bims-alfold 2025-02-16 papers

J Mol Biol. 2025 Feb 11. pii: S0022-2836(25)00062-2. [Epub ahead of print] 168996

ModelArchive: a deposition database for computational macromolecular structural models.

Gerardo Tauriello, Andrew M Waterhouse, Juergen Haas, Dario Behringer, Stefan Bienert, Thomas Garello, Torsten Schwede.

A wide range of applications in life science research benefit from the availability of three-dimensional structures of biological macromolecules as they provide valuable insights into their molecular function. Recent advances in structure prediction techniques have made it possible to generate high quality computational macromolecular structural models for almost all known proteins. In this context, ModelArchive (https://modelarchive.org/) serves as a deposition database for computational models, complementing the Protein Data Bank (PDB) and PDB-IHM, which require experimental data, and specialised databases such as the AlphaFold DB. ModelArchive contains over 600,000 models contributed by researchers using a variety of modelling techniques. It supports single biological macromolecules and complexes, including any combination of polymers and small molecules. Each deposited model can be referenced in manuscripts using an immutable accession code provided by ModelArchive. Depositors are required to provide a minimal set of information about the modelling process and the expected accuracy of the resulting model, enabling scientific reproducibility and maximising the potential reuse of the models. The vast majority of models in ModelArchive use the ModelCIF format which includes coordinates and metadata, allows for programmatic validation of the models, and makes the models interoperable with structures obtained from other sources such as the PDB. The ModelArchive web service provides access to the models and search queries. Model findability is also provided in external services either through APIs or by importing data from ModelArchive.

Keywords: FAIR databases; ModelArchive; ModelCIF; macromolecular structure prediction; structural biology

DOI: https://doi.org/10.1016/j.jmb.2025.168996

QRB Discov. 2025 ;6 e3

Frontiers in integrative structural modeling of macromolecular assemblies.

Kartik Majila, Shreyas Arvindekar, Muskaan Jindal, Shruthi Viswanath.

Integrative modeling enables structure determination for large macromolecular assemblies by combining data from multiple experiments with theoretical and computational predictions. Recent advancements in AI-based structure prediction and cryo electron-microscopy have sparked renewed enthusiasm for integrative modeling; structures from AI-based methods can be integrated with in situ maps to characterize large assemblies. This approach previously allowed us and others to determine the architectures of diverse macromolecular assemblies, such as nuclear pore complexes, chromatin remodelers, and cell-cell junctions. Experimental data spanning several scales was used in these studies, ranging from high-resolution data, such as X-ray crystallography and AlphaFold structure, to low-resolution data, such as cryo-electron tomography maps and data from co-immunoprecipitation experiments. Two recurrent modeling challenges emerged across a range of studies. First, these assemblies contained significant fractions of disordered regions, necessitating the development of new methods for modeling disordered regions in the context of ordered regions. Second, methods needed to be developed to utilize the information from cryo-electron tomography, a timely challenge as structural biology is increasingly moving towards in situ characterization. Here, we recapitulate recent developments in the modeling of disordered proteins and the analysis of cryo-electron tomography data and highlight other opportunities for method development in the context of integrative modeling.

Keywords: Conformational ensembles; Electron cryo-tomography; Generative modeling; Integrative modeling; Intrinsically disordered proteins; Macromolecular assemblies; Protein language models

DOI: https://doi.org/10.1017/qrd.2024.15

J Chem Inf Model. 2025 Feb 14.

The Need for Continuing Blinded Pose- and Activity Prediction Benchmarks.

Christian Kramer, John Chodera, Kelly L Damm-Ganamet, Michael K Gilson, Judith Günther, Uta Lessel, Richard A Lewis, David Mobley, Eva Nittinger, Adam Pecina, Matthieu Schapira, W Patrick Walters.

Computational tools for structure-based drug design (SBDD) are widely used in drug discovery and can provide valuable insights to advance projects in an efficient and cost-effective manner. However, despite the importance of SBDD to the field, the underlying methodologies and techniques have many limitations. In particular, binding pose and activity predictions (P-AP) are still not consistently reliable. We strongly believe that a limiting factor is the lack of a widely accepted and established community benchmarking process that independently assesses the performance and drives the development of methods, similar to the CASP benchmarking challenge for protein structure prediction. Here, we provide an overview of P-AP, unblinded benchmarking data sets, and blinded benchmarking initiatives (concluded and ongoing) and offer a perspective on learnings and the future of the field. To accelerate a breakthrough on the development of novel P-AP methods, it is necessary for the community to establish and support a long-term benchmark challenge that provides nonbiased training/test/validation sets, a systematic independent validation, and a forum for scientific discussions.

DOI: https://doi.org/10.1021/acs.jcim.4c02296

Prog Mol Biol Transl Sci. 2025 ;pii: S1877-1173(24)00217-5. [Epub ahead of print]211 1-15

Functional diversity of intrinsically disordered proteins and their structural heterogeneity: Protein structure-function continuum.

Vladimir N Uversky.

The fact that protein universe is enriched in intrinsic disorder is an accepted truism now. It is also recognized that the phenomenon of protein intrinsic disorder contains keys to answer numerous questions that do not have obvious solutions within the classic "lock-and-key"-based structure-function paradigm. In fact, reality is much more complex than the traditional "one-gene - one-protein - one-function" model, as many (if not most) proteins are multifunctional. This multifunctionality is commonly rooted in the presence of the intrinsically disordered or structurally flexible regions in a protein. Here, in addition to various events at the DNA (genetic variations), mRNA (alternative splicing, alternative promoter usage, alternative initiation of translation, and mRNA editing), and protein levels (post-translational modifications), intrinsic disorder and protein functionality are crucial for generation of proteoforms, which are functionally and structurally different protein forms produced from a single gene. Therefore, since a given protein exists as a dynamic conformational ensemble containing multiple proteoforms characterized by a broad spectrum of structural features and possessing various functional potentials, "protein structure-function continuum" model represents a more realistic way to correlate protein structure and function.

Keywords: Alternative splicing; Induced fit; Intrinsically disordered protein; Intrinsically disordered region; Lock-and-key; Multifunctionality; Posttranslational modification; Protein interactions; Proteoform; Structure-function continuum

DOI: https://doi.org/10.1016/bs.pmbts.2024.11.006

Gut Microbiome (Camb). 2025 ;6 e3

GutBugDB: a web resource to predict the human gut microbiome-mediated biotransformation of biotic and xenobiotic molecules.

Usha Longwani, Ashok K Sharma, Aditya S Malwe, Shubham K Jaiswal, Vineet K Sharma.

There has been a growing recognition of the significant role played by the human gut microbiota in altering the bioavailability as well as the pharmacokinetic and pharmacodynamic aspects of orally ingested xenobiotic and biotic molecules. The determination of species-specific contributions to the metabolism of biotic and xenobiotic molecules has the potential to aid in the development of new therapeutic and nutraceutical molecules that can modulate human gut microbiota. Here we present "GutBugDB," an open-access digital repository that provides information on potential gut microbiome-mediated biotransformation of biotic and xenobiotic molecules using the predictions from the GutBug tool. This database is constructed using metabolic proteins from 690 gut bacterial genomes and 363,872 protein enzymes assigned with their EC numbers (with representative Expasy ID and domains present). It provides information on gut microbiome enzyme-mediated metabolic biotransformation for 1439 FDA-approved drugs and nutraceuticals. GutBugDB is publicly available at https://metabiosys.iiserb.ac.in/gutbugdb/.

Keywords: biotransformation; database; human gut microbiome; machine learning; xenobiotic metabolism

DOI: https://doi.org/10.1017/gmb.2024.15

Mol Biol Rep. 2025 Feb 12. 52(1): 226

Protein subinteractomes of human microsomal cytochromes P450.

Pavel V Ershov, Evgeniy O Yablokov, Yuri V Mezentsev, Alexis S Ivanov.

Microsomal cytochromes P450 (micCYPs) are monooxygenases located in the endoplasmic reticulum and other endomembranes of human cells. micCYPs receive electrons from specific redox partners and perform enzymatic transformations of drugs and different endogenous substrates. The large biodiversity of micCYPs leads to the idea that protein-protein interactions (PPIs) involving micCYPs are not limited to classical redox partners. This review aims to perform a systems biology analysis of the complete set of PPIs for all 33 micCYPs studied, as well as to examine the subinteractome of each micCYP. We have retrieved 287 PPIs from interactomic databases, involving 246 unique protein interactors that share a similar profile of subcellular localization with micCYPs. The number of protein interactors per micCYP unevenly varies from one to 47. Interactors of micCYPs are involved in cellular metabolism, signal transduction, cell-cell junctions, cytoskeleton organization, and intracellular or transmembrane transport. Notably, up to one-third of all interactors belong to the latter group, half of which consists of membrane transporters of compounds, metabolites, and ions (e.g., CACNA2D1, ORAI1, SCN3B, SLC7A2, SLC19A3, and SLC11A2). The CYP2C8 subinteractome is enriched with proteins involved in autophagy; CYP2S1- ERBB2 and EPH-Ephrin signaling; CYP3A4- glucuronidation. Proteins UBC, PGRMC1, and FANCG are the most frequent common interactors across various micCYPs. Nine and 12 interactors of micCYPs are involved in phosphorylation and ubiquitination, respectively; 20 interactors are 'moonlighting' proteins that are represented in the CYP3A4 subinteractome. Furthermore, micCYPs such as CYP2C9, 3A5, 2E1, 2A6, 4F2, and 4A11 may be involved in potentially binary interactions with other micCYPs. The functional implication of these CYP-CYP pairs is likely associated with modulation of their activity. Analysis of transcriptomic data revealed that some micCYP/interactor pairs exhibit tissue-, time-, and disease-specific gene expression patterns. Drugs that are metabolized by micCYPs in some cases can influence the expression of corresponding interactors at the gene or protein levels. These findings suggest that micCYPs may play roles in functions beyond their monooxygenase activity, as indicated by the spectrum of PPIs analyzed.

Keywords: CYP; Cytochrome P450; Drug-protein interactions; Gene expression; Interactomics; Protein-protein interactions

DOI: https://doi.org/10.1007/s11033-025-10341-5