A Fifth of the Protein World: Rossmann-like Proteins as an Evolutionarily Successful Structural unit

Testis-specific RNA methyltransferase NSUN7 contains a re-arranged catalytic site

The RNA methyltransferase NSUN7 has been reported to be involved in the regulation of longitudinal columns positioning in sperm flagella, but its catalytic mechanism remains unclear. In this study, we investigated the functional role of NSUN7's methylation in the longitudinal column positioning by generating a mouse strain with a substitution of the putative catalytic cysteine in motif IV to alanine (Nsun7C382A). Contrary to predictions based on the typical reaction mechanism of NOP2/Sun family methyltransferases, Nsun7C382A mice did not exhibit any phenotypical characteristics of knockouts and had normal fertility, sperm motility, and longitudinal column positioning, similar to wild-type mice. Structural modelling suggests that NSUN7 possesses an unusual catalytic site composition, including three highly conserved cysteines from motifs IV, VI and VIII. The distance between the canonical catalytic cysteines in motifs IV and VI is significantly greater than in related methyltransferases, while a cysteine from motif VIII is positioned closer to motif VI. These findings allow us to assume that NSUN7 may have an essential function in the spermatogenesis of mice independent on its methyltransferase activity.

Leveraging AI to explore structural contexts of post-translational modifications in drug binding

Post-translational modifications (PTMs) play a crucial role in allowing cells to expand the functionality of their proteins and adaptively regulate their signaling pathways. Defects in PTMs have been linked to numerous developmental disorders and human diseases, including cancer, diabetes, heart, neurodegenerative and metabolic diseases. PTMs are important targets in drug discovery, as they can significantly influence various aspects of drug interactions including binding affinity. The structural consequences of PTMs, such as phosphorylation-induced conformational changes or their effects on ligand binding affinity, have historically been challenging to study on a large scale, primarily due to reliance on experimental methods. Recent advancements in computational power and artificial intelligence, particularly in deep learning algorithms and protein structure prediction tools like AlphaFold3, have opened new possibilities for exploring the structural context of interactions between PTMs and drugs. These AI-driven methods enable accurate modeling of protein structures including prediction of PTM-modified regions and simulation of ligand-binding dynamics on a large scale. In this work, we identified small molecule binding-associated PTMs that can influence drug binding across all human proteins listed as small molecule targets in the DrugDomain database, which we developed recently. 6,131 identified PTMs were mapped to structural domains from Evolutionary Classification of Protein Domains (ECOD) database.Scientific contribution: Using recent AI-based approaches for protein structure prediction (AlphaFold3, RoseTTAFold All-Atom, Chai-1), we generated 14,178 models of PTM-modified human proteins with docked ligands. Our results demonstrate that these methods can predict PTM effects on small molecule binding, but precise evaluation of their accuracy requires a much larger benchmarking set. We also found that phosphorylation of NADPH-Cytochrome P450 Reductase, observed in cervical and lung cancer, causes significant structural disruption in the binding pocket, potentially impairing protein function. All data and generated models are available from DrugDomain database v1.1 ( http://prodata.swmed.edu/DrugDomain/ ) and GitHub ( https://github.com/kirmedvedev/DrugDomain ). This resource is the first to our knowledge in offering structural context for small molecule binding-associated PTMs on a large scale.

Structural dynamics of human fatty acid synthase in the condensing cycle

Long-chain fatty acids are the building blocks of fat in human bodies. In mammals, fatty acid synthase (FASN) contains multiple enzymatic domains to catalyse all chemical reactions needed for de novo fatty acid synthesis1. Although the chemical reactions carried out by these enzymatic domains are well defined, how the dimeric FASN with an open architecture continuously catalyses such reactions to synthesize a complete fatty acid remains elusive. Here, using a strategy of tagging and purifying endogenous FASN in HEK293T cells for single-particle cryo-electron microscopy studies, we characterized the structural dynamics of endogenous human FASN. We captured conformational snapshots of various functional substates in the condensing cycle and developed a procedure to analyse the particle distribution landscape of FASN with different orientations between its condensing and modifying wings. Together, our findings reveal that FASN function does not require a large rotational motion between its two main functional domains during the condensing cycle, and that the catalytic reactions in the condensing cycle carried out by the two monomers are unsynchronized. Our data thus provide a new composite view of FASN dynamics during the fatty acid synthesis condensing cycle.

Tubulin Polymerization Promoting Proteins: Functional Diversity With Implications in Neurological Disorders

Tubulin Polymerization Promoting Proteins (TPPPs) are highly conserved across species but remain poorly understood. There are three TPPP genes in humans, with only one homologous TPPP gene in invertebrates, such as Drosophila and C. elegans. The human TPPP (TPPP1/p25/p25α) is enriched in the brain and shares sequence similarities with the invertebrate TPPPs. TPPP/p25 associates with microtubules and plays a pivotal role in microtubule dynamics, bundling, and polymerization, thereby stabilizing the microtubular network. This is essential for cytoskeletal organization and proper functioning of neurons and glial cells, including axonal growth, regeneration, migration, trafficking, synapse formation, and myelination of axons. However, studies have also uncovered that besides its cytoplasmic/microtubular localization, TPPP/p25 is present in other subcellular compartments, including the mitochondria and nucleus, underscoring the presence of additional novel functions. At the molecular level, TPPP/p25 is predicted to exist as an intrinsically disordered protein and is implicated in neurological and neurodegenerative disorders, including Parkinson's and related disorders and Multiple Sclerosis. In this article, we provide a comprehensive overview of TPPP/p25, highlighting its evolutionary conservation, cellular and subcellular localization, established and emerging functions in the nervous system, interacting partners, potential clinical relevance to human neurological disorders, and conclude with unresolved questions and future areas of study.

In silico approach on structural and functional characterization of heat shock protein from Sulfobacillus acidophilus

The 70 kDa heat shock proteins (Hsp70 s) are highly conserved and ubiquitous molecular chaperones. Hsp70 proteins are intimately involved in different biological activities including maintaining protein homeostasis and resisting environmental stress for survival. Characterizations of eukaryotic Hsp70 s with diverse functions are well established but investigations needed for prokaryotes. For better understanding, the sequences of Sulfobacillus acidophilus were retrieved from UniProt. Retrieved stress proteins were renamed as SaHsp70 s and performed an in silico analysis to identify sequential, structural properties and functional attributes. The in silico characterization of these proteins revealed that they are acidic, mostly thermostable globular protein with NAD(P)-binding Rossmann-folding. Molecular mass of SaHsp70 s ranged from 31.9 to 68.5 kDa and mainly localized in the cytoplasm. Phylogeny revealed the evolutionary distance and relationship among retrieved proteins. Domain analyzed only SaHsp70 - 1, SaHsp70 - 3, and SaHsp70 - 14 have actual conserved domain for Hsp70 and share the same clade on phylogenetic tree. Major part of each protein was abundant with α-helix and random coil which make it thermally stable and suitable for interacting with other proteins. SAVES and ProSA server proves the reliability, stability, and consistency tertiary structure of SaHsp70 s. Functional analysis was done in terms of membrane protein topology, PPI network generation, active and proteolytic cleavage sites prediction, conserved motif and domain detection. CastP predicted Gly, Lys, Thr, Glu, Pro, Gln, Arg and Val act as catalytic residue, are important for metal ions binding. Intramolecular interaction analysis suggested Lys67, Thr12, Thr170, Gly 168, Gly 169, and Glu 141 of SaHsp70 - 14 proteins could play central role in various complex cellular functions like stress mitigation, thermal stability, and related developmental processes.

Discovery of additional genomic segments reveals the fluidity of jingmenvirus genomic organization

Jingmenviruses are a distinct group of flavi-like viruses characterized by a genome consisting of four to five segments. Here, we report the discovery of three novel putative jingmenviruses, identified by mining publicly available metagenomics data from mosquito and arachnid samples. Strikingly, these novel jingmenvirus sequences contain up to six genomic segments, with pairs of homologous segments coding for putative structural proteins. Following this discovery, we found an additional homologous segment for two other jingmenvirus genomes, which had gone unnoticed in the initial publications. The presence of a single version of the segments coding for non-structural proteins suggests that we have indeed identified jingmenviruses with infectious units that contain up to six segments. We compared these novel jingmenvirus sequences to published sequences, in particular the segments with multiple open reading frames (ORFs), and we propose that the putative translation initiation mechanisms involved for these segments are ribosomal frameshift resulting in the fusion of ORFs and leaky scanning for overlapping ORFs. These putative mechanisms, conserved for all jingmenvirus sequences analysed, including in homologous segments, require biological confirmation. We also generated structural models of two putative structural proteins in the duplicated segments, and the corresponding alignments enabled us to confirm or identify the homologous relationship between sequences that shared limited nucleotide or amino acid identity. Altogether, these results highlight the fluid nature of jingmenviruses, which is a hallmark of multipartite viruses. Different combinations of segments packaged in different virus particles could facilitate the acquisition or loss of genomic segments and a segment duplication following genomic drift. Our data therefore contribute to the evidence of the multipartite nature of jingmenviruses and the evolutionary role this organization may play.

Leveraging AI to Explore Structural Contexts of Post-Translational Modifications in Drug Binding

Post-translational modifications (PTMs) play a crucial role in allowing cells to expand the functionality of their proteins and adaptively regulate their signaling pathways. Defects in PTMs have been linked to numerous developmental disorders and human diseases, including cancer, diabetes, heart, neurodegenerative and metabolic diseases. PTMs are important targets in drug discovery, as they can significantly influence various aspects of drug interactions including binding affinity. The structural consequences of PTMs, such as phosphorylation-induced conformational changes or their effects on ligand binding affinity, have historically been challenging to study on a large scale, primarily due to reliance on experimental methods. Recent advancements in computational power and artificial intelligence, particularly in deep learning algorithms and protein structure prediction tools like AlphaFold3, have opened new possibilities for exploring the structural context of interactions between PTMs and drugs. These AI-driven methods enable accurate modeling of protein structures including prediction of PTM-modified regions and simulation of ligand-binding dynamics on a large scale. In this work, we identified small molecule binding-associated PTMs that can influence drug binding across all human proteins listed as small molecule targets in the DrugDomain database, which we developed recently. 6,131 identified PTMs were mapped to structural domains from Evolutionary Classification of Protein Domains (ECOD) database. Scientific contribution. Using recent AI-based approaches for protein structure prediction (AlphaFold3, RoseTTAFold All-Atom, Chai-1), we generated 14,178 models of PTM-modified human proteins with docked ligands. Our results demonstrate that these methods can predict PTM effects on small molecule binding, but precise evaluation of their accuracy requires a much larger benchmarking set. We also found that phosphorylation of NADPH-Cytochrome P450 Reductase, observed in cervical and lung cancer, causes significant structural disruption in the binding pocket, potentially impairing protein function. All data and generated models are available from DrugDomain database v1.1 (http://prodata.swmed.edu/DrugDomain/) and GitHub (https://github.com/kirmedvedev/DrugDomain). This resource is the first to our knowledge in offering structural context for small molecule binding-associated PTMs on a large scale.

Refinement and curation of homologous groups facilitated by structure prediction

Domain classification of protein predictions released in the AlphaFold Database (AFDB) has been a recent focus of the Evolutionary Classification of protein Domains (ECOD). Although a primary focus of our recent work has been the partition and assignment of domains from these predictions, we here show how these diverse predictions can be used to examine the reference domain set more closely. Using results from DPAM, our AlphaFold-specific domain parsing algorithm, we examine hierarchical groupings that share significant levels of homologous links, both between groups that were not previously assessed to be definitively homologous and between groups that were not previously observed to share significant homologous links. Combined with manual analysis, these large datasets of structural and sequence similarities allow us to merge homologous groups in multiple cases which we detail within. These domains tend to be families of domains from families that are either small, previously had few experimental representatives, or had unknown function. The exception to this is the chromodomains, a large homologous group which were increased from "possibly homologous" to "definitely homologous" to increase the consistency of ECOD based their strong homologous links to the SH3 domains.

Decoding KRAS dynamics: Exploring the impact of mutations and inhibitor binding

KRAS (Kirsten rat sarcoma viral oncogene homologue), the most common mutated protein in human cancers, is the leading cause of morbidity and mortality. Before Sotorasib (AMG-510) was approved for non-small cell lung cancer treatment in 2020, the oncogenic KRAS mutations were believed to be non-druggable. High-resolution X-ray crystal structures of GDP-bound KRAS mutants with and without inhibitor are resolved and deposited in the Protein Data Bank (PDB). Nevertheless, to develop inhibitors targeting oncogenic KRAS mutants, understanding the dynamics of protein conformations and respective binding sites is crucial. In the present study, multiple molecular dynamics (MD) simulations were conducted on wild-type and mutant KRAS structures to understand how G12C or G12D mutations lead to the stabilization of the active state and how KRAS inhibitors lock the mutated conformations in their inactive state. The study found that the guanosine diphosphate (GDP)-bound KRAS mutants, G12C and G12D, were locked in the inactive state, in terms of stability, when the KRAS inhibitors, AMG-510 and MRTX1133, respectively, bind to the respective Switch-II (S-II) pocket. Covalent inhibitor AMG-510 locked the inactive GDP-bound KRASG12C mutant more efficiently when compared to the non-covalent inhibitor MRTX1133. The Cα atom distance between key highly dynamic amino acids from P-loop, Switch-I, and Switch-II domains, lying within 4 Å of the inhibitor, were stable in the KRAS mutant with bound inhibitors (AMG-510 or MRTX1133), but were varying largely in the absence of any inhibitor throughout the microsecond simulation. According to the per-residue energy decomposition results, S-II amino acids in inhibitor-free KRASG12C and KRASG12D mutants showed larger variations in energy values as compared to AMG-510-bound KRASG12C and MRTX1133-bound KRASG12D, respectively. For example, the inhibitor-free KRASG12C exhibited larger variations in energy values in the S-II residues, namely, Thr58, Gln61, Glu63, and Arg68, as compared to the AMG-510-bound KRASG12C. The study found that the higher stability of AMG-510 in torsion angles was due to its covalent nature of binding to the KRASG12C mutant. The S-II amino acids, namely, Thr58, Glu63, and Arg68 remained stable in AMG-510-bound KRASG12C. The study showed that AMG-510 binding significantly stabilizes the amino acids surrounding it, surpassing that of MRTX1133. The insights gained in the present study is expected to be useful in the design and development of new KRAS-targeted drugs.

Kiwa is a bacterial membrane-embedded defence supercomplex activated by phage-induced membrane changes

Bacteria and archaea deploy diverse, sophisticated defence systems to counter virus infection, yet many immunity mechanisms remain poorly understood. Here, we characterise the Kiwa defence system as a membrane-associated supercomplex that senses changes in the membrane induced by phage infection and plasmid conjugation. This supercomplex, comprising KwaA tetramers bound to KwaB dimers, as its basic repeating unit, detects structural stress via KwaA, activating KwaB, which binds ejected phage DNA through its DUF4868 domain, stalling phage DNA replication forks and thus disrupting replication and late transcription. We show that phage-encoded DNA mimic protein Gam, which inhibits RecBCD, also targets Kiwa through KwaB recognition. However, Gam binding to one defence system precludes its inhibition of the other. These findings reveal a distinct mechanism of bacterial immune coordination, where sensing of membrane disruptions and inhibitor partitioning enhance protection against phages and plasmids.

ECOD: integrating classifications of protein domains from experimental and predicted structures

The evolutionary classification of protein domains (ECOD) classifies protein domains using a combination of sequence and structural data (http://prodata.swmed.edu/ecod). Here we present the culmination of our previous efforts at classifying domains from predicted structures, principally from the AlphaFold Database (AFDB), by integrating these domains with our existing classification of PDB structures. This combined classification includes both domains from our previous, purely experimental, classification of domains as well as domains from our provisional classification of 48 proteomes in AFDB predicted from model organisms and organisms of concern to global health. ECOD classifies over 1.8 M domains from over 1000 000 proteins collectively deposited in the PDB and AFDB. Additionally, we have changed the F-group classification reference used for ECOD, deprecating our original ECODf library and instead relying on direct collaboration with the Pfam sequence family database to inform our classification. Pfam provides similar coverage of ECOD with family classification while being more accurate and less redundant. By eliminating duplication of effort, we can improve both classifications. Finally, we discuss the initial deployment of DrugDomain, a database of domain-ligand interactions, on ECOD and discuss future plans.

Deep learning for NAD/NADP cofactor prediction and engineering using transformer attention analysis in enzymes

Understanding and manipulating the cofactor preferences of NAD(P)-dependent oxidoreductases, the most widely distributed enzyme group in nature, is increasingly crucial in bioengineering. However, large-scale identification of the cofactor preferences and the design of mutants to switch cofactor specificity remain as complex tasks. Here, we introduce DISCODE (Deep learning-based Iterative pipeline to analyze Specificity of COfactors and to Design Enzyme), a novel transformer-based deep learning model to predict NAD(P) cofactor preferences. For model training, a total of 7,132 NAD(P)-dependent enzyme sequences were collected. Leveraging whole-length sequence information, DISCODE classifies the cofactor preferences of NAD(P)-dependent oxidoreductase protein sequences without structural or taxonomic limitation. The model showed 97.4% and 97.3% of accuracy and F1 score, respectively. A notable feature of DISCODE is the interpretability of its transformer layers. Analysis of attention layers in the model enables identification of several residues that showed significantly higher attention weights. They were well aligned with structurally important residues that closely interact with NAD(P), facilitating the identification of key residues for determining cofactor specificities. These key residues showed high consistency with verified cofactor switching mutants. Integrated into an enzyme design pipeline, DISCODE coupled with attention analysis, enables a fully automated approach to redesign cofactor specificity.

Architecture and function of yeast phosphatidate phosphatase Pah1 domains/regions

Phosphatidate (PA) phosphatase, which catalyzes the Mg2+-dependent dephosphorylation of PA to produce diacylglycerol, provides a direct precursor for the synthesis of the storage lipid triacylglycerol and the membrane phospholipids phosphatidylcholine and phosphatidylethanolamine. The enzyme controlling the key phospholipid PA also plays a crucial role in diverse aspects of lipid metabolism and cell physiology. PA phosphatase is a peripheral membrane enzyme that is composed of multiple domains/regions required for its catalytic function and subcellular localization. In this review, we discuss the domains/regions of PA phosphatase from the yeast Saccharomyces cerevisiae with reference to the homologous enzyme from mammalian cells.

Shifting redox reaction equilibria on demand using an orthogonal redox cofactor

Nature's two redox cofactors, nicotinamide adenine dinucleotide (NAD+) and nicotinamide adenine dinucleotide phosphate (NADP+), are held at different reduction potentials, driving catabolism and anabolism in opposite directions. In biomanufacturing, there is a need to flexibly control redox reaction direction decoupled from catabolism and anabolism. We established nicotinamide mononucleotide (NMN+) as a noncanonical cofactor orthogonal to NAD(P)+. Here we present the development of Nox Ortho, a reduced NMN+ (NMNH)-specific oxidase, that completes the toolkit to modulate NMNH:NMN+ ratio together with an NMN+-specific glucose dehydrogenase (GDH Ortho). The design principle discovered from Nox Ortho engineering and modeling is facilely translated onto six different enzymes to create NMN(H)-orthogonal biocatalysts with a consistent ~103-106-fold cofactor specificity switch from NAD(P)+ to NMN+. We assemble these enzymes to produce stereo-pure 2,3-butanediol in cell-free systems and in Escherichia coli, enabled by NMN(H)'s distinct redox ratio firmly set by its designated driving forces, decoupled from both NAD(H) and NADP(H).

Fundamentals and Exceptions of the LysR-type Transcriptional Regulators

LysR-type transcriptional regulators (LTTRs) are emerging as a promising group of macromolecules for the field of biosensors. As the largest family of bacterial transcription factors, the LTTRs represent a vast and mostly untapped repertoire of sensor proteins. To fully harness these regulators for transcription factor-based biosensor development, it is crucial to understand their underlying mechanisms and functionalities. In the first part, this Review discusses the established model and features of LTTRs. As dual-function regulators, these inducible transcription factors exude precise control over their regulatory targets. In the second part of this Review, an overview is given of the exceptions to the "classic" LTTR model. While a general regulatory mechanism has helped elucidate the intricate regulation performed by LTTRs, it is essential to recognize the variations within the family. By combining this knowledge, characterization of new regulators can be done more efficiently and accurately, accelerating the expansion of transcriptional sensors for biosensor development. Unlocking the pool of LTTRs would significantly expand the currently limited range of detectable molecules and regulatory functions available for the implementation of novel synthetic genetic circuitry.

Protein surface chemistry encodes an adaptive tolerance to desiccation

Cellular desiccation - the loss of nearly all water from the cell - is a recurring stress in an increasing number of ecosystems that can drive protein unfolding and aggregation. For cells to survive, at least some of the proteome must resume function upon rehydration. Which proteins tolerate desiccation, and the molecular determinants that underlie this tolerance, are largely unknown. Here, we apply quantitative and structural proteomic mass spectrometry to show that certain proteins possess an innate capacity to tolerate rehydration following extreme water loss. Structural analysis points to protein surface chemistry as a key determinant for desiccation tolerance, which we test by showing that rational surface mutants can convert a desiccation sensitive protein into a tolerant one. Desiccation tolerance also has strong overlap with cellular function, with highly tolerant proteins responsible for production of small molecule building blocks, and intolerant proteins involved in energy-consuming processes such as ribosome biogenesis. As a result, the rehydrated proteome is preferentially enriched with metabolite and small molecule producers and depleted of some of the cell's heaviest consumers. We propose this functional bias enables cells to kickstart their metabolism and promote cell survival following desiccation and rehydration.

Characterization of lignin-degrading enzyme PmdC, which catalyzes a key step in the synthesis of polymer precursor 2-pyrone-4,6-dicarboxylic acid

Pyrone-2,4-dicarboxylic acid (PDC) is a valuable polymer precursor that can be derived from the microbial degradation of lignin. The key enzyme in the microbial production of PDC is 4-carboxy-2-hydroxymuconate-6-semialdehyde (CHMS) dehydrogenase, which acts on the substrate CHMS. We present the crystal structure of CHMS dehydrogenase (PmdC from Comamonas testosteroni) bound to the cofactor NADP, shedding light on its three-dimensional architecture, and revealing residues responsible for binding NADP. Using a combination of structural homology, molecular docking, and quantum chemistry calculations, we have predicted the binding site of CHMS. Key histidine residues in a conserved sequence are identified as crucial for binding the hydroxyl group of CHMS and facilitating dehydrogenation with NADP. Mutating these histidine residues results in a loss of enzyme activity, leading to a proposed model for the enzyme's mechanism. These findings are expected to help guide efforts in protein and metabolic engineering to enhance PDC yields in biological routes to polymer feedstock synthesis.

Protein Fold Usages in Ribosomes: Another Glance to the Past

The analysis of protein fold usage, similar to codon usage, offers profound insights into the evolution of biological systems and the origins of modern proteomes. While previous studies have examined fold distribution in modern genomes, our study focuses on the comparative distribution and usage of protein folds in ribosomes across bacteria, archaea, and eukaryotes. We identify the prevalence of certain 'super-ribosome folds,' such as the OB fold in bacteria and the SH3 domain in archaea and eukaryotes. The observed protein fold distribution in the ribosomes announces the future power-law distribution where only a few folds are highly prevalent, and most are rare. Additionally, we highlight the presence of three copies of proto-Rossmann folds in ribosomes across all kingdoms, showing its ancient and fundamental role in ribosomal structure and function. Our study also explores early mechanisms of molecular convergence, where different protein folds bind equivalent ribosomal RNA structures in ribosomes across different kingdoms. This comparative analysis enhances our understanding of ribosomal evolution, particularly the distinct evolutionary paths of the large and small subunits, and underscores the complex interplay between RNA and protein components in the transition from the RNA world to modern cellular life. Transcending the concept of folds also makes it possible to group a large number of ribosomal proteins into five categories of urfolds or metafolds, which could attest to their ancestral character and common origins. This work also demonstrates that the gradual acquisition of extensions by simple but ordered folds constitutes an inexorable evolutionary mechanism. This observation supports the idea that simple but structured ribosomal proteins preceded the development of their disordered extensions.

DrugDomain: The evolutionary context of drugs and small molecules bound to domains

Interactions between proteins and small organic compounds play a crucial role in regulating protein functions. These interactions can modulate various aspects of protein behavior, including enzymatic activity, signaling cascades, and structural stability. By binding to specific sites on proteins, small organic compounds can induce conformational changes, alter protein-protein interactions, or directly affect catalytic activity. Therefore, many drugs available on the market today are small molecules (72% of all approved drugs in the last 5 years). Proteins are composed of one or more domains: evolutionary units that convey function or fitness either singly or in concert with others. Understanding which domain(s) of the target protein binds to a drug can lead to additional opportunities for discovering novel targets. The evolutionary classification of protein domains (ECOD) classifies domains into an evolutionary hierarchy that focuses on distant homology. Previously, no structure-based protein domain classification existed that included information about both the interaction between small molecules or drugs and the structural domains of a target protein. This data is especially important for multidomain proteins and large complexes. Here, we present the DrugDomain database that reports the interaction between ECOD of human target proteins and DrugBank molecules and drugs. The pilot version of DrugDomain describes the interaction of 5160 DrugBank molecules associated with 2573 human proteins. It describes domains for all experimentally determined structures of these proteins and incorporates AlphaFold models when such structures are unavailable. The DrugDomain database is available online: http://prodata.swmed.edu/DrugDomain/.

Structural characterization of two prototypical repressors of SorC family reveals tetrameric assemblies on DNA and mechanism of function

The SorC family of transcriptional regulators plays a crucial role in controlling the carbohydrate metabolism and quorum sensing. We employed an integrative approach combining X-ray crystallography and cryo-electron microscopy to investigate architecture and functional mechanism of two prototypical representatives of two sub-classes of the SorC family: DeoR and CggR from Bacillus subtilis. Despite possessing distinct DNA-binding domains, both proteins form similar tetrameric assemblies when bound to their respective DNA operators. Structural analysis elucidates the process by which the CggR-regulated gapA operon is derepressed through the action of two effectors: fructose-1,6-bisphosphate and newly confirmed dihydroxyacetone phosphate. Our findings provide the first comprehensive understanding of the DNA binding mechanism of the SorC-family proteins, shedding new light on their functional characteristics.

Structure and function of bacterial transcription regulators of the SorC family

The SorC family is a large group of bacterial transcription regulators involved in controlling carbohydrate catabolism and quorum sensing. SorC proteins consist of a conserved C-terminal effector-binding domain and an N-terminal DNA-binding domain, whose type divides the family into two subfamilies: SorC/DeoR and SorC/CggR. Proteins of the SorC/CggR subfamily are known to regulate the key node of glycolysis-triose phosphate interconversion. On the other hand, SorC/DeoR proteins are involved in a variety of peripheral carbohydrate catabolic pathways and quorum sensing functions, including virulence. Despite the abundance and importance of this family, SorC proteins seem to be on the periphery of scientific interest, which might be caused by the fragmentary information about its representatives. This review aims to compile the existing knowledge and provide material to inspire future questions about the SorC protein family.

Structure classification of the proteins from Salmonella enterica pangenome revealed novel potential pathogenicity islands

Salmonella enterica is a pathogenic bacterium known for causing severe typhoid fever in humans, making it important to study due to its potential health risks and significant impact on public health. This study provides evolutionary classification of proteins from Salmonella enterica pangenome. We classified 17,238 domains from 13,147 proteins from 79,758 Salmonella enterica strains and studied in detail domains of 272 proteins from 14 characterized Salmonella pathogenicity islands (SPIs). Among SPIs-related proteins, 90 proteins function in the secretion machinery. 41% domains of SPI proteins have no previous sequence annotation. By comparing clinical and environmental isolates, we identified 3682 proteins that are overrepresented in clinical group that we consider as potentially pathogenic. Among domains of potentially pathogenic proteins only 50% domains were annotated by sequence methods previously. Moreover, 36% (1330 out of 3682) of potentially pathogenic proteins cannot be classified into Evolutionary Classification of Protein Domains database (ECOD). Among classified domains of potentially pathogenic proteins the most populated homology groups include helix-turn-helix (HTH), Immunoglobulin-related, and P-loop domains-related. Functional analysis revealed overrepresentation of these protein in biological processes related to viral entry into host cell, antibiotic biosynthesis, DNA metabolism and conformation change, and underrepresentation in translational processes. Analysis of the potentially pathogenic proteins indicates that they form 119 clusters or novel potential pathogenicity islands (NPPIs) within the Salmonella genome, suggesting their potential contribution to the bacterium's virulence. One of the NPPIs revealed significant overrepresentation of potentially pathogenic proteins. Overall, our analysis revealed that identified potentially pathogenic proteins are poorly studied.

The discovery of novel noncoding RNAs in 50 bacterial genomes

Structured noncoding RNAs (ncRNAs) contribute to many important cellular processes involving chemical catalysis, molecular recognition and gene regulation. Few ncRNA classes are broadly distributed among organisms from all three domains of life, but the list of rarer classes that exhibit surprisingly diverse functions is growing. We previously developed a computational pipeline that enables the near-comprehensive identification of structured ncRNAs expressed from individual bacterial genomes. The regions between protein coding genes are first sorted based on length and the fraction of guanosine and cytidine nucleotides. Long, GC-rich intergenic regions are then examined for sequence and structural similarity to other bacterial genomes. Herein, we describe the implementation of this pipeline on 50 bacterial genomes from varied phyla. More than 4700 candidate intergenic regions with the desired characteristics were identified, which yielded 44 novel riboswitch candidates and numerous other putative ncRNA motifs. Although experimental validation studies have yet to be conducted, this rate of riboswitch candidate discovery is consistent with predictions that many hundreds of novel riboswitch classes remain to be discovered among the bacterial species whose genomes have already been sequenced. Thus, many thousands of additional novel ncRNA classes likely remain to be discovered in the bacterial domain of life.

Chalkophomycin Biosynthesis Revealing Unique Enzyme Architecture for a Hybrid Nonribosomal Peptide Synthetase and Polyketide Synthase

Chalkophomycin is a novel chalkophore with antibiotic activities isolated from Streptomyces sp. CB00271, while its potential in studying cellular copper homeostasis makes it an important probe and drug lead. The constellation of N-hydroxylpyrrole, 2H-oxazoline, diazeniumdiolate, and methoxypyrrolinone functional groups into one compact molecular architecture capable of coordinating cupric ions draws interest to unprecedented enzymology responsible for chalkophomycin biosynthesis. To elucidate the biosynthetic machinery for chalkophomycin production, the chm biosynthetic gene cluster from S. sp. CB00271 was identified, and its involvement in chalkophomycin biosynthesis was confirmed by gene replacement. The chm cluster was localized to a ~31 kb DNA region, consisting of 19 open reading frames that encode five nonribosomal peptide synthetases (ChmHIJLO), one modular polyketide synthase (ChmP), six tailoring enzymes (ChmFGMNQR), two regulatory proteins (ChmAB), and four resistance proteins (ChmA'CDE). A model for chalkophomycin biosynthesis is proposed based on functional assignments from sequence analysis and structure modelling, and is further supported by analogy to over 100 chm-type gene clusters in public databases. Our studies thus set the stage to fully investigate chalkophomycin biosynthesis and to engineer chalkophomycin analogues through a synthetic biology approach.

Architecture, Function, Regulation, and Evolution of α-Glucans Metabolic Enzymes in Prokaryotes

Bacteria have acquired sophisticated mechanisms for assembling and disassembling polysaccharides of different chemistry. α-d-Glucose homopolysaccharides, so-called α-glucans, are the most widespread polymers in nature being key components of microorganisms. Glycogen functions as an intracellular energy storage while some bacteria also produce extracellular assorted α-glucans. The classical bacterial glycogen metabolic pathway comprises the action of ADP-glucose pyrophosphorylase and glycogen synthase, whereas extracellular α-glucans are mostly related to peripheral enzymes dependent on sucrose. An alternative pathway of glycogen biosynthesis, operating via a maltose 1-phosphate polymerizing enzyme, displays an essential wiring with the trehalose metabolism to interconvert disaccharides into polysaccharides. Furthermore, some bacteria show a connection of intracellular glycogen metabolism with the genesis of extracellular capsular α-glucans, revealing a relationship between the storage and structural function of these compounds. Altogether, the current picture shows that bacteria have evolved an intricate α-glucan metabolism that ultimately relies on the evolution of a specific enzymatic machinery. The structural landscape of these enzymes exposes a limited number of core catalytic folds handling many different chemical reactions. In this Review, we present a rationale to explain how the chemical diversity of α-glucans emerged from these systems, highlighting the underlying structural evolution of the enzymes driving α-glucan bacterial metabolism.

Insights into virulence: structure classification of the Vibrio parahaemolyticus RIMD mobilome

IMPORTANCE

The pandemic Vpar strain RIMD causes seafood-borne illness worldwide. Previous comparative genomic studies have revealed pathogenicity islands in RIMD that contribute to the success of the strain in infection. However, not all virulence determinants have been identified, and many of the proteins encoded in known pathogenicity islands are of unknown function. Based on the EOCD database, we used evolution-based classification of structure models for the RIMD proteome to improve our functional understanding of virulence determinants acquired by the pandemic strain. We further identify and classify previously unknown mobile protein domains as well as fast evolving residue positions in structure models that contribute to virulence and adaptation with respect to a pre-pandemic strain. Our work highlights key contributions of phage in mediating seafood born illness, suggesting this strain balances its avoidance of phage predators with its successful colonization of human hosts.

The 3 31 Nucleotide Minihelix tRNA Evolution Theorem and the Origin of Life

There are no theorems (proven theories) in the biological sciences. We propose that the 3 31 nt minihelix tRNA evolution theorem be universally accepted as one. The 3 31 nt minihelix theorem completely describes the evolution of type I and type II tRNAs from ordered precursors (RNA repeats and inverted repeats). Despite the diversification of tRNAome sequences, statistical tests overwhelmingly support the theorem. Furthermore, the theorem relates the dominant pathway for the origin of life on Earth, specifically, how tRNAomes and the genetic code may have coevolved. Alternate models for tRNA evolution (i.e., 2 minihelix, convergent and accretion models) are falsified. In the context of the pre-life world, tRNA was a molecule that, via mutation, could modify anticodon sequences and teach itself to code. Based on the tRNA sequence, we relate the clearest history to date of the chemical evolution of life. From analysis of tRNA evolution, ribozyme-mediated RNA ligation was a primary driving force in the evolution of complexity during the pre-life-to-life transition. TRNA formed the core for the evolution of living systems on Earth.

Putative NAD(P)-Binding Rossmann Fold Protein Is Involved in Chitosan-Induced Peroxidase Activity and Lipoxygenase Expression in Physcomitrium patens

Oxidative burst, the rapid production of high levels of reactive oxygen species in response to external stimuli, is an early defense reaction against pathogens. The fungal elicitor chitosan causes an oxidative burst in the moss Physcomitrium patens (formerly Physcomitrella patens), mainly due to the peroxidase enzyme Prx34. To better understand the chitosan responses in P. patens, we conducted a screen of part of a P. patens mutant collection to isolate plants with less peroxidase activity than wild-type (WT) plants after chitosan treatment. We isolated a P. patens mutant that affected the gene encoding NAD(P)-binding Rossmann fold protein (hereafter, Rossmann fold protein). Three Rossmann fold protein-knockout (KO) plants (named Rossmann fold KO lines) were generated and used to assess extracellular peroxidase activity and expression of defense-responsive genes, including alternative oxidase, lipoxygenase (LOX), NADPH oxidase, and peroxidase (Prx34) in response to chitosan treatment. Extracellular (apoplastic) peroxidase activity was significantly lower in Rossmann fold KO lines than in WT plants after chitosan treatments. Expression of the LOX gene in Rossmann fold KO plants was significantly lower before and after chitosan treatment when compared with WT. Peroxidase activity assays together with gene expression analyses suggest that the Rossmann fold protein might be an important component of the signaling pathway leading to oxidative burst and basal expression of the LOX gene in P. patens. [Formula: see text] Copyright © 2023 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

Decreased liver B vitamin-related enzymes as a metabolic hallmark of cancer cachexia

Cancer cachexia is a complex metabolic disorder accounting for ~20% of cancer-related deaths, yet its metabolic landscape remains unexplored. Here, we report a decrease in B vitamin-related liver enzymes as a hallmark of systemic metabolic changes occurring in cancer cachexia. Metabolomics of multiple mouse models highlights cachexia-associated reductions of niacin, vitamin B6, and a glycine-related subset of one-carbon (C1) metabolites in the liver. Integration of proteomics and metabolomics reveals that liver enzymes related to niacin, vitamin B6, and glycine-related C1 enzymes dependent on B vitamins decrease linearly with their associated metabolites, likely reflecting stoichiometric cofactor-enzyme interactions. The decrease of B vitamin-related enzymes is also found to depend on protein abundance and cofactor subtype. These metabolic/proteomic changes and decreased protein malonylation, another cachexia feature identified by protein post-translational modification analysis, are reflected in blood samples from mouse models and gastric cancer patients with cachexia, underscoring the clinical relevance of our findings.

Pan-cancer structurome reveals overrepresentation of beta sandwiches and underrepresentation of alpha helical domains

The recent progress in the prediction of protein structures marked a historical milestone. AlphaFold predicted 200 million protein models with an accuracy comparable to experimental methods. Protein structures are widely used to understand evolution and to identify potential drug targets for the treatment of various diseases, including cancer. Thus, these recently predicted structures might convey previously unavailable information about cancer biology. Evolutionary classification of protein domains is challenging and different approaches exist. Recently our team presented a classification of domains from human protein models released by AlphaFold. Here we evaluated the pan-cancer structurome, domains from over and under expressed proteins in 21 cancer types, using the broadest levels of the ECOD classification: the architecture (A-groups) and possible homology (X-groups) levels. Our analysis reveals that AlphaFold has greatly increased the three-dimensional structural landscape for proteins that are differentially expressed in these 21 cancer types. We show that beta sandwich domains are significantly overrepresented and alpha helical domains are significantly underrepresented in the majority of cancer types. Our data suggest that the prevalence of the beta sandwiches is due to the high levels of immunoglobulins and immunoglobulin-like domains that arise during tumor development-related inflammation. On the other hand, proteins with exclusively alpha domains are important elements of homeostasis, apoptosis and transmembrane transport. Therefore cancer cells tend to reduce representation of these proteins to promote successful oncogeneses.

Biological Catalysis and Information Storage Have Relied on N-Glycosyl Derivatives of β-D-Ribofuranose since the Origins of Life

Most naturally occurring nucleotides and nucleosides are N-glycosyl derivatives of β-d-ribose. These N-ribosides are involved in most metabolic processes that occur in cells. They are essential components of nucleic acids, forming the basis for genetic information storage and flow. Moreover, these compounds are involved in numerous catalytic processes, including chemical energy production and storage, in which they serve as cofactors or coribozymes. From a chemical point of view, the overall structure of nucleotides and nucleosides is very similar and simple. However, their unique chemical and structural features render these compounds versatile building blocks that are crucial for life processes in all known organisms. Notably, the universal function of these compounds in encoding genetic information and cellular catalysis strongly suggests their essential role in the origins of life. In this review, we summarize major issues related to the role of N-ribosides in biological systems, especially in the context of the origin of life and its further evolution, through the RNA-based World(s), toward the life we observe today. We also discuss possible reasons why life has arisen from derivatives of β-d-ribofuranose instead of compounds based on other sugar moieties.

Conserved folding landscape of monomeric initiator caspases

The apoptotic caspase subfamily evolved into two subfamilies-monomeric initiators and dimeric effectors; both subfamilies share a conserved caspase-hemoglobinase fold with a protease domain containing a large subunit and a small subunit. Sequence variations in the conserved caspase-hemoglobinase fold resulted in changes in oligomerization, enzyme specificity, and regulation, making caspases an excellent model for examining the mechanisms of molecular evolution in fine-tuning structure, function, and allosteric regulation. We examined the urea-induced equilibrium folding/unfolding of two initiator caspases, monomeric caspase-8 and cFLIPL, over a broad pH range. Both proteins unfold by a three-state equilibrium mechanism that includes a partially folded intermediate. In addition, both proteins undergo a conserved pH-dependent conformational change that is controlled by an evolutionarily conserved mechanism. We show that the conformational free energy landscape of the caspase monomer is conserved in the monomeric and dimeric subfamilies. Molecular dynamics simulations in the presence or the absence of urea, coupled with limited trypsin proteolysis and mass spectrometry, show that the small subunit is unstable in the protomer and unfolds prior to the large subunit. In addition, the unfolding of helix 2 in the large subunit results in disruption of a conserved allosteric site. Because the small subunit forms the interface for dimerization, our results highlight an important driving force for the evolution of the dimeric caspase subfamily through stabilizing the small subunit.

A review of visualisations of protein fold networks and their relationship with sequence and function

Proteins form arguably the most significant link between genotype and phenotype. Understanding the relationship between protein sequence and structure, and applying this knowledge to predict function, is difficult. One way to investigate these relationships is by considering the space of protein folds and how one might move from fold to fold through similarity, or potential evolutionary relationships. The many individual characterisations of fold space presented in the literature can tell us a lot about how well the current Protein Data Bank represents protein fold space, how convergence and divergence may affect protein evolution, how proteins affect the whole of which they are part, and how proteins themselves function. A synthesis of these different approaches and viewpoints seems the most likely way to further our knowledge of protein structure evolution and thus, facilitate improved protein structure design and prediction.

Creative destruction: New protein folds from old

Mechanisms of emergence and divergence of protein folds pose central questions in biological sciences. Incremental mutation and stepwise adaptation explain relationships between topologically similar protein folds. However, the universe of folds is diverse and riotous, suggesting more potent and creative forces are at play. Sequence and structure similarity are observed between distinct folds, indicating that proteins with distinct folds may share common ancestry. We found evidence of common ancestry between three distinct β-barrel folds: Scr kinase family homology (SH3), oligonucleotide/oligosaccharide-binding (OB), and cradle loop barrel (CLB). The data suggest a mechanism of fold evolution that interconverts SH3, OB, and CLB. This mechanism, which we call creative destruction, can be generalized to explain many examples of fold evolution including circular permutation. In creative destruction, an open reading frame duplicates or otherwise merges with another to produce a fused polypeptide. A merger forces two ancestral domains into a new sequence and spatial context. The fused polypeptide can explore folding landscapes that are inaccessible to either of the independent ancestral domains. However, the folding landscapes of the fused polypeptide are not fully independent of those of the ancestral domains. Creative destruction is thus partially conservative; a daughter fold inherits some motifs from ancestral folds. After merger and refolding, adaptive processes such as mutation and loss of extraneous segments optimize the new daughter fold. This model has application in disease states characterized by genetic instability. Fused proteins observed in cancer cells are likely to experience remodeled folding landscapes and realize altered folds, conferring new or altered functions.

Insertions and deletions mediated functional divergence of Rossmann fold enzymes

Nucleobase-containing coenzymes are hypothesized to be relics of an early RNA-based world that preceded the emergence of proteins. Despite the importance of coenzyme-protein synergisms, their emergence and evolution remain understudied. An excellent target to address this issue is the Rossmann fold, the most catalytically diverse and abundant protein architecture in nature. We investigated two main Rossmann lineages: the nicotinamide adenine dinucleotide phosphate (NAD(P)) and the S-adenosyl methionine (SAM)- binding superfamilies. To identify the evolutionary changes that lead to a coenzyme specificity switch on these superfamilies, we performed structural and sequence-based Hidden Markov model analysis to systematically search for key motifs in their coenzyme-binding pockets. Our analyses revealed that through insertions and deletions (InDels) and a residue substitution, the ancient β1-loop-α1 coenzyme-binding structure of NAD(P) could be reshaped into the SAM-binding β1-loop-α1 structure. To experimentally prove this obsevation, we removed three amino acids from the NAD(P)-binding pocket and solved the structure of the resulting mutant, revealing the characteristic loop features of the SAM-binding pocket. To confirm the binding to SAM, we performed isothermal titration calorimetry measurements. Molecular dynamics simulations also corroborated the role of InDels in abolishing NAD binding and acquiring SAM binding. Our results uncovered how nature may have utilized insertions and deletions to optimize the different coenzyme-binding pockets and the distinct functionalities observed for Rossmann superfamilies. This work also proposes a general mechanism by which protein templates could have been recycled through the course of evolution to adopt different coenzymes and confer distinct chemistries.

CMTr mediated 2'-O-ribose methylation status of cap-adjacent nucleotides across animals

Cap methyltransferases (CMTrs) O methylate the 2' position of the ribose (cOMe) of cap-adjacent nucleotides of animal, protist, and viral mRNAs. Animals generally have two CMTrs, whereas trypanosomes have three, and many viruses encode one in their genome. In the splice leader of mRNAs in trypanosomes, the first four nucleotides contain cOMe, but little is known about the status of cOMe in animals. Here, we show that cOMe is prominently present on the first two cap-adjacent nucleotides with species- and tissue-specific variations in Caenorhabditis elegans, honeybees, zebrafish, mouse, and human cell lines. In contrast, Drosophila contains cOMe primarily on the first cap-adjacent nucleotide. De novo RoseTTA modeling of CMTrs reveals close similarities of the overall structure and near identity for the catalytic tetrad, and for cap and cofactor binding for human, Drosophila and C. elegans CMTrs. Although viral CMTrs maintain the overall structure and catalytic tetrad, they have diverged in cap and cofactor binding. Consistent with the structural similarity, both CMTrs from Drosophila and humans methylate the first cap-adjacent nucleotide of an AGU consensus start. Because the second nucleotide is also methylated upon heat stress in Drosophila, these findings argue for regulated cOMe important for gene expression regulation.

Uniform binding and negative catalysis at the origin of enzymes

Enzymes are well known for their catalytic abilities, some even reaching "catalytic perfection" in the sense that the reaction they catalyze has reached the physical bound of the diffusion rate. However, our growing understanding of enzyme superfamilies has revealed that only some share a catalytic chemistry while others share a substrate-handle binding motif, for example, for a particular phosphate group. This suggests that some families emerged through a "substrate-handle-binding-first" mechanism ("binding-first" for brevity) instead of "chemistry-first" and we are, therefore, left to wonder what the role of non-catalytic binders might have been during enzyme evolution. In the last of their eight seminal, back-to-back articles from 1976, John Albery and Jeremy Knowles addressed the question of enzyme evolution by arguing that the simplest mode of enzyme evolution is what they defined as "uniform binding" (parallel stabilization of all enzyme-bound states to the same degree). Indeed, we show that a uniform-binding proto-catalyst can accelerate a reaction, but only when catalysis is already present, that is, when the transition state is already stabilized to some degree. Thus, we sought an alternative explanation for the cases where substrate-handle-binding preceded any involvement of a catalyst. We find that evolutionary starting points that exhibit negative catalysis can redirect the reaction's course to a preferred product without need for rate acceleration or product release; that is, if they do not stabilize, or even destabilize, the transition state corresponding to an undesired product. Such a mechanism might explain the emergence of "binding-first" enzyme families like the aldolase superfamily.

Characterizing and explaining the impact of disease-associated mutations in proteins without known structures or structural homologs

Mutations in human proteins lead to diseases. The structure of these proteins can help understand the mechanism of such diseases and develop therapeutics against them. With improved deep learning techniques, such as RoseTTAFold and AlphaFold, we can predict the structure of proteins even in the absence of structural homologs. We modeled and extracted the domains from 553 disease-associated human proteins without known protein structures or close homologs in the Protein Databank. We noticed that the model quality was higher and the Root mean square deviation (RMSD) lower between AlphaFold and RoseTTAFold models for domains that could be assigned to CATH families as compared to those which could only be assigned to Pfam families of unknown structure or could not be assigned to either. We predicted ligand-binding sites, protein-protein interfaces and conserved residues in these predicted structures. We then explored whether the disease-associated missense mutations were in the proximity of these predicted functional sites, whether they destabilized the protein structure based on ddG calculations or whether they were predicted to be pathogenic. We could explain 80% of these disease-associated mutations based on proximity to functional sites, structural destabilization or pathogenicity. When compared to polymorphisms, a larger percentage of disease-associated missense mutations were buried, closer to predicted functional sites, predicted as destabilizing and pathogenic. Usage of models from the two state-of-the-art techniques provide better confidence in our predictions, and we explain 93 additional mutations based on RoseTTAFold models which could not be explained based solely on AlphaFold models.

A Method for Assessing the Robustness of Protein Structures by Randomizing Packing Interactions

Many single-domain proteins are not only stable and water-soluble, but they also populate few to no intermediates during folding. This reduces interactions between partially folded proteins, misfolding, and aggregation, and makes the proteins tractable in biotechnological applications. Natural proteins fold thus, not necessarily only because their structures are well-suited for folding, but because their sequences optimize packing and fit their structures well. In contrast, folding experiments on the de novo designed Top7 suggest that it populates several intermediates. Additionally, in de novo protein design, where sequences are designed for natural and new non-natural structures, tens of sequences still need to be tested before success is achieved. Both these issues may be caused by the specific scaffolds used in design, i.e., some protein scaffolds may be more tolerant to packing perturbations and varied sequences. Here, we report a computational method for assessing the response of protein structures to packing perturbations. We then benchmark this method using designed proteins and find that it can identify scaffolds whose folding gets disrupted upon perturbing packing, leading to the population of intermediates. The method can also isolate regions of both natural and designed scaffolds that are sensitive to such perturbations and identify contacts which when present can rescue folding. Overall, this method can be used to identify protein scaffolds that are more amenable to whole protein design as well as to identify protein regions which are sensitive to perturbations and where further mutations should be avoided during protein engineering.

Rossmann-toolbox: a deep learning-based protocol for the prediction and design of cofactor specificity in Rossmann fold proteins

The Rossmann fold enzymes are involved in essential biochemical pathways such as nucleotide and amino acid metabolism. Their functioning relies on interaction with cofactors, small nucleoside-based compounds specifically recognized by a conserved βαβ motif shared by all Rossmann fold proteins. While Rossmann methyltransferases recognize only a single cofactor type, the S-adenosylmethionine, the oxidoreductases, depending on the family, bind nicotinamide (nicotinamide adenine dinucleotide, nicotinamide adenine dinucleotide phosphate) or flavin-based (flavin adenine dinucleotide) cofactors. In this study, we showed that despite its short length, the βαβ motif unambiguously defines the specificity towards the cofactor. Following this observation, we trained two complementary deep learning models for the prediction of the cofactor specificity based on the sequence and structural features of the βαβ motif. A benchmark on two independent test sets, one containing βαβ motifs bearing no resemblance to those of the training set, and the other comprising 38 experimentally confirmed cases of rational design of the cofactor specificity, revealed the nearly perfect performance of the two methods. The Rossmann-toolbox protocols can be accessed via the webserver at https://lbs.cent.uw.edu.pl/rossmann-toolbox and are available as a Python package at https://github.com/labstructbioinf/rossmann-toolbox.

Completeness and Consistency in Structural Domain Classifications

Domain classifications are a useful resource for computational analysis of the protein structure, but elements of their composition are often opaque to potential users. We perform a comparative analysis of our classification ECOD against the SCOPe, SCOP2, and CATH domain classifications with respect to their constituent domain boundaries and hierarchal organization. The coverage of these domain classifications with respect to ECOD and to the PDB was assessed by structure and by sequence. We also conducted domain pair analysis to determine broad differences in hierarchy between domains shared by ECOD and other classifications. Finally, we present domains from the major facilitator superfamily (MFS) of transporter proteins and provide evidence that supports their split into domains and for multiple conformations within these families. We find that the ECOD and CATH provide the most extensive structural coverage of the PDB. ECOD and SCOPe have the most consistent domain boundary conditions, whereas CATH and SCOP2 both differ significantly.