Mild chemistry synthesis of ultrathin Bi2O2S nanosheets exhibiting 2D-ferroelectricity at room temperature.

Modern technology demands miniaturization of electronic components to build small, light, and portable devices. Hence, discovery and synthesis of new non-toxic, low cost, ultra-thin ferroelectric materials having potential applications in various electronic and optoelectronic devices are of paramount importance. However, achieving room-temperature ferroelectricity in two dimensional (2D) ultra-thin systems remains a major challenge as conventional three-dimensional ferroelectric materials lose their ferroelectricity when the thickness is brought down below a critical value owing to the depolarization field. Herein, we report room-temperature ferroelectricity in ultra-thin single-crystalline 2D nanosheets of Bi2O2S synthesized by a simple, rapid, and scalable solution-based soft chemistry method. The ferroelectric ground state of Bi2O2S nanosheets is confirmed by temperature-dependent dielectric measurements as well as piezoelectric force microscopy and spectroscopy. High resolution transmission electron microscopy analysis and density functional theory-based calculations suggest that the ferroelectricity in Bi2O2S nanosheets arises due to the local distortion of Bi2O2 layers, which destroys the local inversion symmetry of Bi2O2S.

Enhancing alphafold-multimer-based protein complex structure prediction with MULTICOM in CASP15.

To enhance the AlphaFold-Multimer-based protein complex structure prediction, we developed a quaternary structure prediction system (MULTICOM) to improve the input fed to AlphaFold-Multimer and evaluate and refine its outputs. MULTICOM samples diverse multiple sequence alignments (MSAs) and templates for AlphaFold-Multimer to generate structural predictions by using both traditional sequence alignments and Foldseek-based structure alignments, ranks structural predictions through multiple complementary metrics, and refines the structural predictions via a Foldseek structure alignment-based refinement method. The MULTICOM system with different implementations was blindly tested in the assembly structure prediction in the 15th Critical Assessment of Techniques for Protein Structure Prediction (CASP15) in 2022 as both server and human predictors. MULTICOM_qa ranked 3rd among 26 CASP15 server predictors and MULTICOM_human ranked 7th among 87 CASP15 server and human predictors. The average TM-score of the first predictions submitted by MULTICOM_qa for CASP15 assembly targets is ~0.76, 5.3% higher than ~0.72 of the standard AlphaFold-Multimer. The average TM-score of the best of top 5 predictions submitted by MULTICOM_qa is ~0.80, about 8% higher than ~0.74 of the standard AlphaFold-Multimer. Moreover, the Foldseek Structure Alignment-based Multimer structure Generation (FSAMG) method outperforms the widely used sequence alignment-based multimer structure generation.

Impact of AlphaFold on structure prediction of protein complexes: The CASP15-CAPRI experiment.

We present the results for CAPRI Round 54, the 5th joint CASP-CAPRI protein assembly prediction challenge. The Round offered 37 targets, including 14 homodimers, 3 homo-trimers, 13 heterodimers including 3 antibody-antigen complexes, and 7 large assemblies. On average ~70 CASP and CAPRI predictor groups, including more than 20 automatics servers, submitted models for each target. A total of 21 941 models submitted by these groups and by 15 CAPRI scorer groups were evaluated using the CAPRI model quality measures and the DockQ score consolidating these measures. The prediction performance was quantified by a weighted score based on the number of models of acceptable quality or higher submitted by each group among their five best models. Results show substantial progress achieved across a significant fraction of the 60+ participating groups. High-quality models were produced for about 40% of the targets compared to 8% two years earlier. This remarkable improvement is due to the wide use of the AlphaFold2 and AlphaFold2-Multimer software and the confidence metrics they provide. Notably, expanded sampling of candidate solutions by manipulating these deep learning inference engines, enriching multiple sequence alignments, or integration of advanced modeling tools, enabled top performing groups to exceed the performance of a standard AlphaFold2-Multimer version used as a yard stick. This notwithstanding, performance remained poor for complexes with antibodies and nanobodies, where evolutionary relationships between the binding partners are lacking, and for complexes featuring conformational flexibility, clearly indicating that the prediction of protein complexes remains a challenging problem.

Improving AlphaFold2-based protein tertiary structure prediction with MULTICOM in CASP15.

Since the 14th Critical Assessment of Techniques for Protein Structure Prediction (CASP14), AlphaFold2 has become the standard method for protein tertiary structure prediction. One remaining challenge is to further improve its prediction. We developed a new version of the MULTICOM system to sample diverse multiple sequence alignments (MSAs) and structural templates to improve the input for AlphaFold2 to generate structural models. The models are then ranked by both the pairwise model similarity and AlphaFold2 self-reported model quality score. The top ranked models are refined by a novel structure alignment-based refinement method powered by Foldseek. Moreover, for a monomer target that is a subunit of a protein assembly (complex), MULTICOM integrates tertiary and quaternary structure predictions to account for tertiary structural changes induced by protein-protein interaction. The system participated in the tertiary structure prediction in 2022 CASP15 experiment. Our server predictor MULTICOM_refine ranked 3rd among 47 CASP15 server predictors and our human predictor MULTICOM ranked 7th among all 132 human and server predictors. The average GDT-TS score and TM-score of the first structural models that MULTICOM_refine predicted for 94 CASP15 domains are ~0.80 and ~0.92, 9.6% and 8.2% higher than ~0.73 and 0.85 of the standard AlphaFold2 predictor respectively.

Sodium-Glucose Cotransporter-2 (SGLT-2) Inhibitors in Heart Failure: An Umbrella Review.

Heart failure remains a leading cause of hospitalization and death, and presents a significant challenge for healthcare providers despite the advancements in its management. This umbrella review aimed to pool the results of meta-analyses on the use of sodium-glucose cotransporter-2 (SGLT-2) inhibitors in the treatment of heart failure patients. A literature search was done on five databases: PubMed, Cochrane Library, Scopus, Global Index Medicus, and Science Direct for articles with full texts available online. Meta-analyses of five or more randomized controlled trials (RCTs) were included; the assessment of multiple systematic reviews (AMSTAR) was used to assess the quality of included studies. A systematic search identified 10 relevant meta-analyses of RCTs, with primary analyses including outcome data from 171,556 heart failure patients. A pooled review showed that SGLT-2 inhibitors significantly reduced the risk of heart failure hospitalization, cardiovascular death, mortality, serious adverse events, and improved quality of life. SGLT-2 inhibitors are likely safe and effective in managing patients with heart failure especially considering the acute outcomes.

Architectural Effect on Self-Assembly and Biorecognition of Randomly Grafted Linear and Branched Polymers at Liquid Crystal-Water Interfaces.

Because of simple synthetic strategies, randomly functionalized amphiphilic polymers have gained much attention. Recent studies have demonstrated that such polymers can be reorganized into different nanostructures, such as spheres, cylinders, vesicles, etc., similar to amphiphilic block copolymers. Our study investigated the self-assembly of randomly functionalized hyperbranched polymers (HBP) and their linear analogues (LP) in solution and at the liquid crystal-water (LC-water) interfaces. Regardless of their architecture, the designed amphiphiles self-assembled into spherical nanoaggregates in solution and mediated the ordering transitions of LC molecules at the LC-water interface. However, the amount of amphiphiles required for LP was 10 times lower than that required for HBP amphiphiles to mediate the same ordering transition of LC molecules. Further, of the two compositionally similar amphiphiles (linear and branched), only the linear architecture responds to biorecognition events. The architectural effect can be attributed to both of these differences mentioned above.

Combining pairwise structural similarity and deep learning interface contact prediction to estimate protein complex model accuracy in CASP15.

Estimating the accuracy of quaternary structural models of protein complexes and assemblies (EMA) is important for predicting quaternary structures and applying them to studying protein function and interaction. The pairwise similarity between structural models is proven useful for estimating the quality of protein tertiary structural models, but it has been rarely applied to predicting the quality of quaternary structural models. Moreover, the pairwise similarity approach often fails when many structural models are of low quality and similar to each other. To address the gap, we developed a hybrid method (MULTICOM_qa) combining a pairwise similarity score (PSS) and an interface contact probability score (ICPS) based on the deep learning inter-chain contact prediction for estimating protein complex model accuracy. It blindly participated in the 15th Critical Assessment of Techniques for Protein Structure Prediction (CASP15) in 2022 and performed very well in estimating the global structure accuracy of assembly models. The average per-target correlation coefficient between the model quality scores predicted by MULTICOM_qa and the true quality scores of the models of CASP15 assembly targets is 0.66. The average per-target ranking loss in using the predicted quality scores to rank the models is 0.14. It was able to select good models for most targets. Moreover, several key factors (i.e., target difficulty, model sampling difficulty, skewness of model quality, and similarity between good/bad models) for EMA are identified and analyzed. The results demonstrate that combining the multi-model method (PSS) with the complementary single-model method (ICPS) is a promising approach to EMA.

Universal Piezo-Photocatalytic Wastewater Treatment on Realistic Pollutant Feedstocks by Bi4TaO8Cl: Origin of High Efficiency and Adjustable Synergy.

Clean water is a fundamental human right but millions struggle for it daily. Herein, we demonstrate a new piezo-photocatalyst with immense structural diversity for universal wastewater decontamination. Single-crystalline Bi4TaO8Cl nanoplates with exposed piezoelectric facets exhibit visible-light response, piezoelectric behavior with coercive voltages of ±5 V yielding 0.35% crystal deformation, and pressure-induced band-bending of >2.5 eV. Using five common contaminants of textile and pharmaceutical industries, we show that the nanoplates can mineralize them in all piezocatalytic, photocatalytic, and piezo-photocatalytic approaches with efficiencies higher than most catalysts developed for just one contaminant. Their efficiencies for feedstocks differing over 2 orders of magnitude in concentrations, the highest to date, are also demonstrated to simulate real-life situations. These extensive studies established that combining piezocatalytic and photocatalytic approaches can lead to a tremendous synergy exceeding >45%. The origin of synergy has been illustrated for the first time using band-bending models and improved charge transfer from valence and conduction band electronic surfaces. We further quantified synergy across reactants, concentrations, and ultrasonic frequency and power to demonstrate their versatility and unpredictability. Finally, seven parameters that contribute to synergy but create unpredictability have been identified for the rational design of piezo-photocatalysts for wastewater treatment.

Enhancing AlphaFold-Multimer-based Protein Complex Structure Prediction with MULTICOM in CASP15.

AlphaFold-Multimer has emerged as the state-of-the-art tool for predicting the quaternary structure of protein complexes (assemblies or multimers) since its release in 2021. To further enhance the AlphaFold-Multimer-based complex structure prediction, we developed a new quaternary structure prediction system (MULTICOM) to improve the input fed to AlphaFold-Multimer and evaluate and refine the outputs generated by AlphaFold2-Multimer. Specifically, MULTICOM samples diverse multiple sequence alignments (MSAs) and templates for AlphaFold-Multimer to generate structural models by using both traditional sequence alignments and new Foldseek-based structure alignments, ranks structural models through multiple complementary metrics, and refines the structural models via a Foldseek structure alignment-based refinement method. The MULTICOM system with different implementations was blindly tested in the assembly structure prediction in the 15th Critical Assessment of Techniques for Protein Structure Prediction (CASP15) in 2022 as both server and human predictors. Our server (MULTICOM_qa) ranked 3rd among 26 CASP15 server predictors and our human predictor (MULTICOM_human) ranked 7th among 87 CASP15 server and human predictors. The average TM-score of the first models predicted by MULTICOM_qa for CASP15 assembly targets is ~0.76, 5.3% higher than ~0.72 of the standard AlphaFold-Multimer. The average TM-score of the best of top 5 models predicted by MULTICOM_qa is ~0.80, about 8% higher than ~0.74 of the standard AlphaFold-Multimer. Moreover, the novel Foldseek Structure Alignment-based Model Generation (FSAMG) method based on AlphaFold-Multimer outperforms the widely used sequence alignment-based model generation. The source code of MULTICOM is available at: https://github.com/BioinfoMachineLearning/MULTICOM3.

Combining pairwise structural similarity and deep learning interface contact prediction to estimate protein complex model accuracy in CASP15.

Estimating the accuracy of quaternary structural models of protein complexes and assemblies (EMA) is important for predicting quaternary structures and applying them to studying protein function and interaction. The pairwise similarity between structural models is proven useful for estimating the quality of protein tertiary structural models, but it has been rarely applied to predicting the quality of quaternary structural models. Moreover, the pairwise similarity approach often fails when many structural models are of low quality and similar to each other. To address the gap, we developed a hybrid method (MULTICOM_qa) combining a pairwise similarity score (PSS) and an interface contact probability score (ICPS) based on the deep learning inter-chain contact prediction for estimating protein complex model accuracy. It blindly participated in the 15th Critical Assessment of Techniques for Protein Structure Prediction (CASP15) in 2022 and ranked first out of 24 predictors in estimating the global accuracy of assembly models. The average per-target correlation coefficient between the model quality scores predicted by MULTICOM_qa and the true quality scores of the models of CASP15 assembly targets is 0.66. The average per-target ranking loss in using the predicted quality scores to rank the models is 0.14. It was able to select good models for most targets. Moreover, several key factors (i.e., target difficulty, model sampling difficulty, skewness of model quality, and similarity between good/bad models) for EMA are identified and analayzed. The results demonstrate that combining the multi-model method (PSS) with the complementary single-model method (ICPS) is a promising approach to EMA. The source code of MULTICOM_qa is available at https://github.com/BioinfoMachineLearning/MULTICOM_qa .

Deep learning for reconstructing protein structures from cryo-EM density maps: Recent advances and future directions.

Cryo-Electron Microscopy (cryo-EM) has emerged as a key technology to determine the structure of proteins, particularly large protein complexes and assemblies in recent years. A key challenge in cryo-EM data analysis is to automatically reconstruct accurate protein structures from cryo-EM density maps. In this review, we briefly overview various deep learning methods for building protein structures from cryo-EM density maps, analyze their impact, and discuss the challenges of preparing high-quality data sets for training deep learning models. Looking into the future, more advanced deep learning models of effectively integrating cryo-EM data with other sources of complementary data such as protein sequences and AlphaFold-predicted structures need to be developed to further advance the field.

Introduction of Ferrocene as a Facilitator for the Construction of Supramolecular Polymers.

Proper monomer design is the key to enhancing the strength of noncovalent interactions between the molecules toward the efficient formation of supramolecular polymers (SPs). We have designed and synthesized 1,n'-disubstituted ferrocene-azobenzene-long alkyl chains, Fc(CONH-Azo-TDP)2 , to afford SPs with a high probability. The design exploits the ''molecular ball-bearing'' property of the ferrocene core, which allows two azobenzene arms to rotate in the planes of cyclopentadienyl rings, generating the most suitable molecular conformation required for SP formation. This ferrocene monomer formed a supergel consisting of SPs supported by strong intermolecular (H-bonding and π-π stacking) interactions and higher enthalpy gain than the reference molecules, where the central ferrocene core was replaced by flexible aliphatic as well as rigid benzene linkers. The molecular conformation involved in SPs, the strength of noncovalent interactions, and the process of supramolecular polymerization were investigated through NMR, UV-Vis, XRD and TEM studies. The results demonstrate that ferrocene may act as a good modulator for constructing efficient SPs.

Bi4TaO8Cl as a New Class of Layered Perovskite Oxyhalide Materials for Piezopotential Driven Efficient Seawater Splitting.

Piezocatalytic water splitting is an emerging approach to generate "green hydrogen" that can address several drawbacks of photocatalytic and electrocatalytic approaches. However, existing piezocatalysts are few and with minimal structural flexibility for engineering properties. Moreover, the scope of utilizing unprocessed water is yet unknown and may widely differ from competing techniques due to the constantly varying nature of surface potential. Herein, we present Bi4TaO8Cl as a representative of a class of layered perovskite oxyhalide piezocatalysts with high hydrogen production efficiency and exciting tailorable features including the layer number, multiple cation-anion combination options, etc. In the absence of any cocatalyst and scavenger, an ultrahigh production rate is achievable (1.5 mmol g-1 h-1), along with simultaneous generation of value-added H2O2. The production rate using seawater is somewhat less yet appreciably superior to photocatalytic H2 production by most oxides as well as piezocatalysts and has been illustrated using a double-layer model for further development.

Confinement Matters: Stabilization of CdS Nanoparticles inside a Postmodified MOF toward Photocatalytic Hydrogen Evolution.

Insights into developing innovative routes for the stabilization of photogenerated charge-separated states by suppressing charge recombination in photocatalysts is a topic of immense importance. Herein, we report the synthesis of a metal-organic framework (MOF)-based composite where CdS nanoparticles (NPs) are confined inside the nanosized pores of Zr4+-based MOF-808, namely, CdS@MOF-808. Anchoring l-cysteine into the nanospace of MOF-808 via postsynthetic ligand exchange allows the capture of Cd2+ ions from their aqueous solution, which are further utilized for in situ growth of CdS NPs inside the nanosized MOF pores. The formation of CdS@MOF-808 opens up a possibility for visible-light photocatalysis as CdS NPs (1-2 nm) are a well-studied semiconductor system with a band gap of ∼2.6 eV. The confinement of the CdS NPs inside the MOF pores, close to the Zr4+ cluster, opens up a shorter electron transfer route from CdS to the catalytic Zr4+ cluster and shows a high rate of H2 evolution (10.41 mmol g-1 h-1) from water with a loading of 3.56 wt % CdS. In contrast, a similar composite in which CdS NPs are stabilized on the external surface of MOF-808 reveals poor activity (0.15 mmol g-1 h-1). CdS NPs stabilized on the MOF-808 surface show slower and inefficient electron transfer kinetics compared to CdS stabilized inside the nanospace of the MOF, as realized by the transient absorption measurements. Therefore, this work unveils the critical role of stabilizing the photosensitizer NPs in close proximity of the catalytic sites in MOF systems towards developing highly efficient H2 evolution photocatalysts.

A deep dilated convolutional residual network for predicting interchain contacts of protein homodimers.

MOTIVATION: Deep learning has revolutionized protein tertiary structure prediction recently. The cutting-edge deep learning methods such as AlphaFold can predict high-accuracy tertiary structures for most individual protein chains. However, the accuracy of predicting quaternary structures of protein complexes consisting of multiple chains is still relatively low due to lack of advanced deep learning methods in the field. Because interchain residue-residue contacts can be used as distance restraints to guide quaternary structure modeling, here we develop a deep dilated convolutional residual network method (DRCon) to predict interchain residue-residue contacts in homodimers from residue-residue co-evolutionary signals derived from multiple sequence alignments of monomers, intrachain residue-residue contacts of monomers extracted from true/predicted tertiary structures or predicted by deep learning, and other sequence and structural features. RESULTS: Tested on three homodimer test datasets (Homo_std dataset, DeepHomo dataset and CASP-CAPRI dataset), the precision of DRCon for top L/5 interchain contact predictions (L: length of monomer in a homodimer) is 43.46%, 47.10% and 33.50% respectively at 6 Å contact threshold, which is substantially better than DeepHomo and DNCON2_inter and similar to Glinter. Moreover, our experiments demonstrate that using predicted tertiary structure or intrachain contacts of monomers in the unbound state as input, DRCon still performs well, even though its accuracy is lower than using true tertiary structures in the bound state are used as input. Finally, our case study shows that good interchain contact predictions can be used to build high-accuracy quaternary structure models of homodimers. AVAILABILITY AND IMPLEMENTATION: The source code of DRCon is available at https://github.com/jianlin-cheng/DRCon. The datasets are available at https://zenodo.org/record/5998532#.YgF70vXMKsB. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Charge-Transfer Excitation within a Hybrid-(G)KS Framework through Cartesian Grid DFT.

Organic molecules that exhibit charge-transfer (CT) excited states are known to play an important role in processes linked to electron transfer properties and molecular conductance. In this article, we present a simple technique based on "Becke's excitation theorem" that offers an accurate picture of these electronic states. It expresses the correlated energy splitting between triplet and its corresponding singlet states by a two-electron integral, which is numerically evaluated by our recently developed strategy on Cartesian grid. We first examine the consistency of our adopted numerical strategy to evaluate the integral with the original prescribed technique. Then we assess the method on weakly bound CT complexes with three different functionals (BLYP, B3LYP, and LC-BLYP). The accuracy on asymptotic limit of CT excitation is also explored. Finally in order to illustrate the strength and feasibility, it is further extended to a few "challenging" molecules. The method, when employed with hybrid B3LYP functional, turns out to be quite accurate to describe CT excitation energy.

Enzyme producing insect gut microbes: an unexplored biotechnological aspect.

To explore the unmapped biotechnologically important microbial platforms for human welfare, the insect gut system is such a promising arena. Insects, the inhabitant of all ecological niches, harbor a healthy diversified microbial population in their versatile gut environment. This deep-rooted symbiotic relationship between insects and gut microbes is the result of several indispensable microbial performances that include: enzyme production, detoxification of plant defense compounds and insecticides, maintenance of life cycle, host fertility, bioremediation, pest biocontrol, production of antimicrobial compounds, and in addition provide vitamins, amino acids, and lactic acids to their hosts. Insects have developed such symbiotic interactions with different microorganisms for nutritional benefits like the digestion of dietary compounds by the production of several key hydrolytic enzymes viz: amylase, cellulase, lignocellulase, protease, lipase, xylanase, pectinase, chitinase, laccase, etc. The nutritional enrichment offered by these microbes to insects may be the key factor in the evolutionary attainment of this group. Around one million insect species are grouped under 31 orders, however, only ten of such groups' have been studied in relation to enzyme-producing gut microbes. Moreover, insect gut symbionts are a potential source of biotechnologically active biomolecules as these microbes go through a course of selection pressures in their host gut environment. As symbiosis has pronounced potential regarding the production of novel compounds, especially enzymes with multidimensional industrial capabilities, so there are ample scopes to explore this treasure box for human welfare. Biological significance as well as industrially compatible capabilities can categorize these insect gut symbionts as an unexplored biotechnological aspect.

Distance-based reconstruction of protein quaternary structures from inter-chain contacts.

Predicting the quaternary structure of protein complex is an important problem. Inter-chain residue-residue contact prediction can provide useful information to guide the ab initio reconstruction of quaternary structures. However, few methods have been developed to build quaternary structures from predicted inter-chain contacts. Here, we develop the first method based on gradient descent optimization (GD) to build quaternary structures of protein dimers utilizing inter-chain contacts as distance restraints. We evaluate GD on several datasets of homodimers and heterodimers using true/predicted contacts and monomer structures as input. GD consistently performs better than both simulated annealing and Markov Chain Monte Carlo simulation. Starting from an arbitrarily quaternary structure randomly initialized from the tertiary structures of protein chains and using true inter-chain contacts as input, GD can reconstruct high-quality structural models for homodimers and heterodimers with average TM-score ranging from 0.92 to 0.99 and average interface root mean square distance from 0.72 Å to 1.64 Å. On a dataset of 115 homodimers, using predicted inter-chain contacts as restraints, the average TM-score of the structural models built by GD is 0.76. For 46% of the homodimers, high-quality structural models with TM-score ≥ 0.9 are reconstructed from predicted contacts. There is a strong correlation between the quality of the reconstructed models and the precision and recall of predicted contacts. Only a moderate precision or recall of inter-chain contact prediction is needed to build good structural models for most homodimers. Moreover, GD improves the quality of quaternary structures predicted by AlphaFold2 on a Critical Assessment of Techniques for Protein Structure Prediction-Critical Assessments of Predictions of Interactions dataset.

DeepComplex: A Web Server of Predicting Protein Complex Structures by Deep Learning Inter-chain Contact Prediction and Distance-Based Modelling.

Proteins interact to form complexes. Predicting the quaternary structure of protein complexes is useful for protein function analysis, protein engineering, and drug design. However, few user-friendly tools leveraging the latest deep learning technology for inter-chain contact prediction and the distance-based modelling to predict protein quaternary structures are available. To address this gap, we develop DeepComplex, a web server for predicting structures of dimeric protein complexes. It uses deep learning to predict inter-chain contacts in a homodimer or heterodimer. The predicted contacts are then used to construct a quaternary structure of the dimer by the distance-based modelling, which can be interactively viewed and analysed. The web server is freely accessible and requires no registration. It can be easily used by providing a job name and an email address along with the tertiary structure for one chain of a homodimer or two chains of a heterodimer. The output webpage provides the multiple sequence alignment, predicted inter-chain residue-residue contact map, and predicted quaternary structure of the dimer. DeepComplex web server is freely available at http://tulip.rnet.missouri.edu/deepcomplex/web_index.html.

Prediction of protein assemblies, the next frontier: The CASP14-CAPRI experiment.

We present the results for CAPRI Round 50, the fourth joint CASP-CAPRI protein assembly prediction challenge. The Round comprised a total of twelve targets, including six dimers, three trimers, and three higher-order oligomers. Four of these were easy targets, for which good structural templates were available either for the full assembly, or for the main interfaces (of the higher-order oligomers). Eight were difficult targets for which only distantly related templates were found for the individual subunits. Twenty-five CAPRI groups including eight automatic servers submitted ~1250 models per target. Twenty groups including six servers participated in the CAPRI scoring challenge submitted ~190 models per target. The accuracy of the predicted models was evaluated using the classical CAPRI criteria. The prediction performance was measured by a weighted scoring scheme that takes into account the number of models of acceptable quality or higher submitted by each group as part of their five top-ranking models. Compared to the previous CASP-CAPRI challenge, top performing groups submitted such models for a larger fraction (70-75%) of the targets in this Round, but fewer of these models were of high accuracy. Scorer groups achieved stronger performance with more groups submitting correct models for 70-80% of the targets or achieving high accuracy predictions. Servers performed less well in general, except for the MDOCKPP and LZERD servers, who performed on par with human groups. In addition to these results, major advances in methodology are discussed, providing an informative overview of where the prediction of protein assemblies currently stands.