Searching protein space for ancient sub-domain segments

Molecular handcraft of a well-folded protein chimera

Modular assembly is a compelling pathway to create new proteins, a concept supported by protein engineering and millennia of evolution. Natural evolution provided a repository of building blocks, known as domains, which trace back to even shorter segments that underwent numerous 'copy-paste' processes culminating in the scaffolds we see today. Utilizing the subdomain-database Fuzzle, we constructed a fold-chimera by integrating a flavodoxin-like fragment into a periplasmic binding protein. This chimera is well-folded and a crystal structure reveals stable interfaces between the fragments. These findings demonstrate the adaptability of α/β-proteins and offer a stepping stone for optimization. By emphasizing the practicality of fragment databases, our work pioneers new pathways in protein engineering. Ultimately, the results substantiate the conjecture that periplasmic binding proteins originated from a flavodoxin-like ancestor.

AlphaFold2-guided description of CoBaHMA, a novel family of bacterial domains within the heavy-metal-associated superfamily

Three-dimensional (3D) structure information, now available at the proteome scale, may facilitate the detection of remote evolutionary relationships in protein superfamilies. Here, we illustrate this with the identification of a novel family of protein domains related to the ferredoxin-like superfold, by combining (i) transitive sequence similarity searches, (ii) clustering approaches, and (iii) the use of AlphaFold2 3D structure models. Domains of this family were initially identified in relation with the intracellular biomineralization of calcium carbonates by Cyanobacteria. They are part of the large heavy-metal-associated (HMA) superfamily, departing from the latter by specific sequence and structural features. In particular, most of them share conserved basic amino acids  (hence their name CoBaHMA for Conserved Basic residues HMA), forming a positively charged surface, which is likely to interact with anionic partners. CoBaHMA domains are found in diverse modular organizations in bacteria, existing in the form of monodomain proteins or as part of larger proteins, some of which are membrane proteins involved in transport or lipid metabolism. This suggests that the CoBaHMA domains may exert a regulatory function, involving interactions with anionic lipids. This hypothesis might have a particular resonance in the context of the compartmentalization observed for cyanobacterial intracellular calcium carbonates.

Back in time to the Gly-rich prototype of the phosphate binding elementary function

Binding of nucleotides and their derivatives is one of the most ancient elementary functions dating back to the Origin of Life. We review here the works considering one of the key elements in binding of (di)nucleotide-containing ligands - phosphate binding. We start from a brief discussion of major participants, conditions, and events in prebiotic evolution that resulted in the Origin of Life. Tracing back to the basic functions, including metal and phosphate binding, and, potentially, formation of primitive protein-protein interactions, we focus here on the phosphate binding. Critically assessing works on the structural, functional, and evolutionary aspects of phosphate binding, we perform a simple computational experiment reconstructing its most ancient and generic sequence prototype. The profiles of the phosphate binding signatures have been derived in form of position-specific scoring matrices (PSSMs), their peculiarities depending on the type of the ligands have been analyzed, and evolutionary connections between them have been delineated. Then, the apparent prototype that gave rise to all relevant phosphate-binding signatures had also been reconstructed. We show that two major signatures of the phosphate binding that discriminate between the binding of dinucleotide- and nucleotide-containing ligands are GxGxxG and GxxGxG, respectively. It appears that the signature archetypal for dinucleotide-containing ligands is more generic, and it can frequently bind phosphate groups in nucleotide-containing ligands as well. The reconstructed prototype's key signature GxGGxG underlies the role of glycine residues in providing flexibility and interactions necessary for binding the phosphate groups. The prototype also contains other ancient amino acids, valine, and alanine, showing versatility towards evolutionary design and functional diversification.

Persistent homology reveals strong phylogenetic signal in 3D protein structures

Changes that occur in proteins over time provide a phylogenetic signal that can be used to decipher their evolutionary history and the relationships between organisms. Sequence comparison is the most common way to access this phylogenetic signal, while those based on 3D structure comparisons are still in their infancy. In this study, we propose an effective approach based on Persistent Homology Theory (PH) to extract the phylogenetic information contained in protein structures. PH provides efficient and robust algorithms for extracting and comparing geometric features from noisy datasets at different spatial resolutions. PH has a growing number of applications in the life sciences, including the study of proteins (e.g. classification, folding). However, it has never been used to study the phylogenetic signal they may contain. Here, using 518 protein families, representing 22,940 protein sequences and structures, from 10 major taxonomic groups, we show that distances calculated with PH from protein structures correlate strongly with phylogenetic distances calculated from protein sequences, at both small and large evolutionary scales. We test several methods for calculating PH distances and propose some refinements to improve their relevance for addressing evolutionary questions. This work opens up new perspectives in evolutionary biology by proposing an efficient way to access the phylogenetic signal contained in protein structures, as well as future developments of topological analysis in the life sciences.

Genome-Wide Identification, Characterization, and Expression Analysis of the BES1 Family Genes under Abiotic Stresses in Phoebe bournei

The BRI1 EMS suppressor 1(BES1) transcription factor is a crucial regulator in the signaling pathway of Brassinosteroid (BR) and plays an important role in plant growth and response to abiotic stress. Although the identification and functional validation of BES1 genes have been extensively explored in various plant species, the understanding of their role in woody plants-particularly the endangered species Phoebe bournei (Hemsl.) Yang-remains limited. In this study, we identified nine members of the BES1 gene family in the genome of P. bournei; these nine members were unevenly distributed across four chromosomes. In our further evolutionary analysis of PbBES1, we discovered that PbBES1 can be divided into three subfamilies (Class I, Class II, and Class IV) based on the evolutionary tree constructed with Arabidopsis thaliana, Oryza sativa, and Solanum lycopersicum. Each subfamily contains 2-5 PbBES1 genes. There were nine pairs of homologous BES1 genes in the synteny analysis of PbBES1 and AtBES1. Three segmental replication events and one pair of tandem duplication events were present among the PbBES1 family members. Additionally, we conducted promoter cis-acting element analysis and discovered that PbBES1 contains binding sites for plant growth and development, cell cycle regulation, and response to abiotic stress. PbBES1.2 is highly expressed in root bark, stem bark, root xylem, and stem xylem. PbBES1.3 was expressed in five tissues. Moreover, we examined the expression profiles of five representative PbBES1 genes under heat and drought stress. These experiments preliminarily verified their responsiveness and functional roles in mediating responses to abiotic stress. This study provides important clues to elucidate the functional characteristics of the BES1 gene family, and at the same time provides new insights and valuable information for the regulation of resistance in P. bournei.

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

MOTIVATION

The detection of homology through sequence comparison is a typical first step in the study of protein function and evolution. In this work, we explore the applicability of protein language models to this task.

RESULTS

We introduce pLM-BLAST, a tool inspired by BLAST, that detects distant homology by comparing single-sequence representations (embeddings) derived from a protein language model, ProtT5. Our benchmarks reveal that pLM-BLAST maintains a level of accuracy on par with HHsearch for both highly similar sequences (with >50% identity) and markedly divergent sequences (with <30% identity), while being significantly faster. Additionally, pLM-BLAST stands out among other embedding-based tools due to its ability to compute local alignments. We show that these local alignments, produced by pLM-BLAST, often connect highly divergent proteins, thereby highlighting its potential to uncover previously undiscovered homologous relationships and improve protein annotation.

AVAILABILITY AND IMPLEMENTATION

pLM-BLAST is accessible via the MPI Bioinformatics Toolkit as a web server for searching precomputed databases (https://toolkit.tuebingen.mpg.de/tools/plmblast). It is also available as a standalone tool for building custom databases and performing batch searches (https://github.com/labstructbioinf/pLM-BLAST).

Evolutionary Conserved Short Linear Motifs Provide Insights into the Cellular Response to Stress

Short linear motifs (SLiMs) are evolutionarily conserved functional modules of proteins composed of 3 to 10 residues and involved in multiple cellular functions. Here, we performed a search for SLiMs that exert sequence similarity to two segments of alpha-fetoprotein (AFP), a major mammalian embryonic and cancer-associated protein. Biological activities of the peptides, LDSYQCT (AFP_14-20) and EMTPVNPGV (GIP-9), have been previously confirmed under in vitro and in vivo conditions. In our study, we retrieved a vast array of proteins that contain SLiMs of interest from both prokaryotic and eukaryotic species, including viruses, bacteria, archaea, invertebrates, and vertebrates. Comprehensive Gene Ontology enrichment analysis showed that proteins from multiple functional classes, including enzymes, transcription factors, as well as those involved in signaling, cell cycle, and quality control, and ribosomal proteins were implicated in cellular adaptation to environmental stress conditions. These include response to oxidative and metabolic stress, hypoxia, DNA and RNA damage, protein degradation, as well as antimicrobial, antiviral, and immune response. Thus, our data enabled insights into the common functions of SLiMs evolutionary conserved across all taxonomic categories. These SLiMs can serve as important players in cellular adaptation to stress, which is crucial for cell functioning.

Embeddings from protein language models predict conservation and variant effects

The emergence of SARS-CoV-2 variants stressed the demand for tools allowing to interpret the effect of single amino acid variants (SAVs) on protein function. While Deep Mutational Scanning (DMS) sets continue to expand our understanding of the mutational landscape of single proteins, the results continue to challenge analyses. Protein Language Models (pLMs) use the latest deep learning (DL) algorithms to leverage growing databases of protein sequences. These methods learn to predict missing or masked amino acids from the context of entire sequence regions. Here, we used pLM representations (embeddings) to predict sequence conservation and SAV effects without multiple sequence alignments (MSAs). Embeddings alone predicted residue conservation almost as accurately from single sequences as ConSeq using MSAs (two-state Matthews Correlation Coefficient-MCC-for ProtT5 embeddings of 0.596 ± 0.006 vs. 0.608 ± 0.006 for ConSeq). Inputting the conservation prediction along with BLOSUM62 substitution scores and pLM mask reconstruction probabilities into a simplistic logistic regression (LR) ensemble for Variant Effect Score Prediction without Alignments (VESPA) predicted SAV effect magnitude without any optimization on DMS data. Comparing predictions for a standard set of 39 DMS experiments to other methods (incl. ESM-1v, DeepSequence, and GEMME) revealed our approach as competitive with the state-of-the-art (SOTA) methods using MSA input. No method outperformed all others, neither consistently nor statistically significantly, independently of the performance measure applied (Spearman and Pearson correlation). Finally, we investigated binary effect predictions on DMS experiments for four human proteins. Overall, embedding-based methods have become competitive with methods relying on MSAs for SAV effect prediction at a fraction of the costs in computing/energy. Our method predicted SAV effects for the entire human proteome (~ 20 k proteins) within 40 min on one Nvidia Quadro RTX 8000. All methods and data sets are freely available for local and online execution through bioembeddings.com, https://github.com/Rostlab/VESPA , and PredictProtein.

Similar protein segments shared between domains of different evolutionary lineages

The emergence of novel proteins, beyond these that can be readily made by duplication and recombination of preexisting domains, is elusive. De novo emergence from random sequences is unlikely because the vast majority of random chains would not even fold, let alone function. An alternative explanation is that novel proteins emerge by duplication and fusion of pre‐existing polypeptide segments. In this case, traces of such ancient events may remain within contemporary proteins in the form of reused segments. Together with the late Dan Tawfik, we detected such similar segments, far shorter than intact protein domains, which are found in different environments. The detection of these, “bridging themes,” was based on a unique search strategy, where in addition to searching for similarity of shared fragments, so‐called “themes,” we also explicitly searched for cases in which the sequence segments before and after the theme are dissimilar (both in sequence and structure). Here, using a similar strategy, we further expanded the search and discovered almost 500 additional “bridging themes,” linking domains that are often from ancient folds. The themes, of 20 residues or more (average 53), do not retain their structure despite sharing 37% sequence identity on average. Indeed, conformation flexibility may confer an evolutionary advantage, in that it fits in multiple environments. We elaborate on two interesting themes, shared between Rossmann/Trefoil‐Plexin‐like domains and a β‐propeller‐like domain.

Evidence for the emergence of β-trefoils by 'Peptide Budding' from an IgG-like β-sandwich

As sequence and structure comparison algorithms gain sensitivity, the intrinsic interconnectedness of the protein universe has become increasingly apparent. Despite this general trend, β-trefoils have emerged as an uncommon counterexample: They are an isolated protein lineage for which few, if any, sequence or structure associations to other lineages have been identified. If β-trefoils are, in fact, remote islands in sequence-structure space, it implies that the oligomerizing peptide that founded the β-trefoil lineage itself arose de novo. To better understand β-trefoil evolution, and to probe the limits of fragment sharing across the protein universe, we identified both 'β-trefoil bridging themes' (evolutionarily-related sequence segments) and 'β-trefoil-like motifs' (structure motifs with a hallmark feature of the β-trefoil architecture) in multiple, ostensibly unrelated, protein lineages. The success of the present approach stems, in part, from considering β-trefoil sequence segments or structure motifs rather than the β-trefoil architecture as a whole, as has been done previously. The newly uncovered inter-lineage connections presented here suggest a novel hypothesis about the origins of the β-trefoil fold itself-namely, that it is a derived fold formed by 'budding' from an Immunoglobulin-like β-sandwich protein. These results demonstrate how the evolution of a folded domain from a peptide need not be a signature of antiquity and underpin an emerging truth: few protein lineages escape nature's sewing table.

Structural basis of cytokine-mediated activation of ALK family receptors

Anaplastic lymphoma kinase (ALK)¹ and the related leukocyte tyrosine kinase (LTK)² are recently deorphanized receptor tyrosine kinases³. Together with their activating cytokines, ALKAL1 and ALKAL2^4-6 (also called FAM150A and FAM150B or AUGβ and AUGα, respectively), they are involved in neural development⁷, cancer^7-9 and autoimmune diseases¹⁰. Furthermore, mammalian ALK recently emerged as a key regulator of energy expenditure and weight gain¹¹, consistent with a metabolic role for Drosophila ALK¹². Despite such functional pleiotropy and growing therapeutic relevance^13,14, structural insights into ALK and LTK and their complexes with cognate cytokines have remained scarce. Here we show that the cytokine-binding segments of human ALK and LTK comprise a novel architectural chimera of a permuted TNF-like module that braces a glycine-rich subdomain featuring a hexagonal lattice of long polyglycine type II helices. The cognate cytokines ALKAL1 and ALKAL2 are monomeric three-helix bundles, yet their binding to ALK and LTK elicits similar dimeric assemblies with two-fold symmetry, that tent a single cytokine molecule proximal to the cell membrane. We show that the membrane-proximal EGF-like domain dictates the apparent cytokine preference of ALK. Assisted by these diverse structure-function findings, we propose a structural and mechanistic blueprint for complexes of ALK family receptors, and thereby extend the repertoire of ligand-mediated dimerization mechanisms adopted by receptor tyrosine kinases.

Improved RAD51 binders through motif shuffling based on the modularity of BRC repeats

Exchanges of protein sequence modules support leaps in function unavailable through point mutations during evolution. Here we study the role of the two RAD51-interacting modules within the eight binding BRC repeats of BRCA2. We created 64 chimeric repeats by shuffling these modules and measured their binding to RAD51. We found that certain shuffled module combinations were stronger binders than any of the module combinations in the natural repeats. Surprisingly, the contribution from the two modules was poorly correlated with affinities of natural repeats, with a weak BRC8 repeat containing the most effective N-terminal module. The binding of the strongest chimera, BRC8-2, to RAD51 was improved by -2.4 kCal/mol compared to the strongest natural repeat, BRC4. A crystal structure of RAD51:BRC8-2 complex shows an improved interface fit and an extended β-hairpin in this repeat. BRC8-2 was shown to function in human cells, preventing the formation of nuclear RAD51 foci after ionizing radiation.

Exploiting enzyme evolution for computational protein design

Recent years have seen an explosion of interest in understanding the physicochemical parameters that shape enzyme evolution, as well as substantial advances in computational enzyme design. This review discusses three areas where evolutionary information can be used as part of the design process: (i) using ancestral sequence reconstruction (ASR) to generate new starting points for enzyme design efforts; (ii) learning from how nature uses conformational dynamics in enzyme evolution to mimic this process in silico; and (iii) modular design of enzymes from smaller fragments, again mimicking the process by which nature appears to create new protein folds. Using showcase examples, we highlight the importance of incorporating evolutionary information to continue to push forward the boundaries of enzyme design studies.