Biology subjects

Marks, D. S.

Publications and source records attributed to Marks, D. S..

36 records · Page 2Linked to original sources

bioRxiv2021

MaveDB v2: a curated community database with over three million variant effects from multiplexed functional assays

A central problem in genomics is understanding the effect of individual DNA variants. Multiplexed Assays of Variant Effect (MAVEs) can help address this challenge by measuring all possible single nucleotide variant effects in a gene or regulatory sequence simultaneously. Here we describe MaveDB v2, which has become the database of record for MAVEs. MaveDB now contains a large fraction of published studies, comprising over two hundred datasets and three million variant effect measurements. We created tools and APIs to streamline data submission and access, transforming MaveDB into a hub for the analysis and dissemination of these impactful datasets.

genomics↗

bioRxiv2021

Coevolution of interacting proteins through non-contacting and non-specific mutations

Proteins often accumulate neutral mutations that do not affect current functions1 but can profoundly influence future mutational possibilities and functions2-4. Understanding such hidden potential has major implications for protein design and evolutionary forecasting5-7, but has been limited by a lack of systematic efforts to identify potentiating mutations8,9. Here, through the comprehensive analysis of a bacterial toxin-antitoxin system, we identified all possible single substitutions in the toxin that enable it to tolerate otherwise interface-disrupting mutations in its antitoxin. Strikingly, the majority of enabling mutations in the toxin do not contact, and promote tolerance non-specifically to, many different antitoxin mutations, despite covariation in homologs occurring primarily between specific pairs of contacting residues across the interface. In addition, the enabling mutations we identified expand future mutational paths that both maintain old toxin-antitoxin interactions and form new ones. These non-specific mutations are missed by widely used covariation and machine learning methods10,11. Identifying such enabling mutations will be critical for ensuring continued binding of therapeutically relevant proteins, such as antibodies, aimed at evolving targets12-14.

evolutionary biology↗

bioRxiv2021

Natural and designed proteins inspired by extremotolerant organisms can form condensates and attenuate apoptosis in human cells

Many organisms can survive extreme conditions and successfully recover to normal life. This extremotolerant behavior has been attributed in part to repetitive, amphipathic, and intrinsically disordered proteins that are upregulated in the protected state. Here, we assemble a library of approximately 300 naturally-occurring and designed extremotolerance-associated proteins to assess their ability to protect human cells from chemically-induced apoptosis. We show that several proteins from tardigrades, nematodes, and the Chinese giant salamander are apoptosis protective. Notably, we identify a region of the human ApoE protein with similarity to extremotolerance-associated proteins that also protects against apoptosis. This region mirrors the phase separation behavior seen with such proteins, like the tardigrade protein CAHS2. Moreover, we identify a synthetic protein, DHR81, that shares this combination of elevated phase separation propensity and apoptosis protection. Finally, we demonstrate that driving protective proteins into the condensate state increases apoptosis protection, and highlight the ability for DHR81 condensates to sequester caspase-7. Taken together, this work draws a link between extremotolerance-associated proteins, condensate formation, and designing human cellular protection.

synthetic biology↗

bioRxiv2021

A generative nonparametric Bayesian model for whole genomes

Generative probabilistic modeling of biological sequences has widespread existing and potential use across biology and biomedicine, particularly given advances in high-throughput sequencing, synthesis and editing. However, we still lack methods with nucleotide resolution that are tractable at the scale of whole genomes and that can achieve high predictive accuracy either in theory or practice. In this article we propose a new generative sequence model, the Bayesian embedded autoregressive (BEAR) model, which uses a parametric autoregressive model to specify a conjugate prior over a nonparametric Bayesian Markov model. We explore, theoretically and empirically, applications of BEAR models to a variety of statistical problems including density estimation, robust parameter estimation, goodness-of-fit tests, and two-sample tests. We prove rigorous asymptotic consistency results including nonparametric posterior concentration rates. We scale inference in BEAR models to datasets containing tens of billions of nucleotides. On genomic, transcriptomic, and metagenomic sequence data we show that BEAR models provide large increases in predictive performance as compared to parametric autoregressive models, among other results. BEAR models offer a flexible and scalable framework, with theoretical guarantees, for building and critiquing generative models at the whole genome scale.

genomics↗

bioRxiv2020

Rapid generation of potent antibodies by autonomous hypermutation in yeast

The predominant approach for antibody generation remains animal immunization, which can yield exceptionally selective and potent antibody clones owing to the powerful evolutionary process of somatic hypermutation. However, animal immunization is inherently slow, has poor compatibility with certain antigens (e.g., integral membrane proteins), and suffers from self-tolerance and immunodominance, which limit the functional spectrum of antibodies that can be obtained. Here, we describe Autonomous Hypermutation yEast surfAce Display (AHEAD), a synthetic recombinant antibody generation technology that imitates somatic hypermutation inside engineered yeast. In AHEAD, antibody fragments are encoded on an error-prone orthogonal DNA replication system, resulting in Saccharomyces cerevisiae populations that continuously mutate surface-displayed antibody repertoires. Simple cycles of yeast culturing and enrichment for antigen binding drive the evolution of high-affinity antibody clones in a readily parallelizable process that takes as little as 2 weeks. We applied AHEAD to generate nanobodies against the SARS-CoV-2 S glycoprotein, a GPCR, and other targets. The SARS-CoV-2 nanobodies, concurrently evolved from an open-source naive nanobody library in 8 independent experiments, reached subnanomolar affinities through the sequential fixation of multiple mutations over 3-8 AHEAD cycles that saw [~]580-fold and [~]925-fold improvements in binding affinities and pseudovirus neutralization potencies, respectively. These experiments highlight the defining speed, parallelizability, and effectiveness of AHEAD and provide a template for streamlined antibody generation at large with salient utility in rapid response to current and future viral outbreaks.

bioengineering↗

bioRxiv2020

Generative probabilistic biological sequence models that account for mutational variability

Generative probabilistic modeling of biological sequences has widespread existing and potential application across biology and biomedicine, from evolutionary biology to epidemiology to protein design. Many standard sequence analysis methods preprocess data using a multiple sequence alignment (MSA) algorithm, one of the most widely used computational methods in all of science. However, as we show in this article, training generative probabilistic models with MSA preprocessing leads to statistical pathologies in the context of sequence prediction and forecasting. To address these problems, we propose a principled drop-in alternative to MSA preprocessing in the form of a structured observation distribution (the ``MuE" distribution). The MuE is a latent alignment model in which not only the alignment variable but also the regressor sequence can be latent. We prove theoretically that the MuE distribution comprehensively generalizes popular methods for inferring biological sequence alignments, and provide a precise characterization of how such biological models have differed from natural language latent alignment models. We show empirically that models that use the MuE as an observation distribution outperform comparable methods across a variety of datasets, and apply MuE models to a novel problem for generative probabilistic sequence models: forecasting pathogen evolution.

genomics↗

bioRxiv2020

Multiplexed measurement of variant abundance and activity reveals VKOR topology, active site and human variant impact

Vitamin K epoxide reductase (VKOR) drives the vitamin K cycle, activating vitamin K-dependent blood clotting factors. VKOR is also the target of the widely used anticoagulant drug, warfarin Despite VKORs pivotal role in coagulation, its structure and active site remain poorly understood. In addition, VKOR variants can cause vitamin K-dependent clotting factor deficiency 2 or alter warfarin response. Here, we used multiplexed, sequencing-based assays to measure the effects of 2,695 VKOR missense variants on abundance and 697 variants on activity in cultured human cells. The large-scale functional data, along with an evolutionary coupling analysis, supports a four transmembrane domain topology, with variants in transmembrane domains exhibiting strongly deleterious effects on abundance and activity. Functionally constrained regions of the protein define the active site, and we find that, of four conserved cysteines putatively critical for function, only three are absolutely required. Finally, 25% of human VKOR missense variants show reduced abundance or activity, possibly conferring warfarin sensitivity or causing disease.

genomics↗

bioRxiv2019

Proteome-scale discovery of protein interactions with residue-level resolution using sequence coevolution

The majority of protein interactions in most organisms are unknown, and experimental methods for determining protein interactions can yield divergent results. Here we use an orthogonal, purely computational method based on sequence coevolution to discover protein interactions at large scale. In the model organism Escherichia coli, 53% of protein pairs in the proteome are eligible for our method given currently available sequenced genomes. When assaying the entire cell envelope proteome, which is understudied due to experimental challenges, we found 620 likely interactions and their predicted structures, increasing the space of known interactions by 529. Our results show that genomic sequencing data can be used to predict and resolve protein interactions to atomic resolution at large scale. Predictions and code are freely available at https://marks.hms.harvard.edu/ecolicomplex and https://github.com/debbiemarkslab/EVcouplings

bioinformatics↗

bioRxiv2019

Accelerating Protein Design Using Autoregressive Generative Models

The ability to design functional sequences and predict effects of variation is central to protein engineering and biotherapeutics. State-of-art computational methods rely on models that leverage evolutionary information but are inadequate for important applications where multiple sequence alignments are not robust. Such applications include the prediction of variant effects of indels, disordered proteins, and the design of proteins such as antibodies due to the highly variable complementarity determining regions. We introduce a deep generative model adapted from natural language processing for prediction and design of diverse functional sequences without the need for alignments. The model performs state-of-art prediction of missense and indel effects and we successfully design and test a diverse 105-nanobody library that shows better expression than a 1000-fold larger synthetic library. Our results demonstrate the power of the alignment-free autoregressive model in generalizing to regions of sequence space traditionally considered beyond the reach of prediction and design.

systems biology↗

bioRxiv2019

Protein structure from experimental evolution

Natural evolution encodes rich information about the structure and function of biomolecules in the genetic record. Previously, statistical analysis of co-variation patterns in natural protein families has enabled the accurate computation of 3D structures. Here, we explored whether similar information can be generated by laboratory evolution, starting from a single gene and performing multiple cycles of mutagenesis and functional selection. We evolved two bacterial antibiotic resistance proteins, {beta}-lactamase PSE1 and acetyltransferase AAC6, and obtained hundreds of thousands of diverse functional sequences. Using evolutionary coupling analysis, we inferred residue interactions in good agreement with contacts in the crystal structures, confirming genetic encoding of structural constraints in the selected sequences. Computational protein folding with contact constraints yielded 3D structures with the same fold as that of natural relatives. Evolution experiments combined with inference of residue interactions from sequence information opens the door to a new experimental method for the determination of protein structures.

evolutionary biology↗

bioRxiv2018

The EVcouplings Python framework for coevolutionary sequence analysis

SummaryCoevolutionary sequence analysis has become a commonly used technique for de novo prediction of the structure and function of proteins, RNA, and protein complexes. This approach requires extensive computational pipelines that integrate multiple tools, databases, and data processing steps. We present the EVcouplings framework, a fully integrated open-source application and Python package for coevolutionary analysis. The framework enables generation of sequence alignments, calculation and evaluation of evolutionary couplings (ECs), and de novo prediction of structure and mutation effects. The application has an easy to use command line interface to run workflows with user control over all analysis parameters, while the underlying modular Python package allows interactive data analysis and rapid development of new workflows. Through this multi-layered approach, the EVcouplings framework makes the full power of coevolutionary analyses available to entry-level and advanced users.\n\nAvailabilityhttps://github.com/debbiemarkslab/evcouplings\n\nContactsander.research@gmail.com, debbie@hms.harvard.edu

bioinformatics↗

bioRxiv2018

Genome-wide discovery of epistatic loci affecting antibiotic resistance using evolutionary couplings

The analysis of whole genome sequencing data should, in theory, allow the discovery of interdependent loci that cause antibiotic resistance. In practice, however, identifying this epistasis remains a challenge as the vast number of possible interactions erodes statistical power. To solve this problem, we extend a method that has been successfully used to identify epistatic residues in proteins to infer genomic loci that are strongly coupled and associated with antibiotic resistance. Our method reduces the number of tests required for an epistatic genome-wide association study and increases the likelihood of identifying causal epistasis. We discovered 38 loci and 250 epistatic pairs that influence the dose needed to inhibit growth for five different antibiotics in 1,102 isolates of Neisseria gonorrhoeae that were confirmed in an independent dataset of 495 isolates. Many known resistance-affecting loci were recovered; however, the majority of loci occurred in unreported genes, including murE which was associated with cefixime. About half of the novel epistasis we report involved at least one locus previously associated with antibiotic resistance, including interactions between gyrA and parC associated with ciprofloxacin. Still, many combinations involved unreported loci and genes. Our work provides a systematic identification of epistasis pairs affecting antibiotic resistance in N. gonorrhoeae and a generalizable method for epistatic genome-wide association studies.

genomics↗

bioRxiv2018

3D protein structure from genetic epistasis experiments

High-throughput experimental techniques have made possible the systematic sampling of the single mutation landscape for many proteins, defined as the change in protein fitness as the result of point mutation sequence changes. In a more limited number of cases, and for small proteins only, we also have nearly full coverage of all possible double mutants. By comparing the phenotypic effect of two simultaneous mutations with that of the individual amino acid changes, we can evaluate epistatic effects that reflect non-additive cooperative processes. The observation that epistatic residue pairs often are in contact in the 3D structure led to the hypothesis that a systematic epistatic screen contains sufficient information to identify the 3D fold of a protein. To test this hypothesis, we examined experimental double mutants for evidence of epistasis and identified residue contacts at 86% accuracy, including secondary structure elements and evidence for an alternative all--helical conformation. Positively epistatic contacts - corresponding to compensatory mutations, restoring fitness - were the most informative. Folded models generated from top-ranked epistatic pairs, when compared with the known structure, were accurate within 2.4 [A] over 53 residues, indicating the possibility that 3D protein folds can be determined experimentally with good accuracy from functional assays of mutant libraries, at least for small proteins. These results suggest a new experimental approach for determining protein structure.

systems biology↗

bioRxiv2018

Structure and mutagenic analysis of the lipid II flippase MurJ from Escherichia coli

The peptidoglycan cell wall provides an essential protective barrier in almost all bacteria, defining cellular morphology and conferring resistance to osmotic stress and other environmental hazards. The precursor to peptidoglycan, lipid II, is assembled on the inner leaflet of the plasma membrane. However, peptidoglycan polymerization occurs on the outer face of the plasma membrane, and lipid II must be flipped across the membrane by the MurJ protein prior to its use in peptidoglycan synthesis. Due to its central role in cell wall assembly, MurJ is of fundamental importance in microbial cell biology and is a prime target for novel antibiotic development. However, relatively little is known regarding the mechanisms of MurJ function, and structural data are only available for MurJ from the extremophile Thermosipho africanus. Here, we report the crystal structure of substrate-free MurJ from the Gram-negative model organism Escherichia coli, revealing an inward-open conformation. Taking advantage of the genetic tractability of E. coli, we performed high-throughput mutagenesis and next-generation sequencing to assess mutational tolerance at every amino acid in the protein, providing a detailed functional and structural map for the enzyme and identifying sites for inhibitor development. Finally, through the use of sequence co-evolution analysis we identify functionally important interactions in the outward-open state of the protein, supporting a rocker-switch model for lipid II transport.

biochemistry↗

bioRxiv2018

Viral gain-of-function experiments uncover residues under diversifying selection in nature

Viral gain-of-function mutations are commonly observed in the laboratory; however, it is unknown whether those mutations also evolve in nature. We identify two key residues in the host recognition protein of bacteriophage {lambda} that are necessary to exploit a new receptor; both residues repeatedly evolved among homologs from environmental samples. Our results provide evidence for widespread host-shift evolution in nature and a proof of concept for integrating experiments with genomic epidemiology.

evolutionary biology↗

bioRxiv2017

Deep generative models of genetic variation capture mutation effects

The functions of proteins and RNAs are determined by a myriad of interactions between their constituent residues, but most quantitative models of how molecular phenotype depends on genotype must approximate this by simple additive effects. While recent models have relaxed this constraint to also account for pairwise interactions, these approaches do not provide a tractable path towards modeling higher-order dependencies. Here, we show how latent variable models with nonlinear dependencies can be applied to capture beyond-pairwise constraints in biomolecules. We present a new probabilistic model for sequence families, DeepSequence, that can predict the effects of mutations across a variety of deep mutational scanning experiments significantly better than site independent or pairwise models that are based on the same evolutionary data. The model, learned in an unsupervised manner solely from sequence information, is grounded with biologically motivated priors, reveals latent organization of sequence families, and can be used to extrapolate to new parts of sequence space.

genetics↗

bioRxiv2017

Noise control is a primary function of microRNAs and post-transcriptional regulation

microRNAs are pervasive post-transcriptional regulators of protein-coding genes in multicellular organisms. Two fundamentally different models have been proposed for the function of microRNAs in gene regulation. In the first model, microRNAs act as repressors, reducing protein concentrations by accelerating mRNA decay and inhibiting translation. In the second model, in contrast, the role of microRNAs is not to reduce protein concentrations per se but to reduce fluctuations in these concentrations. Here we present genome-wide evidence that mammalian microRNAs frequently function as noise controllers rather than repressors. Moreover, we show that post-transcriptional noise control has been widely adopted across species from bacteria to animals, with microRNAs specifically employed to reduce noise in regulatory and context-specific processes in animals. Our results substantiate the detrimental nature of expression noise, reveal a universal strategy to control it, and suggest that microRNAs represent an evolutionary innovation for adaptive noise control in animals.\n\nHighlightsO_LIGenome-wide evidence that microRNAs function as noise controllers for genes with context-specific functions\nC_LIO_LIPost-transcriptional noise control is universal from bacteria to animals\nC_LIO_LIAnimals have evolved noise control for regulatory and context-specific processes\nC_LI

genetics↗

bioRxiv2017

Genetic variation in human drug-related genes

Variability in drug efficacy and adverse effects are observed in clinical practice. While the extent of genetic variability in classical pharmacokinetic genes is rather well understood, the role of genetic variation in drug targets is typically less studied. Based on 60,706 human exomes from the ExAC dataset, we performed an in-depth computational analysis of the prevalence of functional-variants in in 806 drug-related genes, including 628 known drug targets. We find that most genetic variants in these genes are very rare (f < 0.1%) and thus likely not observed in clinical trials. Overall, however, four in five patients are likely to carry a functional-variant in a target for commonly prescribed drugs and many of these might alter drug efficacy. We further computed the likelihood of 1,236 FDA approved drugs to be affected by functional-variants in their targets and show that the patient-risk varies for many drugs with respect to geographic ancestry. A focused analysis of oncological drug targets indicates that the probability of a patient carrying germline variants in oncological drug targets is with 44% high enough to suggest that not only somatic alterations, but also germline variants carried over into the tumor genome should be included in therapeutic decision-making.

bioinformatics↗