Macromolecular crystallography generally requires the recovery of missing phase information from diffraction data to reconstruct an electron-density map of the crystallized molecule. Most recent structures have been solved using molecular replacement as a phasing method, requiring an a priori structure that is closely related to the target protein to serve as a search model; when no such search model exists, molecular replacement is not possible. New advances in computational machine-learning methods, however, have resulted in major advances in protein structure predictions from sequence information. Methods that generate predicted structural models of sufficient accuracy provide a powerful approach to molecular replacement. Taking advantage of these advances, AlphaFold predictions were applied to enable structure determination of a bacterial protein of unknown function (UniProtKB Q63NT7, NCBI locus BPSS0212) based on diffraction data that had evaded phasing attempts using MIR and anomalous scattering methods. Using both X-ray and micro-electron (microED) diffraction data, it was possible to solve the structure of the main fragment of the protein using a predicted model of that domain as a starting point. The use of predicted structural models importantly expands the promise of electron diffraction, where structure determination relies critically on molecular replacement.

The availability of highly accurate protein structure predictions from AlphaFold2 (AF2) and similar tools has hugely expanded the applicability of molecular replacement (MR) for crystal structure solution. Many structures can be solved routinely using raw models, structures processed to remove unreliable parts or models split into distinct structural units. There is therefore an open question around how many and which cases still require experimental phasing methods such as single-wavelength anomalous diffraction (SAD). Here, this question is addressed using a large set of PDB depositions that were solved by SAD. A large majority (87%) could be solved using unedited or minimally edited AF2 predictions. A further 18 (4%) yield straightforwardly to MR after splitting of the AF2 prediction using Slice'N'Dice, although different splitting methods succeeded on slightly different sets of cases. It is also found that further unique targets can be solved by alternative modelling approaches such as ESMFold (four cases), alternative MR approaches such as ARCIMBOLDO and AMPLE (two cases each), and multimeric model building with AlphaFold-Multimer or UniFold (three cases). Ultimately, only 12 cases, or 3% of the SAD-phased set, did not yield to any form of MR tested here, offering valuable hints as to the number and the characteristics of cases where experimental phasing remains essential for macromolecular structure solution.

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Google DeepMind has unexpectedly released the source code and model weights of AlphaFold 3 for academic use, marking a significant advance that could accelerate scientific…

Google DeepMind’s AlphaFold 3 is available to access by researchers around the world via open-source. Google DeepMind, the tech giant’s… Continue reading Google DeepMind opens AlphaFold 3 up to researchers worldwide

The post Google DeepMind opens AlphaFold 3 up to researchers worldwide appeared first on ReadWrite.

Google DeepMind has silently open-sourced its frontier artificial intelligence (AI) model that can predict the interaction between proteins and other molecules. Dubbed AlphaFold 3, the large language model is the successor of AlphaFold 2, whose research led to the creators of the large language model (LLM) Demis Hassabis and John Jumper getting the Nobel Prize in Chemistry in 2024.

Компания Google DeepMind опубликовала исходные тексты системы машинного обучения AlphaFold 3, предназначенной для предсказания трёхмерной структуры белков и моделирования взаимодействия белков с другими типами молекул. За создание алгоритмов машинного обучения, реализованных во второй версии AlphaFold, в этом году присуждена Нобелевская премия по химии. Связанный с AlphaFold 3 инструментарий написан на Python и C++, и распространяется под лицензией CC BY-NC-SA 4.0. Натренированные модели предоставляются на основе пользовательского соглашения. Отдельно запущен сервер, позволяющий экспериментировать с AlphaFold 3 в online-режиме.