Deep learning takes on protein folding: In 1972, Anfinsen won a Nobel prize for demonstrating a connection between a protein’s amino acid sequence and its three-dimensional structure. Since 1994, scientists have competed in the biannual Critical Assessment of Structure Prediction (CASP) protein-folding challenge. Deep learning methods took center stage at CASP14, with DeepMind’s Alphafold2 achieving remarkable accuracy. Baek et al . explored network architectures based on the DeepMind framework. They used a three-track network to process sequence, distance, and coordinate information simultaneously and achieved accuracies approaching those of DeepMind. The method, RoseTTA fold, can solve challenging x-ray crystallography and cryo–electron microscopy modeling problems and generate accurate models of protein-protein complexes. —VV

Background: All archaeal and many bacterial genomes contain Clustered Regularly Interspaced Short Palindrome Repeats (CRISPR) and variable arrays of the CRISPR-associated (cas) genes that have been previously implicated in a novel form of DNA repair on the basis of comparative analysis of their protein product sequences. However, the proximity of CRISPR and cas genes strongly suggests that they have related functions which is hard to reconcile with the repair hypothesis. Results: The protein sequences of the numerous cas gene products were classified into ~25 distinct protein families; several new functional and structural predictions are described. Comparative-genomic analysis of CRISPR and cas genes leads to the hypothesis that the CRISPR-Cas system (CASS) is a mechanism of defense against invading phages and plasmids that functions analogously to the eukaryotic RNA interference (RNAi) systems. Specific functional analogies are drawn between several components of CASS and proteins involved in eukaryotic RNAi, including the double-stranded RNA-specific helicase-nuclease (dicer), the endonuclease cleaving target mRNAs (slicer), and the RNA-dependent RNA polymerase. However, none of the CASS components is orthologous to its apparent eukaryotic functional counterpart. It is proposed that unique inserts of CRISPR, some of which are homologous to fragments of bacteriophage and plasmid genes, function as prokaryotic siRNAs (psiRNA), by base-pairing with the target mRNAs and promoting their degradation or translation shutdown. Specific hypothetical schemes are developed for the functioning of the predicted prokaryotic siRNA system and for the formation of new CRISPR units with unique inserts encoding psiRNA conferring immunity to the respective newly encountered phages or plasmids. The unique inserts in CRISPR show virtually no similarity even between closely related bacterial strains which suggests their rapid turnover, on evolutionary scale. Corollaries of this finding are that, even among closely related prokaryotes, the most commonly encountered phages and plasmids are different and/or that the dominant phages and plasmids turn over rapidly. Conclusion: We proposed previously that Cas proteins comprise a novel DNA repair system. The association of the cas genes with CRISPR and, especially, the presence, in CRISPR units, of unique inserts homologous to phage and plasmid genes make us abandon this hypothesis. It appears most likely that CASS is a prokaryotic system of defense against phages and plasmids that functions via the RNAi mechanism. The functioning of this system seems to involve integration of fragments of foreign genes into archaeal and bacterial chromosomes yielding heritable immunity to the respective agents. However, it appears that this inheritance is extremely unstable on the evolutionary scale such that the repertoires of unique psiRNAs are completely replaced even in closely related prokaryotes, presumably, in response to rapidly changing repertoires of dominant phages and plasmids. This article was reviewed by: Eric Bapteste, Patrick Forterre, and Martijn Huynen. Open peer review: Reviewed by Eric Bapteste, Patrick Forterre, and Martijn Huynen. For the full reviews, please go to the Reviewers' comments section.

Zinc fingers are small protein domains in which zinc plays a structural role contributing to the stability of the domain. Zinc fingers are structurally diverse and are present among proteins that perform a broad range of functions in various cellular processes, such as replication and repair, transcription and translation, metabolism and signaling, cell proliferation and apoptosis. Zinc fingers typically function as interaction modules and bind to a wide variety of compounds, such as nucleic acids, proteins and small molecules. Here we present a comprehensive classification of zinc finger spatial structures. We find that each available zinc finger structure can be placed into one of eight fold groups that we define based on the structural properties in the vicinity of the zinc‐binding site. Three of these fold groups comprise the majority of zinc fingers, namely, C2H2‐like finger, treble clef finger and the zinc ribbon. Evolutionary relatedness of proteins within fold groups is not implied, but each group is divided into families of potential homologs. We compare our classification to existing groupings of zinc fingers and find that we define more encompassing fold groups, which bring together proteins whose similarities have previously remained unappreciated. We analyze functional properties of different zinc fingers and overlay them onto our classification. The classification helps in understanding the relationship between the structure, function and evolutionary history of these domains. The results are available as an online database of zinc finger structures.

Motivation: Amino acid sequence alignments are widely used in the analysis of protein structure, function and evolutionary relationships. Proteins within a superfamily usually share the same fold and possess related functions. These structural and functional constraints are reflected in the alignment conservation patterns. Positions of functional and/or structural importance tend to be more conserved. Conserved positions are usually clustered in distinct motifs surrounded by sequence segments of low conservation. Poorly conserved regions might also arise from the imperfections in multiple alignment algorithms and thus indicate possible alignment errors. Quantification of conservation by attributing a conservation index to each aligned position makes motif detection more convenient. Mapping these conservation indices onto a protein spatial structure helps to visualize spatial conservation features of the molecule and to predict functionally and/or structurally important sites. Analysis of conservation indices could be a useful tool in detection of potentially misaligned regions and will aid in improvement of multiple alignments. Results: We developed a program to calculate a conservation index at each position in a multiple sequence alignment using several methods. Namely, amino acid frequencies at each position are estimated and the conservation index is calculated from these frequencies. We utilize both unweighted frequencies and frequencies weighted using two different strategies. Three conceptually different approaches (entropy-based, variance-based and matrix score-based) are implemented in the algorithm to define the conservation index. Calculating conservation indices for 35522 positions in 284 alignments from SMART database we demonstrate that different methods result in highly correlated (correlation coefficient more than 0.85) conservation indices. Conservation indices show statistically significant correlation between sequentially adjacent positions \batchmode \documentclass[fleqn,10pt,legalpaper]{article} \usepackage{amssymb} \usepackage{amsfonts} \usepackage{amsmath} \pagestyle{empty} \begin{document} \(i\) \end{document}and \batchmode \documentclass[fleqn,10pt,legalpaper]{article} \usepackage{amssymb} \usepackage{amsfonts} \usepackage{amsmath} \pagestyle{empty} \begin{document} \(i\ +\ j\) \end{document}, where \batchmode \documentclass[fleqn,10pt,legalpaper]{article} \usepackage{amssymb} \usepackage{amsfonts} \usepackage{amsmath} \pagestyle{empty} \begin{document} \(j\ {<}\ 13\) \end{document}, and averaging of the indices over the window of three positions is optimal for motif detection. Positions with gaps display substantially lower conservation properties. We compare conservation properties of the SMART alignments or FSSP structural alignments to those of the ClustalW alignments. The results suggest that conservation indices should be a valuable tool of alignment quality assessment and might be used as an objective function for refinement of multiple alignments. Availability: The C code of the AL2CO program and its pre-compiled versions for several platforms as well as the details of the analysis are freely available at ftp://iole.swmed.edu/pub/al2co/. Contact: grishin@chop.swmed.edu * To whom correspondence should be addressed.

Understanding the evolution of a protein, including both close and distant relationships, often reveals insight into its structure and function. Fast and easy access to such up-to-date information facilitates research. We have developed a hierarchical evolutionary classification of all proteins with experimentally determined spatial structures, and presented it as an interactive and updatable online database. ECOD (Evolutionary Classification of protein Domains) is distinct from other structural classifications in that it groups domains primarily by evolutionary relationships (homology), rather than topology (or “fold”). This distinction highlights cases of homology between domains of differing topology to aid in understanding of protein structure evolution. ECOD uniquely emphasizes distantly related homologs that are difficult to detect, and thus catalogs the largest number of evolutionary links among structural domain classifications. Placing distant homologs together underscores the ancestral similarities of these proteins and draws attention to the most important regions of sequence and structure, as well as conserved functional sites. ECOD also recognizes closer sequence-based relationships between protein domains. Currently, approximately 100,000 protein structures are classified in ECOD into 9,000 sequence families clustered into close to 2,000 evolutionary groups. The classification is assisted by an automated pipeline that quickly and consistently classifies weekly releases of PDB structures and allows for continual updates. This synchronization with PDB uniquely distinguishes ECOD among all protein classifications. Finally, we present several case studies of homologous proteins not recorded in other classifications, illustrating the potential of how ECOD can be used to further biological and evolutionary studies. Protein structural domain databases offer a vital resource for structural bioinformatics. These databases provide functional inference for homologous structures, supply templates for structural prediction experiments, and differentiate between homologs and analogs. The rate of structure determination and deposition has increased dramatically over recent years, overwhelming the ability of current classifications to incorporate all new structures. We have developed a fast and reliable methodology for updating domain databases automatically, and created a revised hierarchy for domain classification that emphasizes evolutionary relationships. By classifying all known structures in our database with continuing automatic updates, we provide an up-to-date alternative to current resources. We illustrate several concepts that guided our classification scheme with examples of homology between domains in ECOD that are not observed in other resources.

The K homology (KH) module is a widespread RNA-binding motif that has been detected by sequence similarity searches in such proteins as heterogeneous nuclear ribonucleoprotein K (hnRNP K) and ribosomal protein S3. Analysis of spatial structures of KH domains in hnRNP K and S3 reveals that they are topologically dissimilar and thus belong to different protein folds. Thus KH motif proteins provide a rare example of protein domains that share significant sequence similarity in the motif regions but possess globally distinct structures. The two distinct topologies might have arisen from an ancestral KH motif protein by N- and C-terminal extensions, or one of the existing topologies may have evolved from the other by extension, displacement and deletion. C-terminal extension (deletion) requires ss-sheet rearrangement through the insertion (removal) of a ss-strand in a manner similar to that observed in serine protease inhibitors serpins. Current analysis offers a new look on how proteins can change fold in the course of evolution.