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The recent assembly and annotation of the 26 maize nested association mapping (NAM) population founder inbreds have enabled large-scale pan-genomic comparative studies. These studies have expanded our understanding of agronomically important traits by integrating pan-transcriptomic data with trait-specific gene candidates from previous association mapping results. In contrast to the availability of pan-transcriptomic data, obtaining reliable protein-protein interaction (PPI) data has remained a challenge due to its high cost and complexity. We generated predicted PPI networks for each of the 26 genomes using the established STRING database. The individual genome-interactomes were then integrated to generate core- and pan-interactomes. We deployed the PPI clustering algorithm ClusterONE to identify numerous PPI clusters that were functionally annotated using gene ontology (GO) functional enrichment, demonstrating a diverse range of enriched GO terms across different clusters. Additional cluster annotations were generated by integrating gene co-expression data and gene description annotations, providing additional useful information. We show that the functionally annotated PPI clusters establish a useful framework for protein function prediction and prioritization of candidate genes of interest. Our study not only provides a comprehensive resource of predicted PPI networks for 26 maize genomes, but also offers annotated interactome clusters for predicting protein functions and prioritizing gene candidates. The source code for the Python implementation of the analysis workflow and a standalone web application for accessing the analysis results are available at https://github.com/eporetsky/PanPPI.

Randomly barcoded transposon mutant libraries are powerful tools for studying gene function and organization, assessing gene essentiality and pathways, discovering potential therapeutic targets, and understanding the physiology of gut bacteria and their interactions with the host. However, construction of high-quality libraries with uniform representation can be challenging. In this review, we survey various strategies for barcoded library construction, including transposition systems, methods of transposon delivery, optimal library size, and transconjugant selection schemes. We discuss the advantages and limitations of each approach, as well as factors to consider when selecting a strategy. In addition, we highlight experimental and computational advances in arraying condensed libraries from mutant pools. We focus on examples of successful library construction in gut bacteria and their application to gene function studies and drug discovery. Given the need for understanding gene function and organization in gut bacteria, we provide a comprehensive guide for researchers to construct randomly barcoded transposon mutant libraries.

During de novo emergence, new protein coding genes emerge from previously nongenic sequences. The de novo proteins they encode are dissimilar in composition and predicted biochemical properties to conserved proteins. However, functional de novo proteins indeed exist. Both identification of functional de novo proteins and their structural characterization are experimentally laborious. To identify functional and structured de novo proteins in silico, we applied recently developed machine learning based tools and found that most de novo proteins are indeed different from conserved proteins both in their structure and sequence. However, some de novo proteins are predicted to adopt known protein folds, participate in cellular reactions, and to form biomolecular condensates. Apart from broadening our understanding of de novo protein evolution, our study also provides a large set of testable hypotheses for focused experimental studies on structure and function of de novo proteins in Drosophila. [ABSTRACT FROM AUTHOR]

Galectins (Gals) are a type of S-type lectin that are widespread and evolutionarily conserved among metazoans, and can act as pattern recognition receptors (PRRs) to recognize pathogen-associated molecular patterns (PAMPs). In this study, 10 Gals (ToGals) were identified in the Golden pompano (Trachinotus ovatus), and their conserved domains, motifs, and collinearity relationships were analyzed. The expression of ToGals was regulated following infection to Cryptocaryon irritans and Streptococcus agalactiae, indicating that ToGals participate in immune responses against microbial pathogens. Further analysis was conducted on one important member, Galectin-3, subcellular localization showing that ToGal-3like protein is expressed both in the nucleus and cytoplasm. Recombinant protein obtained through prokaryotic expression showed that rToGal-3like can agglutinate red blood cells of rabbit, carp and golden pompano and also agglutinate and kill Staphylococcus aureus, Bacillus subtilis, Vibrio vulnificus, S. agalactiae, Pseudomonas aeruginosa, and Aeromonas hydrophila. This study lays the foundation for further research on the immune roles of Gals in teleosts. [ABSTRACT FROM AUTHOR]

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Abstract The identification of protein binding residues helps to understand their biological processes as protein function is often defined through ligand binding, such as to other proteins, small molecules, ions, or nucleotides. Methods predicting binding residues often err for intrinsically disordered proteins or regions (IDPs/IDPRs), often also referred to as molecular recognition features (MoRFs). Here, we presented a novel machine learning (ML) model trained to specifically predict binding regions in IDPRs. The proposed model, IDBindT5, leveraged embeddings from the protein language model (pLM) ProtT5 to reach a balanced accuracy of 57.2 ± 3.6% (95% confidence interval). Assessed on the same data set, this did not differ at the 95% CI from the state-of-the-art (SOTA) methods ANCHOR2 and DeepDISOBind that rely on expert-crafted features and evolutionary information from multiple sequence alignments (MSAs). Assessed on other data, methods such as SPOT-MoRF reached higher MCCs. IDBindT5’s SOTA predictions are much faster than other methods, easily enabling full-proteome analyses. Our findings emphasize the potential of pLMs as a promising approach for exploring and predicting features of disordered proteins. The model and a comprehensive manual are publicly available at https://github.com/jahnl/binding_in_disorder .

Abstract Background In last two decades, the use of high-throughput sequencing technologies has accelerated the pace of discovery of proteins. However, due to the time and resource limitations of rigorous experimental functional characterization, the functions of a vast majority of them remain unknown. As a result, computational methods offering accurate, fast and large-scale assignment of functions to new and previously unannotated proteins are sought after. Leveraging the underlying associations between the multiplicity of features that describe proteins could reveal functional insights into the diverse roles of proteins and improve performance on the automatic function prediction task. Results We present GO-LTR, a multi-view multi-label prediction model that relies on a high-order tensor approximation of model weights combined with non-linear activation functions. The model is capable of learning high-order relationships between multiple input views representing the proteins and predicting high-dimensional multi-label output consisting of protein functional categories. We demonstrate the competitiveness of our method on various performance measures. Experiments show that GO-LTR learns polynomial combinations between different protein features, resulting in improved performance. Additional investigations establish GO-LTR’s practical potential in assigning functions to proteins under diverse challenging scenarios: very low sequence similarity to previously observed sequences, rarely observed and highly specific terms in the gene ontology. Implementation The code and data used for training GO-LTR is available at https://github.com/aalto-ics-kepaco/GO-LTR-prediction .

Polyglutamine (polyQ) disorders are a group of neurodegenerative diseases characterized by the excessive expansion of CAG (cytosine, adenine, guanine) repeats within host proteins. The quest to unravel the complex diseases mechanism has led researchers to adopt both theoretical and experimental methods, each offering unique insights into the underlying pathogenesis. This review emphasizes the significance of combining multiple approaches in the study of polyQ disorders, focusing on the structure–function correlations and the relevance of polyQ-related protein dynamics in neurodegeneration. By integrating computational/theoretical predictions with experimental observations, one can establish robust structure–function correlations, aiding in the identification of key molecular targets for therapeutic interventions. PolyQ proteins' dynamics, influenced by their length and interactions with other molecular partners, play a pivotal role in the polyQ-related pathogenic cascade. Moreover, conformational dynamics of polyQ proteins can trigger aggregation, leading to toxic assembles that hinder proper cellular homeostasis. Understanding these intricacies offers new avenues for therapeutic strategies by fine-tuning polyQ kinetics, in order to prevent and control disease progression. Last but not least, this review highlights the importance of integrating multidisciplinary efforts to advancing research in this field, bringing us closer to the ultimate goal of finding effective treatments against polyQ disorders. [ABSTRACT FROM AUTHOR]

According to the extensive distribution of GYF domain proteins and the diversity of its functions, it is necessary to study GYF domain proteins further and conduct evolutionary analysis. Since AtEXA1 is the most well-studied GYF domain protein in plants, this study mainly focuses on the proteins similar to AtEXA1. Analysis of 8 053 GYF domain proteins in eukaryotes ranging from unicellular lower fungi to higher organisms showed that AtEXA1-like proteins are mainly found in plant species, while GYF proteins from animals have low similarity to AtEXA1. It was also found that AtEXA1-like proteins are present in almost all agricultural crops. This study demonstrates the conservation of all GYF domain proteins, initially deconstructs the evolution of GYF domain proteins, and provides implications for subsequent studies on the functional characteristics of GYF domain proteins. [ABSTRACT FROM AUTHOR]

Computational methods have significantly transformed biomedical research, offering a comprehensive exploration of disease mechanisms and molecular protein functions. This article reviews a spectrum of computational tools and network analysis databases that play a crucial role in identifying potential interactions and signaling networks contributing to the onset of disease states. The utilization of protein/gene interaction and genetic variation databases, coupled with pathway analysis can facilitate the identification of potential drug targets. By bridging the gap between molecular-level information and disease understanding, this review contributes insights into the impactful utilization of computational methods, paving the way for targeted interventions and therapeutic advancements in biomedical research. [ABSTRACT FROM AUTHOR]

Background: In last two decades, the use of high-throughput sequencing technologies has accelerated the pace of discovery of proteins. However, due to the time and resource limitations of rigorous experimental functional characterization, the functions of a vast majority of them remain unknown. As a result, computational methods offering accurate, fast and large-scale assignment of functions to new and previously unannotated proteins are sought after. Leveraging the underlying associations between the multiplicity of features that describe proteins could reveal functional insights into the diverse roles of proteins and improve performance on the automatic function prediction task. Results: We present GO-LTR, a multi-view multi-label prediction model that relies on a high-order tensor approximation of model weights combined with non-linear activation functions. The model is capable of learning high-order relationships between multiple input views representing the proteins and predicting high-dimensional multi-label output consisting of protein functional categories. We demonstrate the competitiveness of our method on various performance measures. Experiments show that GO-LTR learns polynomial combinations between different protein features, resulting in improved performance. Additional investigations establish GO-LTR's practical potential in assigning functions to proteins under diverse challenging scenarios: very low sequence similarity to previously observed sequences, rarely observed and highly specific terms in the gene ontology. Implementation: The code and data used for training GO-LTR is available at https://github.com/aalto-ics-kepaco/GO-LTR-prediction. [ABSTRACT FROM AUTHOR]

A protein's sequence determines its conformational energy landscape. This, in turn, determines the protein's function. Understanding the evolution of new protein functions therefore requires understanding how mutations alter the protein energy landscape. Ancestral sequence reconstruction (ASR) has proven a valuable tool for tackling this problem. In ASR, one phylogenetically infers the sequences of ancient proteins, allowing characterization of their properties. When coupled to biophysical, biochemical, and functional characterization, ASR can reveal how historical mutations altered the energy landscape of ancient proteins, allowing the evolution of enzyme activity, altered conformations, binding specificity, oligomerization, and many other protein features. In this article, we review how ASR studies have been used to dissect the evolution of energy landscapes. We also discuss ASR studies that reveal how energy landscapes have shaped protein evolution. Finally, we propose that thinking about evolution from the perspective of an energy landscape can improve how we approach and interpret ASR studies. [ABSTRACT FROM AUTHOR]

The formation of a heterogeneous set of advanced glycation end products (AGEs) is the final outcome of a non-enzymatic process that occurs in vivo on long-life biomolecules. This process, known as glycation, starts with the reaction between reducing sugars, or their autoxidation products, with the amino groups of proteins, DNA, or lipids, thus gaining relevance under hyperglycemic conditions. Once AGEs are formed, they might affect the biological function of the biomacromolecule and, therefore, induce the development of pathophysiological events. In fact, the accumulation of AGEs has been pointed as a triggering factor of obesity, diabetes-related diseases, coronary artery disease, neurological disorders, or chronic renal failure, among others. Given the deleterious consequences of glycation, evolution has designed endogenous mechanisms to undo glycation or to prevent it. In addition, many exogenous molecules have also emerged as powerful glycation inhibitors. This review aims to provide an overview on what glycation is. It starts by explaining the similarities and differences between glycation and glycosylation. Then, it describes in detail the molecular mechanism underlying glycation reactions, and the bio-molecular targets with higher propensity to be glycated. Next, it discusses the precise effects of glycation on protein structure, function, and aggregation, and how computational chemistry has provided insights on these aspects. Finally, it reports the most prevalent diseases induced by glycation, and the endogenous mechanisms and the current therapeutic interventions against it. [ABSTRACT FROM AUTHOR]

Clostridium botulinum strain Hall produces potent botulinum neurotoxin type A1, which causes food-borne, infant, and wound botulism in humans. Antibiotics and botulinum antitoxins can control growth and prevent botulinum toxicity. However, limited information on a protein with an unknown function hinders the discovery of new drug targets for this disease. In this study, a combined bioinformatics approach with literature support was applied to predict, assign, and validate operome functions. Our functional annotation scheme was based on sequence motifs, conserved domains, structures, protein folds, and evolutionary relationships. Approximately 14.62 % of the operome exhibited sequence similarity to known proteins, with 6.65 % predicted functions for 293 proteins, including 121 proteins exclusive to C. botulinum. Structural analysis revealed a significant presence of the Rossmann fold (26 %) and miscellaneous folds (43 %) among the operome. Transporters (>85) and transcriptional regulators (>45) were prevalent, underscoring their importance in C. botulinum adaptive strategies. The newly identified operome contributed to the diverse cellular and metabolic processes of this organism. The function of its operome was involved in amino acid metabolism and botulinum neurotoxin biosynthesis. In this study, we identified and characterized 13 new virulence proteins from the operome to determine their structure–function relationships. These new metabolic and virulence proteins allow the organism to colonize and interact with the human gastrointestinal tract. This study provides a quest for new drugs and targets for treating the underlying diseases of C. botulinum in humans.

Disordered linkers (DLs) are intrinsically disordered regions that facilitate movement between adjacent functional regions/domains, contributing to many key cellular functions. The recently completed second Critical Assessments of protein Intrinsic Disorder prediction (CAID2) experiment evaluated DL predictions by considering a rather narrow scenario when predicting 40 proteins that are already known to have DLs. We expand this evaluation by using a much larger set of nearly 350 test proteins from CAID2 and by investigating three distinct scenarios: (1) prediction residues in DLs vs. in non-DL regions (typical use of DL predictors); (2) prediction of residues in DLs vs. other disordered residues (to evaluate whether predictors can differentiate residues in DLs from other types of intrinsically disordered residues); and (3) prediction of proteins harboring DLs. We find that several methods provide relatively accurate predictions of DLs in the first scenario. However, only one method, APOD, accurately identifies DLs among other types of disordered residues (scenario 2) and predicts proteins harboring DLs (scenario 3). We also find that APOD's predictive performance is modest, motivating further research into the development of new and more accurate DL predictors. We note that these efforts will benefit from a growing amount of training data and the availability of sophisticated deep network models and emphasize that future methods should provide accurate results across the three scenarios. [ABSTRACT FROM AUTHOR]

Proteins play an important role in life activities and are the basic units for performing functions. Accurately annotating functions to proteins is crucial for understanding the intricate mechanisms of life and developing effective treatments for complex diseases. Traditional biological experiments struggle to keep pace with the growing number of known proteins. With the development of high-throughput sequencing technology, a wide variety of biological data provides the possibility to accurately predict protein functions by computational methods. Consequently, many computational methods have been proposed. Due to the diversity of application scenarios, it is necessary to conduct a comprehensive evaluation of these computational methods to determine the suitability of each algorithm for specific cases. In this study, we present a comprehensive benchmark, BeProf, to process data and evaluate representative computational methods. We first collect the latest datasets and analyze the data characteristics. Then, we investigate and summarize 17 state-of-the-art computational methods. Finally, we propose a novel comprehensive evaluation metric, design eight application scenarios and evaluate the performance of existing methods on these scenarios. Based on the evaluation, we provide practical recommendations for different scenarios, enabling users to select the most suitable method for their specific needs. All of these servers can be obtained from https://csuligroup.com/BEPROF and https://github.com/CSUBioGroup/BEPROF. [ABSTRACT FROM AUTHOR]

Porcine epidemic diarrhea (PED) virus (PEDV) is one of the main pathogens causing diarrhea in piglets and fattening pigs. The clinical signs of PED are vomiting, acute diarrhea, dehydration, and mortality resulting in significant economic losses and becoming a major challenge in the pig industry. PEDV possesses various crucial structural and functional proteins, which play important roles in viral structure, infection, replication, assembly, and release, as well as in escaping host innate immunity. Over the past few years, there has been progress in the study of PEDV pathogenesis, revealing the crucial role of the interaction between PEDV viral proteins and host cytokines in PEDV infection. At present, the main control measure against PEDV is vaccine immunization of sows, but the protective effect for emerging virus strains is still insufficient, and there is no ideal safe and efficient vaccine. Although scientists have persistently delved their research into the intricate structure and functionalities of the PEDV genome and viral proteins for years, the pathogenic mechanism of PEDV remains incompletely elucidated. Here, we focus on reviewing the research progress of PEDV structural and nonstructural proteins to facilitate the understanding of biological processes such as PEDV infection and pathogenesis. [ABSTRACT FROM AUTHOR]

Substitution models of evolution are necessary for diverse evolutionary analyses including phylogenetic tree and ancestral sequence reconstructions. At the protein level, empirical substitution models are traditionally used due to their simplicity, but they ignore the variability of substitution patterns among protein sites. Next, in order to improve the realism of the modeling of protein evolution, a series of structurally constrained substitution models were presented, but still they usually ignore constraints on the protein activity. Here, we present a substitution model of protein evolution with selection on both protein structure and enzymatic activity, and that can be applied to phylogenetics. In particular, the model considers the binding affinity of the enzyme–substrate complex as well as structural constraints that include the flexibility of structural flaps, hydrogen bonds, amino acids backbone radius of gyration, and solvent-accessible surface area that are quantified through molecular dynamics simulations. We applied the model to the HIV-1 protease and evaluated it by phylogenetic likelihood in comparison with the best-fitting empirical substitution model and a structurally constrained substitution model that ignores the enzymatic activity. We found that accounting for selection on the protein activity improves the fitting of the modeled functional regions with the real observations, especially in data with high molecular identity, which recommends considering constraints on the protein activity in the development of substitution models of evolution. [ABSTRACT FROM AUTHOR]

Abstract Gain-of-function (GOF) variants give rise to increased/novel protein functions whereas loss-of-function (LOF) variants lead to diminished protein function. Experimental approaches for identifying GOF and LOF are generally slow and costly, whilst available computational methods have not been optimized to discriminate between GOF and LOF variants. We have developed LoGoFunc, a machine learning method for predicting pathogenic GOF, pathogenic LOF, and neutral genetic variants, trained on a broad range of gene-, protein-, and variant-level features describing diverse biological characteristics. LoGoFunc outperforms other tools trained solely to predict pathogenicity for identifying pathogenic GOF and LOF variants and is available at https://itanlab.shinyapps.io/goflof/ .

Membrane proteins are critical mediators for tumor progression and present enormous therapeutic potentials. Although gene profiling can identify their cancer-specific signatures, systematic correlations between protein functions and tumor-related mechanisms are still unclear. We present here the CrMP-Sol database (https://bio-gateway.aigene.org.cn/g/CrMP), which aims to breach the gap between the two. Machine learning was used to extract key functional descriptions for protein visualization in the 3D-space, where spatial distributions provide function-based predictive connections between proteins and cancer types. CrMP-Sol also presents QTY-enabled water-soluble designs to facilitate native membrane protein studies despite natural hydrophobicity. Five examples with varying transmembrane helices in different categories were used to demonstrate the feasibility. Native and redesigned proteins exhibited highly similar characteristics, predicted structures and binding pockets, and slightly different docking poses against known ligands, although task-specific designs are still required for proteins more susceptible to internal hydrogen bond formations. The database can accelerate therapeutic developments and biotechnological applications of cancer-related membrane proteins. [ABSTRACT FROM AUTHOR]

Simple Summary: In recent years, grouper industry has been severely devastated by vibriosis. The present study explored the function of IL-15Rα (α Chain of Interleukin-15 Receptor) response to Vibrio harveyi infection from mRNA and protein level, respectively. We concluded that: a) at the mRNA level, IL-15Rα was significantly upregulated in each tissue after V. harveyi infection of grouper, as well as in HKL cells after stimulated by pathogenic stimuli. b) at the protein level, IL-15Rα can increase the cytokine expression level and promote the proliferation of HKL cells, and can also improve the survival of groupers after V. harveyi infection. Our study contributes to a better understanding of the immune responses of Ec-IL15Rα (Epinephelus coioides IL15Rα) in preventing Vibrio infection in groupers. Interleukin-15 (IL15) is a proinflammatory cytokine that could induce the production of inflammatory cytokines. In this study, the α chain of the IL15 receptor of Epinephelus coioides (Ec-IL15Rα), a natural regulator of IL15, was identified, and immune response functions of fish were determined and characterized. Ec-IL15Rα contains a 720 bp open reading frame that encodes 239 amino acids, including four typical conserved cysteine residues with a highly conserved sushi domain. Ec-IL15Rα is closely related to Epinephelus lanceolatus and is the most clustered with teleost. Subcellular localization studies showed that Ec-IL15Rα was situated in the cytoplasm and cell membrane. Ec-IL15Rα was detected in 11 tissues, with the highest expression in the liver and blood. Meanwhile, the Ec-IL15Rα transcriptional levels substantially increased in nine tissues after Vibrio harveyi infection. Ec-IL15Rα was significantly up-regulated in HKLs by ConA, PHA, LPS and poly I:C stimulation. In vitro analysis, the recombinant protein of rEc-IL15Rα stimulates HKL proliferation and IL1R, IL6R, IL10, and IL16 expression. Challenge experiments revealed that IL15Rα protein showed an increase of 6.67–10% survival protection rate after V. harveyi infection. This study provides a better understanding of the immune protection of IL15Rα in vertebrate fish. [ABSTRACT FROM AUTHOR]

The function of protein is crucial for understanding the mechanisms of cellular and biological activities, as well as for studying the mechanisms of diseases. Traditional experimental and sequence alignment methods are insufficient to support large-scale protein functional annotation when in the face of the rapid growth of sequence databases. For this situation, this paper proposed EGnet（Evolutionary Graph Network） model, which utilized the protein pre-training language model ESM2 and One-hot encoding to obtain the protein sequence encoding. The model integrated the coevolutionary information between residues, including PI（Paired Interaction） and SPI（Strong Paired Interaction）, through sequence self-attention and physical calculations. Subsequently, the two types of coevolutionary information and the sequence encoding used in inputs for a multi-layered cascaded graph convolutional network to learn the node features of the sequence encoding and achieve end-to-end protein function prediction. Compared with earlier methods, EGnet achieved better performance on the EC（Enzyme Commission） category labels in the ENZYME database, which reached 0.89 in the F-score and 0.91 in the AUPR. The results indicate that EGnet can achieve good performance by using only a single sequence to predict protein function, providing a rapid and effective method for protein function annotation. [ABSTRACT FROM AUTHOR]

The Protein Kinase Ontology (ProKinO) is an integrated knowledge graph that conceptualizes the complex relationships among protein kinase sequence, structure, function, and disease in a human and machine-readable format. In this study, we have significantly expanded ProKinO by incorporating additional data on expression patterns and drug interactions. Furthermore, we have developed a completely new browser from the ground up to render the knowledge graph visible and interactive on the web. We have enriched ProKinO with new classes and relationships that capture information on kinase ligand binding sites, expression patterns, and functional features. These additions extend ProKinO's capabilities as a discovery tool, enabling it to uncover novel insights about understudied members of the protein kinase family. We next demonstrate the application of ProKinO. Specifically, through graph mining and aggregate SPARQL queries, we identify the p21-activated protein kinase 5 (PAK5) as one of the most frequently mutated dark kinases in human cancers with abnormal expression in multiple cancers, including a previously unappreciated role in acute myeloid leukemia. We have identified recurrent oncogenic mutations in the PAK5 activation loop predicted to alter substrate binding and phosphorylation. Additionally, we have identified common ligand/drug binding residues in PAK family kinases, underscoring ProKinO's potential application in drug discovery. The updated ontology browser and the addition of a web component, ProtVista, which enables interactive mining of kinase sequence annotations in 3D structures and Alphafold models, provide a valuable resource for the signaling community. The updated ProKinO database is accessible at https://prokino.uga.edu. [ABSTRACT FROM AUTHOR]

Gain-of-function (GOF) variants give rise to increased/novel protein functions whereas loss-of-function (LOF) variants lead to diminished protein function. Experimental approaches for identifying GOF and LOF are generally slow and costly, whilst available computational methods have not been optimized to discriminate between GOF and LOF variants. We have developed LoGoFunc, a machine learning method for predicting pathogenic GOF, pathogenic LOF, and neutral genetic variants, trained on a broad range of gene-, protein-, and variant-level features describing diverse biological characteristics. LoGoFunc outperforms other tools trained solely to predict pathogenicity for identifying pathogenic GOF and LOF variants and is available at https://itanlab.shinyapps.io/goflof/. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic measures forced students to stay home and confined them to remote learning. This had a large impact on laboratory experiments, which are often impossible to complete from home. This article is a resource for instructors/educators to introduce the topic of structural biology and crystallographic methods. The main focus is to describe a hands-on crystallization laboratory exercise that can be carried out remotely at home with safe household products. X-ray crystallography is a vital technique for determining protein structure and function. This information can be used to understand fundamental biological processes and to help in the design of life-saving medications. Here, a method was developed to teach crystallography using reagents and equipment that can be found in grocery stores. The steps involved in a crystallography experiment are detailed with links and references to additional resources. [ABSTRACT FROM AUTHOR]

Covalent attachment of the fatty acid palmitate or stearate to the cytoplasmic domain of viral glycoproteins is often crucial for viral replication. This has previously been studied for the hemagglutinin (HA) of influenza A virus and the responsible enzymes have been identified, but similar studies have not been performed with HA of influenza B virus, which contains palmitate linked to two cysteines. We show here that the modification is required for efficient virus replication since exchange of both cysteines or the cysteine located at the end of the cytoplasmic tail prevented the generation of viable viruses. Viruses with an exchange of the membrane-proximal cysteine rapidly reverted back to wild-type virus. Blocking exit of proteins from the endoplasmic reticulum (ER) revealed that palmitoylation of HA of influenza B virus occurs in the ER, whereas acylation of HA of influenza A virus occurs in the Golgi. Infecting cells deficient in Asp-His-His-Cys (DHHC) palmitoyltransferases revealed that HA of influenza B virus is acylated by the ER-localized DHHCs 1, 2, 4, and 6, which are thus different from the enzymes previously identified for acylation of HA of influenza A virus. A comparison of predicted and experimentally determined protein structures suggests that the exclusive acylation of the HA of influenza B virus with palmitate is not a function of the responsible DHHCs and that the transmembrane region may be critical for the acylation of HA of influenza A and B viruses by different DHHCs. IMPORTANCE Influenza viruses are a public health concern since they cause seasonal outbreaks and occasionally pandemics. Our study investigates the importance of a protein modification called “palmitoylation” in the replication of influenza B virus. Palmitoylation involves attaching fatty acids to the viral protein hemagglutinin and has previously been studied for influenza A virus. We found that this modification is important for the influenza B virus to replicate, as mutating the sites where palmitate is attached prevented the virus from generating viable particles. Our experiments also showed that this modification occurs in the endoplasmic reticulum. We identified the specific enzymes responsible for this modification, which are different from those involved in palmitoylation of HA of influenza A virus. Overall, our research illuminates the similarities and differences in fatty acid attachment to HA of influenza A and B viruses and identifies the responsible enzymes, which might be promising targets for anti-viral therapy. [ABSTRACT FROM AUTHOR]

Post-translational modifications (PTMs) are crucial regulatory mechanisms that alter the properties of a protein by covalently attaching a modified chemical group to some of its amino acid residues. PTMs modulate essential physiological processes such as signal transduction, metabolism, protein localization, and turnover and have clinical relevance in cancer and age-related pathologies. Majority of proteins undergo post-translational modifications, irrespective of their occurrence in or after protein biosynthesis. Post-translational modifications link to amino acid termini or side chains, causing the protein backbone to get cleaved, spliced, or cyclized, to name a few. These chemical modifications expand the diversity of the proteome and regulate protein activity, structure, locations, functions, and protein-protein interactions (PPIs). This ability to modify the physical and chemical properties and functions of proteins render PTMs vital. To date, over 200 different protein modifications have been reported, owing to advanced detection technologies. Some of these modifications include phosphorylation, glycosylation, methylation, acetylation, and ubiquitination. Here, we discuss about the existing as well as some novel post-translational protein modifications, with their implications in aberrant states, which will help us better understand the modified sites in different proteins and the effect of PTMs on protein functions in core biological processes and progression in cancer. [ABSTRACT FROM AUTHOR]

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

Abstract Membrane proteins are critical mediators for tumor progression and present enormous therapeutic potentials. Although gene profiling can identify their cancer-specific signatures, systematic correlations between protein functions and tumor-related mechanisms are still unclear. We present here the CrMP-Sol database ( https://bio-gateway.aigene.org.cn/g/CrMP ), which aims to breach the gap between the two. Machine learning was used to extract key functional descriptions for protein visualization in the 3D-space, where spatial distributions provide function-based predictive connections between proteins and cancer types. CrMP-Sol also presents QTY-enabled water-soluble designs to facilitate native membrane protein studies despite natural hydrophobicity. Five examples with varying transmembrane helices in different categories were used to demonstrate the feasibility. Native and redesigned proteins exhibited highly similar characteristics, predicted structures and binding pockets, and slightly different docking poses against known ligands, although task-specific designs are still required for proteins more susceptible to internal hydrogen bond formations. The database can accelerate therapeutic developments and biotechnological applications of cancer-related membrane proteins.

Abstract Background Although the genome of Saccharomyces cerevisiae (S. cerevisiae) was the first one of a eukaryote organism that was fully sequenced (in 1996), a complete understanding of the potential of encoded biomolecular mechanisms has not yet been achieved. Here, we wish to quantify how far the goal of a full list of S. cerevisiae gene functions still is. Results The scientific literature about S. cerevisiae protein-coding genes has been mapped onto the yeast genome via the mentioning of names for genomic regions in scientific publications. The match was quantified with the ratio of a given gene name’s occurrences to those of any gene names in the article. We find that ~ 230 elite genes with ≥ 75 full publication equivalents (FPEs, FPE = 1 is an idealized publication referring to just a single gene) command ~ 45% of all literature. At the same time, about two thirds of the genes (each with less than 10 FPEs) are described in just 12% of the literature (in average each such gene has just ~ 1.5% of the literature of an elite gene). About 600 genes have not been mentioned in any dedicated article. Compared with other groups of genes, the literature growth rates were highest for uncharacterized or understudied genes until late nineties of the twentieth century. Yet, these growth rates deteriorated and became negative thereafter. Thus, yeast function discovery for previously uncharacterized genes has returned to the level of ~ 1980. At the same time, literature for anyhow well-studied genes (with a threshold T10 (≥ 10 FPEs) and higher) remains steadily growing. Conclusions Did the early full genome sequencing of yeast boost gene function discovery? The data proves that the moment of publishing the full genome in reality coincides with the onset of decline of gene function discovery for previously uncharacterized genes. If the current status of literature about yeast molecular mechanisms can be extrapolated into the future, it will take about another ~ 50 years to complete the yeast gene function list. We found that a small group of scientific journals contributed extraordinarily to publishing early reports relevant to yeast gene function discoveries.

ABSTRACT With the advance of high-throughput sequencing, the molecular basis of coevolutionary interaction between viruses and host microorganisms is predominantly elucidated by in silico genomic analyses, which revealed potential communication of genetic materials related to microbial immune systems such as the restriction-modification (R-M) system. However, the sequence-dependent information is often insufficient to output a conclusive argument without biochemical characterizations, particularly for homologs of rare genes considered less accurately annotated. We proposed a 1-day and one-pot workflow covering in vitro protein synthesis and enzymatic assays to confirm the exact function of putative R-M genes only with manual pipetting operations of microliter-scale liquids. The proof-of-demonstration experiments mainly focused on a series of putative R-M enzymes from our recently found deep-sea temperate bacteriophage and its host bacterium. Two new restriction endonucleases and two new methyltransferases with respective unambiguous substrate specificities, superior catalytic performance, or unique sequence preferences were quickly identified. A frequent discrepancy between sequence similarity search and single-molecule methylation-sensitive sequencing toward the prediction of recognition motifs can get settled with the established direct biochemical characterization. The proposed approach under the cell-free one-pot concept allows for preliminary characterizations of diverse categories (e.g., Types I, II, and III) of putative R-M systems at most laboratories with minimum equipment and time costs.IMPORTANCEThe elucidation of the molecular basis of virus-host coevolutionary interactions is boosted with state-of-the-art sequencing technologies. However, the sequence-only information is often insufficient to output a conclusive argument without biochemical characterizations. We proposed a 1-day and one-pot approach to confirm the exact function of putative restriction-modification (R-M) genes that presumably mediate microbial coevolution. The experiments mainly focused on a series of putative R-M enzymes from a deep-sea virus and its host bacterium. The results quickly unveiled unambiguous substrate specificities, superior catalytic performance, and unique sequence preferences for two new restriction enzymes (capable of cleaving DNA) and two new methyltransferases (capable of modifying DNA with methyl groups). The reality of the functional R-M system reinforced a model of mutually beneficial interactions with the virus in the deep-sea microbial ecosystem. The cell culture-independent approach also holds great potential for exploring novel and biotechnologically significant R-M enzymes from microbial dark matter.

The Protein Kinase Ontology (ProKinO) is an integrated knowledge graph that conceptualizes the complex relationships among protein kinase sequence, structure, function, and disease in a human and machine-readable format. In this study, we have significantly expanded ProKinO by incorporating additional data on expression patterns and drug interactions. Furthermore, we have developed a completely new browser from the ground up to render the knowledge graph visible and interactive on the web. We have enriched ProKinO with new classes and relationships that capture information on kinase ligand binding sites, expression patterns, and functional features. These additions extend ProKinO’s capabilities as a discovery tool, enabling it to uncover novel insights about understudied members of the protein kinase family. We next demonstrate the application of ProKinO. Specifically, through graph mining and aggregate SPARQL queries, we identify the p21-activated protein kinase 5 (PAK5) as one of the most frequently mutated dark kinases in human cancers with abnormal expression in multiple cancers, including a previously unappreciated role in acute myeloid leukemia. We have identified recurrent oncogenic mutations in the PAK5 activation loop predicted to alter substrate binding and phosphorylation. Additionally, we have identified common ligand/drug binding residues in PAK family kinases, underscoring ProKinO’s potential application in drug discovery. The updated ontology browser and the addition of a web component, ProtVista, which enables interactive mining of kinase sequence annotations in 3D structures and Alphafold models, provide a valuable resource for the signaling community. The updated ProKinO database is accessible at https://prokino.uga.edu.

The development of aging is associated with the disruption of key cellular processes manifested as well-established hallmarks of aging. Intrinsically disordered proteins (IDPs) and intrinsically disordered regions (IDRs) have no stable tertiary structure that provide them a power to be configurable hubs in signaling cascades and regulate many processes, potentially including those related to aging. There is a need to clarify the roles of IDPs/IDRs in aging. The dataset of 1702 aging-related proteins was collected from established aging databases and experimental studies. There is a noticeable presence of IDPs/IDRs, accounting for about 36% of the aging-related dataset, which is however less than the disorder content of the whole human proteome (about 40%). A Gene Ontology analysis of the used here aging proteome reveals an abundance of IDPs/IDRs in one-third of aging-associated processes, especially in genome regulation. Signaling pathways associated with aging also contain IDPs/IDRs on different hierarchical levels, revealing the importance of "structure-function continuum" in aging. Protein–protein interaction network analysis showed that IDPs present in different clusters associated with different aging hallmarks. Protein cluster with IDPs enrichment has simultaneously high liquid–liquid phase separation (LLPS) probability, “nuclear” localization and DNA-associated functions, related to aging hallmarks: genomic instability, telomere attrition, epigenetic alterations, and stem cells exhaustion. Intrinsic disorder, LLPS, and aggregation propensity should be considered as features that could be markers of pathogenic proteins. Overall, our analyses indicate that IDPs/IDRs play significant roles in aging-associated processes, particularly in the regulation of DNA functioning. IDP aggregation, which can lead to loss of function and toxicity, could be critically harmful to the cell. A structure-based analysis of aging and the identification of proteins that are particularly susceptible to disturbances can enhance our understanding of the molecular mechanisms of aging and open up new avenues for slowing it down. [ABSTRACT FROM AUTHOR]

Background: Although the genome of Saccharomyces cerevisiae (S. cerevisiae) was the first one of a eukaryote organism that was fully sequenced (in 1996), a complete understanding of the potential of encoded biomolecular mechanisms has not yet been achieved. Here, we wish to quantify how far the goal of a full list of S. cerevisiae gene functions still is. Results: The scientific literature about S. cerevisiae protein-coding genes has been mapped onto the yeast genome via the mentioning of names for genomic regions in scientific publications. The match was quantified with the ratio of a given gene name's occurrences to those of any gene names in the article. We find that ~ 230 elite genes with ≥ 75 full publication equivalents (FPEs, FPE = 1 is an idealized publication referring to just a single gene) command ~ 45% of all literature. At the same time, about two thirds of the genes (each with less than 10 FPEs) are described in just 12% of the literature (in average each such gene has just ~ 1.5% of the literature of an elite gene). About 600 genes have not been mentioned in any dedicated article. Compared with other groups of genes, the literature growth rates were highest for uncharacterized or understudied genes until late nineties of the twentieth century. Yet, these growth rates deteriorated and became negative thereafter. Thus, yeast function discovery for previously uncharacterized genes has returned to the level of ~ 1980. At the same time, literature for anyhow well-studied genes (with a threshold T10 (≥ 10 FPEs) and higher) remains steadily growing. Conclusions: Did the early full genome sequencing of yeast boost gene function discovery? The data proves that the moment of publishing the full genome in reality coincides with the onset of decline of gene function discovery for previously uncharacterized genes. If the current status of literature about yeast molecular mechanisms can be extrapolated into the future, it will take about another ~ 50 years to complete the yeast gene function list. We found that a small group of scientific journals contributed extraordinarily to publishing early reports relevant to yeast gene function discoveries. [ABSTRACT FROM AUTHOR]

The paper presents the effect of heat treatment at 80 °C at different times (3, 5, 7, and 9 min) on the structural and functional properties of Phaseolus vulgaris L. protein (PVP, bean protein powder). Surface and structure properties of PVP after heat treatment were analyzed using a Fourier transform infrared spectrometer (FTIR), a fluorescence spectrophotometer, a visible light spectrophotometer, a laser particle size analyzer, and other equipment. The secondary structure and surface hydrophobicity (H0) of PVP changed significantly after heat treatment: the β-sheet content decreased from 25.32 ± 0.09% to 24.66 ± 0.09%, the random coil content increased from 23.91 ± 0.11% to 25.68 ± 0.08%, and the H0 rose by 28.96–64.99%. In addition, the functional properties of PVP after heat treatment were analyzed. After heat treatment, the emulsifying activity index (EAI) of PVP increased from 78.52 ± 2.01 m2/g to 98.21 ± 1.33 m2/g, the foaming ability (FA) improved from 87.31 ± 2.56% to 95.82 ± 2.96%, and the foam stability (FS) rose from 53.23 ± 1.72% to 58.71 ± 2.18%. Finally, the degree of hydrolysis (DH) of PVP after gastrointestinal simulated digestion in vitro was detected by the Ortho-Phthal (OPA) method. Heat treatment enhanced the DH of PVP from 62.34 ± 0.31% to 73.64 ± 0.53%. It was confirmed that heat treatment changed the structural properties of PVP and improved its foamability, emulsification, and digestibility. It provides ideas for improving PVP's potential and producing new foods with rich nutrition, multiple functions, and easy absorption. [ABSTRACT FROM AUTHOR]

The heme oxygenase-1 (HMOX1) gene is a very critical player in cell homeostasis and takes part in heme catabolism. The HMOX1 gene possesses antioxidant, antiapoptotic anti-inflammatory, and antithrombotic properties. This study aimed to identify the deleterious SNPs which may alter the functional and structural attributes of the HMOX1 protein.

Abstract Background Although Escherichia coli (E. coli) is the most studied prokaryote organism in the history of life sciences, many molecular mechanisms and gene functions encoded in its genome remain to be discovered. This work aims at quantifying the illumination of the E. coli gene function space by the scientific literature and how close we are towards the goal of a complete list of E. coli gene functions. Results The scientific literature about E. coli protein-coding genes has been mapped onto the genome via the mentioning of names for genomic regions in scientific articles both for the case of the strain K-12 MG1655 as well as for the 95%-threshold softcore genome of 1324 E. coli strains with known complete genome. The article match was quantified with the ratio of a given gene name’s occurrence to the mentioning of any gene names in the paper. The various genome regions have an extremely uneven literature coverage. A group of elite genes with ≥ 100 full publication equivalents (FPEs, FPE = 1 is an idealized publication devoted to just a single gene) attracts the lion share of the papers. For K-12, ~ 65% of the literature covers just 342 elite genes; for the softcore genome, ~ 68% of the FPEs is about only 342 elite gene families (GFs). We also find that most genes/GFs have at least one mentioning in a dedicated scientific article (with the exception of at least 137 protein-coding transcripts for K-12 and 26 GFs from the softcore genome). Whereas the literature growth rates were highest for uncharacterized or understudied genes until 2005–2010 compared with other groups of genes, they became negative thereafter. At the same time, literature for anyhow well-studied genes started to grow explosively with threshold T10 (≥ 10 FPEs). Typically, a body of ~ 20 actual articles generated over ~ 15 years of research effort was necessary to reach T10. Lineage-specific co-occurrence analysis of genes belonging to the accessory genome of E. coli together with genomic co-localization and sequence-analytic exploration hints previously completely uncharacterized genes yahV and yddL being associated with osmotic stress response/motility mechanisms. Conclusion If the numbers of scientific articles about uncharacterized and understudied genes remain at least at present levels, full gene function lists for the strain K-12 MG1655 and the E. coli softcore genome are in reach within the next 25–30 years. Once the literature body for a gene crosses 10 FPEs, most of the critical fundamental research risk appears overcome and steady incremental research becomes possible.

Disordered linkers (DLs) are intrinsically disordered regions that facilitate movement between adjacent functional regions/domains, contributing to many key cellular functions. The recently completed second Critical Assessments of protein Intrinsic Disorder prediction (CAID2) experiment evaluated DL predictions by considering a rather narrow scenario when predicting 40 proteins that are already known to have DLs. We expand this evaluation by using a much larger set of nearly 350 test proteins from CAID2 and by investigating three distinct scenarios: (1) prediction residues in DLs vs. in non-DL regions (typical use of DL predictors); (2) prediction of residues in DLs vs. other disordered residues (to evaluate whether predictors can differentiate residues in DLs from other types of intrinsically disordered residues); and (3) prediction of proteins harboring DLs. We find that several methods provide relatively accurate predictions of DLs in the first scenario. However, only one method, APOD, accurately identifies DLs among other types of disordered residues (scenario 2) and predicts proteins harboring DLs (scenario 3). We also find that APOD’s predictive performance is modest, motivating further research into the development of new and more accurate DL predictors. We note that these efforts will benefit from a growing amount of training data and the availability of sophisticated deep network models and emphasize that future methods should provide accurate results across the three scenarios.

Porcine epidemic diarrhea (PED) virus (PEDV) is one of the main pathogens causing diarrhea in piglets and fattening pigs. The clinical signs of PED are vomiting, acute diarrhea, dehydration, and mortality resulting in significant economic losses and becoming a major challenge in the pig industry. PEDV possesses various crucial structural and functional proteins, which play important roles in viral structure, infection, replication, assembly, and release, as well as in escaping host innate immunity. Over the past few years, there has been progress in the study of PEDV pathogenesis, revealing the crucial role of the interaction between PEDV viral proteins and host cytokines in PEDV infection. At present, the main control measure against PEDV is vaccine immunization of sows, but the protective effect for emerging virus strains is still insufficient, and there is no ideal safe and efficient vaccine. Although scientists have persistently delved their research into the intricate structure and functionalities of the PEDV genome and viral proteins for years, the pathogenic mechanism of PEDV remains incompletely elucidated. Here, we focus on reviewing the research progress of PEDV structural and nonstructural proteins to facilitate the understanding of biological processes such as PEDV infection and pathogenesis.

Post-translational modifications (PTMs) are crucial regulatory mechanisms that alter the properties of a protein by covalently attaching a modified chemical group to some of its amino acid residues. PTMs modulate essential physiological processes such as signal transduction, metabolism, protein localization, and turnover and have clinical relevance in cancer and age-related pathologies. Majority of proteins undergo post-translational modifications, irrespective of their occurrence in or after protein biosynthesis. Post-translational modifications link to amino acid termini or side chains, causing the protein backbone to get cleaved, spliced, or cyclized, to name a few. These chemical modifications expand the diversity of the proteome and regulate protein activity, structure, locations, functions, and protein-protein interactions (PPIs). This ability to modify the physical and chemical properties and functions of proteins render PTMs vital. To date, over 200 different protein modifications have been reported, owing to advanced detection technologies. Some of these modifications include phosphorylation, glycosylation, methylation, acetylation, and ubiquitination. Here, we discuss about the existing as well as some novel post-translational protein modifications, with their implications in aberrant states, which will help us better understand the modified sites in different proteins and the effect of PTMs on protein functions in core biological processes and progression in cancer.

In recent years, improvements in protein function prediction methods have led to increased success in annotating protein sequences. However, the functions of over 30% of protein-coding genes remain unknown for many sequenced genomes. Protein functions vary widely, from catalyzing chemical reactions to binding DNA or RNA or forming structures in the cell, and some types of functions are challenging to predict due to the physical features associated with those functions. Other complications in understanding protein functions arise due to the fact that many proteins have more than one function or very small differences in sequence or structure that correspond to different functions. We will discuss some of the recent developments in predicting protein functions and some of the remaining challenges.

In the era of constantly increasing amounts of the available protein data, a relevant and interpretable visualization becomes crucial, especially for tasks requiring human expertise. Poincaré disk projection has previously demonstrated its important efficiency for visualization of biological data such as single-cell RNAseq data. Here, we develop a new method PoincaréMSA for visual representation of complex relationships between protein sequences based on Poincaré maps embedding. We demonstrate its efficiency and potential for visualization of protein family topology as well as evolutionary and functional annotation of uncharacterized sequences. PoincaréMSA is implemented in open source Python code with available interactive Google Colab notebooks as described at https://www.dsimb.inserm.fr/POINCARE_MSA. [ABSTRACT FROM AUTHOR]

RNA molecules undergo a number of chemical modifications whose effects can alter their structure and molecular interactions. Previous studies have shown that RNA editing can impact the formation of ribonucleoprotein complexes and influence the assembly of membrane‐less organelles such as stress granules. For instance, N6‐methyladenosine (m6A) enhances SG formation and N1‐methyladenosine (m1A) prevents their transition to solid‐like aggregates. Yet, very little is known about adenosine to inosine (A‐to‐I) modification that is very abundant in human cells and not only impacts mRNAs but also noncoding RNAs. Here, we introduce the CROSSalive predictor of A‐to‐I effects on RNA structure based on high‐throughput in‐cell experiments. Our method shows an accuracy of 90% in predicting the single and double‐stranded content of transcripts and identifies a general enrichment of double‐stranded regions caused by A‐to‐I in long intergenic noncoding RNAs (lincRNAs). For the individual cases of NEAT1, NORAD, and XIST, we investigated the relationship between A‐to‐I editing and interactions with RNA‐binding proteins using available CLIP data and catRAPID predictions. We found that A‐to‐I editing is linked to the alteration of interaction sites with proteins involved in phase separation, which suggests that RNP assembly can be influenced by A‐to‐I. CROSSalive is available at http://service.tartaglialab.com/new_submission/crossalive. [ABSTRACT FROM AUTHOR]

Understanding the mitotic DNA damage response (DDR) is critical to our comprehension of cancer, premature aging and developmental disorders which are marked by DNA repair deficiencies. In this study we use a micro-focused laser to induce DNA damage in selected mitotic chromosomes to study the subsequent repair response. Our findings demonstrate that (1) mitotic cells are capable of DNA repair as evidenced by DNA synthesis at damage sites, (2) Repair is attenuated when DNA-PKcs and ATM are simultaneously compromised, (3) Laser damage may permit the observation of previously undetected DDR proteins when damage is elicited by other methods in mitosis, and (4) Twenty five percent of mitotic DNA-damaged cells undergo a subsequent mitosis. Together these findings suggest that mitotic DDR is more complex than previously thought and may involve factors from multiple repair pathways that are better understood in interphase.