Proteins often function as part of permanent or transient multimeric complexes, and understanding function of these assemblies requires knowledge of their three-dimensional structures. While the ability of AlphaFold to predict structures of individual proteins with unprecedented accuracy has revolutionized structural biology, modeling structures of protein assemblies remains challenging. To address this challenge, we developed a protocol for predicting structures of protein complexes involving model sampling followed by scoring focused on the subunit-subunit interaction interface. In this protocol, we diversified AlphaFold models by varying construction and pairing of multiple sequence alignments as well as increasing the number of recycles. In cases when AlphaFold failed to assemble a full protein complex or produced unreliable results, additional diverse models were constructed by docking of monomers or subcomplexes. All the models were then scored using a newly developed method, VoroIF-jury, which relies only on structural information. Notably, VoroIF-jury is independent of AlphaFold self-assessment scores and therefore can be used to rank models originating from different structure prediction methods. We tested our protocol in CASP15 and obtained top results, significantly outperforming the standard AlphaFold-Multimer pipeline. Analysis of our results showed that the accuracy of our assembly models was capped mainly by structure sampling rather than model scoring. This observation suggests that better sampling, especially for the antibody-antigen complexes, may lead to further improvement. Our protocol is expected to be useful for modeling and/or scoring protein assemblies.

/pdf/prediction-of-protein-assemblies-by-structure-sampling-1uu1vc3t.pdf

Prediction of protein assemblies by structure sampling followed by interface-focused scoring

Sulfoquinovose is a widespread organosulfur substrate for Roseobacter clade bacteria in the ocean

The recent public release of the latest version of the AlphaFold database has given us access to over 200 million predicted protein structures. We use a “shape-mer” approach, a structural fragmentation method analogous to sequence k-mers, to describe these structures and look for novelties - both in terms of proteins with rare or novel structural composition and possible functional annotation of under-studied proteins. Data and code will be made available at https://github.com/TurtleTools/afdb-shapemer-darkness

/pdf/what-is-hidden-in-the-darkness-characterization-of-alphafold-2wen7f8h.pdf

What is hidden in the darkness? Characterization of AlphaFold structural space

The prediction of RNA three-dimensional structures remains an unsolved problem. Here, we report double-blind assessments of RNA structure predictions in CASP15, the first CASP exercise in which RNA modeling was assessed. Forty two predictor groups submitted models for at least one of twelve RNA-containing targets. These models were evaluated by the RNA-Puzzles organizers and, separately, by a CASP-recruited team using metrics (GDT, lDDT) and approaches (Z-score rankings) initially developed for assessment of proteins and generalized here for RNA assessment. The two assessments independently ranked the same predictor groups as first (AIchemy_RNA2), second (Chen), and third (RNAPolis and GeneSilico, tied); predictions from deep learning approaches were significantly worse than these top ranked groups, who did not use deep learning. Further analyses based on direct comparison of predicted models to cryogenic electron microscopy (cryo-EM) maps and X-ray diffraction data support these rankings. With the exception of two RNA-protein complexes, models submitted by CASP15 groups correctly predicted the global topology of the RNA targets. Comparisons of CASP15 submissions to designed RNA nanostructures as well as molecular replacement trials highlight the potential utility of current RNA modeling approaches for RNA nanotechnology and structural biology, respectively. Nevertheless, challenges remain in modeling fine details such as non-canonical pairs, in ranking among submitted models, and in prediction of multiple structures resolved by cryo-EM or crystallography.

/pdf/assessment-of-three-dimensional-rna-structure-prediction-in-tm58bm5b.pdf

Assessment of three-dimensional RNA structure prediction in CASP15

Summary Homology detection by sequence comparison is a typical first step in the study of protein function and evolution. Here, we describe a new homology detection tool, pLM-BLAST, that uses a modified Smith-Waterman algorithm for unsupervised comparison of single-sequence representations obtained from a protein language model (such as ProtT5) trained on millions of sequences. In our benchmarks, pLM-BLAST has shown the ability to detect homology between highly divergent proteins, demonstrating its applicability to tasks such as protein classification, domain annotation, and function prediction. Availability and Implementation pLM-BLAST is available as a web server in the MPI Bioinformatics Toolkit (https://toolkit.tuebingen.mpg.de/tools/plmblast), where it can be used to search precomputed databases. It is also available as a standalone tool to build custom databases and run batch searches (https://github.com/labstructbioinf/pLM-BLAST).

https://www.biorxiv.org/content/biorxiv/early/2022/12/01/2022.11.24.517862.full.pdf

pLM-BLAST – distant homology detection based on direct comparison of sequence representations from protein language models

Structure comparison and alignment are of fundamental importance in structural biology studies. We developed the first universal platform, US-align, to uniformly align monomer and complex structures of different macromolecules-proteins, RNAs and DNAs. The pipeline is built on a uniform TM-score objective function coupled with a heuristic alignment searching algorithm. Large-scale benchmarks demonstrated consistent advantages of US-align over state-of-the-art methods in pairwise and multiple structure alignments of different molecules. Detailed analyses showed that the main advantage of US-align lies in the extensive optimization of the unified objective function powered by efficient heuristic search iterations, which substantially improve the accuracy and speed of the structural alignment process. Meanwhile, the universal protocol fusing different molecular and structural types helps facilitate the heterogeneous oligomer structure comparison and template-based protein-protein and protein-RNA/DNA docking. 

/pdf/us-align-universal-structure-alignments-of-proteins-nucleic-2hb2uquo.pdf

US-align: universal structure alignments of proteins, nucleic acids, and macromolecular complexes

MOTIVATION
The full description of nucleic acid conformation involves eight torsion angles per nucleotide. To simplify this description, we previously developed a representation of the nucleic acid backbone that assigns each nucleotide a pair of pseudo-torsion angles (eta and theta defined by P and C4' atoms; or eta' and theta' defined by P and C1' atoms). A Java program, AMIGOS II, is currently available for calculating eta and theta angles for RNA and for performing motif searches based on eta and theta angles. However, AMIGOS II lacks the ability to parse DNA structures and to calculate eta' and theta' angles. It also has little visualization capacity for 3D structure, making it difficult for users to interpret the computational results.


RESULTS
We present AMIGOS III, a PyMOL plugin that calculates the pseudo-torsion angles eta, theta, eta', and theta' for both DNA and RNA structures and performs motif searching based on these angles. Compared to AMIGOS II, AMIGOS III offers improved pseudo-torsion angle visualization for RNA and faster nucleic acid worm database generation; it also introduces pseudo-torsion angle visualization for DNA and nucleic acid worm visualization. Its integration into PyMOL enables easy preparation of tertiary structure inputs and intuitive visualization of involved structures.


AVAILABILITY AND IMPLEMENTATION
https://github.com/pylelab/AMIGOSIII.


SUPPLEMENTARY INFORMATION
Supplementary data are available at Bioinformatics online.

AMIGOS III: pseudo-torsion angle visualization and motif-based structure comparison of nucleic acids

Abstract Motivation The full description of nucleic acid conformation involves eight torsion angles per nucleotide. To simplify this description, we previously developed a representation of the nucleic acid backbone that assigns each nucleotide a pair of pseudo-torsion angles (eta and theta defined by P and C4ʹ atoms; or etaʹ and thetaʹ defined by P and C1ʹ atoms). A Java program, AMIGOS II, is currently available for calculating eta and theta angles for RNA and for performing motif searches based on eta and theta angles. However, AMIGOS II lacks the ability to parse DNA structures and to calculate etaʹ and thetaʹ angles. It also has little visualization capacity for 3D structure, making it difficult for users to interpret the computational results. Results We present AMIGOS III, a PyMOL plugin that calculates the pseudo-torsion angles eta, theta, etaʹ and thetaʹ for both DNA and RNA structures and performs motif searching based on these angles. Compared to AMIGOS II, AMIGOS III offers improved pseudo-torsion angle visualization for RNA and faster nucleic acid worm database generation; it also introduces pseudo-torsion angle visualization for DNA and nucleic acid worm visualization. Its integration into PyMOL enables easy preparation of tertiary structure inputs and intuitive visualization of involved structures. Availability and implementation https://github.com/pylelab/AMIGOSIII. Supplementary information Supplementary data are available at Bioinformatics online.

https://doi.org/10.1093/bioinformatics/btac207

Morgan Shine

Papers

US-align: universal structure alignments of proteins, nucleic acids, and macromolecular complexes

AMIGOS III: pseudo-torsion angle visualization and motif-based structure comparison of nucleic acids

AMIGOS III: pseudo-torsion angle visualization and motif-based structure comparison of nucleic acids