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The protein inverse folding problem, designing amino acid sequences that fold into desired protein structures, is a critical challenge in biological sciences. Despite numerous data-driven and knowledge-driven methods, there remains a need for a user-friendly toolkit that effectively integrates these approaches for in-silico protein design. In this paper, we present DIProT, an interactive protein design toolkit. DIProT leverages a non-autoregressive deep generative model to solve the inverse folding problem, combined with a protein structure prediction model. This integration allows users to incorporate prior knowledge into the design process, evaluate designs in silico, and form a virtual design loop with human feedback. Our inverse folding model demonstrates competitive performance in terms of effectiveness and efficiency on TS50 and CATH4.2 datasets, with promising sequence recovery and inference time. Case studies further illustrate how DIProT can facilitate user-guided protein design. [ABSTRACT FROM AUTHOR]

AlphaFold2 is an Artificial Intelligence-based program developed to predict the 3D structure of proteins given only their amino acid sequence at atomic resolution. Due to the accuracy and efficiency at which AlphaFold2 can generate 3D structure predictions and its widespread adoption into various aspects of biochemical research, the technique of protein structure prediction should be considered for incorporation into the undergraduate biochemistry curriculum. A module for introducing AlphaFold2 into a senior-level biochemistry laboratory classroom was developed. The module's focus was to have students predict the structures of proteins from the MPOX 22 global outbreak virus isolate genome, which had no structures elucidated at that time. The goal of this study was to both determine the impact the module had on students and to develop a framework for introducing AlphaFold2 into the undergraduate curriculum so that instructors for biochemistry courses, regardless of their background in bioinformatics, could adapt the module into their classrooms. Author summary: AlphaFold2 is software that combines sequence similarity and structure templating with the power of Artificial Intelligence (AI) to bridge the connection between the primary protein structure (amino acid sequence) and higher-level 3D structure (secondary, tertiary, and quaternary). AlphaFold2's impressive and easily accessible nature makes it a bioinformatics tool that has been seeing a wide range of applications in biochemical research. Given this large-scale application, we examined whether Alphafold2 could be integrated into an undergraduate curriculum. We developed a novel module for a senior-level undergraduate biochemistry laboratory class. Our goal was to lay a solid foundation for other undergraduate instructors to be able to adapt this module to fit their classroom needs. While we implemented and ran all predictions on an internal university computing cluster, we recommend ColabFold for those instructors who do not have access to large-scale computational clusters or whose internal clusters cannot scale to their classroom sizes. We have outlined 3 metrics to be quantitatively investigated in the module to give both instructors and students metrics to evaluate model confidence. We have also included a template worksheet, lecture slides, and example scripts to enable instructors to rapidly develop a similar module. We hope that more departments and programs will integrate AlphaFold2 into their undergraduate curriculums, giving students a highly in-demand skill to prepare them for their transition into a career or graduate school. [ABSTRACT FROM AUTHOR]

Type IV pili (T4P) represent one of the most common varieties of surface appendages in archaea. These filaments, assembled from small pilin proteins, can be many microns long and serve diverse functions, including adhesion, biofilm formation, motility, and intercellular communication. Here, we determine atomic structures of two distinct adhesive T4P from Saccharolobus islandicus via cryo-electron microscopy (cryo-EM). Unexpectedly, both pili were assembled from the same pilin polypeptide but under different growth conditions. One filament, denoted mono-pilus, conforms to canonical archaeal T4P structures where all subunits are equivalent, whereas in the other filament, the tri-pilus, the same polypeptide exists in three different conformations. The three conformations in the tri-pilus are very different from the single conformation found in the mono-pilus, and involve different orientations of the outer immunoglobulin-like domains, mediated by a very flexible linker. Remarkably, the outer domains rotate nearly 180° between the mono- and tri-pilus conformations. Both forms of pili require the same ATPase and TadC-like membrane pore for assembly, indicating that the same secretion system can produce structurally very different filaments. Our results show that the structures of archaeal T4P appear to be less constrained and rigid than those of the homologous archaeal flagellar filaments that serve as helical propellers. Type IV pili (T4P) are long surface appendages assembled from small pilin proteins that participate in diverse functions such as adhesion and biofilm formation in archaea. Here, the authors show that the same pilin polypeptide can adopt four conformations and assemble two distinct T4P structures under different growth conditions. [ABSTRACT FROM AUTHOR]

Descriptors play a pivotal role in enzyme design for the greener synthesis of biochemicals, as they could characterize enzymes and chemicals from the physicochemical and evolutionary perspective. This study examined the effects of various descriptors on the performance of Random Forest model used for enzyme-chemical relationships prediction. We curated activity data of seven specific enzyme families from the literature and developed the pipeline for evaluation the machine learning model performance using 10-fold cross-validation. The influence of protein and chemical descriptors was assessed in three scenarios, which were predicting the activity of unknown relations between known enzymes and known chemicals (new relationship evaluation), predicting the activity of novel enzymes on known chemicals (new enzyme evaluation), and predicting the activity of new chemicals on known enzymes (new chemical evaluation). The results showed that protein descriptors significantly enhanced the classification performance of model on new enzyme evaluation in three out of the seven datasets with the greatest number of enzymes, whereas chemical descriptors appear no effect. A variety of sequence-based and structure-based protein descriptors were constructed, among which the esm-2 descriptor achieved the best results. Using enzyme families as labels showed that descriptors could cluster proteins well, which could explain the contributions of descriptors to the machine learning model. As a counterpart, in the new chemical evaluation, chemical descriptors made significant improvement in four out of the seven datasets, while protein descriptors appear no effect. We attempted to evaluate the generalization ability of the model by correlating the statistics of the datasets with the performance of the models. The results showed that datasets with higher sequence similarity were more likely to get better results in the new enzyme evaluation and datasets with more enzymes were more likely beneficial from the protein descriptor strategy. This work provides guidance for the development of machine learning models for specific enzyme families. [ABSTRACT FROM AUTHOR]

Background: Protein solubility is a critically important physicochemical property closely related to protein expression. For example, it is one of the main factors to be considered in the design and production of antibody drugs and a prerequisite for realizing various protein functions. Although several solubility prediction models have emerged in recent years, many of these models are limited to capturing information embedded in one-dimensional amino acid sequences, resulting in unsatisfactory predictive performance. Results: In this study, we introduce a novel Graph Attention network-based protein Solubility model, GATSol, which represents the 3D structure of proteins as a protein graph. In addition to the node features of amino acids extracted by the state-of-the-art protein large language model, GATSol utilizes amino acid distance maps generated using the latest AlphaFold technology. Rigorous testing on independent eSOL and the Saccharomyces cerevisiae test datasets has shown that GATSol outperforms most recently introduced models, especially with respect to the coefficient of determination R2, which reaches 0.517 and 0.424, respectively. It outperforms the current state-of-the-art GraphSol by 18.4% on the S. cerevisiae_test set. Conclusions: GATSol captures 3D dimensional features of proteins by building protein graphs, which significantly improves the accuracy of protein solubility prediction. Recent advances in protein structure modeling allow our method to incorporate spatial structure features extracted from predicted structures into the model by relying only on the input of protein sequences, which simplifies the entire graph neural network prediction process, making it more user-friendly and efficient. As a result, GATSol may help prioritize highly soluble proteins, ultimately reducing the cost and effort of experimental work. The source code and data of the GATSol model are freely available at https://github.com/binbinbinv/GATSol. [ABSTRACT FROM AUTHOR]

This study aimed to investigate azole resistance mechanisms in Aspergillus flavus, which involve cyp51A and cyp51B genes. Real-time Reverse Transcriptase qPCR method was applied to determine the overexpression of cyp51A and cyp51B genes for 34 A. flavus isolates. PCR sequencing of these two genes was used to detect the presence of gene mutations. Susceptibility test found sensitivity to voriconazole (VOR) in all strains. 14.7% and 8.8% of isolates were resistant to itraconazole (IT) and posaconazole (POS), respectively, with a cross-resistance in 5.8%. For the double resistant isolates (IT/POS), the expression of cyp51A was up to 17-fold higher. PCR sequencing showed the presence of 2 mutations in cyp51A: a synonymous point mutation (P61P) in eight isolates, which did not affect the structure of CYP51A protein, and another non synonymous mutation (G206L) for only the TN-33 strain (cross IT/POS resistance) causing an amino acid change in the protein sequence. However, we noted in cyp51B the presence of the only non-synonymous mutation (L177G) causing a change in amino acids in the protein sequence for the TN-31 strain, which exhibits IT/POS cross-resistance. A short single intron of 67 bp was identified in the cyp51A gene, whereas three short introns of 54, 53, and 160 bp were identified in the cyp51B gene. According to the models provided by PatchDock software, the presence of non-synonymous mutations did not affect the interaction of CYP51A and CYP51B proteins with antifungals. In our study, the overexpression of the cyp51A and cyp51B genes is the primary mechanism responsible for resistance in A. flavus collection. Nevertheless, other resistance mechanisms can be involved. [ABSTRACT FROM AUTHOR]

Background: A major challenge of evolutionary biology is making underlying concepts accessible to wide audiences. One method for doing so is to utilise multi-media formats that have potential to engage and inform through entertainment. This pilot study outlines and discusses a sonification concert that integrated musical performance with a range of evolutionary concepts and ideas fundamental to an understanding of evolution, such as protein sequences. We aimed to showcase sound-art objects and live-coding performances created using sonification as a mechanism for presenting complex biological processes to both researcher and non-researchers. We sought to evaluate the effectiveness of this art-adjacent practice for public engagement with evolutionary biology research, and also to gather feedback to guide future events. Toward this end, we held a live concert showcasing biologically-based algorithmic music exploring links between evolutionary biology research, sound art, and musical performance. The event had three main acts: a generative audio-visual piece giving an artistic representation of SARS coronavirus based on a parameter-mapping sonification of protein sequence of the replicase polyprotein; a pre-recorded string ensemble demonstrating the effects of codon selection on translation speed using parameter-mapping sonification; and a live-coded music piece interactively sonifying protein structures. Results: Our event attracted 90 attendees. We evaluated success using direct observation and written feedback forms with a 58% response rate: 95% of respondents stated they had enjoyed the event and 63% indicated they were inspired by it. Conclusions: Presenting the sonic outputs of sonification research in a concert format showed good potential for the pursuit of public engagement with evolutionary biology research, demonstrating the ability to engage curiosity and inspire an audience while also conveying scientific content alongside the nuanced and complex world of modern evolutionary biology. [ABSTRACT FROM AUTHOR]

The development of turn-based inhibitors of protein–protein interactions has attracted considerable attention in medicinal chemistry. Our group has synthesized a series of peptides derived from an amino-functionalized ferrocene to investigate their potential to mimic protein turn structures. Detailed DFT and spectroscopic studies (IR, NMR, CD) have shown that, for peptides, the backbone chirality and bulkiness of the amino acid side chains determine the hydrogen-bond pattern, allowing tuning of the size of the preferred hydrogen-bonded ring in turn-folded structures. However, their biological potential is more dependent on their lipophilicity. In addition, our pioneering work on the chiroptical properties of aminoferrocene-containing peptides enables the correlation of their geometry with the sign of the CD signal in the absorption region of the ferrocene chromophore. These studies have opened up the possibility of using aminoferrocene and its derivatives as chirooptical probes for the determination of various chirality elements, such as the central chirality of amino acids and the helicity of peptide sequences. [ABSTRACT FROM AUTHOR]

In cyanobacteria and chloroplasts (in algae and plants), ATP synthase plays a pivotal role as a photosynthetic membrane complex responsible for producing ATP from adenosine diphosphate and inorganic phosphate, utilizing a proton motive force gradient induced by photosynthesis. These two ATP synthases exhibit similarities in gene organization, amino acid sequences of subunits, structure, and functional mechanisms, suggesting that cyanobacterial ATP synthase is probably the evolutionary precursor to chloroplast ATP synthase. In this review, we explore the precise synthesis and assembly of ATP synthase subunits to address the uneven stoichiometry within the complex during transcription, translation, and assembly processes. We also compare the regulatory strategies governing ATP synthase activity to meet varying energy demands in cyanobacteria and chloroplasts amid fluctuating natural environments. Furthermore, we delve into the role of ATP synthase in stress tolerance and photosynthetic carbon fixation efficiency in oxygenic photosynthetic organisms (OPsOs), along with the current researches on modifying ATP synthase to enhance carbon fixation efficiency under stress conditions. This review aims to offer theoretical insights and serve as a reference for understanding the functional mechanisms of ATP synthase, sparking innovative ideas for enhancing photosynthetic carbon fixation efficiency by utilizing ATP synthase as an effective module in OPsOs. [ABSTRACT FROM AUTHOR]

Background: Acyl-CoA-Binding proteins (ACBPs) function as coenzyme A transporters and play important roles in regulating plant growth and development in response to abiotic stress and phytohormones, as well as in membrane repair. To date, the ACBP family has not been a comprehensively characterized in barley (Hordeum vulgare L.). Results: Eight ACBP genes were identified in the barley genome and named as HvACBP1–8. The analysis of the proteins structure and promoter elements of HvACBP suggested its potential functions in plant growth, development, and stress response. These HvACBPs are expressed in specific tissues and organs following induction by abiotic stressors such as drought, salinity, UV-B exposure, temperature extremes, and exposure to exogenous phytohormones. The HvACBP7 and HvACBP8 amino acid sequences were conserved during the domestication of Tibetan Qingke barley. Conclusions: Acyl-CoA-binding proteins may play important roles in barley growth and environmental adaptation. This study provides foundation for further analyses of the biological functions of HvACBPs in the barley stress response. [ABSTRACT FROM AUTHOR]

Predicting the interaction affinity between drugs and target proteins is crucial for rapid and accurate drug discovery and repositioning. Therefore, more accurate prediction of DTA has become a key area of research in the field of drug discovery and drug repositioning. However, traditional experimental methods have disadvantages such as long operation cycles, high manpower requirements, and high economic costs, making it difficult to predict specific interactions between drugs and target proteins quickly and accurately. Some methods mainly use the SMILES sequence of drugs and the primary structure of proteins as inputs, ignoring the graph information such as bond encoding, degree centrality encoding, spatial encoding of drug molecule graphs, and the structural information of proteins such as secondary structure and accessible surface area. Moreover, previous methods were based on protein sequences to learn feature representations, neglecting the completeness of information. To address the completeness of drug and protein structure information, we propose a Transformer graph-based early fusion research approach for drug-target affinity prediction (GEFormerDTA). Our method reduces prediction errors caused by insufficient feature learning. Experimental results on Davis and KIBA datasets showed a better prediction of drugtarget affinity than existing affinity prediction methods. [ABSTRACT FROM AUTHOR]

Computer modeling and protein structure visualization tools are effective and engaging ways of presenting various molecular biology concepts to high school and college students. Here, we describe a series of activities and exercises that use online bioinformatics databases and programs to search for and obtain protein sequence and structure data and use it to build homology models of proteins. Exercises in homology modeling can serve the pedagogical purpose of introducing and illustrating the concept of homology within gene and protein families, which results in conservation of the 3D structures of proteins and allows us to predict structures when experimental data are not available. [ABSTRACT FROM AUTHOR]

Most intracellular activities of living organisms are performed by proteins that have unique and complex 3-Dimensional (3D) structures, playing very important roles in their operations. Due to importance and challenges of predicting 3D structures for proteins, laboratory methods with limitations such as time consuming and high cost have been developed. Computational methods use a sequence of amino acids to obtain the 3D structure. They encounter with a nondeterministic problem having polynomial completion time (NP-Complete problem); with a chain of amino acids as input, and a protein, with 3D structure, as output. In this paper, a new population-based algorithm, under Predatory Search Strategy-Particle Swarm Optimization (PSS-PSO) framework, is presented for predicting the 3D structure; using Hydrophobic-Polar (HP) model on Face-Centered Cubic (FCC) lattice. In this approach, name TRL-PSSPSO, two new moves are proposed to direct each solution toward the native structure: Local Move for reaching a dense hydrophobic core and large H-H contacts and Meta Move for reaching optimal structure, by using Transfer learning. Two datasets are considered for evaluating and the results on some set of standard protein benchmarks show outperforming the state-of-the-art approaches. They show that the proposed approach can improve the accuracy of template-free prediction in an acceptable manner. [ABSTRACT FROM AUTHOR]

Bacteria use a variety of defense systems to protect themselves from phage infection. In turn, phages have evolved diverse counter-defense measures to overcome host defenses. Here, we use protein structural similarity and gene co-occurrence analyses to screen >66 million viral protein sequences and >330,000 metagenome-assembled genomes for the identification of anti-phage and counter-defense systems. We predict structures for ~300,000 proteins and perform large-scale, pairwise comparison to known anti-CRISPR (Acr) and anti-phage proteins to identify structural homologs that otherwise may not be uncovered using primary sequence search. This way, we identify a Bacteroidota phage Acr protein that inhibits Cas12a, and an Akkermansia muciniphila anti-phage defense protein, termed BxaP. Gene bxaP is found in loci encoding Bacteriophage Exclusion (BREX) and restriction-modification defense systems, but confers immunity independently. Our work highlights the advantage of combining protein structural features and gene co-localization information in studying host-phage interactions. Bacteria use various defense systems to protect themselves from phage infection, and phages have evolved diverse counter-defense measures to overcome host defenses. Here, the authors use protein structural similarity and gene co-occurrence analyses for identification of new anti-phage and counter-defense systems. [ABSTRACT FROM AUTHOR]

Motivation Proteins accomplish cellular functions by interacting with each other, which makes the prediction of interaction sites a fundamental problem. As experimental methods are expensive and time consuming, computational prediction of the interaction sites has been studied extensively. Structure-based programs are the most accurate, while the sequence-based ones are much more widely applicable, as the sequences available outnumber the structures by two orders of magnitude. Ideally, we would like a tool that has the quality of the former and the applicability of the latter. Results We provide here the first solution that achieves these two goals. Our new sequence-based program, Seq-InSite, greatly surpasses the performance of sequence-based models, matching the quality of state-of-the-art structure-based predictors, thus effectively superseding the need for models requiring structure. The predictive power of Seq-InSite is illustrated using an analysis of evolutionary conservation for four protein sequences. Availability and implementation Seq-InSite is freely available as a web server at http://seq-insite.csd.uwo.ca/ and as free source code, including trained models and all datasets used for training and testing, at https://github.com/lucian-ilie/Seq-InSite. [ABSTRACT FROM AUTHOR]

The aim of this work is the full characterization of all the nocturnin (noc) paralogues expressed in a teleost, the goldfish. An in silico analysis of the evolutive origin of noc in Osteichthyes is performed, including the splicing variants and new paralogues appearing after teleostean 3R genomic duplication and the cyprinine 4Rc. After sequencing the full-length mRNA of goldfish, we obtained two isoforms for noc-a (noc-aa and noc-ab) with two splice variants (I and II), and only one for noc-b (noc-bb) with two transcripts (II and III). Using the splicing variant II, the prediction of the secondary and tertiary structures renders a well-conserved 3D distribution of four α-helices and nine β-sheets in the three noc isoforms. A synteny analysis based on the localization of noc genes in the patrilineal or matrilineal subgenomes and a phylogenetic tree of protein sequences were accomplished to stablish a classification and a long-lasting nomenclature of noc in goldfish, and valid to be extrapolated to allotetraploid Cyprininae. Finally, both goldfish and zebrafish showed a broad tissue expression of all the noc paralogues. Moreover, the enriched expression of specific paralogues in some tissues argues in favour of neo- or subfunctionalization. [ABSTRACT FROM AUTHOR]

Protein structure analysis is essential in various bioinformatics domains such as drug discovery, disease diagnosis, and evolutionary studies. Within structural biology, the classification of protein structures is pivotal, employing machine learning algorithms to categorize structures based on data from databases like the Protein Data Bank (PDB). To predict protein functions, embeddings based on protein sequences have been employed. Creating numerical embeddings that preserve vital information while considering protein structure and sequence presents several challenges. The existing literature lacks a comprehensive and effective approach that combines structural and sequence-based features to achieve efficient protein classification. While large language models (LLMs) have exhibited promising outcomes for protein function prediction, their focus primarily lies on protein sequences, disregarding the 3D structures of proteins. The quality of embeddings heavily relies on how well the geometry of the embedding space aligns with the underlying data structure, posing a critical research question. Traditionally, Euclidean space has served as a widely utilized framework for embeddings. In this study, we propose a novel method for designing numerical embeddings in Euclidean space for proteins by leveraging 3D structure information, specifically employing the concept of contact maps. These embeddings are synergistically combined with features extracted from LLMs and traditional feature engineering techniques to enhance the performance of embeddings in supervised protein analysis. Experimental results on benchmark datasets, including PDB Bind and STCRDAB, demonstrate the superior performance of the proposed method for protein function prediction. [ABSTRACT FROM AUTHOR]