Researchers found that a metabolite produced by gut bacteria can weaken the barrier that protects the brain and promote changes associated with the cognitive disease. For years, scientists have known that the gut microbiome of people with Alzheimer's differs from that of healthy people. What remains unclear is whether these differences are a consequence of the disease, contribute to it, or simply accompany it. It is possible that part of the puzzle lies not in the gut bacteria themselves but in the molecules they produce, which end up circulating throughout the body. A team of scientists believes it has found one such molecule.

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The AI-based program AlphaFold predicts a protein's 3D structure with remarkable accuracy. However, it tends to reduce heterogeneous structures to a single dominant conformation, or shape, and overlooks experimental conditions that can alter local structure. Researchers at the Institute of Science and Technology Austria (ISTA) and international collaborators have now developed a way to guide AlphaFold with experimental data. Their approach, published in, paves the way for improved future predictive models. Our understanding of molecular structures is considerably influenced by X-ray crystallography--a technique that has served as structural biology's central pillar for many decades.

The second day's play at RoboCup 2026 has drawn to a close with another bumper set of matches. Teams have come from far and wide to take part in the humanoid soccer competition this year, with 17 different countries represented. China is the most represented country, boasting 15 teams across the three divisions. Other countries taking part are geographically widespread, ranging from Colombia to Malaysia, from Germany to Australia. In advance of the competition, all applying teams provided a video, team description paper, and information about the robots and software that they use.

Protein engineering is a field primed for artificial intelligence research. Each protein is made up of amino acids; to optimize a protein function, researchers modify proteins by switching out one of 20 different amino acids for another. For a protein that is just 50 amino acids in length, this leads to approximately 1.13 10 potential combinations to test. This number of potential combinations, impossible to test in the lab, makes protein engineering an ideal challenge for AI. Modeling which of these combinations will give the best results is a perfect problem for the technology's massive computing power.

This year, RoboCup will be held in Incheon, South Korea, from 2-6 July. The event will see teams take part in competitions, training sessions, and a symposium. It's an exciting time for RoboCup, as there have been some updates to the leagues and competition format . Most prominently, the soccer leagues will have a primary focus on humanoid robots. A workshop focused on sharing projects, experiences, and innovations in educational robotics.

Many drug and antibody discovery pathways focus on intricately folded cell membrane proteins. When molecules of a drug candidate bind to these proteins, like a key going into a lock, they trigger chemical cascades that alter cellular behavior. Understanding how proteins fold and move is therefore essential for developing drugs that interact well with their targets. Artificial intelligence (AI) is a very useful tool to generate novel protein structures, but most systems - including Google DeepMind's AlphaFold - focus on producing static'snapshots' of proteins. Subtle rearrangements of atoms in structures called side chains, which influence a protein's interactions with other molecules, are not captured.

Inverse protein folding addresses the challenge of designing amino acid sequences that fold into a predetermined tertiary structure, bridging geometric and evolutionary constraints to advance protein engineering. Inspired by the pivotal role of multiple sequence alignments (MSAs) in structure prediction models like AlphaFold, we hypothesize that structural alignments can provide an informative prior for inverse folding. In this study, we introduce DualMPNN, a dual-stream message passing neural network that leverages structurally homologous templates to guide amino acid sequence design of predefined query structures. DualMPNN processes the query and template proteins via two interactive branches, coupled through alignment-aware cross-stream attention mechanisms that enable exchange of geometric and co-evolutionary signals. Comprehensive evaluations across on CATH 4.2, TS50 and T500 benchmarks demonstrate DualMPNN achieves state-ofthe-art recovery rates of 65.51%, 70.99%, and 70.37%, significantly outperforming base model ProteinMPNN by 15.64%, 16.56%, 12.29%, respectively. Further template quality analysis and structural foldability assessment underscore the value of structural alignment priors for protein design.

The inverse folding problem, aiming to design amino acid sequences that fold into desired three-dimensional structures, is pivotal for various biotechnological applications. Here, we introduce a novel approach leveraging Direct Preference Optimization (DPO) to fine-tune an inverse folding model using feedback from a protein folding model. Given a target protein structure, we begin by sampling candidate sequences from the inverse-folding model, then predict the three-dimensional structure of each sequence with the folding model to generate pairwise structuralpreference labels. These labels are used to fine-tune the inverse-folding model under the DPO objective. Our results on the CATH 4.2 test set demonstrate that DPO fine-tuning not only improves sequence recovery of baseline models but also leads to a significant improvement in average TM-Score from 0.77 to 0.81, indicating enhanced structure similarity. Furthermore, iterative application of our DPO-based method on challenging protein structures yields substantial gains, with an average TM-Score increase of 79.5% with regard to the baseline model. This work establishes a promising direction for enhancing protein sequence design ability from structure feedback by effectively utilizing preference optimization .

Graph neural networks (GNNs) have emerged as powerful tools for learning protein structures by capturing spatial relationships at the residue level. However, existing GNN-based methods often face challenges in learning multiscale representations and modeling long-range dependencies efficiently. In this work, we propose an efficient multiscale graph-based learning framework tailored to proteins. Our proposed framework contains two crucial components: (1) It constructs a hierarchical graph representation comprising a collection of fine-grained subgraphs, each corresponding to a secondary structure motif (e.g., α-helices, β-strands, loops), and a single coarse-grained graph that connects these motifs based on their spatial arrangement and relative orientation.