Team-wise Effective Communication in Multi-Agent Reinforcement Learning

Effective communication is crucial for the success of multi-agent systems, as it promotes collaboration for attaining joint objectives and enhances competitive efforts towards individual goals. In the context of multi-agent reinforcement learning, determining “whom”, “how” and “what” to communicate are crucial factors for developing effective policies. Therefore, we propose TeamComm, a novel framework for multi-agent communication reinforcement learning. First, it introduces a dynamic team reasoning policy, allowing agents to dynamically form teams and adapt their communication partners based on task requirements and environment states in cooperative or competitive scenarios. Second, TeamComm utilizes heterogeneous communication channels consisting of intra- and inter-team to achieve diverse information flow. Lastly, TeamComm leverages the information bottleneck principle to optimize communication content, guiding agents to convey relevant and valuable information. Through experimental evaluations on three popular environments with seven different scenarios, we empirically demonstrate the superior performance of TeamComm compared to existing methods.</p

Dysferlin Forms a Dimer Mediated by the C2 Domains and the Transmembrane Domain In Vitro and in Living Cells

Dysferlin was previously identified as a key player in muscle membrane repair and its deficiency leads to the development of muscular dystrophy and cardiomyopathy. However, little is known about the oligomerization of this protein in the plasma membrane. Here we report for the first time that dysferlin forms a dimer in vitro and in living adult skeletal muscle fibers isolated from mice. Endogenous dysferlin from rabbit skeletal muscle exists primarily as a ∼460 kDa species in detergent-solubilized muscle homogenate, as shown by sucrose gradient fractionation, gel filtration and cross-linking assays. Fluorescent protein (YFP) labeled human dysferlin forms a dimer in vitro, as demonstrated by fluorescence correlation spectroscopy (FCS) and photon counting histogram (PCH) analyses. Dysferlin also dimerizes in living cells, as probed by fluorescence resonance energy transfer (FRET). Domain mapping FRET experiments showed that dysferlin dimerization is mediated by its transmembrane domain and by multiple C2 domains. However, C2A did not significantly contribute to dimerization; notably, this is the only C2 domain in dysferlin known to engage in a Ca-dependent interaction with cell membranes. Taken together, the data suggest that Ca-insensitive C2 domains mediate high affinity self-association of dysferlin in a parallel homodimer, leaving the Ca-sensitive C2A domain free to interact with membranes

An analysis and evaluation of the WeFold collaborative for protein structure prediction and its pipelines in CASP11 and CASP12

Every two years groups worldwide participate in the Critical Assessment of Protein Structure Prediction (CASP) experiment to blindly test the strengths and weaknesses of their computational methods. CASP has significantly advanced the field but many hurdles still remain, which may require new ideas and collaborations. In 2012 a web-based effort called WeFold, was initiated to promote collaboration within the CASP community and attract researchers from other fields to contribute new ideas to CASP. Members of the WeFold coopetition (cooperation and competition) participated in CASP as individual teams, but also shared components of their methods to create hybrid pipelines and actively contributed to this effort. We assert that the scale and diversity of integrative prediction pipelines could not have been achieved by any individual lab or even by any collaboration among a few partners. The models contributed by the participating groups and generated by the pipelines are publicly available at the WeFold website providing a wealth of data that remains to be tapped. Here, we analyze the results of the 2014 and 2016 pipelines showing improvements according to the CASP assessment as well as areas that require further adjustments and research

An analysis and evaluation of the WeFold collaborative for protein structure prediction and its pipelines in CASP11 and CASP12

Every two years groups worldwide participate in the Critical Assessment of Protein Structure Prediction (CASP) experiment to blindly test the strengths and weaknesses of their computational methods. CASP has significantly advanced the field but many hurdles still remain, which may require new ideas and collaborations. In 2012 a web-based effort called WeFold, was initiated to promote collaboration within the CASP community and attract researchers from other fields to contribute new ideas to CASP. Members of the WeFold coopetition (cooperation and competition) participated in CASP as individual teams, but also shared components of their methods to create hybrid pipelines and actively contributed to this effort. We assert that the scale and diversity of integrative prediction pipelines could not have been achieved by any individual lab or even by any collaboration among a few partners. The models contributed by the participating groups and generated by the pipelines are publicly available at the WeFold website providing a wealth of data that remains to be tapped. Here, we analyze the results of the 2014 and 2016 pipelines showing improvements according to the CASP assessment as well as areas that require further adjustments and research

The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens

Background The Critical Assessment of Functional Annotation (CAFA) is an ongoing, global, community-driven effort to evaluate and improve the computational annotation of protein function. Results Here, we report on the results of the third CAFA challenge, CAFA3, that featured an expanded analysis over the previous CAFA rounds, both in terms of volume of data analyzed and the types of analysis performed. In a novel and major new development, computational predictions and assessment goals drove some of the experimental assays, resulting in new functional annotations for more than 1000 genes. Specifically, we performed experimental whole-genome mutation screening in Candida albicans and Pseudomonas aureginosa genomes, which provided us with genome-wide experimental data for genes associated with biofilm formation and motility. We further performed targeted assays on selected genes in Drosophila melanogaster, which we suspected of being involved in long-term memory. Conclusion We conclude that while predictions of the molecular function and biological process annotations have slightly improved over time, those of the cellular component have not. Term-centric prediction of experimental annotations remains equally challenging; although the performance of the top methods is significantly better than the expectations set by baseline methods in C. albicans and D. melanogaster, it leaves considerable room and need for improvement. Finally, we report that the CAFA community now involves a broad range of participants with expertise in bioinformatics, biological experimentation, biocuration, and bio-ontologies, working together to improve functional annotation, computational function prediction, and our ability to manage big data in the era of large experimental screens.Peer reviewe

Cryo-EM model validation recommendations based on outcomes of the 2019 EMDataResource challenge

This paper describes outcomes of the 2019 Cryo-EM Model Challenge. The goals were to (1) assess the quality of models that can be produced from cryogenic electron microscopy (cryo-EM) maps using current modeling software, (2) evaluate reproducibility of modeling results from different software developers and users and (3) compare performance of current metrics used for model evaluation, particularly Fit-to-Map metrics, with focus on near-atomic resolution. Our findings demonstrate the relatively high accuracy and reproducibility of cryo-EM models derived by 13 participating teams from four benchmark maps, including three forming a resolution series (1.8 to 3.1 Å). The results permit specific recommendations to be made about validating near-atomic cryo-EM structures both in the context of individual experiments and structure data archives such as the Protein Data Bank. We recommend the adoption of multiple scoring parameters to provide full and objective annotation and assessment of the model, reflective of the observed cryo-EM map density

The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens

BackgroundThe Critical Assessment of Functional Annotation (CAFA) is an ongoing, global, community-driven effort to evaluate and improve the computational annotation of protein function.ResultsHere, we report on the results of the third CAFA challenge, CAFA3, that featured an expanded analysis over the previous CAFA rounds, both in terms of volume of data analyzed and the types of analysis performed. In a novel and major new development, computational predictions and assessment goals drove some of the experimental assays, resulting in new functional annotations for more than 1000 genes. Specifically, we performed experimental whole-genome mutation screening in Candida albicans and Pseudomonas aureginosa genomes, which provided us with genome-wide experimental data for genes associated with biofilm formation and motility. We further performed targeted assays on selected genes in Drosophila melanogaster, which we suspected of being involved in long-term memory.ConclusionWe conclude that while predictions of the molecular function and biological process annotations have slightly improved over time, those of the cellular component have not. Term-centric prediction of experimental annotations remains equally challenging; although the performance of the top methods is significantly better than the expectations set by baseline methods in C. albicans and D. melanogaster, it leaves considerable room and need for improvement. Finally, we report that the CAFA community now involves a broad range of participants with expertise in bioinformatics, biological experimentation, biocuration, and bio-ontologies, working together to improve functional annotation, computational function prediction, and our ability to manage big data in the era of large experimental screens.</p

Additional file 1: of DeepQA: improving the estimation of single protein model quality with deep belief networks

Table S1. Z-score improvement of applying DeepQA for CASP11 top performance protein tertiary structure prediction methods. Table S2. TM-score and RMSD score (and their Z-score) of DeepQA on ab initio datasets. Table S3. TM-score and RMSD score of ProQ2 on ab initio datasets. Table S4. Average per-target correlation and loss for DeepQA and ResQ on 54 targets of CASP11. (DOCX 34 kb