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Proteins are essential macromolecules that carry out a plethora of biological functions. The thermal stability of proteins is an important property that affects their function and determines their suitability for various applications. However, current experimental approaches, primarily thermal proteome profiling, are expensive, labor-intensive, and have limited proteome and species coverage. To close the gap between available experimental data and sequence information, a novel protein thermal stability predictor called DeepSTABp has been developed. DeepSTABp uses a transformer-based protein language model for sequence embedding and state-of-the-art feature extraction in combination with other deep learning techniques for end-to-end protein melting temperature prediction. DeepSTABp can predict the thermal stability of a wide range of proteins, making it a powerful and efficient tool for large-scale prediction. The model captures the structural and biological properties that impact protein stability, and it allows for the identification of the structural features that contribute to protein stability. DeepSTABp is available to the public via a user-friendly web interface, making it accessible to researchers in various fields.

Protein structures are more strongly conserved in evolution than are amino acid sequences. Comparative structural analysis is particularly important for inferring the origins of viral proteins that typically evolve at high rates. Viruses with large, double-stranded DNA genomes captured the majority of their genes from their hosts at different stages of evolution. The origins of many virus genes are readily detected through significant sequence similarity with cellular homologs. In particular, this is the case for virus enzymes, such as DNA and RNA polymerases or nucleotide kinases, that retain their catalytic activity after capture by an ancestral virus. However, a large fraction of virus genes have no readily detectable cellular homologs, meaning that their origins remain enigmatic. We explored the potential origins of such proteins that are encoded in the genomes of orthopoxviruses, a thoroughly studied virus genus that includes major human pathogens. To this end, we used AlphaFold2 to predict the structures of all 214 proteins that are encoded by orthopoxviruses. Among the proteins of unknown provenance, structure prediction yielded clear indications of origin for 14 of them and validated several inferences that were previously made via sequence analysis. A notable emerging trend is the exaptation of enzymes from cellular organisms for nonenzymatic, structural roles in virus reproduction that is accompanied by the disruption of catalytic sites and by an overall drastic divergence that precludes homology detection at the sequence level. Among the 16 orthopoxvirus proteins that were found to be inactivated enzyme derivatives are the poxvirus replication processivity factor A20, which is an inactivated NAD-dependent DNA ligase; the major core protein A3, which is an inactivated deubiquitinase; F11, which is an inactivated prolyl hydroxylase; and more similar cases. For nearly one-third of the orthopoxvirus virion proteins, no significantly similar structures were identified, suggesting exaptation with subsequent major structural rearrangement that yielded unique protein folds. IMPORTANCE Protein structures are more strongly conserved in evolution than are amino acid sequences. Comparative structural analysis is particularly important for inferring the origins of viral proteins that typically evolve at high rates. We used a powerful protein structure modeling method, namely, AlphaFold2, to model the structures of all orthopoxvirus proteins and compared them to all available protein structures. Multiple cases of recruitment of host enzymes for structural roles in viruses, accompanied by the disruption of catalytic sites, were discovered. However, many viral proteins appear to have evolved unique structural folds.

Recent advances in the field of protein structure determination using liquid-state NMR enable the elucidation of multi-state protein conformations that can provide insight into correlated and non-correlated protein dynamics at atomic resolution. So far, NMR-derived multi-state structures were typically evaluated by means of visual inspection of structure superpositions, target function values that quantify the violation of experimented restraints and root-mean-square deviations that quantify similarity between conformers. As an alternative or complementary approach, we present here the use of a recently introduced structural correlation measure, PDBcor, that quantifies the clustering of protein states as an additional measure for multi-state protein structure analysis. It can be used for various assays including the validation of experimental distance restraints, optimization of the number of protein states, estimation of protein state populations, identification of key distance restraints, NOE network analysis and semiquantitative analysis of the protein correlation network. We present applications for the final quality analysis stages of typical multi-state protein structure calculations.

Proteins play a major role in biosensors in which they provide catalytic activity and specificity in molecular recognition. However, the immobilization process is far from straightforward as it often affects the protein functionality. Extensive interaction of the protein with the surface or significant surface crowding can lead to changes in the mobility and conformation of the protein structure. This review will provide insights as to how an analysis of the physico-chemical features of the protein surface before the immobilization process can help to identify the optimal immobilization approach. Such an analysis can help to preserve the functionality of the protein when on a biosensor surface.

Regulation of amino acid’s biosynthetic pathway is of significant importance to maintain homeostasis and cell functions. Amino acids regulate their biosynthetic pathway by end-product feedback inhibition of enzymes catalyzing committed steps of a pathway. Discovery of new feedback resistant enzyme variants to enhance industrial production of amino acids is a key objective in industrial biotechnology. Deregulation of feedback inhibition has been achieved for various enzymes using in vitro and in silico mutagenesis techniques. As enzyme’s function, its substrate binding capacity, catalysis activity, regulation and stability are dependent on its structural characteristics, here, we provide detailed structural analysis of all feedback sensitive enzyme targets in amino acid biosynthetic pathways. Current review summarizes information regarding structural characteristics of various enzyme targets and effect of mutations on their structures and functions especially in terms of deregulation of feedback inhibition. Furthermore, applicability of various experimental as well as computational mutagenesis techniques to accomplish feedback resistance has also been discussed in detail to have an insight into various aspects of research work reported in this particular field of study.

Interactions of proteins with other molecules are often mediated by a set of critical binding motifs on their surfaces. Most traditional binder designs relied on motifs borrowed from known binder molecules, which highly restricted their applicability to novel targets or new binding sites. This work presents a deep learning network MotifGen that predicts potential binder motifs directly from receptor structures without further supporting information. MotifGen generates motif profiles at the receptor surface for 14 types of functional groups or 6 chemical interaction classes. These profiles are highly human-interpretable and can be further utilized as pre-trained embedding inputs for versatile few-shot binder design applications. We demonstrate MotifGen's effectiveness through its applications to peptide binder design and small molecule binding site prediction, where it either surpassed existing methods or added significant value when integrated. Our motif-centric approach can offer a new design strategy for novel binder discovery for challenging receptor targets. Scientific contribution  We introduce a new deep-learning based computational strategy for identifying potential binder motifs given a receptor structure. These predicted binder motifs can be directly applied to the design of various drugs types, including peptides and small molecules. To demonstrate its utility, we show its applications in peptide binder sequence discrimination and binding site prediction tasks, both of which are crucial tasks in structure-based drug design.

Research background. Peanut allergy poses a significant threat to human health due to the increased risk of long-term morbidity at low doses. Modifying protein structure to affect sensitization is a popular topic. Experimental approach. In this study, the purified peanut allergen Ara h 1 was enzymatically hydrolysed using Flavourzyme, alkaline protease or a combination of both. The binding ability of Ara h 1 to antibodies, gene expression and secretion levels of the proinflammatory factors interleukin-5 and interleukin-6 in Caco-2 cells was measured. Changes in the secondary and tertiary structures before and after treatment with Ara h 1 were analysed by circular dichroism and sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE). Results and conclusions. The results indicated a decrease of the allergenicity and proinflammatory ability of Ara h 1. The evaluation showed that the Flavourzyme and alkaline protease treatments caused particle shortening and aggregation. The fluorescence emission peak increased by 3.4-fold after the combined treatment with both proteases. Additionally, the secondary structure underwent changes and the hydrophobicity also increased 8.95-fold after the combined treatment. Novelty and scientific contribution. These findings partially uncover the mechanism of peanut sensitization and provide an effective theoretical basis for the development of a new method of peanut desensitization.

The evolutionary conserved Notch signaling pathway functions as a mediator of direct cell–cell communication between neighboring cells during development. Notch plays a crucial role in various fundamental biological processes in a wide range of tissues. Accordingly, the aberrant signaling of this pathway underlies multiple genetic pathologies such as developmental syndromes, congenital disorders, neurodegenerative diseases, and cancer. Over the last two decades, significant data have shown that the Notch signaling pathway displays a significant function in the mature brains of vertebrates and invertebrates beyond neuronal development and specification during embryonic development. Neuronal connection, synaptic plasticity, learning, and memory appear to be regulated by this pathway. Specific mutations in human Notch family proteins have been linked to several neurodegenerative diseases including Alzheimer’s disease, CADASIL, and ischemic injury. Neurodegenerative diseases are incurable disorders of the central nervous system that cause the progressive degeneration and/or death of brain nerve cells, affecting both mental function and movement (ataxia). There is currently a lot of study being conducted to better understand the molecular mechanisms by which Notch plays an essential role in the mature brain. In this study, an in silico analysis of polymorphisms and mutations in human Notch family members that lead to neurodegenerative diseases was performed in order to investigate the correlations among Notch family proteins and neurodegenerative diseases. Particular emphasis was placed on the study of mutations in the Notch3 protein and the structure analysis of the mutant Notch3 protein that leads to the manifestation of the CADASIL syndrome in order to spot possible conserved mutations and interpret the effect of these mutations in the Notch3 protein structure. Conserved mutations of cysteine residues may be candidate pharmacological targets for the potential therapy of CADASIL syndrome.

As the number of macromolecular structures in the worldwide Protein Data Bank (wwPDB) continues to grow rapidly, more attention is being paid to the quality of its data, especially for use in aggregated structural and dynamics analyses. In this study, we systematically analyzed 3.5 Å regions around all metal ions across all PDB entries with supporting electron density maps available from the PDB in Europe. All resulting metal ion-centric regions were evaluated with respect to four quality-control criteria involving electron density resolution, atom occupancy, symmetry atom exclusion, and regional electron density discrepancy. The resulting list of metal binding sites passing all four criteria possess high regional structural quality and should be beneficial to a wide variety of downstream analyses. This study demonstrates an approach for the pan-PDB evaluation of metal binding site structural quality with respect to underlying X-ray crystallographic experimental data represented in the available electron density maps of proteins. For non-crystallographers in particular, we hope to change the focus and discussion of structural quality from a global evaluation to a regional evaluation, since all structural entries in the wwPDB appear to have both regions of high and low structural quality.

The microbial production of medium‐chain carboxylates has attracted considerable interest owing to their potential applications in biofuels and specialty chemicals; however, the underlying biosynthetic mechanisms remain incompletely understood. The present study evaluates the medium‐chain carboxylate‐producing microbe Megaspahera hexanoica using genomic analysis, transcriptome analysis, and metabolic engineering. Additionally, the n‐caproate synthesis pathway of M. hexanoica is characterized with fructose as an electron donor, and the substrate specificity of the respective proteins is evaluated by constructing an n‐caproate biosynthetic pathway in Escherichia coli. Among all r‐BOX or RBO genes, thl_1583, which encodes β‐ketothiolase (MhTHL), is identified as the most critical enzyme for the carbon chain elongation mechanism in M. hexanoica. Therefore, MhTHL is compared with other well‐studied β‐ketothiolases (CkTHL from Clostridium kluyveri, ReBktB from Ralstonia eutropha (Cupriavidus necator), EcAtoB from E. coli, and CaTHL from C. acetobutylicum). MhTHL is found to exhibit the highest n‐caproate production, as evidenced by the protein crystal structure of MhTHL. Structural comparisons with other thiolases show that MhTHL has a larger substrate‐binding pocket than ReBktB. Thiolase mutants generated by site‐directed mutagenesis reveal that two residues (Leu87 and Val351) are essential for determining the size of the substrate‐binding pocket. The medium‐chain carboxylate‐producing microbe Megasphaera hexanoica is analyzed through genomics, transcriptomics, and metabolic engineering. The n‐caproate biosynthetic pathway is reconstructed in Escherichia coli, highlighting MhTHL as a key β‐ketothiolase. Structural analysis and mutagenesis reveal that MhTHL has a distinct substrate‐binding pocket, crucial for n‐caproate production.