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.Cancer Research UK, Grant/Award Number: FC001003; Changzhou Science and Technology Bureau, Grant/Award Number: CE20200503; Department of Energy and Climate Change, Grant/Award Numbers: DE-AR001213, DE-SC0020400, DE-SC0021303; H2020 European Institute of Innovation and Technology, Grant/Award Numbers: 675728, 777536, 823830; Institut national de recherche en informatique et en automatique (INRIA), Grant/Award Number: Cordi-S; Lietuvos Mokslo Taryba, Grant/Award Numbers: S-MIP-17-60, S-MIP-21-35; Medical Research Council, Grant/Award Number: FC001003; Japan Society for the Promotion of Science KAKENHI, Grant/Award Number: JP19J00950; Ministerio de Ciencia e Innovación, Grant/Award Number: PID2019-110167RB-I00; Narodowe Centrum Nauki, Grant/Award Numbers: UMO-2017/25/B/ST4/01026, UMO-2017/26/M/ST4/00044, UMO-2017/27/B/ST4/00926; National Institute of General Medical Sciences, Grant/Award Numbers: R21GM127952, R35GM118078, RM1135136, T32GM132024; National Institutes of Health, Grant/Award Numbers: R01GM074255, R01GM078221, R01GM093123, R01GM109980, R01GM133840, R01GN123055, R01HL142301, R35GM124952, R35GM136409; National Natural Science Foundation of China, Grant/Award Number: 81603152; National Science Foundation, Grant/Award Numbers: AF1645512, CCF1943008, CMMI1825941, DBI1759277, DBI1759934, DBI1917263, DBI20036350, IIS1763246, MCB1925643; NWO, Grant/Award Number: TOP-PUNT 718.015.001; Wellcome Trust, Grant/Award Number: FC00100

Deep transfer learning for inter-chain contact predictions of transmembrane protein complexes

Abstract Membrane proteins are encoded by approximately a quarter of human genes. Inter-chain residue-residue contact information is important for structure prediction of membrane protein complexes and valuable for understanding their molecular mechanism. Although many deep learning methods have been proposed to predict the intra-protein contacts or helix-helix interactions in membrane proteins, it is still challenging to accurately predict their inter-chain contacts due to the limited number of transmembrane proteins. Addressing the challenge, here we develop a deep transfer learning method for predicting inter-chain contacts of transmembrane protein complexes, named DeepTMP, by taking advantage of the knowledge pre-trained from a large data set of non-transmembrane proteins. DeepTMP utilizes a geometric triangle-aware module to capture the correct inter-chain interaction from the coevolution information generated by protein language models. DeepTMP is extensively evaluated on a test set of 52 self-associated transmembrane protein complexes, and compared with state-of-the-art methods including DeepHomo2.0, CDPred, GLINTER, DeepHomo, and DNCON2_Inter. It is shown that DeepTMP considerably improves the precision of inter-chain contact prediction and outperforms the existing approaches in both accuracy and robustness

Prognostic Significance of Homocysteine Level on Neurological Outcome in Brain Arteriovenous Malformations

Objective. We aimed to investigate the serum homocysteine (Hcy) level in patients with brain arteriovenous malformation (bAVM) and their impact on neurological outcome during hospitalization. Method. We retrospectively reviewed patients diagnosed with bAVMs in Beijing Tiantan Hospital from January 2019 to August 2020. Patients were divided into two groups according to the mRS (modified Rankin Scale) score at discharge. Clinical and laboratory characteristics were compared. Logistic regression analyses were performed to identify the potential risk factors for short-term neurological outcome. Results. A total of 175 bAVM patients were enrolled in the study, including 139 patients with favorable outcome (mRS≤2) and 36 patients with unfavorable outcome (mRS>2). Hyperhomocysteinemia was identified in 32.6% of cases (n=57). Serum Hcy level was related to seizure manifestation (P=0.034) and short-term neurological outcome (P=0.027). Logistic regression analysis showed that serum glucose (OR 1.897, 95% CI 1.115-3.229; P=0.018) and Hcy level (OR 0.838, 95% CI 0.720-0.976; P=0.023) were significantly associated with short-term disability. Conclusion. Our results indicated that the lower serum Hcy level is strongly associated with in-hospital unfavorable outcome. Further prospective studies of Hcy natural history and managements in bAVMs are required, which would be valuable for evaluating the disease-modifying efficacy of oral nutritional supplements in bAVM patients

Homocysteine Level and Risk of Hemorrhage in Brain Arteriovenous Malformations

Objective. We aimed to investigate the risk factors associated with hemorrhage and clarify the relation of homocysteine (Hcy) with brain arteriovenous malformations (bAVMs). Method. We retrospectively reviewed bAVM patients from Beijing Tiantan Hospital between January 2019 and December 2019. Clinical and laboratory variables were analyzed in enrolled patients with bAVMs. Potential predictors associated with hemorrhage were evaluated by logistic regression analysis. Results. A total of 143 bAVM patients were identified in the study, including 69 unruptured and 74 ruptured cases. Patients with hemorrhage were less likely to have hyperhomocysteinemia (P=0.023). Logistic regression analysis showed that increased maximum diameter of bAVM lesions (odds ratio (OR) 0.634, 95% confidence intervals (CI) 0.479-0.839; P=0.001) and serum Hcy level (OR 0.956, 95% CI 0.920-0.993; P=0.021) were associated with lower risk of hemorrhage in bAVMs. Conclusion. The present study provided evidence regarding the association between serum Hcy and hemorrhage in patients with bAVMs. Higher Hcy level was correlated with a lower risk of rupture. Detection of factors for subsequent hemorrhage is necessary to develop therapeutic strategies for bAVMs preferably

Automated Spectrophotometric Determination of Carbonate Ion Concentration in Seawater Using a Portable Syringe Pump Based Analyzer

Observations of seawater carbonate ion concentrations are critical to assess the ecological effects of ocean acidification. Nevertheless, currently available methods are labor intensive or too complex for field applications. Here, we report the design and performance of the first fully automated portable carbonate ion analyzer. Measurements are based on reaction of carbonate and chloride ions with Pb(II) followed by quantitative UV spectrophotometric detection of the PbCO30 complex. The core hardware is a syringe pump equipped with a multi-position valve that is controlled by software written in LabVIEW. Measurement precision is 1.1% (n = 13) with a measurement frequency of 12 h−1. The analyzer was used to continuously monitor carbonate ion concentration variations in a 2500 L coral reef tank for five days (test 1), and used for shipboard underway and vertical profile analysis during a 13-day cruise (test 2). The analyzer attained a combined standard uncertainty of 3.0%, which meets the Global Ocean Acidification Observing Network\u27s “weather level” goal. Through use of a syringe pump mechanism for mixing seawater and reagent solution, the analyzer is robust, functionally flexible, and quite suitable for continuous environmental monitoring under harsh conditions

Development of an Integrated Syringe-Pump-Based Environmental-Water Analyzer (<i>i</i>SEA) and Application of It for Fully Automated Real-Time Determination of Ammonium in Fresh Water

The development of a multipurpose integrated syringe-pump-based environmental-water analyzer (<i>i</i>SEA) and its application for spectrophotometric determination of ammonium is presented. The <i>i</i>SEA consists of a mini-syringe pump equipped with a selection valve and laboratory-programmed software written by LabVIEW. The chemistry is based on a modified indophenol method using <i>o</i>-phenylphenol. The effect of reagent concentrations and sample temperatures was evaluated. This fully automated analyzer had a detection limit of 0.12 μM with sample throughput of 12 h^–1. Relative standard deviations at different concentrations (0–20 μM) were 0.23–3.36% (<i>n</i> = 3–11) and 1.0% (<i>n</i> = 144, in 24 h of continuous measurement, ∼5 μM). Calibration curves were linear (<i>R</i>² = 0.9998) over the range of 0–20 and 0–70 μM for the detection at 700 and 600 nm, respectively. The <i>i</i>SEA was applied in continuous real-time monitoring of ammonium variations in a river for 24 h and 14 days. A total of 1802 samples were measured, and only 0.4% was outlier data (≥3 sigma residuals). Measurements of reference materials and different aqueous samples (<i>n</i> = 26) showed no significant difference between results obtained by reference and present methods. The system is compact (18 cm × 22 cm × 24 cm), portable (4.8 kg), and robust (high-resolution real-time monitoring in harsh environments) and consumes a small amount of chemicals (20–30 μL/run) and sample/standards (2.9 mL/run)

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

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

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

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 similar to 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