Proteins-Structure Function and Bioinformatics最新文献

Biomolecules rely on water and ions for stable folding, but these interactions are often transient, dynamic, or disordered and thus hidden from experiments and evaluation challenges that represent biomolecules as single, ordered structures. Here, we compare blindly predicted ensembles of water and ion structure to the cryo-EM densities observed around the Tetrahymena ribozyme at 2.2-2.3 Å resolution, collected through target R1260 in the CASP16 competition. Twenty-six groups participated in this solvation "cryo-ensemble" prediction challenge, submitting over 350 million atoms in total, offering the first opportunity to compare blind predictions of dynamic solvent shell ensembles to cryo-EM density. Predicted atomic ensembles were converted to density through local alignment and these densities were compared to the cryo-EM densities using Pearson correlation, Spearman correlation, mutual information, and precision-recall curves. These predictions show that an ensemble representation is able to capture information of transient or dynamic water and ions better than traditional atomic models, but there remains a large accuracy gap to the performance ceiling set by experimental uncertainty. Overall, molecular dynamics approaches best matched the cryo-EM density, with blind predictions from bussilab_plain_md, SoutheRNA, bussilab_replex, coogs2, and coogs3 outperforming the baseline molecular dynamics prediction. This study indicates that simulations of water and ions can be quantitatively evaluated with cryo-EM maps. We propose that further community-wide blind challenges can drive and evaluate progress in modeling water, ions, and other previously hidden components of biomolecular systems.

The CASP16 Ensemble Prediction experiment assessed advances in methods for modeling proteins, nucleic acids, and their complexes in multiple conformational states. Targets included systems with experimental structures determined in two or three states, evaluated by direct comparison to experimental coordinates, as well as domain-linker-domain (D-L-D) targets assessed against statistical models generated from NMR and SAXS data. This paper focuses on the former class of multi-state targets. Ten ensembles were released as community challenges, including ligand-induced conformational changes, protein-DNA complexes, a trimeric protein, a stem-loop RNA, and multiple oligomeric states of a single RNA. For five targets, some groups produced reasonably accurate models of both reference states (best TM-score > 0.75). However, with the exception of one protein-ligand complex (T1214), where an apo structure was available as a template, predictors generally failed to capture key structural details distinguishing the states. Overall, accuracy was significantly lower than for single-state targets in other CASP experiments. The most successful approaches generated multiple AlphaFold2 models using enhanced multiple sequence alignments and sampling protocols, followed by model quality-based selection. Although the AlphaFold3 server performed well on several targets, individual groups outperformed it in specific cases. By contrast, predictions for one protein-DNA complex, three RNA targets, and multiple oligomeric RNA states consistently fell short (TM-score < 0.75). These results highlight both progress and persistent challenges in multi-state prediction. Despite recent advances, accurate modeling of conformational ensembles, particularly RNA and large multimeric assemblies, remains an important frontier for structural biology.

The Critical Assessment of Structure Prediction (CASP) brings together a diverse group of scientists, from deep learning experts to NMR specialists, all aimed at developing accurate prediction algorithms that can effectively characterize the structural aspects of biomolecules relevant to their functions. Engagement within the CASP community has traditionally been limited to the prediction season and the conference, with limited discourse in the 1.5 years between CASP seasons. CASP special interest groups (SIGs) were established in 2023 to encourage continuous dialogue within the community. The online seminar series has drawn global participation from across disciplines and career stages. This has facilitated cross-disciplinary discussions fostering collaborations. The archives of these seminars have become a vital learning tool for newcomers to the field, lowering the barrier to entry.

During the 16th Critical Assessment of Structure Prediction (CASP16), the Vfold team participated in the two RNA categories: RNA Monomers and RNA Multimers. The Vfold RNA structure prediction method is hierarchical and hybrid, incorporating physics-based models (Vfold2D and VfoldMCPX) for 2D structure prediction, template-based and molecular dynamics simulation-based models (Vfold-Pipeline, IsRNA and RNAJP) for 3D structure prediction. Additionally, Vfold integrates knowledge from templates and the state-of-the-art machine learning model AlphaFold3 into our physics-based models. This integration enhances the prediction accuracy. Here we describe the Vfold approach in CASP16 using selected targets and show how the integration of traditional structure prediction methods with machine learning models can improve RNA structure prediction accuracy.

The assessment of monomer targets in the Critical Assessment of Structure Prediction Round 16 (CASP16) underscores that the problem of single-domain protein fold prediction is nearly solved-no target folds were incorrectly predicted across all Evaluation Units. However, challenges remain in accurately modeling truncated sequences, irregular secondary structures, and interaction-induced conformational changes. The release of AlphaFold3 (AF3) during CASP16, and its effective integration by many groups, demonstrated its superiority over AlphaFold2 (AF2), particularly in confidence estimation and model selection. Additional improvements in multiple sequence alignments (MSAs) and fragment-based prediction, that is, selecting the optimal fragment of the full sequence for modeling, also contributed to enhanced prediction accuracy. The top three groups-all from the Yang lab-consistently outperformed others across CASP16 monomer targets, reflecting their robust modeling pipelines and successful adoption of AF3. CASP16 also introduced three new challenges: Phase 0, in which stoichiometry was withheld; Phase 2, which supplied ~8000 MassiveFold models per target to test model selection strategies; and Model 6, which limited predictors to using MSAs provided by the organizers. While we evaluated group performance in these additional challenges, the insights gained were limited due to low participation and caveats in the design of experiments. We suggest improvements for the organization of these challenges and encourage broader engagement from the prediction community. The progress in monomer modeling from CASP15 to CASP16 was subtle, but more groups in CASP16 were able to outperform ColabFold, reflecting the community's improved ability in optimizing AF2 and the growing adoption of AF3. We anticipate that the recent release of the AF3 source code will stimulate future progress through user-driven optimization and innovations in model architecture. Finally, model ranking remains a persistent weakness across most groups, highlighting a critical area for future development.

The CASP16 evaluation of model accuracy (EMA) experiment assessed the ability of predictors to estimate the accuracy of predicted models, with a particular emphasis on multimeric assemblies. Expanding on the CASP15 framework, CASP16 introduced a new evaluation mode (QMODE3) focused on selecting high-quality models from large-scale AlphaFold2-derived model pools generated by MassiveFold. Three primary evaluation tasks were therefore conducted: QMODE1 assessed global structure accuracy, QMODE2 focused on the accuracy of interface residues, and QMODE3 tested model selection performance. Predictors were evaluated using a diverse set of OpenStructure-based metrics, and a novel penalty-based ranking scheme was developed for QMODE3 to handle score interdependence and varying prediction quality distributions. Additionally, we explored the accuracy and utility of predicted local confidence measures now made available on a per-atom basis by methods that invoke AlphaFold3. Results showed that methods incorporating AlphaFold3-derived features-particularly per-atom pLDDT-performed best in estimating local accuracy and in utility for experimental structure solution. For QMODE3, performance varied significantly across monomeric, homomeric, and heteromeric target categories and underscored the ongoing challenge of evaluating complex assemblies.

CASP (critical assessment of structure prediction) conducts community experiments to determine the state of the art in calculating macromolecular structures. The CASP data management system is continually evolving to address the changing needs of the experiments. For CASP16, we expanded the infrastructure to enable data handling of newly introduced categories and fully support pilot categories introduced in CASP15. This technical note also documents the integration of the CASP and CAPRI (Critical Assessment of PRedicted Interactions) systems.

Biomolecular structure prediction has reached an unprecedented level of accuracy, partly attributed to the use of advanced deep learning algorithms. We participated in the CASP16 experiments across the categories of protein domains, protein multimers, and RNA monomers, achieving official rankings of first, second, and fourth (top for server groups), respectively. We hypothesized that by leveraging state-of-the-art structure predictors such as AlphaFold2, AlphaFold3, trRosettaX2, and trRosettaRNA2, accurate structure predictions could be achieved through careful optimization of input information. For protein structure prediction, we enhanced the input sequences by removing intrinsically disordered regions, a simple yet effective approach that yielded accurate models for protein domains. However, fewer than 25% of the protein multimers were predicted with high quality. In RNA structure prediction, optimizing the secondary structure input for trRosettaRNA2 resulted in more accurate predictions than AlphaFold3. In summary, our prediction results in CASP16 indicate that protein domain structure prediction has achieved high accuracy. However, predicting protein multimers and RNA structures remains challenging, and we anticipate new advancements in these areas in the coming years.

Protein-ligand interaction prediction is pivotal in early-stage drug development, enabling large-scale virtual screening, drug optimization, and reverse target searching. In this work, we present Graph_RG, our top-performing model in the CASP16 small molecule track's protein-ligand affinity prediction category, achieving a N-weighted Kendall's Tau of 0.42-significantly outperforming other submissions (second-best: 0.36). Beyond accuracy, Graph_RG is noncomplex dependent, hence exhibits exceptional computational efficiency, operating > 100 000× faster than conformation-search dependent prediction methods, thus enabling billion- to 10-billion-scale screening on standard servers. We further discuss the potential improvements for Graph_RG, including dataset optimization, atomic vector representation enhancements, and model architecture upgrades. We also introduce the potential broader applications in large-scale drug screening, reverse target identification, and GPCR-specific drug discovery. We also point out the development of an interactive web platform hosting Graph_RG and its derivative models to enhance accessibility. By integrating community feedback and iterative model refinement, this initiative bridges the gap between AI-driven predictions and practical drug discovery, fostering advancements in both computational methodologies and biomedical applications.

We present the methods and results of our protein complex and RNA structure predictions at CASP16. Our approach integrated multiple state-of-the-art deep learning models with a consensus-based scoring method. To enhance the depth of multiple sequence alignments (MSAs), we employed a large metagenomic sequence database. Model ranking was performed with a state-of-the-art consensus ranking method, to which we added more scoring terms. These predictions were further refined manually based on literature evidence. For RNA, we adopted an ensemble approach that incorporated multiple state-of-the-art methods, centered around our NuFold framework. As a result, our KiharaLab group ranked first in protein complex prediction and third in RNA structure prediction. A detailed analysis of targets that significantly differed from those of other groups highlighted both the strengths of our MSA and scoring strategies, as well as areas requiring further improvement.