amino acid variants of a self-assembling viral structural protein. SyMAPS studies on the MS2 bacteriophage coat protein revealed a high-resolution fitness landscape that challenges some conventional assumptions of protein engineering. An additional round of selection identified a previously unknown variant (CP[T71H]) that is stable at neutral pH but less tolerant to acidic conditions than the wild-type coat protein. The capsids formed by this variant could be more amenable to disassembly in late endosomes or early lysosomes-a feature that is advantageous for delivery applications. In addition to providing a mutability blueprint for virus-like particles, SyMAPS can be readily applied to other self-assembling proteins.

Mapping mutational effects along the evolutionary landscape of HIV envelope

The immediate evolutionary space accessible to HIV is largely determined by how single amino acid mutations affect fitness. These mutational effects can shift as the virus evolves. However, the prevalence of such shifts in mutational effects remains unclear. Here, we quantify the effects on viral growth of all amino acid mutations to two HIV envelope (Env) proteins that differ at >100 residues. Most mutations similarly affect both Envs, but the amino acid preferences of a minority of sites have clearly shifted. These shifted sites usually prefer a specific amino acid in one Env, but tolerate many amino acids in the other. Surprisingly, shifts are only slightly enriched at sites that have substituted between the Envs—and many occur at residues that do not even contact substitutions. Therefore, long-range epistasis can unpredictably shift Env’s mutational tolerance during HIV evolution, although the amino acid preferences of most sites are conserved between moderately diverged viral strains.

The virus that causes AIDS, or HIV, has a protein called Env on its surface, which is essential for the virus to infect cells. Env can also be recognized by the immune system, which then targets the virus for destruction or blocks it from infecting cells. Unfortunately, Env evolves very quickly, which means that HIV can evade our defenses. However, there are limits to how much this protein can change, since it still needs to perform its essential role in helping viruses enter cells.

In the century since HIV first appeared in human populations, the virus has evolved considerably. There are now many HIV strains that infect people, and they bear Env proteins with substantially different sequences. However, it is not clear if these changes in sequence have resulted in Envs from distinct strains being able to tolerate different mutations.

To examine this question, Haddox et al. compared how the Envs from two strains of HIV react to modifications in their sequences. They created all possible individual mutations in the proteins, and the resulting collections of mutated viruses were then tested for their ability to infect cells in the laboratory.

Most mutations had similar effects in both Env proteins. This allowed Haddox et al. to identify portions of the protein that easily accommodate changes, and portions that must remain unchanged for viruses to remain infectious—at least in the laboratory. Some of these mutations are under different types of pressures when the virus faces the immune system, and those were identified using computational approaches.

However, some mutations were tolerated differently by the two Env proteins. Therefore, viral strains differ in how their Env proteins can evolve. The parts of Env that showed differences in mutational tolerance between the strains were not necessarily the parts that differ in sequence. This shows that changes in sequence in one part of the protein can modify how other portions evolve.

It remains to be determined whether changes in tolerance to mutations translate into differences in how the virus can escape immunity. This is an important question given that the rapid evolution of Env is a major obstacle to creating a vaccine for HIV.

Novel Synthesis and Phenotypic Analysis of Mutant Clouds for Hepatitis E Virus Genotype 1

Many RNA viruses exist as an ensemble of genetically diverse, replicating populations known as a mutant cloud. The genetic diversity (cloud size) and composition of this mutant cloud may influence several important phenotypic features of the virus, including its replication capacity. We applied a straightforward, bacterium-free approach using error-prone PCR coupled with reverse genetics to generate infectious mutant RNA clouds with various levels of genetic diversity from a genotype 1 strain of hepatitis E virus (HEV). Cloning and sequencing of a genomic fragment encompassing 70% of open reading frame 1 (ORF1) or of the full genome from variants in the resultant clouds showed the occurrence of nucleotide mutations at a frequency on the order of 10^-3 per nucleotide copied and the existence of marked genetic diversity, with a high normalized Shannon entropy value. The mutant clouds showed transient replication in cell culture, while wild-type HEV did not. Cross-sectional data from these cell cultures supported the existence of differential effects of clouds of various sizes and compositions on phenotypic characteristics, such as the replication level of (+)-RNA progeny, the amounts of double-stranded RNA (a surrogate for the rate of viral replication) and ORF1 protein, and the expression of interferon-stimulated genes. Since mutant cloud size and composition influenced the viral phenotypic properties, a better understanding of this relationship may help to provide further insights into virus evolution and prediction of emerging viral diseases.IMPORTANCE Several biological or practical limitations currently prevent the study of phenotypic behavior of a mutant cloud in vitro We developed a simple and rapid method for synthesizing mutant clouds of hepatitis E virus (HEV), a single-stranded (+)-RNA [ss(+) RNA] virus, with various and controllable levels of genetic diversity, which could then be used in a cell culture system to study the effects of cloud size and composition on viral phenotype. In a cross-sectional analysis, we demonstrated that a particular mutant cloud which had an extremely high genetic diversity had a replication rate exceeding that of wild-type HEV. This method should thus provide a useful model for understanding the phenotypic behavior of ss(+) RNA viruses.

Effects of Mutations on Replicative Fitness and Major Histocompatibility Complex Class I Binding Affinity Are Among the Determinants Underlying Cytotoxic-T-Lymphocyte Escape of HIV-1 Gag Epitopes

Certain “protective” major histocompatibility complex class I (MHC-I) alleles, such as B*57 and B*27, are associated with long-term control of HIV-1 in vivo mediated by the CD8⁺ cytotoxic-T-lymphocyte (CTL) response. However, the mechanism of such superior protection is not fully understood. Here we combined high-throughput fitness profiling of mutations in HIV-1 Gag, in silico prediction of MHC-peptide binding affinity, and analysis of intraperson virus evolution to systematically compare differences with respect to CTL escape mutations between epitopes targeted by protective MHC-I alleles and those targeted by nonprotective MHC-I alleles. We observed that the effects of mutations on both viral replication and MHC-I binding affinity are among the determinants of CTL escape. Mutations in Gag epitopes presented by protective MHC-I alleles are associated with significantly higher fitness cost and lower reductions in binding affinity with respect to MHC-I. A linear regression model accounting for the effect of mutations on both viral replicative capacity and MHC-I binding can explain the protective efficacy of MHC-I alleles. Finally, we found a consistent pattern in the evolution of Gag epitopes in long-term nonprogressors versus progressors. Overall, our results suggest that certain protective MHC-I alleles allow superior control of HIV-1 by targeting epitopes where mutations typically incur high fitness costs and small reductions in MHC-I binding affinity.

Understanding the mechanism of viral control achieved in long-term nonprogressors with protective HLA alleles provides insights for developing functional cure of HIV infection. Through the characterization of CTL escape mutations in infected persons, previous researchers hypothesized that protective alleles target epitopes where escape mutations significantly reduce viral replicative capacity. However, these studies were usually limited to a few mutations observed in vivo. Here we utilized our recently developed high-throughput fitness profiling method to quantitatively measure the fitness of mutations across the entirety of HIV-1 Gag. The data enabled us to integrate the results with in silico prediction of MHC-peptide binding affinity and analysis of intraperson virus evolution to systematically determine the differences in CTL escape mutations between epitopes targeted by protective HLA alleles and those targeted by nonprotective HLA alleles. We observed that the effects of Gag epitope mutations on HIV replicative fitness and MHC-I binding affinity are among the major determinants of CTL escape.

Experimental Estimation of the Effects of All Amino-Acid Mutations to HIV's Envelope Protein on Viral Replication in Cell Culture

HIV is notorious for its capacity to evade immunity and anti-viral drugs through rapid sequence evolution. Knowledge of the functional effects of mutations to HIV is critical for understanding this evolution. HIV's most rapidly evolving protein is its envelope (Env). Here we use deep mutational scanning to experimentally estimate the effects of all amino-acid mutations to Env on viral replication in cell culture. Most mutations are under purifying selection in our experiments, although a few sites experience strong selection for mutations that enhance HIV's replication in cell culture. We compare our experimental measurements of each site's preference for each amino acid to the actual frequencies of these amino acids in naturally occurring HIV sequences. Our measured amino-acid preferences correlate with amino-acid frequencies in natural sequences for most sites. However, our measured preferences are less concordant with natural amino-acid frequencies at surface-exposed sites that are subject to pressures absent from our experiments such as antibody selection. Our data enable us to quantify the inherent mutational tolerance of each site in Env. We show that the epitopes of broadly neutralizing antibodies have a significantly reduced inherent capacity to tolerate mutations, rigorously validating a pervasive idea in the field. Overall, our results help disentangle the role of inherent functional constraints and external selection pressures in shaping Env's evolution.

Crystal structure of an HIV assembly and maturation switch

Virus assembly and maturation proceed through the programmed operation of molecular switches, which trigger both local and global structural rearrangements to produce infectious particles. HIV-1 contains an assembly and maturation switch that spans the C-terminal domain (CTD) of the capsid (CA) region and the first spacer peptide (SP1) of the precursor structural protein, Gag. The crystal structure of the CTD-SP1 Gag fragment is a goblet-shaped hexamer in which the cup comprises the CTD and an ensuing type II β-turn, and the stem comprises a 6-helix bundle. The β-turn is critical for immature virus assembly and the 6-helix bundle regulates proteolysis during maturation. This bipartite character explains why the SP1 spacer is a critical element of HIV-1 Gag but is not a universal property of retroviruses. Our results also indicate that HIV-1 maturation inhibitors suppress unfolding of the CA-SP1 junction and thereby delay access of the viral protease to its substrate.

DOI:http://dx.doi.org/10.7554/eLife.17063.001

Viruses like HIV must undergo a process called maturation in order to successfully infect cells. Maturation involves a dramatic rearrangement in the architecture of the virus. That is to say, the virus’s internal protein coat – called the capsid – must change from an immature sphere into a mature cone-shaped coat. Notably, this maturation process is what is disrupted by the protease inhibitors that are a major component of anti-HIV drug cocktails.

Structural changes in small portions of the capsid protein, termed molecular switches, commonly trigger the viral capsids to reorganize. The HIV capsid has two of these switches, and Wagner, Zadrozny et al. set out to understand how one of them – called the CA-SP1 switch – works.

Solving the three-dimensional structure of the immature form of the CA-SP1 switch revealed that it forms a well-structured bundle of six helices. This helical bundle captures another section of the capsid protein that would otherwise be cut by a viral protease. The CA-SP1 switch therefore controls how quickly the protein is cut and the start of the maturation process.

Wagner, Zadrozny et al. then discovered that other small molecule inhibitors of HIV, called maturation inhibitors, work by binding to and disrupting the transformation of the CA-SP1 switch. Finally, further experiments showed that the formation of the CA-SP1 helical bundle controls when the immature capsid shell forms and coordinates the process with the capsid gaining the genetic material of the virus.

The new structure means that researchers now know what the HIV capsid looks like at the start and end of maturation. The next challenge will be to figure out exactly how the capsid changes from one form to the next as HIV matures.

DOI:http://dx.doi.org/10.7554/eLife.17063.002

Estimating Exceptionally Rare Germline and Somatic Mutation Frequencies via Next Generation Sequencing

We used targeted next generation deep-sequencing (Safe Sequencing System) to measure ultra-rare de novo mutation frequencies in the human male germline by attaching a unique identifier code to each target DNA molecule. Segments from three different human genes (FGFR3, MECP2 and PTPN11) were studied. Regardless of the gene segment, the particular testis donor or the 73 different testis pieces used, the frequencies for any one of the six different mutation types were consistent. Averaging over the C>T/G>A and G>T/C>A mutation types the background mutation frequency was 2.6x10^-5 per base pair, while for the four other mutation types the average background frequency was lower at 1.5x10^-6 per base pair. These rates far exceed the well documented human genome average frequency per base pair (~10⁻⁸) suggesting a non-biological explanation for our data. By computational modeling and a new experimental procedure to distinguish between pre-mutagenic lesion base mismatches and a fully mutated base pair in the original DNA molecule, we argue that most of the base-dependent variation in background frequency is due to a mixture of deamination and oxidation during the first two PCR cycles. Finally, we looked at a previously studied disease mutation in the PTPN11 gene and could easily distinguish true mutations from the SSS background. We also discuss the limits and possibilities of this and other methods to measure exceptionally rare mutation frequencies, and we present calculations for other scientists seeking to design their own such experiments.

Detection and sequence/structure mapping of biophysical constraints to protein variation in saturated mutational libraries and protein sequence alignments with a dedicated server

Background

Protein variability can now be studied by measuring high-resolution tolerance-to-substitution maps and fitness landscapes in saturated mutational libraries. But these rich and expensive datasets are typically interpreted coarsely, restricting detailed analyses to positions of extremely high or low variability or dubbed important beforehand based on existing knowledge about active sites, interaction surfaces, (de)stabilizing mutations, etc.

Results

Our new webserver PsychoProt (freely available without registration at http://psychoprot.epfl.ch or at http://lucianoabriata.altervista.org/psychoprot/index.html) helps to detect, quantify, and sequence/structure map the biophysical and biochemical traits that shape amino acid preferences throughout a protein as determined by deep-sequencing of saturated mutational libraries or from large alignments of naturally occurring variants.

Discussion

We exemplify how PsychoProt helps to (i) unveil protein structure-function relationships from experiments and from alignments that are consistent with structures according to coevolution analysis, (ii) recall global information about structural and functional features and identify hitherto unknown constraints to variation in alignments, and (iii) point at different sources of variation among related experimental datasets or between experimental and alignment-based data. Remarkably, metabolic costs of the amino acids pose strong constraints to variability at protein surfaces in nature but not in the laboratory. This and other differences call for caution when extrapolating results from in vitro experiments to natural scenarios in, for example, studies of protein evolution.

Conclusion

We show through examples how PsychoProt can be a useful tool for the broad communities of structural biology and molecular evolution, particularly for studies about protein modeling, evolution and design.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-016-1124-4) contains supplementary material, which is available to authorized users.

DeepSNVMiner: a sequence analysis tool to detect emergent, rare mutations in subsets of cell populations

Background. Massively parallel sequencing technology is being used to sequence highly diverse populations of DNA such as that derived from heterogeneous cell mixtures containing both wild-type and disease-related states. At the core of such molecule tagging techniques is the tagging and identification of sequence reads derived from individual input DNA molecules, which must be first computationally disambiguated to generate read groups sharing common sequence tags, with each read group representing a single input DNA molecule. This disambiguation typically generates huge numbers of reads groups, each of which requires additional variant detection analysis steps to be run specific to each read group, thus representing a significant computational challenge. While sequencing technologies for producing these data are approaching maturity, the lack of available computational tools for analysing such heterogeneous sequence data represents an obstacle to the widespread adoption of this technology. Results. Using synthetic data we successfully detect unique variants at dilution levels of 1 in a 1,000,000 molecules, and find DeeepSNVMiner obtains significantly lower false positive and false negative rates compared to popular variant callers GATK, SAMTools, FreeBayes and LoFreq, particularly as the variant concentration levels decrease. In a dilution series with genomic DNA from two cells lines, we find DeepSNVMiner identifies a known somatic variant when present at concentrations of only 1 in 1,000 molecules in the input material, the lowest concentration amongst all variant callers tested. Conclusions. Here we present DeepSNVMiner; a tool to disambiguate tagged sequence groups and robustly identify sequence variants specific to subsets of starting DNA molecules that may indicate the presence of a disease. DeepSNVMiner is an automated workflow of custom sequence analysis utilities and open source tools able to differentiate somatic DNA variants from artefactual sequence variants that likely arose during DNA amplification. The workflow remains flexible such that it may be customised to variants of the data production protocol used, and supports reproducible analysis through detailed logging and reporting of results. DeepSNVMiner is available for academic non-commercial research purposes at https://github.com/mattmattmattmatt/DeepSNVMiner.

How Good Are Statistical Models at Approximating Complex Fitness Landscapes?

Fitness landscapes determine the course of adaptation by constraining and shaping evolutionary trajectories. Knowledge of the structure of a fitness landscape can thus predict evolutionary outcomes. Empirical fitness landscapes, however, have so far only offered limited insight into real-world questions, as the high dimensionality of sequence spaces makes it impossible to exhaustively measure the fitness of all variants of biologically meaningful sequences. We must therefore revert to statistical descriptions of fitness landscapes that are based on a sparse sample of fitness measurements. It remains unclear, however, how much data are required for such statistical descriptions to be useful. Here, we assess the ability of regression models accounting for single and pairwise mutations to correctly approximate a complex quasi-empirical fitness landscape. We compare approximations based on various sampling regimes of an RNA landscape and find that the sampling regime strongly influences the quality of the regression. On the one hand it is generally impossible to generate sufficient samples to achieve a good approximation of the complete fitness landscape, and on the other hand systematic sampling schemes can only provide a good description of the immediate neighborhood of a sequence of interest. Nevertheless, we obtain a remarkably good and unbiased fit to the local landscape when using sequences from a population that has evolved under strong selection. Thus, current statistical methods can provide a good approximation to the landscape of naturally evolving populations.

Coupling high-throughput genetics with phylogenetic information reveals an epistatic interaction on the influenza A virus M segment

Background

Epistasis is one of the central themes in viral evolution due to its importance in drug resistance, immune escape, and interspecies transmission. However, there is a lack of experimental approach to systematically probe for epistatic residues.

Results

By utilizing the information from natural occurring sequences and high-throughput genetics, this study established a novel strategy to identify epistatic residues. The rationale is that a substitution that is deleterious in one strain may be prevalent in nature due to the presence of a naturally occurring compensatory substitution. Here, high-throughput genetics was applied to influenza A virus M segment to systematically identify deleterious substitutions. Comparison with natural sequence variation showed that a deleterious substitution M1 Q214H was prevalent in circulating strains. A coevolution analysis was then performed and indicated that M1 residues 121, 207, 209, and 214 naturally coevolved as a group. Subsequently, we experimentally validated that M1 A209T was a compensatory substitution for M1 Q214H.

Conclusions

This work provided a proof-of-concept to identify epistatic residues by coupling high-throughput genetics with phylogenetic information. In particular, we were able to identify an epistatic interaction between M1 substitutions A209T and Q214H. This analytic strategy can potentially be adapted to study any protein of interest, provided that the information on natural sequence variants is available.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-2358-7) contains supplementary material, which is available to authorized users.

Rational Protein Engineering Guided by Deep Mutational Scanning

Sequence-function relationship in a protein is commonly determined by the three-dimensional protein structure followed by various biochemical experiments. However, with the explosive increase in the number of genome sequences, facilitated by recent advances in sequencing technology, the gap between protein sequences available and three-dimensional structures is rapidly widening. A recently developed method termed deep mutational scanning explores the functional phenotype of thousands of mutants via massive sequencing. Coupled with a highly efficient screening system, this approach assesses the phenotypic changes made by the substitution of each amino acid sequence that constitutes a protein. Such an informational resource provides the functional role of each amino acid sequence, thereby providing sufficient rationale for selecting target residues for protein engineering. Here, we discuss the current applications of deep mutational scanning and consider experimental design.

Functional Constraint Profiling of a Viral Protein Reveals Discordance of Evolutionary Conservation and Functionality

Viruses often encode proteins with multiple functions due to their compact genomes. Existing approaches to identify functional residues largely rely on sequence conservation analysis. Inferring functional residues from sequence conservation can produce false positives, in which the conserved residues are functionally silent, or false negatives, where functional residues are not identified since they are species-specific and therefore non-conserved. Furthermore, the tedious process of constructing and analyzing individual mutations limits the number of residues that can be examined in a single study. Here, we developed a systematic approach to identify the functional residues of a viral protein by coupling experimental fitness profiling with protein stability prediction using the influenza virus polymerase PA subunit as the target protein. We identified a significant number of functional residues that were influenza type-specific and were evolutionarily non-conserved among different influenza types. Our results indicate that type-specific functional residues are prevalent and may not otherwise be identified by sequence conservation analysis alone. More importantly, this technique can be adapted to any viral (and potentially non-viral) protein where structural information is available.

The analysis of sequence conservation is a common approach to identify functional residues within a protein. However, not all functional residues are conserved as natural evolution and species diversification permit continuous innovation of protein functionality through the retention of advantageous mutations. Non-conserved functional residues, which are often species-specific, may not be identified by conventional analysis of sequence conservation despite being biologically important. Here we described a novel approach to identify functional residues within a protein by coupling a high-throughput experimental fitness profiling approach with computational protein modeling. Our methodology is independent of sequence conservation and is applicable to any protein where structural information is available. In this study, we systematically mapped the functional residues on the influenza A PA protein and revealed that non-conserved functional residues are prevalent. Our results not only have significant implication on how functionality evolves during natural evolution, but also highlight the caveats when applying conservation-based approaches to identify functional residues within a protein.