NAD metabolic dependency in cancer is shaped by gene amplification and enhancer remodelling.

Precision oncology hinges on linking tumour genotype with molecularly targeted drugs1; however, targeting the frequently dysregulated metabolic landscape of cancer has proven to be a major challenge2. Here we show that tissue context is the major determinant of dependence on the nicotinamide adenine dinucleotide (NAD) metabolic pathway in cancer. By analysing more than 7,000 tumours and 2,600 matched normal samples of 19 tissue types, coupled with mathematical modelling and extensive in vitro and in vivo analyses, we identify a simple and actionable set of 'rules'. If the rate-limiting enzyme of de novo NAD synthesis, NAPRT, is highly expressed in a normal tissue type, cancers that arise from that tissue will have a high frequency of NAPRT amplification and be completely and irreversibly dependent on NAPRT for survival. By contrast, tumours that arise from normal tissues that do not express NAPRT highly are entirely dependent on the NAD salvage pathway for survival. We identify the previously unknown enhancer that underlies this dependence. Amplification of NAPRT is shown to generate a pharmacologically actionable tumour cell dependence for survival. Dependence on another rate-limiting enzyme of the NAD synthesis pathway, NAMPT, as a result of enhancer remodelling is subject to resistance by NMRK1-dependent synthesis of NAD. These results identify a central role for tissue context in determining the choice of NAD biosynthetic pathway, explain the failure of NAMPT inhibitors, and pave the way for more effective treatments.

Machine Learning of Three-Dimensional Protein Structures to Predict the Functional Impacts of Genome Variation.

Research in the human genome sciences generates a substantial amount of genetic data for hundreds of thousands of individuals, which concomitantly increases the number of variants of unknown significance (VUS). Bioinformatic analyses can successfully reveal rare variants and variants with clear associations with disease-related phenotypes. These studies have had a significant impact on how clinical genetic screens are interpreted and how patients are stratified for treatment. There are few, if any, computational methods for variants comparable to biological activity predictions. To address this gap, we developed a machine learning method that uses protein three-dimensional structures from AlphaFold to predict how a variant will influence changes to a gene's downstream biological pathways. We trained state-of-the-art machine learning classifiers to predict which protein regions will most likely impact transcriptional activities of two proto-oncogenes, nuclear factor erythroid 2 (NFE2L2)-related factor 2 (NRF2) and c-Myc. We have identified classifiers that attain accuracies higher than 80%, which have allowed us to identify a set of key protein regions that lead to significant perturbations in c-Myc or NRF2 transcriptional pathway activities.

Advances in the Management of Early-Stage Triple-Negative Breast Cancer.

Triple-negative breast cancer (TNBC) is a subtype of breast cancer with both inter- and intratumor heterogeneity, thought to result in a more aggressive course and worse outcomes. Neoadjuvant therapy (NAT) has become the preferred treatment modality of early-stage TNBC as it allows for the downstaging of tumors in the breast and axilla, monitoring early treatment response, and most importantly, provides important prognostic information that is essential to determining post-surgical therapies to improve outcomes. It focuses on combinations of systemic drugs to optimize pathologic complete response (pCR). Excellent response to NAT has allowed surgical de-escalation in ideal candidates. Further, treatment algorithms guide the systemic management of patients based on their pCR status following surgery. The expanding knowledge of molecular pathways, genomic sequencing, and the immunological profile of TNBC has led to the use of immune checkpoint inhibitors and targeted agents, including PARP inhibitors, further revolutionizing the therapeutic landscape of this clinical entity. However, subgroups most likely to benefit from these novel approaches in TNBC remain elusive and are being extensively studied. In this review, we describe current practices and promising therapeutic options on the horizon for TNBC, surgical advances, and future trends in molecular determinants of response to therapy in early-stage TNBC.

Characterizing posttranslational modifications in prokaryotic metabolism using a multiscale workflow.

Understanding the complex interactions of protein posttranslational modifications (PTMs) represents a major challenge in metabolic engineering, synthetic biology, and the biomedical sciences. Here, we present a workflow that integrates multiplex automated genome editing (MAGE), genome-scale metabolic modeling, and atomistic molecular dynamics to study the effects of PTMs on metabolic enzymes and microbial fitness. This workflow incorporates complementary approaches across scientific disciplines; provides molecular insight into how PTMs influence cellular fitness during nutrient shifts; and demonstrates how mechanistic details of PTMs can be explored at different biological scales. As a proof of concept, we present a global analysis of PTMs on enzymes in the metabolic network of Escherichia coli Based on our workflow results, we conduct a more detailed, mechanistic analysis of the PTMs in three proteins: enolase, serine hydroxymethyltransferase, and transaldolase. Application of this workflow identified the roles of specific PTMs in observed experimental phenomena and demonstrated how individual PTMs regulate enzymes, pathways, and, ultimately, cell phenotypes.

ssbio: a Python framework for structural systems biology.

Summary: Working with protein structures at the genome-scale has been challenging in a variety of ways. Here, we present ssbio, a Python package that provides a framework to easily work with structural information in the context of genome-scale network reconstructions, which can contain thousands of individual proteins. The ssbio package provides an automated pipeline to construct high quality genome-scale models with protein structures (GEM-PROs), wrappers to popular third-party programs to compute associated protein properties, and methods to visualize and annotate structures directly in Jupyter notebooks, thus lowering the barrier of linking 3D structural data with established systems workflows. Availability and implementation: ssbio is implemented in Python and available to download under the MIT license at http://github.com/SBRG/ssbio. Documentation and Jupyter notebook tutorials are available at http://ssbio.readthedocs.io/en/latest/. Interactive notebooks can be launched using Binder at https://mybinder.org/v2/gh/SBRG/ssbio/master?filepath=Binder.ipynb. Supplementary information: Supplementary data are available at Bioinformatics online.

Machine learning in computational biology to accelerate high-throughput protein expression.

MOTIVATION: The Human Protein Atlas (HPA) enables the simultaneous characterization of thousands of proteins across various tissues to pinpoint their spatial location in the human body. This has been achieved through transcriptomics and high-throughput immunohistochemistry-based approaches, where over 40 000 unique human protein fragments have been expressed in E. coli. These datasets enable quantitative tracking of entire cellular proteomes and present new avenues for understanding molecular-level properties influencing expression and solubility. RESULTS: Combining computational biology and machine learning identifies protein properties that hinder the HPA high-throughput antibody production pipeline. We predict protein expression and solubility with accuracies of 70% and 80%, respectively, based on a subset of key properties (aromaticity, hydropathy and isoelectric point). We guide the selection of protein fragments based on these characteristics to optimize high-throughput experimentation. AVAILABILITY AND IMPLEMENTATION: We present the machine learning workflow as a series of IPython notebooks hosted on GitHub (https://github.com/SBRG/Protein_ML). The workflow can be used as a template for analysis of further expression and solubility datasets. CONTACT: ebrunk@ucsd.edu or johanr@biotech.kth.se. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Adaptation to the coupling of glycolysis to toxic methylglyoxal production in tpiA deletion strains of Escherichia coli requires synchronized and counterintuitive genetic changes.

Methylglyoxal is a highly toxic metabolite that can be produced in all living organisms. Methylglyoxal was artificially elevated by removal of the tpiA gene from a growth optimized Escherichia coli strain. The initial response to elevated methylglyoxal and its toxicity was characterized, and detoxification mechanisms were studied using adaptive laboratory evolution. We found that: 1) Multi-omics analysis revealed biological consequences of methylglyoxal toxicity, which included attack on macromolecules including DNA and RNA and perturbation of nucleotide levels; 2) Counter-intuitive cross-talk between carbon starvation and inorganic phosphate signalling was revealed in the tpiA deletion strain that required mutations in inorganic phosphate signalling mechanisms to alleviate; and 3) The split flux through lower glycolysis depleted glycolytic intermediates requiring a host of synchronized and coordinated mutations in non-intuitive network locations in order to re-adjust the metabolic flux map to achieve optimal growth. Such mutations included a systematic inactivation of the Phosphotransferase System (PTS) and alterations in cell wall biosynthesis enzyme activity. This study demonstrated that deletion of major metabolic genes followed by ALE was a productive approach to gain novel insight into the systems biology underlying optimal phenotypic states.

Adaptive laboratory evolution resolves energy depletion to maintain high aromatic metabolite phenotypes in Escherichia coli strains lacking the Phosphotransferase System.

Aromatic metabolites provide the backbone for numerous industrial and pharmaceutical compounds of high value. The Phosphotransferase System (PTS) is common to many bacteria, and is the primary mechanism for glucose uptake by Escherichia coli. The PTS was removed to conserve phosphoenolpyruvate (pep), which is a precursor for aromatic metabolites and consumed by the PTS, for aromatic metabolite production. Replicate adaptive laboratory evolution (ALE) of PTS and detailed omics data sets collected revealed that the PTS bridged the gap between respiration and fermentation, leading to distinct high fermentative and high respiratory rate phenotypes. It was also found that while all strains retained high levels of aromatic amino acid (AAA) biosynthetic precursors, only one replicate from the high glycolytic clade retained high levels of intracellular AAAs. The fast growth and high AAA precursor phenotypes could provide a starting host for cell factories targeting the overproduction aromatic metabolites.

Multiple Optimal Phenotypes Overcome Redox and Glycolytic Intermediate Metabolite Imbalances in Escherichia coli pgi Knockout Evolutions.

A mechanistic understanding of how new phenotypes develop to overcome the loss of a gene product provides valuable insight on both the metabolic and regulatory functions of the lost gene. The pgi gene, whose product catalyzes the second step in glycolysis, was deleted in a growth-optimized Escherichia coli K-12 MG1655 strain. The initial knockout (KO) strain exhibited an 80% drop in growth rate that was largely recovered in eight replicate, but phenotypically distinct, cultures after undergoing adaptive laboratory evolution (ALE). Multi-omic data sets showed that the loss of pgi substantially shifted pathway usage, leading to a redox and sugar phosphate stress response. These stress responses were overcome by unique combinations of innovative mutations selected for by ALE. Thus, the coordinated mechanisms from genome to metabolome that lead to multiple optimal phenotypes after the loss of a major gene product were revealed.IMPORTANCE A mechanistic understanding of how microbes are able to overcome the loss of a gene through regulatory and metabolic changes is not well understood. Eight independent adaptive laboratory evolution (ALE) experiments with pgi knockout strains resulted in eight phenotypically distinct endpoints that were able to overcome the gene loss. Utilizing multi-omics analysis, the coordinated mechanisms from genome to metabolome that lead to multiple optimal phenotypes after the loss of a major gene product were revealed.

Model-driven discovery of underground metabolic functions in Escherichia coli.

Enzyme promiscuity toward substrates has been discussed in evolutionary terms as providing the flexibility to adapt to novel environments. In the present work, we describe an approach toward exploring such enzyme promiscuity in the space of a metabolic network. This approach leverages genome-scale models, which have been widely used for predicting growth phenotypes in various environments or following a genetic perturbation; however, these predictions occasionally fail. Failed predictions of gene essentiality offer an opportunity for targeting biological discovery, suggesting the presence of unknown underground pathways stemming from enzymatic cross-reactivity. We demonstrate a workflow that couples constraint-based modeling and bioinformatic tools with KO strain analysis and adaptive laboratory evolution for the purpose of predicting promiscuity at the genome scale. Three cases of genes that are incorrectly predicted as essential in Escherichia coli--aspC, argD, and gltA--are examined, and isozyme functions are uncovered for each to a different extent. Seven isozyme functions based on genetic and transcriptional evidence are suggested between the genes aspC and tyrB, argD and astC, gabT and puuE, and gltA and prpC. This study demonstrates how a targeted model-driven approach to discovery can systematically fill knowledge gaps, characterize underground metabolism, and elucidate regulatory mechanisms of adaptation in response to gene KO perturbations.

A Multi-scale Computational Platform to Mechanistically Assess the Effect of Genetic Variation on Drug Responses in Human Erythrocyte Metabolism.

Progress in systems medicine brings promise to addressing patient heterogeneity and individualized therapies. Recently, genome-scale models of metabolism have been shown to provide insight into the mechanistic link between drug therapies and systems-level off-target effects while being expanded to explicitly include the three-dimensional structure of proteins. The integration of these molecular-level details, such as the physical, structural, and dynamical properties of proteins, notably expands the computational description of biochemical network-level properties and the possibility of understanding and predicting whole cell phenotypes. In this study, we present a multi-scale modeling framework that describes biological processes which range in scale from atomistic details to an entire metabolic network. Using this approach, we can understand how genetic variation, which impacts the structure and reactivity of a protein, influences both native and drug-induced metabolic states. As a proof-of-concept, we study three enzymes (catechol-O-methyltransferase, glucose-6-phosphate dehydrogenase, and glyceraldehyde-3-phosphate dehydrogenase) and their respective genetic variants which have clinically relevant associations. Using all-atom molecular dynamic simulations enables the sampling of long timescale conformational dynamics of the proteins (and their mutant variants) in complex with their respective native metabolites or drug molecules. We find that changes in a protein's structure due to a mutation influences protein binding affinity to metabolites and/or drug molecules, and inflicts large-scale changes in metabolism.

Genetic-Algorithm-Based Optimization of a Peptidic Scaffold for Sequestration and Hydration of CO2.

Biomimicry is a strategy that makes practical use of evolution to find efficient and sustainable ways to produce chemical compounds or engineer products. Exploring the natural machinery of enzymes for the production of desired compounds is a highly profitable investment, but the design of efficient biomimetic systems remains a considerable challenge. An ideal biomimetic system self-assembles in solution, binds a desired range of substrates and catalyzes reactions with turnover rates similar to the native system. To this end, tailoring catalytic functionality in engineered peptides generally requires site-directed mutagenesis or the insertion of additional amino acids, which entails an intensive search across chemical and sequence space. Here we discuss a novel strategy for the computational design of biomimetic compounds and processes that consists of a) characterization of the wild-type and biomimetic systems; b) identification of key descriptors for optimization; c) an efficient search through sequence and chemical space to tailor the catalytic capabilities of the biomimetic system. Through this proof-of-principle study, we are able to decisively understand and identify whether a given scaffold is useful, appropriate and tailorable for a given, desired task.

Free-energy landscape and characteristic forces for the initiation of DNA unzipping.

DNA unzipping, the separation of its double helix into single strands, is crucial in modulating a host of genetic processes. Although the large-scale separation of double-stranded DNA has been studied with a variety of theoretical and experimental techniques, the minute details of the very first steps of unzipping are still unclear. Here, we use atomistic molecular-dynamics simulations, coarse-grained simulations, and a statistical-mechanical model to study the initiation of DNA unzipping by an external force. Calculation of the potential of mean force profiles for the initial separation of the first few terminal basepairs in a DNA oligomer revealed that forces ranging between 130 and 230 pN are needed to disrupt the first basepair, and these values are an order of magnitude larger than those needed to disrupt basepairs in partially unzipped DNA. The force peak has an echo of ∼50 pN at the distance that unzips the second basepair. We show that the high peak needed to initiate unzipping derives from a free-energy basin that is distinct from the basins of subsequent basepairs because of entropic contributions, and we highlight the microscopic origin of the peak. To our knowledge, our results suggest a new window of exploration for single-molecule experiments.

A mechanochemical switch to control radical intermediates.

B12-dependent enzymes employ radical species with exceptional prowess to catalyze some of the most chemically challenging, thermodynamically unfavorable reactions. However, dealing with highly reactive intermediates is an extremely demanding task, requiring sophisticated control strategies to prevent unwanted side reactions. Using hybrid quantum mechanical/molecular mechanical simulations, we follow the full catalytic cycle of an AdoB12-dependent enzyme and present the details of a mechanism that utilizes a highly effective mechanochemical switch. When the switch is "off", the 5'-deoxyadenosyl radical moiety is stabilized by releasing the internal strain of an enzyme-imposed conformation. Turning the switch "on," the enzyme environment becomes the driving force to impose a distinct conformation of the 5'-deoxyadenosyl radical to avoid deleterious radical transfer. This mechanochemical switch illustrates the elaborate way in which enzymes attain selectivity of extremely chemically challenging reactions.

Lessons from nature: computational design of biomimetic compounds and processes.

Through millions of years of evolution, Nature has accomplished the development of highly efficient and sustainable processes and the idea to understand and copy natural strategies is therefore very appealing. However, in spite of intense experimental and computational research, it has turned out to be a difficult task to design efficient biomimetic systems. Here we discuss a novel strategy for the computational design of biomimetic compounds and processes that consists of i) target selection; ii) atomistic and electronic characterization of the wild type system and the biomimetic compounds; iii) identification of key descriptors through feature selection iv) choice of biomimetic template and v) efficient search of chemical and sequence space for optimization of the biomimetic system. As a proof-of-principles study, this general approach is illustrated for the computational design of a 'green' catalyst mimicking the action of the zinc metalloenzyme Human Carbonic Anhydrase (HCA). HCA is a natural model for CO2 fixation since the enzyme is able to convert CO2 into bicarbonate. Very recently, a weakly active HCA mimic based on a trihelical peptide bundle was synthetized. We have used quantum mechanical/molecular mechanical (QM/MM) Car-Parrinello simulations to study the mechanisms of action of HCA and its peptidic mimic and employed the obtained information to guide the design of improved biomimetic analogues. Applying a genetic algorithm based optimization procedure, we were able to re-engineer and optimize the biomimetic system towards its natural counter part. In a second example, we discuss a similar strategy for the design of biomimetic sensitizers for use in dye-sensitized solar cells.

Computational, structural, and kinetic evidence that Vibrio vulnificus FrsA is not a cofactor-independent pyruvate decarboxylase.

The fermentation-respiration switch (FrsA) protein in Vibrio vulnificus was recently reported to catalyze the cofactor-independent decarboxylation of pyruvate. We now report quantum mechanical/molecular mechenical calculations that examine the energetics of C-C bond cleavage for a pyruvate molecule bound within the putative active site of FrsA. These calculations suggest that the barrier to C-C bond cleavage in the bound substrate is 28 kcal/mol, which is similar to that estimated for the uncatalyzed decarboxylation of pyruvate in water at 25 °C. In agreement with the theoretical predictions, no pyruvate decarboxylase activity was detected for recombinant FrsA protein that could be crystallized and structurally characterized. These results suggest that the functional annotation of FrsA as a cofactor-independent pyruvate decarboxylase is incorrect.

Mechanism to trigger unfolding in O(6) -alkylguanine-DNA alkyltransferase.

O(6) -alkylguanine-DNA alkyltransferase (AGT) adopts a non-enzymatic suicide mechanism for the repair of methylated guanine bases by transferring the methyl adduct to itself, thereby initiating unfolding and fast degradation. Classical molecular dynamics simulations provide quantitative evidence that two conserved glycine residues at the centre of an α-helix make the structure susceptible to structural perturbations. The stability of this helix, designated the "recognition helix", is an important factor during the early onset of unfolding of human AGT (hAGT). By combining theory and experiment, we found that helical stability is controlled by key factors in the surrounding protein structure. By using a "double-clip" mechanism, nearby residues hydrogen bond to both the base and centre of the helix. This double clip stabilises this site in the protein in the absence of substrate, but the helix is destabilised upon alkylation. The present investigation aimed to establish why alkylation of hAGT leads to conformational changes and how the protein environment functions as a switch, thus turning the stability of the protein "on" or "off" to tune degradability.

Directed evolution of the suicide protein O6-alkylguanine-DNA alkyltransferase for increased reactivity results in an alkylated protein with exceptional stability.

Here we present a biophysical, structural, and computational analysis of the directed evolution of the human DNA repair protein O(6)-alkylguanine-DNA alkyltransferase (hAGT) into SNAP-tag, a self-labeling protein tag. Evolution of hAGT led not only to increased protein activity but also to higher stability, especially of the alkylated protein, suggesting that the reactivity of the suicide enzyme can be influenced by stabilizing the product of the irreversible reaction. Whereas wild-type hAGT is rapidly degraded in cells after alkyl transfer, the high stability of benzylated SNAP-tag prevents proteolytic degradation. Our data indicate that the intrinstic stability of a key α helix is an important factor in triggering the unfolding and degradation of wild-type hAGT upon alkyl transfer, providing new insights into the structure-function relationship of the DNA repair protein.

Role of environment for catalysis of the DNA repair enzyme MutY.

Control of the N-glycosylase reaction by the DNA repair enzyme, MutY, entails the organization of solvent molecules. Classical molecular dynamics and QM/MM simulations were used to investigate the solvent and environment effects contributing to catalysis. Our findings suggest that the entire reaction is an energetically neutral process, in which the first step is rate determining, requiring protonation of adenine (N(7)) to initiate cleavage, and the second step is strongly exothermic, involving hydrolysis of an oxacarbenium ion intermediate. Although water molecules are catalytically active in both steps, the first step requires an entirely different level of solvent organization compared to the second. Needed to secure protonation at N(7), a long-term solvation pattern is established which facilitates the involvement of three out of the five structured water molecules in the active site. This persistent arrangement coordinates the catalytically active water molecules into prime positions to assist the proton transfer: (i) a water molecule frequently bridges the catalytic residues and (ii) the bridging water molecule is assisted by 1-2 other 'supporting' water molecules. To maintain this configuration, MutY, surprisingly, uses hydrophobic residues in combination with hydrophilic residues to tune the microenvironment into a 'water trap'. Hydrophilic residues prolong solvent residence times by maintaining hydrogen-bonding networks, whereas the hydrophobic residues constrain the positioning of the catalytic water molecules that assist the proton-transfer event. In this way, the enzyme uses both entropic and enthalpic considerations to guide the water-assisted reaction.

Integrating computational methods to retrofit enzymes to synthetic pathways.

Microbial production of desired compounds provides an efficient framework for the development of renewable energy resources. To be competitive to traditional chemistry, one requirement is to utilize the full capacity of the microorganism to produce target compounds with high yields and turnover rates. We use integrated computational methods to generate and quantify the performance of novel biosynthetic routes that contain highly optimized catalysts. Engineering a novel reaction pathway entails addressing feasibility on multiple levels, which involves handling the complexity of large-scale biochemical networks while respecting the critical chemical phenomena at the atomistic scale. To pursue this multi-layer challenge, our strategy merges knowledge-based metabolic engineering methods with computational chemistry methods. By bridging multiple disciplines, we provide an integral computational framework that could accelerate the discovery and implementation of novel biosynthetic production routes. Using this approach, we have identified and optimized a novel biosynthetic route for the production of 3HP from pyruvate.