Specific Protein Families Research Articles

A conditional protein diffusion model generates artificial programmable endonuclease sequences with enhanced activity

Deep learning-based methods for generating functional proteins address the growing need for novel biocatalysts, allowing for precise tailoring of functionalities to meet specific requirements. This advancement leads to the development of highly efficient and specialized proteins with diverse applications across scientific, technological, and biomedical fields. This study establishes a pipeline for protein sequence generation with a conditional protein diffusion model, namely CPDiffusion, to create diverse sequences of proteins with enhanced functions. CPDiffusion accommodates protein-specific conditions, such as secondary structures and highly conserved amino acids. Without relying on extensive training data, CPDiffusion effectively captures highly conserved residues and sequence features for specific protein families. We applied CPDiffusion to generate artificial sequences of Argonaute (Ago) proteins based on the backbone structures of wild-type (WT) Kurthia massiliensis Ago (KmAgo) and Pyrococcus furiosus Ago (PfAgo), which are complex multi-domain programmable endonucleases. The generated sequences deviate by up to nearly 400 amino acids from their WT templates. Experimental tests demonstrated that the majority of the generated proteins for both KmAgo and PfAgo show unambiguous activity in DNA cleavage, with many of them exhibiting superior activity as compared to the WT. These findings underscore CPDiffusion’s remarkable success rate in generating novel sequences for proteins with complex structures and functions in a single step, leading to enhanced activity. This approach facilitates the design of enzymes with multi-domain molecular structures and intricate functions through in silico generation and screening, all accomplished without the need for supervision from labeled data.

Relative Expression of a Salinity Stress‐Responsive Na+/H+ Exchanger (NHX) in Root and Leaf Tissues of the African Leafy Vegetable, Amaranthus dubius

ABSTRACTAmaranthus dubius, an African leafy vegetable (ALV), is an easy‐to‐grow, annual shrub and a highly nutritious food source, containing elevated levels of essential nutrients in the leaves. Many ALVs, including A. dubius, can tolerate salinity stress, enabling their cultivation on marginal land. However, the widespread propagation of A. dubius as a stable food source has thus far not been realised due partially to the high frequency at which hybridisation occurs, resulting in high genotypic and phenotypic variability. Therefore, to increase the agricultural output capacity of this species on salt‐affected marginal lands, it is important to screen, select and then clonally propagate the identified salinity‐tolerant genotypes to ensure true‐to‐type fidelity in the regenerated population. It is also important, thereafter, to elucidate their underlying gene expression of the stress response. The present study exposed 4‐week‐old A. dubius seedlings to 100, 200 and 400 mM NaCl to determine their degree of salt tolerance. Genotypes were then screened, selected and clonally propagated through cuttings, based on high growth rates and biomass, and salt tolerance. Generally, growth and physiological parameters decreased as substrate salinity increased. However, individual salt‐stressed genotypes demonstrated similar vigour to nonstressed plants and were able to maintain total protein and chlorophyll concentrations despite increasing salinity. The relative expression of an NHX1‐like transcript was quantified in 15 genotypes using degenerately primed real‐time qPCR. The relative expression of the putative NHX1 gene was 6.7 times greater in root tissues of seedlings treated with 400 mM NaCl (10.7 ± 1.8) compared to the roots of untreated seedlings (1.6 ± 1.3), and 2.8‐fold more than leaf tissues harvested from seedlings treated with 400 mM NaCl. Furthermore, the relative electrical conductivity (EC) of root tissues was 10 times greater than the EC of leaf tissues from the same 400 mM NaCl treatment. Numerous genotypes yielded similar chlorophyll content between 200 and 400 mM NaCl treatments, with genotypes salinity‐1 (S1) (3.5 ± 0.2 μg/cm2) and S34 (4.0 ± 0.4 μg/cm2) having the highest concentrations of chlorophyll in the 400 mM group, which was positively correlated with total protein content. Following micropropagation through direct organogenesis, selected clones maintained true‐to‐type traits such as similar chlorophyll, protein and NHX1‐like expression as their parent plants when exposed to 400 mM NaCl. This study revealed that some genotypes demonstrated salt stress tolerance capabilities rivalling established halophytes by regulating the constitutive or inducible expression of an NHX1‐like protein in roots and leaves. The correlation between protein content and NHX1‐like expression was nonlinear and nonproportional, demonstrating the complexity of this response and necessitating further exploration of specific protein families or functional groups conferring salinity tolerance in this species.

A Comprehensive Review on Machine Learning Techniques for Protein Family Prediction.

Proteomics is a field dedicated to the analysis of proteins in cells, tissues, and organisms, aiming to gain insights into their structures, functions, and interactions. A crucial aspect within proteomics is protein family prediction, which involves identifying evolutionary relationships between proteins by examining similarities in their sequences or structures. This approach holds great potential for applications such as drug discovery and functional annotation of genomes. However, current methods for protein family prediction have certain limitations, including limited accuracy, high false positive rates, and challenges in handling large datasets. Some methods also rely on homologous sequences or protein structures, which introduce biases and restrict their applicability to specific protein families or structures. To overcome these limitations, researchers have turned to machine learning (ML) approaches that can identify connections between protein features and simplify complex high-dimensional datasets. This paper presents a comprehensive survey of articles that employ various ML techniques for predicting protein families. The primary objective is to explore and improve ML techniques specifically for protein family prediction, thus advancing future research in the field. Through qualitative and quantitative analyses of ML techniques, it is evident that multiple methods utilizing a range of classifiers have been applied for protein family prediction. However, there has been limited focus on developing novel classifiers for protein family classification, highlighting the urgent need for improved approaches in this area. By addressing these challenges, this research aims to enhance the accuracy and effectiveness of protein family prediction, ultimately facilitating advancements in proteomics and its diverse applications.

Updated Insights into the Phylogenetics, Phylodynamics, and Genetic Diversity of Nipah Virus (NiV).

Nipah virus (NiV), a biosafety level 4 agent, was first identified in human clinical cases during an outbreak in 1998 in Malaysia and Singapore. While flying foxes are the primary host and viral vector, the infection is associated with a severe clinical presentation in humans, resulting in a high mortality rate. Therefore, NiV is considered a virus with an elevated epidemic potential which is further underscored by its recent emergence (September 2023) as an outbreak in India. Given the situation, it is paramount to understand the molecular dynamics of the virus to shed more light on its evolution and prevent potential future outbreaks. In this study, we conducted Bayesian phylogenetic analysis on all available NiV complete genomes, including partial N-gene NiV sequences (≥1000 bp) in public databases since the first human case, registered in 1998. We observed the distribution of genomes into three main clades corresponding to the genotypes Malaysia, Bangladesh and India, with the Malaysian clade being the oldest in evolutionary terms. The Bayesian skyline plot showed a recent increase in the viral population size since 2019. Protein analysis showed the presence of specific protein families (Hendra_C) in bats that might keep the infection in an asymptomatic state in bats, which also serve as viral vectors. Our results further indicate a shortage of complete NiV genomes, which would be instrumental in gaining a better understanding of NiV's molecular evolution and preventing future outbreaks. Our investigation also underscores the critical need to strengthen genomic surveillance based on complete NiV genomes that will aid thorough genetic characterization of the circulating NiV strains and the phylogenetic relationships between the henipaviruses. This approach will better prepare us to tackle the challenges posed by the NiV virus and other emerging viruses.

A Meta-Analysis on Presynaptic Changes in Alzheimer's Disease.

A key aspect of synaptic dysfunction in Alzheimer's disease (AD) is loss of synaptic proteins. Previous publications showed that the presynaptic machinery is more strongly affected than postsynaptic proteins. However, it has also been reported that presynaptic protein loss is highly variable and shows region- and protein-specificity. The objective of this meta-analysis was to provide an update on the available literature and to further characterize patterns of presynaptic protein loss in AD. Systematic literature search was conducted for studies published between 2015-2022 which quantified presynaptic proteins in postmortem tissue from AD patients and healthy controls. Three-level random effects meta-analyses of twenty-two identified studies was performed to characterize overall presynaptic protein loss and changes in specific regions, proteins, protein families, and functional categories. Meta-analysis confirmed overall loss of presynaptic proteins in AD patients. Subgroup analysis revealed region specificity of protein loss, with largest effects in temporal and frontal cortex. Results concerning different groups of proteins were also highly variable. Strongest and most consistently affected was the family of synaptosome associated proteins, especially SNAP25. Among the most severely affected were proteins regulating dense core vesicle exocytosis and the synaptic vesicle cycle. Results confirm previous literature related to presynaptic protein loss in AD patients and provide further in-depth characterization of most affected proteins and presynaptic functions.

Identification and engineering of potent cyclic peptides with selective or promiscuous binding through biochemical profiling and bioinformatic data analysis.

As our understanding of biological systems grows, so does the need to selectively target individual or multiple members of specific protein families in order to probe their function. Many targets of current biological and pharmaceutical interest are part of a large family of closely related proteins and achieving ligand selectivity often remains either an elusive or time-consuming endeavour. Cyclic peptides (CPs) occupy a key niche in ligand space, able to achieve high affinity and selectivity while retaining synthetic accessibility. De novo cyclic peptide ligands can be rapidly generated against a given target using mRNA display. In this study we harness mRNA display technology and the wealth of next generation sequencing (NGS) data generated to explore both experimental approaches and bioinformatic, statistical data analysis of peptide enrichment in cross-screen selections to rapidly generate high affinity CPs with differing intra-family protein selectivity profiles against fibroblast growth factor receptor (FGF-R) family proteins. Using these methods, CPs with distinct selectivity profiles can be generated which can serve as valuable tool compounds to decipher biological questions.

To the Origin of Fungi: Analysis of MFS Transporters of First Assembled Aphelidium Genome Highlights Dissimilarity of Osmotrophic Abilities between Aphelida and Fungi.

Aphelids are a holomycotan group, represented exclusively by parasitoids infecting algae. They form a sister lineage to Fungi in the phylogenetic tree and represent a key group for reconstruction of the evolution of Holomycota and for analysis of the origin of Fungi. The newly assembled genome of Aphelidium insullamus (Holomycota, Aphelida) with a total length of 18.9 Mb, 7820 protein-coding genes and a GC percentage of 52.05% was obtained by a hybrid assembly based on Oxford Nanopore long reads and Illumina paired reads. In order to trace the origin and the evolution of fungal osmotrophy and its presence or absence in Aphelida, we analyzed the set of main fungal transmembrane transporters, which are proteins of the Major Facilitator superfamily (MFS), in the predicted aphelid proteomes. This search has shown an absence of a specific fungal protein family Drug:H+ antiporters-2 (DAH-2) and specific fungal orthologs of the sugar porters (SP) family, and the presence of common opisthokont's orthologs of the SP family in four aphelid genomes. The repertoire of SP orthologs in aphelids turned out to be less diverse than in free-living opisthokonts, and one of the most limited among opisthokonts. We argue that aphelids do not show signs of similarity with fungi in terms of their osmotrophic abilities, despite the sister relationships of these groups. Moreover, the osmotrophic abilities of aphelids appear to be reduced in comparison with free-living unicellular opisthokonts. Therefore, we assume that the evolution of fungi-specific traits began after the separation of fungal and aphelid lineages, and there are no essential reasons to consider aphelids as a prototype of the fungal ancestor.

Conserved unique peptide patterns (CUPP) online platform 2.0: implementation of+1000 JGI fungal genomes.

Carbohydrate-processing enzymes, CAZymes, are classified into families based on sequence and three-dimensional fold. Because many CAZyme families contain members of diverse molecular function (different EC-numbers), sophisticated tools are required to further delineate these enzymes. Such delineation is provided by the peptide-based clustering method CUPP, Conserved Unique Peptide Patterns. CUPP operates synergistically with the CAZy family/subfamily categorizations to allow systematic exploration of CAZymes by defining small protein groups with shared sequence motifs. The updated CUPP library contains 21,930 of such motif groups including 3,842,628 proteins. The new implementation of the CUPP-webserver, https://cupp.info/, now includes all published fungal and algal genomes from the Joint Genome Institute (JGI), genome resources MycoCosm and PhycoCosm, dynamically subdivided into motif groups of CAZymes. This allows users to browse the JGI portals for specific predicted functions or specific protein families from genome sequences. Thus, a genome can be searched for proteins having specific characteristics. All JGI proteins have a hyperlink to a summary page which links to the predicted gene splicing including which regions have RNA support. The new CUPP implementation also includes an update of the annotation algorithm that uses only a fourth of the RAM while enabling multi-threading, providing an annotation speed below 1 ms/protein.

Localization of Multiple Jellyfish Toxins Shows Specificity for Functionally Distinct Polyps and Nematocyst Types in a Colonial Hydrozoan.

Hydractinia symbiolongicarpus is a colonial hydrozoan that displays a division of labor through morphologically distinct and functionally specialized polyp types. As with all cnidarians, their venoms are housed in nematocysts, which are scattered across an individual. Here, we investigate the spatial distribution of a specific protein family, jellyfish toxins, in which multiple paralogs are differentially expressed across the functionally specialized polyps. Jellyfish toxins (JFTs) are known pore-forming toxins in the venoms of medically relevant species such as box jellyfish (class Cubozoa), but their role in other medusozoan venoms is less clear. Utilizing a publicly available single-cell dataset, we confirmed that four distinct H. symbiolongicarpus JFT paralogs are expressed in nematocyst-associated clusters, supporting these as true venom components in H. symbiolongicarpus. In situ hybridization and immunohistochemistry were used to localize the expression of these JFTs across the colony. These expression patterns, in conjunction with known nematocyst type distributions, suggest that two of these JFTs, HsymJFT1c-I and HsymJFT1c-II, are localized to specific types of nematocysts. We further interpret JFT expression patterns in the context of known regions of nematogenesis and differential rates of nematocyst turnover. Overall, we show that JFT expression patterns in H. symbiolongicarpus are consistent with the subfunctionalization of JFT paralogs across a partitioned venom system within the colony, such that each JFT is expressed within a specific set of functionally distinct polyp types and, in some cases, specific nematocyst types.

Synthesis and Biological Evaluation of a Series of Quinoline‐Based Quinazolinones and Carbamic Anhydride Derivatives

AbstractQuinoline‐based compounds are one of the most important classes of N‐heterocyclics exhibiting a wide spectrum of biological activities. There is a constant demand for the synthesis of new quinoline‐based molecules suitable for therapeutic applications. Here a new strategy is developed to synthesize different quinoline‐based quinazolinones catalytically using InCl3 through a cyclocondensation reaction of isatoic anhydride and aniline with 2‐substituted‐quinoline‐3‐carbaldehyde to produce 2‐(2‐phenoxyquinolin‐3‐yl)‐3‐phenyl‐2,3‐ dihydroquinazolin‐4(1H)‐one derivatives (>90 % yields). In contrast, the reduced electrophilicity at the metal center allows the hydrazide ligand to bind In(III) and serendipitously forms benzoic(2‐phenoxyquinoline‐3‐carbonyl) carbamic anhydride (∼70 % yields). The mechanistic aspects of these reactions were rationally explained. The structure and purity of all the isolated derivatives were assessed by spectroscopic and analytical data. In view of a strong correlation existing between inflammation and cancer progression, the ex‐vivo anti‐inflammatory effect of the ligands was established. The anti‐cancer property of the proposed ligand was delineated with a specific SIRT protein family. Further, the computational docking studies on the binding abilities of 22 synthesized compounds with the Sirt1 protein were discussed in detail.

Differences between Two Groups of Burmese Vipers (Viperidae: Azemiops) in the Proteomic Profiles, Immunoreactivity and Biochemical Functions of Their Venoms.

Two recently revised Azemiops snakes with apparent differences in their external appearances and skeletal morphologies but unclear genetic boundaries have been proposed. Some researchers have refrained from using the newly proposed taxonomy because these two “species” might be two clades corresponding to different geographical populations of Azemiops feae. To improve the understanding of the kinship of these two Burmese viper groups, more of their characteristics should be explored in depth. We performed a comparative analysis of the proteomic profiles and biochemical activities of snake venoms from these two groups (Sichuan A. feae and Zhejiang A. feae) and evaluated the immunorecognition capacity of commercial antivenoms toward them. Eight protein families were identified in venoms from these two groups, while phospholipase B was only detected in venom from Sichuan A. feae. These protein families displayed varying degrees of differences in relative abundance between venoms, and phospholipase A2 (Sichuan A. feae: 57.15%; Zhejiang A. feae: 65.94%) was the predominated component. Gloydius brevicaudus antivenom exhibited the strongest capacity to immunologically recognize these two venoms, but this was mainly limited to components with high molecular masses, some of which differed between venoms. Additionally, Zhejiang A. feae venom was more toxic than Sichuan A. feae venom, and the venoms expressed remarkable differences in enzymatic activities, probably resulting from the variation in the relative abundance of specific protein families. Our findings unveil differences between the two Burmese viper groups in terms of proteomic profiles, immunoreactivity, and the biochemical functions of their venoms. This information will facilitate the management of snakebites caused by these snakes.

Novel Driver Strength Index highlights important cancer genes in TCGA PanCanAtlas patients.

BackgroundCancer driver genes are usually ranked by mutation frequency, which does not necessarily reflect their driver strength. We hypothesize that driver strength is higher for genes preferentially mutated in patients with few driver mutations overall, because these few mutations should be strong enough to initiate cancer.MethodsWe propose formulas for the Driver Strength Index (DSI) and the Normalized Driver Strength Index (NDSI), the latter independent of gene mutation frequency. We validate them using TCGA PanCanAtlas datasets, established driver prediction algorithms and custom computational pipelines integrating SNA, CNA and aneuploidy driver contributions at the patient-level resolution.ResultsDSI and especially NDSI provide substantially different gene rankings compared to the frequency approach. E.g., NDSI prioritized members of specific protein families, including G proteins GNAQ, GNA11 and GNAS, isocitrate dehydrogenases IDH1 and IDH2, and fibroblast growth factor receptors FGFR2 and FGFR3. KEGG analysis shows that top NDSI-ranked genes comprise EGFR/FGFR2/GNAQ/GNA11–NRAS/HRAS/KRAS–BRAF pathway, AKT1–MTOR pathway, and TCEB1–VHL–HIF1A pathway.ConclusionOur indices are able to select for driver gene attributes not selected by frequency sorting, potentially for driver strength. Genes and pathways prioritized are likely the strongest contributors to cancer initiation and progression and should become future therapeutic targets.

Comprehensive Collection and Prediction of ABC Transmembrane Protein Structures in the AI Era of Structural Biology.

The number of unique transmembrane (TM) protein structures doubled in the last four years, which can be attributed to the revolution of cryo-electron microscopy. In addition, AlphaFold2 (AF2) also provided a large number of predicted structures with high quality. However, if a specific protein family is the subject of a study, collecting the structures of the family members is highly challenging in spite of existing general and protein domain-specific databases. Here, we demonstrate this and assess the applicability and usability of automatic collection and presentation of protein structures via the ABC protein superfamily. Our pipeline identifies and classifies transmembrane ABC protein structures using the PFAM search and also aims to determine their conformational states based on special geometric measures, conftors. Since the AlphaFold database contains structure predictions only for single polypeptide chains, we performed AF2-Multimer predictions for human ABC half transporters functioning as dimers. Our AF2 predictions warn of possibly ambiguous interpretation of some biochemical data regarding interaction partners and call for further experiments and experimental structure determination. We made our predicted ABC protein structures available through a web application, and we joined the 3D-Beacons Network to reach the broader scientific community through platforms such as PDBe-KB.

Comparative studies of AlphaFold, RoseTTAFold and Modeller: a case study involving the use of G-protein-coupled receptors.

Neural network (NN)-based protein modeling methods have improved significantly in recent years. Although the overall accuracy of the two non-homology-based modeling methods, AlphaFold and RoseTTAFold, is outstanding, their performance for specific protein families has remained unexamined. G-protein-coupled receptor (GPCR) proteins are particularly interesting since they are involved in numerous pathways. This work directly compares the performance of these novel deep learning-based protein modeling methods for GPCRs with the most widely used template-based software—Modeller. We collected the experimentally determined structures of 73 GPCRs from the Protein Data Bank. The official AlphaFold repository and RoseTTAFold web service were used with default settings to predict five structures of each protein sequence. The predicted models were then aligned with the experimentally solved structures and evaluated by the root-mean-square deviation (RMSD) metric. If only looking at each program’s top-scored structure, Modeller had the smallest average modeling RMSD of 2.17 Å, which is better than AlphaFold’s 5.53 Å and RoseTTAFold’s 6.28 Å, probably since Modeller already included many known structures as templates. However, the NN-based methods (AlphaFold and RoseTTAFold) outperformed Modeller in 21 and 15 out of the 73 cases with the top-scored model, respectively, where no good templates were available for Modeller. The larger RMSD values generated by the NN-based methods were primarily due to the differences in loop prediction compared to the crystal structures.

RNA at the surface of phase-separated condensates impacts their size and number

Although it is now recognized that specific RNAs and protein families are critical for the biogenesis of ribonucleoprotein (RNP) condensates, how these molecular constituents determine condensate size and morphology is unknown. To circumvent the biochemical complexity of endogenous RNP condensates, the use of programmable tools to reconstitute condensate formation with minimal constituents can be instrumental. Here we report a methodology to form RNA-containing condensates in living cells programmed to specifically recruit a single RNA species. Our bioengineered condensates are made of ArtiGranule scaffolds composed of an orthogonal protein that can bind to a specific heterologously expressed RNA. These scaffolds undergo liquid-liquid phase separation in cells and can be chemically controlled to prevent condensation or to trigger condensate dissolution. We found that the targeted RNAs localize at the condensate surface, either as isolated RNA molecules or as a homogenous corona of RNA molecules around the condensate. The recruitment of RNA changes the material properties of condensates by hardening the condensate body. Moreover, the condensate size scales with RNA surface density; the higher the RNA density is, the smaller and more frequent the condensates are. These results suggest a mechanism based on physical constraints, provided by RNAs at the condensate surface, that limit condensate growth and coalescence.

Metabolomics and proteomics study reveals the effects of benzo[a]pyrene on the viability and migration of KYSE-150 esophageal cells

A representative polycyclic aromatic hydrocarbon, benzo[a]pyrene (B[a]P), has been widely detected in environmental compartments and is highly carcinogenic to humans. Oral ingestion of B[a]P is the dominant exposure pathway. The esophagus acts as the first contact point when B[a]P enters the human body. However, its role in the development of human esophageal cancer is rarely discussed. Herein, we employed untargeted metabolomics in combination with proteomics to explore B[a]P-related intracellular responses in human esophageal cell lines. Our results demonstrated that B[a]P exposure induced significant metabolic disorders, further leading to overproduction of reactive oxygen species (ROS) and disturbance of the cellular viability process and migration ability of esophageal cells. In response, glutathione (GSH) was consumed to meet the demand for cellular detoxification, and thioredoxin (TXN) was upregulated to balance the cellular redox. These alterations caused the reregulation of some specific protein families, including S100A proteins, ribosomal proteins, and histone H1 proteins. Such changes impeded the viability and migration of esophageal cells, which could adversely affect wound healing of the epithelium. These cellular responses indicate that B[a]P will cause serious cellular damage to esophageal cells and increase the carcinogenic risk even as a result of short-term exposure. SynopsisOur omics study demonstrated how benzo[a]pyrene hampered the migration of esophageal cells and proposed a plausible mechanism underlying its carcinogenicity, which may contribute to our understanding of environmental pollutants.

Integrated Multi-Omics Investigations of Metalloproteinases in Colon Cancer: Focus on MMP2 and MMP9.

Colorectal cancer (CRC) develops by genetic and epigenetic alterations. However, the molecular mechanisms underlying metastatic dissemination remain unclear and could benefit from multi-omics investigations of specific protein families. Matrix metalloproteinases (MMPs) are proteolytic enzymes involved in ECM remodeling and the processing of bioactive molecules. Increased MMP expression promotes the hallmarks of tumor progression, including angiogenesis, invasion, and metastasis, and is correlated with a shortened survival. Nevertheless, the collective role and the possible coordination of MMP members in CRC are poorly investigated. Here, we performed a multi-omics analysis of MMP expression in CRC using data mining and experimental investigations. Several databases were used to deeply mine different expressions between tumor and normal tissues, the genetic and epigenetic alterations, the prognostic value as well as the interrelationships with tumor immune-infiltrating cells (TIICs). A special focus was placed on to MMP2 and MMP9: their expression was correlated with immune markers and the interaction network of co-expressed genes disclosed their implication in epithelial to mesenchymal transition (EMT) and immune response. Finally, the activity levels of MMP2 and MMP9 in a cohort of colon cancer samples, including tissues and the corresponding sera, was also investigated by zymography. Our findings suggested that MMPs could have a high potency, as they are targeted in colon cancer, and might serve as novel biomarkers, especially for their involvement in the immune response. However, further studies are needed to explore the detailed biological functions and molecular mechanisms of MMPs in CRC, also in consideration of their expression and different regulation in several tissues.

Efficient generative modeling of protein sequences using simple autoregressive models

Generative models emerge as promising candidates for novel sequence-data driven approaches to protein design, and for the extraction of structural and functional information about proteins deeply hidden in rapidly growing sequence databases. Here we propose simple autoregressive models as highly accurate but computationally efficient generative sequence models. We show that they perform similarly to existing approaches based on Boltzmann machines or deep generative models, but at a substantially lower computational cost (by a factor between 102 and 103). Furthermore, the simple structure of our models has distinctive mathematical advantages, which translate into an improved applicability in sequence generation and evaluation. Within these models, we can easily estimate both the probability of a given sequence, and, using the model’s entropy, the size of the functional sequence space related to a specific protein family. In the example of response regulators, we find a huge number of ca. 1068 possible sequences, which nevertheless constitute only the astronomically small fraction 10−80 of all amino-acid sequences of the same length. These findings illustrate the potential and the difficulty in exploring sequence space via generative sequence models.

L-Type Calcium Channel: Predicting Pathogenic/Likely Pathogenic Status for Variants of Uncertain Clinical Significance.

(1) Background: Defects in gene CACNA1C, which encodes the pore-forming subunit of the human Cav1.2 channel (hCav1.2), are associated with cardiac disorders such as atrial fibrillation, long QT syndrome, conduction disorders, cardiomyopathies, and congenital heart defects. Clinical manifestations are known only for 12% of CACNA1C missense variants, which are listed in public databases. Bioinformatics approaches can be used to predict the pathogenic/likely pathogenic status for variants of uncertain clinical significance. Choosing a bioinformatics tool and pathogenicity threshold that are optimal for specific protein families increases the reliability of such predictions. (2) Methods and Results: We used databases ClinVar, Humsavar, gnomAD, and Ensembl to compose a dataset of pathogenic/likely pathogenic and benign variants of hCav1.2 and its 20 paralogues: voltage-gated sodium and calcium channels. We further tested the performance of sixteen in silico tools in predicting pathogenic variants. ClinPred demonstrated the best performance, followed by REVEL and MCap. In the subset of 309 uncharacterized variants of hCav1.2, ClinPred predicted the pathogenicity for 188 variants. Among these, 36 variants were also categorized as pathogenic/likely pathogenic in at least one paralogue of hCav1.2. (3) Conclusions: The bioinformatics tool ClinPred and the paralogue annotation method consensually predicted the pathogenic/likely pathogenic status for 36 uncharacterized variants of hCav1.2. An analogous approach can be used to classify missense variants of other calcium channels and novel variants of hCav1.2.

Uncovering of cytochrome P450 anatomy by SecStrAnnotator

Protein structural families are groups of homologous proteins defined by the organization of secondary structure elements (SSEs). Nowadays, many families contain vast numbers of structures, and the SSEs can help to orient within them. Communities around specific protein families have even developed specialized SSE annotations, always assigning the same name to the equivalent SSEs in homologous proteins. A detailed analysis of the groups of equivalent SSEs provides an overview of the studied family and enriches the analysis of any particular protein at hand. We developed a workflow for the analysis of the secondary structure anatomy of a protein family. We applied this analysis to the model family of cytochromes P450 (CYPs)—a family of important biotransformation enzymes with a community-wide used SSE annotation. We report the occurrence, typical length and amino acid sequence for the equivalent SSE groups, the conservation/variability of these properties and relationship to the substrate recognition sites. We also suggest a generic residue numbering scheme for the CYP family. Comparing the bacterial and eukaryotic part of the family highlights the significant differences and reveals a well-known anomalous group of bacterial CYPs with some typically eukaryotic features. Our workflow for SSE annotation for CYP and other families can be freely used at address https://sestra.ncbr.muni.cz.