Proteins[JOURNAL]
Sun
200 papers
Performance of pyDock in 8th CAPRI: Energy-Based Scoring Applied to Docking and AlphaFold Models.
Rodríguez-Lumbreras LA, Rosell M, Romero-Durana M … +3 more , Monteagudo V, Glaser F, Fernandez-Recio J
Proteins · 2025 Aug · PMID 40851418 · Publisher ↗
The 8th CAPRI edition has shown a significant evolution in the field of protein-protein complex structure prediction. We have participated in all 11 targets proposed in this edition, involving domain-domain, protein-prot...
The 8th CAPRI edition has shown a significant evolution in the field of protein-protein complex structure prediction. We have participated in all 11 targets proposed in this edition, involving domain-domain, protein-protein, protein-peptide, and protein-DNA interactions, including homo- and hetero-meric interfaces. Our prediction strategy has significantly evolved during this edition due to the appearance of ground-breaking AI-based predicting methodologies, like AlphaFold (AF). As predictors, while for the first targets our modeling approach was mostly based on our standard pyDock protocol, after target T187 and onwards, interacting subunits were routinely modeled by AlphaFold2. In the last round (targets T231-T234) we also generated complex models with AlphaFold-Multimer, which were scored by a combination of pyDock energy and AF model confidence. As scorers, we mostly used pyDock to directly score the provided models, applying the same restraints and additional filters as in predictors. Overall, our performance in this 8th CAPRI edition was in line with that in past editions. Considering successful targets as those with submitted models of acceptable (or better) quality within top 5 (predictors) or top 10 (scorers) for the entire complex or at least for one of their assessment units (AUs), we succeeded in 45% of cases as predictors (ranking 4th among participants) and in 64% of cases as scorers (ranking 3rd among participants). The targets where we failed both as predictors and as scorers were actually challenging for all participants. This experiment has shown that the problem of protein-protein docking is not yet solved, and has confirmed the value of energy-based scoring and other approaches in combination with AI-based predictions.
Model Quality Assessment for CASP16.
Fadini A, Studer G, Read RJ
Proteins · 2026 Jan · PMID 40847537 · Full text
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 frame...
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.
CASP16 Protein Monomer Structure Prediction Assessment.
Yuan R, Zhang J, Kryshtafovych A … +4 more , Schaeffer RD, Zhou J, Cong Q, Grishin NV
Proteins · 2026 Jan · PMID 40820259 · Full text
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 incorrect...
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.
A Review on Efficient and Scalable Graph-Based Clustering Algorithms for Protein Complex Identification in PPI Networks.
Patra S, Sahoo TR
Proteins · 2026 Feb · PMID 40820243 · Publisher ↗
Network clustering is employed in bioinformatics and data mining studies to investigate the structural and functional properties of protein-protein interaction (PPI) networks. In multiple studies over the past two decade...
Network clustering is employed in bioinformatics and data mining studies to investigate the structural and functional properties of protein-protein interaction (PPI) networks. In multiple studies over the past two decades, network clustering has proven valuable for uncovering functional modules and elucidating the functions of previously undiscovered proteins. Protein complexes are vital cellular components that play a crucial role in generating biological activity. Experimental techniques have inherent limitations in inferring protein complexes. Given these constraints, numerous computational methods have emerged over the past decade for predicting protein complexes. Typically, these methods take the input PPI data and generate predicted protein complexes as output subnetworks. Most of these methods have shown encouraging outcomes in predicting protein complexes. Prediction is challenging for sparse, small, and overlapping complexes. New strategies should include explicit knowledge about the biological characteristics of proteins to increase performance. Furthermore, specific issues should be considered more effectively in the future while developing new complex prediction algorithms. The bioinformatics community has developed various techniques for clustering PPI networks, which we identified, analyzed, and compared in this paper. This review evaluates various graph clustering algorithms for protein complex identification, facilitating the benchmarking of existing methods, identifying limitations, motivating the development of novel computational tools, and ultimately improving biological insight and therapeutic progress. Through the assessment of strengths and limitations, researchers may develop efficient and scalable algorithms designed explicitly for biological data, integrating graph-based methodologies with machine learning and deep learning approaches. This study is an invaluable tool for new researchers in the area to recognize upcoming trends, including dynamic PPI networks and temporal complex identification.
Approaches to Study Proteins Encoded by Essential Genes.
Cronan JE
Proteins · 2026 Feb · PMID 40817436 · Full text
Although the phenotypes and functions of nonessential proteins can be studied by deletion of their coding sequences (both gene copies in diploid organisms), essential genes cannot be deleted unless loss of the encoded pr...
Although the phenotypes and functions of nonessential proteins can be studied by deletion of their coding sequences (both gene copies in diploid organisms), essential genes cannot be deleted unless loss of the encoded protein can be bypassed. Bypass is often achieved by supplementation with the product of the enzyme. However, supplementation cannot bypass loss of essential genes such as those encoding enzymes of DNA or RNA synthesis. To study proteins encoded by essential genes that cannot be bypassed, the mutations must be conditional in nature. The mutant cells must be able to grow under a permissive condition, but fail to grow under a different condition, the nonpermissive condition. Several methods have been developed to obtain conditional mutations in essential genes. Mutations that result in proteins abnormally sensitive to high temperatures are called temperature-sensitive (Ts) mutants and are a widely used type of conditional mutation. An alternative to Ts mutants is the "degron" system to target proteins for destruction by cellular proteases. Approaches to conditionally control the functions of proteins encoded by essential genes, plus the advantages and disadvantages of these and other approaches, will be considered.
Highlights of Model Quality Assessment in CASP16.
Fadini A, Adiyaman R, Alhaddad SN … +21 more , Behzadi B, Cheng J, Cui X, Edmunds NS, Freddolino L, Genc AG, Liang F, Liu D, Liu J, Liu Q, McGuffin LJ, Neupane P, Peng C, Shortle DR, Sun M, Wang H, Wuyun Q, Zhang G, Zhao X, Zheng W, Read RJ
Proteins · 2026 Jan · PMID 40810260 · Full text
Model quality assessment (MQA) remains a critical component of structural bioinformatics for both structure predictors and experimentalists seeking to use predictions for downstream applications. In CASP16, the Evaluatio...
Model quality assessment (MQA) remains a critical component of structural bioinformatics for both structure predictors and experimentalists seeking to use predictions for downstream applications. In CASP16, the Evaluation of Model Accuracy (EMA) category featured both global and local quality estimation for multimeric assemblies (QMODE1 and QMODE2), as well as a novel QMODE3 challenge-requiring predictors to identify the best five models from thousands generated by MassiveFold. This paper presents detailed results from several leading CASP16 EMA methods, highlighting the strengths and limitations of the approaches.
Enhancing RNA 3D Structure Prediction: A Hybrid Approach Combining Expert Knowledge and Computational Tools in CASP16.
Xiao B, Shi Y, Huang L
Proteins · 2026 Jan · PMID 40778521 · Publisher ↗
RNA three-dimensional structures are critical for their roles in gene expression and regulation. However, predicting RNA structures remains challenging due to complex tertiary interactions, ion dependency, molecular flex...
RNA three-dimensional structures are critical for their roles in gene expression and regulation. However, predicting RNA structures remains challenging due to complex tertiary interactions, ion dependency, molecular flexibility, and the limited availability of known 3D structures. To address these challenges, our team (GuangzhouRNA-human) employed a hybrid strategy combining computational tools with expert refinement in the CASP16 RNA structure prediction challenge, achieving second place based on the sum Z-score. Our approach integrates multiple techniques through modular workflows, including template-based modeling for targets with homologous templates and ab initio prediction using deep learning tools (e.g., AlphaFold3 and DeepFoldRNA) for novel sequences. Additionally, we incorporate experimental constraints and iterative optimization to enhance prediction accuracy. For targets shorter than 200 nucleotides (nt) with homologous templates, our method demonstrated exceptional performance, achieving 75% of predictions with root-mean-square deviations (RMSD) below 5 Å, and all predictions falling under 10 Å. Furthermore, our strategy demonstrated promising results for targets without homologous templates, such as R1209, through comprehensive literature reviews and structural selection. Despite these advances, RNA structure prediction continues to face challenges, particularly in predicting complex topologies like pseudoknots and coaxial stacking. Future improvements in integrating computational tools with expert knowledge are essential to enhance the accuracy and applicability of RNA tertiary structure prediction.
Accurate Biomolecular Structure Prediction in CASP16 With Optimized Inputs to State-Of-The-Art Predictors.
Wang W, Luo Y, Peng Z … +1 more , Yang J
Proteins · 2026 Jan · PMID 40762404 · Publisher ↗
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 prot...
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.
Structure Modeling Protocols for Protein Multimer and RNA in CASP16 With Enhanced MSAs, Model Ranking, and Deep Learning.
Kagaya Y, Nakamura T, Verburgt J … +8 more , Jain A, Terashi G, Punuru P, Tugolukova E, Park JH, Saha A, Huang D, Kihara D
Proteins · 2026 Jan · PMID 40751131 · Full text
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...
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.
Uncovering Sequence and Structural Characteristics of Fungal Expansin-Related Proteins With Potential to Drive Substrate Targeting.
Pohto A, Koitto T, Dahiya D … +6 more , Castro A, Sidorova E, Huusela M, Baker SE, Tsang A, Master E
Proteins · 2026 Feb · PMID 40750902 · Full text
Expansins loosen plant cell wall networks through disrupting non-covalent bonds between cellulose microfibrils and matrix polysaccharides. Whereas expansins were first discovered in plants, expansin-related proteins have...
Expansins loosen plant cell wall networks through disrupting non-covalent bonds between cellulose microfibrils and matrix polysaccharides. Whereas expansins were first discovered in plants, expansin-related proteins have since been identified in bacteria and fungi. The biological function of microbial expansins remains unclear; however, several studies have shown distinct binding preferences toward different structural polysaccharides. Earlier studies of bacterial expansin-related proteins uncovered sequence and structural features that correlate to substrate binding. Herein, 20 fungal expansin-related sequences were recombinantly produced in Komagataella phaffii, and the purified proteins were compared in terms of substrate binding to cellulosic and chitinous substrates. The impact of pH on the zeta potential of prioritized substrates was also measured, and Principal Component Analysis was performed to uncover correlations between protein characteristics (e.g., pI, hydrophobicity, surface charge distribution) and measured substrate binding preferences. Whereas acidic proteins with a predicted pI less than 5.0 preferentially bound to chitin, basic proteins with pI greater than 8.0 preferentially bound to xylan and xylan-containing fiber. Similar to many cellulases, binding to cellulose was correlated to relatively high aromatic amino acid content in the protein sequence and presence of a carbohydrate binding module (CBM), which in the case of expansins is a C-terminal CBM63. Whereas overall sequence characteristics could be correlated to substrate binding preference, the identity of amino acids occupying conserved positions that impact protein activity was better correlated with loosenin versus expansin classifications.
Ligand Binding Prediction on Pharmaceutical and Nucleic Acid Targets by the CoDock Group in CASP16.
Kong R, Jiang Z, Lu X … +2 more , Xie L, Chang S
Proteins · 2026 Jan · PMID 40750568 · Publisher ↗
Ligand binding prediction is a critical component of structure-based drug design, gaining prominence in Critical Assessment of protein Structure Prediction (CASP) since its introduction in CASP15. In CASP16, the challeng...
Ligand binding prediction is a critical component of structure-based drug design, gaining prominence in Critical Assessment of protein Structure Prediction (CASP) since its introduction in CASP15. In CASP16, the challenges expanded to include protein-ligand and nucleic acid-ligand binding predictions, alongside binding affinity ranking, posing greater computational and methodological demands. This study presents a sophisticated prediction strategy combining template-based docking, multiple receptor conformations, and AI-driven scoring to address these challenges. For protein-ligand systems (L1000-L4000), we leveraged structural templates from PDB, ligand similarity analysis, and tools like CoDock-Ligand and AutoDock Vina to predict binding poses. Key successes included accurate predictions for targets like SARS-CoV-2 Mpro (L4000) and Autotaxin (L3000), though challenges persisted with binding site flexibility and pose ranking. The prediction of ligand pose achieved satisfactory results, with more than 66% of the distribution having RMSD less than 3 Å. Nucleic acid-ligand predictions (e.g., ZTP riboswitch) yielded mixed results, highlighting limitations in RNA/DNA structural accuracy. Affinity prediction employed diverse methods, with machine learning-based SVR_Conjoint outperforming physics-based approaches (Kendall's Tau = 0.43). Our strategy demonstrated robustness in CASP16, yet underscored the need for advancements in handling conformational dynamics and scoring accuracy. This work provides a framework for future ligand binding prediction efforts in computational drug discovery.
Rationalizing Enhanced Affinity of Engineered T-Cell Receptors in Cancer Immunotherapy Through Interaction Energy Calculations and Residue Correlation Analysis.
Frezzini M, Narzi D
Proteins · 2026 Feb · PMID 40747799 · Full text
The advancement of T cell engineering has significantly transformed the field of cancer immunotherapy. In particular, T cells equipped with modified T cell receptors present a promising therapeutic strategy, especially f...
The advancement of T cell engineering has significantly transformed the field of cancer immunotherapy. In particular, T cells equipped with modified T cell receptors present a promising therapeutic strategy, especially for addressing solid tumors. Nonetheless, critical obstacles, including suboptimal clinical response rates, off-target toxicity, and the immunosuppressive nature of the tumor microenvironment, have impeded the full clinical implementation of this approach. Understanding the molecular determinants governing the interaction between T-cell receptors and major histocompatibility complex molecules is pivotal not only for designing TCRs capable of selectively and effectively recognizing MHC on cancer cells but also for minimizing off-target toxicity, thereby improving the safety profile of TCR-based therapies. In this study, we used a test case involving a natural TCR (c728) and its affinity-enhanced variant (c796), which differ by a single conservative mutation in the region. Through molecular dynamics simulations, MM/PBSA binding energy and Free Energy Perturbation calculations, residue-specific energy decomposition, and correlation analyses, we dissected the molecular basis of the engineered TCR's six-fold increase in binding affinity for the peptide-MHC complex compared to its parental counterpart. Interestingly, our results indicate that this affinity enhancement is not directly attributable to the mutation itself but rather to the dynamic interplay of both proximal and distal residues that are either directly correlated with the mutation or connected via allosteric pathways. Our findings, which align with experimental data, highlight the nuanced role of structural flexibility and allosteric communication in shaping TCR-pMHC interactions. By demonstrating the utility of combining computational techniques to unravel these dynamics, this work emphasizes how similar approaches can guide the rational design of engineered TCRs with improved efficacy and specificity, advancing their application in cancer immunotherapy.
The Crystal Structure of Human Transport and Golgi Organization 2 Homolog (TANGO2) Protein Reveals an αββα-Fold Arrangement.
Cooper A, Powers A, Battaile KP … +4 more , Mohsen AW, Johnson DK, Lovell S, Ghaloul-Gonzalez L
Proteins · 2026 Feb · PMID 40726205 · Full text
Transport and Golgi Organization 2 Homolog (TANGO2) protein deficiency disorder (TDD) is a rare autosomal recessive disorder characterized by multi-systemic abnormalities and significant phenotypic variability including...
Transport and Golgi Organization 2 Homolog (TANGO2) protein deficiency disorder (TDD) is a rare autosomal recessive disorder characterized by multi-systemic abnormalities and significant phenotypic variability including neurodevelopmental delay, seizures, intermittent ataxia, hypothyroidism, rhabdomyolysis, life-threatening metabolic derangements, and cardiac arrhythmias. Mutations in TANGO2 result in mitochondrial dysfunction, abnormal lipid homeostasis with cardiolipin deficiency, and impaired Golgi-ER trafficking in TANGO2 patient-derived cells. Despite the wide recognition of the clinical manifestations of TDD and numerous molecular studies, the precise function of TANGO2 and the pathophysiology of TDD remain poorly understood. A computationally derived three-dimensional structure model suggested that TANGO2 adopts an αββα-fold, similar to the N-terminal nucleophile aminohydrolase (Ntn) superfamily of proteins, but the experimentally verified structure has not been available thus far. Here, we present the first crystal structure of the recombinant human TANGO2, determined at 1.70 Å resolution. The X-ray structure data confirmed its predicted tertiary fold with similarity to the Ntn-hydrolase family of proteins, and the comparative analysis of the active site architecture, including residues involved in catalysis and putative ligand binding site, suggests a potential hydrolase function. Additional examination of the common mutation sites found in TDD patients provides insight regarding their potential effect on protein structure integrity.
Structural and Enzymological Characterization of Phosphoserine Phosphatase From Brucella melitensis.
Scaillet T, Pierson É, Fillet M … +1 more , Wouters J
Proteins · 2026 Feb · PMID 40719280 · Publisher ↗
Amino acid L-serine (L-Ser) is a precursor of various biomolecules, including other amino acids, glutathione, and nucleotides. The metabolism of this amino acid is crucial in diseases such as brucellosis. Previous studie...
Amino acid L-serine (L-Ser) is a precursor of various biomolecules, including other amino acids, glutathione, and nucleotides. The metabolism of this amino acid is crucial in diseases such as brucellosis. Previous studies have revealed that the enzymes involved in L-Ser biosynthesis are essential for Brucella replication, making them potential targets for the development of new drugs. Here, we focus on Brucella melitensis phosphoserine phosphatase (BmPSP), which catalyzes the dephosphorylation of phosphoserine in L-Ser. The enzyme is characterized through enzymatic and structural studies, leading to the discovery of its first crystallographic structures. The interactions of BmPSP with different ligands are also investigated. We demonstrate that the substitution of its Mg cofactor with Ca inhibits the enzyme and results in a slight movement of catalytic residues in the active site. Crystallographic structures of BmPSP in complex with substrate, reaction products, and substrate analogs are also detailed, revealing the interaction between these molecules and the active site residues. This structural study provides a better understanding of phosphoserine phosphatases, highlighting the involvement of two highly conserved residues in the mechanism of substrate entry into the active site.
Responses to Ligand Binding in the Bacterial DNA Sliding Clamp "β-Clamp" Manifest in Dynamic Allosteric Effects.
Simonsen S, Prestel A, Østerlund EC … +3 more , Otterlei M, Jørgensen TJD, Kragelund BB
Proteins · 2025 Dec · PMID 40714812 · Full text
The homo-dimeric, ring-shaped bacterial DNA sliding clamp, β-clamp, is a central hub in DNA replication and repair. It interacts with a plethora of proteins via their short linear motifs, binding to the same hydrophobic...
The homo-dimeric, ring-shaped bacterial DNA sliding clamp, β-clamp, is a central hub in DNA replication and repair. It interacts with a plethora of proteins via their short linear motifs, binding to the same hydrophobic binding pocket on β-clamp. Although the structure, functions, and interactions of β-clamp have been amply studied, less focus has been on understanding its dynamics and how this is influenced by ligand binding. In this work, we have made a backbone nuclear magnetic resonance (NMR) assignment of the 83 kDa dimeric β-clamp and used NMR in combination with hydrogen-deuterium exchange mass spectrometry to scrutinize the dynamics of β-clamp and how ligand binding affects this. We found that the binding of a short peptide from the polymerase III α subunit affects the dynamics and stability of β-clamp. The effect not only appears locally around the binding pocket but also globally through dynamic allosteric connections to distant regions of the protein, including the dimer interface. The dissipated dynamic effect from ligand binding is likely a consequence of a unique binding pocket architecture that connects distant parts of the structure and may reflect a mechanism of structural plasticity in protein hubs, where different ligands impose differential responses in the structure and dynamics of β-clamp, resulting in diverse functional responses.
Structural Variability of Pfam Domains Based on Alphafold2 Predictions.
Poretsky E, Andorf CM, Sen TZ
Proteins · 2025 Dec · PMID 40696837 · Full text
Understanding the biological functions of proteins is one of the main goals of functional genomics. Such understanding will help control and manipulate biological processes to enhance desirable traits, including improved...
Understanding the biological functions of proteins is one of the main goals of functional genomics. Such understanding will help control and manipulate biological processes to enhance desirable traits, including improved abiotic and biotic stress resistance in humans, animals, plants, and microbes. Protein domains, regarded as the functional building blocks of proteins, have been used extensively to predict protein function. Sequence-based approaches for protein function prediction, including the use of protein domain prediction from resources like the Pfam database, remain popular due to their reliability, low cost, and ease of use. Although the sequence variability of Pfam domains has been reported in several studies, their structural variability has been understudied. Here, we have extracted the Pfam domain structural portion from the predicted structures of the 16 model organism proteomes in the AlphaFold2 database. Our analysis revealed that many families contained between 20% and 40% members with no assigned regular secondary structures, demonstrating within-family structural variability. To better understand this structural variability, we used FoldSeek and agglomerative clustering to identify structural variability in Pfam families. We then analyzed specific cases to provide structural details for this variability. In this study, we have used two popular prediction applications/resources, Alphafold2 and Pfam, to demonstrate inherent variability in protein domain predictions by comparing their predicted structures. Our study shows that detection of structural variability in Pfam families can facilitate curation and refinement of Pfam families, while demonstrating the need to develop more accurate protein domain prediction workflows.
The SIRT1 N-Terminal Domain as a Common Binding Interface for PPARγ Anchoring.
Malospirito CC, Jara GE, Antunes VU … +5 more , Elias GB, Dias MMG, Batista FAH, de Oliveira PSL, Figueira ACM
Proteins · 2025 Dec · PMID 40673659 · Full text
Insulin resistance, a global health threat linked to type 2 diabetes and obesity, can be addressed by modulating the activity of the Sirtuin 1 (SIRT1), a deacetylase that enhances insulin sensitivity by deacetylating the...
Insulin resistance, a global health threat linked to type 2 diabetes and obesity, can be addressed by modulating the activity of the Sirtuin 1 (SIRT1), a deacetylase that enhances insulin sensitivity by deacetylating the Peroxisome Proliferator-Activated Receptor Gamma (PPARγ) at lysine 268 and 293. Understanding the binding interfaces between SIRT1 and PPARγ is critical to developing new strategies to combat insulin resistance. In this study, we present four experimentally supported binding models of SIRT1 with acetylated PPARγ: one at position 268 and three at position 293 (SIRT1-PPARγ and SIRT1-PPARγ models). These models were generated through an integration of in silico modeling and in vitro binding affinity assays. Our models revealed that the SIRT1:PPARγ binding interface is structured by SIRT1's 3-helix bundle in N-terminus domain (NTD(3HB)) and the catalytic domain (CD). The CD accommodated the acetylated peptide in its active site, while NTD(3HB) anchors PPARγ at a region between loops α1-β1 and α2'-α3 within PPARγ's ligand binding domain (LBD). Notably, the SIRT1-NTD(3HB) consistently bound to the same region of PPARγ in both models, highlighting a common mode for interaction. Through molecular dynamic simulation and binding assays, we demonstrated that either removal of SIRT1-NTD(3HB) or mutation within PPARγ-LBD significantly reduces binding affinity, underscoring the role of NTD(3HB) in substrate anchoring. Additionally, we provided evidence of SIRT1 dimerization, with substrate binding inducing its dissociation to form a heterodimer with PPARγ. These findings underscore the importance of the SIRT1 NTD(3HB) in PPARγ anchoring and offer insights into the activation mechanism of SIRT1, with potential implications for drug development targeting insulin resistance.
Biomolecular Interaction Prediction in the Pre- and Post-AlphaFold Era: The 8th CAPRI Evaluation.
Lensink MF, Raouraoua N, Brysbaert G … +3 more , Velankar S, Wodak SJ, Bonvin AMJJ
Proteins · 2025 Jul · PMID 40653974 · Publisher ↗
We report on the 8th CAPRI Evaluation period, capturing the assessment of CAPRI Rounds 47 to 55 (excluding the CASP and COVID-related Rounds), which have witnessed the transition to AI-driven prediction tools such as Alp...
We report on the 8th CAPRI Evaluation period, capturing the assessment of CAPRI Rounds 47 to 55 (excluding the CASP and COVID-related Rounds), which have witnessed the transition to AI-driven prediction tools such as AlphaFold and related alternatives. The prediction Rounds in this evaluation are characterized by a high level of difficulty due to various factors, including the nature of the targets, the intricacy of the interfaces to be predicted, and conformational changes. A total of 11 targets encompassing 21 interfaces, mostly in the difficult prediction category, were evaluated. While a retrospective analysis reveals a strong performance of AlphaFold on those targets, human predictors still outperform AI on difficult targets, particularly those involving antibodies and nucleic acids. Almost 25 years after its birth, CAPRI remains a vibrant and collaborative initiative with active participation from approximately 50 predictor and scorer groups and 10 servers. Continued contributions from experimentalists providing targets to such blind experiments, and further advances in AI, sampling strategies, and improvement in scoring methods will be key to overcoming remaining structural prediction challenges in complex biomolecular systems.
Supervised Learning of Protein Melting Temperature: Cross-Species vs. Species-Specific Prediction.
López SG, Salomon J, Boomsma W
Proteins · 2025 Dec · PMID 40653882 · Full text
Protein melting temperatures are important proxies for stability, and frequently probed in protein engineering campaigns, for instance for enzyme discovery and protein optimization. With the emergence of large datasets o...
Protein melting temperatures are important proxies for stability, and frequently probed in protein engineering campaigns, for instance for enzyme discovery and protein optimization. With the emergence of large datasets of melting temperatures for diverse natural proteins, it has become possible to train models to predict this quantity, and the literature has reported impressive performance values in terms of Spearman rho. The high correlation scores suggest that it should be possible to accurately predict melting temperature changes in engineered variants, and to reliably identify naturally thermostable proteins. However, in practice, results in these settings are often disappointing. In this paper, we explore this apparent discrepancy. We show that Spearman rho over cross-species data gives an overly optimistic impression of prediction performance, and that this metric reflects the ability to distinguish global differences in amino acid composition between species, rather than the specific effects of genetic variation. We proceed by investigating whether cross-species training on melting temperature is beneficial at all, compared to training specific models for each species. We address this question using four different transfer-learning approaches and a fine-tuning procedure. Surprisingly, we consistently find no benefit of cross-species training. We conclude that (1) current models for supervised prediction of melting temperature perform substantially worse than the literature suggests, and (2) that reliable transfer across species is still a challenging problem. An implementation of this work is available at https://github.com/deltadedirac/thermocontrast_tm.
Structural Mimicry Without Glyoxalase I Functional Convergence: A Homogentisate 1,2-Dioxygenase From Acinetobacter.
Seo PW, Hwangbo SA, Kim JS … +1 more , Park SY
Proteins · 2025 Dec · PMID 40650421 · Publisher ↗
Homogentisate 1,2-dioxygenase (HGD) is a non-heme iron enzyme that plays a crucial role in phenylalanine and tyrosine metabolism. Acinetobacter-derived HGD (AcHGD) exhibits structural similarity to glyoxalase I (GLO1) bu...
Homogentisate 1,2-dioxygenase (HGD) is a non-heme iron enzyme that plays a crucial role in phenylalanine and tyrosine metabolism. Acinetobacter-derived HGD (AcHGD) exhibits structural similarity to glyoxalase I (GLO1) but lacks GLO1 activity. In this study, we analyzed the crystal structure of AcHGD at a resolution of 1.5 Å and investigated the molecular basis for its lack of GLO1 activity using enzymatic assays, isothermal titration calorimetry (ITC), and site-directed mutagenesis. Metal ion dependency assays revealed that AcHGD exhibits high specificity for Fe, supporting its role as a non-heme iron (II)-dependent dioxygenase. Structural analysis revealed that AcHGD adopts a β-barrel fold similar to GLO1 and coordinates Zn through a 2-His-1-carboxylate facial triad. However, its substrate-binding tunnel is narrower than that of GLO1, preventing the binding of S-D-lactoylglutathione, the natural substrate of GLO1. Moreover, introducing GLO1-like mutations in the active site failed to confer GLO1 activity and instead abolished HGD activity. ITC analysis confirmed that AcHGD binds strongly to homogentisate but does not interact with S-D-lactoylglutathione. These findings demonstrate that despite its structural resemblance to GLO1, AcHGD lacks GLO1 activity due to differences in substrate specificity and active site architecture. This study provides insights into the structure-function relationship and evolutionary divergence between HGD and GLO1 enzymes.
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