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Specifying, assessing, and selecting among candidate statistical models is fundamental to ecological research. Commonly used approaches to model selection are based on predictive scores and include information criteria such as Akaike’s information criterion, and cross validation. Based on data splitting, cross validation is particularly versatile because it can be used even when it is not possible to derive a likelihood (e.g., many forms of machine learning) or count parameters precisely (e.g., mixed-effects models). However, much of the literature on cross validation is technical and spread across statistical journals, making it difficult for ecological analysts to assess and choose among the wide range of options. Here we provide a comprehensive, accessible review that explains important—but often overlooked—technical aspects of cross validation for model selection, such as: bias correction, estimation uncertainty, choice of scores, and selection rules to mitigate overfitting. We synthesize the relevant statistical advances to make recommendations for the choice of cross-validation technique and we present two ecological case studies to illustrate their application. In most instances, we recommend using exact or approximate leave-one-out cross validation to minimize bias, or otherwise k-fold with bias correction if k < 10. To mitigate overfitting when using cross validation, we recommend calibrated selection via our recently introduced modified one-standard-error rule. We advocate for the use of predictive scores in model selection across a range of typical modeling goals, such as exploration, hypothesis testing, and prediction, provided that models are specified in accordance with the stated goal. We also emphasize, as others have done, that inference on parameter estimates is biased if preceded by model selection and instead requires a carefully specified single model or further technical adjustments.

Homologous recombination (HR) is a pathway to faithfully repair DNA double-strand breaks (DSBs). At the core of this pathway is a DNA recombinase, which, as a nucleoprotein filament on ssDNA, pairs with homologous DNA as a template to repair the damaged site. In eukaryotes Rad51 is the recombinase capable of carrying out essential steps including strand invasion, homology search on the sister chromatid and strand exchange. Importantly, a tightly regulated process involving many protein factors has evolved to ensure proper localisation of this DNA repair machinery and its correct timing within the cell cycle. Dysregulation of any of the proteins involved can result in unchecked DNA damage, leading to uncontrolled cell division and cancer. Indeed, many are tumour suppressors and are key targets in the development of new cancer therapies. Over the past 40 years, our structural and mechanistic understanding of homologous recombination has steadily increased with notable recent advancements due to the advances in single particle cryo electron microscopy. These have resulted in higher resolution structural models of the signalling proteins ATM (ataxia telangiectasia mutated), and ATR (ataxia telangiectasia and Rad3-related protein), along with various structures of Rad51. However, structural information of the other major players involved, such as BRCA1 (breast cancer type 1 susceptibility protein) and BRCA2 (breast cancer type 2 susceptibility protein), has been limited to crystal structures of isolated domains and low-resolution electron microscopy reconstructions of the full-length proteins. Here we summarise the current structural understanding of homologous recombination, focusing on key proteins in recruitment and signalling events as well as the mediators for the Rad51 recombinase.

Nature has evolved biosynthetic pathways to molecules possessing reactive warheads that inspired the development of many therapeutic agents, including penicillin antibiotics. Peptides armed with electrophilic warheads have proven to be particularly effective covalent inhibitors, providing essential antimicrobial, antiviral and anticancer agents. Here we provide a full characterization of the pathways that nature deploys to assemble peptides with β-lactone warheads, which are potent proteasome inhibitors with promising anticancer activity. Warhead assembly involves a three-step cryptic methylation sequence, which is likely required to reduce unfavorable electrostatic interactions during the sterically demanding β-lactonization. Amide-bond synthetase and adenosine triphosphate (ATP)-grasp enzymes couple amino acids to the β-lactone warhead, generating the bioactive peptide products. After reconstituting the entire pathway to β-lactone peptides in vitro, we go on to deliver a diverse range of analogs through enzymatic cascade reactions. Our approach is more efficient and cleaner than the synthetic methods currently used to produce clinically important warhead-containing peptides. Total in vitro biosynthesis can reveal unusual pathways evolved by nature to produce natural products. Here the authors report on enzymatic cascades, comprising a cryptic methylation sequence, efficiently delivering β-lactone-containing peptide proteasome inhibitors with promising anticancer activity.

Replication Protein A (RPA), the major eukaryotic single stranded DNA-binding protein, binds to exposed ssDNA to protect it from nucleases, participates in a myriad of nucleic acid transactions and coordinates the recruitment of other important players. RPA is a heterotrimer and coats long stretches of single-stranded DNA (ssDNA). The precise molecular architecture of the RPA subunits and its DNA binding domains (DBDs) during assembly is poorly understood. Using cryo electron microscopy we obtained a 3D reconstruction of the RPA trimerisation core bound with ssDNA (∼55 kDa) at ∼4.7 Å resolution and a dimeric RPA assembly on ssDNA. FRET-based solution studies reveal dynamic rearrangements of DBDs during coordinated RPA binding and this activity is regulated by phosphorylation at S178 in RPA70. We present a structural model on how dynamic DBDs promote the cooperative assembly of multiple RPAs on long ssDNA. Replication Protein A (RPA) coats single stranded DNA (ssDNA) generated during DNA recombination, replication and repair. Here the authors present a structural model suggesting how RPA’s DNA-binding domains promote cooperative assembly of multiple RPAs on long ssDNA.

Information-theoretic approaches to model selection, such as Akaike’s information criterion (AIC) and cross validation, provide a rigorous framework to select among candidate hypotheses in ecology, yet the persistent concern of overfitting undermines the interpretation of inferred processes. A common misconception is that overfitting is due to the choice of criterion or model score, despite research demonstrating that selection uncertainty associated with score estimation is the predominant influence. Here we introduce a novel selection rule that identifies a parsimonious model by directly accounting for estimation uncertainty, while still retaining an information-theoretic interpretation. The new rule, which is a modification of the existing one-standard-error rule, mitigates overfitting and reduces the likelihood that spurious effects will be included in the selected model, thereby improving its inferential properties. We present the rule and illustrative examples in the context of maximum-likelihood estimation and Kullback-Leibler discrepancy, although the rule is applicable in a more general setting, including Bayesian model selection and other types of discrepancy.

Break-induced replication (BIR) facilitates single-ended DNA double-strand break (DSB) repair. Upon homologous recombination-mediated strand invasion into a homologous repair template, BIR is catalysed by a minimal replisome comprising PCNA, DNA polymerase δ (Pol δ), and the Pif1 helicase. Here, we identify an interaction between Pol δ and single-stranded DNA (ssDNA)-binding protein RPA mediated by an RPA-binding module (RBM) within Pol δ subunit Pol32 and RPA subunit Rfa1. Pol32 RBM phosphorylation at Thr256 and Thr257 increases its affinity for Rfa1, while corresponding phospho-mimetic amino-acid substitutions promote BIR efficiency in vivo. This suggests that Pol32 functions as a rheostat whose phosphorylation enhances Pol δ’s affinity for RPA-bound BIR intermediates, thereby boosting BIR efficiency. Modelling indicates that Pol32 phospho-RBM-Rfa1 interactions mirror the binding mode of RBMs in Pif1 and the FANCM helicase and BIR antagonist Mph1. This implies a key role for RPA in the dynamic orchestration of the enzymes mediating BIR.

The cell cycle checkpoint kinase Mec1ATR and its integral partner Ddc2ATRIP are vital for the DNA damage and replication stress response. Mec1–Ddc2 “senses” single-stranded DNA (ssDNA) by being recruited to the ssDNA binding Replication Protein A (RPA) via Ddc2. In this study, we show that a DNA damage–induced phosphorylation circuit modulates checkpoint recruitment and function. We demonstrate that Ddc2–RPA interactions modulate the association between RPA and ssDNA and that Rfa1-phosphorylation aids in the further recruitment of Mec1–Ddc2. We also uncover an underappreciated role for Ddc2 phosphorylation that enhances its recruitment to RPA-ssDNA that is important for the DNA damage checkpoint in yeast. The crystal structure of a phosphorylated Ddc2 peptide in complex with its RPA interaction domain provides molecular details of how checkpoint recruitment is enhanced, which involves Zn2+. Using electron microscopy and structural modeling approaches, we propose that Mec1–Ddc2 complexes can form higher order assemblies with RPA when Ddc2 is phosphorylated. Together, our results provide insight into Mec1 recruitment and suggest that formation of supramolecular complexes of RPA and Mec1–Ddc2, modulated by phosphorylation, would allow for rapid clustering of damage foci to promote checkpoint signaling.

In response to DNA damage or replication fork stalling, the basal activity of Mec1 ATR is stimulated in a cell-cycle-dependent manner, leading to cell-cycle arrest and the promotion of DNA repair. Mec1 ATR dysfunction leads to cell death in yeast and causes chromosome instability and embryonic lethality in mammals. Thus, ATR is a major target for cancer therapies in homologous recombination–deficient cancers. Here we identify a single mutation in Mec1, conserved in ATR, that results in constitutive activity. Using cryo-electron microscopy, we determine the structures of this constitutively active form (Mec1(F2244L)-Ddc2) at 2.8 Å and the wild type at 3.8 Å, both in complex with Mg 2+ -AMP-PNP. These structures yield a near-complete atomic model for Mec1–Ddc2 and uncover the molecular basis for low basal activity and the conformational changes required for activation. Combined with biochemical and genetic data, we discover key regulatory regions and propose a Mec1 activation mechanism. Cryo-EM structures and functional analyses of wild-type and constitutively active Mec1–Ddc2 complexes reveal the basis of Mec1 kinase activation and how Dpb11 stimulates Mec1 activity.

Southeast Asia is highly biodiverse and currently experiences among the highest rates of tropical deforestation globally, but impacts on biodiversity are not well synthesized. We use Bayesian multi-level modeling to meta-analyse 831 pairwise comparisons of biodiversity in sites subject to land-use driven deforestation (for example, plantations or logged forest) versus undisturbed sites (control sites). After controlling for hierarchical dependencies, we show that biodiversity is a fifth lower in sites with these land-use driven deforestation (95% credible interval = 16%–28%, mean = 22%). This reduction was greater when forest losses were of high-intensity (34% reduction in biodiversity) compared to low-intensity (18% reduction), and effects were consistent across biogeographic regions and taxa. Oil-palm plantations led to the greatest reduction in biodiversity (39%, CI 27%–48%), and agroforests the least (24%, CI 10%–37%). We also find that biodiversity was reduced by 26% (CI 4%–42%) in secondary forest sites compared to undisturbed control sites, but biodiversity was the same in intermediate or mature-aged secondary forest compared to control sites (although species composition was potentially altered). Overall, our study provides a new line of evidence of the substantial detrimental impacts of land-use driven deforestation and particular types of land-use on the biodiversity of Southeast Asia.

Rising crop production over the last half century has had far-reaching consequences for human welfare and the environment. With food demand projected to rise, one of the central challenges in minimizing agriculture's impacts on the climate and biodiversity is to increase crop production with higher yields rather than more cropland. However, quantifying progress is challenging. When analyzed at the most aggregated, global level, yields can be defined as the total crop output per unit area per year, but aggregate yields are driven by multiple factors, only some of which have a clear relationship to improved agricultural production. To date, there is no research that simultaneously determines how much of rising crop production has been met by rising aggregate yields versus cropland expansion, while also quantifying the unique contribution of each yield driver. Using LMDI decomposition analysis, we find that rising aggregate yields contributed far more than cropland expansion (89% compared to 11%). That is, growing global food demand has by and large been met by growing more crops on the same amount of land, rather than expanding cropland. Our second-stage decomposition showed that nearly two-thirds of aggregate yield improvements have come from pure yield, or the output of a given crop per unit of harvested cropland area in a given country per unit area per year. The remainder has come from less-discussed drivers of aggregate yields, including cropping intensity, changes in the geographic distribution of cropland, and crop composition. Further, we use attribution analysis to show the contributions to different decomposition factors from countries grouped by climate, income, and region, as well as from different crops. Such granular yet comprehensive breakdowns of crop production and aggregate yields offer more accurate forecasts and can help focus policies on the most promising levers to meet rising food demand sustainably.