Neodymium isotopes in peat reveal past local environmental disturbances

Funding The Stawek profile radiocarbon dating and investigation and neodymium measurements were financed by the National Science Centre, Poland, grants no. 2019/03/X/ST10/00849 and 2020/39/D/ST10/00641. The Głęboczek profile radiocarbon dating and investigation were financed by the National Science Centre, Poland, grant no. 2015/17/B/ST10/01656.Peer reviewedPublisher PD

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 homo-dimers, 3 homo-trimers, 13 hetero-dimers 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 21941 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 5 best models. Results show substantial progress achieved across a significant fraction of the 60+ participating groups. High-quality models were produced for about 40% for the targets compared to 8% two years earlier, a remarkable improvement resulting from the wide use of the AlphaFold2 and AlphaFold-Multimer software. Creative use was made of the deep learning inference engines affording the sampling of a much larger number of models and enriching the multiple sequence alignments with sequences from various sources. Wide use was also made of the AlphaFold confidence metrics to rank models, permitting top performing groups to exceed the results of the public AlphaFold-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

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

Small peatland with a big story: 600-year paleoecological and historical data from a kettle-hole peatland in Western Russia

International audiencePeatlands are important records of past environmental changes. Based on a multiproxy analysis, the main factors influencing the evolution of a peatland can be divided into autogenic and allogenic. Among the important allogenic factors, apart from climate change, are deforestation and drainage, which are directly associated with human impact. Numerous consequences arise from these processes, the most important of which are physical and chemical denudation in the catchment and the related hydrological disturbances in the catchment and peatland. The present study determined how human activities and the past climatic variability mutually influenced the development of a small peatland ecosystem. The main goals of the study were: (1) to trace the local changes of the peatland history over the past 600 years, (2) to investigate their relationship with changes in regional hydroclimate patterns, and (3) to estimate the sensitivity of a small peatland to natural and human impact. Our reconstructions were based on a multiproxy analysis, including the analysis of pollen, macrofossils, Chironomidae, Cladocera, and testate amoebae. Our results showed that, depending on the changes in water level, the history of peatland can be divided into three phases as follows: 1/the phase of stable natural conditions, 2/phase of weak changes, and 3/phase of significant changes in the catchment. Additionally, to better understand the importance of the size of catchment and the size of the depositional basin in the evolution of the studied peatland ecosystem, we compared data from two peatlands – large and small – located close to each other. The results of our study indicated that “size matters,” and that larger peatlands are much more resilient and resistant to rapid changes occurring in the direct catchment due to human activities, whereas small peatlands are more sensitive and perfect as archives of environmental changes

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