Proteins[JOURNAL]
Sun
200 papers
The RNA-Puzzles Assessments of RNA-Only Targets in CASP16.
Westhof E, Sun H, Bu F … +1 more , Miao Z
Proteins · 2026 Jan · PMID 41040059 · Full text
RNA-Puzzles was launched in 2011 as a collaborative effort dedicated to advancing and improving RNA 3D structure prediction. The automatic evaluation protocols for comparisons between prediction and experiment developed...
RNA-Puzzles was launched in 2011 as a collaborative effort dedicated to advancing and improving RNA 3D structure prediction. The automatic evaluation protocols for comparisons between prediction and experiment developed within RNA-Puzzles are applied to the 2024 CASP16 competition. The scores evaluate stereochemical parameters, Watson-Crick pairs, non-Watson-Crick pairs, and base stacking in addition to standard global parameters such as RMSD, TM-score, GDT, or lDDT. Several targets were particularly difficult owing to their size or multimerization. As noted in previous evaluations, although predictions that perform well on secondary structure may also achieve acceptable overall folds, they are insufficient to guarantee chemical precision or to correctly identify residues involved in non-Watson-Crick interactions. Both are essential for obtaining a valid three-dimensional architecture and for understanding the biological function of RNAs.
The CASP 16 Experimental Protein-Ligand Datasets.
Tosstorff A, Rudolph MG, Benz J … +9 more , Kuhn B, Kramer C, Sharpe M, Huang CY, Metz A, Hazemann J, Ritz D, Sweeney AM, Gilson MK
Proteins · 2026 Jan · PMID 41040057 · Publisher ↗
This paper presents the experimental protein-ligand datasets used as benchmarks in the CASP 16 blind prediction experiment-the first CASP round to incorporate targets from pharmaceutical discovery projects. We have assem...
This paper presents the experimental protein-ligand datasets used as benchmarks in the CASP 16 blind prediction experiment-the first CASP round to incorporate targets from pharmaceutical discovery projects. We have assembled and characterized protein-ligand complexes for four proteins that are known or candidate drug targets: human chymase, human cathepsin G, human autotaxin, and the SARS-CoV-2 main protease. The collection encompasses over 200 co-crystal structures at resolutions better than 2.7 Å, paired with binding affinity measurements for approximately 160 compounds covering a broad affinity range (nanomolar to high micromolar). These data enabled the CASP16 pose-prediction and affinity-prediction challenges. Many systems feature potentially challenging characteristics, including chymase's electropositive surface and acidic ligands, which require proper handling of titratable ligand groups; autotaxin complexes with and without zinc coordination; and a SARS-CoV-2 protease crystal form exhibiting an unusually open active site conformation. We describe the experimental approaches-from protein production and crystallization to binding assay development-that yielded these reference data. Contributed by scientists at F. Hoffmann-La Roche and Idorsia Pharmaceuticals, these datasets represent actual drug discovery projects and therefore provide a realistic testbed for assessing how computational methods perform on pharmaceutically relevant targets. An accompanying paper in the present special journal issue provides a comprehensive assessment of the pose and affinity predictions for these pharmaceutical protein-ligand systems.
Structural Basis for M2-2-MAVS Proteins Interaction in Human Metapneumovirus (HMPV): Exploring the Immune Evasion Mechanism Through Biomolecular Modeling, Structural Mutagenesis and Classical Simulations.
Alshabrmi FM, Alatawi EA
Proteins · 2026 Mar · PMID 41023768 · Publisher ↗
Human metapneumovirus (HMPV) was first discovered in the Netherlands in 2001 and is now considered one of the most important contributors to viral respiratory diseases. It is often asymptomatic in healthy adults but can...
Human metapneumovirus (HMPV) was first discovered in the Netherlands in 2001 and is now considered one of the most important contributors to viral respiratory diseases. It is often asymptomatic in healthy adults but can cause serious illness among immunocompromised or older patients. In response to the infection, the viral immune evasion mechanism remains a key approach for evading the immune response. In hMPV, the M2-2 protein interacts with the hMAVS protein to evade the immune response. It is essential to understand how the mechanism takes place for designing potential therapeutic agents. Thus, herein, we provide structural mechanisms of the interaction between M2-2 and MAVS through biomolecular interactions, in silico alanine scanning, and classical simulation approaches (repeated). We selected the HADDOCK-generated complex from the docking results, leaving the others from ZDOCK, Cluspro, and PyDOCK. Using alanine scanning, 18 interface residues were identified consensually, among which 8 residues, P29A, E30A, M31A, W33A, E37A, Q39A, E40A, and K48A, significantly affected the binding and were selected for the subsequent analysis. The docking results of these alanine mutants reported a significant reduction in the HADDOCK score, electrostatic energies, and vdW forces. Moreover, the stability of these mutations has been significantly compromised during simulation, while the total binding free energy also corroborates with the docking scores. From the detailed hydrogen-bond analysis, the interactions were significantly reduced in the mutants' complexes compared to the wild type, suggesting that alanine substitutions weaken the M2-1 and MAVS interaction by disrupting its finely tuned interaction network, highlighting potential vulnerabilities in its binding mechanism. The dissociation constant (K) results further validated discrepancies in the binding strength caused by the alanine substitutions. This study provides insights into the immune evasion mechanism of the hMPV virus and provides a basis for therapeutic development.
Molecular Dynamics Analysis of Inhibitor Binding Interactions in the Vibrio cholerae Respiratory Complex NQR.
DePaolo-Boisvert JA, Tuz K, Minh DDL … +1 more , Juarez OX
Proteins · 2026 Feb · PMID 41017720 · Full text
The sodium-pumping ubiquinone oxidoreductase sodium pumping quinone reductase (NQR) is an important enzyme in the respiratory chain of multiple pathogenic gram-negative bacteria. NQR has been proposed as a viable antibio...
The sodium-pumping ubiquinone oxidoreductase sodium pumping quinone reductase (NQR) is an important enzyme in the respiratory chain of multiple pathogenic gram-negative bacteria. NQR has been proposed as a viable antibiotic target due to its importance in supporting energy-consuming reactions and its absence in human cells. In this study, molecular dynamics simulations were conducted to characterize the interactions between the ubiquinone binding pocket of Vibrio cholerae NQR with its substrate analogue ubiquinone-4 and three potent inhibitors: HQNO, aurachin-D42, and korormicin-A. Through interaction fingerprinting, distance calculations, and clustering analysis, important binding motifs for each of these ligands were identified. Subunit B residues K54, F137, E144, V145, V155, E157, G158, F159, and F160 were frequently identified as establishing either hydrogen bonding interactions or hydrophobic interactions with these three ligands. The findings of this in silico study are interpreted in view of mutagenesis analyses previously published in the literature. The elucidation of important binding interactions associated with the inhibitors is critical as it informs structure-activity relationships, which are essential for the development of novel antibiotics targeting NQR.
Beyond Single Chains: Benchmarking Macromolecular Complex Prediction Methods With the Continuous Automated Model EvaluatiOn (CAMEO).
Robin X, Škrinjar P, Waterhouse AM … +4 more , Studer G, Tauriello G, Durairaj J, Schwede T
Proteins · 2026 Jan · PMID 41017158 · Full text
Independent, blind assessment of structure prediction methods is essential for establishing state-of-the-art performance, identifying limitations, and guiding future developments. The Continuous Automated Model EvaluatiO...
Independent, blind assessment of structure prediction methods is essential for establishing state-of-the-art performance, identifying limitations, and guiding future developments. The Continuous Automated Model EvaluatiOn (CAMEO) platform provides weekly, automated benchmarking of structure prediction servers, complementing the biennial Critical Assessment of Structure Prediction (CASP) experiments.
Alternative Conformation Prediction Using Deep Learning With Multi-MSA Strategy and Structural Clustering in CASP16.
Wuyun Q, Liu Q, Ni W … +6 more , Peng C, Zhang Z, Zhou X, Hu G, Freddolino L, Zheng W
Proteins · 2026 Jan · PMID 41014267 · Publisher ↗
We report the results from the "MIEnsembles-Server" and "Zheng" groups for structure ensemble predictions in CASP16, both of which employed the EnsembleFold pipeline. Initially, multiple sequence alignments (MSAs) were g...
We report the results from the "MIEnsembles-Server" and "Zheng" groups for structure ensemble predictions in CASP16, both of which employed the EnsembleFold pipeline. Initially, multiple sequence alignments (MSAs) were generated using DeepMSA2 for proteins and rMSA for RNA targets. These MSAs were processed by newly developed deep learning methods-D-I-TASSER2 for protein monomer structure prediction, DMFold2 for protein complex structure prediction, ExFold for RNA structure prediction, and DeepProtNA for protein-nucleic acid complex structure prediction-to yield diverse structural decoys. The generated decoys were clustered into representative models corresponding to distinct conformational states using the structural clustering tool MolClust. Protein monomer targets underwent additional refinement via replica-exchange Monte Carlo (REMC) simulations with D-I-TASSER2, and these refined decoys were re-clustered with MolClust to finalize the ensemble predictions. For the 19 ensemble targets in CASP16, the final EnsembleFold models achieved an average TM-score of 0.657, representing improvements of 10.2% compared to the baseline AlphaFold3 program. Notably, EnsembleFold achieved particularly good performance for hybrid protein/nucleic-acid targets, leading to its efficacy in ensemble prediction tasks. Analysis of the resulting structural ensembles highlighted three significant insights: (i) Models derived from distinct DeepMSA2-generated MSAs typically represent different conformational states for ensemble targets; (ii) REMC simulations significantly enhance model diversity, facilitating the identification of alternative conformations; (iii) The structural clustering approach effectively identifies and selects accurate representative models for each conformational state. We further discuss potential improvements in Quality Assessment (QA) scoring methods that could further enhance the reliability and accuracy of ensemble predictions in the future.
Iterative Modeling via Structural Diffusion (IMSD): Exploring Fold-Switching Pathways in Metamorphic Proteins Using AlphaFold2-Based Generative Diffusion Model UFConf.
Luzik DA, Skrynnikov NR
Proteins · 2026 Feb · PMID 40990820 · Full text
Metamorphic proteins (MPs) can fold into two or more distinct spatial structures. Increasing interest in MPs has spurred the search for computational tools to predict proteins fold-switching potential and model their ref...
Metamorphic proteins (MPs) can fold into two or more distinct spatial structures. Increasing interest in MPs has spurred the search for computational tools to predict proteins fold-switching potential and model their refolding pathways. Here we address this problem by using the recently reported generative diffusion predictor UFConf, based on the AlphaFold2 network. We have developed a new UFConf-driven algorithm dubbed IMSD (iterative modeling via structural diffusion) to model the MP's path from one conformational state to another. In brief, we begin with the experimental structure of state A, perturb it through the "noising" process, and infer a number of models (replicas) through the reverse diffusion or "denoising" process. From this set of models, we choose the one that is closest to the alternative structure B; then we use it as a starting point to perform another round of noising/denoising and thus generate the next batch of replicas. Repeating this process in an iterative fashion, we have been able to map the entire path from state A to state B for metamorphic proteins GA98, SA1 V90T, and the C-terminal domain of RfaH. The obtained representation of the fold-switching pathways in these MPs is consistent with the dual-funnel energy landscape observed in the previous modeling studies and shows good agreement with the available experimental data. The new UFConf-based IMSD protocol can be viewed as a part of the emerging generation of modeling tools aiming to model protein dynamics by means of deep learning technology.
Docking With Rosetta and Deep Learning Approaches in CAPRI Rounds 47-55.
Harmalkar A, Chu LS, Canner SW … +6 more , Samanta R, Frick R, Davila-Hernandez FA, Sarma S, Hitawala F, Gray JJ
Proteins · 2025 Sep · PMID 40980933 · Full text
Critical Assessment of PRediction of Interactions (CAPRI) rounds 47 through 55 introduced 49 targets comprising multistage assemblies, antibody-antigen complexes, and flexible interfaces. For these rounds, we combined va...
Critical Assessment of PRediction of Interactions (CAPRI) rounds 47 through 55 introduced 49 targets comprising multistage assemblies, antibody-antigen complexes, and flexible interfaces. For these rounds, we combined various Rosetta docking approaches (RosettaDock, ReplicaDock, and SymDock) with deep learning approaches (AlphaFold2, IgFold, and AlphaRED). Since prior CAPRI rounds, we have developed methods to better capture conformational changes, updated our scoring function, and integrated structure prediction tools such as AlphaFold2 in our docking routines. Here, we highlight several notable CAPRI targets and address the major challenges in the blind prediction of protein-protein interactions, including binding-induced conformational changes, large multimeric proteins, and antibody-antigen interactions. Although predictors have achieved modest improvements in accuracy for simpler targets post-AlphaFold2, performance for more flexible complexes remains limited. We employed RosettaDock 4.0, ReplicaDock 2.0, and AlphaRED to enhance backbone conformational sampling for flexible complexes. Our docking routines improved the DockQ score (0.77 vs. 0.62 for AF2-multimer) for a GP2 bacteriophage protein (T194), effectively capturing binding-induced conformational changes. Additionally, we introduce a fold-and-dock approach for predicting the assembly of a surface-layer SAP protein derived from Bacillus anthracis (T160), a large hetero-multimer comprising six distinct sub-units. For large symmetric complexes, we used Rosetta-based SymDock 2.0, successfully predicting a human DNA repair protein complex with A10 stoichiometry (T230) with high CAPRI-quality ranking. We also address the challenges in modeling antibody/nanobody-antigen interactions, particularly through the integration of deep learning tools and docking methods. Despite advances with tools like IgFold and AlphaFold2, accurately predicting CDR H3 loops and antibody-antigen binding interfaces remains challenging. Combining ReplicaDock 2.0 with deep learning highlights these difficulties and underscores the need for extensive sampling and CDR-focused strategies to improve prediction accuracy.
Metal-Coordination Specificity and Structural Dynamics of C. elegans Metallothionein I: Insights From 3D Modeling and MD Simulations.
de Oliveira NR, Siqueira AS, Bueno PSA … +2 more , Gonçalves EC, Zanette J
Proteins · 2026 Feb · PMID 40977118 · Full text
Metallothioneins (MTLs) are small, cysteine-rich proteins known for their ability to bind metal ions and exhibit flexible, disordered structures. The structural and functional characteristics of metallothionein I (MTL-1)...
Metallothioneins (MTLs) are small, cysteine-rich proteins known for their ability to bind metal ions and exhibit flexible, disordered structures. The structural and functional characteristics of metallothionein I (MTL-1) from Caenorhabditis elegans were investigated, focusing on its behavior in both metal free (MTL-1 Apo) and metal-bond states with Zn, Cd, Cu, Hg, and Pb divalent metal ions. Using molecular dynamics simulations and 3D modeling via AlphaFold, we characterized the flexibility and stability of MTL. The MTL-1 Apo form displayed high flexibility, aligning with its intrinsically disordered protein (IDP) nature, with 89.3% of its structure composed of coils, bends, and turns. Metal binding significantly enhanced the protein's stability, particularly with Zn, Cd, Cu, and Hg, reducing root mean square deviation (RMSD), root mean square fluctuation (RMSF), accessible surface area (SASA) and radius of gyration (R ) values, indicating structural compaction. Conversely, Pb showed a weaker stabilizing effect, with a more dynamic and less stable structure. Structural analysis revealed that conserved cysteine residues coordinate the metal through strong thiolate interactions, with additional contributions from non-cysteine residues, such as Glu and Lys. The study underscores the importance of incorporating intrinsically disordered protein models in MD simulations to provide deeper insights into how metallothionein's flexibility and stability vary in response to different metal ions, offering a structural perspective on their biological interactions and behavior under diverse environmental conditions. While thermodynamic aspects were not directly assessed, the results reveal consistent conformation trends across different metal coordination states.
In Silico Characterization of Bromo-DragonFLY Binding to the 5-HT Receptor: Molecular Insights Into a Potent Designer Psychedelic.
Tariq SS, Qureshi U, Mushtaq M … +5 more , Munsif S, Nur-E-Alam M, Hawwal MF, Wang Y, Ul-Haq Z
Proteins · 2026 Feb · PMID 40959960 · Publisher ↗
Bromo-DragonFLY (BDF), a potent designer psychedelic drug with hallucinogenic properties, has recently emerged as a significant recreational substance. Named for its dragonfly-like molecular structure, BDF induces prolon...
Bromo-DragonFLY (BDF), a potent designer psychedelic drug with hallucinogenic properties, has recently emerged as a significant recreational substance. Named for its dragonfly-like molecular structure, BDF induces prolonged psychedelic effects, with hallucinations lasting several days. Clinical reports highlight severe toxicity, including confusion, tachycardia, hypertension, seizures, renal failure, and, in extreme cases, death. BDF acts as a potent agonist of the 5-HT2A serotonin receptor subtype, which mediates the behavioral and psychedelic effects of hallucinogens. Despite its increasing prevalence and associated clinical implications, the precise molecular mechanisms underlying BDF's interaction with 5-HT2A remain inadequately characterized, particularly from an in silico perspective. This study addresses this gap by employing a comprehensive in silico framework to investigate the molecular interactions of BDF with the 5-HT receptor. Molecular docking was used to identify binding sites, while all-atom molecular dynamics (MD) simulations provided insights into the stability of the protein-ligand complex, assessing deviations, local flexibility, and time-dependent gyration patterns. The results revealed stable and compact complex formation between BDF and 5-HT, characterized by minimal per-residue fluctuations and high hydrogen bond occupancy, suggesting a highly stable interaction as shown experimentally. Additionally, principal component analysis, leveraging machine learning algorithms, demonstrated consistent motion, while free energy profiles highlighted stable energy basins with minimal variations for the BDF-5-HT complex. These findings suggest strong binding affinities of BDF with the serotonin receptor, leading to highly stable complex formation. This study provides a foundational understanding of BDF's molecular interactions, offering critical insights into its role as a potent psychedelic agent and laying the groundwork for future investigations into the risks posed by novel designer drugs.
AlphaFold Kinase Optimizer: Enhancing Virtual Screening Performance Through Automated Refinement of AlphaFold-Based Kinase Structures.
Evteev S, Ivanenkov Y, Aiginin A … +8 more , Kuznetsov M, Shayakhmetov R, Knyazev M, Bezrukov D, Malyshev A, Malkov M, Aliper A, Zhavoronkov A
Proteins · 2026 Feb · PMID 40955709 · Publisher ↗
AlphaFold (AF) is a valuable tool for generating protein 3D structures, but its application in structure-based drug design is limited. In this study, we introduce AF Optimizer-a new deep learning-assisted approach that r...
AlphaFold (AF) is a valuable tool for generating protein 3D structures, but its application in structure-based drug design is limited. In this study, we introduce AF Optimizer-a new deep learning-assisted approach that refines binding site geometry based on neural network scores and calculated free binding energy. We refined TTK protein geometry using AF Optimizer and performed virtual screening using the optimized version of the AF-generated protein model. The application of the model showed a decrease in steric clashes with ligands from known crystal complexes, more precise results of molecular docking and virtual screening, and hits enrichment during a prospective in vitro study.
Kinetic Characterization of Inhibition of Cathepsins L and S by Peptides With Anticancer Potential.
Chepikova OE, Bunik VI, Rodionov IV … +3 more , Gorokhovets NV, Zamyatnin AA, Savvateeva LV
Proteins · 2026 Feb · PMID 40935811 · Publisher ↗
Cysteine cathepsins have been suggested as attractive therapeutic targets due to their critical role in several pathologies. In particular, inhibitors of cysteine cathepsins reduce the viability of tumor cells. The prese...
Cysteine cathepsins have been suggested as attractive therapeutic targets due to their critical role in several pathologies. In particular, inhibitors of cysteine cathepsins reduce the viability of tumor cells. The present study uses enzyme kinetics to characterize the interaction of human cathepsins L and S with their peptide substrate acetyl-QLLR-7-amino-4-methylcoumarin (Ac-QLLR-AMC) and peptide inhibitors with anti-tumor activity: FFSFGGAL (CS-PEP1) and acetyl-PLVE-fluoromethyl-ketone (Ac-PLVE-fmk). Due to multiple cellular locations of cathepsins, our study is conducted under different pH conditions, simulating lysosomal and cytosolic environments (pH 4.6 and 6.5-7.0). Catalytic activities of both cathepsins are higher at pH 6.5-7.0 compared to pH 4.6. Affinities for the substrate or inhibitor CS-PEP1 are higher for cathepsin L than S independent of pH, but show different pH sensitivities, reciprocating different pI's of the cathepsins. Mixed inhibition by CS-PEP1 is demonstrated for both cathepsins. While preincubation of cathepsins with CS-PEP1 does not enhance the inhibition, Ac-PLVE-fmk inactivates both cathepsins in the preincubation medium. A strong increase in the inactivation rate is observed with increasing pH in the interval including pK of the active site cysteine residues of cathepsins, in agreement with the irreversible modification by mono-fluoromethyl ketones of the catalytic thiolate anion. At pH 4.6, cathepsin L has a higher affinity for Ac-PLVE-fmk, but a slower rate of the irreversible modification compared to cathepsin S. Our findings highlight opportunities for differential targeting of L and S cathepsins by peptide inhibitors in different cellular compartments, providing directions for cathepsin- and location-specific drug design.
Functional Relevance of CASP16 Nucleic Acid Predictions as Evaluated by Structure Providers.
Kretsch RC, Albrecht R, Andersen ES … +30 more , Chen HA, Chiu W, Das R, Gezelle JG, Hartmann MD, Höbartner C, Hu Y, Jadhav S, Johnson PE, Jones CP, Koirala D, Kristoffersen EL, Largy E, Lewicka A, Mackereth CD, Marcia M, Nigro M, Ojha M, Piccirilli JA, Rice PA, Shin H, Steckelberg AL, Su Z, Srivastava Y, Wang L, Wu Y, Xie J, Zwergius NH, Moult J, Kryshtafovych A
Proteins · 2026 Jan · PMID 40905273 · Full text
Accurate biomolecular structure prediction enables the prediction of mutational effects, the speculation of function based on predicted structural homology, the analysis of ligand binding modes, experimental model buildi...
Accurate biomolecular structure prediction enables the prediction of mutational effects, the speculation of function based on predicted structural homology, the analysis of ligand binding modes, experimental model building, and many other applications. Such algorithms to predict essential functional and structural features remain out of reach for biomolecular complexes containing nucleic acids. Here, we report a quantitative and qualitative evaluation of nucleic acid structures for the CASP16 blind prediction challenge by 12 of the experimental groups who provided nucleic acid targets. Blind predictions accurately model secondary structure and some aspects of tertiary structure, including reasonable global folds for some complex RNAs; however, predictions often lack accuracy in the regions of highest functional importance. All models have inaccuracies in non-canonical regions where, for example, the nucleic-acid backbone bends, deviating from an A-form helix geometry, or a base forms a non-standard hydrogen bond (not a Watson-Crick base pair). These bends and non-canonical interactions are integral to forming functionally important regions such as RNA enzymatic active sites. Additionally, the modeling of conserved and functional interfaces between nucleic acids and ligands, proteins, or other nucleic acids remains poor. For some targets, the experimental structures may not represent the only structure the biomolecular complex occupies in solution or in its functional life cycle, posing a future challenge for the community.
Effect of L110M Mutation on the Structure and Stability of ATTR(105-115) Peptide Assembly: A Computational Study.
Bhattacharya P, Mittal S
Proteins · 2026 Feb · PMID 40905130 · Publisher ↗
The mechanisms driving amyloid assembly have long intrigued structural biologists, as they offer insights into systemic fibrotic changes and the dynamic behavior of transthyretin (TTR) aggregation, crucial for developing...
The mechanisms driving amyloid assembly have long intrigued structural biologists, as they offer insights into systemic fibrotic changes and the dynamic behavior of transthyretin (TTR) aggregation, crucial for developing amyloid-targeted therapies. In TTR-associated amyloidosis, amyloid fibrils form via destabilization of the tetramer into dimers and monomers. While many TTR mutations have been studied, the atomistic impact of multiple mutations on amyloid transthyretin (ATTR) self-assembly remains underexplored. To the best of our knowledge, this is the first computational analysis reporting the impact of the L110M mutation on ATTR peptide aggregation. Using triplicate 1 μs all-atom molecular dynamics (MD) simulations, totaling 18 μs, the conformational dynamics of cross-β amyloid fibrils in the ATTR(105-115) segment were examined for both wild-type and L110M mutant TTR. The L110M mutation consistently enhanced the β-sheet content in all oligomers, with increases of ~1%, ~5%, and ~4% over the wild-type in the 2-, 4-, and 8-peptide systems, respectively. Molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) calculations revealed higher effective binding free energy for the L110M mutant, with residue M110 contributing significantly to stabilization. These results suggest that L110M modestly enhances conformational order and stability in the TTR peptide assemblies without major structural disruption, deepening our understanding of amyloidogenesis in TTR-related disorders.
Updates to the CASP Infrastructure in 2024.
Kryshtafovych A, Milostan M, Lensink MF … +4 more , Velankar S, Bonvin AMJJ, Moult J, Fidelis K
Proteins · 2026 Jan · PMID 40890987 · Full text
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 add...
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.
Markovian Timescales of Intramolecular Disulfide Pairing in Cyclotides.
Venkatesan J, Roy D
Proteins · 2026 Feb · PMID 40877196 · Publisher ↗
Kinetics of intramolecular disulphide pairing in a six-cysteine containing plant toxin peptide cycloviolacin O1 (CyO1) having a cyclic backbone and a cyclic cystine knot (CCK) is studied using a Hidden Markov Model (HMM)...
Kinetics of intramolecular disulphide pairing in a six-cysteine containing plant toxin peptide cycloviolacin O1 (CyO1) having a cyclic backbone and a cyclic cystine knot (CCK) is studied using a Hidden Markov Model (HMM) created from molecular dynamics simulation trajectories. Starting from a fully reduced form of CyO1 (peptide-D), the kinetic model is created to track the peptide's evolution to a native-like state (peptide-N) where all three correct pairs of S-S linkages are most likely to be observed. The structural evolution and fluctuation of peptide-D through many partially folded S-S intermediates and the associated propensity, along with the timescale of formation of a single or simultaneously two or three S-S pairs, is studied using this Markov chain. The phenomenon of intramolecular S-S pairing, as observed in proteins and peptides, is fast, with a computed rate constant of ~10 s in line with experimental observations in the bacterial disulphide bond redox protein DsbD. Rate networks and transition path theory analysis are used to find the most probable pathway for peptide-D to evolve into peptide-N.
The Evolving Landscape of Amyloid Research.
Bonilauri B
Proteins · 2026 Feb · PMID 40874670 · Full text
The exponential growth of biomedical and life sciences literature, including research on amyloid biology, has made it increasingly challenging to track new discoveries and gain a comprehensive understanding of the evolut...
The exponential growth of biomedical and life sciences literature, including research on amyloid biology, has made it increasingly challenging to track new discoveries and gain a comprehensive understanding of the evolution of specific research fields. Advances in natural language models (NLM) and artificial intelligence (AI) approaches now enable large-scale analysis of scientific publications, uncovering hidden patterns and facilitating data-driven insights. Here, a two-dimensional mapping of the global amyloid research landscape is presented, using the transformer-based large language model PubMedBERT, in combination with t-SNE and Latent Dirichlet Allocation (LDA), to analyze more than 140 000 abstracts from the PubMed database. This analysis provides a comprehensive visualization of the amyloid field, capturing key trends such as the historical progression of amyloid research, the emergence of dominant subfields, the distribution of contributing authors and their respective countries, and the identification of latent research topics over time, including chemicals and small molecules. By integrating AI-driven text analysis with large-scale bibliometric data, this study offers a novel perspective on the evolution of amyloid research, facilitating a deeper interdisciplinary understanding. This work serves as a valuable interactive resource for researchers while highlighting the potential of machine learning-driven literature mapping in identifying knowledge gaps and guiding future investigations.
MassiveFold Data for CASP16-CAPRI: A Systematic Massive Sampling Experiment.
Raouraoua N, Lensink MF, Brysbaert G
Proteins · 2026 Jan · PMID 40874652 · Full text
Massive sampling with AlphaFold2 has become a widely used approach in protein structure prediction. Here we present the MassiveFold CASP16-CAPRI dataset, a systematic, large-scale sampling of both monomeric and multimeri...
Massive sampling with AlphaFold2 has become a widely used approach in protein structure prediction. Here we present the MassiveFold CASP16-CAPRI dataset, a systematic, large-scale sampling of both monomeric and multimeric protein targets. By exploiting maximal parallelization, we produced up to 8040 models per target and shared them with the community for collaborative selection and scoring. This collective effort minimizes redundant computation and environmental impact, while granting resource-limited groups - especially those focused on scoring - access to high quality structures. In our analysis, we define an interface-difficulty classification based on DockQ metrics, showing that massive sampling yields the greatest gains on most of the challenging interfaces. Crucially, this classification can be predicted from the median ipTM scores of a routine AF2 run, enabling users to selectively deploy massive sampling only when it is most needed. Combined with a reduction of the massive sampling from 8040 to 2475 predictions, such targeted strategies dramatically cut computation time and resource use with minimal loss of accuracy. Finally, we underscore the persistent challenge of choosing optimal models from massive sampling datasets, emphasizing the need for more robust scoring methods. The MassiveFold datasets, together with AlphaFold ranking scores and CASP and CAPRI assessment metrics, are publicly available at https://github.com/GBLille/CASP16-CAPRI_MassiveFold_Data to accelerate further progress in protein structure prediction and assembly modeling.
Critical Assessment of Protein Intrinsic Disorder Round 3 - Predicting Disorder in the Era of Protein Language Models.
Mehdiabadi M, Del Conte A, Nugnes MV … +3 more , Aspromonte MC, Tosatto SCE, Piovesan D
Proteins · 2026 Jan · PMID 40859602 · Full text
Intrinsic disorder (ID) in proteins is a complex phenomenon, encompassing a continuum from entirely disordered regions to structured domains with flexible segments. The absence of a ground truth for all forms of disorder...
Intrinsic disorder (ID) in proteins is a complex phenomenon, encompassing a continuum from entirely disordered regions to structured domains with flexible segments. The absence of a ground truth for all forms of disorder, combined with the possibility of structural transitions between ordered and disordered states under specific conditions, makes accurate prediction of ID especially challenging. The Critical Assessment of Protein Intrinsic Disorder (CAID) evaluates ID prediction methods using diverse benchmarks derived from DisProt, a manually curated database of experimentally validated annotations. This paper presents findings from the third round (CAID3), in which 24 new methods were assessed along with the predictors from previous rounds. Compared to CAID2, the top-performing methods in CAID3 demonstrated significant gains in average precision: over 31% improvement in predicting linker regions, and 15% in disorder prediction. This round introduces a new binding sub-challenge focused on identifying binding regions within known IDR boundaries. The results indicate that this task remains challenging, highlighting the potential for improvement. The top-performing methods in CAID3 are mostly new and commonly used embeddings from protein language models (pLMs), underscoring the growing impact of pLMs in tackling the complexities of disordered proteins and advancing ID prediction.
AlphaFold3 at CASP16.
Elofsson A
Proteins · 2026 Jan · PMID 40851426 · Full text
The CASP16 experiment provided the first opportunity to benchmark AlphaFold3. In contrast to AlphaFold2, AlphaFold3 can predict the structure of non-protein molecules. According to the benchmark presented by the develope...
The CASP16 experiment provided the first opportunity to benchmark AlphaFold3. In contrast to AlphaFold2, AlphaFold3 can predict the structure of non-protein molecules. According to the benchmark presented by the developers, it is expected to perform slightly better than AlphaFold2 for proteins. In this study, we assess the performance of AlphaFold3 using both automatic server submissions (AF3-server) and manual predictions from the Elofsson group (Elofsson). All predictions were generated via the AlphaFold3 web server, with manual interventions applied to large targets and ligands. Compared to AlphaFold2-based methods, we found that AlphaFold3 performs slightly better for protein complexes. However, when massive sampling is applied to AlphaFold2, the difference disappears. It was also noted that, according to the official ranking from CASP, the AF3-server performs better than AlphaFold2 for easier targets, but not for harder targets. Furthermore, the performance of the AF3-server is comparable to the best methods when considering the top-ranked predictions, but slightly behind when examining the best among the five submitted models. Here, there exist targets where AF3-server, the top-ranked method, is worse than lower-ranked models, indicating that a venue for progress could be to develop better strategies for identifying the best out of the generated models. When using AF3-server to predict the stoichiometry of larger protein complexes, the accuracy is limited, especially for heteromeric targets. When analyzing the predictions including nucleic acids, it was found that, in general, the accuracy is relatively low. However, the AF3-server performance was not far behind that of the top-ranked method. In summary, AF3-server offers a user-friendly tool that provides predictions comparable to state-of-the-art methods in all categories of CASP.
About
- Frequency
- Sun
- Papers found
- 200
- RSS feed
- Subscribe