Recep Adiyaman
bioinformatics

Issue #33: ModelCIF update: Supporting Emerging Classes of Computational Macromolecular Models.

January 27, 2026 Daily Intelligence
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ModelCIF update: Supporting Emerging Classes of Computational Macromolecular Models.

šŸ§¬ Abstract

The recent development of highly accurate protein structure prediction tools has led to a rapid expansion in the scope of computational structural biology, enabling a much wider range of modelling studies than ever before. These new in silico opportunities help life science researchers understand how proteins interact with their environment and support design of new molecules with desired properties. Ultimately, they have broad applications, e.g. in medicine, drug discovery or engineering. To ensure reproducibility and to facilitate data exchange and reuse, predicted structures or computed structure models can be stored using ModelCIF, a rich data representation designed to include the atomic coordinates/metadata. The previously published version of ModelCIF (1.4.4; 2022-12-21) mainly covered protein structure predictions generated by homology and ab initio modelling. In this work, we present an extension of the ModelCIF (https://github.com/ihmwg/ModelCIF) data standard and its associated tools. This extension supports important new use cases, including modelling protein-ligand and protein-protein interactions, sampling multiple conformational states and designing proteins de novo. We define guidelines for storage and validation of modelling results for those use cases by applying new and existing ModelCIF categories to capture protocols, inputs and outputs. Additionally, we outline updates to the software tools and resources that implement these new standards and provide functionality for model generation, validation, archiving, and visualisation. By enabling consistent metadata capture across different modelling workflows, this framework aims to support the FAIR dissemination of computational models, thereby promoting reproducibility and reusability in downstream applications.

Why it matters: Critical for improving fold accuracy and reducing structural uncertainty in de novo design.

ā­ Additional Signals

Tailored pyrrole-based imidazothiazole scaffolds: Synthetic elaboration, enzyme kinetic profiling and DFT-guided molecular docking toward Antidiabetic therapeutics.

The current research study highlights the successful biological evaluation of novel imidazo-thiadiazole based pyrrole derivatives, with the aim of targeting diabetes mellitus through alpha-amylase and alpha-glucosidase inhibition. These compounds exhibited promising anti-diabetic activity, notably compound 8 emerged as a leading candidate (3.50 Ā± 0.20, and 4.10 Ā± 0.10 ĀµM) which outperformed the potential of acarbose (6.20 Ā± 0.10 and 6.70 Ā± 0.20 ĀµM), a reference drug. The enhanced biological potential of compound 8 is likely due to incorporation of hydroxyl substituents, which may strengthen its binding affinity and selectivity towards the targeted enzymes. Molecular docking revealed stable interactions with key amino acids residues of targeted enzymes, providing mechanistic basis for its potent inhibitory activity. To further established their therapeutic relevance, enzyme kinetic study was conducted which confirmed their mode of inhibition while ADMET analysis indicated favorable pharmacokinetics and safety profiles. Moreover, pharmacophore modeling and molecular dynamics simulations reinforced the stability and binding efficiency of lead compounds under dynamic biological conditions. All the experimental results and in silico validations demonstrate that potent compounds possess significant anti-diabetic activity profile. Their ability to outperform an existing diabetes mellitus inhibitor and maintaining a favorable safety profile suggest that these compounds have potential to be further used in drug development and optimization against Diabetes Mellitus.

Identification of novel umami peptides in fermented milk and elucidation of their umami mechanism via molecular docking and molecular dynamics simulations.

A streamlined workflow integrating multi-model machine learning, bioinformatics filtering, sensory evaluation, molecular docking and dynamics simulations was applied to mine umami peptides in fermented milk. Based on dual selection criteria-(i) unanimous umami prediction by UMPred-FRL, Umami_YYDS, Umami-MRNN, Mlp4Umami, Umami_TD, (ii) favorable in silico properties (non-toxicity, non-allergenicity, good solubility, stability, potential bioactivity)-ten out of the 1505 peptides identified by peptidomics were shortlisted as umami peptide candidates. Sensory evaluation confirmed that eight imparted an umami taste. Molecular docking revealed that umami peptides interact with TAS1R1/TAS1R3 primarily through hydrogen bonds formed between their hydrophilic residues (predominantly Lys, Tyr) and receptor hydrophilic residues (notably Lys/Arg in TAS1R1, Asn in TAS1R3). Residues Arg307/Met375/Lys379 of TAS1R1, and Arg327/443/Ala329/Val437/Met452 of TAS1R3 were key interaction sites. Molecular dynamics simulations showed that the three peptides with the highest umami taste-EVFTKK, SKKTVDME, VMGVSKVKE-formed stable and compact complexes with TAS1R1/TAS1R3. This work enhances understanding of the umami characteristics of fermented milk.

Stereoselective Synthesis, Anticolon Cancer Activity, Molecular Docking, and Dynamics Simulation Studies of Spirooxindole Derivatives.

Spirooxindoles have been reported to be effective anticancer drug candidates by displaying promising pre-clinical results. Therefore, to find out a lead spirocyclic oxindole template, a series of spirooxindole derivatives bearing pyrrolizidine (14a-e) and N-methyl pyrrolidine (15a-e) were synthesized using an efficient multicomponent, one-pot, and stereoselective [3+2] cycloaddition reaction and evaluated in vitro against HT29 and HCT116 human colon cancer cell lines. The pyrrolizidine and N-methyl pyrrolidine spirooxindole derivatives were synthesised in excellent regio- and stereoselectivity using previously optimized reaction conditions. They were evaluated in vitro against cell lines HT29 and HCT116. In silico ADME profiling, molecular docking, and dynamics simulation studies were performed to ascertain the probable mode of action of the lead derivative. The spirooxindoles were characterized using FTIR, ESI-MS, 1H and 13C NMR, purity was determined by RP-HPLC, and stereochemistry was confirmed by X-ray crystallography. Compound 14a produced the best anti-colon cancer activity with IC50 values of 62.66 and 9.55 Ī¼M against HT29 and HCT116 human colon cancer cell lines, respectively. The in silico studies revealed that MDM2 protein inhibition is a probable mode of anti-colon cancer activity, supported by the data obtained in the molecular docking and molecular dynamics study. The described [3+2] cycloaddition reaction proved to be a highly efficient and catalyst- free reaction. The in vitro cell viability assays and in silico studies revealed that more spirooxindoles can be designed with a varied degree of substitution to target colon cancer.

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Protein Language Model-Guided Engineering of a 2,3-Butanediol Dehydrogenase for the Enantioselective Synthesis of Cyclic Ī±-Hydroxy Ketones.

(2R,3R)-butanediol dehydrogenases (BDHs) are promising catalysts for the production of Ī±-hydroxy ketones, which are highly valuable compounds in the synthesis of fine chemicals and pharmaceuticals. However, (2R,3R)-BDHs display limited stereoselectivity, thus restricting wider applications. In this study, we engineered a (2R,3R)-BDH from Bacillus subtilis (BsBDH) to enhance and invert its stereoselectivity toward 1,2-cyclohexanediol (1,2-CHD) for the production of chiral 2-hydroxycyclohexanone. The hot spots 115, 118, 293 of BsBDH were initially identified using the protein language model ESM-1v. Subsequently, to obtain a stable scaffold to engineer stereoselectivity, we devised a strategy of position analysis and source search, achieving a true-positive rate of 88.2% in designing thermostable single variants. Furthermore, iterative saturation mutagenesis was applied to the hot spots of the thermostable variant 6M2, and obtained a trans-CHD preference variant LTF (ee > 99%) and a cis-CHD preference variant 10M (ee > 99%). Several high-activity variants were also obtained, including 6M2/F115C/L118F and 6M2/F115L/L118M, which demonstrated the activity improvements toward 25 substrates, with the highest enhancement reaching 5183.1-fold. Additionally, molecular dynamics (MD) simulations and the incorporation of non-canonical amino acids (ncAAs) were utilized to elucidate the mechanisms underlying the variants. The engineered BsBDH variants exhibit promising potential for the biocatalytic production of Ī±-hydroxyketones.

Physics Informed Differentiable Solvers for Learning Parametric Solution Manifolds in Heterogeneous Physical Systems

Learning the full family of solutions to parameterized partial differential equations (PDEs) is a central challenge to our ability to model the behavior of heterogeneous systems, with a variety of fundamental and application-oriented implications in fields such as hydrogeology where system properties exhibit significant (and often uncertain) spatial heterogeneity. We address this by reformulating a Physics-Informed Neural Network (PINN) as a differentiable solver that learns the continuous solution manifold for steady-state Darcy flow. Our framework requires only a single training run, circumventing the need for costly re-training for each new parameter instance. Its versatility is demonstrated through two representations of spatially heterogeneous hydraulic conductivity fields: a direct analytical form and a novel data-driven formulation resting on an autoencoder to create a low-dimensional latent encoding. A key innovation is the integration of the differentiable decoder into the physics-informed loss function, enabling on-the-fly reconstruction of complex conductivity fields via automatic differentiation. The approach yields accurate, mass-conserving flow solutions and supports efficient uncertainty quantification, providing a general methodology for physics-constrained data-driven modeling of heterogeneous systems.

A comparative analysis of BMI and skinfold measurements in the assessment of body composition parameters.

To measure biceps, triceps, subscapular, and suprailiac skinfold thicknesses, and to construct population growth charts for these skinfolds and for the sum of the 4 skinfold thicknesses. One aim was also to derive the percentage of body fat from skinfold thicknesses, and to determine whether BMI and MUAC could be used to measure body fatness. The research methodology involved a cross-sectional study design, with data collected from children 0-18 years of age across different age groups and in both sexes in the United Arab Emirates (UAE). We included at least 200 children in each age-sex group. Height, weight, biceps skinfold, triceps skinfold, subscapular skinfold, suprailiac skinfold, and mid-upper-arm circumference were measured in each child. We determined whether the calculation of percentage body fat from the skinfold measurements correlated with BMI in the United Arab Emirates population. We also determined whether any of the above is a good indicator of fatness in children. Statistical tests used were Pearson’s correlation, partial correlations and concordance coefficient. The total number of children studied was 19,960 children (9646 boys and 10,314 girls). BMI, upper-arm circumference, sum of four skinfolds, and percentage body fat charts were constructed using the LMS smoothing method. BMI significantly correlated with the sum of skinfold thicknesses and mid-upper-arm circumference. Prevalence of obesity and overweight in ages 13-17 years was respectively 9.94% and 15.16% in females and 6.08% and 14.16% in males. Derived body fat charts were found not to be accurate. BMI and MUAC were not concordant with the sum of 4 skinfold thicknesses. National BMI, upper-arm circumference, and sum of four skinfolds charts have been constructed as a reference standard for the UAE. The sum of four skinfold thicknesses provides a more accurate measure of adiposity than BMI or MUAC in UAE children. These UAE-specific growth charts enable better assessment of childhood obesity.

šŸ’” Pipeline Tip

Normalise thermal B-factors when comparing different crystal structures.

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Deep learning is not a magic wand, but a powerful lens for structural biology. ā€” Recep Adiyaman

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