Accurate structure prediction of biomolecular interactions with AlphaFold 3.

The introduction of AlphaFold 21 has spurred a revolution in modelling the structure of proteins and their interactions, enabling a huge range of applications in protein modelling and design2-6. Here we describe our AlphaFold 3 model with a substantially updated diffusion-based architecture that is capable of predicting the joint structure of complexes including proteins, nucleic acids, small molecules, ions and modified residues. The new AlphaFold model demonstrates substantially improved accuracy over many previous specialized tools: far greater accuracy for protein-ligand interactions compared with state-of-the-art docking tools, much higher accuracy for protein-nucleic acid interactions compared with nucleic-acid-specific predictors and substantially higher antibody-antigen prediction accuracy compared with AlphaFold-Multimer v.2.37,8. Together, these results show that high-accuracy modelling across biomolecular space is possible within a single unified deep-learning framework.

Increased cover of native and exotic plants on the rims of harvester ant (Hymenoptera: Formicidae) nests under grazing and drought.

Harvester ants create habitats along nest rims, which some plants use as refugia. These refugia can enhance ecosystem stability to disturbances like drought and grazing, but their potential role in invasion ecology is not yet tested. Here we examine the effects of drought and grazing on nest-rim refugia of 2 harvester ant species: Pogonomyrmex occidentals and P. rugosus. We selected 4 rangeland sites with high harvester ant nest densities in northern Arizona, USA, with pre-existing grazing exclosures adjacent to heavily grazed habitat. Our objective was to determine whether nest refugia were used by native or exotic plant species for each site and scenario of drought and grazing. We measured vegetation cover on nest surfaces, on nest rims, and at 3 distances (3, 5, and 10 m) from nests. At each site, we sampled 2 treatments (grazed/excluded) during 2 seasons (drought/monsoon). We found that nest rims increased vegetation cover compared with background levels at all sites and in almost all scenarios of treatment and season, indicating that nest rims provide important refugia for plants from drought and cattle grazing. In some cases, plants enhanced on nest rims were native grasses such as blue gramma (Bouteloua gracilis) or forbs such as sunflowers (Helianthus petiolaris). However, nest rims at all sites enhanced exotic species, particularly Russian thistle (Salsola tragus), purslane (Portulaca oleracea), and bull thistle (Cirsium vulgare). These results suggest that harvester ants play important roles in invasion ecology and restoration. We discuss potential mechanisms for why certain plant species use nest-rim refugia and how harvester ant nests contribute to plant community dynamics.

Applying and improving AlphaFold at CASP14.

We describe the operation and improvement of AlphaFold, the system that was entered by the team AlphaFold2 to the "human" category in the 14th Critical Assessment of Protein Structure Prediction (CASP14). The AlphaFold system entered in CASP14 is entirely different to the one entered in CASP13. It used a novel end-to-end deep neural network trained to produce protein structures from amino acid sequence, multiple sequence alignments, and homologous proteins. In the assessors' ranking by summed z scores (>2.0), AlphaFold scored 244.0 compared to 90.8 by the next best group. The predictions made by AlphaFold had a median domain GDT_TS of 92.4; this is the first time that this level of average accuracy has been achieved during CASP, especially on the more difficult Free Modeling targets, and represents a significant improvement in the state of the art in protein structure prediction. We reported how AlphaFold was run as a human team during CASP14 and improved such that it now achieves an equivalent level of performance without intervention, opening the door to highly accurate large-scale structure prediction.

A single-institution phase I feasibility study of dose-escalated IMRT for non-operative locally advanced esophageal carcinoma.

BACKGROUND AND PURPOSE: Radiation dose escalation to improve poor outcomes with chemoradiation in locally advanced esophageal carcinoma is limited in part by increased toxicity. This Phase I study investigates the use of IMRT to improve tolerability of dose escalation. MATERIALS AND METHODS: A single-institution, prospective study was conducted between 2007 and 2013 for individuals with inoperable esophageal carcinoma. Gross disease received 60 Gy in 30 fractions and at-risk sites received 54 Gy with simultaneous integrated boost. Concurrent chemotherapy primarily consisted of cisplatin/5-FU. The primary objective was to assess feasibility (<15% rate of grade 4-5 toxicity). Secondary objectives included assessment of overall survival (OS), progression free survival (PFS), and locoregional (LRR) and distant recurrence. RESULTS: Twenty-six patients were enrolled with median follow up of 17.6 months (range 0.1 to 152.0). The majority were AJCC 7th edition Stage III (54%), distal esophagus primary (81%), and adenocarcinoma histology (85%). Twenty-one patients (81%) completed their course of radiation therapy, while only 55% received 2 cycles of concurrent cisplatin/5-FU. One grade 5 and one grade 4 cardiac event occurred, both during chemoradiation and before receiving 50 Gy. The 3-year OS was 48.6% (95% CI: 32.5 to 72.2%) and PFS was 28.5% (95% CI: 14.6 to 55.5%). Half developed distant failure with LRR occurring in 10 patients (38%), isolated in 5 patients. CONCLUSION: While feasibility was demonstrated, toxicity and compliance remained limiting factors with outcomes similar to historical controls. There remains an uncertain role for dose escalation in definitive management of locally advanced esophageal cancer.

Highly accurate protein structure prediction with AlphaFold.

Proteins are essential to life, and understanding their structure can facilitate a mechanistic understanding of their function. Through an enormous experimental effort1-4, the structures of around 100,000 unique proteins have been determined5, but this represents a small fraction of the billions of known protein sequences6,7. Structural coverage is bottlenecked by the months to years of painstaking effort required to determine a single protein structure. Accurate computational approaches are needed to address this gap and to enable large-scale structural bioinformatics. Predicting the three-dimensional structure that a protein will adopt based solely on its amino acid sequence-the structure prediction component of the 'protein folding problem'8-has been an important open research problem for more than 50 years9. Despite recent progress10-14, existing methods fall far short of atomic accuracy, especially when no homologous structure is available. Here we provide the first computational method that can regularly predict protein structures with atomic accuracy even in cases in which no similar structure is known. We validated an entirely redesigned version of our neural network-based model, AlphaFold, in the challenging 14th Critical Assessment of protein Structure Prediction (CASP14)15, demonstrating accuracy competitive with experimental structures in a majority of cases and greatly outperforming other methods. Underpinning the latest version of AlphaFold is a novel machine learning approach that incorporates physical and biological knowledge about protein structure, leveraging multi-sequence alignments, into the design of the deep learning algorithm.

Highly accurate protein structure prediction for the human proteome.

Protein structures can provide invaluable information, both for reasoning about biological processes and for enabling interventions such as structure-based drug development or targeted mutagenesis. After decades of effort, 17% of the total residues in human protein sequences are covered by an experimentally determined structure1. Here we markedly expand the structural coverage of the proteome by applying the state-of-the-art machine learning method, AlphaFold2, at a scale that covers almost the entire human proteome (98.5% of human proteins). The resulting dataset covers 58% of residues with a confident prediction, of which a subset (36% of all residues) have very high confidence. We introduce several metrics developed by building on the AlphaFold model and use them to interpret the dataset, identifying strong multi-domain predictions as well as regions that are likely to be disordered. Finally, we provide some case studies to illustrate how high-quality predictions could be used to generate biological hypotheses. We are making our predictions freely available to the community and anticipate that routine large-scale and high-accuracy structure prediction will become an important tool that will allow new questions to be addressed from a structural perspective.

Targeted free energy estimation via learned mappings.

Free energy perturbation (FEP) was proposed by Zwanzig [J. Chem. Phys. 22, 1420 (1954)] more than six decades ago as a method to estimate free energy differences and has since inspired a huge body of related methods that use it as an integral building block. Being an importance sampling based estimator, however, FEP suffers from a severe limitation: the requirement of sufficient overlap between distributions. One strategy to mitigate this problem, called Targeted FEP, uses a high-dimensional mapping in configuration space to increase the overlap of the underlying distributions. Despite its potential, this method has attracted only limited attention due to the formidable challenge of formulating a tractable mapping. Here, we cast Targeted FEP as a machine learning problem in which the mapping is parameterized as a neural network that is optimized so as to increase the overlap. We develop a new model architecture that respects permutational and periodic symmetries often encountered in atomistic simulations and test our method on a fully periodic solvation system. We demonstrate that our method leads to a substantial variance reduction in free energy estimates when compared against baselines, without requiring any additional data.

Racemisation in Chemistry and Biology.

The two enantiomers of a compound often have profoundly different biological properties and thus their liability to racemisation in aqueous solutions is an important piece of information. The authors reviewed the available data concerning the process of racemisation in vivo, in the presence of biological molecules (e.g., racemase enzymes, serum albumin, cofactors and derivatives) and under purely chemical but aqueous conditions (acid, base and other aqueous systems). Mechanistic studies are described critically in light of reported kinetic data. The types of experimental measurement that can be used to effectively determine rate constants of racemisation in various conditions are discussed and the data they provide is summarised. The proposed origins of enzymatic racemisation are presented and suggest ways to promote the process that are different from processes taking place in bulk water. Experimental and computational studies that provide understanding and quantitative predictions of racemisation risk are also presented.

Framing Bias in the Interpretation of Quality Improvement Data: Evidence From an Experiment.

BACKGROUND: A growing body of public management literature sheds light on potential shortcomings to quality improvement (QI) and performance management efforts. These challenges stem from heuristics individuals use when interpreting data. Evidence from studies of citizens suggests that individuals' evaluation of data is influenced by the linguistic framing or context of that information and may bias the way they use such information for decision-making. This study extends prospect theory into the field of public health QI by utilizing an experimental design to test for equivalency framing effects on how public health professionals interpret common QI indicators. METHODS: An experimental design utilizing randomly assigned survey vignettes is used to test for the influence of framing effects in the interpretation of QI data. The web-based survey assigned a national sample of 286 city and county health officers to a "positive frame" group or a "negative frame" group and measured perceptions of organizational performance. The majority of respondents self-report as organizational leadership. RESULTS: Public health managers are indeed susceptible to these framing effects and to a similar degree as citizens. Specifically, they tend to interpret QI information presented in a "positive frame" as indicating a higher level of performance as the same underlying data presenting in a "negative frame." These results are statistically significant and pass robustness checks when regressed against control variables and alternative sources of information. CONCLUSION: This study helps identify potential areas of reform within the reporting aspects of QI systems. Specifically, there is a need to fully contextualize data when presenting even to subject matter experts to reduce the existence of bias when making decisions and introduce training in data presentation and basic numeracy prior to fully engaging in QI initiatives.

The problem of racemization in drug discovery and tools to predict it.

INTRODUCTION: Racemization has long been an ignored risk in drug development, probably because of a lack of convenient access to good tools for its detection and an absence of methods to predict racemization risk. As a result, the potential effects of racemization have been systematically underestimated. Areas covered: Herein, the potential effects of racemization are discussed through a review of drugs for which activity and side effects for both enantiomers are known. Subsequently, drugs known to racemize are discussed and the authors review methods to predict racemization risk. Application of a method quantitatively predicting racemization risk to databases of compounds from the medicinal chemistry literature shows that success in clinical trials is negatively correlated with racemization risk. Expert opinion: It is envisioned that a quantitative method of predicting racemization risk will remove a blind spot from the drug development pipeline. Removal of the blind spot will make drug development more efficient and result in less late-stage attrition of the drug pipeline.

Quantitative Prediction of Rate Constants for Aqueous Racemization To Avoid Pointless Stereoselective Syntheses.

Racemization has a large impact upon the biological properties of molecules but the chemical scope of compounds with known rate constants for racemization in aqueous conditions was hitherto limited. To address this remarkable blind spot, we have measured the kinetics for racemization of 28 compounds using circular dichroism and 1 H NMR spectroscopy. We show that rate constants for racemization (measured by ourselves and others) correlate well with deprotonation energies from quantum mechanical (QM) and group contribution calculations. Such calculations thus provide predictions of the second-order rate constants for general-base-catalyzed racemization that are usefully accurate. When applied to recent publications describing the stereoselective synthesis of compounds of purported biological value, the calculations reveal that racemization would be sufficiently fast to render these expensive syntheses pointless.

Energy landscapes for machine learning.

Machine learning techniques are being increasingly used as flexible non-linear fitting and prediction tools in the physical sciences. Fitting functions that exhibit multiple solutions as local minima can be analysed in terms of the corresponding machine learning landscape. Methods to explore and visualise molecular potential energy landscapes can be applied to these machine learning landscapes to gain new insight into the solution space involved in training and the nature of the corresponding predictions. In particular, we can define quantities analogous to molecular structure, thermodynamics, and kinetics, and relate these emergent properties to the structure of the underlying landscape. This Perspective aims to describe these analogies with examples from recent applications, and suggest avenues for new interdisciplinary research.

Energy landscapes for a machine learning application to series data.

Methods developed to explore and characterise potential energy landscapes are applied to the corresponding landscapes obtained from optimisation of a cost function in machine learning. We consider neural network predictions for the outcome of local geometry optimisation in a triatomic cluster, where four distinct local minima exist. The accuracy of the predictions is compared for fits using data from single and multiple points in the series of atomic configurations resulting from local geometry optimisation and for alternative neural networks. The machine learning solution landscapes are visualised using disconnectivity graphs, and signatures in the effective heat capacity are analysed in terms of distributions of local minima and their properties.

Intrinsically disordered energy landscapes.

Analysis of an intrinsically disordered protein (IDP) reveals an underlying multifunnel structure for the energy landscape. We suggest that such 'intrinsically disordered' landscapes, with a number of very different competing low-energy structures, are likely to characterise IDPs, and provide a useful way to address their properties. In particular, IDPs are present in many cellular protein interaction networks, and several questions arise regarding how they bind to partners. Are conformations resembling the bound structure selected for binding, or does further folding occur on binding the partner in a induced-fit fashion? We focus on the p53 upregulated modulator of apoptosis (PUMA) protein, which adopts an α-helical conformation when bound to its partner, and is involved in the activation of apoptosis. Recent experimental evidence shows that folding is not necessary for binding, and supports an induced-fit mechanism. Using a variety of computational approaches we deduce the molecular mechanism behind the instability of the PUMA peptide as a helix in isolation. We find significant barriers between partially folded states and the helix. Our results show that the favoured conformations are molten-globule like, stabilised by charged and hydrophobic contacts, with structures resembling the bound state relatively unpopulated in equilibrium.

Equilibrium molecular thermodynamics from Kirkwood sampling.

We present two methods for barrierless equilibrium sampling of molecular systems based on the recently proposed Kirkwood method (J. Chem. Phys. 2009, 130, 134102). Kirkwood sampling employs low-order correlations among internal coordinates of a molecule for random (or non-Markovian) sampling of the high dimensional conformational space. This is a geometrical sampling method independent of the potential energy surface. The first method is a variant of biased Monte Carlo, where Kirkwood sampling is used for generating trial Monte Carlo moves. Using this method, equilibrium distributions corresponding to different temperatures and potential energy functions can be generated from a given set of low-order correlations. Since Kirkwood samples are generated independently, this method is ideally suited for massively parallel distributed computing. The second approach is a variant of reservoir replica exchange, where Kirkwood sampling is used to construct a reservoir of conformations, which exchanges conformations with the replicas performing equilibrium sampling corresponding to different thermodynamic states. Coupling with the Kirkwood reservoir enhances sampling by facilitating global jumps in the conformational space. The efficiency of both methods depends on the overlap of the Kirkwood distribution with the target equilibrium distribution. We present proof-of-concept results for a model nine-atom linear molecule and alanine dipeptide.

Superposition-Enhanced Estimation of Optimal Temperature Spacings for Parallel Tempering Simulations.

Effective parallel tempering simulations rely crucially on a properly chosen sequence of temperatures. While it is desirable to achieve a uniform exchange acceptance rate across neighboring replicas, finding a set of temperatures that achieves this end is often a difficult task, in particular for systems undergoing phase transitions. Here we present a method for determination of optimal replica spacings, which is based upon knowledge of local minima in the potential energy landscape. Working within the harmonic superposition approximation, we derive an analytic expression for the parallel tempering acceptance rate as a function of the replica temperatures. For a particular system and a given database of minima, we show how this expression can be used to determine optimal temperatures that achieve a desired uniform acceptance rate. We test our strategy for two atomic clusters that exhibit broken ergodicity, demonstrating that our method achieves uniform acceptance as well as significant efficiency gains.

Structure of the physical therapy benefit in a typical Blue Cross Blue Shield preferred provider organization plan available in the individual insurance market in 2011.

BACKGROUND: The Affordable Care Act of 2010 establishes American Health Benefit Exchanges. The benefit design of insurance plans in state health insurance exchanges will be based on the structure of existing small-employer-sponsored plans. OBJECTIVE: The purpose of this study was to describe the structure of the physical therapy benefit in a typical Blue Cross Blue Shield (BCBS) preferred provider organization (PPO) health insurance plan available in the individual insurance market in 2011. DESIGN: A cross-sectional survey design was used. METHODS: The physical therapy benefit within 39 BCBS PPO plans in 2011 was studied for a standard consumer with a standard budget. First, whether physical therapy was a benefit in the plan was determined. If so, then the structure of the benefit was described in terms of whether the physical therapy benefit was a stand-alone benefit or part of a combined-discipline benefit and whether a visit or financial limit was placed on the physical therapy benefit. RESULTS: Physical therapy was included in all BCBS plans that were studied. Ninety-three percent of plans combined physical therapy with other disciplines. Two thirds of plans placed a limit on the number of visits covered. LIMITATIONS: The results of the study are limited to 1 standard consumer, 1 association of insurance companies, 1 form of insurance (a PPO), and 1 PPO plan in each of the 39 states that were studied. CONCLUSIONS: Physical therapy is a covered benefit in a typical BCBS PPO health insurance plan. Physical therapy most often is combined with other therapy disciplines, and the number of covered visits is limited in two thirds of plans.

Toward the mechanism of ionic dissociation in water.

We investigate the solvent effects leading to dissociation of sodium chloride in water. Thermodynamic analysis reveals dissociation to be driven energetically and opposed entropically, with the loss in entropy due to an increasing number of solvent molecules entering the highly coordinated ionic solvation shell. We show through committor analysis that the ion-ion distance is an insufficient reaction coordinate, in agreement with previous findings. By application of committor analysis on various constrained solvent ensembles, we find that the dissociation event is generally sensitive to solvent fluctuations at long ranges, with both sterics and electrostatics of importance. The dynamics of the reaction reveal that solvent rearrangements leading to dissociation occur on time scales from 0.5 to 5 ps or longer, and that, near the transition state, inertial effects enhance the reaction probability of a given trajectory.

Replica exchange with nonequilibrium switches: enhancing equilibrium sampling by increasing replica overlap.

We describe a replica exchange strategy where trial swap configurations are generated by nonequilibrium switching simulations. By devoting simulation time to the switching simulations, one can systematically increase an effective overlap between replicas, which leads to an increased exchange acceptance rate and less correlated equilibrium samples. In this paper, we derive our method for a general class of stochastic dynamics, and discuss various strategies for enhancing replica overlap through novel dynamical schemes and prudent choices of switching protocols. We then demonstrate our method on a model system of alanine dipeptide in implicit solvent, characterizing decreases in data correlations and gains in sampling efficiency.